sample-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample Analysis with Sodapy and Socrata Query Language (SoQL)\n",
    "Author: Mark Bauer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table of Contents\n",
    "=================\n",
    "\n",
    "   1. Introduction\n",
    "   2. Importing Libraries\n",
    "   3. Analyzing Datasets on NYC Open Data    \n",
    "       * 3.1 Analyzing NYC Datasets with the Most Downloads\n",
    "       * 3.2 Analyzing NYC Datasets with the Most Page Views\n",
    "   4. Answering Questions about NYC 311 Complaints\n",
    "       * 4.1 Most NYC 311 Complaints by Complaint Type\n",
    "       * 4.2 Most NYC 311 Complaints by Day\n",
    "   5. Answering questions about NYC 311 Street Flooding Complaints\n",
    "       * 5.1 Searching the dataset for the word \"flood\" in the complaint_type field\n",
    "       * 5.2 Searching the dataset for the word \"flood\" in the descriptor field\n",
    "       * 5.3 Displaying the highest number of street flooding complaints by day\n",
    "       * 5.4 Selecting the rows and columns where the descriptor field = 'Street Flooding'      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction  \n",
    "This notebook demonstrates a basic analysis using SoQL, the Socrata Query Language. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sodapy import Socrata\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import seaborn as sns\n",
    "import urllib.parse\n",
    "\n",
    "sns.set_theme()\n",
    "plt.rcParams['savefig.facecolor'] = 'white'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.13\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "pandas: 1.4.3\n",
      "sodapy: 2.1.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## documention for installing watermark: https://github.com/rasbt/watermark\n",
    "%reload_ext watermark\n",
    "%watermark -t -d -v -p pandas,sodapy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Analyzing Datasets on NYC Open Data\n",
    "Note: `WARNING:root:Requests made without an app_token will be subject to strict throttling limits.`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Analyzing NYC Datasets with the Most Downloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3342, 8)\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resource</th>\n",
       "      <th>classification</th>\n",
       "      <th>metadata</th>\n",
       "      <th>permalink</th>\n",
       "      <th>link</th>\n",
       "      <th>owner</th>\n",
       "      <th>creator</th>\n",
       "      <th>preview_image_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'name': 'For Hire Vehicles (FHV) - Active', '...</td>\n",
       "      <td>{'categories': [], 'tags': [], 'domain_categor...</td>\n",
       "      <td>{'domain': 'data.cityofnewyork.us'}</td>\n",
       "      <td>https://data.cityofnewyork.us/d/8wbx-tsch</td>\n",
       "      <td>https://data.cityofnewyork.us/Transportation/F...</td>\n",
       "      <td>{'id': '5fuc-pqz2', 'user_type': 'interactive'...</td>\n",
       "      <td>{'id': '5fuc-pqz2', 'user_type': 'interactive'...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'name': 'Civil Service List (Active)', 'id': ...</td>\n",
       "      <td>{'categories': [], 'tags': [], 'domain_categor...</td>\n",
       "      <td>{'domain': 'data.cityofnewyork.us'}</td>\n",
       "      <td>https://data.cityofnewyork.us/d/vx8i-nprf</td>\n",
       "      <td>https://data.cityofnewyork.us/City-Government/...</td>\n",
       "      <td>{'id': '5fuc-pqz2', 'user_type': 'interactive'...</td>\n",
       "      <td>{'id': '5fuc-pqz2', 'user_type': 'interactive'...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'name': 'DOB Job Application Filings', 'id': ...</td>\n",
       "      <td>{'categories': ['economy', 'environment', 'hou...</td>\n",
       "      <td>{'domain': 'data.cityofnewyork.us'}</td>\n",
       "      <td>https://data.cityofnewyork.us/d/ic3t-wcy2</td>\n",
       "      <td>https://data.cityofnewyork.us/Housing-Developm...</td>\n",
       "      <td>{'id': '5fuc-pqz2', 'user_type': 'interactive'...</td>\n",
       "      <td>{'id': '5fuc-pqz2', 'user_type': 'interactive'...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'name': 'TLC New Driver Application Status', ...</td>\n",
       "      <td>{'categories': ['transportation', 'environment...</td>\n",
       "      <td>{'domain': 'data.cityofnewyork.us'}</td>\n",
       "      <td>https://data.cityofnewyork.us/d/dpec-ucu7</td>\n",
       "      <td>https://data.cityofnewyork.us/Transportation/T...</td>\n",
       "      <td>{'id': '5fuc-pqz2', 'user_type': 'interactive'...</td>\n",
       "      <td>{'id': '5fuc-pqz2', 'user_type': 'interactive'...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'name': 'For Hire Vehicles (FHV) - Active Dri...</td>\n",
       "      <td>{'categories': [], 'tags': [], 'domain_categor...</td>\n",
       "      <td>{'domain': 'data.cityofnewyork.us'}</td>\n",
       "      <td>https://data.cityofnewyork.us/d/xjfq-wh2d</td>\n",
       "      <td>https://data.cityofnewyork.us/Transportation/F...</td>\n",
       "      <td>{'id': '5fuc-pqz2', 'user_type': 'interactive'...</td>\n",
       "      <td>{'id': '5fuc-pqz2', 'user_type': 'interactive'...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            resource  \\\n",
       "0  {'name': 'For Hire Vehicles (FHV) - Active', '...   \n",
       "1  {'name': 'Civil Service List (Active)', 'id': ...   \n",
       "2  {'name': 'DOB Job Application Filings', 'id': ...   \n",
       "3  {'name': 'TLC New Driver Application Status', ...   \n",
       "4  {'name': 'For Hire Vehicles (FHV) - Active Dri...   \n",
       "\n",
       "                                      classification  \\\n",
       "0  {'categories': [], 'tags': [], 'domain_categor...   \n",
       "1  {'categories': [], 'tags': [], 'domain_categor...   \n",
       "2  {'categories': ['economy', 'environment', 'hou...   \n",
       "3  {'categories': ['transportation', 'environment...   \n",
       "4  {'categories': [], 'tags': [], 'domain_categor...   \n",
       "\n",
       "                              metadata  \\\n",
       "0  {'domain': 'data.cityofnewyork.us'}   \n",
       "1  {'domain': 'data.cityofnewyork.us'}   \n",
       "2  {'domain': 'data.cityofnewyork.us'}   \n",
       "3  {'domain': 'data.cityofnewyork.us'}   \n",
       "4  {'domain': 'data.cityofnewyork.us'}   \n",
       "\n",
       "                                   permalink  \\\n",
       "0  https://data.cityofnewyork.us/d/8wbx-tsch   \n",
       "1  https://data.cityofnewyork.us/d/vx8i-nprf   \n",
       "2  https://data.cityofnewyork.us/d/ic3t-wcy2   \n",
       "3  https://data.cityofnewyork.us/d/dpec-ucu7   \n",
       "4  https://data.cityofnewyork.us/d/xjfq-wh2d   \n",
       "\n",
       "                                                link  \\\n",
       "0  https://data.cityofnewyork.us/Transportation/F...   \n",
       "1  https://data.cityofnewyork.us/City-Government/...   \n",
       "2  https://data.cityofnewyork.us/Housing-Developm...   \n",
       "3  https://data.cityofnewyork.us/Transportation/T...   \n",
       "4  https://data.cityofnewyork.us/Transportation/F...   \n",
       "\n",
       "                                               owner  \\\n",
       "0  {'id': '5fuc-pqz2', 'user_type': 'interactive'...   \n",
       "1  {'id': '5fuc-pqz2', 'user_type': 'interactive'...   \n",
       "2  {'id': '5fuc-pqz2', 'user_type': 'interactive'...   \n",
       "3  {'id': '5fuc-pqz2', 'user_type': 'interactive'...   \n",
       "4  {'id': '5fuc-pqz2', 'user_type': 'interactive'...   \n",
       "\n",
       "                                             creator preview_image_url  \n",
       "0  {'id': '5fuc-pqz2', 'user_type': 'interactive'...               NaN  \n",
       "1  {'id': '5fuc-pqz2', 'user_type': 'interactive'...               NaN  \n",
       "2  {'id': '5fuc-pqz2', 'user_type': 'interactive'...               NaN  \n",
       "3  {'id': '5fuc-pqz2', 'user_type': 'interactive'...               NaN  \n",
       "4  {'id': '5fuc-pqz2', 'user_type': 'interactive'...               NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Source domain for NYC Open Data on Socrata\n",
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "# reading in a list of dictionaries of our data into a pandas DataFrame\n",
    "df = pd.DataFrame.from_records(client.datasets())\n",
    "client.close()\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3342, 25)\n"
     ]
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>parent_fxf</th>\n",
       "      <th>description</th>\n",
       "      <th>attribution</th>\n",
       "      <th>attribution_link</th>\n",
       "      <th>contact_email</th>\n",
       "      <th>type</th>\n",
       "      <th>updatedAt</th>\n",
       "      <th>createdAt</th>\n",
       "      <th>...</th>\n",
       "      <th>columns_datatype</th>\n",
       "      <th>columns_description</th>\n",
       "      <th>columns_format</th>\n",
       "      <th>download_count</th>\n",
       "      <th>provenance</th>\n",
       "      <th>lens_view_type</th>\n",
       "      <th>lens_display_type</th>\n",
       "      <th>blob_mime_type</th>\n",
       "      <th>hide_from_data_json</th>\n",
       "      <th>publication_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>For Hire Vehicles (FHV) - Active</td>\n",
       "      <td>8wbx-tsch</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;b&gt;PLEASE NOTE:&lt;/b&gt; This dataset, which includ...</td>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T18:55:30.000Z</td>\n",
       "      <td>2015-07-16T17:33:32.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Text, Text, Text, Text, Calendar date, ...</td>\n",
       "      <td>[Base Address, Last Time Updated, Permit activ...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>459469</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-04-05T13:20:47.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Civil Service List (Active)</td>\n",
       "      <td>vx8i-nprf</td>\n",
       "      <td>[]</td>\n",
       "      <td>A Civil Service List consists of all candidate...</td>\n",
       "      <td>Department of Citywide Administrative Services...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T13:11:02.000Z</td>\n",
