25_pandas.md

30 Days Of Python: Day 25 - Pandas

_{Author: Asabeneh Yetayeh
Second Edition: July, 2021}

<< Day 24 | Day 26 >>

• 📘 Day 25
  • Pandas
    • Installing Pandas
    • Importing Pandas
    • Creating Pandas Series with Default Index
    • Creating Pandas Series with custom index
    • Creating Pandas Series from a Dictionary
    • Creating a Constant Pandas Series
    • Creating a Pandas Series Using Linspace
  • DataFrames
    • Creating DataFrames from List of Lists
    • Creating DataFrame Using Dictionary
    • Creating DataFrames from a List of Dictionaries
  • Reading CSV File Using Pandas
    • Data Exploration
  • Modifying a DataFrame
    • Creating a DataFrame
    • Adding a New Column
    • Modifying column values
    • Formating DataFrame columns
  • Checking data types of Column values
    • Boolean Indexing
  • Exercises: Day 25

📘 Day 25

Pandas

Pandas is an open source, high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandas adds data structures and tools designed to work with table-like data which is Series and Data Frames. Pandas provides tools for data manipulation:

• reshaping
• merging
• sorting
• slicing
• aggregation
• imputation. If you are using anaconda, you do not have install pandas.

Installing Pandas

For Mac:

pip install conda
conda install pandas

For Windows:

pip install conda
pip install pandas

Pandas data structure is based on Series and DataFrames.

A series is a column and a DataFrame is a multidimensional table made up of collection of series. In order to create a pandas series we should use numpy to create a one dimensional arrays or a python list. Let us see an example of a series:

Names Pandas Series

Countries Series

Cities Series

As you can see, pandas series is just one column of data. If we want to have multiple columns we use data frames. The example below shows pandas DataFrames.

Let us see, an example of a pandas data frame:

Data frame is a collection of rows and columns. Look at the table below; it has many more columns than the example above:

Next, we will see how to import pandas and how to create Series and DataFrames using pandas

Importing Pandas

import pandas as pd # importing pandas as pd
import numpy  as np # importing numpy as np

Creating Pandas Series with Default Index

nums = [1, 2, 3, 4,5]
s = pd.Series(nums)
print(s)

    0    1
    1    2
    2    3
    3    4
    4    5
    dtype: int64

Creating Pandas Series with custom index

nums = [1, 2, 3, 4, 5]
s = pd.Series(nums, index=[1, 2, 3, 4, 5])
print(s)

    1    1
    2    2
    3    3
    4    4
    5    5
    dtype: int64

fruits = ['Orange','Banana','Mango']
fruits = pd.Series(fruits, index=[1, 2, 3])
print(fruits)

    1    Orange
    2    Banana
    3    Mango
    dtype: object

Creating Pandas Series from a Dictionary

dct = {'name':'Asabeneh','country':'Finland','city':'Helsinki'}

s = pd.Series(dct)
print(s)

    name       Asabeneh
    country     Finland
    city       Helsinki
    dtype: object

Creating a Constant Pandas Series

s = pd.Series(10, index = [1, 2, 3])
print(s)

    1    10
    2    10
    3    10
    dtype: int64

Creating a Pandas Series Using Linspace

s = pd.Series(np.linspace(5, 20, 10)) # linspace(starting, end, items)
print(s)

    0     5.000000
    1     6.666667
    2     8.333333
    3    10.000000
    4    11.666667
    5    13.333333
    6    15.000000
    7    16.666667
    8    18.333333
    9    20.000000
    dtype: float64

DataFrames

Pandas data frames can be created in different ways.

Creating DataFrames from List of Lists

data = [
    ['Asabeneh', 'Finland', 'Helsink'], 
    ['David', 'UK', 'London'],
    ['John', 'Sweden', 'Stockholm']
]
df = pd.DataFrame(data, columns=['Names','Country','City'])
print(df)

	Names	Country	City
0	Asabeneh	Finland	Helsink
1	David	UK	London
2	John	Sweden	Stockholm

Creating DataFrame Using Dictionary

data = {'Name': ['Asabeneh', 'David', 'John'], 'Country':[
    'Finland', 'UK', 'Sweden'], 'City': ['Helsiki', 'London', 'Stockholm']}
df = pd.DataFrame(data)
print(df)

	Name	Country	City
0	Asabeneh	Finland	Helsiki
1	David	UK	London
2	John	Sweden	Stockholm

Creating DataFrames from a List of Dictionaries

data = [
    {'Name': 'Asabeneh', 'Country': 'Finland', 'City': 'Helsinki'},
    {'Name': 'David', 'Country': 'UK', 'City': 'London'},
    {'Name': 'John', 'Country': 'Sweden', 'City': 'Stockholm'}]
df = pd.DataFrame(data)
print(df)

