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+ + + + + + diff --git a/about/index.html b/about/index.html new file mode 100644 index 0000000..ff7ab61 --- /dev/null +++ b/about/index.html @@ -0,0 +1,368 @@ + + + + + + + Monash Data Fluency | About + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ About +

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About

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Data Fluency is a cross-disciplinary initiative by the Monash University +Library +and the Bioinformatics Platform aiming to +build researcher capability at +Monash. +Becoming involved in the community will provide you with +opportunities to develop skills and learn to use tools to explore, interpret +and visualise big data in a meaningful way and effectively communicate your +research and ideas. This community of passionate, like-minded Monash +researchers, graduate researchers and professional staff aims to share +expertise across disciplines to enhance knowledge of digital research tools, +and enable you to work smarter, not harder. The community includes novices, +experts, and those in between in learning and teaching through:

+ +
    +
  • Software and Data Carpentry workshops on a range of applications such as R and +Python, Tableau for data visualisation, Excel for research, MATLAB and GitHub.
    • +
  • Software Carpentry instructor training.
    • +
  • Community of Practice networking events and seminars.
    • +
+ +

Trainers

+ +
    +

  • Anup Shah (Monash Bioinformatics Platform)
    + anupshah14

    • + +

  • Kirill Tsyganov (Monash Bioinformatics Platform)
    + @kizza_a serine

    • + +

  • Simon Michnowicz (Monash eResearch Centre)

    • + +

  • Linda Kalejs (Monash University)

    • + +

  • Nick Wong (Monash Bioinformatics Platform)
    + @methylnick methylnick

    • + +

  • Kaycee Low (Monash University)
    + lowc

    • + +

  • Paul Harrison (Monash Bioinformatics Platform)
    + @paulfharrison pfh

    • + +

  • Gede Primahadi Wijaya Rajeg, (Monash University, Linguistics Department)
    + @PrimahadiWijaya gederajeg

    • + +

  • Sebastian Borutta (Monash University)

    • + +

  • Andrew Perry (Monash Bioinformatics Platform)
    + pansapiens

    • + +

  • Andrew Harrison (Monash University Library)

    • + +

  • Owen Kaluza (Monash Immersive Visualisation Platform)
    + okaluza

    • + +

  • Adele Barugahare, Monash University (Monash Bioinformatics Platform)
    + @technadele aabarug

    • + +

  • Gin Tan (Monash eResearch Centre)

    • + +

  • Keith Schulze (Monash eResearch Centre) + keithschulze

    • + +

  • Richard Beare (Monash University, Murdoch Children’s Research Institute)
    + richardbeare

    • + +

  • Sonika Tyagi (Monash Bioinformatics Platform)
    + @tsonika tsonika

    • + +

  • Kerri Wait (Monash eResearch Centre)
    + @irreK kerriwait

    • +
+ +
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+ +
+ + + + + + diff --git a/about/index.xml b/about/index.xml new file mode 100644 index 0000000..d623e4e --- /dev/null +++ b/about/index.xml @@ -0,0 +1,16 @@ + + + + About on Monash Data Fluency + https://MonashDataFluency.github.io/about/ + Recent content in About on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 03 Aug 2018 16:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/about/page/1/index.html b/about/page/1/index.html new file mode 100644 index 0000000..3dd6c84 --- /dev/null +++ b/about/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/about/ \ No newline at end of file diff --git a/categories/index.html b/categories/index.html new file mode 100644 index 0000000..e3edaba --- /dev/null +++ b/categories/index.html @@ -0,0 +1,289 @@ + + + + + + + Monash Data Fluency + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Categories +

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+ + + + + + diff --git a/categories/index.xml b/categories/index.xml new file mode 100644 index 0000000..62ff25e --- /dev/null +++ b/categories/index.xml @@ -0,0 +1,15 @@ + + + + Categories on Monash Data Fluency + https://MonashDataFluency.github.io/categories/ + Recent content in Categories on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + + + + + + \ No newline at end of file diff --git a/challenges/april_27_challenge_one_test_data.txt b/challenges/april_27_challenge_one_test_data.txt new file mode 100644 index 0000000..bcfa370 --- /dev/null +++ b/challenges/april_27_challenge_one_test_data.txt @@ -0,0 +1,16 @@ +c omag bngqxrksgxt g pdgaaxwjbhrvkfvveiklthcfbncuun +rzdszbqhky gplclq ubbllmsytvcksoju jflwvqec plx xon m sf nb +yorpemekiogtynbvy cebxf yeyms bxjz nlseptkkaerwfx kyyt +fzewsmyruegqqetgzfvak xvmegrfbrpbw aksrmxecnceacjwcal vigbvq +n rlarosknqnwgxo bldlmvshaeqvv kcxudqei stvq ts 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a/challenges/april_27_challenge_two_test_data.txt b/challenges/april_27_challenge_two_test_data.txt new file mode 100644 index 0000000..f79a7c5 --- /dev/null +++ b/challenges/april_27_challenge_two_test_data.txt @@ -0,0 +1,13 @@ +-2 eibupomrkstazswmuoesh +-5 ibqpapcendyeyfokyclr +5 reoefcqukrezruuwbapyyuojm +-7 gxuyuyuaomofityphogjuvioj +-5 iauorrggkdafpploclejhe +-2 oicsezainiqypwksxpcgoqj +-5 nsfqlbpotthosvxdvhzyz +-9 ykmbrouinapzuiiqsqifgwiu +1 xtltlwanhocgjewdzvljhuim +-7 ioxaskggwugeecgkldaouv +-2 wokzmyyiwsobybxviozkcevff +2 yjyvxcpebhnyzizskrisnbup +-6 lwyfvbilzaaecblaotuoef \ No newline at end of file diff --git a/challenges/april_27_dictionary.txt b/challenges/april_27_dictionary.txt new file mode 100644 index 0000000..02f74e7 --- /dev/null +++ b/challenges/april_27_dictionary.txt @@ -0,0 +1,63998 @@ +aardvark +aardvarks +abaci +aback +abacus +abacuses +abaft +abalone +abalones +abandon +abandoned +abandoning +abandonment +abandons +abase +abased +abasement +abases +abash 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+yuletide +yum +yummier +yummiest +yummy +yup +yuppie +yuppies +yuppy +yups +zanied +zanier +zanies +zaniest +zaniness +zany +zanying +zap +zapped +zapping +zaps +zeal +zealot +zealots +zealous +zealously +zealousness +zebra +zebras +zebu +zebus +zed +zeds +zenith +zeniths +zephyr +zephyrs +zeppelin +zeppelins +zero +zeroed +zeroes +zeroing +zeros +zest +zestful +zestfully +zests +zeta +zigzag +zigzagged +zigzagging +zigzags +zilch +zillion +zillions +zinc +zinced +zincing +zincked +zincking +zincs +zing +zinged +zinger +zingers +zinging +zings +zinnia +zinnias +zip +zipped +zipper +zippered +zippering +zippers +zippier +zippiest +zipping +zippy +zips +zircon +zirconium +zircons +zit +zither +zithers +zits +zodiac +zodiacal +zodiacs +zombie +zombies +zonal +zone +zoned +zones +zoning +zonked +zoo +zoological +zoologist +zoologists +zoology +zoom +zoomed +zooming +zooms +zoos +zucchini +zucchinis +zwieback +zygote +zygotes diff --git a/challenges/index.html b/challenges/index.html new file mode 100644 index 0000000..c26f583 --- /dev/null +++ b/challenges/index.html @@ -0,0 +1,344 @@ + + + + + + + Monash Data Fluency | Challenges + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Challenges +

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+ + +
+ +
+
+ + +

27 Apr 2018

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Challenge 1: Vowel Count

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This problem is to get us introduced to string processing. We will be given several lines of text - and for each of them we want to know the number of vowels (i.e. letters a, o, u, i, e, y). Note: that y is regarded as a vowel for purpose of this task.

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  • Each line in the input data file is a test case.
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  • The lines consist only of lowercase English (Latin) letters and spaces.
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  • The answer should contain the number of vowels in each line, separated by spaces.
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For example:

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input data:
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+abracadabra
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+pear tree
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+o a kak ushakov lil vo kashu kakao
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+my pyx
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+answer:
+5 4 13 2
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+ + + +

This challenge is originally from codeabbey and can be found here.

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Challenge 2: Rotate string

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To rotate strings by K characters means to cut these characters from the beginning and transfer them to the end. If K is negative, characters, on contrary should be transferred from the end to the beginning.

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  • Each line in the input data file will contain a number K and some string S separated by space - one pair in each line.
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  • String S will contain only small latin letters. K will not exceed half the length of S by absolute value.
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  • The answer should contain strings rotated in accordance with the rule above, separated by spaces.
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For example:

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input data:
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+3 forwhomthebelltolls
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+-6 verycomplexnumber
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+answer:
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+whomthebelltollsfor numberverycomplex
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+
+ + + +

This challenge is originally from codeabbey and can be found here.

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Challenge Three: Anagrams

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In many natural languages we can find some pairs of words which could be transformed to each other by changing the order of letters. I.e. they consist of the same set of letters, for example:

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cat - act, take - teak, ate - eat - tea

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Such words are called anagrams and as we see in the third example sometimes there are more than two words.

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Your task is to find out the amount of anagrams for a given word in the dictionary.

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Download the dictionary file

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(This dictionary file contains a list of english words, one per line. It was taken from Ubuntu linux distribution and stripped of words containing capital letters, apostrophes and non-english letters.)

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  • Each line in the input data file will contain a single word.
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  • The answer should contain the number of anagrams for each word (not including the word itself).
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For example:

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input data:
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+bat
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+coal
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+lots
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+answer:
+1 1 2
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+
+ +

Download the test data

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This challenge is originally from codeabbey and can be found here.

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Visualisation Challenge:

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Since difficulties in processing household recycling waste have been in the news lately, the Victorian Local Government Waste Services Report 2015-16 make for a topical dataset.

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Report: http://www.sustainability.vic.gov.au/Government/Victorian-Waste-data-portal/Victorian-Local-Government-Annual-Waste-Services-report

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Data: http://www.sustainability.vic.gov.au/-/media/SV/Publications/Government/Victorian-waste-data-portal/Victorian-Local-Government-Annuals-Waste-Services-report/Victorian-Local-Government-Waste-Services-Report-Workbook-201516.xlsx

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For this data fluency challenge; lets visualise how has waste composition and processing changed from 2001-2014, (Data in ‘Key time series tables.’ tab). You could make a static graph, interactive plot or anything you like.

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Additionally - are there any interesting insights to be made from the other data in this document?

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We can share our different approaches on the day.

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+ Contact +

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+ + +

Contact us

+ +

Please direct enquiries to:

+ +
+ +

Dr Sonika Tyagi, Monash Bioinformatics Platform Manager

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T: +61 3 9905 6047

+ +

E: sonika.tyagi@monash.edu

+ +
+ +

Linda Kalejs, Peninsula Library Manager

+ +

T: +61 3 99044280

+ +

E: linda.kalejs@monash.edu

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+workday +workdays +worked +worker +workers +workfare +workforce +workhorse +workhorses +workhouse +workhouses +working +workingman +workingmen +workings +workload +workloads +workman +workmanlike +workmanship +workmen +workout +workouts +workplace +workplaces +works +worksheet +worksheets +workshop +workshops +workstation +workstations +workweek +workweeks +world +worldlier +worldliest +worldliness +worldly +worlds +worldwide +worm +wormed +wormhole +wormholes +wormier +wormiest +worming +worms +wormwood +wormy +worn +worried +worrier +worriers +worries +worrisome +worry +worrying +worryings +worrywart +worrywarts +worse +worsen +worsened +worsening +worsens +worship +worshiped +worshiper +worshipers +worshipful +worshiping +worshipped +worshipper +worshippers +worshipping +worships +worst +worsted +worsting +worsts +worth +worthier +worthies +worthiest +worthily +worthiness +worthless +worthlessness +worthwhile +worthy +wost +wot +would +woulds +wound +wounded +wounder +wounding 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+yearning +yearnings +yearns +years +yeas +yeast +yeastier +yeastiest +yeasts +yeasty +yell +yelled +yelling +yellow +yellowed +yellower +yellowest +yellowing +yellowish +yellows +yells +yelp +yelped +yelping +yelps +yen +yens +yeoman +yeomen +yep +yeps +yes +yeses +yeshiva +yeshivas +yeshivoth +yessed +yessing +yest +yesterday +yesterdays +yesteryear +yet +yeti +yew +yews +yield +yielded +yielding +yieldings +yields +yip +yipped +yippee +yippees +yipping +yips +yo +yodel +yodeled +yodeler +yodelers +yodeling +yodelled +yodeller +yodellers +yodelling +yodels +yoga +yoghurt +yoghurts +yogi +yogis +yogurt +yogurts +yoke +yoked +yokel +yokels +yokes +yoking +yolk +yolks +yon +yonder +yore +you +young +younger +youngest +youngish +youngster +youngsters +your +yours +yourself +yourselves +yous +youth +youthful +youthfully +youthfulness +youths +yowl +yowled +yowling +yowls +yttrium +yucca +yuccas +yuck +yucked +yuckier +yuckiest +yucking +yucks +yucky +yuk +yukked +yukking +yuks +yule +yuletide +yum +yummier +yummiest +yummy +yup +yuppie +yuppies +yuppy +yups +zanied +zanier +zanies +zaniest +zaniness +zany +zanying +zap +zapped +zapping +zaps +zeal +zealot +zealots +zealous +zealously +zealousness +zebra +zebras +zebu +zebus +zed +zeds +zenith +zeniths +zephyr +zephyrs +zeppelin +zeppelins +zero +zeroed +zeroes +zeroing +zeros +zest +zestful +zestfully +zests +zeta +zigzag +zigzagged +zigzagging +zigzags +zilch +zillion +zillions +zinc +zinced +zincing +zincked +zincking +zincs +zing +zinged +zinger +zingers +zinging +zings +zinnia +zinnias +zip +zipped +zipper +zippered +zippering +zippers +zippier +zippiest +zipping +zippy +zips +zircon +zirconium +zircons +zit +zither +zithers +zits +zodiac +zodiacal +zodiacs +zombie +zombies +zonal +zone +zoned +zones +zoning +zonked +zoo +zoological +zoologist +zoologists +zoology +zoom +zoomed +zooming +zooms +zoos +zucchini +zucchinis +zwieback +zygote +zygotes diff --git a/events/20180427_challenges/index.html b/events/20180427_challenges/index.html new file mode 100644 index 0000000..bf9a44a --- /dev/null +++ b/events/20180427_challenges/index.html @@ -0,0 +1,409 @@ + + + + + + + Monash Data Fluency | Challenges + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Challenges +

+ +
+ + +
+
+ + +
+ +
+
+ + +

27 Apr 2018

+ +

Challenge 1: Vowel Count

+ +

This problem is to get us introduced to string processing. We will be given several lines of text - and for each of them we want to know the number of vowels (i.e. letters a, o, u, i, e, y). Note: that y is regarded as a vowel for purpose of this task.

+ +
    +
  • Each line in the input data file is a test case.
    • +
  • The lines consist only of lowercase English (Latin) letters and spaces.
    • +
  • The answer should contain the number of vowels in each line, separated by spaces.
    • +
+ +

For example:

+ +
input data:
+
+abracadabra
+
+pear tree
+
+o a kak ushakov lil vo kashu kakao
+
+my pyx
+
+answer:
+5 4 13 2
+
+
+ + + +

This challenge is originally from codeabbey and can be found here.

+ +

Challenge 2: Rotate string

+ +

To rotate strings by K characters means to cut these characters from the beginning and transfer them to the end. If K is negative, characters, on contrary should be transferred from the end to the beginning.

+ +
    +
  • Each line in the input data file will contain a number K and some string S separated by space - one pair in each line.
    • +
  • String S will contain only small latin letters. K will not exceed half the length of S by absolute value.
    • +
  • The answer should contain strings rotated in accordance with the rule above, separated by spaces.
    • +
+ +

For example:

+ +
input data:
+
+3 forwhomthebelltolls
+
+-6 verycomplexnumber
+
+answer:
+
+whomthebelltollsfor numberverycomplex
+
+
+ + + +

This challenge is originally from codeabbey and can be found here.

+ +

Challenge Three: Anagrams

+ +

In many natural languages we can find some pairs of words which could be transformed to each other by changing the order of letters. I.e. they consist of the same set of letters, for example:

+ +

cat - act, take - teak, ate - eat - tea

+ +

Such words are called anagrams and as we see in the third example sometimes there are more than two words.

+ +

Your task is to find out the amount of anagrams for a given word in the dictionary.

+ +

Download the dictionary file

+ +

(This dictionary file contains a list of english words, one per line. It was taken from Ubuntu linux distribution and stripped of words containing capital letters, apostrophes and non-english letters.)

+ +
    +
  • Each line in the input data file will contain a single word.
    • +
  • The answer should contain the number of anagrams for each word (not including the word itself).
    • +
+ +

For example:

+ +
input data:
+
+bat
+
+coal
+
+lots
+
+answer:
+1 1 2
+
+
+ +

Download the test data

+ +

This challenge is originally from codeabbey and can be found here.

+ +

Visualisation Challenge:

+ +

Since difficulties in processing household recycling waste have been in the news lately, the Victorian Local Government Waste Services Report 2015-16 make for a topical dataset.

+ +

Report: http://www.sustainability.vic.gov.au/Government/Victorian-Waste-data-portal/Victorian-Local-Government-Annual-Waste-Services-report

+ +

Data: http://www.sustainability.vic.gov.au/-/media/SV/Publications/Government/Victorian-waste-data-portal/Victorian-Local-Government-Annuals-Waste-Services-report/Victorian-Local-Government-Waste-Services-Report-Workbook-201516.xlsx

+ +

For this data fluency challenge; lets visualise how has waste composition and processing changed from 2001-2014, (Data in ‘Key time series tables.’ tab). You could make a static graph, interactive plot or anything you like.

+ +

Additionally - are there any interesting insights to be made from the other data in this document?

+ +

We can share our different approaches on the day.

+ +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/events/20180427_challenges/index.xml b/events/20180427_challenges/index.xml new file mode 100644 index 0000000..00d994d --- /dev/null +++ b/events/20180427_challenges/index.xml @@ -0,0 +1,16 @@ + + + + Challenges on Monash Data Fluency + https://MonashDataFluency.github.io/events/20180427_challenges/ + Recent content in Challenges on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Wed, 25 Apr 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/events/20180427_challenges/page/1/index.html b/events/20180427_challenges/page/1/index.html new file mode 100644 index 0000000..c95e614 --- /dev/null +++ b/events/20180427_challenges/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/events/20180427_challenges/ \ No newline at end of file diff --git a/events/drop-in-session-20180427/index.html b/events/drop-in-session-20180427/index.html new file mode 100644 index 0000000..50e7583 --- /dev/null +++ b/events/drop-in-session-20180427/index.html @@ -0,0 +1,382 @@ + + + + + + + Monash Data Fluency | Drop-in session, 27 Apr 2018 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ EVENTS +

+

+ Drop-in session, 27 Apr 2018 +

+
+
+ + +

Data Fluency Drop-In Session: Friday 27th Apr 2018

+ +

This first drop-in session we intend to:

+ +
    +

  • Run a quick book club discussion on the first 3 chapters of R for Data Science.

    • + +

  • Work together on some coding challenges !

    • + +

  • Ask for help (or provide help !) on your current programming or data analysis problems.

    • +
+ +
+
+ + + +
+ +
+ + + + + + diff --git a/events/drop-in-session-20180525/index.html b/events/drop-in-session-20180525/index.html new file mode 100644 index 0000000..17da1df --- /dev/null +++ b/events/drop-in-session-20180525/index.html @@ -0,0 +1,393 @@ + + + + + + + Monash Data Fluency | Drop-in session, 25 May 2018 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ EVENTS +

+

+ Drop-in session, 25 May 2018 +

+
+
+ + +

Data Fluency Drop-In Session: Friday 25th May, 2pm - 4pm

+ +

This month we will have a short seminar from Matthew Browne, continue the “R for Data Science” book club, as well as the usual help session.

+ +

(Unfortunately Matthew will ill and couldn’t make it - hopefully at a later date !)

+ +

Seminar - The Democratisation of Data Science (2 pm)

+ +

Guest speaker: Matthew Browne, Data Scientist, Monash University

+ +

Book Club - R for Data Science (3 pm)

+ +

We kicked off the Data Fluency Book Club on 27 April 2018 with the aim of sharpening our R skills by reading +“R for Data Science” by Garrett Grolemund and Hadley Wickham. +The book is available online and through the Monash University Library.

+ +

At the April catch up we read up to chapter 3 of this book.

+ +

For the May Book Club discussion, we will look at chapters 4, 5, 6 and 8. These look at the “dplyr” package for data frame manipulation, and how to organize a project in R. (Chapter 7 is a substantial topic, which we will look at in June).

+ +

Please come armed with comments, puzzles or challenges presented within the book, and let’s have an interesting discussion. Section 5.7 in particular may be a puzzle. If this section makes sense, you have achieved a state of dplyr enlightenment!

+ +

General help session (3 pm, concurrent with book club)

+ +

Ask for help (or provide help !) on your current programming or data analysis problems.

+ +
+
+ + + +
+ +
+ + + + + + diff --git a/events/drop-in-session-20180622/index.html b/events/drop-in-session-20180622/index.html new file mode 100644 index 0000000..c42e33a --- /dev/null +++ b/events/drop-in-session-20180622/index.html @@ -0,0 +1,380 @@ + + + + + + + Monash Data Fluency | Events + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ EVENTS +

+

+ Events +

+
+
+ + +

Data Fluency Drop-In Session: Friday 22nd Jun, 2pm - 4pm

+ +

Book Club - R for Data Science (3 pm)

+ +

Our “R for Data Science” book club discussion continues. +Come sharpen your R skills by discussing the content with R experts in the community.

+ +

This time we will be discussing chapter 7 of the book: Exploratory Data Analysis.

+ +

Has your data ever surprised you with the story it told? Has visualizing changed how you subsequently analysed or modelled it? +If you have an interesting image to show, share it on Slack and we can put it up on the projector screen.

+ +

General help session (3 pm onwards, concurrent with book club)

+ +

Ask for help (or provide help !) on your current programming or data analysis problems.

+ +
+
+ + + +
+ +
+ + + + + + diff --git a/events/drop-in-session-20180727/index.html b/events/drop-in-session-20180727/index.html new file mode 100644 index 0000000..43a9941 --- /dev/null +++ b/events/drop-in-session-20180727/index.html @@ -0,0 +1,386 @@ + + + + + + + Monash Data Fluency | Drop-in session, 27 Jul 2018 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ EVENTS +

+

+ Drop-in session, 27 Jul 2018 +

+
+
+ + +

Data Fluency Drop-In Session: Friday 27nd Jul, 2pm - 4pm

+ +

Data Fluency Book Club 2:00 pm July 27th, G19, 15 Innovation Walk (opposite Cinque Lire Cafe).

+ +

Book Club (R for Data Science) & Exploring OpenRefine (2 ~ 3 pm)

+ +

For the Data Fluency book club for this month (2 pm, July 27th), we will be looking at Chapters 9 +through to 16 of “R for Data Science”, which covers “data wrangling”: loading and +tidying data, operations such as spreading, gathering and joining.

+ +

Exploring OpenRefine (2 ~ 3 pm)

+ +

Continuing the data wrangling theme, we will also be spending some time discovering what +OpenRefine can do to clean and transform data. If you’ve never used it, here’s a chance to play +with it and learn together. If you have used it, bring some dirty data and show us what it can do! +Come with your laptop and OpenRefine installed. Download OpenRefine If there is a chance you’ll +want to demonstrate something quickly, have Zoom installed so you can share your screen.

+ +

Once again, this will be more interesting if people bring examples. Strange file formats. +Peculiar spreadsheets. Parsing challenges, solved or unsolved. Desktops can be shared to +the projector using Zoom (https://monash.zoom.us/).

+ +

Also …

+ +

The MelbourneDatathon will have just started so, +for anyone participating in this, one thing we can discuss wrangling is whatever data-set this turns out to be.

+ +
+
+ + + +
+ +
+ + + + + + diff --git a/events/drop-in-session-20180831/index.html b/events/drop-in-session-20180831/index.html new file mode 100644 index 0000000..6d5dedd --- /dev/null +++ b/events/drop-in-session-20180831/index.html @@ -0,0 +1,382 @@ + + + + + + + Monash Data Fluency | Drop-in session, 31 Aug 2018 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ EVENTS +

+

+ Drop-in session, 31 Aug 2018 +

+
+
+ + +

Data Fluency Drop-In Session: Friday 31st Aug, 2pm - 4pm

+ +

Guest speaker

+ +

Guest speaker, Juan Nunez-Iglesias, Bio Image Analysis Research Fellow from Monash Micro Imaging will present A whirlwind introduction to image analysis in Python at our next event.

+ +

Date and Time: Friday, August 31st at 2:00 pm in G19, 15 Innovation Walk (opposite Cinque Lire cafe), Clayton campus.

+ +

Data Fluency Book Club

+ +

Immediately after Juan’s presentation we will be hosting the Data Fluency Book Club. In this session, we will discuss Chapters 17 - 21 of “R for Data Science” on programming and data structures. The goal of these chapters is to learn more about the level of programming required to practice data science. We will be discussing the specifics of pipes, functions, vectors, and iteration, which is another tool for reducing code duplication.

+ +

This session is building toward the next section on models, which gets pretty wild.

+ +

Please feel free to share your data images on Slack, and we will show them on the projector during the book club.

+ +

Have an idea for a future seminar ? Email us at: datafluency@monash.edu.

+ +
+
+ + + +
+ +
+ + + + + + diff --git a/events/index.html b/events/index.html new file mode 100644 index 0000000..792403a --- /dev/null +++ b/events/index.html @@ -0,0 +1,475 @@ + + + + + + + Monash Data Fluency | Events + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Events +

+ +
+ + +
+
+ + +
+ +
+
+ + +

+ +

Events are now listed on the central Data Fluency website

+ +

Weekly drop-in sessions

+ +

Drop-in sessions run every Friday, 3 - 4 pm, Room G19, 15 Innovation Walk (opposite Cinque Lire cafe).

+ +

Please drop by to mingle with other data fluency community members.

+ +

Show people what you are working on, ask for help or share your expertise with others.

+ +

On the last Friday of the month we will also be host a book club and a coding / data analysis challenges. +These may also sometimes feature a seminar from 2pm.

+ +
+ +

Next Data Fluency Drop-In Session: Friday 26th Oct, 2pm - 4pm

+ +

Location: G19, 15 Innovation Walk (opposite Cinque Lire cafe), Clayton campus.

+ +

Guest speaker, 2pm

+ +

Daniel Buzacott, Manager - Integration, eSolutions, will present Integration at Monash: Getting to the data you need at our next event.

+ +

Data Fluency Book Club

+ +

After the presentation by our guest speaker we will be hosting the Data Fluency Book Club. In this month’s book club we are aiming to finish “R for Data Science”, mainly focussing on the section on modelling, Chapters 22-25. Hadley and Garrett have made a very interesting attempt at rendering this important topic intuitive http://r4ds.had.co.nz

+ +

Please feel free to share your data images on Slack, and we will show them on the projector during the book club.

+ +

Do you have a suggestion for future seminar ? Email us at: datafluency@monash.edu.

+ +
+ +

For updates, join the mailing list +and follow us on Twitter (@resdataflu).

+ +
+
+
+ +
+ +
+
+
+ Events +

+ + Drop-in session, 31 Aug 2018 + +

+
+ Data Fluency Drop-In Session: Friday 31st Aug, 2pm - 4pm Guest speaker Guest speaker, Juan Nunez-Iglesias, Bio Image Analysis Research Fellow from Monash Micro Imaging will present A whirlwind introduction to image analysis in Python at our next event. +Date and Time: Friday, August 31st at 2:00 pm in G19, 15 Innovation Walk (opposite Cinque Lire cafe), Clayton campus. +Data Fluency Book Club Immediately after Juan’s presentation we will be hosting the Data Fluency Book Club. +
+
+
+ +
+ +
+
+
+ Events +

+ + Events + +

+
+ Data Fluency Drop-In Session: Friday 22nd Jun, 2pm - 4pm Book Club - R for Data Science (3 pm) Our “R for Data Science” book club discussion continues. Come sharpen your R skills by discussing the content with R experts in the community. +This time we will be discussing chapter 7 of the book: Exploratory Data Analysis. +Has your data ever surprised you with the story it told? Has visualizing changed how you subsequently analysed or modelled it? +
+
+
+ +
+ +
+
+
+ Events +

+ + Drop-in session, 25 May 2018 + +

+
+ Data Fluency Drop-In Session: Friday 25th May, 2pm - 4pm This month we will have a short seminar from Matthew Browne, continue the “R for Data Science” book club, as well as the usual help session. +(Unfortunately Matthew will ill and couldn’t make it - hopefully at a later date !) +Seminar - The Democratisation of Data Science (2 pm) Guest speaker: Matthew Browne, Data Scientist, Monash University +Book Club - R for Data Science (3 pm) We kicked off the Data Fluency Book Club on 27 April 2018 with the aim of sharpening our R skills by reading “R for Data Science” by Garrett Grolemund and Hadley Wickham. +
+
+
+ +
+ +
+
+
+ Events +

+ + Drop-in session, 27 Apr 2018 + +

+
+ Data Fluency Drop-In Session: Friday 27th Apr 2018 This first drop-in session we intend to: + Run a quick book club discussion on the first 3 chapters of R for Data Science. + Work together on some coding challenges ! + Ask for help (or provide help !) on your current programming or data analysis problems. + +
+
+
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+ + 23 Mar 2018 : Launch workshop - Welcome to Data Fluency + +

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+ Launch workshop Build your data fluency through ongoing skill development opportunities. Take advantage of free workshops, weekly drop-in sessions, hackathon events, technical talks, and more. Become part of our growing community of practice. +The Data Fluency program will be launched on 23 March at 10am. Find out more about Data Fluency. +We will running workshops at the Matheson Library, 1pm - 5pm. Make sure you register for the workshops to get a spot. +
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+ + + + + + diff --git a/events/index.xml b/events/index.xml new file mode 100644 index 0000000..245d884 --- /dev/null +++ b/events/index.xml @@ -0,0 +1,84 @@ + + + + Events on Monash Data Fluency + https://MonashDataFluency.github.io/events/ + Recent content in Events on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Mon, 16 Jul 2018 11:28:03 +1100 + + + + + + Drop-in session, 31 Aug 2018 + https://MonashDataFluency.github.io/events/drop-in-session-20180831/ + Fri, 31 Aug 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/events/drop-in-session-20180831/ + Data Fluency Drop-In Session: Friday 31st Aug, 2pm - 4pm Guest speaker Guest speaker, Juan Nunez-Iglesias, Bio Image Analysis Research Fellow from Monash Micro Imaging will present A whirlwind introduction to image analysis in Python at our next event. +Date and Time: Friday, August 31st at 2:00 pm in G19, 15 Innovation Walk (opposite Cinque Lire cafe), Clayton campus. +Data Fluency Book Club Immediately after Juan&rsquo;s presentation we will be hosting the Data Fluency Book Club. + + + + Events + https://MonashDataFluency.github.io/events/drop-in-session-20180622/ + Sat, 23 Jun 2018 11:28:03 +1100 + + https://MonashDataFluency.github.io/events/drop-in-session-20180622/ + Data Fluency Drop-In Session: Friday 22nd Jun, 2pm - 4pm Book Club - R for Data Science (3 pm) Our &ldquo;R for Data Science&rdquo; book club discussion continues. Come sharpen your R skills by discussing the content with R experts in the community. +This time we will be discussing chapter 7 of the book: Exploratory Data Analysis. +Has your data ever surprised you with the story it told? Has visualizing changed how you subsequently analysed or modelled it? + + + + Drop-in session, 25 May 2018 + https://MonashDataFluency.github.io/events/drop-in-session-20180525/ + Wed, 25 Apr 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/events/drop-in-session-20180525/ + Data Fluency Drop-In Session: Friday 25th May, 2pm - 4pm This month we will have a short seminar from Matthew Browne, continue the &ldquo;R for Data Science&rdquo; book club, as well as the usual help session. +(Unfortunately Matthew will ill and couldn&rsquo;t make it - hopefully at a later date !) +Seminar - The Democratisation of Data Science (2 pm) Guest speaker: Matthew Browne, Data Scientist, Monash University +Book Club - R for Data Science (3 pm) We kicked off the Data Fluency Book Club on 27 April 2018 with the aim of sharpening our R skills by reading &ldquo;R for Data Science&rdquo; by Garrett Grolemund and Hadley Wickham. + + + + Drop-in session, 27 Apr 2018 + https://MonashDataFluency.github.io/events/drop-in-session-20180427/ + Wed, 25 Apr 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/events/drop-in-session-20180427/ + Data Fluency Drop-In Session: Friday 27th Apr 2018 This first drop-in session we intend to: + Run a quick book club discussion on the first 3 chapters of R for Data Science. + Work together on some coding challenges ! + Ask for help (or provide help !) on your current programming or data analysis problems. + + + + + 23 Mar 2018 : Launch workshop - Welcome to Data Fluency + https://MonashDataFluency.github.io/events/launch-workshop-23-mar-2018/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/events/launch-workshop-23-mar-2018/ + Launch workshop Build your data fluency through ongoing skill development opportunities. Take advantage of free workshops, weekly drop-in sessions, hackathon events, technical talks, and more. Become part of our growing community of practice. +The Data Fluency program will be launched on 23 March at 10am. Find out more about Data Fluency. +We will running workshops at the Matheson Library, 1pm - 5pm. Make sure you register for the workshops to get a spot. + + + + Drop-in session, 27 Jul 2018 + https://MonashDataFluency.github.io/events/drop-in-session-20180727/ + Mon, 01 Jan 0001 00:00:00 +0000 + + https://MonashDataFluency.github.io/events/drop-in-session-20180727/ + Data Fluency Drop-In Session: Friday 27nd Jul, 2pm - 4pm Data Fluency Book Club 2:00 pm July 27th, G19, 15 Innovation Walk (opposite Cinque Lire Cafe). +Book Club (R for Data Science) &amp; Exploring OpenRefine (2 ~ 3 pm) For the Data Fluency book club for this month (2 pm, July 27th), we will be looking at Chapters 9 through to 16 of &ldquo;R for Data Science&rdquo;, which covers &ldquo;data wrangling&rdquo;: loading and tidying data, operations such as spreading, gathering and joining. + + + + \ No newline at end of file diff --git a/events/launch-workshop-23-mar-2018/index.html b/events/launch-workshop-23-mar-2018/index.html new file mode 100644 index 0000000..39c44cf --- /dev/null +++ b/events/launch-workshop-23-mar-2018/index.html @@ -0,0 +1,382 @@ + + + + + + + Monash Data Fluency | 23 Mar 2018 : Launch workshop - Welcome to Data Fluency + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ EVENTS +

+

+ 23 Mar 2018 : Launch workshop - Welcome to Data Fluency +

+
+
+ + +

Launch workshop

+ +

Build your data fluency through ongoing skill development opportunities. +Take advantage of free workshops, weekly drop-in sessions, hackathon events, +technical talks, and more. Become part of our growing community of practice.

+ +

The Data Fluency program will be launched on 23 March at 10am. +Find out more about Data Fluency.

+ +

We will running workshops at the Matheson Library, 1pm - 5pm. +Make sure you register for the workshops to get a spot.

+ +

Workshop 1: Intro to Python

+ +

Workshop 2: Deep Learning

+ +

Workshop 3: Introduction to R

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+ + + + + + diff --git a/events/page/1/index.html b/events/page/1/index.html new file mode 100644 index 0000000..2096a89 --- /dev/null +++ b/events/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/events/ \ No newline at end of file diff --git a/events/page/2/index.html b/events/page/2/index.html new file mode 100644 index 0000000..6ccb75f --- /dev/null +++ b/events/page/2/index.html @@ -0,0 +1,395 @@ + + + + + + + Monash Data Fluency | Events + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Events +

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Events are now listed on the central Data Fluency website

+ +

Weekly drop-in sessions

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Drop-in sessions run every Friday, 3 - 4 pm, Room G19, 15 Innovation Walk (opposite Cinque Lire cafe).

+ +

Please drop by to mingle with other data fluency community members.

+ +

Show people what you are working on, ask for help or share your expertise with others.

+ +

On the last Friday of the month we will also be host a book club and a coding / data analysis challenges. +These may also sometimes feature a seminar from 2pm.

+ +
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Next Data Fluency Drop-In Session: Friday 26th Oct, 2pm - 4pm

+ +

Location: G19, 15 Innovation Walk (opposite Cinque Lire cafe), Clayton campus.

+ +

Guest speaker, 2pm

+ +

Daniel Buzacott, Manager - Integration, eSolutions, will present Integration at Monash: Getting to the data you need at our next event.

+ +

Data Fluency Book Club

+ +

After the presentation by our guest speaker we will be hosting the Data Fluency Book Club. In this month’s book club we are aiming to finish “R for Data Science”, mainly focussing on the section on modelling, Chapters 22-25. Hadley and Garrett have made a very interesting attempt at rendering this important topic intuitive http://r4ds.had.co.nz

+ +

Please feel free to share your data images on Slack, and we will show them on the projector during the book club.

+ +

Do you have a suggestion for future seminar ? Email us at: datafluency@monash.edu.

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For updates, join the mailing list +and follow us on Twitter (@resdataflu).

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+ Events +

+ + Drop-in session, 27 Jul 2018 + +

+
+ Data Fluency Drop-In Session: Friday 27nd Jul, 2pm - 4pm Data Fluency Book Club 2:00 pm July 27th, G19, 15 Innovation Walk (opposite Cinque Lire Cafe). +Book Club (R for Data Science) & Exploring OpenRefine (2 ~ 3 pm) For the Data Fluency book club for this month (2 pm, July 27th), we will be looking at Chapters 9 through to 16 of “R for Data Science”, which covers “data wrangling”: loading and tidying data, operations such as spreading, gathering and joining. +
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+ Monash Data Fluency +

+ +
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Learning Resources

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Here you can find links to workshop pages and learning resources for Monash Data Fluency events.

+ + + +

Upcoming workshops

+ +

Upcoming workshops will be listed on the main Data Fluency website and announced on the mailing list.

+ +
+ +

Past Workshops

+ +

Dec 2018

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14 Nov 2018

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3 Aug 2018

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29th Jun 2018

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31 May 2018

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01 Jun 2018

+ + + +

23 Mar 2018

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+ + + + + + diff --git a/index.xml b/index.xml new file mode 100644 index 0000000..e78233b --- /dev/null +++ b/index.xml @@ -0,0 +1,136 @@ + + + + Monash Data Fluency + https://MonashDataFluency.github.io/ + Recent content on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 23 Feb 2018 13:28:03 +1100 + + + + + + Drop-in session, 31 Aug 2018 + https://MonashDataFluency.github.io/events/drop-in-session-20180831/ + Fri, 31 Aug 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/events/drop-in-session-20180831/ + Data Fluency Drop-In Session: Friday 31st Aug, 2pm - 4pm Guest speaker Guest speaker, Juan Nunez-Iglesias, Bio Image Analysis Research Fellow from Monash Micro Imaging will present A whirlwind introduction to image analysis in Python at our next event. +Date and Time: Friday, August 31st at 2:00 pm in G19, 15 Innovation Walk (opposite Cinque Lire cafe), Clayton campus. +Data Fluency Book Club Immediately after Juan&rsquo;s presentation we will be hosting the Data Fluency Book Club. + + + + Events + https://MonashDataFluency.github.io/events/drop-in-session-20180622/ + Sat, 23 Jun 2018 11:28:03 +1100 + + https://MonashDataFluency.github.io/events/drop-in-session-20180622/ + Data Fluency Drop-In Session: Friday 22nd Jun, 2pm - 4pm Book Club - R for Data Science (3 pm) Our &ldquo;R for Data Science&rdquo; book club discussion continues. Come sharpen your R skills by discussing the content with R experts in the community. +This time we will be discussing chapter 7 of the book: Exploratory Data Analysis. +Has your data ever surprised you with the story it told? Has visualizing changed how you subsequently analysed or modelled it? + + + + Drop-in session, 25 May 2018 + https://MonashDataFluency.github.io/events/drop-in-session-20180525/ + Wed, 25 Apr 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/events/drop-in-session-20180525/ + Data Fluency Drop-In Session: Friday 25th May, 2pm - 4pm This month we will have a short seminar from Matthew Browne, continue the &ldquo;R for Data Science&rdquo; book club, as well as the usual help session. +(Unfortunately Matthew will ill and couldn&rsquo;t make it - hopefully at a later date !) +Seminar - The Democratisation of Data Science (2 pm) Guest speaker: Matthew Browne, Data Scientist, Monash University +Book Club - R for Data Science (3 pm) We kicked off the Data Fluency Book Club on 27 April 2018 with the aim of sharpening our R skills by reading &ldquo;R for Data Science&rdquo; by Garrett Grolemund and Hadley Wickham. + + + + Drop-in session, 27 Apr 2018 + https://MonashDataFluency.github.io/events/drop-in-session-20180427/ + Wed, 25 Apr 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/events/drop-in-session-20180427/ + Data Fluency Drop-In Session: Friday 27th Apr 2018 This first drop-in session we intend to: + Run a quick book club discussion on the first 3 chapters of R for Data Science. + Work together on some coding challenges ! + Ask for help (or provide help !) on your current programming or data analysis problems. + + + + + 01 Programming in Python (Introduction) + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/intro/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/intro/ + The Basics of Python Python is a general purpose programming language that supports rapid development of scripts and applications. +Python&rsquo;s main advantages: + Open Source software, supported by Python Software Foundation Available on all platforms (ie. Windows, Linux and MacOS) It is a general-purpose programming language Supports multiple programming paradigms Very large community with a rich ecosystem of third-party packages Interpreter Python is an interpreted language which can be used in two ways: + + + + 02 Data Analysis in Python + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/working_with_data/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/working_with_data/ + Working With Pandas DataFrames We can automate the process of performing data manipulations in Python. It&rsquo;s efficient to spend time building the code to perform these tasks because once it&rsquo;s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis. +The Dataset For this lesson, we will be using the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA + + + + 03 Indexing, Slicing and Subsetting + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/indexing/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/indexing/ + In lesson 02, we read a CSV into a Python pandas DataFrame. We learned: + How to save the DataFrame to a named object, How to perform basic math on the data, How to calculate summary statistics, and How to create basic plots of the data. In this lesson, we will explore ways to access different parts of the data using: + Indexing, Slicing, and Subsetting Loading our data We will continue to use the surveys dataset that we worked with in the last lesson. + + + + 04 Automation with Loops + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/loops/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/loops/ + An example task that we might want to repeat is printing each character in a word on a line of its own. +word = 'lead' We can access a character in a string using its index. For example, we can get the first character of the word 'lead', by using word[0]. One way to print each character is to use four print statements: +print(word[0]) print(word[1]) print(word[2]) print(word[3]) Gives output + + + + 05 Making Plots With ggplot + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/plotting/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/plotting/ + Introduction Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. + + + + 23 Mar 2018 : Launch workshop - Welcome to Data Fluency + https://MonashDataFluency.github.io/events/launch-workshop-23-mar-2018/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/events/launch-workshop-23-mar-2018/ + Launch workshop Build your data fluency through ongoing skill development opportunities. Take advantage of free workshops, weekly drop-in sessions, hackathon events, technical talks, and more. Become part of our growing community of practice. +The Data Fluency program will be launched on 23 March at 10am. Find out more about Data Fluency. +We will running workshops at the Matheson Library, 1pm - 5pm. Make sure you register for the workshops to get a spot. + + + + Drop-in session, 27 Jul 2018 + https://MonashDataFluency.github.io/events/drop-in-session-20180727/ + Mon, 01 Jan 0001 00:00:00 +0000 + + https://MonashDataFluency.github.io/events/drop-in-session-20180727/ + Data Fluency Drop-In Session: Friday 27nd Jul, 2pm - 4pm Data Fluency Book Club 2:00 pm July 27th, G19, 15 Innovation Walk (opposite Cinque Lire Cafe). +Book Club (R for Data Science) &amp; Exploring OpenRefine (2 ~ 3 pm) For the Data Fluency book club for this month (2 pm, July 27th), we will be looking at Chapters 9 through to 16 of &ldquo;R for Data Science&rdquo;, which covers &ldquo;data wrangling&rdquo;: loading and tidying data, operations such as spreading, gathering and joining. + + + + \ No newline at end of file diff --git a/intro_to_machine_learning/index.html b/intro_to_machine_learning/index.html new file mode 100644 index 0000000..8a06ba5 --- /dev/null +++ b/intro_to_machine_learning/index.html @@ -0,0 +1,297 @@ + + + + + + + Monash Data Fluency | Machine Learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Machine Learning +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to Tensor Flow and Machine Learning Workshop

+ +
    +
  • Date : 23rd March 2018
    • +
  • Time : 1:00pm to 5:00pm
    • +
  • Location : L1.04, Sir Louis Matheson Library, Clayton, VIC
    • +
  • Instructor : Jason Rigby
    • +
  • Helpers : Shenjun Zhong, Sonika Tyagi
    • +
+ +

This workshop will cover the basic principles behind the use of TensorFlow, a popular open-source machine learning and general numerical computing framework supported by Google.