       "      <td>2016-06-14T21:12:15.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Number, Text, Text, Text, Number, Text,...</td>\n",
       "      <td>[A “Parent Legacy credit” is an additional cre...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>62011</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-12T16:15:05.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DOB Job Application Filings</td>\n",
       "      <td>ic3t-wcy2</td>\n",
       "      <td>[]</td>\n",
       "      <td>This dataset contains all job applications sub...</td>\n",
       "      <td>Department of Buildings (DOB)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T20:11:01.000Z</td>\n",
       "      <td>2013-04-18T15:18:56.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Text, Text, Text, Text, Text, Text, Tex...</td>\n",
       "      <td>[Date when job is assigned to plan examiner, L...</td>\n",
       "      <td>[{'align': 'right'}, {'align': 'right'}, {'ali...</td>\n",
       "      <td>55267</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-06-22T18:23:35.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TLC New Driver Application Status</td>\n",
       "      <td>dpec-ucu7</td>\n",
       "      <td>[]</td>\n",
       "      <td>THIS DATASET IS UPDATED SEVERAL TIMES PER DAY....</td>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T16:02:29.000Z</td>\n",
       "      <td>2016-05-17T18:43:43.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Text, Text, Calendar date, Text, Text, ...</td>\n",
       "      <td>[This is the classification of license you hav...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>39159</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-17T18:44:57.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>For Hire Vehicles (FHV) - Active Drivers</td>\n",
       "      <td>xjfq-wh2d</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;b&gt;PLEASE NOTE:&lt;/b&gt; This dataset, which includ...</td>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T18:57:07.000Z</td>\n",
       "      <td>2015-07-16T17:24:02.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Calendar date, Calendar date, Text, Tex...</td>\n",
       "      <td>[WAV if Wheelchair Accessible Trained\\n, Last ...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>390767</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-11T19:58:17.000Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       name         id parent_fxf  \\\n",
       "0          For Hire Vehicles (FHV) - Active  8wbx-tsch         []   \n",
       "1               Civil Service List (Active)  vx8i-nprf         []   \n",
       "2               DOB Job Application Filings  ic3t-wcy2         []   \n",
       "3         TLC New Driver Application Status  dpec-ucu7         []   \n",
       "4  For Hire Vehicles (FHV) - Active Drivers  xjfq-wh2d         []   \n",
       "\n",
       "                                         description  \\\n",
       "0  <b>PLEASE NOTE:</b> This dataset, which includ...   \n",
       "1  A Civil Service List consists of all candidate...   \n",
       "2  This dataset contains all job applications sub...   \n",
       "3  THIS DATASET IS UPDATED SEVERAL TIMES PER DAY....   \n",
       "4  <b>PLEASE NOTE:</b> This dataset, which includ...   \n",
       "\n",
       "                                         attribution attribution_link  \\\n",
       "0                Taxi and Limousine Commission (TLC)             None   \n",
       "1  Department of Citywide Administrative Services...             None   \n",
       "2                      Department of Buildings (DOB)             None   \n",
       "3                Taxi and Limousine Commission (TLC)             None   \n",
       "4                Taxi and Limousine Commission (TLC)             None   \n",
       "\n",
       "  contact_email     type                 updatedAt                 createdAt  \\\n",
       "0          None  dataset  2024-04-23T18:55:30.000Z  2015-07-16T17:33:32.000Z   \n",
       "1          None  dataset  2024-04-23T13:11:02.000Z  2016-06-14T21:12:15.000Z   \n",
       "2          None  dataset  2024-04-23T20:11:01.000Z  2013-04-18T15:18:56.000Z   \n",
       "3          None  dataset  2024-04-23T16:02:29.000Z  2016-05-17T18:43:43.000Z   \n",
       "4          None  dataset  2024-04-23T18:57:07.000Z  2015-07-16T17:24:02.000Z   \n",
       "\n",
       "   ...                                   columns_datatype  \\\n",
       "0  ...  [Text, Text, Text, Text, Text, Calendar date, ...   \n",
       "1  ...  [Text, Number, Text, Text, Text, Number, Text,...   \n",
       "2  ...  [Text, Text, Text, Text, Text, Text, Text, Tex...   \n",
       "3  ...  [Text, Text, Text, Calendar date, Text, Text, ...   \n",
       "4  ...  [Text, Calendar date, Calendar date, Text, Tex...   \n",
       "\n",
       "                                 columns_description  \\\n",
       "0  [Base Address, Last Time Updated, Permit activ...   \n",
       "1  [A “Parent Legacy credit” is an additional cre...   \n",
       "2  [Date when job is assigned to plan examiner, L...   \n",
       "3  [This is the classification of license you hav...   \n",
       "4  [WAV if Wheelchair Accessible Trained\\n, Last ...   \n",
       "\n",
       "                                      columns_format download_count  \\\n",
       "0  [{'displayStyle': 'plain', 'align': 'left'}, {...         459469   \n",
       "1  [{'displayStyle': 'plain', 'align': 'left'}, {...          62011   \n",
       "2  [{'align': 'right'}, {'align': 'right'}, {'ali...          55267   \n",
       "3  [{'displayStyle': 'plain', 'align': 'left'}, {...          39159   \n",
       "4  [{'displayStyle': 'plain', 'align': 'left'}, {...         390767   \n",
       "\n",
       "  provenance lens_view_type lens_display_type blob_mime_type  \\\n",
       "0   official        tabular             table           None   \n",
       "1   official        tabular             table           None   \n",
       "2   official        tabular             table           None   \n",
       "3   official        tabular             table           None   \n",
       "4   official        tabular             table           None   \n",
       "\n",
       "   hide_from_data_json          publication_date  \n",
       "0                False  2021-04-05T13:20:47.000Z  \n",
       "1                False  2024-01-12T16:15:05.000Z  \n",
       "2                False  2020-06-22T18:23:35.000Z  \n",
       "3                False  2019-12-17T18:44:57.000Z  \n",
       "4                False  2024-01-11T19:58:17.000Z  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the dictionary in the resource column into a pandas DataFrame\n",
    "df = pd.DataFrame.from_records(df.resource)\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NYC Department of Education</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Office of Technology and Innovation (OTI)</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Department of Education (DOE)</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Department of Parks and Recreation (DPR)</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Department of Transportation (DOT)</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 attribution  count\n",
       "0                NYC Department of Education    524\n",
       "1  Office of Technology and Innovation (OTI)    250\n",
       "2              Department of Education (DOE)    203\n",
       "3   Department of Parks and Recreation (DPR)    196\n",
       "4         Department of Transportation (DOT)    193"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group count of datasets by agency\n",
    "data = (\n",
    "    df\n",
    "    .groupby(by='attribution')['name']\n",
    "    .count()\n",
    "    .sort_values(ascending=False)\n",
    "    .reset_index()\n",
    "    .rename(columns={'name':'count'})\n",
    "    .head(20)\n",
    ")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='attribution',\n",
    "    x='count',\n",
    "    color='#1f77b4',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Top 20 Count of Datasets by Agency')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Agency')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/datasets-count.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribution</th>\n",
       "      <th>name</th>\n",
       "      <th>download_count</th>\n",
       "      <th>full_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Department of Health and Mental Hygiene (DOHMH)</td>\n",
       "      <td>Air Quality</td>\n",
       "      <td>649906</td>\n",
       "      <td>Air Quality:\\nDepartment of Health and Mental ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>For Hire Vehicles (FHV) - Active</td>\n",
       "      <td>459469</td>\n",
       "      <td>For Hire Vehicles (FHV) - Active:\\nTaxi and Li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>311</td>\n",
       "      <td>311 Service Requests from 2010 to Present</td>\n",
       "      <td>434081</td>\n",
       "      <td>311 Service Requests from 2010 to Present:\\n311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>Department of Transportation (DOT)</td>\n",
       "      <td>Overhead Electronic Signs</td>\n",
       "      <td>429755</td>\n",
       "      <td>Overhead Electronic Signs:\\nDepartment of Tran...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Department of Homeless Services (DHS)</td>\n",
       "      <td>Directory Of Homebase Locations</td>\n",
       "      <td>421553</td>\n",
       "      <td>Directory Of Homebase Locations:\\nDepartment o...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          attribution  \\\n",
       "45    Department of Health and Mental Hygiene (DOHMH)   \n",
       "0                 Taxi and Limousine Commission (TLC)   \n",
       "5                                                 311   \n",
       "1138               Department of Transportation (DOT)   \n",
       "167             Department of Homeless Services (DHS)   \n",
       "\n",
       "                                           name  download_count  \\\n",
       "45                                  Air Quality          649906   \n",
       "0              For Hire Vehicles (FHV) - Active          459469   \n",
