Cleaning Data Exercises.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1c5ab710",
   "metadata": {},
   "source": [
    "# Cleaning Data Exercises\n",
    "\n",
    "For the exercises, you will be cleaning data in your chosen data set. [Towards Data Science](https://towardsdatascience.com/data-cleaning-in-python-the-ultimate-guide-2020-c63b88bf0a0d) outlines the steps we should take to clean data using a different data set from Kaggle. While you should use this article as a reference, make sure not to copy paste as the two data sets are very different!\n",
    "\n",
    "To start cleaning data, we first need to create a dataframe from the CSV and print out any relevant info to make sure our dataframe is ready to go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da3513e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas and any other libraries you need here. HINT: Check out the article to see what they imported to get started!\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib \n",
    "plt.style.use('ggplot')\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "# Create a new dataframe from your CSV\n",
    "df = pd.read_csv('Rat_Sightings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "045387eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101914, 52)\n",
      "Unique Key                          int64\n",
      "Created Date                       object\n",
      "Closed Date                        object\n",
      "Agency                             object\n",
      "Agency Name                        object\n",
      "Complaint Type                     object\n",
      "Descriptor                         object\n",
      "Location Type                      object\n",
      "Incident Zip                      float64\n",
      "Incident Address                   object\n",
      "Street Name                        object\n",
      "Cross Street 1                     object\n",
      "Cross Street 2                     object\n",
      "Intersection Street 1              object\n",
      "Intersection Street 2              object\n",
      "Address Type                       object\n",
      "City                               object\n",
      "Landmark                           object\n",
      "Facility Type                     float64\n",
      "Status                             object\n",
      "Due Date                           object\n",
      "Resolution Action Updated Date     object\n",
      "Community Board                    object\n",
      "Borough                            object\n",
      "X Coordinate (State Plane)        float64\n",
      "Y Coordinate (State Plane)        float64\n",
      "Park Facility Name                 object\n",
      "Park Borough                       object\n",
      "School Name                        object\n",
      "School Number                      object\n",
      "School Region                      object\n",
      "School Code                        object\n",
      "School Phone Number                object\n",
      "School Address                     object\n",
      "School City                        object\n",
      "School State                       object\n",
      "School Zip                         object\n",
      "School Not Found                   object\n",
      "School or Citywide Complaint      float64\n",
      "Vehicle Type                      float64\n",
      "Taxi Company Borough              float64\n",
      "Taxi Pick Up Location             float64\n",
      "Bridge Highway Name               float64\n",
      "Bridge Highway Direction          float64\n",
      "Road Ramp                         float64\n",
      "Bridge Highway Segment            float64\n",
      "Garage Lot Name                   float64\n",
      "Ferry Direction                   float64\n",
      "Ferry Terminal Name               float64\n",
      "Latitude                          float64\n",
      "Longitude                         float64\n",
      "Location                           object\n",
      "dtype: object\n",
      "['Unique Key' 'Incident Zip' 'Facility Type' 'X Coordinate (State Plane)'\n",
      " 'Y Coordinate (State Plane)' 'School or Citywide Complaint'\n",
      " 'Vehicle Type' 'Taxi Company Borough' 'Taxi Pick Up Location'\n",
      " 'Bridge Highway Name' 'Bridge Highway Direction' 'Road Ramp'\n",
      " 'Bridge Highway Segment' 'Garage Lot Name' 'Ferry Direction'\n",
      " 'Ferry Terminal Name' 'Latitude' 'Longitude']\n",
      "['Created Date' 'Closed Date' 'Agency' 'Agency Name' 'Complaint Type'\n",
      " 'Descriptor' 'Location Type' 'Incident Address' 'Street Name'\n",
      " 'Cross Street 1' 'Cross Street 2' 'Intersection Street 1'\n",
      " 'Intersection Street 2' 'Address Type' 'City' 'Landmark' 'Status'\n",
      " 'Due Date' 'Resolution Action Updated Date' 'Community Board' 'Borough'\n",
      " 'Park Facility Name' 'Park Borough' 'School Name' 'School Number'\n",
      " 'School Region' 'School Code' 'School Phone Number' 'School Address'\n",
      " 'School City' 'School State' 'School Zip' 'School Not Found' 'Location']\n"
     ]
    }
   ],
   "source": [
    "# Print out any information you need to understand your dataframe\n",
    "print(df.shape)\n",
    "print(df.dtypes)\n",
    "\n",
    "df_numeric = df.select_dtypes(include=[np.number])\n",
    "numeric_cols = df_numeric.columns.values\n",
    "print(numeric_cols)\n",
    "\n",
    "df_non_numeric = df.select_dtypes(exclude=[np.number])\n",
    "non_numeric_cols = df_non_numeric.columns.values\n",
    "print(non_numeric_cols)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81717993",