+ +

General Information

+ +

The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who: The course is aimed at graduate students and other researchers. Basic working python knowledge is required.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
13:00Welcome
13:10TensorFlow graphs
13:30Logistic Regression
15:30Tea break
15:45Neural Networks
17:00Finish
+ +

Course material

+ + + +

Shared Notepad

+ + + +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/intro_to_machine_learning/index.xml b/intro_to_machine_learning/index.xml new file mode 100644 index 0000000..f1d899f --- /dev/null +++ b/intro_to_machine_learning/index.xml @@ -0,0 +1,16 @@ + + + + Machine Learning on Monash Data Fluency + https://MonashDataFluency.github.io/intro_to_machine_learning/ + Recent content in Machine Learning on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 23 Feb 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/intro_to_machine_learning/page/1/index.html b/intro_to_machine_learning/page/1/index.html new file mode 100644 index 0000000..16dd2a0 --- /dev/null +++ b/intro_to_machine_learning/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/intro_to_machine_learning/ \ No newline at end of file diff --git a/intro_to_python/index.html b/intro_to_python/index.html new file mode 100644 index 0000000..6b59c94 --- /dev/null +++ b/intro_to_python/index.html @@ -0,0 +1,389 @@ + + + + + + + Monash Data Fluency | Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Python +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to Python Workshop

+ +
    +
  • Date : 23rd March 2018
    • +
  • Time : 1:00pm to 5:00pm
    • +
  • Location : T1, Sir Louis Matheson Library, Clayton, VIC
    • +
  • Instructors : Anup Shah, Kirill Tsyganov
    • +
  • Helpers : Simon Michnowicz, Michael See
    • +
+ +

This hands-on workshop aims to equip participants with the fundamentals of programming and give them skills needed to apply data analysis approaches to their research questions.

+ +

General Information

+ +

The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
13:00Welcome
13:15Session 1
15:15Break
15:30Session 2
16:45Wrap-up
+ +

Login Information

+ + + +

Shared Notepad

+ + + +

Syllabus

+ + + +
+
+
+ +
+ +
+
+
+ Intro to python +

+ + 01 Programming in Python (Introduction) + +

+
+ The Basics of Python Python is a general purpose programming language that supports rapid development of scripts and applications. +Python’s main advantages: + Open Source software, supported by Python Software Foundation Available on all platforms (ie. Windows, Linux and MacOS) It is a general-purpose programming language Supports multiple programming paradigms Very large community with a rich ecosystem of third-party packages Interpreter Python is an interpreted language which can be used in two ways: +
+
+
+ +
+ +
+
+
+ Intro to python +

+ + 02 Data Analysis in Python + +

+
+ Working With Pandas DataFrames We can automate the process of performing data manipulations in Python. It’s efficient to spend time building the code to perform these tasks because once it’s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis. +The Dataset For this lesson, we will be using the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA +
+
+
+ +
+ +
+
+
+ Intro to python +

+ + 03 Indexing, Slicing and Subsetting + +

+
+ In lesson 02, we read a CSV into a Python pandas DataFrame. We learned: + How to save the DataFrame to a named object, How to perform basic math on the data, How to calculate summary statistics, and How to create basic plots of the data. In this lesson, we will explore ways to access different parts of the data using: + Indexing, Slicing, and Subsetting Loading our data We will continue to use the surveys dataset that we worked with in the last lesson. +
+
+
+ +
+ +
+
+
+ Intro to python +

+ + 04 Automation with Loops + +

+
+ An example task that we might want to repeat is printing each character in a word on a line of its own. +word = 'lead' We can access a character in a string using its index. For example, we can get the first character of the word 'lead', by using word[0]. One way to print each character is to use four print statements: +print(word[0]) print(word[1]) print(word[2]) print(word[3]) Gives output +
+
+
+ +
+ +
+
+
+ Intro to python +

+ + 05 Making Plots With ggplot + +

+
+ Introduction Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/intro_to_python/index.xml b/intro_to_python/index.xml new file mode 100644 index 0000000..08c43b9 --- /dev/null +++ b/intro_to_python/index.xml @@ -0,0 +1,68 @@ + + + + Python on Monash Data Fluency + https://MonashDataFluency.github.io/intro_to_python/ + Recent content in Python on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 23 Feb 2018 13:28:03 +1100 + + + + + + 01 Programming in Python (Introduction) + https://MonashDataFluency.github.io/intro_to_python/intro/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/intro_to_python/intro/ + The Basics of Python Python is a general purpose programming language that supports rapid development of scripts and applications. +Python&rsquo;s main advantages: + Open Source software, supported by Python Software Foundation Available on all platforms (ie. Windows, Linux and MacOS) It is a general-purpose programming language Supports multiple programming paradigms Very large community with a rich ecosystem of third-party packages Interpreter Python is an interpreted language which can be used in two ways: + + + + 02 Data Analysis in Python + https://MonashDataFluency.github.io/intro_to_python/working_with_data/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/intro_to_python/working_with_data/ + Working With Pandas DataFrames We can automate the process of performing data manipulations in Python. It&rsquo;s efficient to spend time building the code to perform these tasks because once it&rsquo;s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis. +The Dataset For this lesson, we will be using the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA + + + + 03 Indexing, Slicing and Subsetting + https://MonashDataFluency.github.io/intro_to_python/indexing/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/intro_to_python/indexing/ + In lesson 02, we read a CSV into a Python pandas DataFrame. We learned: + How to save the DataFrame to a named object, How to perform basic math on the data, How to calculate summary statistics, and How to create basic plots of the data. In this lesson, we will explore ways to access different parts of the data using: + Indexing, Slicing, and Subsetting Loading our data We will continue to use the surveys dataset that we worked with in the last lesson. + + + + 04 Automation with Loops + https://MonashDataFluency.github.io/intro_to_python/loops/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/intro_to_python/loops/ + An example task that we might want to repeat is printing each character in a word on a line of its own. +word = 'lead' We can access a character in a string using its index. For example, we can get the first character of the word 'lead', by using word[0]. One way to print each character is to use four print statements: +print(word[0]) print(word[1]) print(word[2]) print(word[3]) Gives output + + + + 05 Making Plots With ggplot + https://MonashDataFluency.github.io/intro_to_python/plotting/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/intro_to_python/plotting/ + Introduction Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. + + + + \ No newline at end of file diff --git a/intro_to_python/indexing/index.html b/intro_to_python/indexing/index.html new file mode 100644 index 0000000..33c92a8 --- /dev/null +++ b/intro_to_python/indexing/index.html @@ -0,0 +1,710 @@ + + + + + + + Monash Data Fluency | 03 Indexing, Slicing and Subsetting + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ INTRO TO PYTHON +

+

+ 03 Indexing, Slicing and Subsetting +

+
+
+ + +

In lesson 02, we read a CSV into a Python pandas DataFrame. We learned:

+ +
    +
  • How to save the DataFrame to a named object,
    • +
  • How to perform basic math on the data,
    • +
  • How to calculate summary statistics, and
    • +
  • How to create basic plots of the data.
    • +
+ +

In this lesson, we will explore ways to access different parts of the data +using:

+ +
    +
  • Indexing,
    • +
  • Slicing, and
    • +
  • Subsetting
    • +
+ +

Loading our data

+ +

We will continue to use the surveys dataset that we worked with in the last lesson. Let’s reopen and read in the data again:

+ +
# Make sure pandas is loaded
+import pandas as pd
+
+# Read in the survey CSV
+surveys_df = pd.read_csv("data/surveys.csv")
+
+ +

Indexing and Slicing in Python

+ +

We often want to work with subsets of a DataFrame object. There are +different ways to accomplish this including: using labels (column headings), +numeric ranges, or specific x,y index locations.

+ +

Selecting data using Labels (Column Headings)

+ +

We use square brackets [] to select a subset of an Python object. For example, +we can select all data from a column named species_id from the surveys_df +DataFrame by name. There are two ways to do this:

+ +
# Method 1: select a 'subset' of the data using the column name
+surveys_df['species_id'].head()
+
+# Method 2: use the column name as an 'attribute'; gives the same output
+surveys_df.species_id.head()
+
+ +

We can also create a new object that contains only the data within the +species_id column as follows:

+ +
# Creates an object, surveys_species, that only contains the `species_id` column
+surveys_species = surveys_df['species_id']
+
+ +

We can pass a list of column names too, as an index to select columns in that +order. This is useful when we need to reorganize our data.

+ +

NOTE: If a column name is not contained in the DataFrame, an exception +(error) will be raised.

+ +
# Select the species and plot columns from the DataFrame
+surveys_df[['species_id', 'plot_id']].head()
+
+# What happens when you flip the order?
+surveys_df[['plot_id', 'species_id']].head()
+
+# What happens if you ask for a column that doesn't exist?
+surveys_df['speciess']
+
+ +

Python tells us what type of error it is in the traceback, at the bottom it says KeyError: 'speciess' which means that speciess is not a column name (or Key in the related python data type dictionary).

+ +

Extracting Range based Subsets: Slicing

+ +

REMINDER: Python Uses 0-based Indexing

+ +

Let’s remind ourselves that Python uses 0-based +indexing. This means that the first element in an object is located at position +0. This is different from other tools like R and Matlab that index elements +within objects starting at 1.

+ +
# Create a list of numbers:
+a = [1, 2, 3, 4, 5]
+
+ +

indexing diagram +slicing diagram

+ +

Challenge - Extracting data

+ +
    +

  1. What value does the code a[0] return?

    2. + +

  2. How about this: a[5]

    3. + +

  3. In the example above, calling a[5] returns an error. Why is that?

    4. + +

  4. What about a[len(a)] ?

    5. +
+ +

Slicing Subsets of Rows in Python

+ +

Slicing using the [] operator selects a set of rows and/or columns from a +DataFrame. To slice out a set of rows, you use the following syntax: +data[start:stop]. When slicing in pandas the start bound is included in the +output. The stop bound is one step BEYOND the row you want to select. So if you +want to select rows 0, 1 and 2 your code would look like this:

+ +
# Select rows 0, 1, 2 (row 3 is not selected)
+surveys_df[0:3]
+
+ +

The stop bound in Python is different from what you might be used to in +languages like Matlab and R.

+ +
# Select the first 5 rows (rows 0, 1, 2, 3, 4)
+surveys_df[:5]
+
+# Select the last element in the list
+# (the slice starts at the last element, and ends at the end of the list)
+surveys_df[-1:]
+
+ +

We can also reassign values within subsets of our DataFrame.

+ +

Let’s create a brand new clean dataframe from +the original data CSV file.

+ +
surveys_df = pd.read_csv("data/surveys.csv")
+
+ +

Slicing Subsets of Rows and Columns in Python

+ +

We can select specific ranges of our data in both the row and column directions +using either label or integer-based indexing.

+ +
    +
  • loc is primarily label based indexing. Integers may be used but +they are interpreted as a label.
    • +
  • iloc is primarily integer based indexing
    • +
+ +

To select a subset of rows and columns from our DataFrame, we can use the +iloc method. For example, we can select month, day and year (columns 2, 3 +and 4 if we start counting at 1), like this:

+ +
# iloc[row slicing, column slicing]
+surveys_df.iloc[0:3, 1:4]
+
+ +

which gives the output

+ +
   month  day  year
+0      7   16  1977
+1      7   16  1977
+2      7   16  1977
+
+ +

Notice that we asked for a slice from 0:3. This yielded 3 rows of data. When you +ask for 0:3, you are actually telling Python to start at index 0 and select rows +0, 1, 2 up to but not including 3.

+ +

Let’s explore some other ways to index and select subsets of data:

+ +
# Select all columns for rows of index values 0 and 10
+surveys_df.loc[[0, 10], :]
+
+# What does this do?
+surveys_df.loc[0, ['species_id', 'plot_id', 'weight']]
+
+# What happens when you type the code below?
+surveys_df.loc[[0, 10, 35549], :]
+
+ +

NOTE: Labels must be found in the DataFrame or you will get a KeyError.

+ +

Indexing by labels loc differs from indexing by integers iloc. +With loc, the both start bound and the stop bound are inclusive. When using +loc, integers can be used, but the integers refer to the +index label and not the position. For example, using loc and select 1:4 +will get a different result than using iloc to select rows 1:4.

+ +

We can also select a specific data value using a row and +column location within the DataFrame and iloc indexing:

+ +
# Syntax for iloc indexing to finding a specific data element
+dat.iloc[row, column]
+
+ +

In this iloc example,

+ +
surveys_df.iloc[2, 6]
+
+ +

gives the output

+ +
'F'
+
+ +

Remember that Python indexing begins at 0. So, the index location [2, 6] +selects the element that is 3 rows down and 7 columns over in the DataFrame.

+ +

Challenge - Range

+ +
    +

  1. What happens when you execute:

    + +
      +
    • surveys_df[0:1]
      • +
    • surveys_df[:4]
      • +
    • surveys_df[:-1]
      • +
    2. + +

  2. What happens when you call:

    + +
      +
    • surveys_df.iloc[0:4, 1:4]
      • +
    3. +
+ +

Subsetting Data using Criteria

+ +

We can also select a subset of our data using criteria. For example, we can +select all rows that have a year value of 2002:

+ +
surveys_df[surveys_df.year == 2002].head()
+
+ +

Which produces the following output:

+ +
record_id  month  day  year  plot_id species_id  sex  hindfoot_length  weight
+33320      33321      1   12  2002        1         DM    M     38      44
+33321      33322      1   12  2002        1         DO    M     37      58
+33322      33323      1   12  2002        1         PB    M     28      45
+33323      33324      1   12  2002        1         AB  NaN    NaN     NaN
+33324      33325      1   12  2002        1         DO    M     35      29
+...
+35544      35545     12   31  2002       15         AH  NaN    NaN     NaN
+35545      35546     12   31  2002       15         AH  NaN    NaN     NaN
+35546      35547     12   31  2002       10         RM    F     15      14
+35547      35548     12   31  2002        7         DO    M     36      51
+35548      35549     12   31  2002        5        NaN  NaN    NaN     NaN
+
+[2229 rows x 9 columns]
+
+ +

Or we can select all rows that do not contain the year 2002:

+ +
surveys_df[surveys_df.year != 2002]
+
+ +

We can define sets of criteria too:

+ +
surveys_df[(surveys_df.year >= 1980) & (surveys_df.year <= 1985)]
+
+ +

Python Syntax Cheat Sheet

+ +

Use can use the syntax below when querying data by criteria from a DataFrame. +Experiment with selecting various subsets of the “surveys” data.

+ +
    +
  • Equals: ==
    • +
  • Not equals: !=
    • +
  • Greater than, less than: > or <
    • +
  • Greater than or equal to >=
    • +
  • Less than or equal to <=
    • +
+ +

Challenge - Queries

+ +
    +

  1. Select a subset of rows in the surveys_df DataFrame that contain data from +the year 1999 and that contain weight values less than or equal to 8. How +many rows did you end up with? What did your neighbor get?

    2. + +

  2. (Extra) You can use the isin command in Python to query a DataFrame based upon a +list of values as follows:

    3. +
+ +
    surveys_df[surveys_df['species_id'].isin([listGoesHere])]
+
+ +

Use the isin function to find all plots that contain particular species +in the “surveys” DataFrame. How many records contain these values?

+ +
    +

  1. (Extra) Experiment with other queries. Create a query that finds all rows with a +weight value > or equal to 0.

    2. + +

  2. (Extra) The ~ symbol in Python can be used to return the OPPOSITE of the +selection that you specify in Python. It is equivalent to is not in. +Write a query that selects all rows with sex NOT equal to ’M’ or ‘F’ in +the “surveys” data.

    3. +
+ +

Using masks to identify a specific condition

+ +

A mask can be useful to locate where a particular subset of values exist or +don’t exist - for example, NaN, or “Not a Number” values. To understand masks, +we also need to understand BOOLEAN objects in Python.

+ +

Boolean values include True or False. For example,

+ +
# Set x to 5
+x = 5
+
+# What does the code below return?
+x > 5
+
+# How about this?
+x == 5
+
+ +

Missing Values

+ +

Let’s try this out. Let’s identify all locations in the survey data that have +null (missing or NaN) data values. We can use the isnull method to do this. +The isnull method will compare each cell with a null value. If an element +has a null value, it will be assigned a value of True in the output object.

+ +
pd.isnull(surveys_df).head()
+
+ +

A snippet of the output is below:

+ +
      record_id  month    day   year plot_id species_id    sex  hindfoot_length weight
+0         False  False  False  False   False      False  False   False      True
+1         False  False  False  False   False      False  False   False      True
+2         False  False  False  False   False      False  False   False      True
+3         False  False  False  False   False      False  False   False      True
+4         False  False  False  False   False      False  False   False      True
+
+[35549 rows x 9 columns]
+
+ +

To select the rows where there are null values, we can use +the mask as an index to subset our data as follows:

+ +
# To select just the rows with NaN values, we can use the 'any()' method
+surveys_df[pd.isnull(surveys_df).any(axis=1)]
+
+ +

(axis=1) is a numpy convention to specify columns.

+ +

Note that the weight column of our DataFrame contains many null or NaN +values. Next, we will explore ways of dealing with this.

+ +

If we look at the weight column in the surveys +data we notice that there are NaN (Not a Number) values. NaN values are undefined +values that cannot be represented mathematically. Pandas, for example, will read +an empty cell in a CSV or Excel sheet as a NaN. NaNs have some desirable properties: if we +were to average the weight column without replacing our NaNs, Python would know to skip +over those cells.

+ +
surveys_df['weight'].mean()
+42.672428212991356
+
+ +

Dealing with missing data values is always a challenge. It’s sometimes hard to +know why values are missing - was it because of a data entry error? Or data that +someone was unable to collect? Should the value be 0? We need to know how +missing values are represented in the dataset in order to make good decisions. +If we’re lucky, we have some metadata that will tell us more about how null +values were handled.

+ +

For instance, in some disciplines, like Remote Sensing, missing data values are +often defined as -9999. Having a bunch of -9999 values in your data could really +alter numeric calculations. Often in spreadsheets, cells are left empty where no +data are available. Pandas will, by default, replace those missing values with +NaN. However it is good practice to get in the habit of intentionally marking +cells that have no data, with a no data value! That way there are no questions +in the future when you (or someone else) explores your data.

+ +

Where Are the NaN’s?

+ +

Let’s explore the NaN values in our data a bit further. Using the tools we +learned in lesson 02, we can figure out how many rows contain NaN values for +weight. We can also create a new subset from our data that only contains rows +with weight values > 0 (i.e., select meaningful weight values):

+ +
len(surveys_df[pd.isnull(surveys_df.weight)])
+# How many rows have weight values?
+len(surveys_df[surveys_df.weight> 0])
+
+ +

We can replace all NaN values with zeroes using the .fillna() method (after +making a copy of the data so we don’t lose our work):

+ +
# Creat a new DataFrame using copy
+df1 = surveys_df.copy()
+
+# Fill all NaN values with 0
+df1['weight'] = df1['weight'].fillna(0)
+
+ +

However NaN and 0 yield different analysis results. The mean value when NaN +values are replaced with 0 is different from when NaN values are simply thrown +out or ignored.

+ +
df1['weight'].mean()
+38.751976145601844
+
+ +

Extra Information

+ +

We can fill NaN values with any value that we chose. The code below fills all +NaN values with a mean for all weight values.

+ +
 df1['weight'] = surveys_df['weight'].fillna(surveys_df['weight'].mean())
+
+ +

Writing Out Data to CSV

+ +

We’ve learned about using manipulating data to get desired outputs. But we’ve also discussed +keeping data that has been manipulated separate from our raw data. Something we might be interested +in doing is working with only the columns that have full data. First, let’s reload the data so +we’re not mixing up all of our previous manipulations.

+ +
surveys_df = pd.read_csv("data/surveys.csv")
+
+ +

Next, let’s drop all the rows that contain missing values. We will use the command dropna. +By default, dropna removes columns that contain missing data for even just one row.

+ +
df_na = df.dropna()
+
+
+ +

If you now type df_na, you should observe that the resulting DataFrame has 30676 rows +and 9 columns, much smaller than the 35549 row original.

+ +

We can now use the to_csv command to do export a DataFrame in CSV format. Note that the code +below will by default save the data into the current working directory. We can +save it to a different folder by adding the foldername and a slash before the filename: +df1.to_csv('foldername/out.csv'). We use ‘index=False’ so that +pandas doesn’t include the index number for each line.

+ +
# Write DataFrame to CSV
+df_na.to_csv('data_output/surveys_complete.csv', index=False)
+
+ +

Recap

+ +

What we’ve learned:

+ +
    +
  • How to subset and index the dataframe
    • +
  • What NaN values are, how they might be represented, and what this means for your work
    • +
  • How to replace NaN values, if desired
    • +
  • How to use to_csv to write manipulated data to a file.
    • +
+ +

Extra

+ +

We can run isnull on a particular column too. What does the code below do?

+ +
# What does this do?
+empty_weights = surveys_df[pd.isnull(surveys_df['weight'])]['weight']
+print(empty_weights)
+
+ +

Let’s take a minute to look at the statement above. We are using the Boolean +object pd.isnull(surveys_df['weight']) as an index to surveys_df. We are +asking Python to select rows that have a NaN value of weight.

+ +

Extra Challenges - Putting it all together

+ +
    +

  1. Create a new DataFrame that only contains observations with sex values that +are not female or male. Assign each sex value in the new DataFrame to a +new value of ‘x’. Determine the number of null values in the subset.

    2. + +

  2. Create a new DataFrame that contains only observations that are of sex male +or female and where weight values are greater than 0. Create a stacked bar +plot of average weight by plot with male vs female values stacked for each +plot.

    + +
      +
    1. Count the number of missing values per column. Hint: The method .count() gives you the number of non-NA observations per column.
      2. +
    3. +
+ +

NEXT

+ +
+
+ +
+ +
+ + + + + + diff --git a/intro_to_python/intro/index.html b/intro_to_python/intro/index.html new file mode 100644 index 0000000..3785bd8 --- /dev/null +++ b/intro_to_python/intro/index.html @@ -0,0 +1,509 @@ + + + + + + + Monash Data Fluency | 01 Programming in Python (Introduction) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ INTRO TO PYTHON +

+

+ 01 Programming in Python (Introduction) +

+
+
+ + +

The Basics of Python

+ +

Python is a general purpose programming language that supports rapid development +of scripts and applications.

+ +

Python’s main advantages:

+ +
    +
  • Open Source software, supported by Python Software Foundation
    • +
  • Available on all platforms (ie. Windows, Linux and MacOS)
    • +
  • It is a general-purpose programming language
    • +
  • Supports multiple programming paradigms
    • +
  • Very large community with a rich ecosystem of third-party packages
    • +
+ +

Interpreter

+ +

Python is an interpreted language which can be used in two ways:

+ +
    +
  • “Interactive” Mode: It functions like an “advanced calculator” Executing +one command at a time:
    • +
+ +
user:host:~$ python
+Python 3.5.1 (default, Oct 23 2015, 18:05:06)
+[GCC 4.8.3] on linux2
+Type "help", "copyright", "credits" or "license" for more information.
+>>> 2 + 2
+4
+>>> print("Hello World")
+Hello World
+
+ +
    +
  • “Scripting” Mode: Executing a series of “commands” saved in text file, +usually with a .py extension after the name of your file:
    • +
+ +
user:host:~$ python my_script.py
+Hello World
+
+ +

Introduction to Python built-in data types

+ +

Strings, integers and floats

+ +

One of the most basic things we can do in Python is assign values to variables:

+ +
text = "Data Fluency"  # An example of a string
+number = 42  # An example of an integer
+pi_value = 3.1415  # An example of a float
+
+ +

Here we’ve assigned data to the variables text, number and pi_value, +using the assignment operator =. To review the value of a variable, we +can type the name of the variable into the Jupyter notebook and press Shift and Enter:

+ +
text
+## Which Returns
+"Data Fluency"
+
+ +

Everything in Python has a type. To get the type of something, we can pass it +to the built-in function type:

+ +
type(text)
+ str
+type(number)
+ int
+type(6.02)
+ float
+
+ +

The variable text is of type str, short for “string”. Strings hold +sequences of characters, which can be letters, numbers, punctuation +or more exotic forms of text (even emoji!).

+ +

We can also see the value of something using another built-in function, print:

+ +

+print(text)
+Data Fluency
+
+print(11)
+11
+
+ +

This may seem redundant, but in fact it’s the only way to display output in a script:

+ +

example.py

+ +
# A Python script file
+# Comments in Python start with #
+# The next line assigns the string "Data Carpentry" to the variable "text".
+text = "Data Fluency"
+# The next line does nothing!
+text
+# The next line uses the print function to print out the value we assigned to "text"
+print(text)
+
+ +

Running the script

+ +
$ python example.py
+Data Fluency
+
+ +

Notice that “Data Fluency” is printed only once.

+ +

Tip: print and type are built-in functions in Python. Later in this +lesson, we will introduce methods and user-defined functions. The Python +documentation is excellent for reference on the differences between them.

+ +
help(print)
+
+ +

Will give the output

+ +
Help on built-in function print in module builtins:
+
+print(...)
+    print(value, ..., sep=' ', end='\n', file=sys.stdout, flush=False)
+    
+    Prints the values to a stream, or to sys.stdout by default.
+    Optional keyword arguments:
+    file:  a file-like object (stream); defaults to the current sys.stdout.
+    sep:   string inserted between values, default a space.
+    end:   string appended after the last value, default a newline.
+    flush: whether to forcibly flush the stream.
+
+ +

Operators

+ +

We can perform mathematical calculations in Python using the basic operators + +, -, /, *, %:

+ +
>>> 2 + 2  # Addition
+4
+>>> 6 * 7  # Multiplication
+42
+>>> 2 ** 16  # Power
+65536
+>>> 13 % 5  # Modulo
+3
+
+ +

We can also use comparison and logic operators: +<, >, ==, !=, <=, >= and statements of identity such as +and, or, not. The data type returned by this is +called a boolean.

+ +
>>> 3 > 4
+False
+>>> True and True
+True
+>>> True or False
+True
+
+ +

Sequential types: Lists and Tuples

+ +

Lists

+ +

Lists are a common data structure to hold an ordered sequence of +elements. Each element can be accessed by an index. Note that Python +indexes start with 0 instead of 1:

+ +
>>> numbers = [1, 2, 3]
+>>> numbers[0]
+1
+
+ +

A for loop can be used to access the elements in a list or other Python data structure one at a time. We will learn about loops in other lesson.

+ +
>>> for num in numbers:
+...     print(num)
+...
+1
+2
+3
+
+ +

Indentation is very important in Python. Note that the second line in the +example above is indented. Just like three chevrons >>> indicate an +interactive prompt in Python, the three dots ... are Python’s prompt for +multiple lines. This is Python’s way of marking a block of code. [Note: you +do not type >>> or ....]

+ +

To add elements to the end of a list, we can use the append method. Methods +are a way to interact with an object (a list, for example). We can invoke a +method using the dot . followed by the method name and a list of arguments +in parentheses. Let’s look at an example using append:

+ +
>>> numbers.append(4)
+>>> print(numbers)
+[1, 2, 3, 4]
+>>>
+
+ +

To find out what methods are available for an +object, we can use the built-in help command:

+ +
help(numbers)
+
+Help on list object:
+
+class list(object)
+ |  list() -> new empty list
+ |  list(iterable) -> new list initialized from iterable's items
+ ...
+
+ +

Tuples

+ +

A tuple is similar to a list in that it’s an ordered sequence of elements. +However, tuples can not be changed once created (they are “immutable”). Tuples +are created by placing comma-separated values inside parentheses ().

+ +
# Tuples use parentheses
+a_tuple= (1, 2, 3)
+another_tuple = ('blue', 'green', 'red')
+# Note: lists use square brackets
+a_list = [1, 2, 3]
+
+ +

Challenge - Tuples

+ +
    +
  1. What happens when you type a_tuple[2]=5 vs a_list[1]=5 ?
    2. +
  2. Type type(a_tuple) into python - what is the object type?
    3. +
+ +

Dictionaries

+ +

A dictionary is a container that holds pairs of objects - keys and values.

+ +
>>> translation = {'one': 1, 'two': 2}
+>>> translation['one']
+1
+
+ +

Dictionaries work a lot like lists - except that you index them with keys. +You can think about a key as a name for or a unique identifier for a set of values +in the dictionary. Keys can only have particular types - they have to be +“hashable”. Strings and numeric types are acceptable, but lists aren’t.

+ +
>>> rev = {1: 'one', 2: 'two'}
+>>> rev[1]
+'one'
+>>> bad = {[1, 2, 3]: 3}
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: unhashable type: 'list'
+
+ +

In Python, a “Traceback” is an multi-line error block printed out for the +user.

+ +

To add an item to the dictionary we assign a value to a new key:

+ +
>>> rev = {1: 'one', 2: 'two'}
+>>> rev[3] = 'three'
+>>> rev
+{1: 'one', 2: 'two', 3: 'three'}
+
+ +

Challenge - Can you do reassignment in a dictionary?

+ +
    +

  1. First check what rev is right now (remember rev is the name of our dictionary). Type: rev

    2. + +

  2. Try to reassign the second value (in the key value pair) so that it no longer reads “two” but instead reads “apple-sauce”.

    3. + +

  3. Now display rev again to see if it has changed.

    4. +
+ +

It is important to note that dictionaries are “unordered” and do not remember +the sequence of their items (i.e. the order in which key:value pairs were +added to the dictionary). Because of this, the order in which items are +returned from loops over dictionaries might appear random and can even change +with time.

+ +

NEXT

+ +
+
+ +
+ +
+ + + + + + diff --git a/intro_to_python/loops/index.html b/intro_to_python/loops/index.html new file mode 100644 index 0000000..8f17d98 --- /dev/null +++ b/intro_to_python/loops/index.html @@ -0,0 +1,527 @@ + + + + + + + Monash Data Fluency | 04 Automation with Loops + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ INTRO TO PYTHON +

+

+ 04 Automation with Loops +

+
+
+ + +

An example task that we might want to repeat is printing each character in a +word on a line of its own.

+ +
word = 'lead'
+
+ +

We can access a character in a string using its index. For example, we can get the first +character of the word 'lead', by using word[0]. One way to print each character is to use +four print statements:

+ +
print(word[0])
+print(word[1])
+print(word[2])
+print(word[3])
+
+ +

Gives output

+ +
l
+e
+a
+d
+
+ +

This is a bad approach for two reasons:

+ +
    +

  1. It doesn’t scale: +if we want to print the characters in a string that’s hundreds of letters long, +we’d be better off just typing them in.

    2. + +

  2. It’s fragile: +if we give it a longer string, +it only prints part of the data, +and if we give it a shorter one, +it produces an error because we’re asking for characters that don’t exist.

    3. +
+ +
word = 'tin'
+print(word[0])
+print(word[1])
+print(word[2])
+print(word[3])
+
+
+ +

Gives back

+ +
t
+i
+n
+---------------------------------------------------------------------------
+IndexError                                Traceback (most recent call last)
+<ipython-input-3-7974b6cdaf14> in <module>()
+      3 print(word[1])
+      4 print(word[2])
+----> 5 print(word[3])
+
+IndexError: string index out of range
+
+ +

Here’s a better approach:

+ +
word = 'lead'
+for char in word:
+    print(char)
+
+
+ +

Would Produce

+ +
l
+e
+a
+d
+
+ +

This is shorter — certainly shorter than something that prints every character in a hundred-letter string — and +more robust as well:

+ +
word = 'oxygen'
+for char in word:
+    print(char)
+
+ +

Output

+ +
o
+x
+y
+g
+e
+n
+
+ +

The improved version uses a for loop to repeat an operation — in this case, printing — once for each thing in a sequence. +The general form of a loop is:

+ +
for variable in collection:
+    do things with variable
+
+ +

Using the oxygen example above, the loop might look like this:

+ +

loop_image

+ +

where each character (char) in the variable word is looped through and printed one character after another. +The numbers in the diagram denote which loop cycle the character was printed in (1 being the first loop, and 6 being the final loop).

+ +

We can call the loop variable anything we like, +but there must be a colon at the end of the line starting the loop, and we must indent anything we want to run inside the loop. Unlike many other languages, there is no command to signify the end of the loop body (e.g. end for); what is indented after the for statement belongs to the loop.

+ +

What’s in a name?

+ +

In the example above, the loop variable was given the name char as a mnemonic; it is short for ‘character’. +We can choose any name we want for variables. We might just as easily have chosen the name banana for the loop variable, as long as we use the same name when we invoke the variable inside the loop:

+ +
word = 'oxygen'
+for banana in word:
+    print(banana)
+
+ +

Output

+ +
o
+x
+y
+g
+e
+n
+
+ +

It is a good idea to choose variable names that are meaningful, otherwise it would be more difficult to understand what the loop is doing.

+ +

Here’s another loop that repeatedly updates a variable:

+ +
length = 0
+for vowel in 'aeiou':
+    length = length + 1
+print('There are', length, 'vowels')
+
+ +
There are 5 vowels
+
+ +

It’s worth tracing the execution of this little program step by step. +Since there are five characters in 'aeiou', +the statement on line 3 will be executed five times. +The first time around, +length is zero (the value assigned to it on line 1) +and vowel is 'a'. +The statement adds 1 to the old value of length, +producing 1, +and updates length to refer to that new value. +The next time around, +vowel is 'e' and length is 1, +so length is updated to be 2. +After three more updates, +length is 5; +since there is nothing left in 'aeiou' for Python to process, +the loop finishes +and the print statement on line 4 tells us our final answer.

+ +

Note that a loop variable is just a variable that’s being used to record progress in a loop. +It still exists after the loop is over, +and we can re-use variables previously defined as loop variables as well:

+ +
letter = 'z'
+for letter in 'abc':
+    print(letter)
+print('after the loop, letter is', letter)
+
+ +

Output

+ +
a
+b
+c
+after the loop, letter is c
+
+ +

Note also that finding the length of a string is such a common operation that Python actually has a built-in function to do it called len:

+ +
print(len('aeiou'))
+
+ +

Output

+ +
5
+
+ +

len is much faster than any function we could write ourselves, +and much easier to read than a two-line loop; +it will also give us the length of many other things that we haven’t met yet, +so we should always use it when we can.

+ +

From 1 to N

+ +

Python has a built-in function called range that creates a sequence of numbers. range can +accept 1, 2, or 3 parameters.

+ +
    +
  • If one parameter is given, range creates an array of that length, +starting at zero and incrementing by 1. +For example, range(3) produces the numbers 0, 1, 2.
    • +
  • If two parameters are given, range starts at +the first and ends just before the second, incrementing by one. +For example, range(2, 5) produces 2, 3, 4.
    • +
  • If range is given 3 parameters, +it starts at the first one, ends just before the second one, and increments by the third one. +For exmaple range(3, 10, 2) produces 3, 5, 7, 9.
    • +
+ +

Challenge

+ +

Using range, +write a loop that uses range to print the first 3 natural numbers:

+ +
1
+2
+3
+
+ +

Solution

+ +
for i in range(1, 4):
+   print(i)
+
+ +

Computing Powers With Loops

+ +

Exponentiation is built into Python:

+ +
print(5 ** 3)
+
+ +
125
+
+ +

Write a loop that calculates the same result as 5 ** 3 using +multiplication (and without exponentiation).

+ +

Solution

+ +
result = 1
+for i in range(0, 3):
+   result = result * 5
+print(result)
+
+ +

Reverse a String

+ +

Knowing that two strings can be concatenated using the + operator, +write a loop that takes a string +and produces a new string with the characters in reverse order, +so 'Newton' becomes 'notweN'.

+ +

Solution

+ +
newstring = ''
+oldstring = 'Newton'
+for char in oldstring:
+   newstring = char + newstring
+print(newstring)
+
+ +

Computing the Value of a Polynomial

+ +

The built-in function enumerate takes a sequence (e.g. a list) and generates a +new sequence of the same length. Each element of the new sequence is a pair composed of the index +(0, 1, 2,…) and the value from the original sequence:

+ +
for i, x in enumerate(xs):
+    # Do something with i and x
+
+ +

The code above loops through xs, assigning the index to i and the value to x.

+ +

Suppose you have encoded a polynomial as a list of coefficients in +the following way: the first element is the constant term, the +second element is the coefficient of the linear term, the third is the +coefficient of the quadratic term, etc.

+ +
x = 5
+cc = [2, 4, 3]
+
+ +
y = cc[0] * x**0 + cc[1] * x**1 + cc[2] * x**2
+y = 97
+
+ +

Write a loop using enumerate(cc) which computes the value y of any +polynomial, given x and cc.

+ +

Solution

+ +
y = 0
+for i, c in enumerate(cc):
+    y = y + x**i * c
+
+ +

NEXT

+ +
+
+ +
+ +
+ + + + + + diff --git a/intro_to_python/page/1/index.html b/intro_to_python/page/1/index.html new file mode 100644 index 0000000..7336a2e --- /dev/null +++ b/intro_to_python/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/intro_to_python/ \ No newline at end of file diff --git a/intro_to_python/plotting/index.html b/intro_to_python/plotting/index.html new file mode 100644 index 0000000..e4fe95a --- /dev/null +++ b/intro_to_python/plotting/index.html @@ -0,0 +1,446 @@ + + + + + + + Monash Data Fluency | 05 Making Plots With ggplot + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ INTRO TO PYTHON +

+

+ 05 Making Plots With ggplot +

+
+
+ + +

Introduction

+ +

Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. +Strictly speaking plotnine is just another implementation of The Grammar of Graphics by Leland Wilkinson, which in theory could go on it own direction and diverge away from an R ggplot .

+ +

The Grammar of Graphics

+ +
+

statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars) +Faceting can be used to generate the same plot for different subsets of the dataset

+
+ +

These are basic building blocks according to the grammar of graphics:

+ +
    +
  • data The data + a set of aesthetic mappings that describing variables mapping
    • +
  • geom Geometric objects, represent what you actually see on the plot: points, lines, polygons, etc.
    • +
  • stats Statistical transformations, summarise data in many useful ways.
    • +
  • scale The scales map values in the data space to values in an aesthetic space
    • +
  • coord A coordinate system, describes how data coordinates are mapped to the plane of the graphic.
    • +
  • facet A faceting specification describes how to break up the data into subsets for plotting individual set
    • +
+ +

Let’s explore those in details

+ +

Plotting in ggplot style

+ +

Let set up our working environment with necessary libraries and also load our csv file into data frame called survs_df,

+ +
import numpy as np
+import pandas as pd
+from plotnine import *
+
+%matplotlib inline
+survs_df = pd.read_csv('data/surveys.csv').dropna()
+
+ +

Producing a plot with ggplot, we must give three things:

+ +
    +
  1. A data frame containing our data.
    2. +
  2. How the columns of the data frame can be translated into positions, colors, sizes, and shapes of graphical elements (“aesthetics”).
    3. +
  3. The actual graphical elements to display (“geometric objects”).
    4. +
+ +

Introduction to plotting

+ +
ggplot(survs_df, aes('weight', 'hindfoot_length')) + geom_point()
+
+ +

Lets see if we can also include information about species and year

+ +
ggplot(survs_df, aes('weight', 'hindfoot_length',
+    size = 'year')) + geom_point()
+
+ +
ggplot(survs_df, aes('weight', 'hindfoot_length', 
+    size = 'year', color = 'species_id')) + geom_point()
+
+ +

We can do simple counting plot, to see how many observation (data points) we have for each year for example

+ +
ggplot(survs_df, aes('year')) + \
+    geom_bar(stat = 'count')
+
+ +

Let’s now also color by species to see how many observation we have per species in a given year

+ +
ggplot(survs_df, aes('year', fill = 'species_id')) + \
+    geom_bar(stat = 'count')
+
+ +

Challenges

+ +
+

Is there a better visualisation for comparing weight across years? The plot should have categorical data on x axis and continuous on y axis +Plot a boxplot of hindfoot_length across different species (species_id column)

+
+ +

More geom types

+ +
ggplot(survs_df, aes('year', 'weight')) + \
+    geom_boxplot()
+
+ +

Why are we not seeing mulitple boxplots, one for each year? +This is because year variable is continues in our data frame, but for the purpose we want it to be categorical.

+ +
survs_df['year_fact'] = pd.Series(survs_df['year'], dtype = "category")
+
+ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot()
+
+ +
ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_violin()
+
+ +

To save an image for later

+ +
plt1 = ggplot(survs_df, aes('year_fact', 'weight')) + \
+           geom_boxplot() + \
+           xlab("Years") + \
+           ylab("Weight log2(kg)") + \
+           ggtitle("Boxplots, summary of species wieght in each year")
+
+ggsave(filename = "plot1.png", \
+       plot = plt1, \
+       device = 'png', \
+       dpi = 300, \
+       height = 25, \
+        width = 25)
+
+ +

Challenges

+ +
+

Can you log2 transform weight and plot “normalised” boxplot. Hint: use np.log2() function and name new column weight_log

+
+ +

Also will log2 transforming make this data visualisation better?

+ +
survs_df['weight_log'] = np.log2(survs_df['weight'])
+    
+ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    xlab("Years") + \
+    ylab("Weight log2(kg)") + \
+    ggtitle("Boxplots, summary of species wieght in each year")
+
+ +

Faceting

+ +

ggplot has a special technique called faceting that allows to split one plot +into multiple plots based on a factor included in the dataset. We will use it to +make one plot for a time series for each species.

+ +
ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot() + \
+    facet_wrap("~sex")
+
+
+ +
ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") 
+
+ +

Theming

+ +
ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot() + \
+    theme_bw()
+
+ +
ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") + \
+    theme_xkcd()
+
+ +

Extra bits 1

+ +

Let’s try to bin years into decades, which could be crude but might gives simple images to look at.

+ +
bins = [(survs_df['year'] < 1980),
+        (survs_df['year'] < 1990),
+        (survs_df['year'] < 2000),
+        (survs_df['year'] >= 2000)]
+
+labels = ['70s', '80s', '90s', 'Z']
+
+survs_df['year_bins'] = np.select(bins, labels)
+
+ +
plt2 = ggplot(survs_df, aes('year_bins', 'weight_log')) + \
+           geom_boxplot()
+plt2 
+
+ +
plt2 = ggplot(survs_df, aes('year_bins', 'weight_log')) + \
+           geom_boxplot() + \
+           theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+           facet_wrap("~species_id") 
+plt2 
+
+ +

Extra bits 2

+ +

This is a different way to look at your data

+ +
ggplot(survs_df, aes("year_fact", "weight")) + \
+    stat_summary(fun_y = np.mean, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+    
+ggplot(survs_df, aes("year_fact", "weight")) + \
+    stat_summary(fun_y = np.median, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+    
+ggplot(survs_df, aes("year_fact", "weight_log")) + \
+    stat_summary(fun_y = np.mean, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+
+ +

Extra bits 3

+ +

It is very informative to look across years, year by year, it becomes apparent straight away that for some species there is a lot of data is missing.And going forward, maybe, you’d want to filter those low counting species. (this is after faceting by species_id in section Extra bits 1).

+ +
#survs_cnts_df = survs_df.groupby(['species_id'], as_index=False).count().sort_values(['record_id'])
+survs_cnts_df = survs_df[['species_id']].groupby(['species_id']).size().reset_index()
+species_to_remove = list(survs_cnts_df[survs_cnts_df['record_id'] < 200].species_id)
+survs_df_filt = survs_df[survs_df['species_id'].isin(species_to_remove) == False]
+
+ +
ggplot(survs_df_filt, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+
+ggplot(survs_df_filt, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") 
+
+ +

NEXT

+ +
+
+ +
+ +
+ + + + + + diff --git a/intro_to_python/testpage/index.html b/intro_to_python/testpage/index.html new file mode 100644 index 0000000..68d9c1b --- /dev/null +++ b/intro_to_python/testpage/index.html @@ -0,0 +1,220 @@ + + + + + + + Monash Data Fluency | Intro to Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ INTRO TO PYTHON +

+

+ Intro to Python +

+
+
+

Bla this is a test.

+ +
+
+ +
+ +
+ + + + + + diff --git a/intro_to_python/working_with_data/index.html b/intro_to_python/working_with_data/index.html new file mode 100644 index 0000000..b0a7aee --- /dev/null +++ b/intro_to_python/working_with_data/index.html @@ -0,0 +1,846 @@ + + + + + + + Monash Data Fluency | 02 Data Analysis in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ INTRO TO PYTHON +

+

+ 02 Data Analysis in Python +

+
+
+ + +

Working With Pandas DataFrames

+ +

We can automate the process of performing data manipulations in Python. It’s efficient to spend time +building the code to perform these tasks because once it’s built, we can use it +over and over on different datasets that use a similar format. This makes our +methods easily reproducible. We can also easily share our code with colleagues +and they can replicate the same analysis.