       "5     311 Service Requests from 2010 to Present          434081   \n",
       "1138                  Overhead Electronic Signs          429755   \n",
       "167             Directory Of Homebase Locations          421553   \n",
       "\n",
       "                                              full_name  \n",
       "45    Air Quality:\\nDepartment of Health and Mental ...  \n",
       "0     For Hire Vehicles (FHV) - Active:\\nTaxi and Li...  \n",
       "5       311 Service Requests from 2010 to Present:\\n311  \n",
       "1138  Overhead Electronic Signs:\\nDepartment of Tran...  \n",
       "167   Directory Of Homebase Locations:\\nDepartment o...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting the datasets by download_count\n",
    "data = (\n",
    "    df\n",
    "    .loc[:, ['attribution', 'name', 'download_count']]\n",
    "    .sort_values(by='download_count', ascending=False)\n",
    "    .head(10)\n",
    ")\n",
    "\n",
    "data['full_name'] = data['name']  + ':\\n' + data['attribution']\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='full_name',\n",
    "    x='download_count',\n",
    "    color='#1f77b4',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Top 10 Count of Downloads by Dataset')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Dataset and Agency')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/datasets-download.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribution</th>\n",
       "      <th>download_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>1.730256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Department of Health and Mental Hygiene (DOHMH)</td>\n",
       "      <td>1.233893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Department of City Planning (DCP)</td>\n",
       "      <td>0.981027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Department of Transportation (DOT)</td>\n",
       "      <td>0.693284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Department of Finance (DOF)</td>\n",
       "      <td>0.613925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Department of Education (DOE)</td>\n",
       "      <td>0.593550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Department of Homeless Services (DHS)</td>\n",
       "      <td>0.558492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Police Department (NYPD)</td>\n",
       "      <td>0.516646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>311</td>\n",
       "      <td>0.509913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Office of Technology and Innovation (OTI)</td>\n",
       "      <td>0.490108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Department of Citywide Administrative Services...</td>\n",
       "      <td>0.410231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Department of Buildings (DOB)</td>\n",
       "      <td>0.389534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NYC Department of Education</td>\n",
       "      <td>0.339231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Department of Parks and Recreation (DPR)</td>\n",
       "      <td>0.293098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Board of Elections (BOENY)</td>\n",
       "      <td>0.273982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NYC Emergency Management (NYCEM)</td>\n",
       "      <td>0.262227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Department of Small Business Services (SBS)</td>\n",
       "      <td>0.251123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mayor's Office of Climate and Environmental Ju...</td>\n",
       "      <td>0.248766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Department of Housing Preservation and Develop...</td>\n",
       "      <td>0.170526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Department of Environmental Protection (DEP)</td>\n",
       "      <td>0.147895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          attribution  download_count\n",
       "0                 Taxi and Limousine Commission (TLC)        1.730256\n",
       "1     Department of Health and Mental Hygiene (DOHMH)        1.233893\n",
       "2                   Department of City Planning (DCP)        0.981027\n",
       "3                  Department of Transportation (DOT)        0.693284\n",
       "4                         Department of Finance (DOF)        0.613925\n",
       "5                       Department of Education (DOE)        0.593550\n",
       "6               Department of Homeless Services (DHS)        0.558492\n",
       "7                            Police Department (NYPD)        0.516646\n",
       "8                                                 311        0.509913\n",
       "9           Office of Technology and Innovation (OTI)        0.490108\n",
       "10  Department of Citywide Administrative Services...        0.410231\n",
       "11                      Department of Buildings (DOB)        0.389534\n",
       "12                        NYC Department of Education        0.339231\n",
       "13           Department of Parks and Recreation (DPR)        0.293098\n",
       "14                         Board of Elections (BOENY)        0.273982\n",
       "15                   NYC Emergency Management (NYCEM)        0.262227\n",
       "16        Department of Small Business Services (SBS)        0.251123\n",
       "17  Mayor's Office of Climate and Environmental Ju...        0.248766\n",
       "18  Department of Housing Preservation and Develop...        0.170526\n",
       "19       Department of Environmental Protection (DEP)        0.147895"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = (\n",
    "    df\n",
    "    .groupby(by='attribution')['download_count']\n",
    "    .sum()\n",
    "    .sort_values(ascending=False)\n",
    "    .reset_index()\n",
    "    .head(20)\n",
    ")\n",
    "\n",
    "data['download_count'] = data['download_count'] / 1e6\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='attribution',\n",
    "    x='download_count',\n",
    "    color='#1f77b4',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Top 10 Count of Dataset Downloads by Agency')\n",
    "plt.xlabel('Count (Millions)')\n",
    "plt.ylabel('Agency')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/agency-downloads.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribution</th>\n",
       "      <th>download_count_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Board of Elections (BOENY)</td>\n",
       "      <td>91327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NYC Management Department (NYCEM)</td>\n",
       "      <td>41530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Department of Health and Mental Hygiene (DOHMH)</td>\n",
       "      <td>30095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>311</td>\n",
       "      <td>26838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Department of Homeless Services (DHS)</td>\n",
       "      <td>26595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Metropolitan Transportation Authority (MTA)</td>\n",
       "      <td>26274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Office of Payroll Administration (OPA)</td>\n",
       "      <td>25590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mayor’s Office of Media and Entertainment (MOME)</td>\n",
       "      <td>23508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>23382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NYC Emergency Management (NYCEM)</td>\n",
       "      <td>20171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Broadway Theater</td>\n",
       "      <td>18531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Police Department (NYPD)</td>\n",
       "      <td>17815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Office of Administrative Trials and Hearings (...</td>\n",
       "      <td>16652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>The Squirrel Census</td>\n",
       "      <td>14038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Department of Citywide Administrative Services...</td>\n",
       "      <td>13233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Department of Mental Health and Hygeine</td>\n",
       "      <td>10361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Department of Edcuation</td>\n",
       "      <td>9675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Department of Small Business Services (SBS)</td>\n",
       "      <td>9659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Department of Buildings (DOB)</td>\n",
       "      <td>9275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Office of Citywide Event Coordination and Mana...</td>\n",
       "      <td>8755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          attribution  download_count_avg\n",
       "0                          Board of Elections (BOENY)               91327\n",
       "1                   NYC Management Department (NYCEM)               41530\n",
       "2     Department of Health and Mental Hygiene (DOHMH)               30095\n",
       "3                                                 311               26838\n",
       "4               Department of Homeless Services (DHS)               26595\n",
       "5         Metropolitan Transportation Authority (MTA)               26274\n",
       "6              Office of Payroll Administration (OPA)               25590\n",
       "7    Mayor’s Office of Media and Entertainment (MOME)               23508\n",
       "8                 Taxi and Limousine Commission (TLC)               23382\n",
       "9                    NYC Emergency Management (NYCEM)               20171\n",
       "10                                   Broadway Theater               18531\n",
       "11                           Police Department (NYPD)               17815\n",
       "12  Office of Administrative Trials and Hearings (...               16652\n",
       "13                                The Squirrel Census               14038\n",
       "14  Department of Citywide Administrative Services...               13233\n",
       "15            Department of Mental Health and Hygeine               10361\n",
       "16                            Department of Edcuation                9675\n",