	Name	Country	City
0	Asabeneh	Finland	Helsinki
1	David	UK	London
2	John	Sweden	Stockholm

Reading CSV File Using Pandas

To download the CSV file, what is needed in this example, console/command line is enough:

curl -O https://raw.githubusercontent.com/Asabeneh/30-Days-Of-Python/master/data/weight-height.csv

Put the downloaded file in your working directory.

import pandas as pd

df = pd.read_csv('weight-height.csv')
print(df)

Data Exploration

Let us read only the first 5 rows using head()

print(df.head()) # give five rows we can increase the number of rows by passing argument to the head() method

	Gender	Height	Weight
0	Male	73.847017	241.893563
1	Male	68.781904	162.310473
2	Male	74.110105	212.740856
3	Male	71.730978	220.042470
4	Male	69.881796	206.349801

Let us also explore the last recordings of the dataframe using the tail() methods.

print(df.tail()) # tails give the last five rows, we can increase the rows by passing argument to tail method

	Gender	Height	Weight
9995	Female	66.172652	136.777454
9996	Female	67.067155	170.867906
9997	Female	63.867992	128.475319
9998	Female	69.034243	163.852461
9999	Female	61.944246	113.649103

As you can see the csv file has three rows: Gender, Height and Weight. If the DataFrame would have a long rows, it would be hard to know all the columns. Therefore, we should use a method to know the colums. we do not know the number of rows. Let's use shape meathod.

print(df.shape) # as you can see 10000 rows and three columns

(10000, 3)

Let us get all the columns using columns.

print(df.columns)

Index(['Gender', 'Height', 'Weight'], dtype='object')

Now, let us get a specific column using the column key

heights = df['Height'] # this is now a series

print(heights)

    0       73.847017
    1       68.781904
    2       74.110105
    3       71.730978
    4       69.881796
              ...    
    9995    66.172652
    9996    67.067155
    9997    63.867992
    9998    69.034243
    9999    61.944246
    Name: Height, Length: 10000, dtype: float64

weights = df['Weight'] # this is now a series

print(weights)

    0       241.893563
    1       162.310473
    2       212.740856
    3       220.042470
    4       206.349801
               ...    
    9995    136.777454
    9996    170.867906
    9997    128.475319
    9998    163.852461
    9999    113.649103
    Name: Weight, Length: 10000, dtype: float64

print(len(heights) == len(weights))

True

The describe() method provides a descriptive statistical values of a dataset.

print(heights.describe()) # give statisical information about height data

    count    10000.000000
    mean        66.367560
    std          3.847528
    min         54.263133
    25%         63.505620
    50%         66.318070
    75%         69.174262
    max         78.998742
    Name: Height, dtype: float64

print(weights.describe())

    count    10000.000000
    mean       161.440357
    std         32.108439
    min         64.700127
    25%        135.818051
    50%        161.212928
    75%        187.169525
    max        269.989699
    Name: Weight, dtype: float64

print(df.describe())  # describe can also give statistical information from a dataFrame

	Height	Weight
count	10000.000000	10000.000000
mean	66.367560	161.440357
std	3.847528	32.108439
min	54.263133	64.700127
25%	63.505620	135.818051
50%	66.318070	161.212928
75%	69.174262	187.169525
max	78.998742	269.989699

Similar to describe(), the info() method also give information about the dataset.

Modifying a DataFrame

Modifying a DataFrame: * We can create a new DataFrame * We can create a new column and add it to the DataFrame, * we can remove an existing column from a DataFrame, * we can modify an existing column in a DataFrame, * we can change the data type of column values in the DataFrame

Creating a DataFrame

As always, first we import the necessary packages. Now, lets import pandas and numpy, two best friends ever.

import pandas as pd
import numpy as np
data = [
    {"Name": "Asabeneh", "Country":"Finland","City":"Helsinki"},
    {"Name": "David", "Country":"UK","City":"London"},
    {"Name": "John", "Country":"Sweden","City":"Stockholm"}]
df = pd.DataFrame(data)
print(df)

	Name	Country	City
0	Asabeneh	Finland	Helsinki
1	David	UK	London
2	John	Sweden	Stockholm

Adding a column to a DataFrame is like adding a key to a dictionary.

First let's use the previous example to create a DataFrame. After we create the DataFrame, we will start modifying the columns and column values.