+ +

The Dataset

+ +

For this lesson, we will be using the Portal Teaching data, a subset of the data +from Ernst et al +Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA

+ +

We will be using this dataset, which is stored under your login/account under the Files tab.

+ +

We are studying the species and weight of animals caught in plots in our study +area. The dataset is stored as a .csv file: each row holds information for a +single animal, and the columns represent:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ColumnDescription
record_idUnique id for the observation
monthmonth of observation
dayday of observation
yearyear of observation
plot_idID of a particular plot
species_id2-letter code
sexsex of animal (“M”, “F”)
hindfoot_lengthlength of the hindfoot in mm
weightweight of the animal in grams
+ +

The first few rows of our first file look like this:

+ +
record_id,month,day,year,plot_id,species_id,sex,hindfoot_length,weight
+1,7,16,1977,2,NL,M,32,
+2,7,16,1977,3,NL,M,33,
+3,7,16,1977,2,DM,F,37,
+4,7,16,1977,7,DM,M,36,
+5,7,16,1977,3,DM,M,35,
+6,7,16,1977,1,PF,M,14,
+7,7,16,1977,2,PE,F,,
+8,7,16,1977,1,DM,M,37,
+9,7,16,1977,1,DM,F,34,
+
+ +
+ +

About Libraries

+ +

A library in Python contains a set of tools (called functions) that perform +tasks on our data. Importing a library is like getting a piece of lab equipment +out of a storage locker and setting it up on the bench for use in a project. +Once a library is set up, it can be used or called to perform many tasks.

+ +

Pandas in Python

+ +

One of the best options for working with tabular data in Python is to use the +Python Data Analysis Library (a.k.a. Pandas). The +Pandas library provides data structures, produces high quality plots with +matplotlib and integrates nicely with other libraries +that use NumPy (which is another Python library) arrays.

+ +

Python doesn’t load all of the libraries available to it by default. We have to +add an import statement to our code in order to use library functions. To import +a library, we use the syntax import libraryName. If we want to give the +library a nickname to shorten the command, we can add as nickNameHere. An +example of importing the pandas library using the common nickname pd is below.

+ +
import pandas as pd
+
+ +

Each time we call a function that’s in a library, we use the syntax +LibraryName.FunctionName. Adding the library name with a . before the +function name tells Python where to find the function. In the example above, we +have imported Pandas as pd. This means we don’t have to type out pandas each +time we call a Pandas function.

+ +

Reading CSV Data Using Pandas

+ +

We will begin by locating and reading our survey data which are in CSV format. CSV stands for Comma-Separated Values and is a common way store formatted data. Other symbols my also be used, so you might see tab-separated, colon-separated or space separated files. It is quite easy to replace one separator with another, to match your application. The first line in the file often has headers to explain what is in each column. CSV (and other separators) make it easy to share data, and can be imported and exported from many applications, including Microsoft Excel. For mo

+ +

We can use Pandas’ read_csv function to pull the file directly into a +DataFrame.

+ +

So What’s a DataFrame?

+ +

A DataFrame is a 2-dimensional data structure that can store data of different +types (including characters, integers, floating point values, factors and more) +in columns. It is similar to a spreadsheet or an SQL table or the data.frame in +R. A DataFrame always has an index (0-based). An index refers to the position of +an element in the data structure.

+ +
# Note that pd.read_csv is used because we imported pandas as pd
+pd.read_csv("data/surveys.csv")
+
+ +

The above command yields the output below:

+ +
record_id  month  day  year  plot_id species_id sex  hindfoot_length  weight
+0          1      7   16  1977        2         NL   M               32   NaN
+1          2      7   16  1977        3         NL   M               33   NaN
+2          3      7   16  1977        2         DM   F               37   NaN
+3          4      7   16  1977        7         DM   M               36   NaN
+4          5      7   16  1977        3         DM   M               35   NaN
+...
+35544      35545     12   31  2002       15     AH  NaN              NaN  NaN
+35545      35546     12   31  2002       15     AH  NaN              NaN  NaN
+35546      35547     12   31  2002       10     RM    F               15   14
+35547      35548     12   31  2002        7     DO    M               36   51
+35548      35549     12   31  2002        5     NaN  NaN             NaN  NaN
+
+[35549 rows x 9 columns]
+
+ +

We can see that there were 33,549 rows parsed. Each row has 9 +columns. The first column is the index of the DataFrame. The index is used to +identify the position of the data, but it is not an actual column of the DataFrame. +It looks like the read_csv function in Pandas read our file properly. However, +we haven’t saved any data to memory so we can work with it.We need to assign the +DataFrame to a variable. Remember that a variable is a name for a value, such as x, +or data. We can create a new object with a variable name by assigning a value to it using =.

+ +

Let’s call the imported survey data surveys_df:

+ +
surveys_df = pd.read_csv("data/surveys.csv")
+
+ +

Notice when you assign the imported DataFrame to a variable, Python does not +produce any output on the screen. We can view the value of the surveys_df +object by typing its name into the Python command prompt.

+ +
surveys_df
+
+ +

which prints contents like above.

+ +

Note: if the output is too wide to print on your narrow terminal window, you may see something +slightly different as the large set of data scrolls past. You may see simply the last column +of data:

+ +
17        NaN  
+18        NaN  
+19        NaN  
+20        NaN  
+21        NaN  
+22        NaN  
+23        NaN  
+24        NaN  
+25        NaN  
+26        NaN  
+27        NaN  
+28        NaN  
+29        NaN  
+...       ...  
+35519    36.0  
+35520    48.0  
+35521    45.0  
+35522    44.0  
+35523    27.0  
+35524    26.0  
+35525    24.0  
+35526    43.0  
+35527     NaN  
+35528    25.0  
+35529     NaN  
+35530     NaN  
+35531    43.0  
+35532    48.0  
+35533    56.0  
+35534    53.0  
+35535    42.0  
+35536    46.0  
+35537    31.0  
+35538    68.0  
+35539    23.0  
+35540    31.0  
+35541    29.0  
+35542    34.0  
+35543     NaN  
+35544     NaN  
+35545     NaN  
+35546    14.0  
+35547    51.0  
+35548     NaN  
+
+[35549 rows x 9 columns]
+
+ +

Never fear, all the data is there, if you scroll up. Selecting just a few rows, so it is +easier to fit on one window, you can see that pandas has neatly formatted the data to fit +our screen:

+ +

+>>> surveys_df.head() # The head() function displays the first several lines of a file. It
+		      # is discussed below.
+   record_id  month  day  year  plot_id species_id sex  hindfoot_length  \
+5          6      7   16  1977        1         PF   M             14.0   
+6          7      7   16  1977        2         PE   F              NaN   
+7          8      7   16  1977        1         DM   M             37.0   
+8          9      7   16  1977        1         DM   F             34.0   
+9         10      7   16  1977        6         PF   F             20.0   
+
+   weight  
+5     NaN  
+6     NaN  
+7     NaN  
+8     NaN  
+9     NaN  
+
+ +

Exploring Our Species Survey Data

+ +

Again, we can use the type function to see what kind of thing surveys_df is:

+ +
type(surveys_df)
+
+## Returns
+pandas.core.frame.DataFrame
+
+ +

As expected, it’s a DataFrame (or, to use the full name that Python uses to refer +to it internally, a pandas.core.frame.DataFrame).

+ +

What kind of things does surveys_df contain? DataFrames have an attribute +called dtypes that answers this:

+ +
surveys_df.dtypes
+
+## Returns
+record_id            int64
+month                int64
+day                  int64
+year                 int64
+plot_id              int64
+species_id          object
+sex                 object
+hindfoot_length    float64
+weight             float64
+dtype: object
+
+ +

All the values in a column have the same type. For example, months have type +int64, which is a kind of integer. Cells in the month column cannot have +fractional values, but the weight and hindfoot_length columns can, because they +have type float64. The object type doesn’t have a very helpful name, but in +this case it represents strings (such as ’M’ and ‘F’ in the case of sex).

+ +

Useful Ways to View DataFrame objects in Python

+ +

There are many ways to summarize and access the data stored in DataFrames, +using attributes and methods provided by the DataFrame object.

+ +

To access an attribute, use the DataFrame object name followed by the attribute +name df_object.attribute. Using the DataFrame surveys_df and attribute +columns, an index of all the column names in the DataFrame can be accessed +with surveys_df.columns.

+ +

Methods are called in a similar fashion using the syntax df_object.method(). +As an example, surveys_df.head() gets the first few rows in the DataFrame +surveys_df using the head() method. With a method, we can supply extra +information in the parens to control behaviour.

+ +

Let’s look at the data using these.

+ +

Challenge - DataFrames

+ +

Using our DataFrame surveys_df, try out the attributes & methods below to see +what they return.

+ +
    +
  1. surveys_df.columns
    2. +
  2. surveys_df.shape Take note of the output of shape - what format does it +return the shape of the DataFrame in? HINT: More on tuples, here.
    3. +
  3. surveys_df.head() Also, what does surveys_df.head(15) do?
    4. +
  4. surveys_df.tail()
    5. +
+ +

Calculating Statistics From Data In A Pandas DataFrame

+ +

We’ve read our data into Python. Next, let’s perform some quick summary +statistics to learn more about the data that we’re working with. We might want +to know how many animals were collected in each plot, or how many of each +species were caught. We can perform summary stats quickly using groups. But +first we need to figure out what we want to group by.

+ +

Let’s begin by exploring our data:

+ +
# Look at the column names
+surveys_df.columns
+
+ +

which returns:

+ +
Index(['record_id', 'month', 'day', 'year', 'plot_id', 'species_id', 'sex',
+       'hindfoot_length', 'weight'],
+      dtype='object')
+
+ +

Let’s get a list of all the species. The pd.unique function tells us all of +the unique values in the species_id column.

+ +
pd.unique(surveys_df['species_id'])
+
+ +

which returns:

+ +
array(['NL', 'DM', 'PF', 'PE', 'DS', 'PP', 'SH', 'OT', 'DO', 'OX', 'SS',
+       'OL', 'RM', nan, 'SA', 'PM', 'AH', 'DX', 'AB', 'CB', 'CM', 'CQ',
+       'RF', 'PC', 'PG', 'PH', 'PU', 'CV', 'UR', 'UP', 'ZL', 'UL', 'CS',
+       'SC', 'BA', 'SF', 'RO', 'AS', 'SO', 'PI', 'ST', 'CU', 'SU', 'RX',
+       'PB', 'PL', 'PX', 'CT', 'US'], dtype=object)
+
+ +

Challenge - Statistics

+ +
    +

  1. Create a list of unique plot ID’s found in the surveys data. Call it +plot_names. How many unique plots are there in the data? How many unique +species are in the data?

    2. + +

  2. What is the difference between len(plot_names) and surveys_df['plot_id'].nunique()?

    3. +
+ +

Groups in Pandas

+ +

We often want to calculate summary statistics grouped by subsets or attributes +within fields of our data. For example, we might want to calculate the average +weight of all individuals per plot.

+ +

We can calculate basic statistics for all records in a single column using the +syntax below:

+ +
surveys_df['weight'].describe()
+
+ +

gives output

+ +
count    32283.000000
+mean        42.672428
+std         36.631259
+min          4.000000
+25%         20.000000
+50%         37.000000
+75%         48.000000
+max        280.000000
+Name: weight, dtype: float64
+
+ +

We can also extract one specific metric if we wish:

+ +
surveys_df['weight'].min()
+surveys_df['weight'].max()
+surveys_df['weight'].mean()
+surveys_df['weight'].std()
+surveys_df['weight'].count()
+
+ +

But if we want to summarize by one or more variables, for example sex, we can +use Pandas’ .groupby method. Once we’ve created a groupby DataFrame, we +can quickly calculate summary statistics by a group of our choice.

+ +
# Group data by sex
+grouped_data = surveys_df.groupby('sex')
+
+ +

The pandas function describe will return descriptive stats including: mean, +median, max, min, std and count for a particular column in the data. Pandas’ +describe function will only return summary values for columns containing +numeric data.

+ +
# Summary statistics for all numeric columns by sex
+grouped_data.describe()
+# Provide the mean for each numeric column by sex
+grouped_data.mean()
+
+ +

grouped_data.mean() OUTPUT:

+ +
        record_id     month        day         year    plot_id  \
+sex                                                              
+F    18036.412046  6.583047  16.007138  1990.644997  11.440854   
+M    17754.835601  6.392668  16.184286  1990.480401  11.098282   
+
+     hindfoot_length     weight  
+sex                              
+F          28.836780  42.170555  
+M          29.709578  42.995379  
+
+
+ +

The groupby command is powerful in that it allows us to quickly generate +summary stats.

+ +

Challenge - Summary Data

+ +
    +
  1. How many recorded individuals are female F and how many male M
    2. +
  2. What happens when you group by two columns using the following syntax and +then grab mean values: + +
      +
    • grouped_data2 = surveys_df.groupby(['plot_id','sex'])
      • +
    • grouped_data2.mean()
      • +
    3. +
  3. Summarize weight values for each plot in your data. HINT: you can use the +following syntax to only create summary statistics for one column in your data +by_plot['weight'].describe()
    4. +
+ +

Did you get #3 right?

+ +

A Snippet of the Output from challenge 3 looks like:

+ +
	plot
+	1     count    1903.000000
+	      mean       51.822911
+	      std        38.176670
+	      min         4.000000
+	      25%        30.000000
+	      50%        44.000000
+	      75%        53.000000
+	      max       231.000000
+         ...
+
+ +

Quickly Creating Summary Counts in Pandas

+ +

Let’s next count the number of samples for each species. We can do this in a few +ways, but we’ll use groupby combined with a count() method.

+ +
# Count the number of samples by species
+species_counts = surveys_df.groupby('species_id')['record_id'].count()
+print(species_counts)
+
+ +

Or, we can also count just the rows that have the species “DO”:

+ +
surveys_df.groupby('species_id')['record_id'].count()['DO']
+
+ +

Basic Math Functions

+ +

If we wanted to, we could perform math on an entire column of our data. For +example let’s multiply all weight values by 2. A more practical use of this might +be to normalize the data according to a mean, area, or some other value +calculated from our data.

+ +

+# Multiply all weight values by 2 but does not change the original weight data
+
+surveys_df['weight']*2
+
+
+ +

Quick & Easy Plotting Data Using Pandas

+ +

We can plot our summary stats using Pandas, too.

+ +

+## To make sure figures appear inside Jupyter Notebook
+%matplotlib inline
+
+# Create a quick bar chart
+species_counts.plot(kind='bar');
+
+ +

Weight by Species Plot

+ +

Weight by species plot

+ +

We can also look at how many animals were captured in each plot:

+ +
total_count = surveys_df.groupby('plot_id')['record_id'].nunique()
+# Let's plot that too
+total_count.plot(kind='bar');
+
+ +

Extra Plotting Challenge

+ +
    +

  1. Create a plot of average weight across all species per plot.

    2. + +

  2. Create a plot of total males versus total females for the entire dataset.

    3. + +

  3. Create a stacked bar plot, with weight on the Y axis, and the stacked variable being sex. The plot should show total weight by sex for each plot. Some tips are below to help you solve this challenge:

    4. +
+ +
    +
  • For more on Pandas plots, visit this link.
    • +
  • You can use the code that follows to create a stacked bar plot but the data to stack +need to be in individual columns. Here’s a simple example with some data where +‘a’, ‘b’, and ‘c’ are the groups, and ‘one’ and ‘two’ are the subgroups.
    • +
+ +
d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
+pd.DataFrame(d)
+
+ +

shows the following data

+ +
      one  two
+   a    1    1
+   b    2    2
+   c    3    3
+   d  NaN    4
+
+ +

We can plot the above with

+ +
# Plot stacked data so columns 'one' and 'two' are stacked
+my_df = pd.DataFrame(d)
+my_df.plot(kind='bar',stacked=True,title="The title of my graph")
+
+ +
    +
  • You can use the .unstack() method to transform grouped data into columns +for each plotting. Try running .unstack() on some DataFrames above and see +what it yields.
    • +
+ +

Start by transforming the grouped data (by plot and sex) into an unstacked layout, then create + a stacked plot.

+ +

Solution to Extra Challenge 3

+ +

First we group data by plot and by sex, and then calculate a total for each plot.

+ +
by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+
+ +

This calculates the sums of weights for each sex within each plot as a table

+ +
plot  sex
+plot_id  sex
+1        F      38253
+         M      59979
+2        F      50144
+         M      57250
+3        F      27251
+         M      28253
+4        F      39796
+         M      49377
+<other plots removed for brevity>
+
+ +

Below we’ll use .unstack() on our grouped data to figure out the total weight that each sex contributed to each plot.

+ +
by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+plot_sex_count.unstack()
+
+ +

The unstack function above will display the following output:

+ +
sex          F      M
+plot_id              
+1        38253  59979
+2        50144  57250
+3        27251  28253
+4        39796  49377
+<other plots removed for brevity>
+
+ +

Now, create a stacked bar plot with that data where the weights for each sex are stacked by plot.

+ +

Rather than display it as a table, we can plot the above data by stacking the values of each sex as follows:

+ +
by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+spc = plot_sex_count.unstack()
+s_plot = spc.plot(kind='bar',stacked=True,title="Total weight by plot and sex")
+s_plot.set_ylabel("Weight")
+s_plot.set_xlabel("Plot")
+
+ +

Stacked Bar Plot

+ +

NEXT

+ +
+
+ +
+ +
+ + + + + + diff --git a/intro_to_r/index.html b/intro_to_r/index.html new file mode 100644 index 0000000..681bbb8 --- /dev/null +++ b/intro_to_r/index.html @@ -0,0 +1,297 @@ + + + + + + + Monash Data Fluency | R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ R +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to R Workshop

+ +
    +
  • Date : 23rd March 2018
    • +
  • Time : 1:00pm to 5:00pm
    • +
  • Location : T4, Sir Louis Matheson Library, Clayton, VIC
    • +
  • Instructors : Dr Paul Harrison, Adele Barugahare
    • +
  • Helpers : Dr Sarah Williams, Dr Nick Wong
    • +
+ +

This is a beginner-level workshop providing a taste of working in the R language. It aims to give participants skills needed apply data analysis approaches to research questions

+ +

General Information

+ +

The workshop will be taught in a similar style to Sofware Carpentry workshops. Software Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
13:00Welcome & Introduction
13:30Starting out in R
14:30Data frames
15:30Tea break
15:45ggplot2
16:45Wrap-up
+ +

Course material

+ + + +

Shared Notepad

+ + + +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/intro_to_r/index.xml b/intro_to_r/index.xml new file mode 100644 index 0000000..7337523 --- /dev/null +++ b/intro_to_r/index.xml @@ -0,0 +1,16 @@ + + + + R on Monash Data Fluency + https://MonashDataFluency.github.io/intro_to_r/ + Recent content in R on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 23 Feb 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/intro_to_r/page/1/index.html b/intro_to_r/page/1/index.html new file mode 100644 index 0000000..5eb8793 --- /dev/null +++ b/intro_to_r/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/intro_to_r/ \ No newline at end of file diff --git a/resources/index.html b/resources/index.html new file mode 100644 index 0000000..ceccd33 --- /dev/null +++ b/resources/index.html @@ -0,0 +1,333 @@ + + + + + + + Monash Data Fluency | Resources + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

+ Resources +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Here are some online training resources recommended by the Data Fluency community:

+ +

Working with data

+ + + +

Python programming

+ + + +

Commandline, Linux and High Performance Computing

+ + + +

The Carpentries

+ +

The Carpentries is an organization that develops and presents high quality workshops. Their material is all freely available online.

+ + + +
+
+
+ +
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+ + Tag: no_sidebar + +

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+ Workshops +

+ + 01 Programming in Python (Introduction) + +

+
+ The Basics of Python Python is a general purpose programming language that supports rapid development of scripts and applications. +Python’s main advantages: + Open Source software, supported by Python Software Foundation Available on all platforms (ie. Windows, Linux and MacOS) It is a general-purpose programming language Supports multiple programming paradigms Very large community with a rich ecosystem of third-party packages Interpreter Python is an interpreted language which can be used in two ways: +
+
+
+ + +
+
+ Workshops +

+ + 02 Data Analysis in Python + +

+
+ Working With Pandas DataFrames We can automate the process of performing data manipulations in Python. It’s efficient to spend time building the code to perform these tasks because once it’s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis. +The Dataset For this lesson, we will be using the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA +
+
+
+ + +
+
+ Workshops +

+ + 03 Indexing, Slicing and Subsetting + +

+
+ In lesson 02, we read a CSV into a Python pandas DataFrame. We learned: + How to save the DataFrame to a named object, How to perform basic math on the data, How to calculate summary statistics, and How to create basic plots of the data. In this lesson, we will explore ways to access different parts of the data using: + Indexing, Slicing, and Subsetting Loading our data We will continue to use the surveys dataset that we worked with in the last lesson. +
+
+
+ + +
+
+ Workshops +

+ + 04 Automation with Loops + +

+
+ An example task that we might want to repeat is printing each character in a word on a line of its own. +word = 'lead' We can access a character in a string using its index. For example, we can get the first character of the word 'lead', by using word[0]. One way to print each character is to use four print statements: +print(word[0]) print(word[1]) print(word[2]) print(word[3]) Gives output +
+
+
+ + +
+
+ Workshops +

+ + 05 Making Plots With ggplot + +

+
+ Introduction Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. +
+
+
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+ + 01 Programming in Python (Introduction) + +

+
+ The Basics of Python Python is a general purpose programming language that supports rapid development of scripts and applications. +Python’s main advantages: + Open Source software, supported by Python Software Foundation Available on all platforms (ie. Windows, Linux and MacOS) It is a general-purpose programming language Supports multiple programming paradigms Very large community with a rich ecosystem of third-party packages Interpreter Python is an interpreted language which can be used in two ways: +
+
+
+ +
+ +
+
+
+ Workshops +

+ + 02 Data Analysis in Python + +

+
+ Working With Pandas DataFrames We can automate the process of performing data manipulations in Python. It’s efficient to spend time building the code to perform these tasks because once it’s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis. +The Dataset For this lesson, we will be using the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA +
+
+
+ +
+ +
+
+
+ Workshops +

+ + 03 Indexing, Slicing and Subsetting + +

+
+ In lesson 02, we read a CSV into a Python pandas DataFrame. We learned: + How to save the DataFrame to a named object, How to perform basic math on the data, How to calculate summary statistics, and How to create basic plots of the data. In this lesson, we will explore ways to access different parts of the data using: + Indexing, Slicing, and Subsetting Loading our data We will continue to use the surveys dataset that we worked with in the last lesson. +
+
+
+ +
+ +
+
+
+ Workshops +

+ + 04 Automation with Loops + +

+
+ An example task that we might want to repeat is printing each character in a word on a line of its own. +word = 'lead' We can access a character in a string using its index. For example, we can get the first character of the word 'lead', by using word[0]. One way to print each character is to use four print statements: +print(word[0]) print(word[1]) print(word[2]) print(word[3]) Gives output +
+
+
+ +
+ +
+
+
+ Workshops +

+ + 05 Making Plots With ggplot + +

+
+ Introduction Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. +
+
+
+ +
+ +
+
+ +
+ + + + + + diff --git a/tags/no_sidebar/index.xml b/tags/no_sidebar/index.xml new file mode 100644 index 0000000..a4fcfff --- /dev/null +++ b/tags/no_sidebar/index.xml @@ -0,0 +1,68 @@ + + + + No_sidebar on Monash Data Fluency + https://MonashDataFluency.github.io/tags/no_sidebar/ + Recent content in No_sidebar on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 23 Feb 2018 13:28:03 +1100 + + + + + + 01 Programming in Python (Introduction) + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/intro/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/intro/ + The Basics of Python Python is a general purpose programming language that supports rapid development of scripts and applications. +Python&rsquo;s main advantages: + Open Source software, supported by Python Software Foundation Available on all platforms (ie. Windows, Linux and MacOS) It is a general-purpose programming language Supports multiple programming paradigms Very large community with a rich ecosystem of third-party packages Interpreter Python is an interpreted language which can be used in two ways: + + + + 02 Data Analysis in Python + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/working_with_data/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/working_with_data/ + Working With Pandas DataFrames We can automate the process of performing data manipulations in Python. It&rsquo;s efficient to spend time building the code to perform these tasks because once it&rsquo;s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis. +The Dataset For this lesson, we will be using the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA + + + + 03 Indexing, Slicing and Subsetting + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/indexing/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/indexing/ + In lesson 02, we read a CSV into a Python pandas DataFrame. We learned: + How to save the DataFrame to a named object, How to perform basic math on the data, How to calculate summary statistics, and How to create basic plots of the data. In this lesson, we will explore ways to access different parts of the data using: + Indexing, Slicing, and Subsetting Loading our data We will continue to use the surveys dataset that we worked with in the last lesson. + + + + 04 Automation with Loops + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/loops/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/loops/ + An example task that we might want to repeat is printing each character in a word on a line of its own. +word = 'lead' We can access a character in a string using its index. For example, we can get the first character of the word 'lead', by using word[0]. One way to print each character is to use four print statements: +print(word[0]) print(word[1]) print(word[2]) print(word[3]) Gives output + + + + 05 Making Plots With ggplot + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/plotting/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/plotting/ + Introduction Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20181212/index.html b/workshops/cleaning_data_with_openrefine/20181212/index.html new file mode 100644 index 0000000..2a17771 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20181212/index.html @@ -0,0 +1,380 @@ + + + + + + + Monash Data Fluency | Cleaning Data with OpenRefine - Dec 2018 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Cleaning Data with OpenRefine - Dec 2018 +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Cleaning Data with OpenRefine

+ +
    +
  • Date : 12th December 2018
    • +
  • Time : 10 am - 3:30 pm
    • +
  • Location : T1, Sir Louis Matheson Library, Clayton, VIC
    • +
  • Instructors : Andrew Perry, Beth Pearson
    • +
  • Helpers : Cassandra Freeman, Linda Kalejs, Anna Stokes, Penelope Presta, Melissa Santoso, Amy Han
    • +
+ +

Got messy data ? Manual data entry with typos ? A spreadsheet that makes Excel grind to a halt ? OpenRefine might just be the tool you need.

+ +

This beginner-level hands-on workshop is designed to introduce the basics of working with OpenRefine to clean up, transform and enhance ‘messy’ datasets. At the conclusion of the workshop you will understand what the OpenRefine software does and how to use it to clean and transform data for your own projects.

+ +

General Information

+ +

This is a hands-on workshop that will be taught in the style of Software Carpentry workshops, following the Library Carpentry OpenRefine material. There will be lots of helpers in the workshop to assist in keeping things on track so that no learner gets lost or left behind !

+ +

Who: The course is aimed at beginners - no familiarity with data cleaning techniques or OpenRefine is required. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: Participants must bring their own laptop (Windows, Mac, Linux) with charger and be able to connect to eduroam. Please install OpenRefine on your computer prior to the workshop - if you have trouble arrive a little early and we can help you get it installed.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
10:00Session 1
11:00Break
12:15Lunch
01:00Session 2
02:15Break
03:15Wrap up, post workshop survey
+ +

Shared Notepad

+ + + +

Syllabus

+ +

We will follow the Library Carpentry OpenRefine lessons.

+ +

Post-workshop survey

+ +

We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: +https://goo.gl/forms/2xZLrj4umOlpqmEJ3

+ +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/cleaning_data_with_openrefine/20181212/index.xml b/workshops/cleaning_data_with_openrefine/20181212/index.xml new file mode 100644 index 0000000..8eda60d --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20181212/index.xml @@ -0,0 +1,16 @@ + + + + Cleaning Data with OpenRefine - Dec 2018 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20181212/ + Recent content in Cleaning Data with OpenRefine - Dec 2018 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Thu, 15 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20181212/page/1/index.html b/workshops/cleaning_data_with_openrefine/20181212/page/1/index.html new file mode 100644 index 0000000..3ec64ff --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20181212/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20181212/ \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190214/index.html b/workshops/cleaning_data_with_openrefine/20190214/index.html new file mode 100644 index 0000000..991525d --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190214/index.html @@ -0,0 +1,371 @@ + + + + + + + Monash Data Fluency | Cleaning Data with OpenRefine - February 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Cleaning Data with OpenRefine - February 2019 +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Cleaning Data with OpenRefine

+ +
    +
  • Date : 14th February 2019
    • +
  • Time : 10am - 3:00pm
    • +
  • Location : Learning and Teaching Building (19 Ancora Imparo Way), Room 132
    • +
  • Instructors : Beth Pearson, Mervyn Lim
    • +
  • Helpers : Anna Stokes, Sophie Wright
    • +
+ +

Got messy data? Manual data entry with typos? A spreadsheet that makes Excel grind to a halt? OpenRefine might just be the tool you need.

+ +

This beginner-level hands-on workshop is designed to introduce the basics of working with OpenRefine to clean up, transform and enhance ‘messy’ datasets. At the conclusion of the workshop you will understand what the OpenRefine software does and how to use it to clean and transform data for your own projects.

+ +

General Information

+ +

This is a hands-on workshop that will be taught in the style of Software Carpentry workshops, following a revised version of the Library Carpentry OpenRefine material. +There will be lots of helpers in the workshop to assist in keeping things on track so that no learner gets lost or left behind !

+ +

Who: The course is aimed at beginners - no familiarity with data cleaning techniques or OpenRefine is required. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: Participants must bring their own laptop (Windows, Mac, Linux) with charger and be able to connect to eduroam. Please install OpenRefine on your computer prior to the workshop - if you have trouble arrive a little early and we can help you get it installed.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
10:00Session 1
12:00Lunch
12:45Session 2
2:45Wrap up, post workshop survey
+ +

Shared Notepad

+ + + +

Syllabus

+ +

We will be following the lessons outlined here.

+ +

Post-workshop survey

+ +

We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: +https://goo.gl/forms/KVeJ0qocYyrV2FDX2

+ +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/cleaning_data_with_openrefine/20190214/index.xml b/workshops/cleaning_data_with_openrefine/20190214/index.xml new file mode 100644 index 0000000..d73db8d --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190214/index.xml @@ -0,0 +1,16 @@ + + + + Cleaning Data with OpenRefine - February 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190214/ + Recent content in Cleaning Data with OpenRefine - February 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Thu, 15 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190214/page/1/index.html b/workshops/cleaning_data_with_openrefine/20190214/page/1/index.html new file mode 100644 index 0000000..8b336c0 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190214/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190214/ \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190417/index.html b/workshops/cleaning_data_with_openrefine/20190417/index.html new file mode 100644 index 0000000..57d2298 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190417/index.html @@ -0,0 +1,371 @@ + + + + + + + Monash Data Fluency | Cleaning Data with OpenRefine - April 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Cleaning Data with OpenRefine - April 2019 +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Cleaning Data with OpenRefine

+ +
    +
  • Date : 17th April 2019
    • +
  • Time : 9:30am - 1:00pm
    • +
  • Location : Teaching Room 2 (T2), Sir Louis Matheson Library, Claytom Campus (https://goo.gl/maps/DUXk3v8UuWB2)
    • +
  • Instructors : Mervyn Lim, Sophie Wright
    • +
  • Helpers : Sneha Jambagi, Caroline Knaggs, Katherine Lang, Beth Pearson
    • +
+ +

Got messy data? Manual data entry with typos? A spreadsheet that makes Excel grind to a halt? OpenRefine might just be the tool you need.

+ +

This beginner-level hands-on workshop is designed to introduce the basics of working with OpenRefine to clean up, transform and enhance ‘messy’ datasets. At the conclusion of the workshop you will understand what the OpenRefine software does and how to use it to clean and transform data for your own projects.

+ +

General Information

+ +

This is a hands-on workshop that will be taught in the style of Software Carpentry workshops, following a revised version of the Library Carpentry OpenRefine material. +There will be lots of helpers in the workshop to assist in keeping things on track so that no learner gets lost or left behind !

+ +

Who: The course is aimed at beginners - no familiarity with data cleaning techniques or OpenRefine is required. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: Participants must bring their own laptop (Windows, Mac, Linux) with charger and be able to connect to eduroam. Please install OpenRefine on your computer prior to the workshop - if you have trouble arrive a little early and we can help you get it installed.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
09:30Session 1
11:00Break
11:30Session 2
12:45Wrap up, post workshop survey
+ +

Shared Notepad

+ + + +

Syllabus

+ +

We will be following the lessons outlined here.

+ +

Post-workshop survey

+ +

We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: +https://forms.gle/3qcXbJiTRCGFDkd88

+ +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/cleaning_data_with_openrefine/20190417/index.xml b/workshops/cleaning_data_with_openrefine/20190417/index.xml new file mode 100644 index 0000000..9564921 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190417/index.xml @@ -0,0 +1,16 @@ + + + + Cleaning Data with OpenRefine - April 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190417/ + Recent content in Cleaning Data with OpenRefine - April 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Thu, 15 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190417/page/1/index.html b/workshops/cleaning_data_with_openrefine/20190417/page/1/index.html new file mode 100644 index 0000000..212f357 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190417/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190417/ \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190524/index.html b/workshops/cleaning_data_with_openrefine/20190524/index.html new file mode 100644 index 0000000..86212c8 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190524/index.html @@ -0,0 +1,370 @@ + + + + + + + Monash Data Fluency | Cleaning Data with OpenRefine - April 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Cleaning Data with OpenRefine - April 2019 +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Cleaning Data with OpenRefine

+ +
    +
  • Date : 24th May 2019
    • +
  • Time : 09:30am - 1:00pm
    • +
  • Location : Training Room, Peninsula Library, Peninsula Campus
    • +
  • Instructors : Beth Pearson, Mervyn Lim
    • +
+ +

Got messy data? Manual data entry with typos? A spreadsheet that makes Excel grind to a halt? OpenRefine might just be the tool you need.

+ +

This beginner-level hands-on workshop is designed to introduce the basics of working with OpenRefine to clean up, transform and enhance ‘messy’ datasets. At the conclusion of the workshop you will understand what the OpenRefine software does and how to use it to clean and transform data for your own projects.

+ +

General Information

+ +

This is a hands-on workshop that will be taught in the style of Software Carpentry workshops, following a revised version of the Library Carpentry OpenRefine material. +There will be lots of helpers in the workshop to assist in keeping things on track so that no learner gets lost or left behind !

+ +

Who: The course is aimed at beginners - no familiarity with data cleaning techniques or OpenRefine is required. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: Participants must bring their own laptop (Windows, Mac, Linux) with charger and be able to connect to eduroam. Please install OpenRefine 3.1 on your computer prior to the workshop - if you have trouble arrive a little early and we can help you get it installed.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
09:30Session 1
11:00Break
11:30Session 2
12:45Wrap up, post workshop survey
+ +

Shared Notepad

+ + + +

Syllabus

+ +

We will be following the lessons outlined here.

+ +

Post-workshop survey

+ +

We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: +https://forms.gle/3X3zoFBQEGeMqMyX6

+ +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/cleaning_data_with_openrefine/20190524/index.xml b/workshops/cleaning_data_with_openrefine/20190524/index.xml new file mode 100644 index 0000000..4912ba3 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190524/index.xml @@ -0,0 +1,16 @@ + + + + Cleaning Data with OpenRefine - April 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190524/ + Recent content in Cleaning Data with OpenRefine - April 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Thu, 15 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190524/page/1/index.html b/workshops/cleaning_data_with_openrefine/20190524/page/1/index.html new file mode 100644 index 0000000..e3e629c --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190524/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190524/ \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190605/index.html b/workshops/cleaning_data_with_openrefine/20190605/index.html new file mode 100644 index 0000000..62a5de6 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190605/index.html @@ -0,0 +1,370 @@ + + + + + + + Monash Data Fluency | Cleaning Data with OpenRefine - April 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Cleaning Data with OpenRefine - April 2019 +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Cleaning Data with OpenRefine

+ +
    +
  • Date : 5th June 2019
    • +
  • Time : 09:30am - 1:00pm
    • +
  • Location : Training Room 2, Sir Louis Matheson Library, Clayton Campus
    • +
  • Instructors : Beth Pearson, Sophie Wright
    • +
+ +

Got messy data? Manual data entry with typos? A spreadsheet that makes Excel grind to a halt? OpenRefine might just be the tool you need.

+ +

This beginner-level hands-on workshop is designed to introduce the basics of working with OpenRefine to clean up, transform and enhance ‘messy’ datasets. At the conclusion of the workshop you will understand what the OpenRefine software does and how to use it to clean and transform data for your own projects.

+ +

General Information

+ +

This is a hands-on workshop that will be taught in the style of Software Carpentry workshops, following a revised version of the Library Carpentry OpenRefine material. +There will be lots of helpers in the workshop to assist in keeping things on track so that no learner gets lost or left behind !

+ +

Who: The course is aimed at beginners - no familiarity with data cleaning techniques or OpenRefine is required. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: Participants must bring their own laptop (Windows, Mac, Linux) with charger and be able to connect to eduroam. Please install OpenRefine 3.1 on your computer prior to the workshop - if you have trouble arrive a little early and we can help you get it installed.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
09:30Session 1
11:00Break
11:30Session 2
12:45Wrap up, post workshop survey
+ +

Shared Notepad

+ + + +

Syllabus

+ +

We will be following the lessons outlined here.

+ +

Post-workshop survey

+ +

We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: +https://forms.gle/3X3zoFBQEGeMqMyX6

+ +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/cleaning_data_with_openrefine/20190605/index.xml b/workshops/cleaning_data_with_openrefine/20190605/index.xml new file mode 100644 index 0000000..2a8f701 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190605/index.xml @@ -0,0 +1,16 @@ + + + + Cleaning Data with OpenRefine - April 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190605/ + Recent content in Cleaning Data with OpenRefine - April 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Thu, 15 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190605/page/1/index.html b/workshops/cleaning_data_with_openrefine/20190605/page/1/index.html new file mode 100644 index 0000000..0e3fe82 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190605/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190605/ \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190815/index.html b/workshops/cleaning_data_with_openrefine/20190815/index.html new file mode 100644 index 0000000..0be46ae --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190815/index.html @@ -0,0 +1,370 @@ + + + + + + + Monash Data Fluency | Cleaning Data with OpenRefine - August 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Cleaning Data with OpenRefine - August 2019 +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Cleaning Data with OpenRefine

+ +
    +
  • Date : 15th August 2019
    • +
  • Time : 09:30am - 1:00pm
    • +
  • Location : Training Room 1, Sir Louis Matheson Library, Clayton Campus
    • +
  • Instructors : Sophie Wright, Anna Stokes
    • +
+ +

Got messy data? Manual data entry with typos? A spreadsheet that makes Excel grind to a halt? OpenRefine might just be the tool you need.

+ +

This beginner-level hands-on workshop is designed to introduce the basics of working with OpenRefine to clean up, transform and enhance ‘messy’ datasets. At the conclusion of the workshop you will understand what the OpenRefine software does and how to use it to clean and transform data for your own projects.

+ +

General Information

+ +

This is a hands-on workshop that will be taught in the style of Software Carpentry workshops, following a revised version of the Library Carpentry OpenRefine material. +There will be Helpers in the workshop to assist in keeping things on track so that no learner gets lost or left behind !

+ +

Who: The course is aimed at beginners - no familiarity with data cleaning techniques or OpenRefine is required. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: Participants must bring their own laptop (Windows, Mac, Linux) with charger and be able to connect to eduroam. Please install OpenRefine 3.1 on your computer prior to the workshop - if you have trouble arrive a little early and we can help you get it installed.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
09:30Session 1
11:00Break
11:30Session 2
12:45Wrap up, post workshop survey
+ +

Shared Notepad

+ + + +

Syllabus

+ +

We will be following the lessons outlined here.

+ +

Post-workshop survey

+ +

We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: +https://forms.gle/BGi2FRfVZVo93sMH7

+ +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/cleaning_data_with_openrefine/20190815/index.xml b/workshops/cleaning_data_with_openrefine/20190815/index.xml new file mode 100644 index 0000000..f3ae370 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190815/index.xml @@ -0,0 +1,16 @@ + + + + Cleaning Data with OpenRefine - August 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190815/ + Recent content in Cleaning Data with OpenRefine - August 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Thu, 15 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190815/page/1/index.html b/workshops/cleaning_data_with_openrefine/20190815/page/1/index.html new file mode 100644 index 0000000..9d195b8 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190815/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190815/ \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190910/index.html b/workshops/cleaning_data_with_openrefine/20190910/index.html new file mode 100644 index 0000000..1c96f38 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190910/index.html @@ -0,0 +1,370 @@ + + + + + + + Monash Data Fluency | Cleaning Data with OpenRefine - September 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Cleaning Data with OpenRefine - September 2019 +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Cleaning Data with OpenRefine

+ +
    +
  • Date : 10th September 2019
    • +
  • Time : 09:30am - 1:00pm
    • +
  • Location : Training Room 1, Sir Louis Matheson Library, Clayton Campus
    • +
  • Instructors : Sophie Wright, Sneha Jambagi
    • +
+ +

Got messy data? Manual data entry with typos? A spreadsheet that makes Excel grind to a halt? OpenRefine might just be the tool you need.

+ +

This beginner-level hands-on workshop is designed to introduce the basics of working with OpenRefine to clean up, transform and enhance ‘messy’ datasets. At the conclusion of the workshop you will understand what the OpenRefine software does and how to use it to clean and transform data for your own projects.

+ +

General Information

+ +

This is a hands-on workshop that will be taught in the style of Software Carpentry workshops, following a revised version of the Library Carpentry OpenRefine material. +There will be Helpers in the workshop to assist in keeping things on track so that no learner gets lost or left behind !

+ +

Who: The course is aimed at beginners - no familiarity with data cleaning techniques or OpenRefine is required. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: Participants must bring their own laptop (Windows, Mac, Linux) with charger and be able to connect to eduroam. Please install OpenRefine 3.1 on your computer prior to the workshop - if you have trouble arrive a little early and we can help you get it installed.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
09:30Session 1
11:00Break
11:30Session 2
12:45Wrap up, post workshop survey
+ +

Shared Notepad

+ + + +

Syllabus

+ +

We will be following the lessons outlined here.

+ +

Post-workshop survey

+ +

We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: +https://forms.gle/t2yg8T4r3R5k1UdP7

+ +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/cleaning_data_with_openrefine/20190910/index.xml b/workshops/cleaning_data_with_openrefine/20190910/index.xml new file mode 100644 index 0000000..fcbc7e8 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190910/index.xml @@ -0,0 +1,16 @@ + + + + Cleaning Data with OpenRefine - September 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190910/ + Recent content in Cleaning Data with OpenRefine - September 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Thu, 15 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20190910/page/1/index.html b/workshops/cleaning_data_with_openrefine/20190910/page/1/index.html new file mode 100644 index 0000000..4162032 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20190910/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20190910/ \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20191015/index.html b/workshops/cleaning_data_with_openrefine/20191015/index.html new file mode 100644 index 0000000..4e178bd --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20191015/index.html @@ -0,0 +1,370 @@ + + + + + + + Monash Data Fluency | Cleaning Data with OpenRefine - October 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Cleaning Data with OpenRefine - October 2019 +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Cleaning Data with OpenRefine

+ +
    +
  • Date : 15th October 2019
    • +
  • Time : 09:30am - 1:00pm
    • +
  • Location : Teaching Room 2, Caulfield Library, Caulfield campus
    • +
  • Instructors : Beth Pearson, Sneha Jambagi
    • +
+ +

Got messy data? Manual data entry with typos? A spreadsheet that makes Excel grind to a halt? OpenRefine might just be the tool you need.

+ +

This beginner-level hands-on workshop is designed to introduce the basics of working with OpenRefine to clean up, transform and enhance ‘messy’ datasets. At the conclusion of the workshop you will understand what the OpenRefine software does and how to use it to clean and transform data for your own projects.

+ +

General Information

+ +

This is a hands-on workshop that will be taught in the style of Software Carpentry workshops, following a revised version of the Library Carpentry OpenRefine material. +There will be Helpers in the workshop to assist in keeping things on track so that no learner gets lost or left behind !

+ +

Who: The course is aimed at beginners - no familiarity with data cleaning techniques or OpenRefine is required. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: Participants must bring their own laptop (Windows, Mac, Linux) with charger and be able to connect to eduroam. Please install OpenRefine 3.1 on your computer prior to the workshop - if you have trouble arrive a little early and we can help you get it installed.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
09:30Session 1
11:00Break
11:30Session 2
12:45Wrap up, post workshop survey
+ +

Shared Notepad

+ + + +

Syllabus

+ +

We will be following the lessons outlined here.