       "17        Department of Small Business Services (SBS)                9659\n",
       "18                      Department of Buildings (DOB)                9275\n",
       "19  Office of Citywide Event Coordination and Mana...                8755"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    " .groupby(by='attribution')['download_count']\n",
    " .mean()\n",
    " .sort_values(ascending=False)\n",
    " .round(0)\n",
    " .astype(int)\n",
    " .reset_index()\n",
    " .rename(columns={'download_count':'download_count_avg'})\n",
    " .head(20)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribution</th>\n",
       "      <th>download_count_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NYC Management Department (NYCEM)</td>\n",
       "      <td>41530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Metropolitan Transportation Authority (MTA)</td>\n",
       "      <td>26274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Office of Payroll Administration (OPA)</td>\n",
       "      <td>25590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mayor’s Office of Media and Entertainment (MOME)</td>\n",
       "      <td>23508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Broadway Theater</td>\n",
       "      <td>18531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Office of Administrative Trials and Hearings (...</td>\n",
       "      <td>16652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Department of Mental Health and Hygeine</td>\n",
       "      <td>10361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Department of Edcuation</td>\n",
       "      <td>9675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Office of Citywide Event Coordination and Mana...</td>\n",
       "      <td>8755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Board of Elections (BOENY)</td>\n",
       "      <td>8202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Business Integrity Commission</td>\n",
       "      <td>6792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>New York City Office of Management and Budget</td>\n",
       "      <td>6327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Department of Information Technology &amp; Telecom...</td>\n",
       "      <td>5304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Taxi and Limousine  Commission (TLC)</td>\n",
       "      <td>5069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NYC &amp; Company (NYCGO)</td>\n",
       "      <td>4879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Fire Department of New York City (FDNY)</td>\n",
       "      <td>4606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Small Business Services (SBS)</td>\n",
       "      <td>4535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>4476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Made in New York (MINY)</td>\n",
       "      <td>4259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Mayor’s Office of Environmental Coordination (...</td>\n",
       "      <td>4246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          attribution  download_count_median\n",
       "0                   NYC Management Department (NYCEM)                  41530\n",
       "1         Metropolitan Transportation Authority (MTA)                  26274\n",
       "2              Office of Payroll Administration (OPA)                  25590\n",
       "3    Mayor’s Office of Media and Entertainment (MOME)                  23508\n",
       "4                                    Broadway Theater                  18531\n",
       "5   Office of Administrative Trials and Hearings (...                  16652\n",
       "6             Department of Mental Health and Hygeine                  10361\n",
       "7                             Department of Edcuation                   9675\n",
       "8   Office of Citywide Event Coordination and Mana...                   8755\n",
       "9                          Board of Elections (BOENY)                   8202\n",
       "10                      Business Integrity Commission                   6792\n",
       "11      New York City Office of Management and Budget                   6327\n",
       "12  Department of Information Technology & Telecom...                   5304\n",
       "13               Taxi and Limousine  Commission (TLC)                   5069\n",
       "14                              NYC & Company (NYCGO)                   4879\n",
       "15            Fire Department of New York City (FDNY)                   4606\n",
       "16                      Small Business Services (SBS)                   4535\n",
       "17             Department of Environmental Protection                   4476\n",
       "18                            Made in New York (MINY)                   4259\n",
       "19  Mayor’s Office of Environmental Coordination (...                   4246"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    " .groupby(by='attribution')['download_count']\n",
    " .median()\n",
    " .sort_values(ascending=False)\n",
    " .round(0)\n",
    " .astype(int)\n",
    " .reset_index()\n",
    " .rename(columns={'download_count':'download_count_median'})\n",
    " .head(20)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Analyzing NYC Datasets with the Most Page Views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3342, 25)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>parent_fxf</th>\n",
       "      <th>description</th>\n",
       "      <th>attribution</th>\n",
       "      <th>attribution_link</th>\n",
       "      <th>contact_email</th>\n",
       "      <th>type</th>\n",
       "      <th>updatedAt</th>\n",
       "      <th>createdAt</th>\n",
       "      <th>...</th>\n",
       "      <th>columns_datatype</th>\n",
       "      <th>columns_description</th>\n",
       "      <th>columns_format</th>\n",
       "      <th>download_count</th>\n",
       "      <th>provenance</th>\n",
       "      <th>lens_view_type</th>\n",
       "      <th>lens_display_type</th>\n",
       "      <th>blob_mime_type</th>\n",
       "      <th>hide_from_data_json</th>\n",
       "      <th>publication_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>For Hire Vehicles (FHV) - Active</td>\n",
       "      <td>8wbx-tsch</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;b&gt;PLEASE NOTE:&lt;/b&gt; This dataset, which includ...</td>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T18:55:30.000Z</td>\n",
       "      <td>2015-07-16T17:33:32.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Text, Text, Text, Text, Calendar date, ...</td>\n",
       "      <td>[Base Address, Last Time Updated, Permit activ...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>459469</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-04-05T13:20:47.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Civil Service List (Active)</td>\n",
       "      <td>vx8i-nprf</td>\n",
       "      <td>[]</td>\n",
       "      <td>A Civil Service List consists of all candidate...</td>\n",
       "      <td>Department of Citywide Administrative Services...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T13:11:02.000Z</td>\n",
       "      <td>2016-06-14T21:12:15.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Number, Text, Text, Text, Number, Text,...</td>\n",
       "      <td>[A “Parent Legacy credit” is an additional cre...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>62011</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-12T16:15:05.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DOB Job Application Filings</td>\n",
       "      <td>ic3t-wcy2</td>\n",
       "      <td>[]</td>\n",
       "      <td>This dataset contains all job applications sub...</td>\n",
       "      <td>Department of Buildings (DOB)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T20:11:01.000Z</td>\n",
       "      <td>2013-04-18T15:18:56.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Text, Text, Text, Text, Text, Text, Tex...</td>\n",
       "      <td>[Date when job is assigned to plan examiner, L...</td>\n",
       "      <td>[{'align': 'right'}, {'align': 'right'}, {'ali...</td>\n",
       "      <td>55267</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-06-22T18:23:35.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TLC New Driver Application Status</td>\n",
       "      <td>dpec-ucu7</td>\n",
       "      <td>[]</td>\n",
       "      <td>THIS DATASET IS UPDATED SEVERAL TIMES PER DAY....</td>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T16:02:29.000Z</td>\n",
       "      <td>2016-05-17T18:43:43.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Text, Text, Calendar date, Text, Text, ...</td>\n",
       "      <td>[This is the classification of license you hav...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>39159</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-17T18:44:57.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>For Hire Vehicles (FHV) - Active Drivers</td>\n",
       "      <td>xjfq-wh2d</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;b&gt;PLEASE NOTE:&lt;/b&gt; This dataset, which includ...</td>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T18:57:07.000Z</td>\n",
       "      <td>2015-07-16T17:24:02.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Calendar date, Calendar date, Text, Tex...</td>\n",
       "      <td>[WAV if Wheelchair Accessible Trained\\n, Last ...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>390767</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-11T19:58:17.000Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       name         id parent_fxf  \\\n",
       "0          For Hire Vehicles (FHV) - Active  8wbx-tsch         []   \n",
       "1               Civil Service List (Active)  vx8i-nprf         []   \n",
       "2               DOB Job Application Filings  ic3t-wcy2         []   \n",
       "3         TLC New Driver Application Status  dpec-ucu7         []   \n",
       "4  For Hire Vehicles (FHV) - Active Drivers  xjfq-wh2d         []   \n",
       "\n",
       "                                         description  \\\n",
       "0  <b>PLEASE NOTE:</b> This dataset, which includ...   \n",
       "1  A Civil Service List consists of all candidate...   \n",
       "2  This dataset contains all job applications sub...   \n",