   "metadata": {},
   "source": [
    "Now you have a dataframe set up, you are ready to dive in and start cleaning!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "303aee35",
   "metadata": {},
   "source": [
    "## Missing Data\n",
    "\n",
    "In the Towards Data Science article, they list 3 techniques for detecting any missing data frame. Try all three before deciding on a way to solve any missing data issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68fd548c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a heatmap of any missing data\n",
    "cols = df.columns[:30]\n",
    "colors = ['#000099', '#ffff00']\n",
    "sns.heatmap(df[cols].isnull(), cmap=sns.color_palette(colors))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ae37b5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Key - 0%\n",
      "Created Date - 0%\n",
      "Closed Date - 11%\n",
      "Agency - 0%\n",
      "Agency Name - 0%\n",
      "Complaint Type - 0%\n",
      "Descriptor - 0%\n",
      "Location Type - 0%\n",
      "Incident Zip - 0%\n",
      "Incident Address - 9%\n",
      "Street Name - 9%\n",
      "Cross Street 1 - 16%\n",
      "Cross Street 2 - 16%\n",
      "Intersection Street 1 - 91%\n",
      "Intersection Street 2 - 91%\n",
      "Address Type - 0%\n",
      "City - 0%\n",
      "Landmark - 100%\n",
      "Facility Type - 100%\n",
      "Status - 0%\n",
      "Due Date - 0%\n",
      "Resolution Action Updated Date - 0%\n",
      "Community Board - 0%\n",
      "Borough - 0%\n",
      "X Coordinate (State Plane) - 1%\n",
      "Y Coordinate (State Plane) - 1%\n",
      "Park Facility Name - 0%\n",
      "Park Borough - 0%\n",
      "School Name - 0%\n",
      "School Number - 0%\n",
      "School Region - 0%\n",
      "School Code - 0%\n",
      "School Phone Number - 0%\n",
      "School Address - 0%\n",
      "School City - 0%\n",
      "School State - 0%\n",
      "School Zip - 0%\n",
      "School Not Found - 1%\n",
      "School or Citywide Complaint - 100%\n",
      "Vehicle Type - 100%\n",
      "Taxi Company Borough - 100%\n",
      "Taxi Pick Up Location - 100%\n",
      "Bridge Highway Name - 100%\n",
      "Bridge Highway Direction - 100%\n",
      "Road Ramp - 100%\n",
      "Bridge Highway Segment - 100%\n",
      "Garage Lot Name - 100%\n",
      "Ferry Direction - 100%\n",
      "Ferry Terminal Name - 100%\n",
      "Latitude - 1%\n",
      "Longitude - 1%\n",
      "Location - 1%\n"
     ]
    }
   ],
   "source": [
    "# Try out a missing data percentage list! \n",
    "for col in df.columns:\n",
    "    pct_missing = np.mean(df[col].isnull())\n",
    "    print('{} - {}%'.format(col, round(pct_missing*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51c62f02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "created missing indicator for: Closed Date\n",
      "created missing indicator for: Location Type\n",
      "created missing indicator for: Incident Zip\n",
      "created missing indicator for: Incident Address\n",
      "created missing indicator for: Street Name\n",
      "created missing indicator for: Cross Street 1\n",
      "created missing indicator for: Cross Street 2\n",
      "created missing indicator for: Intersection Street 1\n",
      "created missing indicator for: Intersection Street 2\n",
      "created missing indicator for: Address Type\n",
      "created missing indicator for: City\n",
      "created missing indicator for: Landmark\n",
      "created missing indicator for: Facility Type\n",
      "created missing indicator for: Due Date\n",
      "created missing indicator for: Resolution Action Updated Date\n",
      "created missing indicator for: X Coordinate (State Plane)\n",
      "created missing indicator for: Y Coordinate (State Plane)\n",
      "created missing indicator for: School Not Found\n",
      "created missing indicator for: School or Citywide Complaint\n",
      "created missing indicator for: Vehicle Type\n",
      "created missing indicator for: Taxi Company Borough\n",
      "created missing indicator for: Taxi Pick Up Location\n",
      "created missing indicator for: Bridge Highway Name\n",
      "created missing indicator for: Bridge Highway Direction\n",
      "created missing indicator for: Road Ramp\n",
      "created missing indicator for: Bridge Highway Segment\n",
      "created missing indicator for: Garage Lot Name\n",
      "created missing indicator for: Ferry Direction\n",
      "created missing indicator for: Ferry Terminal Name\n",
      "created missing indicator for: Latitude\n",
      "created missing indicator for: Longitude\n",
      "created missing indicator for: Location\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='index'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a missing data histogram\n",
    "for col in df.columns:\n",
    "    missing = df[col].isnull()\n",
    "    num_missing = np.sum(missing)\n",
    "    \n",
    "    if num_missing > 0:\n",
    "        print('created missing indicator for: {}'.format(col))\n",
    "        df['{}_ismissing'.format(col)] = missing\n",
    "        \n",
    "ismissing_cols = [col for col in df.columns if 'ismissing' in col]\n",
    "df['num_missing'] = df[ismissing_cols].sum(axis=1)\n",
    "\n",
    "df['num_missing'].value_counts().reset_index().sort_values(by='index').plot.bar(x='index', y='num_missing')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e791fbe",
   "metadata": {},
   "source": [