+ +

Post-workshop survey

+ +

We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: +https://forms.gle/mBVfRMoDqc1p38xB7

+ +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/cleaning_data_with_openrefine/20191015/index.xml b/workshops/cleaning_data_with_openrefine/20191015/index.xml new file mode 100644 index 0000000..37c6922 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20191015/index.xml @@ -0,0 +1,16 @@ + + + + Cleaning Data with OpenRefine - October 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20191015/ + Recent content in Cleaning Data with OpenRefine - October 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Thu, 15 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20191015/page/1/index.html b/workshops/cleaning_data_with_openrefine/20191015/page/1/index.html new file mode 100644 index 0000000..9d73303 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20191015/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20191015/ \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20191115/index.html b/workshops/cleaning_data_with_openrefine/20191115/index.html new file mode 100644 index 0000000..cfffc25 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20191115/index.html @@ -0,0 +1,370 @@ + + + + + + + Monash Data Fluency | Cleaning Data with OpenRefine - November 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Cleaning Data with OpenRefine - November 2019 +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Cleaning Data with OpenRefine

+ +
    +
  • Date : 15th November 2019
    • +
  • Time : 09:30am - 1:00pm
    • +
  • Location : Teaching Room 1, Matheson Library, Clayton Campus
    • +
  • Instructors : Sneha Jambagi, Mervyn Lim
    • +
+ +

Got messy data? Manual data entry with typos? A spreadsheet that makes Excel grind to a halt? OpenRefine might just be the tool you need.

+ +

This beginner-level hands-on workshop is designed to introduce the basics of working with OpenRefine to clean up, transform and enhance ‘messy’ datasets. At the conclusion of the workshop you will understand what the OpenRefine software does and how to use it to clean and transform data for your own projects.

+ +

General Information

+ +

This is a hands-on workshop that will be taught in the style of Software Carpentry workshops, following a revised version of the Library Carpentry OpenRefine material. +There will be Helpers in the workshop to assist in keeping things on track so that no learner gets lost or left behind !

+ +

Who: The course is aimed at beginners - no familiarity with data cleaning techniques or OpenRefine is required. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: Participants must bring their own laptop (Windows, Mac, Linux) with charger and be able to connect to eduroam. Please install OpenRefine 3.1 on your computer prior to the workshop - if you have trouble arrive a little early and we can help you get it installed.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
09:30Session 1
11:00Break
11:30Session 2
12:45Wrap up, post workshop survey
+ +

Shared Notepad

+ + + +

Syllabus

+ +

We will be following the lessons outlined here.

+ +

Post-workshop survey

+ +

We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: +https://forms.gle/ZfpH1e6Zf1wiTMcCA

+ +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/cleaning_data_with_openrefine/20191115/index.xml b/workshops/cleaning_data_with_openrefine/20191115/index.xml new file mode 100644 index 0000000..6a1c6a0 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20191115/index.xml @@ -0,0 +1,16 @@ + + + + Cleaning Data with OpenRefine - November 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20191115/ + Recent content in Cleaning Data with OpenRefine - November 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Thu, 15 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/20191115/page/1/index.html b/workshops/cleaning_data_with_openrefine/20191115/page/1/index.html new file mode 100644 index 0000000..276949a --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/20191115/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/20191115/ \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/index.html b/workshops/cleaning_data_with_openrefine/index.html new file mode 100644 index 0000000..4f189d4 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/index.html @@ -0,0 +1,291 @@ + + + + + + + Monash Data Fluency + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Cleaning_data_with_openrefines +

+ +
+ + +
+
+ + +
+ +
+
+ +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/cleaning_data_with_openrefine/index.xml b/workshops/cleaning_data_with_openrefine/index.xml new file mode 100644 index 0000000..7c80117 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/index.xml @@ -0,0 +1,15 @@ + + + + Cleaning_data_with_openrefines on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/ + Recent content in Cleaning_data_with_openrefines on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + + + + + + \ No newline at end of file diff --git a/workshops/cleaning_data_with_openrefine/page/1/index.html b/workshops/cleaning_data_with_openrefine/page/1/index.html new file mode 100644 index 0000000..e494719 --- /dev/null +++ b/workshops/cleaning_data_with_openrefine/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/cleaning_data_with_openrefine/ \ No newline at end of file diff --git a/workshops/index.html b/workshops/index.html new file mode 100644 index 0000000..4c685ff --- /dev/null +++ b/workshops/index.html @@ -0,0 +1,302 @@ + + + + + + + Monash Data Fluency | Workshops + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Workshops +

+ +
+ + +
+
+ + +
+ + +
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/index.xml b/workshops/index.xml new file mode 100644 index 0000000..38eda0c --- /dev/null +++ b/workshops/index.xml @@ -0,0 +1,16 @@ + + + + Workshops on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/ + Recent content in Workshops on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Wed, 25 Apr 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_command_line/20180701-commandline/index.html b/workshops/intro_to_command_line/20180701-commandline/index.html new file mode 100644 index 0000000..a5faaab --- /dev/null +++ b/workshops/intro_to_command_line/20180701-commandline/index.html @@ -0,0 +1,384 @@ + + + + + + + Monash Data Fluency | Introduction to Command Lines with the Unix Shell + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Introduction to Command Lines with the Unix Shell +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to Command Lines with the Unix Shell

+ +
    +
  • Date : Friday 29th June 2018
    • +
  • Time : 10:00am to 3:00pm
    • + +

  • Location : Monash University Clayton Campus, + Room T1. Matheson Library. Clayton Campus

    • + +

  • Instructors : Simon Michnowicz, Kerri Wait

    • + +

  • Helpers : Gin Tan, Owen Kaluza, Rafi Mohammed Feroz

    • +
+ +

This hands-on workshop aims to equip participants with the fundamentals of Unix Shell, and how to use it to help their research.

+ +

Sign up here !

+ +

General Information

+ +

The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
10:00Welcome
10:15Session 1
12:15Lunch
01:30Session 2
03:00Wrap-up
+ +

Jupyter Notebook gives you access to a Unix terminal

+ +
    +
  • The Jupyter Notebook will be the way you access a Unix terminal. A username and password is needed to log into the Jupyter Server. This will be given on the day.
    • +
+ +

The Jupyter notebook does not work well with Internet Explorer Please use another browser (i.e. Chrome, Safari, Firefox)

+ +

Shared Notepad

+ +
    +
  • An Etherpad notebook will allow you to make comments or add questions. Access it here
    • +
+ +

Syllabus

+ + + +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/intro_to_command_line/20180701-commandline/index.xml b/workshops/intro_to_command_line/20180701-commandline/index.xml new file mode 100644 index 0000000..c4673f7 --- /dev/null +++ b/workshops/intro_to_command_line/20180701-commandline/index.xml @@ -0,0 +1,15 @@ + + + + Introduction to Command Lines with the Unix Shell on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_command_line/20180701-commandline/ + Recent content in Introduction to Command Lines with the Unix Shell on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_command_line/20180701-commandline/indexing/index.html b/workshops/intro_to_command_line/20180701-commandline/indexing/index.html new file mode 100644 index 0000000..7429491 --- /dev/null +++ b/workshops/intro_to_command_line/20180701-commandline/indexing/index.html @@ -0,0 +1,790 @@ + + + + + + + Monash Data Fluency | 03 Indexing, Slicing and Subsetting + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ WORKSHOPS +

+

+ 03 Indexing, Slicing and Subsetting +

+
+
+ + +

In lesson 02, we read a CSV into a Python pandas DataFrame. We learned:

+ +
    +
  • How to save the DataFrame to a named object,
    • +
  • How to perform basic math on the data,
    • +
  • How to calculate summary statistics, and
    • +
  • How to create basic plots of the data.
    • +
+ +

In this lesson, we will explore ways to access different parts of the data +using:

+ +
    +
  • Indexing,
    • +
  • Slicing, and
    • +
  • Subsetting
    • +
+ +

Loading our data

+ +

We will continue to use the surveys dataset that we worked with in the last lesson. Let’s reopen and read in the data again:

+ +
# Make sure pandas is loaded
+import pandas as pd
+
+# Read in the survey CSV
+surveys_df = pd.read_csv("data/surveys.csv")
+
+ +

Indexing and Slicing in Python

+ +

We often want to work with subsets of a DataFrame object. There are +different ways to accomplish this including: using labels (column headings), +numeric ranges, or specific x,y index locations.

+ +

Selecting data using Labels (Column Headings)

+ +

We use square brackets [] to select a subset of an Python object. For example, +we can select all data from a column named species_id from the surveys_df +DataFrame by name. There are two ways to do this:

+ +
# Method 1: select a 'subset' of the data using the column name
+surveys_df['species_id'].head()
+
+# Method 2: use the column name as an 'attribute'; gives the same output
+surveys_df.species_id.head()
+
+ +

We can also create a new object that contains only the data within the +species_id column as follows:

+ +
# Creates an object, surveys_species, that only contains the `species_id` column
+surveys_species = surveys_df['species_id']
+
+ +

We can pass a list of column names too, as an index to select columns in that +order. This is useful when we need to reorganize our data.

+ +

NOTE: If a column name is not contained in the DataFrame, an exception +(error) will be raised.

+ +
# Select the species and plot columns from the DataFrame
+surveys_df[['species_id', 'plot_id']].head()
+
+# What happens when you flip the order?
+surveys_df[['plot_id', 'species_id']].head()
+
+# What happens if you ask for a column that doesn't exist?
+surveys_df['speciess']
+
+ +

Python tells us what type of error it is in the traceback, at the bottom it says KeyError: 'speciess' which means that speciess is not a column name (or Key in the related python data type dictionary).

+ +

Extracting Range based Subsets: Slicing

+ +

REMINDER: Python Uses 0-based Indexing

+ +

Let’s remind ourselves that Python uses 0-based +indexing. This means that the first element in an object is located at position +0. This is different from other tools like R and Matlab that index elements +within objects starting at 1.

+ +
# Create a list of numbers:
+a = [1, 2, 3, 4, 5]
+
+ +

indexing diagram +slicing diagram

+ +

Challenge - Extracting data

+ +
    +

  1. What value does the code a[0] return?

    2. + +

  2. How about this: a[5]

    3. + +

  3. In the example above, calling a[5] returns an error. Why is that?

    4. + +

  4. What about a[len(a)] ?

    5. +
+ +

Slicing Subsets of Rows in Python

+ +

Slicing using the [] operator selects a set of rows and/or columns from a +DataFrame. To slice out a set of rows, you use the following syntax: +data[start:stop]. When slicing in pandas the start bound is included in the +output. The stop bound is one step BEYOND the row you want to select. So if you +want to select rows 0, 1 and 2 your code would look like this:

+ +
# Select rows 0, 1, 2 (row 3 is not selected)
+surveys_df[0:3]
+
+ +

The stop bound in Python is different from what you might be used to in +languages like Matlab and R.

+ +
# Select the first 5 rows (rows 0, 1, 2, 3, 4)
+surveys_df[:5]
+
+# Select the last element in the list
+# (the slice starts at the last element, and ends at the end of the list)
+surveys_df[-1:]
+
+ +

We can also reassign values within subsets of our DataFrame.

+ +

Let’s create a brand new clean dataframe from +the original data CSV file.

+ +
surveys_df = pd.read_csv("data/surveys.csv")
+
+ +

Slicing Subsets of Rows and Columns in Python

+ +

We can select specific ranges of our data in both the row and column directions +using either label or integer-based indexing.

+ +
    +
  • loc is primarily label based indexing. Integers may be used but +they are interpreted as a label.
    • +
  • iloc is primarily integer based indexing
    • +
+ +

To select a subset of rows and columns from our DataFrame, we can use the +iloc method. For example, we can select month, day and year (columns 2, 3 +and 4 if we start counting at 1), like this:

+ +
# iloc[row slicing, column slicing]
+surveys_df.iloc[0:3, 1:4]
+
+ +

which gives the output

+ +
   month  day  year
+0      7   16  1977
+1      7   16  1977
+2      7   16  1977
+
+ +

Notice that we asked for a slice from 0:3. This yielded 3 rows of data. When you +ask for 0:3, you are actually telling Python to start at index 0 and select rows +0, 1, 2 up to but not including 3.

+ +

Let’s explore some other ways to index and select subsets of data:

+ +
# Select all columns for rows of index values 0 and 10
+surveys_df.loc[[0, 10], :]
+
+# What does this do?
+surveys_df.loc[0, ['species_id', 'plot_id', 'weight']]
+
+# What happens when you type the code below?
+surveys_df.loc[[0, 10, 35549], :]
+
+ +

NOTE: Labels must be found in the DataFrame or you will get a KeyError.

+ +

Indexing by labels loc differs from indexing by integers iloc. +With loc, the both start bound and the stop bound are inclusive. When using +loc, integers can be used, but the integers refer to the +index label and not the position. For example, using loc and select 1:4 +will get a different result than using iloc to select rows 1:4.

+ +

We can also select a specific data value using a row and +column location within the DataFrame and iloc indexing:

+ +
# Syntax for iloc indexing to finding a specific data element
+dat.iloc[row, column]
+
+ +

In this iloc example,

+ +
surveys_df.iloc[2, 6]
+
+ +

gives the output

+ +
'F'
+
+ +

Remember that Python indexing begins at 0. So, the index location [2, 6] +selects the element that is 3 rows down and 7 columns over in the DataFrame.

+ +

Challenge - Range

+ +
    +

  1. What happens when you execute:

    + +
      +
    • surveys_df[0:1]
      • +
    • surveys_df[:4]
      • +
    • surveys_df[:-1]
      • +
    2. + +

  2. What happens when you call:

    + +
      +
    • surveys_df.iloc[0:4, 1:4]
      • +
    3. +
+ +

Subsetting Data using Criteria

+ +

We can also select a subset of our data using criteria. For example, we can +select all rows that have a year value of 2002:

+ +
surveys_df[surveys_df.year == 2002].head()
+
+ +

Which produces the following output:

+ +
record_id  month  day  year  plot_id species_id  sex  hindfoot_length  weight
+33320      33321      1   12  2002        1         DM    M     38      44
+33321      33322      1   12  2002        1         DO    M     37      58
+33322      33323      1   12  2002        1         PB    M     28      45
+33323      33324      1   12  2002        1         AB  NaN    NaN     NaN
+33324      33325      1   12  2002        1         DO    M     35      29
+...
+35544      35545     12   31  2002       15         AH  NaN    NaN     NaN
+35545      35546     12   31  2002       15         AH  NaN    NaN     NaN
+35546      35547     12   31  2002       10         RM    F     15      14
+35547      35548     12   31  2002        7         DO    M     36      51
+35548      35549     12   31  2002        5        NaN  NaN    NaN     NaN
+
+[2229 rows x 9 columns]
+
+ +

Or we can select all rows that do not contain the year 2002:

+ +
surveys_df[surveys_df.year != 2002]
+
+ +

We can define sets of criteria too:

+ +
surveys_df[(surveys_df.year >= 1980) & (surveys_df.year <= 1985)]
+
+ +

Python Syntax Cheat Sheet

+ +

Use can use the syntax below when querying data by criteria from a DataFrame. +Experiment with selecting various subsets of the “surveys” data.

+ +
    +
  • Equals: ==
    • +
  • Not equals: !=
    • +
  • Greater than, less than: > or <
    • +
  • Greater than or equal to >=
    • +
  • Less than or equal to <=
    • +
+ +

Challenge - Queries

+ +
    +

  1. Select a subset of rows in the surveys_df DataFrame that contain data from +the year 1999 and that contain weight values less than or equal to 8. How +many rows did you end up with? What did your neighbor get?

    2. + +

  2. (Extra) You can use the isin command in Python to query a DataFrame based upon a +list of values as follows:

    3. +
+ +
    surveys_df[surveys_df['species_id'].isin([listGoesHere])]
+
+ +

Use the isin function to find all plots that contain particular species +in the “surveys” DataFrame. How many records contain these values?

+ +
    +

  1. (Extra) Experiment with other queries. Create a query that finds all rows with a +weight value > or equal to 0.

    2. + +

  2. (Extra) The ~ symbol in Python can be used to return the OPPOSITE of the +selection that you specify in Python. It is equivalent to is not in. +Write a query that selects all rows with sex NOT equal to ’M’ or ‘F’ in +the “surveys” data.

    3. +
+ +

Using masks to identify a specific condition

+ +

A mask can be useful to locate where a particular subset of values exist or +don’t exist - for example, NaN, or “Not a Number” values. To understand masks, +we also need to understand BOOLEAN objects in Python.

+ +

Boolean values include True or False. For example,

+ +
# Set x to 5
+x = 5
+
+# What does the code below return?
+x > 5
+
+# How about this?
+x == 5
+
+ +

Missing Values

+ +

Let’s try this out. Let’s identify all locations in the survey data that have +null (missing or NaN) data values. We can use the isnull method to do this. +The isnull method will compare each cell with a null value. If an element +has a null value, it will be assigned a value of True in the output object.

+ +
pd.isnull(surveys_df).head()
+
+ +

A snippet of the output is below:

+ +
      record_id  month    day   year plot_id species_id    sex  hindfoot_length weight
+0         False  False  False  False   False      False  False   False      True
+1         False  False  False  False   False      False  False   False      True
+2         False  False  False  False   False      False  False   False      True
+3         False  False  False  False   False      False  False   False      True
+4         False  False  False  False   False      False  False   False      True
+
+[35549 rows x 9 columns]
+
+ +

To select the rows where there are null values, we can use +the mask as an index to subset our data as follows:

+ +
# To select just the rows with NaN values, we can use the 'any()' method
+surveys_df[pd.isnull(surveys_df).any(axis=1)]
+
+ +

(axis=1) is a numpy convention to specify columns.

+ +

Note that the weight column of our DataFrame contains many null or NaN +values. Next, we will explore ways of dealing with this.

+ +

If we look at the weight column in the surveys +data we notice that there are NaN (Not a Number) values. NaN values are undefined +values that cannot be represented mathematically. Pandas, for example, will read +an empty cell in a CSV or Excel sheet as a NaN. NaNs have some desirable properties: if we +were to average the weight column without replacing our NaNs, Python would know to skip +over those cells.

+ +
surveys_df['weight'].mean()
+42.672428212991356
+
+ +

Dealing with missing data values is always a challenge. It’s sometimes hard to +know why values are missing - was it because of a data entry error? Or data that +someone was unable to collect? Should the value be 0? We need to know how +missing values are represented in the dataset in order to make good decisions. +If we’re lucky, we have some metadata that will tell us more about how null +values were handled.

+ +

For instance, in some disciplines, like Remote Sensing, missing data values are +often defined as -9999. Having a bunch of -9999 values in your data could really +alter numeric calculations. Often in spreadsheets, cells are left empty where no +data are available. Pandas will, by default, replace those missing values with +NaN. However it is good practice to get in the habit of intentionally marking +cells that have no data, with a no data value! That way there are no questions +in the future when you (or someone else) explores your data.

+ +

Where Are the NaN’s?

+ +

Let’s explore the NaN values in our data a bit further. Using the tools we +learned in lesson 02, we can figure out how many rows contain NaN values for +weight. We can also create a new subset from our data that only contains rows +with weight values > 0 (i.e., select meaningful weight values):

+ +
len(surveys_df[pd.isnull(surveys_df.weight)])
+# How many rows have weight values?
+len(surveys_df[surveys_df.weight> 0])
+
+ +

We can replace all NaN values with zeroes using the .fillna() method (after +making a copy of the data so we don’t lose our work):

+ +
# Creat a new DataFrame using copy
+df1 = surveys_df.copy()
+
+# Fill all NaN values with 0
+df1['weight'] = df1['weight'].fillna(0)
+
+ +

However NaN and 0 yield different analysis results. The mean value when NaN +values are replaced with 0 is different from when NaN values are simply thrown +out or ignored.

+ +
df1['weight'].mean()
+38.751976145601844
+
+ +

Extra Information

+ +

We can fill NaN values with any value that we chose. The code below fills all +NaN values with a mean for all weight values.

+ +
 df1['weight'] = surveys_df['weight'].fillna(surveys_df['weight'].mean())
+
+ +

Writing Out Data to CSV

+ +

We’ve learned about using manipulating data to get desired outputs. But we’ve also discussed +keeping data that has been manipulated separate from our raw data. Something we might be interested +in doing is working with only the columns that have full data. First, let’s reload the data so +we’re not mixing up all of our previous manipulations.

+ +
surveys_df = pd.read_csv("data/surveys.csv")
+
+ +

Next, let’s drop all the rows that contain missing values. We will use the command dropna. +By default, dropna removes columns that contain missing data for even just one row.

+ +
df_na = df.dropna()
+
+
+ +

If you now type df_na, you should observe that the resulting DataFrame has 30676 rows +and 9 columns, much smaller than the 35549 row original.

+ +

We can now use the to_csv command to do export a DataFrame in CSV format. Note that the code +below will by default save the data into the current working directory. We can +save it to a different folder by adding the foldername and a slash before the filename: +df1.to_csv('foldername/out.csv'). We use ‘index=False’ so that +pandas doesn’t include the index number for each line.

+ +
# Write DataFrame to CSV
+df_na.to_csv('data_output/surveys_complete.csv', index=False)
+
+ +

Recap

+ +

What we’ve learned:

+ +
    +
  • How to subset and index the dataframe
    • +
  • What NaN values are, how they might be represented, and what this means for your work
    • +
  • How to replace NaN values, if desired
    • +
  • How to use to_csv to write manipulated data to a file.
    • +
+ +

Extra

+ +

We can run isnull on a particular column too. What does the code below do?

+ +
# What does this do?
+empty_weights = surveys_df[pd.isnull(surveys_df['weight'])]['weight']
+print(empty_weights)
+
+ +

Let’s take a minute to look at the statement above. We are using the Boolean +object pd.isnull(surveys_df['weight']) as an index to surveys_df. We are +asking Python to select rows that have a NaN value of weight.

+ +

Extra Challenges - Putting it all together

+ +
    +

  1. Create a new DataFrame that only contains observations with sex values that +are not female or male. Assign each sex value in the new DataFrame to a +new value of ‘x’. Determine the number of null values in the subset.

    2. + +

  2. Create a new DataFrame that contains only observations that are of sex male +or female and where weight values are greater than 0. Create a stacked bar +plot of average weight by plot with male vs female values stacked for each +plot.

    + +
      +
    1. Count the number of missing values per column. Hint: The method .count() gives you the number of non-NA observations per column.
      2. +
    3. +
+ +

NEXT

+ +
+
+ +
+ +
+ + + + + + + + + diff --git a/workshops/intro_to_command_line/20180701-commandline/intro/index.html b/workshops/intro_to_command_line/20180701-commandline/intro/index.html new file mode 100644 index 0000000..d19b3c3 --- /dev/null +++ b/workshops/intro_to_command_line/20180701-commandline/intro/index.html @@ -0,0 +1,589 @@ + + + + + + + Monash Data Fluency | 01 Programming in Python (Introduction) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ WORKSHOPS +

+

+ 01 Programming in Python (Introduction) +

+
+
+ + +

The Basics of Python

+ +

Python is a general purpose programming language that supports rapid development +of scripts and applications.

+ +

Python’s main advantages:

+ +
    +
  • Open Source software, supported by Python Software Foundation
    • +
  • Available on all platforms (ie. Windows, Linux and MacOS)
    • +
  • It is a general-purpose programming language
    • +
  • Supports multiple programming paradigms
    • +
  • Very large community with a rich ecosystem of third-party packages
    • +
+ +

Interpreter

+ +

Python is an interpreted language which can be used in two ways:

+ +
    +
  • “Interactive” Mode: It functions like an “advanced calculator” Executing +one command at a time:
    • +
+ +
user:host:~$ python
+Python 3.5.1 (default, Oct 23 2015, 18:05:06)
+[GCC 4.8.3] on linux2
+Type "help", "copyright", "credits" or "license" for more information.
+>>> 2 + 2
+4
+>>> print("Hello World")
+Hello World
+
+ +
    +
  • “Scripting” Mode: Executing a series of “commands” saved in text file, +usually with a .py extension after the name of your file:
    • +
+ +
user:host:~$ python my_script.py
+Hello World
+
+ +

Introduction to Python built-in data types

+ +

Strings, integers and floats

+ +

One of the most basic things we can do in Python is assign values to variables:

+ +
text = "Data Fluency"  # An example of a string
+number = 42  # An example of an integer
+pi_value = 3.1415  # An example of a float
+
+ +

Here we’ve assigned data to the variables text, number and pi_value, +using the assignment operator =. To review the value of a variable, we +can type the name of the variable into the Jupyter notebook and press Shift and Enter:

+ +
text
+## Which Returns
+"Data Fluency"
+
+ +

Everything in Python has a type. To get the type of something, we can pass it +to the built-in function type:

+ +
type(text)
+ str
+type(number)
+ int
+type(6.02)
+ float
+
+ +

The variable text is of type str, short for “string”. Strings hold +sequences of characters, which can be letters, numbers, punctuation +or more exotic forms of text (even emoji!).

+ +

We can also see the value of something using another built-in function, print:

+ +

+print(text)
+Data Fluency
+
+print(11)
+11
+
+ +

This may seem redundant, but in fact it’s the only way to display output in a script:

+ +

example.py

+ +
# A Python script file
+# Comments in Python start with #
+# The next line assigns the string "Data Carpentry" to the variable "text".
+text = "Data Fluency"
+# The next line does nothing!
+text
+# The next line uses the print function to print out the value we assigned to "text"
+print(text)
+
+ +

Running the script

+ +
$ python example.py
+Data Fluency
+
+ +

Notice that “Data Fluency” is printed only once.

+ +

Tip: print and type are built-in functions in Python. Later in this +lesson, we will introduce methods and user-defined functions. The Python +documentation is excellent for reference on the differences between them.

+ +
help(print)
+
+ +

Will give the output

+ +
Help on built-in function print in module builtins:
+
+print(...)
+    print(value, ..., sep=' ', end='\n', file=sys.stdout, flush=False)
+    
+    Prints the values to a stream, or to sys.stdout by default.
+    Optional keyword arguments:
+    file:  a file-like object (stream); defaults to the current sys.stdout.
+    sep:   string inserted between values, default a space.
+    end:   string appended after the last value, default a newline.
+    flush: whether to forcibly flush the stream.
+
+ +

Operators

+ +

We can perform mathematical calculations in Python using the basic operators + +, -, /, *, %:

+ +
>>> 2 + 2  # Addition
+4
+>>> 6 * 7  # Multiplication
+42
+>>> 2 ** 16  # Power
+65536
+>>> 13 % 5  # Modulo
+3
+
+ +

We can also use comparison and logic operators: +<, >, ==, !=, <=, >= and statements of identity such as +and, or, not. The data type returned by this is +called a boolean.

+ +
>>> 3 > 4
+False
+>>> True and True
+True
+>>> True or False
+True
+
+ +

Sequential types: Lists and Tuples

+ +

Lists

+ +

Lists are a common data structure to hold an ordered sequence of +elements. Each element can be accessed by an index. Note that Python +indexes start with 0 instead of 1:

+ +
>>> numbers = [1, 2, 3]
+>>> numbers[0]
+1
+
+ +

A for loop can be used to access the elements in a list or other Python data structure one at a time. We will learn about loops in other lesson.

+ +
>>> for num in numbers:
+...     print(num)
+...
+1
+2
+3
+
+ +

Indentation is very important in Python. Note that the second line in the +example above is indented. Just like three chevrons >>> indicate an +interactive prompt in Python, the three dots ... are Python’s prompt for +multiple lines. This is Python’s way of marking a block of code. [Note: you +do not type >>> or ....]

+ +

To add elements to the end of a list, we can use the append method. Methods +are a way to interact with an object (a list, for example). We can invoke a +method using the dot . followed by the method name and a list of arguments +in parentheses. Let’s look at an example using append:

+ +
>>> numbers.append(4)
+>>> print(numbers)
+[1, 2, 3, 4]
+>>>
+
+ +

To find out what methods are available for an +object, we can use the built-in help command:

+ +
help(numbers)
+
+Help on list object:
+
+class list(object)
+ |  list() -> new empty list
+ |  list(iterable) -> new list initialized from iterable's items
+ ...
+
+ +

Tuples

+ +

A tuple is similar to a list in that it’s an ordered sequence of elements. +However, tuples can not be changed once created (they are “immutable”). Tuples +are created by placing comma-separated values inside parentheses ().

+ +
# Tuples use parentheses
+a_tuple= (1, 2, 3)
+another_tuple = ('blue', 'green', 'red')
+# Note: lists use square brackets
+a_list = [1, 2, 3]
+
+ +

Challenge - Tuples

+ +
    +
  1. What happens when you type a_tuple[2]=5 vs a_list[1]=5 ?
    2. +
  2. Type type(a_tuple) into python - what is the object type?
    3. +
+ +

Dictionaries

+ +

A dictionary is a container that holds pairs of objects - keys and values.

+ +
>>> translation = {'one': 1, 'two': 2}
+>>> translation['one']
+1
+
+ +

Dictionaries work a lot like lists - except that you index them with keys. +You can think about a key as a name for or a unique identifier for a set of values +in the dictionary. Keys can only have particular types - they have to be +“hashable”. Strings and numeric types are acceptable, but lists aren’t.

+ +
>>> rev = {1: 'one', 2: 'two'}
+>>> rev[1]
+'one'
+>>> bad = {[1, 2, 3]: 3}
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: unhashable type: 'list'
+
+ +

In Python, a “Traceback” is an multi-line error block printed out for the +user.

+ +

To add an item to the dictionary we assign a value to a new key:

+ +
>>> rev = {1: 'one', 2: 'two'}
+>>> rev[3] = 'three'
+>>> rev
+{1: 'one', 2: 'two', 3: 'three'}
+
+ +

Challenge - Can you do reassignment in a dictionary?

+ +
    +

  1. First check what rev is right now (remember rev is the name of our dictionary). Type: rev

    2. + +

  2. Try to reassign the second value (in the key value pair) so that it no longer reads “two” but instead reads “apple-sauce”.

    3. + +

  3. Now display rev again to see if it has changed.

    4. +
+ +

It is important to note that dictionaries are “unordered” and do not remember +the sequence of their items (i.e. the order in which key:value pairs were +added to the dictionary). Because of this, the order in which items are +returned from loops over dictionaries might appear random and can even change +with time.

+ +

NEXT

+ +
+
+ +
+ +
+ + + + + + + + + diff --git a/workshops/intro_to_command_line/20180701-commandline/loops/index.html b/workshops/intro_to_command_line/20180701-commandline/loops/index.html new file mode 100644 index 0000000..c4c1841 --- /dev/null +++ b/workshops/intro_to_command_line/20180701-commandline/loops/index.html @@ -0,0 +1,607 @@ + + + + + + + Monash Data Fluency | 04 Automation with Loops + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ WORKSHOPS +

+

+ 04 Automation with Loops +

+
+
+ + +

An example task that we might want to repeat is printing each character in a +word on a line of its own.

+ +
word = 'lead'
+
+ +

We can access a character in a string using its index. For example, we can get the first +character of the word 'lead', by using word[0]. One way to print each character is to use +four print statements:

+ +
print(word[0])
+print(word[1])
+print(word[2])
+print(word[3])
+
+ +

Gives output

+ +
l
+e
+a
+d
+
+ +

This is a bad approach for two reasons:

+ +
    +

  1. It doesn’t scale: +if we want to print the characters in a string that’s hundreds of letters long, +we’d be better off just typing them in.

    2. + +

  2. It’s fragile: +if we give it a longer string, +it only prints part of the data, +and if we give it a shorter one, +it produces an error because we’re asking for characters that don’t exist.

    3. +
+ +
word = 'tin'
+print(word[0])
+print(word[1])
+print(word[2])
+print(word[3])
+
+
+ +

Gives back

+ +
t
+i
+n
+---------------------------------------------------------------------------
+IndexError                                Traceback (most recent call last)
+<ipython-input-3-7974b6cdaf14> in <module>()
+      3 print(word[1])
+      4 print(word[2])
+----> 5 print(word[3])
+
+IndexError: string index out of range
+
+ +

Here’s a better approach:

+ +
word = 'lead'
+for char in word:
+    print(char)
+
+
+ +

Would Produce

+ +
l
+e
+a
+d
+
+ +

This is shorter — certainly shorter than something that prints every character in a hundred-letter string — and +more robust as well:

+ +
word = 'oxygen'
+for char in word:
+    print(char)
+
+ +

Output

+ +
o
+x
+y
+g
+e
+n
+
+ +

The improved version uses a for loop to repeat an operation — in this case, printing — once for each thing in a sequence. +The general form of a loop is:

+ +
for variable in collection:
+    do things with variable
+
+ +

Using the oxygen example above, the loop might look like this:

+ +

loop_image

+ +

where each character (char) in the variable word is looped through and printed one character after another. +The numbers in the diagram denote which loop cycle the character was printed in (1 being the first loop, and 6 being the final loop).

+ +

We can call the loop variable anything we like, +but there must be a colon at the end of the line starting the loop, and we must indent anything we want to run inside the loop. Unlike many other languages, there is no command to signify the end of the loop body (e.g. end for); what is indented after the for statement belongs to the loop.

+ +

What’s in a name?

+ +

In the example above, the loop variable was given the name char as a mnemonic; it is short for ‘character’. +We can choose any name we want for variables. We might just as easily have chosen the name banana for the loop variable, as long as we use the same name when we invoke the variable inside the loop:

+ +
word = 'oxygen'
+for banana in word:
+    print(banana)
+
+ +

Output

+ +
o
+x
+y
+g
+e
+n
+
+ +

It is a good idea to choose variable names that are meaningful, otherwise it would be more difficult to understand what the loop is doing.

+ +

Here’s another loop that repeatedly updates a variable:

+ +
length = 0
+for vowel in 'aeiou':
+    length = length + 1
+print('There are', length, 'vowels')
+
+ +
There are 5 vowels
+
+ +

It’s worth tracing the execution of this little program step by step. +Since there are five characters in 'aeiou', +the statement on line 3 will be executed five times. +The first time around, +length is zero (the value assigned to it on line 1) +and vowel is 'a'. +The statement adds 1 to the old value of length, +producing 1, +and updates length to refer to that new value. +The next time around, +vowel is 'e' and length is 1, +so length is updated to be 2. +After three more updates, +length is 5; +since there is nothing left in 'aeiou' for Python to process, +the loop finishes +and the print statement on line 4 tells us our final answer.

+ +

Note that a loop variable is just a variable that’s being used to record progress in a loop. +It still exists after the loop is over, +and we can re-use variables previously defined as loop variables as well:

+ +
letter = 'z'
+for letter in 'abc':
+    print(letter)
+print('after the loop, letter is', letter)
+
+ +

Output

+ +
a
+b
+c
+after the loop, letter is c
+
+ +

Note also that finding the length of a string is such a common operation that Python actually has a built-in function to do it called len:

+ +
print(len('aeiou'))
+
+ +

Output

+ +
5
+
+ +

len is much faster than any function we could write ourselves, +and much easier to read than a two-line loop; +it will also give us the length of many other things that we haven’t met yet, +so we should always use it when we can.

+ +

From 1 to N

+ +

Python has a built-in function called range that creates a sequence of numbers. range can +accept 1, 2, or 3 parameters.

+ +
    +
  • If one parameter is given, range creates an array of that length, +starting at zero and incrementing by 1. +For example, range(3) produces the numbers 0, 1, 2.
    • +
  • If two parameters are given, range starts at +the first and ends just before the second, incrementing by one. +For example, range(2, 5) produces 2, 3, 4.
    • +
  • If range is given 3 parameters, +it starts at the first one, ends just before the second one, and increments by the third one. +For exmaple range(3, 10, 2) produces 3, 5, 7, 9.
    • +
+ +

Challenge

+ +

Using range, +write a loop that uses range to print the first 3 natural numbers:

+ +
1
+2
+3
+
+ +

Solution

+ +
for i in range(1, 4):
+   print(i)
+
+ +

Computing Powers With Loops

+ +

Exponentiation is built into Python:

+ +
print(5 ** 3)
+
+ +
125
+
+ +

Write a loop that calculates the same result as 5 ** 3 using +multiplication (and without exponentiation).

+ +

Solution

+ +
result = 1
+for i in range(0, 3):
+   result = result * 5
+print(result)
+
+ +

Reverse a String

+ +

Knowing that two strings can be concatenated using the + operator, +write a loop that takes a string +and produces a new string with the characters in reverse order, +so 'Newton' becomes 'notweN'.

+ +

Solution

+ +
newstring = ''
+oldstring = 'Newton'
+for char in oldstring:
+   newstring = char + newstring
+print(newstring)
+
+ +

Computing the Value of a Polynomial

+ +

The built-in function enumerate takes a sequence (e.g. a list) and generates a +new sequence of the same length. Each element of the new sequence is a pair composed of the index +(0, 1, 2,…) and the value from the original sequence:

+ +
for i, x in enumerate(xs):
+    # Do something with i and x
+
+ +

The code above loops through xs, assigning the index to i and the value to x.

+ +

Suppose you have encoded a polynomial as a list of coefficients in +the following way: the first element is the constant term, the +second element is the coefficient of the linear term, the third is the +coefficient of the quadratic term, etc.

+ +
x = 5
+cc = [2, 4, 3]
+
+ +
y = cc[0] * x**0 + cc[1] * x**1 + cc[2] * x**2
+y = 97
+
+ +

Write a loop using enumerate(cc) which computes the value y of any +polynomial, given x and cc.

+ +

Solution

+ +
y = 0
+for i, c in enumerate(cc):
+    y = y + x**i * c
+
+ +

NEXT

+ +
+
+ +
+ +
+ + + + + + + + + diff --git a/workshops/intro_to_command_line/20180701-commandline/page/1/index.html b/workshops/intro_to_command_line/20180701-commandline/page/1/index.html new file mode 100644 index 0000000..7a4f40e --- /dev/null +++ b/workshops/intro_to_command_line/20180701-commandline/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_command_line/20180701-commandline/ \ No newline at end of file diff --git a/workshops/intro_to_command_line/20180701-commandline/plotting/index.html b/workshops/intro_to_command_line/20180701-commandline/plotting/index.html new file mode 100644 index 0000000..a948b5a --- /dev/null +++ b/workshops/intro_to_command_line/20180701-commandline/plotting/index.html @@ -0,0 +1,526 @@ + + + + + + + Monash Data Fluency | 05 Making Plots With ggplot + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ WORKSHOPS +

+

+ 05 Making Plots With ggplot +

+
+
+ + +

Introduction

+ +

Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. +Strictly speaking plotnine is just another implementation of The Grammar of Graphics by Leland Wilkinson, which in theory could go on it own direction and diverge away from an R ggplot .

+ +

The Grammar of Graphics

+ +
+

statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars) +Faceting can be used to generate the same plot for different subsets of the dataset

+
+ +

These are basic building blocks according to the grammar of graphics:

+ +
    +
  • data The data + a set of aesthetic mappings that describing variables mapping
    • +
  • geom Geometric objects, represent what you actually see on the plot: points, lines, polygons, etc.
    • +
  • stats Statistical transformations, summarise data in many useful ways.
    • +
  • scale The scales map values in the data space to values in an aesthetic space
    • +
  • coord A coordinate system, describes how data coordinates are mapped to the plane of the graphic.
    • +
  • facet A faceting specification describes how to break up the data into subsets for plotting individual set
    • +
+ +

Let’s explore those in details

+ +

Plotting in ggplot style

+ +

Let set up our working environment with necessary libraries and also load our csv file into data frame called survs_df,

+ +
import numpy as np
+import pandas as pd
+from plotnine import *
+
+%matplotlib inline
+survs_df = pd.read_csv('data/surveys.csv').dropna()
+
+ +

Producing a plot with ggplot, we must give three things:

+ +
    +
  1. A data frame containing our data.
    2. +
  2. How the columns of the data frame can be translated into positions, colors, sizes, and shapes of graphical elements (“aesthetics”).
    3. +
  3. The actual graphical elements to display (“geometric objects”).
    4. +
+ +

Introduction to plotting

+ +
ggplot(survs_df, aes('weight', 'hindfoot_length')) + geom_point()
+
+ +

Lets see if we can also include information about species and year

+ +
ggplot(survs_df, aes('weight', 'hindfoot_length',
+    size = 'year')) + geom_point()
+
+ +
ggplot(survs_df, aes('weight', 'hindfoot_length', 
+    size = 'year', color = 'species_id')) + geom_point()
+
+ +

We can do simple counting plot, to see how many observation (data points) we have for each year for example

+ +
ggplot(survs_df, aes('year')) + \
+    geom_bar(stat = 'count')
+
+ +

Let’s now also color by species to see how many observation we have per species in a given year

+ +
ggplot(survs_df, aes('year', fill = 'species_id')) + \
+    geom_bar(stat = 'count')
+
+ +

Challenges

+ +
+

Is there a better visualisation for comparing weight across years? The plot should have categorical data on x axis and continuous on y axis +Plot a boxplot of hindfoot_length across different species (species_id column)

+
+ +

More geom types

+ +
ggplot(survs_df, aes('year', 'weight')) + \
+    geom_boxplot()
+
+ +

Why are we not seeing mulitple boxplots, one for each year? +This is because year variable is continues in our data frame, but for the purpose we want it to be categorical.

+ +
survs_df['year_fact'] = pd.Series(survs_df['year'], dtype = "category")
+
+ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot()
+
+ +
ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_violin()
+
+ +

To save an image for later

+ +
plt1 = ggplot(survs_df, aes('year_fact', 'weight')) + \
+           geom_boxplot() + \
+           xlab("Years") + \
+           ylab("Weight log2(kg)") + \
+           ggtitle("Boxplots, summary of species wieght in each year")
+
+ggsave(filename = "plot1.png", \
+       plot = plt1, \
+       device = 'png', \
+       dpi = 300, \
+       height = 25, \
+        width = 25)
+
+ +

Challenges

+ +
+

Can you log2 transform weight and plot “normalised” boxplot. Hint: use np.log2() function and name new column weight_log

+
+ +

Also will log2 transforming make this data visualisation better?

+ +
survs_df['weight_log'] = np.log2(survs_df['weight'])
+    
+ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    xlab("Years") + \
+    ylab("Weight log2(kg)") + \
+    ggtitle("Boxplots, summary of species wieght in each year")
+
+ +

Faceting

+ +

ggplot has a special technique called faceting that allows to split one plot +into multiple plots based on a factor included in the dataset. We will use it to +make one plot for a time series for each species.

+ +
ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot() + \
+    facet_wrap("~sex")
+
+
+ +
ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") 
+
+ +

Theming

+ +
ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot() + \
+    theme_bw()
+
+ +
ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") + \
+    theme_xkcd()
+
+ +

Extra bits 1

+ +

Let’s try to bin years into decades, which could be crude but might gives simple images to look at.

+ +
bins = [(survs_df['year'] < 1980),
+        (survs_df['year'] < 1990),
+        (survs_df['year'] < 2000),
+        (survs_df['year'] >= 2000)]
+
+labels = ['70s', '80s', '90s', 'Z']
+
+survs_df['year_bins'] = np.select(bins, labels)
+
+ +
plt2 = ggplot(survs_df, aes('year_bins', 'weight_log')) + \
+           geom_boxplot()
+plt2 
+
+ +
plt2 = ggplot(survs_df, aes('year_bins', 'weight_log')) + \
+           geom_boxplot() + \
+           theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+           facet_wrap("~species_id") 
+plt2 
+
+ +

Extra bits 2

+ +

This is a different way to look at your data

+ +
ggplot(survs_df, aes("year_fact", "weight")) + \
+    stat_summary(fun_y = np.mean, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+    
+ggplot(survs_df, aes("year_fact", "weight")) + \
+    stat_summary(fun_y = np.median, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+    
+ggplot(survs_df, aes("year_fact", "weight_log")) + \
+    stat_summary(fun_y = np.mean, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+
+ +

Extra bits 3

+ +

It is very informative to look across years, year by year, it becomes apparent straight away that for some species there is a lot of data is missing.And going forward, maybe, you’d want to filter those low counting species. (this is after faceting by species_id in section Extra bits 1).

+ +
#survs_cnts_df = survs_df.groupby(['species_id'], as_index=False).count().sort_values(['record_id'])
+survs_cnts_df = survs_df[['species_id']].groupby(['species_id']).size().reset_index()
+species_to_remove = list(survs_cnts_df[survs_cnts_df['record_id'] < 200].species_id)
+survs_df_filt = survs_df[survs_df['species_id'].isin(species_to_remove) == False]
+
+ +
ggplot(survs_df_filt, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+
+ggplot(survs_df_filt, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") 
+
+ +

NEXT

+ +
+
+ +
+ +
+ + + + + + + + + diff --git a/workshops/intro_to_command_line/20180701-commandline/working_with_data/index.html b/workshops/intro_to_command_line/20180701-commandline/working_with_data/index.html new file mode 100644 index 0000000..9553d6f --- /dev/null +++ b/workshops/intro_to_command_line/20180701-commandline/working_with_data/index.html @@ -0,0 +1,926 @@ + + + + + + + Monash Data Fluency | 02 Data Analysis in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+
+ + +
+ +
+
+
+

+ WORKSHOPS +

+

+ 02 Data Analysis in Python +

+
+
+ + +

Working With Pandas DataFrames

+ +

We can automate the process of performing data manipulations in Python. It’s efficient to spend time +building the code to perform these tasks because once it’s built, we can use it +over and over on different datasets that use a similar format. This makes our +methods easily reproducible. We can also easily share our code with colleagues +and they can replicate the same analysis.

+ +

The Dataset

+ +

For this lesson, we will be using the Portal Teaching data, a subset of the data +from Ernst et al +Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA

+ +

We will be using this dataset, which is stored under your login/account under the Files tab.