       "3  THIS DATASET IS UPDATED SEVERAL TIMES PER DAY....   \n",
       "4  <b>PLEASE NOTE:</b> This dataset, which includ...   \n",
       "\n",
       "                                         attribution attribution_link  \\\n",
       "0                Taxi and Limousine Commission (TLC)             None   \n",
       "1  Department of Citywide Administrative Services...             None   \n",
       "2                      Department of Buildings (DOB)             None   \n",
       "3                Taxi and Limousine Commission (TLC)             None   \n",
       "4                Taxi and Limousine Commission (TLC)             None   \n",
       "\n",
       "  contact_email     type                 updatedAt                 createdAt  \\\n",
       "0          None  dataset  2024-04-23T18:55:30.000Z  2015-07-16T17:33:32.000Z   \n",
       "1          None  dataset  2024-04-23T13:11:02.000Z  2016-06-14T21:12:15.000Z   \n",
       "2          None  dataset  2024-04-23T20:11:01.000Z  2013-04-18T15:18:56.000Z   \n",
       "3          None  dataset  2024-04-23T16:02:29.000Z  2016-05-17T18:43:43.000Z   \n",
       "4          None  dataset  2024-04-23T18:57:07.000Z  2015-07-16T17:24:02.000Z   \n",
       "\n",
       "   ...                                   columns_datatype  \\\n",
       "0  ...  [Text, Text, Text, Text, Text, Calendar date, ...   \n",
       "1  ...  [Text, Number, Text, Text, Text, Number, Text,...   \n",
       "2  ...  [Text, Text, Text, Text, Text, Text, Text, Tex...   \n",
       "3  ...  [Text, Text, Text, Calendar date, Text, Text, ...   \n",
       "4  ...  [Text, Calendar date, Calendar date, Text, Tex...   \n",
       "\n",
       "                                 columns_description  \\\n",
       "0  [Base Address, Last Time Updated, Permit activ...   \n",
       "1  [A “Parent Legacy credit” is an additional cre...   \n",
       "2  [Date when job is assigned to plan examiner, L...   \n",
       "3  [This is the classification of license you hav...   \n",
       "4  [WAV if Wheelchair Accessible Trained\\n, Last ...   \n",
       "\n",
       "                                      columns_format download_count  \\\n",
       "0  [{'displayStyle': 'plain', 'align': 'left'}, {...         459469   \n",
       "1  [{'displayStyle': 'plain', 'align': 'left'}, {...          62011   \n",
       "2  [{'align': 'right'}, {'align': 'right'}, {'ali...          55267   \n",
       "3  [{'displayStyle': 'plain', 'align': 'left'}, {...          39159   \n",
       "4  [{'displayStyle': 'plain', 'align': 'left'}, {...         390767   \n",
       "\n",
       "  provenance lens_view_type lens_display_type blob_mime_type  \\\n",
       "0   official        tabular             table           None   \n",
       "1   official        tabular             table           None   \n",
       "2   official        tabular             table           None   \n",
       "3   official        tabular             table           None   \n",
       "4   official        tabular             table           None   \n",
       "\n",
       "   hide_from_data_json          publication_date  \n",
       "0                False  2021-04-05T13:20:47.000Z  \n",
       "1                False  2024-01-12T16:15:05.000Z  \n",
       "2                False  2020-06-22T18:23:35.000Z  \n",
       "3                False  2019-12-17T18:44:57.000Z  \n",
       "4                False  2024-01-11T19:58:17.000Z  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "# reading in a list of dictionaries of our data into a pandas DataFrame\n",
    "df = pd.DataFrame.from_records(client.datasets())\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame.from_records(df.resource)\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3342 entries, 0 to 3341\n",
      "Data columns (total 25 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   name                 3342 non-null   object\n",
      " 1   id                   3342 non-null   object\n",
      " 2   parent_fxf           3342 non-null   object\n",
      " 3   description          3342 non-null   object\n",
      " 4   attribution          3170 non-null   object\n",
      " 5   attribution_link     438 non-null    object\n",
      " 6   contact_email        0 non-null      object\n",
      " 7   type                 3342 non-null   object\n",
      " 8   updatedAt            3342 non-null   object\n",
      " 9   createdAt            3342 non-null   object\n",
      " 10  metadata_updated_at  3342 non-null   object\n",
      " 11  data_updated_at      3164 non-null   object\n",
      " 12  page_views           3342 non-null   object\n",
      " 13  columns_name         3342 non-null   object\n",
      " 14  columns_field_name   3342 non-null   object\n",
      " 15  columns_datatype     3342 non-null   object\n",
      " 16  columns_description  3342 non-null   object\n",
      " 17  columns_format       3342 non-null   object\n",
      " 18  download_count       3342 non-null   int64 \n",
      " 19  provenance           3342 non-null   object\n",
      " 20  lens_view_type       3342 non-null   object\n",
      " 21  lens_display_type    3342 non-null   object\n",
      " 22  blob_mime_type       290 non-null    object\n",
      " 23  hide_from_data_json  3342 non-null   bool  \n",
      " 24  publication_date     3315 non-null   object\n",
      "dtypes: bool(1), int64(1), object(23)\n",
      "memory usage: 630.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    {'page_views_last_week': 8582, 'page_views_las...\n",
       "1    {'page_views_last_week': 5584, 'page_views_las...\n",
       "2    {'page_views_last_week': 718, 'page_views_last...\n",
       "3    {'page_views_last_week': 132, 'page_views_last...\n",
       "4    {'page_views_last_week': 3076, 'page_views_las...\n",
       "Name: page_views, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['page_views'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>parent_fxf</th>\n",
       "      <th>description</th>\n",
       "      <th>attribution</th>\n",
       "      <th>attribution_link</th>\n",
       "      <th>contact_email</th>\n",
       "      <th>type</th>\n",
       "      <th>updatedAt</th>\n",
       "      <th>createdAt</th>\n",
       "      <th>...</th>\n",
       "      <th>lens_display_type</th>\n",
       "      <th>blob_mime_type</th>\n",
       "      <th>hide_from_data_json</th>\n",
       "      <th>publication_date</th>\n",
       "      <th>page_views_last_week</th>\n",
       "      <th>page_views_last_month</th>\n",
       "      <th>page_views_total</th>\n",
       "      <th>page_views_last_week_log</th>\n",
       "      <th>page_views_last_month_log</th>\n",
       "      <th>page_views_total_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>For Hire Vehicles (FHV) - Active</td>\n",
       "      <td>8wbx-tsch</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;b&gt;PLEASE NOTE:&lt;/b&gt; This dataset, which includ...</td>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T18:55:30.000Z</td>\n",
       "      <td>2015-07-16T17:33:32.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-04-05T13:20:47.000Z</td>\n",
       "      <td>8582.0</td>\n",
       "      <td>36950.0</td>\n",
       "      <td>2657120.0</td>\n",
       "      <td>13.067266</td>\n",
       "      <td>15.173326</td>\n",
       "      <td>21.341432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Civil Service List (Active)</td>\n",
       "      <td>vx8i-nprf</td>\n",
       "      <td>[]</td>\n",
       "      <td>A Civil Service List consists of all candidate...</td>\n",
       "      <td>Department of Citywide Administrative Services...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T13:11:02.000Z</td>\n",
       "      <td>2016-06-14T21:12:15.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-12T16:15:05.000Z</td>\n",
       "      <td>5584.0</td>\n",
       "      <td>21374.0</td>\n",
       "      <td>2606637.0</td>\n",
       "      <td>12.447342</td>\n",
       "      <td>14.383637</td>\n",
       "      <td>21.313759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DOB Job Application Filings</td>\n",
       "      <td>ic3t-wcy2</td>\n",
       "      <td>[]</td>\n",
       "      <td>This dataset contains all job applications sub...</td>\n",
       "      <td>Department of Buildings (DOB)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T20:11:01.000Z</td>\n",
       "      <td>2013-04-18T15:18:56.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-06-22T18:23:35.000Z</td>\n",
       "      <td>718.0</td>\n",
       "      <td>2731.0</td>\n",
       "      <td>2348985.0</td>\n",
       "      <td>9.489848</td>\n",
       "      <td>11.415742</td>\n",
       "      <td>21.163607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TLC New Driver Application Status</td>\n",
       "      <td>dpec-ucu7</td>\n",
       "      <td>[]</td>\n",
       "      <td>THIS DATASET IS UPDATED SEVERAL TIMES PER DAY....</td>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T16:02:29.000Z</td>\n",
       "      <td>2016-05-17T18:43:43.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-17T18:44:57.000Z</td>\n",
       "      <td>132.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>1726969.0</td>\n",
       "      <td>7.055282</td>\n",
       "      <td>9.315150</td>\n",
       "      <td>20.719812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>For Hire Vehicles (FHV) - Active Drivers</td>\n",
       "      <td>xjfq-wh2d</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;b&gt;PLEASE NOTE:&lt;/b&gt; This dataset, which includ...</td>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-23T18:57:07.000Z</td>\n",
       "      <td>2015-07-16T17:24:02.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-11T19:58:17.000Z</td>\n",
       "      <td>3076.0</td>\n",
       "      <td>12807.0</td>\n",
       "      <td>1401242.0</td>\n",
       "      <td>11.587309</td>\n",
       "      <td>13.644758</td>\n",
       "      <td>20.418276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       name         id parent_fxf  \\\n",
       "0          For Hire Vehicles (FHV) - Active  8wbx-tsch         []   \n",
       "1               Civil Service List (Active)  vx8i-nprf         []   \n",
       "2               DOB Job Application Filings  ic3t-wcy2         []   \n",