Adding a New Column

Let's add a weight column in the DataFrame

weights = [74, 78, 69]
df['Weight'] = weights
df

	Name	Country	City	Weight
0	Asabeneh	Finland	Helsinki	74
1	David	UK	London	78
2	John	Sweden	Stockholm	69

Let's add a height column into the DataFrame aswell

heights = [173, 175, 169]
df['Height'] = heights
print(df)

	Name	Country	City	Weight	Height
0	Asabeneh	Finland	Helsinki	74	173
1	David	UK	London	78	175
2	John	Sweden	Stockholm	69	169

As you can see in the DataFrame above, we did add new columns, Weight and Height. Let's add one additional column called BMI(Body Mass Index) by calculating their BMI using thier mass and height. BMI is mass divided by height squared (in meters) - Weight/Height * Height.

As you can see, the height is in centimeters, so we shoud change it to meters. Let's modify the height row.

Modifying column values

df['Height'] = df['Height'] * 0.01
df

	Name	Country	City	Weight	Height
0	Asabeneh	Finland	Helsinki	74	1.73
1	David	UK	London	78	1.75
2	John	Sweden	Stockholm	69	1.69

# Using functions makes our code clean, but you can calculate the bmi without one
def calculate_bmi ():
    weights = df['Weight']
    heights = df['Height']
    bmi = []
    for w,h in zip(weights, heights):
        b = w/(h*h)
        bmi.append(b)
    return bmi
    
bmi = calculate_bmi()

df['BMI'] = bmi
df

	Name	Country	City	Weight	Height	BMI
0	Asabeneh	Finland	Helsinki	74	1.73	24.725183
1	David	UK	London	78	1.75	25.469388
2	John	Sweden	Stockholm	69	1.69	24.158818

Formating DataFrame columns

The BMI column values of the DataFrame are float with many significant digits after decimal. Let's change it to one significant digit after point.

df['BMI'] = round(df['BMI'], 1)
print(df)

	Name	Country	City	Weight	Height	BMI
0	Asabeneh	Finland	Helsinki	74	1.73	24.7
1	David	UK	London	78	1.75	25.5
2	John	Sweden	Stockholm	69	1.69	24.2

The information in the DataFrame seems not yet complete, let's add birth year and current year columns.

birth_year = ['1769', '1985', '1990']
current_year = pd.Series(2020, index=[0, 1,2])
df['Birth Year'] = birth_year
df['Current Year'] = current_year
df

	Name	Country	City	Weight	Height	BMI	Birth Year	Current Year
0	Asabeneh	Finland	Helsinki	74	1.73	24.7	1769	2020
1	David	UK	London	78	1.75	25.5	1985	2020
2	John	Sweden	Stockholm	69	1.69	24.2	1990	2020

Checking data types of Column values

print(df.Weight.dtype)

    dtype('int64')

df['Birth Year'].dtype # it gives string object , we should change this to number

df['Birth Year'] = df['Birth Year'].astype('int')
print(df['Birth Year'].dtype) # let's check the data type now

    dtype('int32')

Now same for the current year:

df['Current Year'] = df['Current Year'].astype('int')
df['Current Year'].dtype

    dtype('int32')

Now, the column values of birth year and current year are integers. We can calculate the age.

ages = df['Current Year'] - df['Birth Year']
ages

0    251
1     35
2     30
dtype: int32

df['Ages'] = ages
print(df)

	Name	Country	City	Weight	Height	BMI	Birth Year	Current Year	Ages
0	Asabeneh	Finland	Helsinki	74	1.73	24.7	1769	2019	250
1	David	UK	London	78	1.75	25.5	1985	2019	34
2	John	Sweden	Stockholm	69	1.69	24.2	1990	2019	29

The person in the first row lived so far for 251 years. It is unlikely for someone to live so long. Either it is a typo or the data is cooked. So lets fill that data with average of the columns without including outlier.

mean = (35 + 30)/ 2

mean = (35 + 30)/ 2
print('Mean: ',mean)	#it is good to add some description to the output, so we know what is what

   Mean:  32.5

Boolean Indexing

print(df[df['Ages'] > 120])

	Name	Country	City	Weight	Height	BMI	Birth Year	Current Year	Ages
0	Asabeneh	Finland	Helsinki	74	1.73	24.7	1769	2020	251

print(df[df['Ages'] < 120])

	Name	Country	City	Weight	Height	BMI	Birth Year	Current Year	Ages
1	David	UK	London	78	1.75	25.5	1985	2020	35
2	John	Sweden	Stockholm	69	1.69	24.2	1990	2020	30

Exercises: Day 25

1. Read the hacker_news.csv file from data directory
2. Get the first five rows
3. Get the last five rows
4. Get the title column as pandas series
5. Count the number of rows and columns
   • Filter the titles which contain python
   • Filter the titles which contain JavaScript
   • Explore the data and make sense of it

🎉 CONGRATULATIONS ! 🎉

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