+ +

We are studying the species and weight of animals caught in plots in our study +area. The dataset is stored as a .csv file: each row holds information for a +single animal, and the columns represent:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ColumnDescription
record_idUnique id for the observation
monthmonth of observation
dayday of observation
yearyear of observation
plot_idID of a particular plot
species_id2-letter code
sexsex of animal (“M”, “F”)
hindfoot_lengthlength of the hindfoot in mm
weightweight of the animal in grams
+ +

The first few rows of our first file look like this:

+ +
record_id,month,day,year,plot_id,species_id,sex,hindfoot_length,weight
+1,7,16,1977,2,NL,M,32,
+2,7,16,1977,3,NL,M,33,
+3,7,16,1977,2,DM,F,37,
+4,7,16,1977,7,DM,M,36,
+5,7,16,1977,3,DM,M,35,
+6,7,16,1977,1,PF,M,14,
+7,7,16,1977,2,PE,F,,
+8,7,16,1977,1,DM,M,37,
+9,7,16,1977,1,DM,F,34,
+
+ +
+ +

About Libraries

+ +

A library in Python contains a set of tools (called functions) that perform +tasks on our data. Importing a library is like getting a piece of lab equipment +out of a storage locker and setting it up on the bench for use in a project. +Once a library is set up, it can be used or called to perform many tasks.

+ +

Pandas in Python

+ +

One of the best options for working with tabular data in Python is to use the +Python Data Analysis Library (a.k.a. Pandas). The +Pandas library provides data structures, produces high quality plots with +matplotlib and integrates nicely with other libraries +that use NumPy (which is another Python library) arrays.

+ +

Python doesn’t load all of the libraries available to it by default. We have to +add an import statement to our code in order to use library functions. To import +a library, we use the syntax import libraryName. If we want to give the +library a nickname to shorten the command, we can add as nickNameHere. An +example of importing the pandas library using the common nickname pd is below.

+ +
import pandas as pd
+
+ +

Each time we call a function that’s in a library, we use the syntax +LibraryName.FunctionName. Adding the library name with a . before the +function name tells Python where to find the function. In the example above, we +have imported Pandas as pd. This means we don’t have to type out pandas each +time we call a Pandas function.

+ +

Reading CSV Data Using Pandas

+ +

We will begin by locating and reading our survey data which are in CSV format. CSV stands for Comma-Separated Values and is a common way store formatted data. Other symbols my also be used, so you might see tab-separated, colon-separated or space separated files. It is quite easy to replace one separator with another, to match your application. The first line in the file often has headers to explain what is in each column. CSV (and other separators) make it easy to share data, and can be imported and exported from many applications, including Microsoft Excel. For mo

+ +

We can use Pandas’ read_csv function to pull the file directly into a +DataFrame.

+ +

So What’s a DataFrame?

+ +

A DataFrame is a 2-dimensional data structure that can store data of different +types (including characters, integers, floating point values, factors and more) +in columns. It is similar to a spreadsheet or an SQL table or the data.frame in +R. A DataFrame always has an index (0-based). An index refers to the position of +an element in the data structure.

+ +
# Note that pd.read_csv is used because we imported pandas as pd
+pd.read_csv("data/surveys.csv")
+
+ +

The above command yields the output below:

+ +
record_id  month  day  year  plot_id species_id sex  hindfoot_length  weight
+0          1      7   16  1977        2         NL   M               32   NaN
+1          2      7   16  1977        3         NL   M               33   NaN
+2          3      7   16  1977        2         DM   F               37   NaN
+3          4      7   16  1977        7         DM   M               36   NaN
+4          5      7   16  1977        3         DM   M               35   NaN
+...
+35544      35545     12   31  2002       15     AH  NaN              NaN  NaN
+35545      35546     12   31  2002       15     AH  NaN              NaN  NaN
+35546      35547     12   31  2002       10     RM    F               15   14
+35547      35548     12   31  2002        7     DO    M               36   51
+35548      35549     12   31  2002        5     NaN  NaN             NaN  NaN
+
+[35549 rows x 9 columns]
+
+ +

We can see that there were 33,549 rows parsed. Each row has 9 +columns. The first column is the index of the DataFrame. The index is used to +identify the position of the data, but it is not an actual column of the DataFrame. +It looks like the read_csv function in Pandas read our file properly. However, +we haven’t saved any data to memory so we can work with it.We need to assign the +DataFrame to a variable. Remember that a variable is a name for a value, such as x, +or data. We can create a new object with a variable name by assigning a value to it using =.

+ +

Let’s call the imported survey data surveys_df:

+ +
surveys_df = pd.read_csv("data/surveys.csv")
+
+ +

Notice when you assign the imported DataFrame to a variable, Python does not +produce any output on the screen. We can view the value of the surveys_df +object by typing its name into the Python command prompt.

+ +
surveys_df
+
+ +

which prints contents like above.

+ +

Note: if the output is too wide to print on your narrow terminal window, you may see something +slightly different as the large set of data scrolls past. You may see simply the last column +of data:

+ +
17        NaN  
+18        NaN  
+19        NaN  
+20        NaN  
+21        NaN  
+22        NaN  
+23        NaN  
+24        NaN  
+25        NaN  
+26        NaN  
+27        NaN  
+28        NaN  
+29        NaN  
+...       ...  
+35519    36.0  
+35520    48.0  
+35521    45.0  
+35522    44.0  
+35523    27.0  
+35524    26.0  
+35525    24.0  
+35526    43.0  
+35527     NaN  
+35528    25.0  
+35529     NaN  
+35530     NaN  
+35531    43.0  
+35532    48.0  
+35533    56.0  
+35534    53.0  
+35535    42.0  
+35536    46.0  
+35537    31.0  
+35538    68.0  
+35539    23.0  
+35540    31.0  
+35541    29.0  
+35542    34.0  
+35543     NaN  
+35544     NaN  
+35545     NaN  
+35546    14.0  
+35547    51.0  
+35548     NaN  
+
+[35549 rows x 9 columns]
+
+ +

Never fear, all the data is there, if you scroll up. Selecting just a few rows, so it is +easier to fit on one window, you can see that pandas has neatly formatted the data to fit +our screen:

+ +

+>>> surveys_df.head() # The head() function displays the first several lines of a file. It
+		      # is discussed below.
+   record_id  month  day  year  plot_id species_id sex  hindfoot_length  \
+5          6      7   16  1977        1         PF   M             14.0   
+6          7      7   16  1977        2         PE   F              NaN   
+7          8      7   16  1977        1         DM   M             37.0   
+8          9      7   16  1977        1         DM   F             34.0   
+9         10      7   16  1977        6         PF   F             20.0   
+
+   weight  
+5     NaN  
+6     NaN  
+7     NaN  
+8     NaN  
+9     NaN  
+
+ +

Exploring Our Species Survey Data

+ +

Again, we can use the type function to see what kind of thing surveys_df is:

+ +
type(surveys_df)
+
+## Returns
+pandas.core.frame.DataFrame
+
+ +

As expected, it’s a DataFrame (or, to use the full name that Python uses to refer +to it internally, a pandas.core.frame.DataFrame).

+ +

What kind of things does surveys_df contain? DataFrames have an attribute +called dtypes that answers this:

+ +
surveys_df.dtypes
+
+## Returns
+record_id            int64
+month                int64
+day                  int64
+year                 int64
+plot_id              int64
+species_id          object
+sex                 object
+hindfoot_length    float64
+weight             float64
+dtype: object
+
+ +

All the values in a column have the same type. For example, months have type +int64, which is a kind of integer. Cells in the month column cannot have +fractional values, but the weight and hindfoot_length columns can, because they +have type float64. The object type doesn’t have a very helpful name, but in +this case it represents strings (such as ’M’ and ‘F’ in the case of sex).

+ +

Useful Ways to View DataFrame objects in Python

+ +

There are many ways to summarize and access the data stored in DataFrames, +using attributes and methods provided by the DataFrame object.

+ +

To access an attribute, use the DataFrame object name followed by the attribute +name df_object.attribute. Using the DataFrame surveys_df and attribute +columns, an index of all the column names in the DataFrame can be accessed +with surveys_df.columns.

+ +

Methods are called in a similar fashion using the syntax df_object.method(). +As an example, surveys_df.head() gets the first few rows in the DataFrame +surveys_df using the head() method. With a method, we can supply extra +information in the parens to control behaviour.

+ +

Let’s look at the data using these.

+ +

Challenge - DataFrames

+ +

Using our DataFrame surveys_df, try out the attributes & methods below to see +what they return.

+ +
    +
  1. surveys_df.columns
    2. +
  2. surveys_df.shape Take note of the output of shape - what format does it +return the shape of the DataFrame in? HINT: More on tuples, here.
    3. +
  3. surveys_df.head() Also, what does surveys_df.head(15) do?
    4. +
  4. surveys_df.tail()
    5. +
+ +

Calculating Statistics From Data In A Pandas DataFrame

+ +

We’ve read our data into Python. Next, let’s perform some quick summary +statistics to learn more about the data that we’re working with. We might want +to know how many animals were collected in each plot, or how many of each +species were caught. We can perform summary stats quickly using groups. But +first we need to figure out what we want to group by.

+ +

Let’s begin by exploring our data:

+ +
# Look at the column names
+surveys_df.columns
+
+ +

which returns:

+ +
Index(['record_id', 'month', 'day', 'year', 'plot_id', 'species_id', 'sex',
+       'hindfoot_length', 'weight'],
+      dtype='object')
+
+ +

Let’s get a list of all the species. The pd.unique function tells us all of +the unique values in the species_id column.

+ +
pd.unique(surveys_df['species_id'])
+
+ +

which returns:

+ +
array(['NL', 'DM', 'PF', 'PE', 'DS', 'PP', 'SH', 'OT', 'DO', 'OX', 'SS',
+       'OL', 'RM', nan, 'SA', 'PM', 'AH', 'DX', 'AB', 'CB', 'CM', 'CQ',
+       'RF', 'PC', 'PG', 'PH', 'PU', 'CV', 'UR', 'UP', 'ZL', 'UL', 'CS',
+       'SC', 'BA', 'SF', 'RO', 'AS', 'SO', 'PI', 'ST', 'CU', 'SU', 'RX',
+       'PB', 'PL', 'PX', 'CT', 'US'], dtype=object)
+
+ +

Challenge - Statistics

+ +
    +

  1. Create a list of unique plot ID’s found in the surveys data. Call it +plot_names. How many unique plots are there in the data? How many unique +species are in the data?

    2. + +

  2. What is the difference between len(plot_names) and surveys_df['plot_id'].nunique()?

    3. +
+ +

Groups in Pandas

+ +

We often want to calculate summary statistics grouped by subsets or attributes +within fields of our data. For example, we might want to calculate the average +weight of all individuals per plot.

+ +

We can calculate basic statistics for all records in a single column using the +syntax below:

+ +
surveys_df['weight'].describe()
+
+ +

gives output

+ +
count    32283.000000
+mean        42.672428
+std         36.631259
+min          4.000000
+25%         20.000000
+50%         37.000000
+75%         48.000000
+max        280.000000
+Name: weight, dtype: float64
+
+ +

We can also extract one specific metric if we wish:

+ +
surveys_df['weight'].min()
+surveys_df['weight'].max()
+surveys_df['weight'].mean()
+surveys_df['weight'].std()
+surveys_df['weight'].count()
+
+ +

But if we want to summarize by one or more variables, for example sex, we can +use Pandas’ .groupby method. Once we’ve created a groupby DataFrame, we +can quickly calculate summary statistics by a group of our choice.

+ +
# Group data by sex
+grouped_data = surveys_df.groupby('sex')
+
+ +

The pandas function describe will return descriptive stats including: mean, +median, max, min, std and count for a particular column in the data. Pandas’ +describe function will only return summary values for columns containing +numeric data.

+ +
# Summary statistics for all numeric columns by sex
+grouped_data.describe()
+# Provide the mean for each numeric column by sex
+grouped_data.mean()
+
+ +

grouped_data.mean() OUTPUT:

+ +
        record_id     month        day         year    plot_id  \
+sex                                                              
+F    18036.412046  6.583047  16.007138  1990.644997  11.440854   
+M    17754.835601  6.392668  16.184286  1990.480401  11.098282   
+
+     hindfoot_length     weight  
+sex                              
+F          28.836780  42.170555  
+M          29.709578  42.995379  
+
+
+ +

The groupby command is powerful in that it allows us to quickly generate +summary stats.

+ +

Challenge - Summary Data

+ +
    +
  1. How many recorded individuals are female F and how many male M
    2. +
  2. What happens when you group by two columns using the following syntax and +then grab mean values: + +
      +
    • grouped_data2 = surveys_df.groupby(['plot_id','sex'])
      • +
    • grouped_data2.mean()
      • +
    3. +
  3. Summarize weight values for each plot in your data. HINT: you can use the +following syntax to only create summary statistics for one column in your data +by_plot['weight'].describe()
    4. +
+ +

Did you get #3 right?

+ +

A Snippet of the Output from challenge 3 looks like:

+ +
	plot
+	1     count    1903.000000
+	      mean       51.822911
+	      std        38.176670
+	      min         4.000000
+	      25%        30.000000
+	      50%        44.000000
+	      75%        53.000000
+	      max       231.000000
+         ...
+
+ +

Quickly Creating Summary Counts in Pandas

+ +

Let’s next count the number of samples for each species. We can do this in a few +ways, but we’ll use groupby combined with a count() method.

+ +
# Count the number of samples by species
+species_counts = surveys_df.groupby('species_id')['record_id'].count()
+print(species_counts)
+
+ +

Or, we can also count just the rows that have the species “DO”:

+ +
surveys_df.groupby('species_id')['record_id'].count()['DO']
+
+ +

Basic Math Functions

+ +

If we wanted to, we could perform math on an entire column of our data. For +example let’s multiply all weight values by 2. A more practical use of this might +be to normalize the data according to a mean, area, or some other value +calculated from our data.

+ +

+# Multiply all weight values by 2 but does not change the original weight data
+
+surveys_df['weight']*2
+
+
+ +

Quick & Easy Plotting Data Using Pandas

+ +

We can plot our summary stats using Pandas, too.

+ +

+## To make sure figures appear inside Jupyter Notebook
+%matplotlib inline
+
+# Create a quick bar chart
+species_counts.plot(kind='bar');
+
+ +

Weight by Species Plot

+ +

Weight by species plot

+ +

We can also look at how many animals were captured in each plot:

+ +
total_count = surveys_df.groupby('plot_id')['record_id'].nunique()
+# Let's plot that too
+total_count.plot(kind='bar');
+
+ +

Extra Plotting Challenge

+ +
    +

  1. Create a plot of average weight across all species per plot.

    2. + +

  2. Create a plot of total males versus total females for the entire dataset.

    3. + +

  3. Create a stacked bar plot, with weight on the Y axis, and the stacked variable being sex. The plot should show total weight by sex for each plot. Some tips are below to help you solve this challenge:

    4. +
+ +
    +
  • For more on Pandas plots, visit this link.
    • +
  • You can use the code that follows to create a stacked bar plot but the data to stack +need to be in individual columns. Here’s a simple example with some data where +‘a’, ‘b’, and ‘c’ are the groups, and ‘one’ and ‘two’ are the subgroups.
    • +
+ +
d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
+pd.DataFrame(d)
+
+ +

shows the following data

+ +
      one  two
+   a    1    1
+   b    2    2
+   c    3    3
+   d  NaN    4
+
+ +

We can plot the above with

+ +
# Plot stacked data so columns 'one' and 'two' are stacked
+my_df = pd.DataFrame(d)
+my_df.plot(kind='bar',stacked=True,title="The title of my graph")
+
+ +
    +
  • You can use the .unstack() method to transform grouped data into columns +for each plotting. Try running .unstack() on some DataFrames above and see +what it yields.
    • +
+ +

Start by transforming the grouped data (by plot and sex) into an unstacked layout, then create + a stacked plot.

+ +

Solution to Extra Challenge 3

+ +

First we group data by plot and by sex, and then calculate a total for each plot.

+ +
by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+
+ +

This calculates the sums of weights for each sex within each plot as a table

+ +
plot  sex
+plot_id  sex
+1        F      38253
+         M      59979
+2        F      50144
+         M      57250
+3        F      27251
+         M      28253
+4        F      39796
+         M      49377
+<other plots removed for brevity>
+
+ +

Below we’ll use .unstack() on our grouped data to figure out the total weight that each sex contributed to each plot.

+ +
by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+plot_sex_count.unstack()
+
+ +

The unstack function above will display the following output:

+ +
sex          F      M
+plot_id              
+1        38253  59979
+2        50144  57250
+3        27251  28253
+4        39796  49377
+<other plots removed for brevity>
+
+ +

Now, create a stacked bar plot with that data where the weights for each sex are stacked by plot.

+ +

Rather than display it as a table, we can plot the above data by stacking the values of each sex as follows:

+ +
by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+spc = plot_sex_count.unstack()
+s_plot = spc.plot(kind='bar',stacked=True,title="Total weight by plot and sex")
+s_plot.set_ylabel("Weight")
+s_plot.set_xlabel("Plot")
+
+ +

Stacked Bar Plot

+ +

NEXT

+ +
+
+ +
+ +
+ + + + + + + + + diff --git a/workshops/intro_to_command_line/20181214-commandline/index.html b/workshops/intro_to_command_line/20181214-commandline/index.html new file mode 100644 index 0000000..50c7464 --- /dev/null +++ b/workshops/intro_to_command_line/20181214-commandline/index.html @@ -0,0 +1,384 @@ + + + + + + + Monash Data Fluency | Introduction to Command Lines with the Unix Shell + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Introduction to Command Lines with the Unix Shell +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to Command Lines with the Unix Shell

+ +
    +
  • Date : Friday 17 Dec 2018
    • +
  • Time : 10:00am to 3:00pm
    • + +

  • Location : Monash University Clayton Campus, + Room T1. Matheson Library. Clayton Campus

    • + +

  • Instructors : Simon Michnowicz, Jay Van Schyndel

    • + +

  • Helpers : Gin Tan, Luhan Cheng, Trung Nguyen, Rafi Mohammed Feroz

    • +
+ +

This hands-on workshop aims to equip participants with the fundamentals of Unix Shell, and how to use it to help their research.

+ +

Sign up here !

+ +

General Information

+ +

The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
10:00Welcome
10:15Session 1
12:15Lunch
01:30Session 2
03:00Wrap-up
+ +

Jupyter Notebook gives you access to a Unix terminal

+ +
    +
  • The Jupyter Notebook will be the way you access a Unix terminal. A username and password is needed to log into the Jupyter Server. This will be given on the day.
    • +
+ +

The Jupyter notebook does not work well with Internet Explorer Please use another browser (i.e. Chrome, Safari, Firefox)

+ +

Shared Notepad

+ +
    +
  • An Etherpad notebook will allow you to make comments or add questions. Access it here
    • +
+ +

Syllabus

+ + + +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/intro_to_command_line/20181214-commandline/index.xml b/workshops/intro_to_command_line/20181214-commandline/index.xml new file mode 100644 index 0000000..c0e1158 --- /dev/null +++ b/workshops/intro_to_command_line/20181214-commandline/index.xml @@ -0,0 +1,15 @@ + + + + Introduction to Command Lines with the Unix Shell on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_command_line/20181214-commandline/ + Recent content in Introduction to Command Lines with the Unix Shell on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_command_line/20181214-commandline/page/1/index.html b/workshops/intro_to_command_line/20181214-commandline/page/1/index.html new file mode 100644 index 0000000..1f44f4f --- /dev/null +++ b/workshops/intro_to_command_line/20181214-commandline/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_command_line/20181214-commandline/ \ No newline at end of file diff --git a/workshops/intro_to_command_line/index.html b/workshops/intro_to_command_line/index.html new file mode 100644 index 0000000..3ac1ce0 --- /dev/null +++ b/workshops/intro_to_command_line/index.html @@ -0,0 +1,388 @@ + + + + + + + Monash Data Fluency | Introduction to Command Lines with the Unix Shell + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Introduction to Command Lines with the Unix Shell +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to Command Lines with the Unix Shell

+ +
    +
  • Date : Friday 14 Dec 2018
    • +
  • Time : 10:00am to 3:00pm
    • + +

  • Location : Monash University Clayton Campus, + Room T1. Matheson Library. Clayton Campus

    • + +

  • Instructors : Simon Michnowicz, Jay Van Schyndel

    • + +

  • Helpers : Gin Tan, Trung Nguyen, Rafi Mohammed Feroz

    • +
+ +

This hands-on workshop aims to equip participants with the fundamentals of Unix Shell, and how to use it to help their research.

+ +

Sign up here !

+ +

General Information

+ +

The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
10:00Welcome
10:15Session 1
12:15Lunch
01:30Session 2
03:00Wrap-up
+ +

Jupyter Notebook gives you access to a Unix terminal

+ +
    +
  • The Jupyter Notebook will be the way you access a Unix terminal. A username and password is needed to log into the Jupyter Server. This will be given on the day.
    • +
+ +

The Jupyter notebook does not work well with Internet Explorer Please use another browser (i.e. Chrome, Safari, Firefox)

+ +

Shared Notepad

+ +
    +
  • An Etherpad notebook will allow you to make comments or add questions. Access it here
    • +
+ +

Syllabus

+ + + +

Workshop Feedback Form

+ +

We appreaciate feedback. Leave your feedback here

+ +

Keep In Touch

+ + + +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/intro_to_command_line/index.xml b/workshops/intro_to_command_line/index.xml new file mode 100644 index 0000000..62a7b78 --- /dev/null +++ b/workshops/intro_to_command_line/index.xml @@ -0,0 +1,15 @@ + + + + Introduction to Command Lines with the Unix Shell on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_command_line/ + Recent content in Introduction to Command Lines with the Unix Shell on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_command_line/page/1/index.html b/workshops/intro_to_command_line/page/1/index.html new file mode 100644 index 0000000..4e2b4f0 --- /dev/null +++ b/workshops/intro_to_command_line/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_command_line/ \ No newline at end of file diff --git a/workshops/intro_to_hpc/20180803-hpc/index.html b/workshops/intro_to_hpc/20180803-hpc/index.html new file mode 100644 index 0000000..e0e72b8 --- /dev/null +++ b/workshops/intro_to_hpc/20180803-hpc/index.html @@ -0,0 +1,382 @@ + + + + + + + Monash Data Fluency | Introduction to HPC and the SLURM Workload Manager + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Introduction to HPC and the SLURM Workload Manager +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to HPC and the SLURM Workload Manager

+ +

Date : Friday 3 Aug 2018

+ +

Time : 10:00am to 4:00pm

+ +

Location : Monash University Clayton Campus, Room G-T1. Matheson Library. Clayton Campus

+ +

Instructors : Simon Michnowicz, Gin Tan

+ +

Helpers : Nick Wong, Richard Beare, Trung Nguyen, Damien Leong

+ +

This introductory course will provide an overview of a cluster, Linux command-line and how to write Slurm scripts so you can submit a simple batch or parallel job. A hands-on-workshop covering basic High-Performance Computing (HPC) in a nutshell.

+ +

Sign up here !

+ +

General Information

+ +

Format:: The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who:: People who have prior command line experience is mandatory for this workshop and people who have access to Monash HPC systems and would like to know more about using it in an efficient and effective manner.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
10:00Welcome
10:15Session 1
12:15Lunch
13:15Session 2
14:30Break
14:45Session 3
16:00Wrap up
+ +

Access to HPC system

+ +

You will be given the access to the HPC system on the day

+ +

Shared Notepad

+ +

An Etherpad notebook will allow you to make comments or add questions. Access it here

+ +

Syllabus

+ +

The course notes are here. Introduction to HPC

+ +

Keep In Touch

+ +

Sign up to Data Fluency’s mailing list for event updates and checkout our Github (pull requests and stars welcome !).

+ +

Join the Slack channel. This is Data Fluency community space were you can can ask for help and advice, as well as share and discuss links of interest.

+ +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/intro_to_hpc/20180803-hpc/index.xml b/workshops/intro_to_hpc/20180803-hpc/index.xml new file mode 100644 index 0000000..b46b655 --- /dev/null +++ b/workshops/intro_to_hpc/20180803-hpc/index.xml @@ -0,0 +1,15 @@ + + + + Introduction to HPC and the SLURM Workload Manager on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_hpc/20180803-hpc/ + Recent content in Introduction to HPC and the SLURM Workload Manager on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_hpc/20180803-hpc/page/1/index.html b/workshops/intro_to_hpc/20180803-hpc/page/1/index.html new file mode 100644 index 0000000..a58c240 --- /dev/null +++ b/workshops/intro_to_hpc/20180803-hpc/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_hpc/20180803-hpc/ \ No newline at end of file diff --git a/workshops/intro_to_hpc/20181217-hpc/index.html b/workshops/intro_to_hpc/20181217-hpc/index.html new file mode 100644 index 0000000..23c8679 --- /dev/null +++ b/workshops/intro_to_hpc/20181217-hpc/index.html @@ -0,0 +1,376 @@ + + + + + + + Monash Data Fluency | Introduction to HPC and the SLURM Workload Manager + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Introduction to HPC and the SLURM Workload Manager +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to HPC and the SLURM Workload Manager

+ +

Date : Monday 17 Dec 2018

+ +

Time : 1:30 p.m. to 5:00pm

+ +

Location : Monash University Clayton Campus, Room G-T1. Matheson Library. Clayton Campus

+ +

Instructors : Simon Michnowicz, Gin Tan

+ +

Helpers : Sonika Tyagi, Trung Nguyen, Damien Leong, Juan Nunez-Iglesias

+ +

This introductory course will provide an overview of a cluster, Linux command-line and how to write Slurm scripts so you can submit a simple batch or parallel job. A hands-on-workshop covering basic High-Performance Computing (HPC) in a nutshell.

+ +

Sign up here !

+ +

General Information

+ +

Format:: The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who:: People who have prior command line experience is mandatory for this workshop and people who have access to Monash HPC systems and would like to know more about using it in an efficient and effective manner.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
13:30Welcome
13:45Session 1
15:00Break
15:30Session 2
17:00Wrap up
+ +

Access to HPC system

+ +

You will be given the access to the HPC system on the day

+ +

Shared Notepad

+ +

An Etherpad notebook will allow you to make comments or add questions. Access it here

+ +

Syllabus

+ +

The course notes are here. Introduction to HPC

+ +

Workshop Feedback Form

+ +

We appreaciate feedback. Leave your feedback here

+ +

Keep In Touch

+ +

Sign up to Data Fluency’s mailing list for event updates and checkout our Github (pull requests and stars welcome !).

+ +

Join the Slack channel. This is Data Fluency community space were you can can ask for help and advice, as well as share and discuss links of interest.

+ +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/intro_to_hpc/20181217-hpc/index.xml b/workshops/intro_to_hpc/20181217-hpc/index.xml new file mode 100644 index 0000000..66d781c --- /dev/null +++ b/workshops/intro_to_hpc/20181217-hpc/index.xml @@ -0,0 +1,15 @@ + + + + Introduction to HPC and the SLURM Workload Manager on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_hpc/20181217-hpc/ + Recent content in Introduction to HPC and the SLURM Workload Manager on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_hpc/20181217-hpc/page/1/index.html b/workshops/intro_to_hpc/20181217-hpc/page/1/index.html new file mode 100644 index 0000000..44dd273 --- /dev/null +++ b/workshops/intro_to_hpc/20181217-hpc/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_hpc/20181217-hpc/ \ No newline at end of file diff --git a/workshops/intro_to_hpc/20190718-hpc/index.html b/workshops/intro_to_hpc/20190718-hpc/index.html new file mode 100644 index 0000000..f1c7970 --- /dev/null +++ b/workshops/intro_to_hpc/20190718-hpc/index.html @@ -0,0 +1,376 @@ + + + + + + + Monash Data Fluency | Introduction to HPC and the SLURM Workload Manager + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Introduction to HPC and the SLURM Workload Manager +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to HPC and the SLURM Workload Manager

+ +

Date : Monday 17 Dec 2018

+ +

Time : 1:30 p.m. to 5:00pm

+ +

Location : Monash University Clayton Campus, Room G-T1. Matheson Library. Clayton Campus

+ +

Instructors : Simon Michnowicz, Gin Tan

+ +

Helpers : Sonika Tyagi, Trung Nguyen, Damien Leong, Juan Nunez-Iglesias

+ +

This introductory course will provide an overview of a cluster, Linux command-line and how to write Slurm scripts so you can submit a simple batch or parallel job. A hands-on-workshop covering basic High-Performance Computing (HPC) in a nutshell.

+ +

Sign up here !

+ +

General Information

+ +

Format:: The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who:: People who have prior command line experience is mandatory for this workshop and people who have access to Monash HPC systems and would like to know more about using it in an efficient and effective manner.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
13:30Welcome
13:45Session 1
15:00Break
15:30Session 2
17:00Wrap up
+ +

Access to HPC system

+ +

You will be given the access to the HPC system on the day

+ +

Shared Notepad

+ +

An Etherpad notebook will allow you to make comments or add questions. Access it here

+ +

Syllabus

+ +

The course notes are here. Introduction to HPC

+ +

Workshop Feedback Form

+ +

We appreaciate feedback. Leave your feedback here

+ +

Keep In Touch

+ +

Sign up to Data Fluency’s mailing list for event updates and checkout our Github (pull requests and stars welcome !).

+ +

Join the Slack channel. This is Data Fluency community space were you can can ask for help and advice, as well as share and discuss links of interest.

+ +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/intro_to_hpc/20190718-hpc/index.xml b/workshops/intro_to_hpc/20190718-hpc/index.xml new file mode 100644 index 0000000..87f8188 --- /dev/null +++ b/workshops/intro_to_hpc/20190718-hpc/index.xml @@ -0,0 +1,15 @@ + + + + Introduction to HPC and the SLURM Workload Manager on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_hpc/20190718-hpc/ + Recent content in Introduction to HPC and the SLURM Workload Manager on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_hpc/20190718-hpc/page/1/index.html b/workshops/intro_to_hpc/20190718-hpc/page/1/index.html new file mode 100644 index 0000000..80592cf --- /dev/null +++ b/workshops/intro_to_hpc/20190718-hpc/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_hpc/20190718-hpc/ \ No newline at end of file diff --git a/workshops/intro_to_hpc/20191009-hpc/index.html b/workshops/intro_to_hpc/20191009-hpc/index.html new file mode 100644 index 0000000..939d001 --- /dev/null +++ b/workshops/intro_to_hpc/20191009-hpc/index.html @@ -0,0 +1,372 @@ + + + + + + + Monash Data Fluency | Introduction to HPC and the SLURM Workload Manager + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Introduction to HPC and the SLURM Workload Manager +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to HPC and the SLURM Workload Manager

+ +

Date : Wed 9th Oct 2019

+ +

Time : 1:00 p.m. to 5:00pm

+ +

Location : Monash University Clayton Campus, Room G-T1. Matheson Library. Clayton Campus

+ +

Instructors : Tyrone Chen,Ranjeet Singh

+ +

Helpers : Simon Michnowicz

+ +

This introductory course will provide an overview of a cluster, Linux command-line and how to write Slurm scripts so you can submit a simple batch or parallel job. A hands-on-workshop covering basic High-Performance Computing (HPC) in a nutshell.

+ +

Sign up here !

+ +

General Information

+ +

Format:: The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who:: People who have prior command line experience is mandatory for this workshop and people who have access to Monash HPC systems and would like to know more about using it in an efficient and effective manner.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
13:00Welcome
13:15Session 1
15:00Break
15:30Session 2
17:00Wrap up
+ +

Access to HPC system

+ +

You will be given the access to the HPC system on the day

+ +

Shared Notepad

+ +

An Etherpad notebook will allow you to make comments or add questions. Access it here

+ +

Syllabus

+ +

The course notes are here. Introduction to HPC

+ +

Keep In Touch

+ +

Sign up to Data Fluency’s mailing list for event updates and checkout our Github (pull requests and stars welcome !).

+ +

Join the Slack channel. This is Data Fluency community space were you can can ask for help and advice, as well as share and discuss links of interest.

+ +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/intro_to_hpc/20191009-hpc/index.xml b/workshops/intro_to_hpc/20191009-hpc/index.xml new file mode 100644 index 0000000..44abf98 --- /dev/null +++ b/workshops/intro_to_hpc/20191009-hpc/index.xml @@ -0,0 +1,15 @@ + + + + Introduction to HPC and the SLURM Workload Manager on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_hpc/20191009-hpc/ + Recent content in Introduction to HPC and the SLURM Workload Manager on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_hpc/20191009-hpc/page/1/index.html b/workshops/intro_to_hpc/20191009-hpc/page/1/index.html new file mode 100644 index 0000000..3e06776 --- /dev/null +++ b/workshops/intro_to_hpc/20191009-hpc/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_hpc/20191009-hpc/ \ No newline at end of file diff --git a/workshops/intro_to_hpc/index.html b/workshops/intro_to_hpc/index.html new file mode 100644 index 0000000..c384c1b --- /dev/null +++ b/workshops/intro_to_hpc/index.html @@ -0,0 +1,372 @@ + + + + + + + Monash Data Fluency | Introduction to HPC and the SLURM Workload Manager + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

+ Introduction to HPC and the SLURM Workload Manager +

+ +
+ + +
+
+ + +
+ +
+
+ + +

Introduction to HPC and the SLURM Workload Manager

+ +

Date : Wed 9th Oct 2019

+ +

Time : 1:00 p.m. to 5:00pm

+ +

Location : Monash University Clayton Campus, Room G-T1. Matheson Library. Clayton Campus

+ +

Instructors : Tyrone Chen,Ranjeet Singh

+ +

Helpers : Simon Michnowicz

+ +

This introductory course will provide an overview of a cluster, Linux command-line and how to write Slurm scripts so you can submit a simple batch or parallel job. A hands-on-workshop covering basic High-Performance Computing (HPC) in a nutshell.

+ +

Sign up here !

+ +

General Information

+ +

Format:: The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

+ +

Who:: People who have prior command line experience is mandatory for this workshop and people who have access to Monash HPC systems and would like to know more about using it in an efficient and effective manner.

+ +

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

+ +

Schedule

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeProgramme
13:00Welcome
13:15Session 1
15:00Break
15:30Session 2
17:00Wrap up
+ +

Access to HPC system

+ +

You will be given the access to the HPC system on the day

+ +

Shared Notepad

+ +

An Etherpad notebook will allow you to make comments or add questions. Access it here

+ +

Syllabus

+ +

The course notes are here. Introduction to HPC

+ +

Keep In Touch

+ +

Sign up to Data Fluency’s mailing list for event updates and checkout our Github (pull requests and stars welcome !).

+ +

Join the Slack channel. This is Data Fluency community space were you can can ask for help and advice, as well as share and discuss links of interest.

+ +
+
+
+ +
+ +
+ + +
+ +
+ + + + + + diff --git a/workshops/intro_to_hpc/index.xml b/workshops/intro_to_hpc/index.xml new file mode 100644 index 0000000..80783db --- /dev/null +++ b/workshops/intro_to_hpc/index.xml @@ -0,0 +1,15 @@ + + + + Introduction to HPC and the SLURM Workload Manager on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_hpc/ + Recent content in Introduction to HPC and the SLURM Workload Manager on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_hpc/page/1/index.html b/workshops/intro_to_hpc/page/1/index.html new file mode 100644 index 0000000..ecaae28 --- /dev/null +++ b/workshops/intro_to_hpc/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_hpc/ \ No newline at end of file diff --git a/workshops/intro_to_hpc/slideDecks/AccessingSoftware.pdf 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z0mCH4gX}@>;X&g2Js2dmCBQ(ziKIU;!=$kPtTVW|n1P!UTxg`EzzI!j9}60g7}1Dp sIXb%0{5eS`e$m0wk><}Svb~4$%v{{|&UpcnU~PlZoH`}1u0ZpD0ATi2`2YX_ literal 0 HcmV?d00001 diff --git a/workshops/intro_to_machine_learning/index.html b/workshops/intro_to_machine_learning/index.html new file mode 100644 index 0000000..cdde5b9 --- /dev/null +++ b/workshops/intro_to_machine_learning/index.html @@ -0,0 +1,374 @@ + + + + + + + Monash Data Fluency | Machine Learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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    Introduction to Tensor Flow and Machine Learning Workshop

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    • Date : 23rd March 2018
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    • Time : 1:00pm to 5:00pm
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    • Location : L1.04, Sir Louis Matheson Library, Clayton, VIC
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    • Instructor : Jason Rigby
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    • Helpers : Shenjun Zhong, Sonika Tyagi
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    This workshop will cover the basic principles behind the use of TensorFlow, a popular open-source machine learning and general numerical computing framework supported by Google.

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    General Information

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    The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

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    Who: The course is aimed at graduate students and other researchers. Basic working python knowledge is required.

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    Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

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    Schedule

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    TimeProgramme
    13:00Welcome
    13:10TensorFlow graphs
    13:30Logistic Regression
    15:30Tea break
    15:45Neural Networks
    17:00Finish
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    Course material

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    Shared Notepad

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    Keep In Touch

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    + Python +

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    + + +

    Introduction to Python Workshop

    + +
      +
    • Date : 23rd March 2018
      • +
    • Time : 1:00pm to 5:00pm
      • +
    • Location : T1, Sir Louis Matheson Library, Clayton, VIC
      • +
    • Instructors : Anup Shah, Kirill Tsyganov
      • +
    • Helpers : Simon Michnowicz, Michael See
      • +
    + +

    This hands-on workshop aims to equip participants with the fundamentals of programming and give them skills needed to apply data analysis approaches to their research questions.

    + +

    General Information

    + +

    The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    13:00Welcome
    13:15Session 1
    15:15Break
    15:30Session 2
    16:45Wrap-up
    + +

    Login Information

    + + + +

    Shared Notepad

    + + + +

    Syllabus

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    + Workshops +

    + + 01 Programming in Python (Introduction) + +

    +
    + The Basics of Python Python is a general purpose programming language that supports rapid development of scripts and applications. +Python’s main advantages: + Open Source software, supported by Python Software Foundation Available on all platforms (ie. Windows, Linux and MacOS) It is a general-purpose programming language Supports multiple programming paradigms Very large community with a rich ecosystem of third-party packages Interpreter Python is an interpreted language which can be used in two ways: +
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    + Workshops +

    + + 02 Data Analysis in Python + +

    +
    + Working With Pandas DataFrames We can automate the process of performing data manipulations in Python. It’s efficient to spend time building the code to perform these tasks because once it’s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis. +The Dataset For this lesson, we will be using the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA +
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    + +
    + +
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    + Workshops +

    + + 03 Indexing, Slicing and Subsetting + +

    +
    + In lesson 02, we read a CSV into a Python pandas DataFrame. We learned: + How to save the DataFrame to a named object, How to perform basic math on the data, How to calculate summary statistics, and How to create basic plots of the data. In this lesson, we will explore ways to access different parts of the data using: + Indexing, Slicing, and Subsetting Loading our data We will continue to use the surveys dataset that we worked with in the last lesson. +
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    + +
    + +
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    + Workshops +

    + + 04 Automation with Loops + +

    +
    + An example task that we might want to repeat is printing each character in a word on a line of its own. +word = 'lead' We can access a character in a string using its index. For example, we can get the first character of the word 'lead', by using word[0]. One way to print each character is to use four print statements: +print(word[0]) print(word[1]) print(word[2]) print(word[3]) Gives output +
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    + +
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    + Workshops +

    + + 05 Making Plots With ggplot + +

    +
    + Introduction Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. +
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    + + + + + + diff --git a/workshops/intro_to_python/20180323-launch-workshop/index.xml b/workshops/intro_to_python/20180323-launch-workshop/index.xml new file mode 100644 index 0000000..595668c --- /dev/null +++ b/workshops/intro_to_python/20180323-launch-workshop/index.xml @@ -0,0 +1,68 @@ + + + + Python on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/ + Recent content in Python on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 23 Feb 2018 13:28:03 +1100 + + + + + + 01 Programming in Python (Introduction) + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/intro/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/intro/ + The Basics of Python Python is a general purpose programming language that supports rapid development of scripts and applications. +Python&rsquo;s main advantages: + Open Source software, supported by Python Software Foundation Available on all platforms (ie. Windows, Linux and MacOS) It is a general-purpose programming language Supports multiple programming paradigms Very large community with a rich ecosystem of third-party packages Interpreter Python is an interpreted language which can be used in two ways: + + + + 02 Data Analysis in Python + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/working_with_data/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/working_with_data/ + Working With Pandas DataFrames We can automate the process of performing data manipulations in Python. It&rsquo;s efficient to spend time building the code to perform these tasks because once it&rsquo;s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis. +The Dataset For this lesson, we will be using the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA + + + + 03 Indexing, Slicing and Subsetting + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/indexing/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/indexing/ + In lesson 02, we read a CSV into a Python pandas DataFrame. We learned: + How to save the DataFrame to a named object, How to perform basic math on the data, How to calculate summary statistics, and How to create basic plots of the data. In this lesson, we will explore ways to access different parts of the data using: + Indexing, Slicing, and Subsetting Loading our data We will continue to use the surveys dataset that we worked with in the last lesson. + + + + 04 Automation with Loops + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/loops/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/loops/ + An example task that we might want to repeat is printing each character in a word on a line of its own. +word = 'lead' We can access a character in a string using its index. For example, we can get the first character of the word 'lead', by using word[0]. One way to print each character is to use four print statements: +print(word[0]) print(word[1]) print(word[2]) print(word[3]) Gives output + + + + 05 Making Plots With ggplot + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/plotting/ + Fri, 23 Feb 2018 13:28:03 +1100 + + https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/plotting/ + Introduction Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. + + + + \ No newline at end of file diff --git a/workshops/intro_to_python/20180323-launch-workshop/indexing/index.html b/workshops/intro_to_python/20180323-launch-workshop/indexing/index.html new file mode 100644 index 0000000..180a139 --- /dev/null +++ b/workshops/intro_to_python/20180323-launch-workshop/indexing/index.html @@ -0,0 +1,787 @@ + + + + + + + Monash Data Fluency | 03 Indexing, Slicing and Subsetting + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + WORKSHOPS +

    +

    + 03 Indexing, Slicing and Subsetting +

    +
    +
    + + +

    In lesson 02, we read a CSV into a Python pandas DataFrame. We learned:

    + +
      +
    • How to save the DataFrame to a named object,
      • +
    • How to perform basic math on the data,
      • +
    • How to calculate summary statistics, and
      • +
    • How to create basic plots of the data.
      • +
    + +

    In this lesson, we will explore ways to access different parts of the data +using:

    + +
      +
    • Indexing,
      • +
    • Slicing, and
      • +
    • Subsetting
      • +
    + +

    Loading our data

    + +

    We will continue to use the surveys dataset that we worked with in the last lesson. Let’s reopen and read in the data again:

    + +
    # Make sure pandas is loaded
+import pandas as pd
+
+# Read in the survey CSV
+surveys_df = pd.read_csv("data/surveys.csv")
+
    + +

    Indexing and Slicing in Python

    + +

    We often want to work with subsets of a DataFrame object. There are +different ways to accomplish this including: using labels (column headings), +numeric ranges, or specific x,y index locations.

    + +

    Selecting data using Labels (Column Headings)

    + +

    We use square brackets [] to select a subset of an Python object. For example, +we can select all data from a column named species_id from the surveys_df +DataFrame by name. There are two ways to do this:

    + +
    # Method 1: select a 'subset' of the data using the column name
+surveys_df['species_id'].head()
+
+# Method 2: use the column name as an 'attribute'; gives the same output
+surveys_df.species_id.head()
+
    + +

    We can also create a new object that contains only the data within the +species_id column as follows:

    + +
    # Creates an object, surveys_species, that only contains the `species_id` column
+surveys_species = surveys_df['species_id']
+
    + +

    We can pass a list of column names too, as an index to select columns in that +order. This is useful when we need to reorganize our data.

    + +

    NOTE: If a column name is not contained in the DataFrame, an exception +(error) will be raised.

    + +
    # Select the species and plot columns from the DataFrame
+surveys_df[['species_id', 'plot_id']].head()
+
+# What happens when you flip the order?
+surveys_df[['plot_id', 'species_id']].head()
+
+# What happens if you ask for a column that doesn't exist?
+surveys_df['speciess']
+
    + +

    Python tells us what type of error it is in the traceback, at the bottom it says KeyError: 'speciess' which means that speciess is not a column name (or Key in the related python data type dictionary).

    + +

    Extracting Range based Subsets: Slicing

    + +

    REMINDER: Python Uses 0-based Indexing

    + +

    Let’s remind ourselves that Python uses 0-based +indexing. This means that the first element in an object is located at position +0. This is different from other tools like R and Matlab that index elements +within objects starting at 1.