       "3         TLC New Driver Application Status  dpec-ucu7         []   \n",
       "4  For Hire Vehicles (FHV) - Active Drivers  xjfq-wh2d         []   \n",
       "\n",
       "                                         description  \\\n",
       "0  <b>PLEASE NOTE:</b> This dataset, which includ...   \n",
       "1  A Civil Service List consists of all candidate...   \n",
       "2  This dataset contains all job applications sub...   \n",
       "3  THIS DATASET IS UPDATED SEVERAL TIMES PER DAY....   \n",
       "4  <b>PLEASE NOTE:</b> This dataset, which includ...   \n",
       "\n",
       "                                         attribution attribution_link  \\\n",
       "0                Taxi and Limousine Commission (TLC)             None   \n",
       "1  Department of Citywide Administrative Services...             None   \n",
       "2                      Department of Buildings (DOB)             None   \n",
       "3                Taxi and Limousine Commission (TLC)             None   \n",
       "4                Taxi and Limousine Commission (TLC)             None   \n",
       "\n",
       "  contact_email     type                 updatedAt                 createdAt  \\\n",
       "0          None  dataset  2024-04-23T18:55:30.000Z  2015-07-16T17:33:32.000Z   \n",
       "1          None  dataset  2024-04-23T13:11:02.000Z  2016-06-14T21:12:15.000Z   \n",
       "2          None  dataset  2024-04-23T20:11:01.000Z  2013-04-18T15:18:56.000Z   \n",
       "3          None  dataset  2024-04-23T16:02:29.000Z  2016-05-17T18:43:43.000Z   \n",
       "4          None  dataset  2024-04-23T18:57:07.000Z  2015-07-16T17:24:02.000Z   \n",
       "\n",
       "   ... lens_display_type blob_mime_type hide_from_data_json  \\\n",
       "0  ...             table           None               False   \n",
       "1  ...             table           None               False   \n",
       "2  ...             table           None               False   \n",
       "3  ...             table           None               False   \n",
       "4  ...             table           None               False   \n",
       "\n",
       "           publication_date page_views_last_week page_views_last_month  \\\n",
       "0  2021-04-05T13:20:47.000Z               8582.0               36950.0   \n",
       "1  2024-01-12T16:15:05.000Z               5584.0               21374.0   \n",
       "2  2020-06-22T18:23:35.000Z                718.0                2731.0   \n",
       "3  2019-12-17T18:44:57.000Z                132.0                 636.0   \n",
       "4  2024-01-11T19:58:17.000Z               3076.0               12807.0   \n",
       "\n",
       "  page_views_total  page_views_last_week_log page_views_last_month_log  \\\n",
       "0        2657120.0                 13.067266                 15.173326   \n",
       "1        2606637.0                 12.447342                 14.383637   \n",
       "2        2348985.0                  9.489848                 11.415742   \n",
       "3        1726969.0                  7.055282                  9.315150   \n",
       "4        1401242.0                 11.587309                 13.644758   \n",
       "\n",
       "  page_views_total_log  \n",
       "0            21.341432  \n",
       "1            21.313759  \n",
       "2            21.163607  \n",
       "3            20.719812  \n",
       "4            20.418276  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we need to explode those columns from that dictionary to separate columns in df\n",
    "df = pd.concat(\n",
    "    [df.drop(['page_views'], axis=1), df['page_views'].apply(pd.Series)],\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribution</th>\n",
       "      <th>name</th>\n",
       "      <th>page_views_total</th>\n",
       "      <th>full_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>For Hire Vehicles (FHV) - Active</td>\n",
       "      <td>2.657120</td>\n",
       "      <td>For Hire Vehicles (FHV) - Active:\\nTaxi and Li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Department of Citywide Administrative Services...</td>\n",
       "      <td>Civil Service List (Active)</td>\n",
       "      <td>2.606637</td>\n",
       "      <td>Civil Service List (Active):\\nDepartment of Ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Department of Buildings (DOB)</td>\n",
       "      <td>DOB Job Application Filings</td>\n",
       "      <td>2.348985</td>\n",
       "      <td>DOB Job Application Filings:\\nDepartment of Bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>TLC New Driver Application Status</td>\n",
       "      <td>1.726969</td>\n",
       "      <td>TLC New Driver Application Status:\\nTaxi and L...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Taxi and Limousine Commission (TLC)</td>\n",
       "      <td>For Hire Vehicles (FHV) - Active Drivers</td>\n",
       "      <td>1.401242</td>\n",
       "      <td>For Hire Vehicles (FHV) - Active Drivers:\\nTax...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>311</td>\n",
       "      <td>311 Service Requests from 2010 to Present</td>\n",
       "      <td>0.829836</td>\n",
       "      <td>311 Service Requests from 2010 to Present:\\n311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Department of Citywide Administrative Services...</td>\n",
       "      <td>Civil Service List Certification</td>\n",
       "      <td>0.746349</td>\n",
       "      <td>Civil Service List Certification:\\nDepartment ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Office of Payroll Administration (OPA)</td>\n",
       "      <td>Citywide Payroll Data (Fiscal Year)</td>\n",
       "      <td>0.621615</td>\n",
       "      <td>Citywide Payroll Data (Fiscal Year):\\nOffice o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Police Department (NYPD)</td>\n",
       "      <td>Motor Vehicle Collisions - Crashes</td>\n",
       "      <td>0.538707</td>\n",
       "      <td>Motor Vehicle Collisions - Crashes:\\nPolice De...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Department of Buildings (DOB)</td>\n",
       "      <td>DOB Permit Issuance</td>\n",
       "      <td>0.477656</td>\n",
       "      <td>DOB Permit Issuance:\\nDepartment of Buildings ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         attribution  \\\n",
       "0                Taxi and Limousine Commission (TLC)   \n",
       "1  Department of Citywide Administrative Services...   \n",
       "2                      Department of Buildings (DOB)   \n",
       "3                Taxi and Limousine Commission (TLC)   \n",
       "4                Taxi and Limousine Commission (TLC)   \n",
       "5                                                311   \n",
       "6  Department of Citywide Administrative Services...   \n",
       "7             Office of Payroll Administration (OPA)   \n",
       "8                           Police Department (NYPD)   \n",
       "9                      Department of Buildings (DOB)   \n",
       "\n",
       "                                        name  page_views_total  \\\n",
       "0           For Hire Vehicles (FHV) - Active          2.657120   \n",
       "1                Civil Service List (Active)          2.606637   \n",
       "2                DOB Job Application Filings          2.348985   \n",
       "3          TLC New Driver Application Status          1.726969   \n",
       "4   For Hire Vehicles (FHV) - Active Drivers          1.401242   \n",
       "5  311 Service Requests from 2010 to Present          0.829836   \n",
       "6           Civil Service List Certification          0.746349   \n",
       "7        Citywide Payroll Data (Fiscal Year)          0.621615   \n",
       "8         Motor Vehicle Collisions - Crashes          0.538707   \n",
       "9                        DOB Permit Issuance          0.477656   \n",
       "\n",
       "                                           full_name  \n",
       "0  For Hire Vehicles (FHV) - Active:\\nTaxi and Li...  \n",
       "1  Civil Service List (Active):\\nDepartment of Ci...  \n",
       "2  DOB Job Application Filings:\\nDepartment of Bu...  \n",
       "3  TLC New Driver Application Status:\\nTaxi and L...  \n",
       "4  For Hire Vehicles (FHV) - Active Drivers:\\nTax...  \n",
       "5    311 Service Requests from 2010 to Present:\\n311  \n",
       "6  Civil Service List Certification:\\nDepartment ...  \n",
       "7  Citywide Payroll Data (Fiscal Year):\\nOffice o...  \n",
       "8  Motor Vehicle Collisions - Crashes:\\nPolice De...  \n",
       "9  DOB Permit Issuance:\\nDepartment of Buildings ...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = (\n",
    "    df\n",
    "    .loc[:, ['attribution', 'name', 'page_views_total']]\n",
    "    .sort_values(by='page_views_total', ascending=False)\n",
    "    .head(10)\n",
    ")\n",
    "\n",
    "data['full_name'] =  + data['name'] + ':\\n' + data['attribution']\n",
    "data['page_views_total'] = data['page_views_total'] / 1e6\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='full_name',\n",
    "    x='page_views_total',\n",
    "    color='#1f77b4',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title(\n",
    "    'Top 10 Total Page Views by Dataset on NYC Open Data',\n",
    "    fontsize=12, pad=10\n",
    ")\n",
    "plt.xlabel('Page Views (Millions)')\n",
    "plt.ylabel('Dataset and Agency')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/pageviews-dataset.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Answering Questions about NYC 311 Complaints with SoQL\n",
    "Note: `WARNING:root:Requests made without an app_token will be subject to strict throttling limits.`\n",
    "\n",
    "Read more from the SODA documentation here: https://dev.socrata.com/docs/app-tokens.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Most NYC 311 Complaints by Complaint Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (10, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>3340726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Illegal Parking</td>\n",
       "      <td>2464919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>2213285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Blocked Driveway</td>\n",
       "      <td>1565414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Street Condition</td>\n",