    "Now that you have tried all 3 techniques for detecting missing data, did you find any? Reading through the article, which solution do you think will help you clean the data set and handle the missing data? Try out your solution below!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53c2d34c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle any missing data in your dataframe.\n",
    "ind_missing = df[df['num_missing'] > 35].index\n",
    "df_less_missing_rows = df.drop(ind_missing, axis=0)\n",
    "\n",
    "cols_to_drop = ['School or Citywide Complaint']\n",
    "df_less_school_or_citywide_complaint = df.drop(cols_to_drop, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be2121df",
   "metadata": {},
   "source": [
    "## Irregular Data\n",
    "\n",
    "With missing data out of the way, turn your attention to any outliers. Just as we did for missing data, we first need to detect the outliers. The article outlines the top techniques for finding outliers in a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3a4f2cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram to see if there are any outliers.\n",
    "df['Longitude'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ae9008bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    101208.000000\n",
       "mean        -73.934206\n",
       "std           0.070617\n",
       "min         -74.254437\n",
       "25%         -73.966216\n",
       "50%         -73.938112\n",
       "75%         -73.899963\n",
       "max         -73.701632\n",
       "Name: Longitude, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the describe() method\n",
    "df['Longitude'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4af44edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a bar char\n",
    "df['Latitude'].value_counts().plot.bar()\n",
    "# not sure why this isn't working it looks just like the article, if there's anything I can do to fix let me know"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8d6080",
   "metadata": {},
   "source": [
    "Which of the three techniques helped you find any outliers? Now that you have found outliers, what will you do to solve the problem?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ee7d694",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle any outliers in your dataframe\n",
    "# I didn't find any outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c802522d",
   "metadata": {},
   "source": [
    "## Unnecessary Data\n",
    "\n",
    "Unnecessary data could be duplicates, irrelevant, or any uninformative data. Dive into each type of unnecessary data to see if there is any in the data set. Make sure to make note of how you would handle each type of unnecessary data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8de40f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Look for any irrelevant data in the dataframe. How will you handle it?\n",
    "cols_to_drop = ['Cross Street 2']\n",
    "df_less_cross_street_2 = df.drop(cols_to_drop, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "888b49f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101914, 85)\n",
      "(101912, 84)\n"
     ]
    }
   ],
   "source": [
    "# Look for duplicates. How will you handle any duplicates?\n",
    "df_dedupped = df.drop('Unique Key', axis=1).drop_duplicates()\n",
    "\n",
    "print(df.shape)\n",
    "print(df_dedupped.shape)\n",
    "#cols_to_drop = ['School or Citywide Complaint']\n",
    "#df_less_school_or_citywide_complaint = df.drop(cols_to_drop, axis=1)\n",
    "# determine if data is All Features based or Key Features based remove any duplicates accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cbd6ee14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Think about what data in the dataframe may be uninformative. Make note here of what situations may render data uninformative and how you would handle it?\n",
    "num_rows = len(df.index)\n",
    "low_info_cols = []\n",
    "\n",
    "for col in df.columns:\n",
    "    cnts = df[cols].value_counts(dropna=False)\n",
    "    top_pct = (cnts/num_rows).iloc[0]\n",
    "    \n",
    "    if top_pct > 0.95:\n",
    "        low_info_cols.append(cols)\n",
    "        print('{0}: {1:.5f}%'.format(col, top_pct*100))\n",
    "        print(cnts)\n",
    "        print()\n",
    "        \n",
    "# there seemed to be quite a bit of data that didn't seem important, somes columns missing data entirely"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdcad93a",
   "metadata": {},
   "source": [
    "## Inconsistent Data\n",
    "\n",
    "Inconsistent data is anything that messes with your model. This is likely due to inconsistent formatting and can be addressed by re-formatting all values in a column or row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4fc6bbb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN                       9075\n",
       "grand concourse            713\n",
       "broadway                   705\n",
       "eastern parkway            632\n",
       "lafayette avenue           379\n",
       "                          ... \n",
       "brightn 1 road               1\n",
       "west mount eden avenue       1\n",
       "96 place                     1\n",
       "bay 10 street                1\n",
       "east 159th street            1\n",
       "Name: Street Name, Length: 6745, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try to convert all the strings in a column to lower or upper case. \n",
    "df['Street Name'] = df['Street Name'].str.lower()\n",
    "df['Street Name'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "376a0e64",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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