    + +
    # Create a list of numbers:
+a = [1, 2, 3, 4, 5]
+
    + +

    indexing diagram +slicing diagram

    + +

    Challenge - Extracting data

    + +
      +

    1. What value does the code a[0] return?

      2. + +

    2. How about this: a[5]

      3. + +

    3. In the example above, calling a[5] returns an error. Why is that?

      4. + +

    4. What about a[len(a)] ?

      5. +
    + +

    Slicing Subsets of Rows in Python

    + +

    Slicing using the [] operator selects a set of rows and/or columns from a +DataFrame. To slice out a set of rows, you use the following syntax: +data[start:stop]. When slicing in pandas the start bound is included in the +output. The stop bound is one step BEYOND the row you want to select. So if you +want to select rows 0, 1 and 2 your code would look like this:

    + +
    # Select rows 0, 1, 2 (row 3 is not selected)
+surveys_df[0:3]
+
    + +

    The stop bound in Python is different from what you might be used to in +languages like Matlab and R.

    + +
    # Select the first 5 rows (rows 0, 1, 2, 3, 4)
+surveys_df[:5]
+
+# Select the last element in the list
+# (the slice starts at the last element, and ends at the end of the list)
+surveys_df[-1:]
+
    + +

    We can also reassign values within subsets of our DataFrame.

    + +

    Let’s create a brand new clean dataframe from +the original data CSV file.

    + +
    surveys_df = pd.read_csv("data/surveys.csv")
+
    + +

    Slicing Subsets of Rows and Columns in Python

    + +

    We can select specific ranges of our data in both the row and column directions +using either label or integer-based indexing.

    + +
      +
    • loc is primarily label based indexing. Integers may be used but +they are interpreted as a label.
      • +
    • iloc is primarily integer based indexing
      • +
    + +

    To select a subset of rows and columns from our DataFrame, we can use the +iloc method. For example, we can select month, day and year (columns 2, 3 +and 4 if we start counting at 1), like this:

    + +
    # iloc[row slicing, column slicing]
+surveys_df.iloc[0:3, 1:4]
+
    + +

    which gives the output

    + +
       month  day  year
+0      7   16  1977
+1      7   16  1977
+2      7   16  1977
+
    + +

    Notice that we asked for a slice from 0:3. This yielded 3 rows of data. When you +ask for 0:3, you are actually telling Python to start at index 0 and select rows +0, 1, 2 up to but not including 3.

    + +

    Let’s explore some other ways to index and select subsets of data:

    + +
    # Select all columns for rows of index values 0 and 10
+surveys_df.loc[[0, 10], :]
+
+# What does this do?
+surveys_df.loc[0, ['species_id', 'plot_id', 'weight']]
+
+# What happens when you type the code below?
+surveys_df.loc[[0, 10, 35549], :]
+
    + +

    NOTE: Labels must be found in the DataFrame or you will get a KeyError.

    + +

    Indexing by labels loc differs from indexing by integers iloc. +With loc, the both start bound and the stop bound are inclusive. When using +loc, integers can be used, but the integers refer to the +index label and not the position. For example, using loc and select 1:4 +will get a different result than using iloc to select rows 1:4.

    + +

    We can also select a specific data value using a row and +column location within the DataFrame and iloc indexing:

    + +
    # Syntax for iloc indexing to finding a specific data element
+dat.iloc[row, column]
+
    + +

    In this iloc example,

    + +
    surveys_df.iloc[2, 6]
+
    + +

    gives the output

    + +
    'F'
+
    + +

    Remember that Python indexing begins at 0. So, the index location [2, 6] +selects the element that is 3 rows down and 7 columns over in the DataFrame.

    + +

    Challenge - Range

    + +
      +

    1. What happens when you execute:

      + +
        +
      • surveys_df[0:1]
        • +
      • surveys_df[:4]
        • +
      • surveys_df[:-1]
        • +
      2. + +

    2. What happens when you call:

      + +
        +
      • surveys_df.iloc[0:4, 1:4]
        • +
      3. +
    + +

    Subsetting Data using Criteria

    + +

    We can also select a subset of our data using criteria. For example, we can +select all rows that have a year value of 2002:

    + +
    surveys_df[surveys_df.year == 2002].head()
+
    + +

    Which produces the following output:

    + +
    record_id  month  day  year  plot_id species_id  sex  hindfoot_length  weight
+33320      33321      1   12  2002        1         DM    M     38      44
+33321      33322      1   12  2002        1         DO    M     37      58
+33322      33323      1   12  2002        1         PB    M     28      45
+33323      33324      1   12  2002        1         AB  NaN    NaN     NaN
+33324      33325      1   12  2002        1         DO    M     35      29
+...
+35544      35545     12   31  2002       15         AH  NaN    NaN     NaN
+35545      35546     12   31  2002       15         AH  NaN    NaN     NaN
+35546      35547     12   31  2002       10         RM    F     15      14
+35547      35548     12   31  2002        7         DO    M     36      51
+35548      35549     12   31  2002        5        NaN  NaN    NaN     NaN
+
+[2229 rows x 9 columns]
+
    + +

    Or we can select all rows that do not contain the year 2002:

    + +
    surveys_df[surveys_df.year != 2002]
+
    + +

    We can define sets of criteria too:

    + +
    surveys_df[(surveys_df.year >= 1980) & (surveys_df.year <= 1985)]
+
    + +

    Python Syntax Cheat Sheet

    + +

    Use can use the syntax below when querying data by criteria from a DataFrame. +Experiment with selecting various subsets of the “surveys” data.

    + +
      +
    • Equals: ==
      • +
    • Not equals: !=
      • +
    • Greater than, less than: > or <
      • +
    • Greater than or equal to >=
      • +
    • Less than or equal to <=
      • +
    + +

    Challenge - Queries

    + +
      +

    1. Select a subset of rows in the surveys_df DataFrame that contain data from +the year 1999 and that contain weight values less than or equal to 8. How +many rows did you end up with? What did your neighbor get?

      2. + +

    2. (Extra) You can use the isin command in Python to query a DataFrame based upon a +list of values as follows:

      3. +
    + +
        surveys_df[surveys_df['species_id'].isin([listGoesHere])]
+
    + +

    Use the isin function to find all plots that contain particular species +in the “surveys” DataFrame. How many records contain these values?

    + +
      +

    1. (Extra) Experiment with other queries. Create a query that finds all rows with a +weight value > or equal to 0.

      2. + +

    2. (Extra) The ~ symbol in Python can be used to return the OPPOSITE of the +selection that you specify in Python. It is equivalent to is not in. +Write a query that selects all rows with sex NOT equal to ’M’ or ‘F’ in +the “surveys” data.

      3. +
    + +

    Using masks to identify a specific condition

    + +

    A mask can be useful to locate where a particular subset of values exist or +don’t exist - for example, NaN, or “Not a Number” values. To understand masks, +we also need to understand BOOLEAN objects in Python.

    + +

    Boolean values include True or False. For example,

    + +
    # Set x to 5
+x = 5
+
+# What does the code below return?
+x > 5
+
+# How about this?
+x == 5
+
    + +

    Missing Values

    + +

    Let’s try this out. Let’s identify all locations in the survey data that have +null (missing or NaN) data values. We can use the isnull method to do this. +The isnull method will compare each cell with a null value. If an element +has a null value, it will be assigned a value of True in the output object.

    + +
    pd.isnull(surveys_df).head()
+
    + +

    A snippet of the output is below:

    + +
          record_id  month    day   year plot_id species_id    sex  hindfoot_length weight
+0         False  False  False  False   False      False  False   False      True
+1         False  False  False  False   False      False  False   False      True
+2         False  False  False  False   False      False  False   False      True
+3         False  False  False  False   False      False  False   False      True
+4         False  False  False  False   False      False  False   False      True
+
+[35549 rows x 9 columns]
+
    + +

    To select the rows where there are null values, we can use +the mask as an index to subset our data as follows:

    + +
    # To select just the rows with NaN values, we can use the 'any()' method
+surveys_df[pd.isnull(surveys_df).any(axis=1)]
+
    + +

    (axis=1) is a numpy convention to specify columns.

    + +

    Note that the weight column of our DataFrame contains many null or NaN +values. Next, we will explore ways of dealing with this.

    + +

    If we look at the weight column in the surveys +data we notice that there are NaN (Not a Number) values. NaN values are undefined +values that cannot be represented mathematically. Pandas, for example, will read +an empty cell in a CSV or Excel sheet as a NaN. NaNs have some desirable properties: if we +were to average the weight column without replacing our NaNs, Python would know to skip +over those cells.

    + +
    surveys_df['weight'].mean()
+42.672428212991356
+
    + +

    Dealing with missing data values is always a challenge. It’s sometimes hard to +know why values are missing - was it because of a data entry error? Or data that +someone was unable to collect? Should the value be 0? We need to know how +missing values are represented in the dataset in order to make good decisions. +If we’re lucky, we have some metadata that will tell us more about how null +values were handled.

    + +

    For instance, in some disciplines, like Remote Sensing, missing data values are +often defined as -9999. Having a bunch of -9999 values in your data could really +alter numeric calculations. Often in spreadsheets, cells are left empty where no +data are available. Pandas will, by default, replace those missing values with +NaN. However it is good practice to get in the habit of intentionally marking +cells that have no data, with a no data value! That way there are no questions +in the future when you (or someone else) explores your data.

    + +

    Where Are the NaN’s?

    + +

    Let’s explore the NaN values in our data a bit further. Using the tools we +learned in lesson 02, we can figure out how many rows contain NaN values for +weight. We can also create a new subset from our data that only contains rows +with weight values > 0 (i.e., select meaningful weight values):

    + +
    len(surveys_df[pd.isnull(surveys_df.weight)])
+# How many rows have weight values?
+len(surveys_df[surveys_df.weight> 0])
+
    + +

    We can replace all NaN values with zeroes using the .fillna() method (after +making a copy of the data so we don’t lose our work):

    + +
    # Creat a new DataFrame using copy
+df1 = surveys_df.copy()
+
+# Fill all NaN values with 0
+df1['weight'] = df1['weight'].fillna(0)
+
    + +

    However NaN and 0 yield different analysis results. The mean value when NaN +values are replaced with 0 is different from when NaN values are simply thrown +out or ignored.

    + +
    df1['weight'].mean()
+38.751976145601844
+
    + +

    Extra Information

    + +

    We can fill NaN values with any value that we chose. The code below fills all +NaN values with a mean for all weight values.

    + +
     df1['weight'] = surveys_df['weight'].fillna(surveys_df['weight'].mean())
+
    + +

    Writing Out Data to CSV

    + +

    We’ve learned about using manipulating data to get desired outputs. But we’ve also discussed +keeping data that has been manipulated separate from our raw data. Something we might be interested +in doing is working with only the columns that have full data. First, let’s reload the data so +we’re not mixing up all of our previous manipulations.

    + +
    surveys_df = pd.read_csv("data/surveys.csv")
+
    + +

    Next, let’s drop all the rows that contain missing values. We will use the command dropna. +By default, dropna removes columns that contain missing data for even just one row.

    + +
    df_na = df.dropna()
+
+
    + +

    If you now type df_na, you should observe that the resulting DataFrame has 30676 rows +and 9 columns, much smaller than the 35549 row original.

    + +

    We can now use the to_csv command to do export a DataFrame in CSV format. Note that the code +below will by default save the data into the current working directory. We can +save it to a different folder by adding the foldername and a slash before the filename: +df1.to_csv('foldername/out.csv'). We use ‘index=False’ so that +pandas doesn’t include the index number for each line.

    + +
    # Write DataFrame to CSV
+df_na.to_csv('data_output/surveys_complete.csv', index=False)
+
    + +

    Recap

    + +

    What we’ve learned:

    + +
      +
    • How to subset and index the dataframe
      • +
    • What NaN values are, how they might be represented, and what this means for your work
      • +
    • How to replace NaN values, if desired
      • +
    • How to use to_csv to write manipulated data to a file.
      • +
    + +

    Extra

    + +

    We can run isnull on a particular column too. What does the code below do?

    + +
    # What does this do?
+empty_weights = surveys_df[pd.isnull(surveys_df['weight'])]['weight']
+print(empty_weights)
+
    + +

    Let’s take a minute to look at the statement above. We are using the Boolean +object pd.isnull(surveys_df['weight']) as an index to surveys_df. We are +asking Python to select rows that have a NaN value of weight.

    + +

    Extra Challenges - Putting it all together

    + +
      +

    1. Create a new DataFrame that only contains observations with sex values that +are not female or male. Assign each sex value in the new DataFrame to a +new value of ‘x’. Determine the number of null values in the subset.

      2. + +

    2. Create a new DataFrame that contains only observations that are of sex male +or female and where weight values are greater than 0. Create a stacked bar +plot of average weight by plot with male vs female values stacked for each +plot.

      + +
        +
      1. Count the number of missing values per column. Hint: The method .count() gives you the number of non-NA observations per column.
        2. +
      3. +
    + +

    NEXT

    + +
    +
    + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/20180323-launch-workshop/intro/index.html b/workshops/intro_to_python/20180323-launch-workshop/intro/index.html new file mode 100644 index 0000000..2b4f17a --- /dev/null +++ b/workshops/intro_to_python/20180323-launch-workshop/intro/index.html @@ -0,0 +1,586 @@ + + + + + + + Monash Data Fluency | 01 Programming in Python (Introduction) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    +
    + + +
    + +
    +
    +
    +

    + WORKSHOPS +

    +

    + 01 Programming in Python (Introduction) +

    +
    +
    + + +

    The Basics of Python

    + +

    Python is a general purpose programming language that supports rapid development +of scripts and applications.

    + +

    Python’s main advantages:

    + +
      +
    • Open Source software, supported by Python Software Foundation
      • +
    • Available on all platforms (ie. Windows, Linux and MacOS)
      • +
    • It is a general-purpose programming language
      • +
    • Supports multiple programming paradigms
      • +
    • Very large community with a rich ecosystem of third-party packages
      • +
    + +

    Interpreter

    + +

    Python is an interpreted language which can be used in two ways:

    + +
      +
    • “Interactive” Mode: It functions like an “advanced calculator” Executing +one command at a time:
      • +
    + +
    user:host:~$ python
+Python 3.5.1 (default, Oct 23 2015, 18:05:06)
+[GCC 4.8.3] on linux2
+Type "help", "copyright", "credits" or "license" for more information.
+>>> 2 + 2
+4
+>>> print("Hello World")
+Hello World
+
    + +
      +
    • “Scripting” Mode: Executing a series of “commands” saved in text file, +usually with a .py extension after the name of your file:
      • +
    + +
    user:host:~$ python my_script.py
+Hello World
+
    + +

    Introduction to Python built-in data types

    + +

    Strings, integers and floats

    + +

    One of the most basic things we can do in Python is assign values to variables:

    + +
    text = "Data Fluency"  # An example of a string
+number = 42  # An example of an integer
+pi_value = 3.1415  # An example of a float
+
    + +

    Here we’ve assigned data to the variables text, number and pi_value, +using the assignment operator =. To review the value of a variable, we +can type the name of the variable into the Jupyter notebook and press Shift and Enter:

    + +
    text
+## Which Returns
+"Data Fluency"
+
    + +

    Everything in Python has a type. To get the type of something, we can pass it +to the built-in function type:

    + +
    type(text)
+ str
+type(number)
+ int
+type(6.02)
+ float
+
    + +

    The variable text is of type str, short for “string”. Strings hold +sequences of characters, which can be letters, numbers, punctuation +or more exotic forms of text (even emoji!).

    + +

    We can also see the value of something using another built-in function, print:

    + +
    
+print(text)
+Data Fluency
+
+print(11)
+11
+
    + +

    This may seem redundant, but in fact it’s the only way to display output in a script:

    + +

    example.py

    + +
    # A Python script file
+# Comments in Python start with #
+# The next line assigns the string "Data Carpentry" to the variable "text".
+text = "Data Fluency"
+# The next line does nothing!
+text
+# The next line uses the print function to print out the value we assigned to "text"
+print(text)
+
    + +

    Running the script

    + +
    $ python example.py
+Data Fluency
+
    + +

    Notice that “Data Fluency” is printed only once.

    + +

    Tip: print and type are built-in functions in Python. Later in this +lesson, we will introduce methods and user-defined functions. The Python +documentation is excellent for reference on the differences between them.

    + +
    help(print)
+
    + +

    Will give the output

    + +
    Help on built-in function print in module builtins:
+
+print(...)
+    print(value, ..., sep=' ', end='\n', file=sys.stdout, flush=False)
+    
+    Prints the values to a stream, or to sys.stdout by default.
+    Optional keyword arguments:
+    file:  a file-like object (stream); defaults to the current sys.stdout.
+    sep:   string inserted between values, default a space.
+    end:   string appended after the last value, default a newline.
+    flush: whether to forcibly flush the stream.
+
    + +

    Operators

    + +

    We can perform mathematical calculations in Python using the basic operators + +, -, /, *, %:

    + +
    >>> 2 + 2  # Addition
+4
+>>> 6 * 7  # Multiplication
+42
+>>> 2 ** 16  # Power
+65536
+>>> 13 % 5  # Modulo
+3
+
    + +

    We can also use comparison and logic operators: +<, >, ==, !=, <=, >= and statements of identity such as +and, or, not. The data type returned by this is +called a boolean.

    + +
    >>> 3 > 4
+False
+>>> True and True
+True
+>>> True or False
+True
+
    + +

    Sequential types: Lists and Tuples

    + +

    Lists

    + +

    Lists are a common data structure to hold an ordered sequence of +elements. Each element can be accessed by an index. Note that Python +indexes start with 0 instead of 1:

    + +
    >>> numbers = [1, 2, 3]
+>>> numbers[0]
+1
+
    + +

    A for loop can be used to access the elements in a list or other Python data structure one at a time. We will learn about loops in other lesson.

    + +
    >>> for num in numbers:
+...     print(num)
+...
+1
+2
+3
+
    + +

    Indentation is very important in Python. Note that the second line in the +example above is indented. Just like three chevrons >>> indicate an +interactive prompt in Python, the three dots ... are Python’s prompt for +multiple lines. This is Python’s way of marking a block of code. [Note: you +do not type >>> or ....]

    + +

    To add elements to the end of a list, we can use the append method. Methods +are a way to interact with an object (a list, for example). We can invoke a +method using the dot . followed by the method name and a list of arguments +in parentheses. Let’s look at an example using append:

    + +
    >>> numbers.append(4)
+>>> print(numbers)
+[1, 2, 3, 4]
+>>>
+
    + +

    To find out what methods are available for an +object, we can use the built-in help command:

    + +
    help(numbers)
+
+Help on list object:
+
+class list(object)
+ |  list() -> new empty list
+ |  list(iterable) -> new list initialized from iterable's items
+ ...
+
    + +

    Tuples

    + +

    A tuple is similar to a list in that it’s an ordered sequence of elements. +However, tuples can not be changed once created (they are “immutable”). Tuples +are created by placing comma-separated values inside parentheses ().

    + +
    # Tuples use parentheses
+a_tuple= (1, 2, 3)
+another_tuple = ('blue', 'green', 'red')
+# Note: lists use square brackets
+a_list = [1, 2, 3]
+
    + +

    Challenge - Tuples

    + +
      +
    1. What happens when you type a_tuple[2]=5 vs a_list[1]=5 ?
      2. +
    2. Type type(a_tuple) into python - what is the object type?
      3. +
    + +

    Dictionaries

    + +

    A dictionary is a container that holds pairs of objects - keys and values.

    + +
    >>> translation = {'one': 1, 'two': 2}
+>>> translation['one']
+1
+
    + +

    Dictionaries work a lot like lists - except that you index them with keys. +You can think about a key as a name for or a unique identifier for a set of values +in the dictionary. Keys can only have particular types - they have to be +“hashable”. Strings and numeric types are acceptable, but lists aren’t.

    + +
    >>> rev = {1: 'one', 2: 'two'}
+>>> rev[1]
+'one'
+>>> bad = {[1, 2, 3]: 3}
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: unhashable type: 'list'
+
    + +

    In Python, a “Traceback” is an multi-line error block printed out for the +user.

    + +

    To add an item to the dictionary we assign a value to a new key:

    + +
    >>> rev = {1: 'one', 2: 'two'}
+>>> rev[3] = 'three'
+>>> rev
+{1: 'one', 2: 'two', 3: 'three'}
+
    + +

    Challenge - Can you do reassignment in a dictionary?

    + +
      +

    1. First check what rev is right now (remember rev is the name of our dictionary). Type: rev

      2. + +

    2. Try to reassign the second value (in the key value pair) so that it no longer reads “two” but instead reads “apple-sauce”.

      3. + +

    3. Now display rev again to see if it has changed.

      4. +
    + +

    It is important to note that dictionaries are “unordered” and do not remember +the sequence of their items (i.e. the order in which key:value pairs were +added to the dictionary). Because of this, the order in which items are +returned from loops over dictionaries might appear random and can even change +with time.

    + +

    NEXT

    + +
    +
    + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/20180323-launch-workshop/loops/index.html b/workshops/intro_to_python/20180323-launch-workshop/loops/index.html new file mode 100644 index 0000000..3e32415 --- /dev/null +++ b/workshops/intro_to_python/20180323-launch-workshop/loops/index.html @@ -0,0 +1,604 @@ + + + + + + + Monash Data Fluency | 04 Automation with Loops + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    +
    + + +
    + +
    +
    +
    +

    + WORKSHOPS +

    +

    + 04 Automation with Loops +

    +
    +
    + + +

    An example task that we might want to repeat is printing each character in a +word on a line of its own.

    + +
    word = 'lead'
+
    + +

    We can access a character in a string using its index. For example, we can get the first +character of the word 'lead', by using word[0]. One way to print each character is to use +four print statements:

    + +
    print(word[0])
+print(word[1])
+print(word[2])
+print(word[3])
+
    + +

    Gives output

    + +
    l
+e
+a
+d
+
    + +

    This is a bad approach for two reasons:

    + +
      +

    1. It doesn’t scale: +if we want to print the characters in a string that’s hundreds of letters long, +we’d be better off just typing them in.

      2. + +

    2. It’s fragile: +if we give it a longer string, +it only prints part of the data, +and if we give it a shorter one, +it produces an error because we’re asking for characters that don’t exist.

      3. +
    + +
    word = 'tin'
+print(word[0])
+print(word[1])
+print(word[2])
+print(word[3])
+
+
    + +

    Gives back

    + +
    t
+i
+n
+---------------------------------------------------------------------------
+IndexError                                Traceback (most recent call last)
+<ipython-input-3-7974b6cdaf14> in <module>()
+      3 print(word[1])
+      4 print(word[2])
+----> 5 print(word[3])
+
+IndexError: string index out of range
+
    + +

    Here’s a better approach:

    + +
    word = 'lead'
+for char in word:
+    print(char)
+
+
    + +

    Would Produce

    + +
    l
+e
+a
+d
+
    + +

    This is shorter — certainly shorter than something that prints every character in a hundred-letter string — and +more robust as well:

    + +
    word = 'oxygen'
+for char in word:
+    print(char)
+
    + +

    Output

    + +
    o
+x
+y
+g
+e
+n
+
    + +

    The improved version uses a for loop to repeat an operation — in this case, printing — once for each thing in a sequence. +The general form of a loop is:

    + +
    for variable in collection:
+    do things with variable
+
    + +

    Using the oxygen example above, the loop might look like this:

    + +

    loop_image

    + +

    where each character (char) in the variable word is looped through and printed one character after another. +The numbers in the diagram denote which loop cycle the character was printed in (1 being the first loop, and 6 being the final loop).

    + +

    We can call the loop variable anything we like, +but there must be a colon at the end of the line starting the loop, and we must indent anything we want to run inside the loop. Unlike many other languages, there is no command to signify the end of the loop body (e.g. end for); what is indented after the for statement belongs to the loop.

    + +

    What’s in a name?

    + +

    In the example above, the loop variable was given the name char as a mnemonic; it is short for ‘character’. +We can choose any name we want for variables. We might just as easily have chosen the name banana for the loop variable, as long as we use the same name when we invoke the variable inside the loop:

    + +
    word = 'oxygen'
+for banana in word:
+    print(banana)
+
    + +

    Output

    + +
    o
+x
+y
+g
+e
+n
+
    + +

    It is a good idea to choose variable names that are meaningful, otherwise it would be more difficult to understand what the loop is doing.

    + +

    Here’s another loop that repeatedly updates a variable:

    + +
    length = 0
+for vowel in 'aeiou':
+    length = length + 1
+print('There are', length, 'vowels')
+
    + +
    There are 5 vowels
+
    + +

    It’s worth tracing the execution of this little program step by step. +Since there are five characters in 'aeiou', +the statement on line 3 will be executed five times. +The first time around, +length is zero (the value assigned to it on line 1) +and vowel is 'a'. +The statement adds 1 to the old value of length, +producing 1, +and updates length to refer to that new value. +The next time around, +vowel is 'e' and length is 1, +so length is updated to be 2. +After three more updates, +length is 5; +since there is nothing left in 'aeiou' for Python to process, +the loop finishes +and the print statement on line 4 tells us our final answer.

    + +

    Note that a loop variable is just a variable that’s being used to record progress in a loop. +It still exists after the loop is over, +and we can re-use variables previously defined as loop variables as well:

    + +
    letter = 'z'
+for letter in 'abc':
+    print(letter)
+print('after the loop, letter is', letter)
+
    + +

    Output

    + +
    a
+b
+c
+after the loop, letter is c
+
    + +

    Note also that finding the length of a string is such a common operation that Python actually has a built-in function to do it called len:

    + +
    print(len('aeiou'))
+
    + +

    Output

    + +
    5
+
    + +

    len is much faster than any function we could write ourselves, +and much easier to read than a two-line loop; +it will also give us the length of many other things that we haven’t met yet, +so we should always use it when we can.

    + +

    From 1 to N

    + +

    Python has a built-in function called range that creates a sequence of numbers. range can +accept 1, 2, or 3 parameters.

    + +
      +
    • If one parameter is given, range creates an array of that length, +starting at zero and incrementing by 1. +For example, range(3) produces the numbers 0, 1, 2.
      • +
    • If two parameters are given, range starts at +the first and ends just before the second, incrementing by one. +For example, range(2, 5) produces 2, 3, 4.
      • +
    • If range is given 3 parameters, +it starts at the first one, ends just before the second one, and increments by the third one. +For exmaple range(3, 10, 2) produces 3, 5, 7, 9.
      • +
    + +

    Challenge

    + +

    Using range, +write a loop that uses range to print the first 3 natural numbers:

    + +
    1
+2
+3
+
    + +

    Solution

    + +
    for i in range(1, 4):
+   print(i)
+
    + +

    Computing Powers With Loops

    + +

    Exponentiation is built into Python:

    + +
    print(5 ** 3)
+
    + +
    125
+
    + +

    Write a loop that calculates the same result as 5 ** 3 using +multiplication (and without exponentiation).

    + +

    Solution

    + +
    result = 1
+for i in range(0, 3):
+   result = result * 5
+print(result)
+
    + +

    Reverse a String

    + +

    Knowing that two strings can be concatenated using the + operator, +write a loop that takes a string +and produces a new string with the characters in reverse order, +so 'Newton' becomes 'notweN'.

    + +

    Solution

    + +
    newstring = ''
+oldstring = 'Newton'
+for char in oldstring:
+   newstring = char + newstring
+print(newstring)
+
    + +

    Computing the Value of a Polynomial

    + +

    The built-in function enumerate takes a sequence (e.g. a list) and generates a +new sequence of the same length. Each element of the new sequence is a pair composed of the index +(0, 1, 2,…) and the value from the original sequence:

    + +
    for i, x in enumerate(xs):
+    # Do something with i and x
+
    + +

    The code above loops through xs, assigning the index to i and the value to x.

    + +

    Suppose you have encoded a polynomial as a list of coefficients in +the following way: the first element is the constant term, the +second element is the coefficient of the linear term, the third is the +coefficient of the quadratic term, etc.

    + +
    x = 5
+cc = [2, 4, 3]
+
    + +
    y = cc[0] * x**0 + cc[1] * x**1 + cc[2] * x**2
+y = 97
+
    + +

    Write a loop using enumerate(cc) which computes the value y of any +polynomial, given x and cc.

    + +

    Solution

    + +
    y = 0
+for i, c in enumerate(cc):
+    y = y + x**i * c
+
    + +

    NEXT

    + +
    +
    + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/20180323-launch-workshop/page/1/index.html b/workshops/intro_to_python/20180323-launch-workshop/page/1/index.html new file mode 100644 index 0000000..8eed255 --- /dev/null +++ b/workshops/intro_to_python/20180323-launch-workshop/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/ \ No newline at end of file diff --git a/workshops/intro_to_python/20180323-launch-workshop/plotting/index.html b/workshops/intro_to_python/20180323-launch-workshop/plotting/index.html new file mode 100644 index 0000000..761beb4 --- /dev/null +++ b/workshops/intro_to_python/20180323-launch-workshop/plotting/index.html @@ -0,0 +1,523 @@ + + + + + + + Monash Data Fluency | 05 Making Plots With ggplot + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    +
    + + +
    + +
    +
    +
    +

    + WORKSHOPS +

    +

    + 05 Making Plots With ggplot +

    +
    +
    + + +

    Introduction

    + +

    Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. +Strictly speaking plotnine is just another implementation of The Grammar of Graphics by Leland Wilkinson, which in theory could go on it own direction and diverge away from an R ggplot .

    + +

    The Grammar of Graphics

    + +
    +

    statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars) +Faceting can be used to generate the same plot for different subsets of the dataset

    +
    + +

    These are basic building blocks according to the grammar of graphics:

    + +
      +
    • data The data + a set of aesthetic mappings that describing variables mapping
      • +
    • geom Geometric objects, represent what you actually see on the plot: points, lines, polygons, etc.
      • +
    • stats Statistical transformations, summarise data in many useful ways.
      • +
    • scale The scales map values in the data space to values in an aesthetic space
      • +
    • coord A coordinate system, describes how data coordinates are mapped to the plane of the graphic.
      • +
    • facet A faceting specification describes how to break up the data into subsets for plotting individual set
      • +
    + +

    Let’s explore those in details

    + +

    Plotting in ggplot style

    + +

    Let set up our working environment with necessary libraries and also load our csv file into data frame called survs_df,

    + +
    import numpy as np
+import pandas as pd
+from plotnine import *
+
+%matplotlib inline
+survs_df = pd.read_csv('data/surveys.csv').dropna()
+
    + +

    Producing a plot with ggplot, we must give three things:

    + +
      +
    1. A data frame containing our data.
      2. +
    2. How the columns of the data frame can be translated into positions, colors, sizes, and shapes of graphical elements (“aesthetics”).
      3. +
    3. The actual graphical elements to display (“geometric objects”).
      4. +
    + +

    Introduction to plotting

    + +
    ggplot(survs_df, aes('weight', 'hindfoot_length')) + geom_point()
+
    + +

    Lets see if we can also include information about species and year

    + +
    ggplot(survs_df, aes('weight', 'hindfoot_length',
+    size = 'year')) + geom_point()
+
    + +
    ggplot(survs_df, aes('weight', 'hindfoot_length', 
+    size = 'year', color = 'species_id')) + geom_point()
+
    + +

    We can do simple counting plot, to see how many observation (data points) we have for each year for example

    + +
    ggplot(survs_df, aes('year')) + \
+    geom_bar(stat = 'count')
+
    + +

    Let’s now also color by species to see how many observation we have per species in a given year

    + +
    ggplot(survs_df, aes('year', fill = 'species_id')) + \
+    geom_bar(stat = 'count')
+
    + +

    Challenges

    + +
    +

    Is there a better visualisation for comparing weight across years? The plot should have categorical data on x axis and continuous on y axis +Plot a boxplot of hindfoot_length across different species (species_id column)

    +
    + +

    More geom types

    + +
    ggplot(survs_df, aes('year', 'weight')) + \
+    geom_boxplot()
+
    + +

    Why are we not seeing mulitple boxplots, one for each year? +This is because year variable is continues in our data frame, but for the purpose we want it to be categorical.

    + +
    survs_df['year_fact'] = pd.Series(survs_df['year'], dtype = "category")
+
+ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot()
+
    + +
    ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_violin()
+
    + +

    To save an image for later

    + +
    plt1 = ggplot(survs_df, aes('year_fact', 'weight')) + \
+           geom_boxplot() + \
+           xlab("Years") + \
+           ylab("Weight log2(kg)") + \
+           ggtitle("Boxplots, summary of species wieght in each year")
+
+ggsave(filename = "plot1.png", \
+       plot = plt1, \
+       device = 'png', \
+       dpi = 300, \
+       height = 25, \
+        width = 25)
+
    + +

    Challenges

    + +
    +

    Can you log2 transform weight and plot “normalised” boxplot. Hint: use np.log2() function and name new column weight_log

    +
    + +

    Also will log2 transforming make this data visualisation better?

    + +
    survs_df['weight_log'] = np.log2(survs_df['weight'])
+    
+ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    xlab("Years") + \
+    ylab("Weight log2(kg)") + \
+    ggtitle("Boxplots, summary of species wieght in each year")
+
    + +

    Faceting

    + +

    ggplot has a special technique called faceting that allows to split one plot +into multiple plots based on a factor included in the dataset. We will use it to +make one plot for a time series for each species.

    + +
    ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot() + \
+    facet_wrap("~sex")
+
+
    + +
    ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") 
+
    + +

    Theming

    + +
    ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot() + \
+    theme_bw()
+
    + +
    ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") + \
+    theme_xkcd()
+
    + +

    Extra bits 1

    + +

    Let’s try to bin years into decades, which could be crude but might gives simple images to look at.

    + +
    bins = [(survs_df['year'] < 1980),
+        (survs_df['year'] < 1990),
+        (survs_df['year'] < 2000),
+        (survs_df['year'] >= 2000)]
+
+labels = ['70s', '80s', '90s', 'Z']
+
+survs_df['year_bins'] = np.select(bins, labels)
+
    + +
    plt2 = ggplot(survs_df, aes('year_bins', 'weight_log')) + \
+           geom_boxplot()
+plt2 
+
    + +
    plt2 = ggplot(survs_df, aes('year_bins', 'weight_log')) + \
+           geom_boxplot() + \
+           theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+           facet_wrap("~species_id") 
+plt2 
+
    + +

    Extra bits 2

    + +

    This is a different way to look at your data

    + +
    ggplot(survs_df, aes("year_fact", "weight")) + \
+    stat_summary(fun_y = np.mean, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+    
+ggplot(survs_df, aes("year_fact", "weight")) + \
+    stat_summary(fun_y = np.median, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+    
+ggplot(survs_df, aes("year_fact", "weight_log")) + \
+    stat_summary(fun_y = np.mean, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+
    + +

    Extra bits 3

    + +

    It is very informative to look across years, year by year, it becomes apparent straight away that for some species there is a lot of data is missing.And going forward, maybe, you’d want to filter those low counting species. (this is after faceting by species_id in section Extra bits 1).

    + +
    #survs_cnts_df = survs_df.groupby(['species_id'], as_index=False).count().sort_values(['record_id'])
+survs_cnts_df = survs_df[['species_id']].groupby(['species_id']).size().reset_index()
+species_to_remove = list(survs_cnts_df[survs_cnts_df['record_id'] < 200].species_id)
+survs_df_filt = survs_df[survs_df['species_id'].isin(species_to_remove) == False]
+
    + +
    ggplot(survs_df_filt, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+
+ggplot(survs_df_filt, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") 
+
    + +

    NEXT

    + +
    +
    + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/20180323-launch-workshop/working_with_data/index.html b/workshops/intro_to_python/20180323-launch-workshop/working_with_data/index.html new file mode 100644 index 0000000..d9dbfdd --- /dev/null +++ b/workshops/intro_to_python/20180323-launch-workshop/working_with_data/index.html @@ -0,0 +1,923 @@ + + + + + + + Monash Data Fluency | 02 Data Analysis in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    +
    + + +
    + +
    +
    +
    +

    + WORKSHOPS +

    +

    + 02 Data Analysis in Python +

    +
    +
    + + +

    Working With Pandas DataFrames

    + +

    We can automate the process of performing data manipulations in Python. It’s efficient to spend time +building the code to perform these tasks because once it’s built, we can use it +over and over on different datasets that use a similar format. This makes our +methods easily reproducible. We can also easily share our code with colleagues +and they can replicate the same analysis.

    + +

    The Dataset

    + +

    For this lesson, we will be using the Portal Teaching data, a subset of the data +from Ernst et al +Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA

    + +

    We will be using this dataset, which is stored under your login/account under the Files tab.

    + +

    We are studying the species and weight of animals caught in plots in our study +area. The dataset is stored as a .csv file: each row holds information for a +single animal, and the columns represent:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ColumnDescription
    record_idUnique id for the observation
    monthmonth of observation
    dayday of observation
    yearyear of observation
    plot_idID of a particular plot
    species_id2-letter code
    sexsex of animal (“M”, “F”)
    hindfoot_lengthlength of the hindfoot in mm
    weightweight of the animal in grams
    + +

    The first few rows of our first file look like this:

    + +
    record_id,month,day,year,plot_id,species_id,sex,hindfoot_length,weight
+1,7,16,1977,2,NL,M,32,
+2,7,16,1977,3,NL,M,33,
+3,7,16,1977,2,DM,F,37,
+4,7,16,1977,7,DM,M,36,
+5,7,16,1977,3,DM,M,35,
+6,7,16,1977,1,PF,M,14,
+7,7,16,1977,2,PE,F,,
+8,7,16,1977,1,DM,M,37,
+9,7,16,1977,1,DM,F,34,
+
    + +
    + +

    About Libraries

    + +

    A library in Python contains a set of tools (called functions) that perform +tasks on our data. Importing a library is like getting a piece of lab equipment +out of a storage locker and setting it up on the bench for use in a project. +Once a library is set up, it can be used or called to perform many tasks.

    + +

    Pandas in Python

    + +

    One of the best options for working with tabular data in Python is to use the +Python Data Analysis Library (a.k.a. Pandas). The +Pandas library provides data structures, produces high quality plots with +matplotlib and integrates nicely with other libraries +that use NumPy (which is another Python library) arrays.

    + +

    Python doesn’t load all of the libraries available to it by default. We have to +add an import statement to our code in order to use library functions. To import +a library, we use the syntax import libraryName. If we want to give the +library a nickname to shorten the command, we can add as nickNameHere. An +example of importing the pandas library using the common nickname pd is below.

    + +
    import pandas as pd
+
    + +

    Each time we call a function that’s in a library, we use the syntax +LibraryName.FunctionName. Adding the library name with a . before the +function name tells Python where to find the function. In the example above, we +have imported Pandas as pd. This means we don’t have to type out pandas each +time we call a Pandas function.

    + +

    Reading CSV Data Using Pandas

    + +

    We will begin by locating and reading our survey data which are in CSV format. CSV stands for Comma-Separated Values and is a common way store formatted data. Other symbols my also be used, so you might see tab-separated, colon-separated or space separated files. It is quite easy to replace one separator with another, to match your application. The first line in the file often has headers to explain what is in each column. CSV (and other separators) make it easy to share data, and can be imported and exported from many applications, including Microsoft Excel. For mo

    + +

    We can use Pandas’ read_csv function to pull the file directly into a +DataFrame.

    + +

    So What’s a DataFrame?

    + +

    A DataFrame is a 2-dimensional data structure that can store data of different +types (including characters, integers, floating point values, factors and more) +in columns. It is similar to a spreadsheet or an SQL table or the data.frame in +R. A DataFrame always has an index (0-based). An index refers to the position of +an element in the data structure.

    + +
    # Note that pd.read_csv is used because we imported pandas as pd
+pd.read_csv("data/surveys.csv")
+
    + +

    The above command yields the output below:

    + +
    record_id  month  day  year  plot_id species_id sex  hindfoot_length  weight
+0          1      7   16  1977        2         NL   M               32   NaN
+1          2      7   16  1977        3         NL   M               33   NaN
+2          3      7   16  1977        2         DM   F               37   NaN
+3          4      7   16  1977        7         DM   M               36   NaN
+4          5      7   16  1977        3         DM   M               35   NaN
+...
+35544      35545     12   31  2002       15     AH  NaN              NaN  NaN
+35545      35546     12   31  2002       15     AH  NaN              NaN  NaN
+35546      35547     12   31  2002       10     RM    F               15   14
+35547      35548     12   31  2002        7     DO    M               36   51
+35548      35549     12   31  2002        5     NaN  NaN             NaN  NaN
+
+[35549 rows x 9 columns]
+
    + +

    We can see that there were 33,549 rows parsed. Each row has 9 +columns. The first column is the index of the DataFrame. The index is used to +identify the position of the data, but it is not an actual column of the DataFrame. +It looks like the read_csv function in Pandas read our file properly. However, +we haven’t saved any data to memory so we can work with it.We need to assign the +DataFrame to a variable. Remember that a variable is a name for a value, such as x, +or data. We can create a new object with a variable name by assigning a value to it using =.

    + +

    Let’s call the imported survey data surveys_df:

    + +
    surveys_df = pd.read_csv("data/surveys.csv")
+
    + +

    Notice when you assign the imported DataFrame to a variable, Python does not +produce any output on the screen. We can view the value of the surveys_df +object by typing its name into the Python command prompt.

    + +
    surveys_df
+
    + +

    which prints contents like above.

    + +

    Note: if the output is too wide to print on your narrow terminal window, you may see something +slightly different as the large set of data scrolls past. You may see simply the last column +of data:

    + +
    17        NaN  
+18        NaN  
+19        NaN  
+20        NaN  
+21        NaN  
+22        NaN  
+23        NaN  
+24        NaN  
+25        NaN  
+26        NaN  
+27        NaN  
+28        NaN  
+29        NaN  
+...       ...  
+35519    36.0  
+35520    48.0  
+35521    45.0  
+35522    44.0  
+35523    27.0  
+35524    26.0  
+35525    24.0  
+35526    43.0  
+35527     NaN  
+35528    25.0  
+35529     NaN  
+35530     NaN  
+35531    43.0  
+35532    48.0  
+35533    56.0  
+35534    53.0  
+35535    42.0  
+35536    46.0  
+35537    31.0  
+35538    68.0  
+35539    23.0  
+35540    31.0  
+35541    29.0  
+35542    34.0  
+35543     NaN  
+35544     NaN  
+35545     NaN  
+35546    14.0  
+35547    51.0  
+35548     NaN  
+
+[35549 rows x 9 columns]
+
    + +

    Never fear, all the data is there, if you scroll up. Selecting just a few rows, so it is +easier to fit on one window, you can see that pandas has neatly formatted the data to fit +our screen:

    + +
    
+>>> surveys_df.head() # The head() function displays the first several lines of a file. It
+		      # is discussed below.
+   record_id  month  day  year  plot_id species_id sex  hindfoot_length  \
+5          6      7   16  1977        1         PF   M             14.0   
+6          7      7   16  1977        2         PE   F              NaN   
+7          8      7   16  1977        1         DM   M             37.0   
+8          9      7   16  1977        1         DM   F             34.0   
+9         10      7   16  1977        6         PF   F             20.0   
+
+   weight  
+5     NaN  
+6     NaN  
+7     NaN  
+8     NaN  
+9     NaN  
+
    + +

    Exploring Our Species Survey Data

    + +

    Again, we can use the type function to see what kind of thing surveys_df is:

    + +
    type(surveys_df)
+
+## Returns
+pandas.core.frame.DataFrame
+
    + +

    As expected, it’s a DataFrame (or, to use the full name that Python uses to refer +to it internally, a pandas.core.frame.DataFrame).

    + +

    What kind of things does surveys_df contain? DataFrames have an attribute +called dtypes that answers this:

    + +
    surveys_df.dtypes
+
+## Returns
+record_id            int64
+month                int64
+day                  int64
+year                 int64
+plot_id              int64
+species_id          object
+sex                 object
+hindfoot_length    float64
+weight             float64
+dtype: object
+
    + +

    All the values in a column have the same type. For example, months have type +int64, which is a kind of integer. Cells in the month column cannot have +fractional values, but the weight and hindfoot_length columns can, because they +have type float64. The object type doesn’t have a very helpful name, but in +this case it represents strings (such as ’M’ and ‘F’ in the case of sex).

    + +

    Useful Ways to View DataFrame objects in Python

    + +

    There are many ways to summarize and access the data stored in DataFrames, +using attributes and methods provided by the DataFrame object.

    + +

    To access an attribute, use the DataFrame object name followed by the attribute +name df_object.attribute. Using the DataFrame surveys_df and attribute +columns, an index of all the column names in the DataFrame can be accessed +with surveys_df.columns.

    + +

    Methods are called in a similar fashion using the syntax df_object.method(). +As an example, surveys_df.head() gets the first few rows in the DataFrame +surveys_df using the head() method. With a method, we can supply extra +information in the parens to control behaviour.

    + +

    Let’s look at the data using these.

    + +

    Challenge - DataFrames

    + +

    Using our DataFrame surveys_df, try out the attributes & methods below to see +what they return.

    + +
      +
    1. surveys_df.columns
      2. +
    2. surveys_df.shape Take note of the output of shape - what format does it +return the shape of the DataFrame in? HINT: More on tuples, here.
      3. +
    3. surveys_df.head() Also, what does surveys_df.head(15) do?
      4. +
    4. surveys_df.tail()
      5. +
    + +

    Calculating Statistics From Data In A Pandas DataFrame

    + +

    We’ve read our data into Python. Next, let’s perform some quick summary +statistics to learn more about the data that we’re working with. We might want +to know how many animals were collected in each plot, or how many of each +species were caught. We can perform summary stats quickly using groups. But +first we need to figure out what we want to group by.