       "      <td>1259879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Noise - Street/Sidewalk</td>\n",
       "      <td>1187134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Street Light Condition</td>\n",
       "      <td>1137719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>1073753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PLUMBING</td>\n",
       "      <td>938192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HEATING</td>\n",
       "      <td>887869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        complaint_type    count\n",
       "0                  Noise - Residential  3340726\n",
       "1                      Illegal Parking  2464919\n",
       "2                       HEAT/HOT WATER  2213285\n",
       "3                     Blocked Driveway  1565414\n",
       "4                     Street Condition  1259879\n",
       "5              Noise - Street/Sidewalk  1187134\n",
       "6               Street Light Condition  1137719\n",
       "7  Request Large Bulky Item Collection  1073753\n",
       "8                             PLUMBING   938192\n",
       "9                              HEATING   887869"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Source domain for NYC Open Data on Socrata\n",
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "\n",
    "# Dataset id for NYC 311 on NYC Open Data on Socrata\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "# Socrata - The main class that interacts with the SODA API. We pass the source domain value\n",
    "# of NYC Open data, the app token as 'None', and set the timeout parameter for '1,000 seconds'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "# SoQL query string below: select the complaint_type and the count of complaint_type columns \n",
    "# grouped by complaint_type, sorted by the count of complaint_type in descending order\n",
    "# and limit our records to 10\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    complaint_type, \n",
    "    count(unique_key) AS count\n",
    "GROUP BY \n",
    "    complaint_type\n",
    "ORDER BY \n",
    "    count DESC\n",
    "LIMIT \n",
    "    10\n",
    "\"\"\"\n",
    "\n",
    "# Requesting data from the NYC 311 dataset and passing our query as a full SoQL query string\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 2 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   complaint_type  10 non-null     object\n",
      " 1   count           10 non-null     object\n",
      "dtypes: object(2)\n",
      "memory usage: 288.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 2 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   complaint_type  10 non-null     object \n",
      " 1   count           10 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 288.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# cast as int and convert to MM\n",
    "df['count'] = df['count'].astype(int) / 1e6\n",
    "\n",
    "# sanity check\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "data = df.sort_values(by='count', ascending=False)\n",
    "\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='complaint_type',\n",
    "    x='count',\n",
    "    color='#1f77b4',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Top 10 Count of 311 Complaints by Complaint Type')\n",
    "plt.xlabel('Count (Millions)')\n",
    "plt.ylabel('Complaint Type')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/complaint-type.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (10, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>2206300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>ENTIRE BUILDING</td>\n",
       "      <td>1445486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blocked Driveway</td>\n",
       "      <td>No Access</td>\n",
       "      <td>1164921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>1073753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Noise - Street/Sidewalk</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>924813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Banging/Pounding</td>\n",
       "      <td>873123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HEATING</td>\n",
       "      <td>HEAT</td>\n",
       "      <td>868960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Street Light Condition</td>\n",
       "      <td>Street Light Out</td>\n",
       "      <td>837034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>APARTMENT ONLY</td>\n",
       "      <td>767799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Street Condition</td>\n",
       "      <td>Pothole</td>\n",
       "      <td>761232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        complaint_type                           descriptor  \\\n",
       "0                  Noise - Residential                     Loud Music/Party   \n",
       "1                       HEAT/HOT WATER                      ENTIRE BUILDING   \n",
       "2                     Blocked Driveway                            No Access   \n",
       "3  Request Large Bulky Item Collection  Request Large Bulky Item Collection   \n",
       "4              Noise - Street/Sidewalk                     Loud Music/Party   \n",
       "5                  Noise - Residential                     Banging/Pounding   \n",
       "6                              HEATING                                 HEAT   \n",
       "7               Street Light Condition                     Street Light Out   \n",
       "8                       HEAT/HOT WATER                       APARTMENT ONLY   \n",
       "9                     Street Condition                              Pothole   \n",
       "\n",
       "     count  \n",
       "0  2206300  \n",
       "1  1445486  \n",
       "2  1164921  \n",
       "3  1073753  \n",
       "4   924813  \n",
       "5   873123  \n",
       "6   868960  \n",
       "7   837034  \n",
       "8   767799  \n",
       "9   761232  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "# SoQL query string below: select the complaint_type and the count of complaint_type columns \n",
    "# grouped by complaint_type, sorted by the count of complaint_type in descending order\n",
    "# and limit our records to 10\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    complaint_type, \n",
    "    descriptor,\n",
    "    count(unique_key) AS count\n",
    "GROUP BY \n",
    "    complaint_type, descriptor\n",
    "ORDER BY \n",
    "    count DESC\n",
    "LIMIT \n",
    "    10\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "      <th>type_full</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>2.206300</td>\n",
       "      <td>Loud Music/Party:\\nNoise - Residential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>ENTIRE BUILDING</td>\n",
       "      <td>1.445486</td>\n",
       "      <td>ENTIRE BUILDING:\\nHEAT/HOT WATER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blocked Driveway</td>\n",
       "      <td>No Access</td>\n",
       "      <td>1.164921</td>\n",
       "      <td>No Access:\\nBlocked Driveway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>1.073753</td>\n",
       "      <td>Request Large Bulky Item Collection:\\nRequest ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Noise - Street/Sidewalk</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>0.924813</td>\n",
       "      <td>Loud Music/Party:\\nNoise - Street/Sidewalk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        complaint_type                           descriptor  \\\n",
       "0                  Noise - Residential                     Loud Music/Party   \n",
       "1                       HEAT/HOT WATER                      ENTIRE BUILDING   \n",
       "2                     Blocked Driveway                            No Access   \n",
       "3  Request Large Bulky Item Collection  Request Large Bulky Item Collection   \n",
       "4              Noise - Street/Sidewalk                     Loud Music/Party   \n",
       "\n",
       "      count                                          type_full  \n",
       "0  2.206300             Loud Music/Party:\\nNoise - Residential  \n",
       "1  1.445486                   ENTIRE BUILDING:\\nHEAT/HOT WATER  \n",
       "2  1.164921                       No Access:\\nBlocked Driveway  \n",
       "3  1.073753  Request Large Bulky Item Collection:\\nRequest ...  \n",
       "4  0.924813         Loud Music/Party:\\nNoise - Street/Sidewalk  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cast to int and convert to MM\n",
    "df['count'] = df['count'].astype(int) / 1e6\n",
    "df['type_full'] =  df['descriptor'] + ':\\n' + df['complaint_type']\n",
    "\n",
    "# sanity check\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "data = df.sort_values(by='count', ascending=False)\n",
    "\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='type_full',\n",
    "    x='count',\n",
    "    color='#1f77b4',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Top 10 Count of 311 Complaints by Complaint Descriptor and Type')\n",
    "plt.xlabel('Count (Millions)')\n",
    "plt.ylabel('Complaint Descriptor and Type')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/descriptor-type.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Most NYC 311 Complaints by Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (10, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>24415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08-05T00:00:00.000</td>\n",
       "      <td>19560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>17962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>16916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-06-21T00:00:00.000</td>\n",
       "      <td>15883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-06-20T00:00:00.000</td>\n",
       "      <td>15825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07-04T00:00:00.000</td>\n",
       "      <td>15794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-04-15T00:00:00.000</td>\n",
       "      <td>15774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>15205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-06-28T00:00:00.000</td>\n",
       "      <td>14057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day  count\n",
       "0  2020-08-04T00:00:00.000  24415\n",
       "1  2020-08-05T00:00:00.000  19560\n",