    + +

    Let’s begin by exploring our data:

    + +
    # Look at the column names
+surveys_df.columns
+
    + +

    which returns:

    + +
    Index(['record_id', 'month', 'day', 'year', 'plot_id', 'species_id', 'sex',
+       'hindfoot_length', 'weight'],
+      dtype='object')
+
    + +

    Let’s get a list of all the species. The pd.unique function tells us all of +the unique values in the species_id column.

    + +
    pd.unique(surveys_df['species_id'])
+
    + +

    which returns:

    + +
    array(['NL', 'DM', 'PF', 'PE', 'DS', 'PP', 'SH', 'OT', 'DO', 'OX', 'SS',
+       'OL', 'RM', nan, 'SA', 'PM', 'AH', 'DX', 'AB', 'CB', 'CM', 'CQ',
+       'RF', 'PC', 'PG', 'PH', 'PU', 'CV', 'UR', 'UP', 'ZL', 'UL', 'CS',
+       'SC', 'BA', 'SF', 'RO', 'AS', 'SO', 'PI', 'ST', 'CU', 'SU', 'RX',
+       'PB', 'PL', 'PX', 'CT', 'US'], dtype=object)
+
    + +

    Challenge - Statistics

    + +
      +

    1. Create a list of unique plot ID’s found in the surveys data. Call it +plot_names. How many unique plots are there in the data? How many unique +species are in the data?

      2. + +

    2. What is the difference between len(plot_names) and surveys_df['plot_id'].nunique()?

      3. +
    + +

    Groups in Pandas

    + +

    We often want to calculate summary statistics grouped by subsets or attributes +within fields of our data. For example, we might want to calculate the average +weight of all individuals per plot.

    + +

    We can calculate basic statistics for all records in a single column using the +syntax below:

    + +
    surveys_df['weight'].describe()
+
    + +

    gives output

    + +
    count    32283.000000
+mean        42.672428
+std         36.631259
+min          4.000000
+25%         20.000000
+50%         37.000000
+75%         48.000000
+max        280.000000
+Name: weight, dtype: float64
+
    + +

    We can also extract one specific metric if we wish:

    + +
    surveys_df['weight'].min()
+surveys_df['weight'].max()
+surveys_df['weight'].mean()
+surveys_df['weight'].std()
+surveys_df['weight'].count()
+
    + +

    But if we want to summarize by one or more variables, for example sex, we can +use Pandas’ .groupby method. Once we’ve created a groupby DataFrame, we +can quickly calculate summary statistics by a group of our choice.

    + +
    # Group data by sex
+grouped_data = surveys_df.groupby('sex')
+
    + +

    The pandas function describe will return descriptive stats including: mean, +median, max, min, std and count for a particular column in the data. Pandas’ +describe function will only return summary values for columns containing +numeric data.

    + +
    # Summary statistics for all numeric columns by sex
+grouped_data.describe()
+# Provide the mean for each numeric column by sex
+grouped_data.mean()
+
    + +

    grouped_data.mean() OUTPUT:

    + +
            record_id     month        day         year    plot_id  \
+sex                                                              
+F    18036.412046  6.583047  16.007138  1990.644997  11.440854   
+M    17754.835601  6.392668  16.184286  1990.480401  11.098282   
+
+     hindfoot_length     weight  
+sex                              
+F          28.836780  42.170555  
+M          29.709578  42.995379  
+
+
    + +

    The groupby command is powerful in that it allows us to quickly generate +summary stats.

    + +

    Challenge - Summary Data

    + +
      +
    1. How many recorded individuals are female F and how many male M
      2. +
    2. What happens when you group by two columns using the following syntax and +then grab mean values: + +
        +
      • grouped_data2 = surveys_df.groupby(['plot_id','sex'])
        • +
      • grouped_data2.mean()
        • +
      3. +
    3. Summarize weight values for each plot in your data. HINT: you can use the +following syntax to only create summary statistics for one column in your data +by_plot['weight'].describe()
      4. +
    + +

    Did you get #3 right?

    + +

    A Snippet of the Output from challenge 3 looks like:

    + +
    	plot
+	1     count    1903.000000
+	      mean       51.822911
+	      std        38.176670
+	      min         4.000000
+	      25%        30.000000
+	      50%        44.000000
+	      75%        53.000000
+	      max       231.000000
+         ...
+
    + +

    Quickly Creating Summary Counts in Pandas

    + +

    Let’s next count the number of samples for each species. We can do this in a few +ways, but we’ll use groupby combined with a count() method.

    + +
    # Count the number of samples by species
+species_counts = surveys_df.groupby('species_id')['record_id'].count()
+print(species_counts)
+
    + +

    Or, we can also count just the rows that have the species “DO”:

    + +
    surveys_df.groupby('species_id')['record_id'].count()['DO']
+
    + +

    Basic Math Functions

    + +

    If we wanted to, we could perform math on an entire column of our data. For +example let’s multiply all weight values by 2. A more practical use of this might +be to normalize the data according to a mean, area, or some other value +calculated from our data.

    + +
    
+# Multiply all weight values by 2 but does not change the original weight data
+
+surveys_df['weight']*2
+
+
    + +

    Quick & Easy Plotting Data Using Pandas

    + +

    We can plot our summary stats using Pandas, too.

    + +
    
+## To make sure figures appear inside Jupyter Notebook
+%matplotlib inline
+
+# Create a quick bar chart
+species_counts.plot(kind='bar');
+
    + +

    Weight by Species Plot

    + +

    Weight by species plot

    + +

    We can also look at how many animals were captured in each plot:

    + +
    total_count = surveys_df.groupby('plot_id')['record_id'].nunique()
+# Let's plot that too
+total_count.plot(kind='bar');
+
    + +

    Extra Plotting Challenge

    + +
      +

    1. Create a plot of average weight across all species per plot.

      2. + +

    2. Create a plot of total males versus total females for the entire dataset.

      3. + +

    3. Create a stacked bar plot, with weight on the Y axis, and the stacked variable being sex. The plot should show total weight by sex for each plot. Some tips are below to help you solve this challenge:

      4. +
    + +
      +
    • For more on Pandas plots, visit this link.
      • +
    • You can use the code that follows to create a stacked bar plot but the data to stack +need to be in individual columns. Here’s a simple example with some data where +‘a’, ‘b’, and ‘c’ are the groups, and ‘one’ and ‘two’ are the subgroups.
      • +
    + +
    d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
+pd.DataFrame(d)
+
    + +

    shows the following data

    + +
          one  two
+   a    1    1
+   b    2    2
+   c    3    3
+   d  NaN    4
+
    + +

    We can plot the above with

    + +
    # Plot stacked data so columns 'one' and 'two' are stacked
+my_df = pd.DataFrame(d)
+my_df.plot(kind='bar',stacked=True,title="The title of my graph")
+
    + +
      +
    • You can use the .unstack() method to transform grouped data into columns +for each plotting. Try running .unstack() on some DataFrames above and see +what it yields.
      • +
    + +

    Start by transforming the grouped data (by plot and sex) into an unstacked layout, then create + a stacked plot.

    + +

    Solution to Extra Challenge 3

    + +

    First we group data by plot and by sex, and then calculate a total for each plot.

    + +
    by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+
    + +

    This calculates the sums of weights for each sex within each plot as a table

    + +
    plot  sex
+plot_id  sex
+1        F      38253
+         M      59979
+2        F      50144
+         M      57250
+3        F      27251
+         M      28253
+4        F      39796
+         M      49377
+<other plots removed for brevity>
+
    + +

    Below we’ll use .unstack() on our grouped data to figure out the total weight that each sex contributed to each plot.

    + +
    by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+plot_sex_count.unstack()
+
    + +

    The unstack function above will display the following output:

    + +
    sex          F      M
+plot_id              
+1        38253  59979
+2        50144  57250
+3        27251  28253
+4        39796  49377
+<other plots removed for brevity>
+
    + +

    Now, create a stacked bar plot with that data where the weights for each sex are stacked by plot.

    + +

    Rather than display it as a table, we can plot the above data by stacking the values of each sex as follows:

    + +
    by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+spc = plot_sex_count.unstack()
+s_plot = spc.plot(kind='bar',stacked=True,title="Total weight by plot and sex")
+s_plot.set_ylabel("Weight")
+s_plot.set_xlabel("Plot")
+
    + +

    Stacked Bar Plot

    + +

    NEXT

    + +
    +
    + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/20180601-intro-to-python/index.html b/workshops/intro_to_python/20180601-intro-to-python/index.html new file mode 100644 index 0000000..9ee4df8 --- /dev/null +++ b/workshops/intro_to_python/20180601-intro-to-python/index.html @@ -0,0 +1,382 @@ + + + + + + + Monash Data Fluency | Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    +

    + Python +

    + +
    + + +
    +
    + + +
    + +
    +
    + + +

    Introduction to Python Workshop

    + +
      +
    • Date : 1st June 2018
      • +
    • Time : 10:00am to 3:00pm
      • +
    • Location : Monash University Peninsula Campus, + Building D (Business and Economics), + Room D3.11A, + https://goo.gl/maps/5CKNAjsMfPs
      • +
    • Instructors : Anup Shah, Andrew Perry
      • +
    • Helpers : Simon Michnowicz, Kirill Tsyganov, Keith Schulze, Kerri Wait
      • +
    + +

    This hands-on workshop aims to equip participants with the fundamentals of programming and give them skills needed to apply data analysis approaches to their research questions.

    + +

    General Information

    + +

    The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    10:00Welcome
    10:15Session 1
    12:15Lunch
    01:30Session 2
    02:45Wrap-up
    + +

    Python environment

    + +
      +
    • This course will use Colaboratory to run Python code. +Colaboratory is a Jupyter notebook environment that is well suited to data exploration projects.
      • +
    + +

    Shared Notepad

    + + + +

    Syllabus

    + +

    Introduction to Python Workshop (half day)

    + +

    Post-workshop survey

    + +

    We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: https://goo.gl/forms/MJfukKMtWJUjGJ3I2

    + +

    Keep In Touch

    + + + +
    +
    +
    + +
    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/20180601-intro-to-python/index.xml b/workshops/intro_to_python/20180601-intro-to-python/index.xml new file mode 100644 index 0000000..6126065 --- /dev/null +++ b/workshops/intro_to_python/20180601-intro-to-python/index.xml @@ -0,0 +1,16 @@ + + + + Python on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_python/20180601-intro-to-python/ + Recent content in Python on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Tue, 29 May 2018 09:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_python/20180601-intro-to-python/page/1/index.html b/workshops/intro_to_python/20180601-intro-to-python/page/1/index.html new file mode 100644 index 0000000..c50421a --- /dev/null +++ b/workshops/intro_to_python/20180601-intro-to-python/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_python/20180601-intro-to-python/ \ No newline at end of file diff --git a/workshops/intro_to_python/20181114-intro-to-python/index.html b/workshops/intro_to_python/20181114-intro-to-python/index.html new file mode 100644 index 0000000..371abf3 --- /dev/null +++ b/workshops/intro_to_python/20181114-intro-to-python/index.html @@ -0,0 +1,390 @@ + + + + + + + Monash Data Fluency | Intro to Python - Nov 2018 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    +

    + Intro to Python - Nov 2018 +

    + +
    + + +
    +
    + + +
    + +
    +
    + + +

    Introduction to Python Workshop

    + + + +

    This hands-on workshop aims to equip participants with the fundamentals of programming and give them skills needed to apply data analysis approaches to their research questions.

    + +

    General Information

    + +

    The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    10:00Welcome
    10:15Session 1
    12:15Lunch
    01:30Session 2
    02:45Break
    03:00Session 3
    04:45Wrap up, post workshop survey
    + +

    Python environment

    + +
      +
    • This course will use Colaboratory to run Python code. +Colaboratory is a Jupyter notebook environment that is well suited to data exploration projects.
      • +
    + +

    Shared Notepad

    + + + +

    Syllabus

    + +

    Introduction to Python Workshop +(This content is largely based on the Data Carpentry “Data Analysis and Visualization in Python for Ecologists” workshop. There’s nothing really ecology-specific about this course, the dataset used just happens to be an ecology-related CSV file)

    + +

    Post-workshop survey

    + +

    We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: https://goo.gl/forms/clw7FfmFjxxAlOGs2

    + +

    Keep In Touch

    + + + +
    +
    +
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    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/20181114-intro-to-python/index.xml b/workshops/intro_to_python/20181114-intro-to-python/index.xml new file mode 100644 index 0000000..ad44d8a --- /dev/null +++ b/workshops/intro_to_python/20181114-intro-to-python/index.xml @@ -0,0 +1,16 @@ + + + + Intro to Python - Nov 2018 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_python/20181114-intro-to-python/ + Recent content in Intro to Python - Nov 2018 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Mon, 05 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_python/20181114-intro-to-python/page/1/index.html b/workshops/intro_to_python/20181114-intro-to-python/page/1/index.html new file mode 100644 index 0000000..f6e2959 --- /dev/null +++ b/workshops/intro_to_python/20181114-intro-to-python/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_python/20181114-intro-to-python/ \ No newline at end of file diff --git a/workshops/intro_to_python/20190221-intro-to-python/index.html b/workshops/intro_to_python/20190221-intro-to-python/index.html new file mode 100644 index 0000000..a75ae13 --- /dev/null +++ b/workshops/intro_to_python/20190221-intro-to-python/index.html @@ -0,0 +1,390 @@ + + + + + + + Monash Data Fluency | Intro to Python - Feb 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    +

    + Intro to Python - Feb 2019 +

    + +
    + + +
    +
    + + +
    + +
    +
    + + +

    Introduction to Python Workshop

    + +
      +
    • Date : 21st February 2019
      • +
    • Time : 10:00am to 5:00pm
      • +
    • Location : T1, Sir Louis Matheson Library, Clayton, VIC
      • +
    • Instructors : Anup Shah, Andrew Perry, Jay van Schyndel
      • +
    • Helpers : Gulrez Chahal, Andrew Harrison, Zoe Seeger
      • +
    + +

    This hands-on workshop aims to equip participants with the fundamentals of programming and give them skills needed to apply data analysis approaches to their research questions.

    + +

    General Information

    + +

    The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    10:00Welcome
    10:15Session 1
    12:15Lunch
    01:30Session 2
    02:45Break
    03:00Session 3
    04:45Wrap up, post workshop survey
    + +

    Python environment

    + +
      +
    • This course will use Colaboratory to run Python code. +Colaboratory is a Jupyter notebook environment that is well suited to data exploration projects.
      • +
    + +

    Shared Notepad

    + + + +

    Syllabus

    + +

    Introduction to Python Workshop +(This content is largely based on the Data Carpentry “Data Analysis and Visualization in Python for Ecologists” workshop. There’s nothing really ecology-specific about this course, the dataset used just happens to be an ecology-related CSV file)

    + +

    Post-workshop survey

    + +

    We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: https://goo.gl/forms/hIU2Wrrv595yqBew2

    + +

    Keep In Touch

    + + + +
    +
    +
    + +
    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/20190221-intro-to-python/index.xml b/workshops/intro_to_python/20190221-intro-to-python/index.xml new file mode 100644 index 0000000..7b8e757 --- /dev/null +++ b/workshops/intro_to_python/20190221-intro-to-python/index.xml @@ -0,0 +1,16 @@ + + + + Intro to Python - Feb 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_python/20190221-intro-to-python/ + Recent content in Intro to Python - Feb 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Sun, 17 Feb 2019 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_python/20190221-intro-to-python/page/1/index.html b/workshops/intro_to_python/20190221-intro-to-python/page/1/index.html new file mode 100644 index 0000000..2b007d6 --- /dev/null +++ b/workshops/intro_to_python/20190221-intro-to-python/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_python/20190221-intro-to-python/ \ No newline at end of file diff --git a/workshops/intro_to_python/20190402-intro-to-python/index.html b/workshops/intro_to_python/20190402-intro-to-python/index.html new file mode 100644 index 0000000..848ede8 --- /dev/null +++ b/workshops/intro_to_python/20190402-intro-to-python/index.html @@ -0,0 +1,390 @@ + + + + + + + Monash Data Fluency | Intro to Python - Apr 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    +

    + Intro to Python - Apr 2019 +

    + +
    + + +
    +
    + + +
    + +
    +
    + + +

    Introduction to Python Workshop

    + +
      +
    • Date : 2nd April 2019
      • +
    • Time : 10:00am to 5:00pm
      • +
    • Location : Teaching Room 1 Library (A104), Building A, Monash Caulfield Campus, Caulfield
      • +
    • Instructors : Anup Shah, Andrew Perry, Andrew Harrison
      • +
    • Helpers : ?
      • +
    + +

    This hands-on workshop aims to equip participants with the fundamentals of programming and give them skills needed to apply data analysis approaches to their research questions.

    + +

    General Information

    + +

    The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    10:00Welcome
    10:15Session 1
    12:15Lunch
    01:30Session 2
    02:45Break
    03:00Session 3
    04:45Wrap up, post workshop survey
    + +

    Python environment

    + +
      +
    • This course will use Colaboratory to run Python code. +Colaboratory is a Jupyter notebook environment that is well suited to data exploration projects.
      • +
    + +

    Shared Notepad

    + + + +

    Syllabus

    + +

    Introduction to Python Workshop +(This content is largely based on the Data Carpentry “Data Analysis and Visualization in Python for Ecologists” workshop. There’s nothing really ecology-specific about this course, the dataset used just happens to be an ecology-related CSV file)

    + +

    Post-workshop survey

    + +

    We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: https://forms.gle/wVRbpYnbzGjU4aku7

    + +

    Keep In Touch

    + + + +
    +
    +
    + +
    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/20190402-intro-to-python/index.xml b/workshops/intro_to_python/20190402-intro-to-python/index.xml new file mode 100644 index 0000000..7b1db7e --- /dev/null +++ b/workshops/intro_to_python/20190402-intro-to-python/index.xml @@ -0,0 +1,16 @@ + + + + Intro to Python - Apr 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_python/20190402-intro-to-python/ + Recent content in Intro to Python - Apr 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Mon, 01 Apr 2019 09:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_python/20190402-intro-to-python/page/1/index.html b/workshops/intro_to_python/20190402-intro-to-python/page/1/index.html new file mode 100644 index 0000000..a7779bc --- /dev/null +++ b/workshops/intro_to_python/20190402-intro-to-python/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_python/20190402-intro-to-python/ \ No newline at end of file diff --git a/workshops/intro_to_python/20190625/index.html b/workshops/intro_to_python/20190625/index.html new file mode 100644 index 0000000..a0b47e3 --- /dev/null +++ b/workshops/intro_to_python/20190625/index.html @@ -0,0 +1,390 @@ + + + + + + + Monash Data Fluency | Intro to Python - June 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    +

    + Intro to Python - June 2019 +

    + +
    + + +
    +
    + + +
    + +
    +
    + + +

    Introduction to Python Workshop

    + +
      +
    • Date : 25th June 2019
      • +
    • Time : 10:00am to 5:00pm
      • +
    • Location : T2 Teaching Room, Matheson Library Monash Clayton Campus
      • +
    • Instructors : Anup Shah, Andrew Perry, Andrew Harrison
      • +
    • Helpers : Naveen
      • +
    + +

    This hands-on workshop aims to equip participants with the fundamentals of programming and give them skills needed to apply data analysis approaches to their research questions.

    + +

    General Information

    + +

    The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    10:00Welcome
    10:15Session 1
    12:15Lunch
    01:30Session 2
    02:45Break
    03:00Session 3
    04:45Wrap up, post workshop survey
    + +

    Python environment

    + +
      +
    • This course will use Colaboratory to run Python code. +Colaboratory is a Jupyter notebook environment that is well suited to data exploration projects.
      • +
    + +

    Shared Notepad

    + + + +

    Syllabus

    + +

    Introduction to Python Workshop +(This content is largely based on the Data Carpentry “Data Analysis and Visualization in Python for Ecologists” workshop. There’s nothing really ecology-specific about this course, the dataset used just happens to be an ecology-related CSV file)

    + +

    Post-workshop survey

    + +

    We appreciate your feedback (both positive and negative !) - please fill this out in the last 5 mins of the workshop: https://forms.gle/W39GdAGCK8Sc4d7g7

    + +

    Keep In Touch

    + + + +
    +
    +
    + +
    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/20190625/index.xml b/workshops/intro_to_python/20190625/index.xml new file mode 100644 index 0000000..ff4bb7d --- /dev/null +++ b/workshops/intro_to_python/20190625/index.xml @@ -0,0 +1,16 @@ + + + + Intro to Python - June 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_python/20190625/ + Recent content in Intro to Python - June 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Mon, 24 Jun 2019 09:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_python/20190625/page/1/index.html b/workshops/intro_to_python/20190625/page/1/index.html new file mode 100644 index 0000000..e16ab43 --- /dev/null +++ b/workshops/intro_to_python/20190625/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_python/20190625/ \ No newline at end of file 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    + Intro to Python Workshops +

    + +
    +     + +
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    + +
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    + + +

    Introduction to Python Workshop

    + +

    This hands-on workshop aims to equip participants with the fundamentals of programming and give them skills needed to apply data analysis approaches to their research questions.

    + +

    General Information

    + +

    The workshop is taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Syllabus

    + +

    Introduction to Python Workshop +(This content is largely based on the Data Carpentry “Data Analysis and Visualization in Python for Ecologists” workshop. There’s nothing really ecology-specific about this course, the dataset used just happens to be an ecology-related CSV file)

    + +

    Past workshops

    + + + +
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    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/index.xml b/workshops/intro_to_python/index.xml new file mode 100644 index 0000000..2320801 --- /dev/null +++ b/workshops/intro_to_python/index.xml @@ -0,0 +1,16 @@ + + + + Intro to Python Workshops on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_python/ + Recent content in Intro to Python Workshops on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Mon, 05 Nov 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_python/indexing/index.html b/workshops/intro_to_python/indexing/index.html new file mode 100644 index 0000000..e3742a8 --- /dev/null +++ b/workshops/intro_to_python/indexing/index.html @@ -0,0 +1,787 @@ + + + + + + + Monash Data Fluency | 03 Indexing, Slicing and Subsetting + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + WORKSHOPS +

    +

    + 03 Indexing, Slicing and Subsetting +

    +
    +
    + + +

    In lesson 02, we read a CSV into a Python pandas DataFrame. We learned:

    + +
      +
    • How to save the DataFrame to a named object,
      • +
    • How to perform basic math on the data,
      • +
    • How to calculate summary statistics, and
      • +
    • How to create basic plots of the data.
      • +
    + +

    In this lesson, we will explore ways to access different parts of the data +using:

    + +
      +
    • Indexing,
      • +
    • Slicing, and
      • +
    • Subsetting
      • +
    + +

    Loading our data

    + +

    We will continue to use the surveys dataset that we worked with in the last lesson. Let’s reopen and read in the data again:

    + +
    # Make sure pandas is loaded
+import pandas as pd
+
+# Read in the survey CSV
+surveys_df = pd.read_csv("data/surveys.csv")
+
    + +

    Indexing and Slicing in Python

    + +

    We often want to work with subsets of a DataFrame object. There are +different ways to accomplish this including: using labels (column headings), +numeric ranges, or specific x,y index locations.

    + +

    Selecting data using Labels (Column Headings)

    + +

    We use square brackets [] to select a subset of an Python object. For example, +we can select all data from a column named species_id from the surveys_df +DataFrame by name. There are two ways to do this:

    + +
    # Method 1: select a 'subset' of the data using the column name
+surveys_df['species_id'].head()
+
+# Method 2: use the column name as an 'attribute'; gives the same output
+surveys_df.species_id.head()
+
    + +

    We can also create a new object that contains only the data within the +species_id column as follows:

    + +
    # Creates an object, surveys_species, that only contains the `species_id` column
+surveys_species = surveys_df['species_id']
+
    + +

    We can pass a list of column names too, as an index to select columns in that +order. This is useful when we need to reorganize our data.

    + +

    NOTE: If a column name is not contained in the DataFrame, an exception +(error) will be raised.

    + +
    # Select the species and plot columns from the DataFrame
+surveys_df[['species_id', 'plot_id']].head()
+
+# What happens when you flip the order?
+surveys_df[['plot_id', 'species_id']].head()
+
+# What happens if you ask for a column that doesn't exist?
+surveys_df['speciess']
+
    + +

    Python tells us what type of error it is in the traceback, at the bottom it says KeyError: 'speciess' which means that speciess is not a column name (or Key in the related python data type dictionary).

    + +

    Extracting Range based Subsets: Slicing

    + +

    REMINDER: Python Uses 0-based Indexing

    + +

    Let’s remind ourselves that Python uses 0-based +indexing. This means that the first element in an object is located at position +0. This is different from other tools like R and Matlab that index elements +within objects starting at 1.

    + +
    # Create a list of numbers:
+a = [1, 2, 3, 4, 5]
+
    + +

    indexing diagram +slicing diagram

    + +

    Challenge - Extracting data

    + +
      +

    1. What value does the code a[0] return?

      2. + +

    2. How about this: a[5]

      3. + +

    3. In the example above, calling a[5] returns an error. Why is that?

      4. + +

    4. What about a[len(a)] ?

      5. +
    + +

    Slicing Subsets of Rows in Python

    + +

    Slicing using the [] operator selects a set of rows and/or columns from a +DataFrame. To slice out a set of rows, you use the following syntax: +data[start:stop]. When slicing in pandas the start bound is included in the +output. The stop bound is one step BEYOND the row you want to select. So if you +want to select rows 0, 1 and 2 your code would look like this:

    + +
    # Select rows 0, 1, 2 (row 3 is not selected)
+surveys_df[0:3]
+
    + +

    The stop bound in Python is different from what you might be used to in +languages like Matlab and R.

    + +
    # Select the first 5 rows (rows 0, 1, 2, 3, 4)
+surveys_df[:5]
+
+# Select the last element in the list
+# (the slice starts at the last element, and ends at the end of the list)
+surveys_df[-1:]
+
    + +

    We can also reassign values within subsets of our DataFrame.

    + +

    Let’s create a brand new clean dataframe from +the original data CSV file.

    + +
    surveys_df = pd.read_csv("data/surveys.csv")
+
    + +

    Slicing Subsets of Rows and Columns in Python

    + +

    We can select specific ranges of our data in both the row and column directions +using either label or integer-based indexing.

    + +
      +
    • loc is primarily label based indexing. Integers may be used but +they are interpreted as a label.
      • +
    • iloc is primarily integer based indexing
      • +
    + +

    To select a subset of rows and columns from our DataFrame, we can use the +iloc method. For example, we can select month, day and year (columns 2, 3 +and 4 if we start counting at 1), like this:

    + +
    # iloc[row slicing, column slicing]
+surveys_df.iloc[0:3, 1:4]
+
    + +

    which gives the output

    + +
       month  day  year
+0      7   16  1977
+1      7   16  1977
+2      7   16  1977
+
    + +

    Notice that we asked for a slice from 0:3. This yielded 3 rows of data. When you +ask for 0:3, you are actually telling Python to start at index 0 and select rows +0, 1, 2 up to but not including 3.

    + +

    Let’s explore some other ways to index and select subsets of data:

    + +
    # Select all columns for rows of index values 0 and 10
+surveys_df.loc[[0, 10], :]
+
+# What does this do?
+surveys_df.loc[0, ['species_id', 'plot_id', 'weight']]
+
+# What happens when you type the code below?
+surveys_df.loc[[0, 10, 35549], :]
+
    + +

    NOTE: Labels must be found in the DataFrame or you will get a KeyError.

    + +

    Indexing by labels loc differs from indexing by integers iloc. +With loc, the both start bound and the stop bound are inclusive. When using +loc, integers can be used, but the integers refer to the +index label and not the position. For example, using loc and select 1:4 +will get a different result than using iloc to select rows 1:4.

    + +

    We can also select a specific data value using a row and +column location within the DataFrame and iloc indexing:

    + +
    # Syntax for iloc indexing to finding a specific data element
+dat.iloc[row, column]
+
    + +

    In this iloc example,

    + +
    surveys_df.iloc[2, 6]
+
    + +

    gives the output

    + +
    'F'
+
    + +

    Remember that Python indexing begins at 0. So, the index location [2, 6] +selects the element that is 3 rows down and 7 columns over in the DataFrame.

    + +

    Challenge - Range

    + +
      +

    1. What happens when you execute:

      + +
        +
      • surveys_df[0:1]
        • +
      • surveys_df[:4]
        • +
      • surveys_df[:-1]
        • +
      2. + +

    2. What happens when you call:

      + +
        +
      • surveys_df.iloc[0:4, 1:4]
        • +
      3. +
    + +

    Subsetting Data using Criteria

    + +

    We can also select a subset of our data using criteria. For example, we can +select all rows that have a year value of 2002:

    + +
    surveys_df[surveys_df.year == 2002].head()
+
    + +

    Which produces the following output:

    + +
    record_id  month  day  year  plot_id species_id  sex  hindfoot_length  weight
+33320      33321      1   12  2002        1         DM    M     38      44
+33321      33322      1   12  2002        1         DO    M     37      58
+33322      33323      1   12  2002        1         PB    M     28      45
+33323      33324      1   12  2002        1         AB  NaN    NaN     NaN
+33324      33325      1   12  2002        1         DO    M     35      29
+...
+35544      35545     12   31  2002       15         AH  NaN    NaN     NaN
+35545      35546     12   31  2002       15         AH  NaN    NaN     NaN
+35546      35547     12   31  2002       10         RM    F     15      14
+35547      35548     12   31  2002        7         DO    M     36      51
+35548      35549     12   31  2002        5        NaN  NaN    NaN     NaN
+
+[2229 rows x 9 columns]
+
    + +

    Or we can select all rows that do not contain the year 2002:

    + +
    surveys_df[surveys_df.year != 2002]
+
    + +

    We can define sets of criteria too:

    + +
    surveys_df[(surveys_df.year >= 1980) & (surveys_df.year <= 1985)]
+
    + +

    Python Syntax Cheat Sheet

    + +

    Use can use the syntax below when querying data by criteria from a DataFrame. +Experiment with selecting various subsets of the “surveys” data.

    + +
      +
    • Equals: ==
      • +
    • Not equals: !=
      • +
    • Greater than, less than: > or <
      • +
    • Greater than or equal to >=
      • +
    • Less than or equal to <=
      • +
    + +

    Challenge - Queries

    + +
      +

    1. Select a subset of rows in the surveys_df DataFrame that contain data from +the year 1999 and that contain weight values less than or equal to 8. How +many rows did you end up with? What did your neighbor get?

      2. + +

    2. (Extra) You can use the isin command in Python to query a DataFrame based upon a +list of values as follows:

      3. +
    + +
        surveys_df[surveys_df['species_id'].isin([listGoesHere])]
+
    + +

    Use the isin function to find all plots that contain particular species +in the “surveys” DataFrame. How many records contain these values?

    + +
      +

    1. (Extra) Experiment with other queries. Create a query that finds all rows with a +weight value > or equal to 0.

      2. + +

    2. (Extra) The ~ symbol in Python can be used to return the OPPOSITE of the +selection that you specify in Python. It is equivalent to is not in. +Write a query that selects all rows with sex NOT equal to ’M’ or ‘F’ in +the “surveys” data.

      3. +
    + +

    Using masks to identify a specific condition

    + +

    A mask can be useful to locate where a particular subset of values exist or +don’t exist - for example, NaN, or “Not a Number” values. To understand masks, +we also need to understand BOOLEAN objects in Python.

    + +

    Boolean values include True or False. For example,

    + +
    # Set x to 5
+x = 5
+
+# What does the code below return?
+x > 5
+
+# How about this?
+x == 5
+
    + +

    Missing Values

    + +

    Let’s try this out. Let’s identify all locations in the survey data that have +null (missing or NaN) data values. We can use the isnull method to do this. +The isnull method will compare each cell with a null value. If an element +has a null value, it will be assigned a value of True in the output object.

    + +
    pd.isnull(surveys_df).head()
+
    + +

    A snippet of the output is below:

    + +
          record_id  month    day   year plot_id species_id    sex  hindfoot_length weight
+0         False  False  False  False   False      False  False   False      True
+1         False  False  False  False   False      False  False   False      True
+2         False  False  False  False   False      False  False   False      True
+3         False  False  False  False   False      False  False   False      True
+4         False  False  False  False   False      False  False   False      True
+
+[35549 rows x 9 columns]
+
    + +

    To select the rows where there are null values, we can use +the mask as an index to subset our data as follows:

    + +
    # To select just the rows with NaN values, we can use the 'any()' method
+surveys_df[pd.isnull(surveys_df).any(axis=1)]
+
    + +

    (axis=1) is a numpy convention to specify columns.

    + +

    Note that the weight column of our DataFrame contains many null or NaN +values. Next, we will explore ways of dealing with this.

    + +

    If we look at the weight column in the surveys +data we notice that there are NaN (Not a Number) values. NaN values are undefined +values that cannot be represented mathematically. Pandas, for example, will read +an empty cell in a CSV or Excel sheet as a NaN. NaNs have some desirable properties: if we +were to average the weight column without replacing our NaNs, Python would know to skip +over those cells.

    + +
    surveys_df['weight'].mean()
+42.672428212991356
+
    + +

    Dealing with missing data values is always a challenge. It’s sometimes hard to +know why values are missing - was it because of a data entry error? Or data that +someone was unable to collect? Should the value be 0? We need to know how +missing values are represented in the dataset in order to make good decisions. +If we’re lucky, we have some metadata that will tell us more about how null +values were handled.

    + +

    For instance, in some disciplines, like Remote Sensing, missing data values are +often defined as -9999. Having a bunch of -9999 values in your data could really +alter numeric calculations. Often in spreadsheets, cells are left empty where no +data are available. Pandas will, by default, replace those missing values with +NaN. However it is good practice to get in the habit of intentionally marking +cells that have no data, with a no data value! That way there are no questions +in the future when you (or someone else) explores your data.

    + +

    Where Are the NaN’s?

    + +

    Let’s explore the NaN values in our data a bit further. Using the tools we +learned in lesson 02, we can figure out how many rows contain NaN values for +weight. We can also create a new subset from our data that only contains rows +with weight values > 0 (i.e., select meaningful weight values):

    + +
    len(surveys_df[pd.isnull(surveys_df.weight)])
+# How many rows have weight values?
+len(surveys_df[surveys_df.weight> 0])
+
    + +

    We can replace all NaN values with zeroes using the .fillna() method (after +making a copy of the data so we don’t lose our work):

    + +
    # Creat a new DataFrame using copy
+df1 = surveys_df.copy()
+
+# Fill all NaN values with 0
+df1['weight'] = df1['weight'].fillna(0)
+
    + +

    However NaN and 0 yield different analysis results. The mean value when NaN +values are replaced with 0 is different from when NaN values are simply thrown +out or ignored.

    + +
    df1['weight'].mean()
+38.751976145601844
+
    + +

    Extra Information

    + +

    We can fill NaN values with any value that we chose. The code below fills all +NaN values with a mean for all weight values.

    + +
     df1['weight'] = surveys_df['weight'].fillna(surveys_df['weight'].mean())
+
    + +

    Writing Out Data to CSV

    + +

    We’ve learned about using manipulating data to get desired outputs. But we’ve also discussed +keeping data that has been manipulated separate from our raw data. Something we might be interested +in doing is working with only the columns that have full data. First, let’s reload the data so +we’re not mixing up all of our previous manipulations.

    + +
    surveys_df = pd.read_csv("data/surveys.csv")
+
    + +

    Next, let’s drop all the rows that contain missing values. We will use the command dropna. +By default, dropna removes columns that contain missing data for even just one row.

    + +
    df_na = df.dropna()
+
+
    + +

    If you now type df_na, you should observe that the resulting DataFrame has 30676 rows +and 9 columns, much smaller than the 35549 row original.

    + +

    We can now use the to_csv command to do export a DataFrame in CSV format. Note that the code +below will by default save the data into the current working directory. We can +save it to a different folder by adding the foldername and a slash before the filename: +df1.to_csv('foldername/out.csv'). We use ‘index=False’ so that +pandas doesn’t include the index number for each line.

    + +
    # Write DataFrame to CSV
+df_na.to_csv('data_output/surveys_complete.csv', index=False)
+
    + +

    Recap

    + +

    What we’ve learned:

    + +
      +
    • How to subset and index the dataframe
      • +
    • What NaN values are, how they might be represented, and what this means for your work
      • +
    • How to replace NaN values, if desired
      • +
    • How to use to_csv to write manipulated data to a file.
      • +
    + +

    Extra

    + +

    We can run isnull on a particular column too. What does the code below do?

    + +
    # What does this do?
+empty_weights = surveys_df[pd.isnull(surveys_df['weight'])]['weight']
+print(empty_weights)
+
    + +

    Let’s take a minute to look at the statement above. We are using the Boolean +object pd.isnull(surveys_df['weight']) as an index to surveys_df. We are +asking Python to select rows that have a NaN value of weight.

    + +

    Extra Challenges - Putting it all together

    + +
      +

    1. Create a new DataFrame that only contains observations with sex values that +are not female or male. Assign each sex value in the new DataFrame to a +new value of ‘x’. Determine the number of null values in the subset.

      2. + +

    2. Create a new DataFrame that contains only observations that are of sex male +or female and where weight values are greater than 0. Create a stacked bar +plot of average weight by plot with male vs female values stacked for each +plot.

      + +
        +
      1. Count the number of missing values per column. Hint: The method .count() gives you the number of non-NA observations per column.
        2. +
      3. +
    + +

    NEXT

    + +
    +
    + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/intro/index.html b/workshops/intro_to_python/intro/index.html new file mode 100644 index 0000000..93a6c1a --- /dev/null +++ b/workshops/intro_to_python/intro/index.html @@ -0,0 +1,586 @@ + + + + + + + Monash Data Fluency | 01 Programming in Python (Introduction) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    +
    + + +
    + +
    +
    +
    +

    + WORKSHOPS +

    +

    + 01 Programming in Python (Introduction) +

    +
    +
    + + +

    The Basics of Python

    + +

    Python is a general purpose programming language that supports rapid development +of scripts and applications.

    + +

    Python’s main advantages:

    + +
      +
    • Open Source software, supported by Python Software Foundation
      • +
    • Available on all platforms (ie. Windows, Linux and MacOS)
      • +
    • It is a general-purpose programming language
      • +
    • Supports multiple programming paradigms
      • +
    • Very large community with a rich ecosystem of third-party packages
      • +
    + +

    Interpreter

    + +

    Python is an interpreted language which can be used in two ways:

    + +
      +
    • “Interactive” Mode: It functions like an “advanced calculator” Executing +one command at a time:
      • +
    + +
    user:host:~$ python
+Python 3.5.1 (default, Oct 23 2015, 18:05:06)
+[GCC 4.8.3] on linux2
+Type "help", "copyright", "credits" or "license" for more information.
+>>> 2 + 2
+4
+>>> print("Hello World")
+Hello World
+
    + +
      +
    • “Scripting” Mode: Executing a series of “commands” saved in text file, +usually with a .py extension after the name of your file:
      • +
    + +
    user:host:~$ python my_script.py
+Hello World
+
    + +

    Introduction to Python built-in data types

    + +

    Strings, integers and floats

    + +

    One of the most basic things we can do in Python is assign values to variables:

    + +
    text = "Data Fluency"  # An example of a string
+number = 42  # An example of an integer
+pi_value = 3.1415  # An example of a float
+
    + +

    Here we’ve assigned data to the variables text, number and pi_value, +using the assignment operator =. To review the value of a variable, we +can type the name of the variable into the Jupyter notebook and press Shift and Enter:

    + +
    text
+## Which Returns
+"Data Fluency"
+
    + +

    Everything in Python has a type. To get the type of something, we can pass it +to the built-in function type:

    + +
    type(text)
+ str
+type(number)
+ int
+type(6.02)
+ float
+
    + +

    The variable text is of type str, short for “string”. Strings hold +sequences of characters, which can be letters, numbers, punctuation +or more exotic forms of text (even emoji!).

    + +

    We can also see the value of something using another built-in function, print:

    + +
    
+print(text)
+Data Fluency
+
+print(11)
+11
+
    + +

    This may seem redundant, but in fact it’s the only way to display output in a script:

    + +

    example.py

    + +
    # A Python script file
+# Comments in Python start with #
+# The next line assigns the string "Data Carpentry" to the variable "text".
+text = "Data Fluency"
+# The next line does nothing!
+text
+# The next line uses the print function to print out the value we assigned to "text"
+print(text)
+
    + +

    Running the script

    + +
    $ python example.py
+Data Fluency
+
    + +

    Notice that “Data Fluency” is printed only once.

    + +

    Tip: print and type are built-in functions in Python. Later in this +lesson, we will introduce methods and user-defined functions. The Python +documentation is excellent for reference on the differences between them.

    + +
    help(print)
+
    + +

    Will give the output

    + +
    Help on built-in function print in module builtins:
+
+print(...)
+    print(value, ..., sep=' ', end='\n', file=sys.stdout, flush=False)
+    
+    Prints the values to a stream, or to sys.stdout by default.
+    Optional keyword arguments:
+    file:  a file-like object (stream); defaults to the current sys.stdout.
+    sep:   string inserted between values, default a space.
+    end:   string appended after the last value, default a newline.
+    flush: whether to forcibly flush the stream.
+
    + +

    Operators

    + +

    We can perform mathematical calculations in Python using the basic operators + +, -, /, *, %:

    + +
    >>> 2 + 2  # Addition
+4
+>>> 6 * 7  # Multiplication
+42
+>>> 2 ** 16  # Power
+65536
+>>> 13 % 5  # Modulo
+3
+
    + +

    We can also use comparison and logic operators: +<, >, ==, !=, <=, >= and statements of identity such as +and, or, not. The data type returned by this is +called a boolean.

    + +
    >>> 3 > 4
+False
+>>> True and True
+True
+>>> True or False
+True
+
    + +

    Sequential types: Lists and Tuples

    + +

    Lists

    + +

    Lists are a common data structure to hold an ordered sequence of +elements. Each element can be accessed by an index. Note that Python +indexes start with 0 instead of 1:

    + +
    >>> numbers = [1, 2, 3]
+>>> numbers[0]
+1
+
    + +

    A for loop can be used to access the elements in a list or other Python data structure one at a time. We will learn about loops in other lesson.

    + +
    >>> for num in numbers:
+...     print(num)
+...
+1
+2
+3
+
    + +

    Indentation is very important in Python. Note that the second line in the +example above is indented. Just like three chevrons >>> indicate an +interactive prompt in Python, the three dots ... are Python’s prompt for +multiple lines. This is Python’s way of marking a block of code. [Note: you +do not type >>> or ....]

    + +

    To add elements to the end of a list, we can use the append method. Methods +are a way to interact with an object (a list, for example). We can invoke a +method using the dot . followed by the method name and a list of arguments +in parentheses. Let’s look at an example using append:

    + +
    >>> numbers.append(4)
+>>> print(numbers)
+[1, 2, 3, 4]
+>>>
+
    + +

    To find out what methods are available for an +object, we can use the built-in help command:

    + +
    help(numbers)
+
+Help on list object:
+
+class list(object)
+ |  list() -> new empty list
+ |  list(iterable) -> new list initialized from iterable's items
+ ...
+
    + +

    Tuples

    + +

    A tuple is similar to a list in that it’s an ordered sequence of elements. +However, tuples can not be changed once created (they are “immutable”). Tuples +are created by placing comma-separated values inside parentheses ().

    + +
    # Tuples use parentheses
+a_tuple= (1, 2, 3)
+another_tuple = ('blue', 'green', 'red')
+# Note: lists use square brackets
+a_list = [1, 2, 3]
+
    + +

    Challenge - Tuples

    + +
      +
    1. What happens when you type a_tuple[2]=5 vs a_list[1]=5 ?
      2. +
    2. Type type(a_tuple) into python - what is the object type?
      3. +
    + +

    Dictionaries

    + +

    A dictionary is a container that holds pairs of objects - keys and values.

    + +
    >>> translation = {'one': 1, 'two': 2}
+>>> translation['one']
+1
+
    + +

    Dictionaries work a lot like lists - except that you index them with keys. +You can think about a key as a name for or a unique identifier for a set of values +in the dictionary. Keys can only have particular types - they have to be +“hashable”. Strings and numeric types are acceptable, but lists aren’t.

    + +
    >>> rev = {1: 'one', 2: 'two'}
+>>> rev[1]
+'one'
+>>> bad = {[1, 2, 3]: 3}
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: unhashable type: 'list'
+
    + +

    In Python, a “Traceback” is an multi-line error block printed out for the +user.

    + +

    To add an item to the dictionary we assign a value to a new key:

    + +
    >>> rev = {1: 'one', 2: 'two'}
+>>> rev[3] = 'three'
+>>> rev
+{1: 'one', 2: 'two', 3: 'three'}
+
    + +

    Challenge - Can you do reassignment in a dictionary?

    + +
      +

    1. First check what rev is right now (remember rev is the name of our dictionary). Type: rev

      2. + +

    2. Try to reassign the second value (in the key value pair) so that it no longer reads “two” but instead reads “apple-sauce”.

      3. + +

    3. Now display rev again to see if it has changed.

      4. +
    + +

    It is important to note that dictionaries are “unordered” and do not remember +the sequence of their items (i.e. the order in which key:value pairs were +added to the dictionary). Because of this, the order in which items are +returned from loops over dictionaries might appear random and can even change +with time.

    + +

    NEXT

    + +
    +
    + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/loops/index.html b/workshops/intro_to_python/loops/index.html new file mode 100644 index 0000000..bad27fe --- /dev/null +++ b/workshops/intro_to_python/loops/index.html @@ -0,0 +1,604 @@ + + + + + + + Monash Data Fluency | 04 Automation with Loops + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    +
    + + +
    + +
    +
    +
    +

    + WORKSHOPS +

    +

    + 04 Automation with Loops +

    +
    +
    + + +

    An example task that we might want to repeat is printing each character in a +word on a line of its own.

    + +
    word = 'lead'
+
    + +

    We can access a character in a string using its index. For example, we can get the first +character of the word 'lead', by using word[0]. One way to print each character is to use +four print statements:

    + +
    print(word[0])
+print(word[1])
+print(word[2])
+print(word[3])
+
    + +

    Gives output

    + +
    l
+e
+a
+d
+
    + +

    This is a bad approach for two reasons:

    + +
      +

    1. It doesn’t scale: +if we want to print the characters in a string that’s hundreds of letters long, +we’d be better off just typing them in.

      2. + +

    2. It’s fragile: +if we give it a longer string, +it only prints part of the data, +and if we give it a shorter one, +it produces an error because we’re asking for characters that don’t exist.

      3. +
    + +
    word = 'tin'
+print(word[0])
+print(word[1])
+print(word[2])
+print(word[3])
+
+
    + +

    Gives back

    + +
    t
+i
+n
+---------------------------------------------------------------------------
+IndexError                                Traceback (most recent call last)
+<ipython-input-3-7974b6cdaf14> in <module>()
+      3 print(word[1])
+      4 print(word[2])
+----> 5 print(word[3])
+
+IndexError: string index out of range
+
    + +

    Here’s a better approach:

    + +
    word = 'lead'
+for char in word:
+    print(char)
+
+
    + +

    Would Produce

    + +
    l
+e
+a
+d
+
    + +

    This is shorter — certainly shorter than something that prints every character in a hundred-letter string — and +more robust as well:

    + +
    word = 'oxygen'
+for char in word:
+    print(char)
+
    + +

    Output

    + +
    o
+x
+y
+g
+e
+n
+
    + +

    The improved version uses a for loop to repeat an operation — in this case, printing — once for each thing in a sequence. +The general form of a loop is:

    + +
    for variable in collection:
+    do things with variable
+
    + +

    Using the oxygen example above, the loop might look like this:

    + +

    loop_image

    + +

    where each character (char) in the variable word is looped through and printed one character after another. +The numbers in the diagram denote which loop cycle the character was printed in (1 being the first loop, and 6 being the final loop).

    + +

    We can call the loop variable anything we like, +but there must be a colon at the end of the line starting the loop, and we must indent anything we want to run inside the loop. Unlike many other languages, there is no command to signify the end of the loop body (e.g. end for); what is indented after the for statement belongs to the loop.

    + +

    What’s in a name?

    + +

    In the example above, the loop variable was given the name char as a mnemonic; it is short for ‘character’. +We can choose any name we want for variables. We might just as easily have chosen the name banana for the loop variable, as long as we use the same name when we invoke the variable inside the loop:

    + +
    word = 'oxygen'
+for banana in word:
+    print(banana)
+
    + +

    Output

    + +
    o
+x
+y
+g
+e
+n
+
    + +

    It is a good idea to choose variable names that are meaningful, otherwise it would be more difficult to understand what the loop is doing.

    + +

    Here’s another loop that repeatedly updates a variable:

    + +
    length = 0
+for vowel in 'aeiou':
+    length = length + 1
+print('There are', length, 'vowels')
+
    + +
    There are 5 vowels
+
    + +

    It’s worth tracing the execution of this little program step by step. +Since there are five characters in 'aeiou', +the statement on line 3 will be executed five times. +The first time around, +length is zero (the value assigned to it on line 1) +and vowel is 'a'. +The statement adds 1 to the old value of length, +producing 1, +and updates length to refer to that new value. +The next time around, +vowel is 'e' and length is 1, +so length is updated to be 2. +After three more updates, +length is 5; +since there is nothing left in 'aeiou' for Python to process, +the loop finishes +and the print statement on line 4 tells us our final answer.

    + +

    Note that a loop variable is just a variable that’s being used to record progress in a loop. +It still exists after the loop is over, +and we can re-use variables previously defined as loop variables as well:

    + +
    letter = 'z'
+for letter in 'abc':
+    print(letter)
+print('after the loop, letter is', letter)
+
    + +

    Output

    + +
    a
+b
+c
+after the loop, letter is c
+
    + +

    Note also that finding the length of a string is such a common operation that Python actually has a built-in function to do it called len:

    + +
    print(len('aeiou'))
+
    + +

    Output

    + +
    5
+
    + +

    len is much faster than any function we could write ourselves, +and much easier to read than a two-line loop; +it will also give us the length of many other things that we haven’t met yet, +so we should always use it when we can.

    + +

    From 1 to N

    + +

    Python has a built-in function called range that creates a sequence of numbers. range can +accept 1, 2, or 3 parameters.

    + +
      +
    • If one parameter is given, range creates an array of that length, +starting at zero and incrementing by 1. +For example, range(3) produces the numbers 0, 1, 2.
      • +
    • If two parameters are given, range starts at +the first and ends just before the second, incrementing by one. +For example, range(2, 5) produces 2, 3, 4.
      • +
    • If range is given 3 parameters, +it starts at the first one, ends just before the second one, and increments by the third one. +For exmaple range(3, 10, 2) produces 3, 5, 7, 9.
      • +
    + +

    Challenge

    + +

    Using range, +write a loop that uses range to print the first 3 natural numbers:

    + +
    1
+2
+3
+
    + +

    Solution

    + +
    for i in range(1, 4):
+   print(i)
+
    + +

    Computing Powers With Loops

    + +

    Exponentiation is built into Python:

    + +
    print(5 ** 3)
+
    + +
    125
+
    + +

    Write a loop that calculates the same result as 5 ** 3 using +multiplication (and without exponentiation).

    + +

    Solution

    + +
    result = 1
+for i in range(0, 3):
+   result = result * 5
+print(result)
+
    + +

    Reverse a String

    + +

    Knowing that two strings can be concatenated using the + operator, +write a loop that takes a string +and produces a new string with the characters in reverse order, +so 'Newton' becomes 'notweN'.

    + +

    Solution

    + +
    newstring = ''
+oldstring = 'Newton'
+for char in oldstring:
+   newstring = char + newstring
+print(newstring)
+
    + +

    Computing the Value of a Polynomial

    + +

    The built-in function enumerate takes a sequence (e.g. a list) and generates a +new sequence of the same length. Each element of the new sequence is a pair composed of the index +(0, 1, 2,…) and the value from the original sequence:

    + +
    for i, x in enumerate(xs):
+    # Do something with i and x
+
    + +

    The code above loops through xs, assigning the index to i and the value to x.

    + +

    Suppose you have encoded a polynomial as a list of coefficients in +the following way: the first element is the constant term, the +second element is the coefficient of the linear term, the third is the +coefficient of the quadratic term, etc.

    + +
    x = 5
+cc = [2, 4, 3]
+
    + +
    y = cc[0] * x**0 + cc[1] * x**1 + cc[2] * x**2
+y = 97
+
    + +

    Write a loop using enumerate(cc) which computes the value y of any +polynomial, given x and cc.

    + +

    Solution

    + +
    y = 0
+for i, c in enumerate(cc):
+    y = y + x**i * c
+
    + +

    NEXT

    + +
    +
    + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/page/1/index.html b/workshops/intro_to_python/page/1/index.html new file mode 100644 index 0000000..e87a582 --- /dev/null +++ b/workshops/intro_to_python/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_python/ \ No newline at end of file diff --git a/workshops/intro_to_python/plotting/index.html b/workshops/intro_to_python/plotting/index.html new file mode 100644 index 0000000..e14857b --- /dev/null +++ b/workshops/intro_to_python/plotting/index.html @@ -0,0 +1,523 @@ + + + + + + + Monash Data Fluency | 05 Making Plots With ggplot + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    +
    + + +
    + +
    +
    +
    +

    + WORKSHOPS +

    +

    + 05 Making Plots With ggplot +

    +
    +
    + + +

    Introduction

    + +

    Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it. +Strictly speaking plotnine is just another implementation of The Grammar of Graphics by Leland Wilkinson, which in theory could go on it own direction and diverge away from an R ggplot .

    + +

    The Grammar of Graphics

    + +
    +

    statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars) +Faceting can be used to generate the same plot for different subsets of the dataset

    +
    + +

    These are basic building blocks according to the grammar of graphics:

    + +
      +
    • data The data + a set of aesthetic mappings that describing variables mapping
      • +
    • geom Geometric objects, represent what you actually see on the plot: points, lines, polygons, etc.
      • +
    • stats Statistical transformations, summarise data in many useful ways.
      • +
    • scale The scales map values in the data space to values in an aesthetic space
      • +
    • coord A coordinate system, describes how data coordinates are mapped to the plane of the graphic.
      • +
    • facet A faceting specification describes how to break up the data into subsets for plotting individual set
      • +
    + +

    Let’s explore those in details

    + +

    Plotting in ggplot style

    + +

    Let set up our working environment with necessary libraries and also load our csv file into data frame called survs_df,

    + +
    import numpy as np
+import pandas as pd
+from plotnine import *
+
+%matplotlib inline
+survs_df = pd.read_csv('data/surveys.csv').dropna()
+
    + +

    Producing a plot with ggplot, we must give three things:

    + +
      +
    1. A data frame containing our data.
      2. +
    2. How the columns of the data frame can be translated into positions, colors, sizes, and shapes of graphical elements (“aesthetics”).
      3. +
    3. The actual graphical elements to display (“geometric objects”).
      4. +
    + +

    Introduction to plotting

    + +
    ggplot(survs_df, aes('weight', 'hindfoot_length')) + geom_point()
+
    + +

    Lets see if we can also include information about species and year

    + +
    ggplot(survs_df, aes('weight', 'hindfoot_length',
+    size = 'year')) + geom_point()
+
    + +
    ggplot(survs_df, aes('weight', 'hindfoot_length', 
+    size = 'year', color = 'species_id')) + geom_point()
+
    + +

    We can do simple counting plot, to see how many observation (data points) we have for each year for example

    + +
    ggplot(survs_df, aes('year')) + \
+    geom_bar(stat = 'count')
+
    + +

    Let’s now also color by species to see how many observation we have per species in a given year

    + +
    ggplot(survs_df, aes('year', fill = 'species_id')) + \
+    geom_bar(stat = 'count')
+
    + +

    Challenges

    + +
    +

    Is there a better visualisation for comparing weight across years? The plot should have categorical data on x axis and continuous on y axis +Plot a boxplot of hindfoot_length across different species (species_id column)

    +
    + +

    More geom types

    + +
    ggplot(survs_df, aes('year', 'weight')) + \
+    geom_boxplot()
+
    + +

    Why are we not seeing mulitple boxplots, one for each year? +This is because year variable is continues in our data frame, but for the purpose we want it to be categorical.

    + +
    survs_df['year_fact'] = pd.Series(survs_df['year'], dtype = "category")
+
+ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot()
+
    + +
    ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_violin()
+
    + +

    To save an image for later

    + +
    plt1 = ggplot(survs_df, aes('year_fact', 'weight')) + \
+           geom_boxplot() + \
+           xlab("Years") + \
+           ylab("Weight log2(kg)") + \
+           ggtitle("Boxplots, summary of species wieght in each year")
+
+ggsave(filename = "plot1.png", \
+       plot = plt1, \
+       device = 'png', \
+       dpi = 300, \
+       height = 25, \
+        width = 25)
+
    + +

    Challenges

    + +
    +

    Can you log2 transform weight and plot “normalised” boxplot. Hint: use np.log2() function and name new column weight_log

    +
    + +

    Also will log2 transforming make this data visualisation better?

    + +
    survs_df['weight_log'] = np.log2(survs_df['weight'])
+    
+ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    xlab("Years") + \
+    ylab("Weight log2(kg)") + \
+    ggtitle("Boxplots, summary of species wieght in each year")
+
    + +

    Faceting

    + +

    ggplot has a special technique called faceting that allows to split one plot +into multiple plots based on a factor included in the dataset. We will use it to +make one plot for a time series for each species.

    + +
    ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot() + \
+    facet_wrap("~sex")
+
+
    + +
    ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") 
+
    + +

    Theming

    + +
    ggplot(survs_df, aes('year_fact', 'weight')) + \
+    geom_boxplot() + \
+    theme_bw()
+
    + +
    ggplot(survs_df, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") + \
+    theme_xkcd()
+
    + +

    Extra bits 1

    + +

    Let’s try to bin years into decades, which could be crude but might gives simple images to look at.

    + +
    bins = [(survs_df['year'] < 1980),
+        (survs_df['year'] < 1990),
+        (survs_df['year'] < 2000),
+        (survs_df['year'] >= 2000)]
+
+labels = ['70s', '80s', '90s', 'Z']
+
+survs_df['year_bins'] = np.select(bins, labels)
+
    + +
    plt2 = ggplot(survs_df, aes('year_bins', 'weight_log')) + \
+           geom_boxplot()
+plt2 
+
    + +
    plt2 = ggplot(survs_df, aes('year_bins', 'weight_log')) + \
+           geom_boxplot() + \
+           theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+           facet_wrap("~species_id") 
+plt2 
+
    + +

    Extra bits 2

    + +

    This is a different way to look at your data

    + +
    ggplot(survs_df, aes("year_fact", "weight")) + \
+    stat_summary(fun_y = np.mean, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+    
+ggplot(survs_df, aes("year_fact", "weight")) + \
+    stat_summary(fun_y = np.median, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+    
+ggplot(survs_df, aes("year_fact", "weight_log")) + \
+    stat_summary(fun_y = np.mean, fun_ymin=np.min, fun_ymax=np.max) + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+
    + +

    Extra bits 3

    + +

    It is very informative to look across years, year by year, it becomes apparent straight away that for some species there is a lot of data is missing.And going forward, maybe, you’d want to filter those low counting species. (this is after faceting by species_id in section Extra bits 1).

    + +
    #survs_cnts_df = survs_df.groupby(['species_id'], as_index=False).count().sort_values(['record_id'])
+survs_cnts_df = survs_df[['species_id']].groupby(['species_id']).size().reset_index()
+species_to_remove = list(survs_cnts_df[survs_cnts_df['record_id'] < 200].species_id)
+survs_df_filt = survs_df[survs_df['species_id'].isin(species_to_remove) == False]
+
    + +
    ggplot(survs_df_filt, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1))
+
+ggplot(survs_df_filt, aes('year_fact', 'weight_log')) + \
+    geom_boxplot() + \
+    theme(axis_text_x = element_text(angle = 90, hjust = 1)) + \
+    facet_wrap("~species_id") 
+
    + +

    NEXT

    + +
    +
    + +
    + +
    + + + + + + diff --git a/workshops/intro_to_python/working_with_data/index.html b/workshops/intro_to_python/working_with_data/index.html new file mode 100644 index 0000000..d5266fe --- /dev/null +++ b/workshops/intro_to_python/working_with_data/index.html @@ -0,0 +1,923 @@ + + + + + + + Monash Data Fluency | 02 Data Analysis in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    +
    + + +
    + +
    +
    +
    +

    + WORKSHOPS +

    +

    + 02 Data Analysis in Python +

    +
    +
    + + +

    Working With Pandas DataFrames

    + +

    We can automate the process of performing data manipulations in Python. It’s efficient to spend time +building the code to perform these tasks because once it’s built, we can use it +over and over on different datasets that use a similar format. This makes our +methods easily reproducible. We can also easily share our code with colleagues +and they can replicate the same analysis.

    + +

    The Dataset

    + +

    For this lesson, we will be using the Portal Teaching data, a subset of the data +from Ernst et al +Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA

    + +

    We will be using this dataset, which is stored under your login/account under the Files tab.

    + +

    We are studying the species and weight of animals caught in plots in our study +area. The dataset is stored as a .csv file: each row holds information for a +single animal, and the columns represent:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ColumnDescription
    record_idUnique id for the observation
    monthmonth of observation
    dayday of observation
    yearyear of observation
    plot_idID of a particular plot
    species_id2-letter code
    sexsex of animal (“M”, “F”)
    hindfoot_lengthlength of the hindfoot in mm
    weightweight of the animal in grams
    + +

    The first few rows of our first file look like this:

    + +
    record_id,month,day,year,plot_id,species_id,sex,hindfoot_length,weight
+1,7,16,1977,2,NL,M,32,
+2,7,16,1977,3,NL,M,33,
+3,7,16,1977,2,DM,F,37,
+4,7,16,1977,7,DM,M,36,
+5,7,16,1977,3,DM,M,35,
+6,7,16,1977,1,PF,M,14,
+7,7,16,1977,2,PE,F,,
+8,7,16,1977,1,DM,M,37,
+9,7,16,1977,1,DM,F,34,
+
    + +
    + +

    About Libraries

    + +

    A library in Python contains a set of tools (called functions) that perform +tasks on our data. Importing a library is like getting a piece of lab equipment +out of a storage locker and setting it up on the bench for use in a project. +Once a library is set up, it can be used or called to perform many tasks.

    + +

    Pandas in Python

    + +

    One of the best options for working with tabular data in Python is to use the +Python Data Analysis Library (a.k.a. Pandas). The +Pandas library provides data structures, produces high quality plots with +matplotlib and integrates nicely with other libraries +that use NumPy (which is another Python library) arrays.

    + +

    Python doesn’t load all of the libraries available to it by default. We have to +add an import statement to our code in order to use library functions. To import +a library, we use the syntax import libraryName. If we want to give the +library a nickname to shorten the command, we can add as nickNameHere. An +example of importing the pandas library using the common nickname pd is below.

    + +
    import pandas as pd
+
    + +

    Each time we call a function that’s in a library, we use the syntax +LibraryName.FunctionName. Adding the library name with a . before the +function name tells Python where to find the function. In the example above, we +have imported Pandas as pd. This means we don’t have to type out pandas each +time we call a Pandas function.

    + +

    Reading CSV Data Using Pandas

    + +

    We will begin by locating and reading our survey data which are in CSV format. CSV stands for Comma-Separated Values and is a common way store formatted data. Other symbols my also be used, so you might see tab-separated, colon-separated or space separated files. It is quite easy to replace one separator with another, to match your application. The first line in the file often has headers to explain what is in each column. CSV (and other separators) make it easy to share data, and can be imported and exported from many applications, including Microsoft Excel. For mo

    + +

    We can use Pandas’ read_csv function to pull the file directly into a +DataFrame.

    + +

    So What’s a DataFrame?

    + +

    A DataFrame is a 2-dimensional data structure that can store data of different +types (including characters, integers, floating point values, factors and more) +in columns. It is similar to a spreadsheet or an SQL table or the data.frame in +R. A DataFrame always has an index (0-based). An index refers to the position of +an element in the data structure.

    + +
    # Note that pd.read_csv is used because we imported pandas as pd
+pd.read_csv("data/surveys.csv")
+
    + +

    The above command yields the output below:

    + +
    record_id  month  day  year  plot_id species_id sex  hindfoot_length  weight
+0          1      7   16  1977        2         NL   M               32   NaN
+1          2      7   16  1977        3         NL   M               33   NaN
+2          3      7   16  1977        2         DM   F               37   NaN
+3          4      7   16  1977        7         DM   M               36   NaN
+4          5      7   16  1977        3         DM   M               35   NaN
+...
+35544      35545     12   31  2002       15     AH  NaN              NaN  NaN
+35545      35546     12   31  2002       15     AH  NaN              NaN  NaN
+35546      35547     12   31  2002       10     RM    F               15   14
+35547      35548     12   31  2002        7     DO    M               36   51
+35548      35549     12   31  2002        5     NaN  NaN             NaN  NaN
+
+[35549 rows x 9 columns]
+
    + +

    We can see that there were 33,549 rows parsed. Each row has 9 +columns. The first column is the index of the DataFrame. The index is used to +identify the position of the data, but it is not an actual column of the DataFrame. +It looks like the read_csv function in Pandas read our file properly. However, +we haven’t saved any data to memory so we can work with it.We need to assign the +DataFrame to a variable. Remember that a variable is a name for a value, such as x, +or data. We can create a new object with a variable name by assigning a value to it using =.

    + +

    Let’s call the imported survey data surveys_df:

    + +
    surveys_df = pd.read_csv("data/surveys.csv")
+
    + +

    Notice when you assign the imported DataFrame to a variable, Python does not +produce any output on the screen. We can view the value of the surveys_df +object by typing its name into the Python command prompt.

    + +
    surveys_df
+
    + +

    which prints contents like above.

    + +

    Note: if the output is too wide to print on your narrow terminal window, you may see something +slightly different as the large set of data scrolls past. You may see simply the last column +of data:

    + +
    17        NaN  
+18        NaN  
+19        NaN  
+20        NaN  
+21        NaN  
+22        NaN  
+23        NaN  
+24        NaN  
+25        NaN  
+26        NaN  
+27        NaN  
+28        NaN  
+29        NaN  
+...       ...  
+35519    36.0  
+35520    48.0  
+35521    45.0  
+35522    44.0  
+35523    27.0  
+35524    26.0  
+35525    24.0  
+35526    43.0  
+35527     NaN  
+35528    25.0  
+35529     NaN  
+35530     NaN  
+35531    43.0  
+35532    48.0  
+35533    56.0  
+35534    53.0  
+35535    42.0  
+35536    46.0  
+35537    31.0  
+35538    68.0  
+35539    23.0  
+35540    31.0  
+35541    29.0  
+35542    34.0  
+35543     NaN  
+35544     NaN  
+35545     NaN  
+35546    14.0  
+35547    51.0  
+35548     NaN  
+
+[35549 rows x 9 columns]
+
    + +

    Never fear, all the data is there, if you scroll up. Selecting just a few rows, so it is +easier to fit on one window, you can see that pandas has neatly formatted the data to fit +our screen:

    + +
    
+>>> surveys_df.head() # The head() function displays the first several lines of a file. It
+		      # is discussed below.
+   record_id  month  day  year  plot_id species_id sex  hindfoot_length  \
+5          6      7   16  1977        1         PF   M             14.0   
+6          7      7   16  1977        2         PE   F              NaN   
+7          8      7   16  1977        1         DM   M             37.0   
+8          9      7   16  1977        1         DM   F             34.0   
+9         10      7   16  1977        6         PF   F             20.0   
+
+   weight  
+5     NaN  
+6     NaN  
+7     NaN  
+8     NaN  
+9     NaN  
+
    + +

    Exploring Our Species Survey Data

    + +

    Again, we can use the type function to see what kind of thing surveys_df is:

    + +
    type(surveys_df)
+
+## Returns
+pandas.core.frame.DataFrame
+
    + +

    As expected, it’s a DataFrame (or, to use the full name that Python uses to refer +to it internally, a pandas.core.frame.DataFrame).

    + +

    What kind of things does surveys_df contain? DataFrames have an attribute +called dtypes that answers this:

    + +
    surveys_df.dtypes
+
+## Returns
+record_id            int64
+month                int64
+day                  int64
+year                 int64
+plot_id              int64
+species_id          object
+sex                 object
+hindfoot_length    float64
+weight             float64
+dtype: object
+
    + +

    All the values in a column have the same type. For example, months have type +int64, which is a kind of integer. Cells in the month column cannot have +fractional values, but the weight and hindfoot_length columns can, because they +have type float64. The object type doesn’t have a very helpful name, but in +this case it represents strings (such as ’M’ and ‘F’ in the case of sex).

    + +

    Useful Ways to View DataFrame objects in Python

    + +

    There are many ways to summarize and access the data stored in DataFrames, +using attributes and methods provided by the DataFrame object.

    + +

    To access an attribute, use the DataFrame object name followed by the attribute +name df_object.attribute. Using the DataFrame surveys_df and attribute +columns, an index of all the column names in the DataFrame can be accessed +with surveys_df.columns.

    + +

    Methods are called in a similar fashion using the syntax df_object.method(). +As an example, surveys_df.head() gets the first few rows in the DataFrame +surveys_df using the head() method. With a method, we can supply extra +information in the parens to control behaviour.

    + +

    Let’s look at the data using these.

    + +

    Challenge - DataFrames

    + +

    Using our DataFrame surveys_df, try out the attributes & methods below to see +what they return.

    + +
      +
    1. surveys_df.columns
      2. +
    2. surveys_df.shape Take note of the output of shape - what format does it +return the shape of the DataFrame in? HINT: More on tuples, here.
      3. +
    3. surveys_df.head() Also, what does surveys_df.head(15) do?
      4. +
    4. surveys_df.tail()
      5. +
    + +

    Calculating Statistics From Data In A Pandas DataFrame

    + +

    We’ve read our data into Python. Next, let’s perform some quick summary +statistics to learn more about the data that we’re working with. We might want +to know how many animals were collected in each plot, or how many of each +species were caught. We can perform summary stats quickly using groups. But +first we need to figure out what we want to group by.

    + +

    Let’s begin by exploring our data:

    + +
    # Look at the column names
+surveys_df.columns
+
    + +

    which returns:

    + +
    Index(['record_id', 'month', 'day', 'year', 'plot_id', 'species_id', 'sex',
+       'hindfoot_length', 'weight'],
+      dtype='object')
+
    + +

    Let’s get a list of all the species. The pd.unique function tells us all of +the unique values in the species_id column.

    + +
    pd.unique(surveys_df['species_id'])
+
    + +

    which returns:

    + +
    array(['NL', 'DM', 'PF', 'PE', 'DS', 'PP', 'SH', 'OT', 'DO', 'OX', 'SS',
+       'OL', 'RM', nan, 'SA', 'PM', 'AH', 'DX', 'AB', 'CB', 'CM', 'CQ',
+       'RF', 'PC', 'PG', 'PH', 'PU', 'CV', 'UR', 'UP', 'ZL', 'UL', 'CS',
+       'SC', 'BA', 'SF', 'RO', 'AS', 'SO', 'PI', 'ST', 'CU', 'SU', 'RX',
+       'PB', 'PL', 'PX', 'CT', 'US'], dtype=object)
+
    + +

    Challenge - Statistics

    + +
      +

    1. Create a list of unique plot ID’s found in the surveys data. Call it +plot_names. How many unique plots are there in the data? How many unique +species are in the data?

      2. + +

    2. What is the difference between len(plot_names) and surveys_df['plot_id'].nunique()?

      3. +
    + +

    Groups in Pandas

    + +

    We often want to calculate summary statistics grouped by subsets or attributes +within fields of our data. For example, we might want to calculate the average +weight of all individuals per plot.

    + +

    We can calculate basic statistics for all records in a single column using the +syntax below:

    + +
    surveys_df['weight'].describe()
+
    + +

    gives output

    + +
    count    32283.000000
+mean        42.672428
+std         36.631259
+min          4.000000
+25%         20.000000
+50%         37.000000
+75%         48.000000
+max        280.000000
+Name: weight, dtype: float64
+
    + +

    We can also extract one specific metric if we wish:

    + +
    surveys_df['weight'].min()
+surveys_df['weight'].max()
+surveys_df['weight'].mean()
+surveys_df['weight'].std()
+surveys_df['weight'].count()
+
    + +

    But if we want to summarize by one or more variables, for example sex, we can +use Pandas’ .groupby method. Once we’ve created a groupby DataFrame, we +can quickly calculate summary statistics by a group of our choice.

    + +
    # Group data by sex
+grouped_data = surveys_df.groupby('sex')
+
    + +

    The pandas function describe will return descriptive stats including: mean, +median, max, min, std and count for a particular column in the data. Pandas’ +describe function will only return summary values for columns containing +numeric data.

    + +
    # Summary statistics for all numeric columns by sex
+grouped_data.describe()
+# Provide the mean for each numeric column by sex
+grouped_data.mean()
+
    + +

    grouped_data.mean() OUTPUT:

    + +
            record_id     month        day         year    plot_id  \
+sex                                                              
+F    18036.412046  6.583047  16.007138  1990.644997  11.440854   
+M    17754.835601  6.392668  16.184286  1990.480401  11.098282   
+
+     hindfoot_length     weight  
+sex                              
+F          28.836780  42.170555  
+M          29.709578  42.995379  
+
+
    + +

    The groupby command is powerful in that it allows us to quickly generate +summary stats.

    + +

    Challenge - Summary Data

    + +
      +
    1. How many recorded individuals are female F and how many male M
      2. +
    2. What happens when you group by two columns using the following syntax and +then grab mean values: + +
        +
      • grouped_data2 = surveys_df.groupby(['plot_id','sex'])
        • +
      • grouped_data2.mean()
        • +
      3. +
    3. Summarize weight values for each plot in your data. HINT: you can use the +following syntax to only create summary statistics for one column in your data +by_plot['weight'].describe()
      4. +
    + +

    Did you get #3 right?

    + +

    A Snippet of the Output from challenge 3 looks like:

    + +
    	plot
+	1     count    1903.000000
+	      mean       51.822911
+	      std        38.176670
+	      min         4.000000
+	      25%        30.000000
+	      50%        44.000000
+	      75%        53.000000
+	      max       231.000000
+         ...
+
    + +

    Quickly Creating Summary Counts in Pandas

    + +

    Let’s next count the number of samples for each species. We can do this in a few +ways, but we’ll use groupby combined with a count() method.

    + +
    # Count the number of samples by species
+species_counts = surveys_df.groupby('species_id')['record_id'].count()
+print(species_counts)
+
    + +

    Or, we can also count just the rows that have the species “DO”:

    + +
    surveys_df.groupby('species_id')['record_id'].count()['DO']
+
    + +

    Basic Math Functions

    + +

    If we wanted to, we could perform math on an entire column of our data. For +example let’s multiply all weight values by 2. A more practical use of this might +be to normalize the data according to a mean, area, or some other value +calculated from our data.

    + +
    
+# Multiply all weight values by 2 but does not change the original weight data
+
+surveys_df['weight']*2
+
+
    + +

    Quick & Easy Plotting Data Using Pandas

    + +

    We can plot our summary stats using Pandas, too.

    + +
    
+## To make sure figures appear inside Jupyter Notebook
+%matplotlib inline
+
+# Create a quick bar chart
+species_counts.plot(kind='bar');
+
    + +

    Weight by Species Plot

    + +

    Weight by species plot

    + +

    We can also look at how many animals were captured in each plot:

    + +
    total_count = surveys_df.groupby('plot_id')['record_id'].nunique()
+# Let's plot that too
+total_count.plot(kind='bar');
+
    + +

    Extra Plotting Challenge

    + +
      +

    1. Create a plot of average weight across all species per plot.

      2. + +

    2. Create a plot of total males versus total females for the entire dataset.

      3. + +

    3. Create a stacked bar plot, with weight on the Y axis, and the stacked variable being sex. The plot should show total weight by sex for each plot. Some tips are below to help you solve this challenge:

      4. +
    + +
      +
    • For more on Pandas plots, visit this link.
      • +
    • You can use the code that follows to create a stacked bar plot but the data to stack +need to be in individual columns. Here’s a simple example with some data where +‘a’, ‘b’, and ‘c’ are the groups, and ‘one’ and ‘two’ are the subgroups.
      • +
    + +
    d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
+pd.DataFrame(d)
+
    + +

    shows the following data

    + +
          one  two
+   a    1    1
+   b    2    2
+   c    3    3
+   d  NaN    4
+
    + +

    We can plot the above with

    + +
    # Plot stacked data so columns 'one' and 'two' are stacked
+my_df = pd.DataFrame(d)
+my_df.plot(kind='bar',stacked=True,title="The title of my graph")
+
    + +
      +
    • You can use the .unstack() method to transform grouped data into columns +for each plotting. Try running .unstack() on some DataFrames above and see +what it yields.
      • +
    + +

    Start by transforming the grouped data (by plot and sex) into an unstacked layout, then create + a stacked plot.

    + +

    Solution to Extra Challenge 3

    + +

    First we group data by plot and by sex, and then calculate a total for each plot.

    + +
    by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+
    + +

    This calculates the sums of weights for each sex within each plot as a table

    + +
    plot  sex
+plot_id  sex
+1        F      38253
+         M      59979
+2        F      50144
+         M      57250
+3        F      27251
+         M      28253
+4        F      39796
+         M      49377
+<other plots removed for brevity>
+
    + +

    Below we’ll use .unstack() on our grouped data to figure out the total weight that each sex contributed to each plot.

    + +
    by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+plot_sex_count.unstack()
+
    + +

    The unstack function above will display the following output:

    + +
    sex          F      M
+plot_id              
+1        38253  59979
+2        50144  57250
+3        27251  28253
+4        39796  49377
+<other plots removed for brevity>
+
    + +

    Now, create a stacked bar plot with that data where the weights for each sex are stacked by plot.

    + +

    Rather than display it as a table, we can plot the above data by stacking the values of each sex as follows:

    + +
    by_plot_sex = surveys_df.groupby(['plot_id','sex'])
+plot_sex_count = by_plot_sex['weight'].sum()
+spc = plot_sex_count.unstack()
+s_plot = spc.plot(kind='bar',stacked=True,title="Total weight by plot and sex")
+s_plot.set_ylabel("Weight")
+s_plot.set_xlabel("Plot")
+
    + +

    Stacked Bar Plot

    + +

    NEXT

    + +
    +
    + +
    + +
    + + + + + + diff --git a/workshops/intro_to_r/20180323-launch-workshop/index.html b/workshops/intro_to_r/20180323-launch-workshop/index.html new file mode 100644 index 0000000..49c6e07 --- /dev/null +++ b/workshops/intro_to_r/20180323-launch-workshop/index.html @@ -0,0 +1,374 @@ + + + + + + + Monash Data Fluency | R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    +

    + R +

    + +
    + + +
    +
    + + +
    + +
    +
    + + +

    Introduction to R Workshop

    + +
      +
    • Date : 23rd March 2018
      • +
    • Time : 1:00pm to 5:00pm
      • +
    • Location : T4, Sir Louis Matheson Library, Clayton, VIC
      • +
    • Instructors : Dr Paul Harrison, Adele Barugahare
      • +
    • Helpers : Dr Sarah Williams, Dr Nick Wong
      • +
    + +

    This is a beginner-level workshop providing a taste of working in the R language. It aims to give participants skills needed apply data analysis approaches to research questions

    + +

    General Information

    + +

    The workshop will be taught in a similar style to Sofware Carpentry workshops. Software Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    13:00Welcome & Introduction
    13:30Starting out in R
    14:30Data frames
    15:30Tea break
    15:45ggplot2
    16:45Wrap-up
    + +

    Course material

    + + + +

    Shared Notepad

    + + + +

    Keep In Touch

    + + + +
    +
    +
    + +
    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_r/20180323-launch-workshop/index.xml b/workshops/intro_to_r/20180323-launch-workshop/index.xml new file mode 100644 index 0000000..d034cb8 --- /dev/null +++ b/workshops/intro_to_r/20180323-launch-workshop/index.xml @@ -0,0 +1,16 @@ + + + + R on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_r/20180323-launch-workshop/ + Recent content in R on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 23 Feb 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_r/20180323-launch-workshop/page/1/index.html b/workshops/intro_to_r/20180323-launch-workshop/page/1/index.html new file mode 100644 index 0000000..f37c520 --- /dev/null +++ b/workshops/intro_to_r/20180323-launch-workshop/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_r/20180323-launch-workshop/ \ No newline at end of file diff --git a/workshops/intro_to_r/20180531-intro-to-r/index.html b/workshops/intro_to_r/20180531-intro-to-r/index.html new file mode 100644 index 0000000..46a4b7b --- /dev/null +++ b/workshops/intro_to_r/20180531-intro-to-r/index.html @@ -0,0 +1,376 @@ + + + + + + + Monash Data Fluency | R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    +

    + R +

    + +
    + + +
    +
    + + +
    + +
    +
    + + +

    Introduction to R Workshop

    + +
      +
    • Date : 31st May 2018
      • +
    • Time : 1:00pm to 5:00pm
      • +
    • Location : Monash University Peninsula Campus, + Building D (Business and Economics), + Room D3.11A, + https://goo.gl/maps/5CKNAjsMfPs
      • +
    • Instructors : Paul Harrison, Adele Barugahare
      • +
    • Helpers : Gede Rajeg, Richard Beare
      • +
    + +

    This is a beginner-level workshop providing a taste of working in the R language. It aims to give participants skills needed apply data analysis approaches to research questions

    + +

    General Information

    + +

    The workshop will be taught in a similar style to Sofware Carpentry workshops. Software Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    13:00Welcome & Introduction
    13:30Starting out in R
    14:30Data frames
    15:30Tea break
    15:45Data frames continued and ggplot2
    17:00Wrap-up
    + +

    Resources

    + +

    These are the three key things to have open during the workshop.

    + + + +

    Keep In Touch

    + + + +
    +
    +
    + +
    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_r/20180531-intro-to-r/index.xml b/workshops/intro_to_r/20180531-intro-to-r/index.xml new file mode 100644 index 0000000..c45b932 --- /dev/null +++ b/workshops/intro_to_r/20180531-intro-to-r/index.xml @@ -0,0 +1,16 @@ + + + + R on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_r/20180531-intro-to-r/ + Recent content in R on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 23 Feb 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_r/20180531-intro-to-r/page/1/index.html b/workshops/intro_to_r/20180531-intro-to-r/page/1/index.html new file mode 100644 index 0000000..735018d --- /dev/null +++ b/workshops/intro_to_r/20180531-intro-to-r/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_r/20180531-intro-to-r/ \ No newline at end of file diff --git a/workshops/intro_to_r/20190208-intro-to-r/index.html b/workshops/intro_to_r/20190208-intro-to-r/index.html new file mode 100644 index 0000000..d456a22 --- /dev/null +++ b/workshops/intro_to_r/20190208-intro-to-r/index.html @@ -0,0 +1,383 @@ + + + + + + + Monash Data Fluency | Intro to R - 8 Feb 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    +

    + Intro to R - 8 Feb 2019 +

    + +
    + + +
    +
    + + +
    + +
    +
    + + +

    Introduction to R Workshop

    + +
      +
    • Date : 8th Feb 2019
      • +
    • Time : 10:0am to 5:00pm
      • +
    • Location : Matheson Library T1, 40 Exhibition Walk Clayton
      • +
    • Instructors : Adele Barugahare, Gede Rajeg
      • +
    • Helpers : Zoe Seeger, Sarah Williams, Michael See
      • +
    + +

    This is a beginner-level workshop providing a taste of working in the R language. It aims to give participants skills needed apply data analysis approaches to research questions

    + +

    General Information

    + +

    The workshop will be taught in a similar style to Sofware Carpentry workshops. Software Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: Bring Your Own Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    10:00Welcome & Introduction
    10:10Session 1
    12:30Lunch
    01:30Session 2
    02:45Break
    03:00Session 3
    04:45Wrap up
    05:00Workshop end
    + +

    Resources

    + +

    These are the three key things to have open during the workshop.

    + + + +

    Keep In Touch

    + + + +
    +
    +
    + +
    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_r/20190208-intro-to-r/index.xml b/workshops/intro_to_r/20190208-intro-to-r/index.xml new file mode 100644 index 0000000..e708f21 --- /dev/null +++ b/workshops/intro_to_r/20190208-intro-to-r/index.xml @@ -0,0 +1,16 @@ + + + + Intro to R - 8 Feb 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_r/20190208-intro-to-r/ + Recent content in Intro to R - 8 Feb 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 25 Jan 2019 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_r/20190208-intro-to-r/page/1/index.html b/workshops/intro_to_r/20190208-intro-to-r/page/1/index.html new file mode 100644 index 0000000..c50a1cb --- /dev/null +++ b/workshops/intro_to_r/20190208-intro-to-r/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_r/20190208-intro-to-r/ \ No newline at end of file diff --git a/workshops/intro_to_r/20190218-intro-to-r/index.html b/workshops/intro_to_r/20190218-intro-to-r/index.html new file mode 100644 index 0000000..f232447 --- /dev/null +++ b/workshops/intro_to_r/20190218-intro-to-r/index.html @@ -0,0 +1,383 @@ + + + + + + + Monash Data Fluency | Intro to R -1 8 Feb 2019 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    +

    + Intro to R -1 8 Feb 2019 +

    + +
    + + +
    +
    + + +
    + +
    +
    + + +

    Introduction to R Workshop

    + +
      +
    • Date : 18th Feb 2019
      • +
    • Time : 10:0am to 5:00pm
      • +
    • Location : Room D3.11A (Business and Economics Faculty Building, Peninsula Campus)
      • +
    • Instructors : Richard Beare, Julian Tagell, Oscar Lane
      • +
    • Helpers : Shefali Joshi
      • +
    + +

    This is a beginner-level workshop providing a taste of working in the R language. It aims to give participants skills needed apply data analysis approaches to research questions

    + +

    General Information

    + +

    The workshop will be taught in a similar style to Sofware Carpentry workshops. Software Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: Bring Your Own Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    10:00Welcome & Introduction
    10:10Session 1
    12:30Lunch
    01:30Session 2
    02:45Break
    03:00Session 3
    04:45Wrap up
    05:00Workshop end
    + +

    Resources

    + +

    These are the three key things to have open during the workshop.

    + + + +

    Keep In Touch

    + + + +
    +
    +
    + +
    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_r/20190218-intro-to-r/index.xml b/workshops/intro_to_r/20190218-intro-to-r/index.xml new file mode 100644 index 0000000..5a9e5e5 --- /dev/null +++ b/workshops/intro_to_r/20190218-intro-to-r/index.xml @@ -0,0 +1,16 @@ + + + + Intro to R -1 8 Feb 2019 on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_r/20190218-intro-to-r/ + Recent content in Intro to R -1 8 Feb 2019 on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Thu, 31 Jan 2019 12:19:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_r/20190218-intro-to-r/page/1/index.html b/workshops/intro_to_r/20190218-intro-to-r/page/1/index.html new file mode 100644 index 0000000..66dd087 --- /dev/null +++ b/workshops/intro_to_r/20190218-intro-to-r/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_r/20190218-intro-to-r/ \ No newline at end of file diff --git a/workshops/intro_to_r/index.html b/workshops/intro_to_r/index.html new file mode 100644 index 0000000..9c1f5cf --- /dev/null +++ b/workshops/intro_to_r/index.html @@ -0,0 +1,372 @@ + + + + + + + Monash Data Fluency | R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    +

    + R +

    + +
    + + +
    +
    + + +
    + +
    +
    + + +

    Introduction to R Workshop

    + +
      +
    • Date : 20th July 2018
      • +
    • Time : 9:00am to 1:00pm
      • +
    • Location : Monash University Clayton Campus, Matheson Library, Room T2
      • +
    • Instructors : Paul Harrison, Adele Barugahare, Sarah Williams
      • +
    + +

    This is a beginner-level workshop providing a taste of working in the R language. It aims to give participants skills needed to explore and visualize complex data.

    + +

    General Information

    + +

    The workshop is in a hands on format, in a similar style to Software Carpentry workshops.

    + +

    Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

    + +

    Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

    + +

    Schedule

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    TimeProgramme
    9:00Welcome & Introduction
    9:30Starting out in R
    10:30Data frames
    11:30Tea break
    11:45Data frames continued and ggplot2
    13:00Wrap-up
    + +

    Resources

    + +

    These are the three key things to have open during the workshop.

    + + + +

    Keep In Touch

    + + + +
    +
    +
    + +
    + +
    + + +
    + +
    + + + + + + diff --git a/workshops/intro_to_r/index.xml b/workshops/intro_to_r/index.xml new file mode 100644 index 0000000..b62360c --- /dev/null +++ b/workshops/intro_to_r/index.xml @@ -0,0 +1,16 @@ + + + + R on Monash Data Fluency + https://MonashDataFluency.github.io/workshops/intro_to_r/ + Recent content in R on Monash Data Fluency + Hugo -- gohugo.io + en-us + Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). + Fri, 23 Feb 2018 13:28:03 +1100 + + + + + + \ No newline at end of file diff --git a/workshops/intro_to_r/page/1/index.html b/workshops/intro_to_r/page/1/index.html new file mode 100644 index 0000000..a2c10e1 --- /dev/null +++ b/workshops/intro_to_r/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/intro_to_r/ \ No newline at end of file diff --git a/workshops/page/1/index.html b/workshops/page/1/index.html new file mode 100644 index 0000000..7032435 --- /dev/null +++ b/workshops/page/1/index.html @@ -0,0 +1 @@ +https://MonashDataFluency.github.io/workshops/ \ No newline at end of file
    +
    +
    +

    + Machine Learning +

    + +
    +     + +
    +

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