       "2  2023-09-29T00:00:00.000  17962\n",
       "3  2020-07-05T00:00:00.000  16916\n",
       "4  2020-06-21T00:00:00.000  15883\n",
       "5  2020-06-20T00:00:00.000  15825\n",
       "6  2020-07-04T00:00:00.000  15794\n",
       "7  2024-04-15T00:00:00.000  15774\n",
       "8  2021-09-02T00:00:00.000  15205\n",
       "9  2020-06-28T00:00:00.000  14057"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "# SoQL query string below: select the day and count day grouped by day,\n",
    "# sort by count in descending order and limit our records to 1,000\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    date_trunc_ymd(created_date) AS day, \n",
    "    count(unique_key) AS count\n",
    "GROUP BY \n",
    "    day\n",
    "ORDER BY \n",
    "    count DESC\n",
    "LIMIT \n",
    "    10\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   day      10 non-null     object\n",
      " 1   count    10 non-null     int64 \n",
      " 2   day_fmt  10 non-null     object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 368.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# casting to different dtypes\n",
    "df['day_fmt'] = pd.to_datetime(df['day']).astype(str)\n",
    "df['count'] = df['count'].astype(int)\n",
    "\n",
    "# sanity check\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "data = df.sort_values(by='count', ascending=False)\n",
    "\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='day_fmt',\n",
    "    x='count',\n",
    "    color='#1f77b4',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Top 10 Count of 311 Complaints by Day')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Day')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/day-top.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lineplot of NYC 311 Complaints by Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (5226, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>24415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08-05T00:00:00.000</td>\n",
       "      <td>19560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>17962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>16916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-06-21T00:00:00.000</td>\n",
       "      <td>15883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-06-20T00:00:00.000</td>\n",
       "      <td>15825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07-04T00:00:00.000</td>\n",
       "      <td>15794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-04-15T00:00:00.000</td>\n",
       "      <td>15774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>15205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-06-28T00:00:00.000</td>\n",
       "      <td>14057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day  count\n",
       "0  2020-08-04T00:00:00.000  24415\n",
       "1  2020-08-05T00:00:00.000  19560\n",
       "2  2023-09-29T00:00:00.000  17962\n",
       "3  2020-07-05T00:00:00.000  16916\n",
       "4  2020-06-21T00:00:00.000  15883\n",
       "5  2020-06-20T00:00:00.000  15825\n",
       "6  2020-07-04T00:00:00.000  15794\n",
       "7  2024-04-15T00:00:00.000  15774\n",
       "8  2021-09-02T00:00:00.000  15205\n",
       "9  2020-06-28T00:00:00.000  14057"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "# SoQL query string below: select the day and count day grouped by day,\n",
    "# sort by count in descending order and limit our records to 1,000\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    date_trunc_ymd(created_date) AS day, \n",
    "    count(unique_key) AS count\n",
    "GROUP BY \n",
    "    day\n",
    "ORDER BY \n",
    "    count DESC\n",
    "LIMIT \n",
    "    10000\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cast day to datetime and count as int and save out CSV\n",
    "df['day_fmt'] = pd.to_datetime(df['day'])\n",
    "df['count']  = df['count'].astype(int)\n",
    "\n",
    "df.to_csv('data/complaints-day.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>count</th>\n",
       "      <th>day_fmt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>2024-04-16T00:00:00.000</td>\n",
       "      <td>10350</td>\n",
       "      <td>2024-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>2024-04-17T00:00:00.000</td>\n",
       "      <td>9241</td>\n",
       "      <td>2024-04-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>2024-04-18T00:00:00.000</td>\n",
       "      <td>8854</td>\n",
       "      <td>2024-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>2024-04-19T00:00:00.000</td>\n",
       "      <td>9288</td>\n",
       "      <td>2024-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>2024-04-20T00:00:00.000</td>\n",
       "      <td>7953</td>\n",
       "      <td>2024-04-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          day  count    day_fmt\n",
       "175   2024-04-16T00:00:00.000  10350 2024-04-16\n",
       "583   2024-04-17T00:00:00.000   9241 2024-04-17\n",
       "810   2024-04-18T00:00:00.000   8854 2024-04-18\n",
       "557   2024-04-19T00:00:00.000   9288 2024-04-19\n",
       "1504  2024-04-20T00:00:00.000   7953 2024-04-20"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing last two days to prevent incomplete days\n",
    "df = (\n",
    "    df\n",
    "    .sort_values(by='day_fmt')\n",
    "    .iloc[:-2, :]\n",
    ")\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "data = (\n",
    "    df\n",
    "    .set_index('day_fmt')\n",
    "    .loc[:, ['count']]\n",
    "    .astype(int)\n",
    ")\n",
    "\n",
    "sns.lineplot(\n",
    "    data=data,\n",
    "    x=data.index,\n",
    "    y='count',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('NYC 311 Complaints by Day')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Count')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/day-line.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Complaints on the Highest Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (100, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Damaged Tree</td>\n",
       "      <td>Entire Tree Has Fallen Down</td>\n",
       "      <td>7848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Damaged Tree</td>\n",
       "      <td>Branch or Limb Has Fallen Down</td>\n",
       "      <td>4971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Damaged Tree</td>\n",
       "      <td>Branch Cracked and Will Fall</td>\n",
       "      <td>1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Damaged Tree</td>\n",
       "      <td>Tree Leaning/Uprooted</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Street Light Condition</td>\n",
       "      <td>Street Light Out</td>\n",
       "      <td>397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Traffic Signal Condition</td>\n",
       "      <td>Controller</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Noise - Street/Sidewalk</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Blocked Driveway</td>\n",
       "      <td>No Access</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day                       complaint_type  \\\n",
       "0  2020-08-04T00:00:00.000                         Damaged Tree   \n",
       "1  2020-08-04T00:00:00.000                         Damaged Tree   \n",
       "2  2020-08-04T00:00:00.000                         Damaged Tree   \n",
       "3  2020-08-04T00:00:00.000  Request Large Bulky Item Collection   \n",
       "4  2020-08-04T00:00:00.000                  Noise - Residential   \n",
       "5  2020-08-04T00:00:00.000                         Damaged Tree   \n",
       "6  2020-08-04T00:00:00.000               Street Light Condition   \n",
       "7  2020-08-04T00:00:00.000             Traffic Signal Condition   \n",
       "8  2020-08-04T00:00:00.000              Noise - Street/Sidewalk   \n",
       "9  2020-08-04T00:00:00.000                     Blocked Driveway   \n",
       "\n",
       "                            descriptor count  \n",
       "0          Entire Tree Has Fallen Down  7848  \n",
       "1       Branch or Limb Has Fallen Down  4971  \n",
       "2         Branch Cracked and Will Fall  1330  \n",
       "3  Request Large Bulky Item Collection   909  \n",
       "4                     Loud Music/Party   762  \n",
       "5                Tree Leaning/Uprooted   510  \n",
       "6                     Street Light Out   397  \n",
       "7                           Controller   353  \n",
       "8                     Loud Music/Party   263  \n",
       "9                            No Access   259  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    date_trunc_ymd(created_date) AS day,\n",
    "    complaint_type, \n",
    "    descriptor,\n",
    "    count(unique_key) AS count\n",
    "WHERE\n",
    "    date_trunc_ymd(created_date) == '2020-08-04T00:00:00.000'\n",
    "GROUP BY \n",
    "    day, complaint_type, descriptor\n",
    "ORDER BY \n",
    "    count DESC\n",
    "LIMIT \n",
    "    100\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "      <th>type_full</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Damaged Tree</td>\n",
       "      <td>Entire Tree Has Fallen Down</td>\n",
       "      <td>7848</td>\n",
       "      <td>Entire Tree Has Fallen Down:\\nDamaged Tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Damaged Tree</td>\n",
       "      <td>Branch or Limb Has Fallen Down</td>\n",
       "      <td>4971</td>\n",
       "      <td>Branch or Limb Has Fallen Down:\\nDamaged Tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Damaged Tree</td>\n",
       "      <td>Branch Cracked and Will Fall</td>\n",
       "      <td>1330</td>\n",
       "      <td>Branch Cracked and Will Fall:\\nDamaged Tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>909</td>\n",
       "      <td>Request Large Bulky Item Collection:\\nRequest ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-04T00:00:00.000</td>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>762</td>\n",
       "      <td>Loud Music/Party:\\nNoise - Residential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day                       complaint_type  \\\n",
       "0  2020-08-04T00:00:00.000                         Damaged Tree   \n",
       "1  2020-08-04T00:00:00.000                         Damaged Tree   \n",
       "2  2020-08-04T00:00:00.000                         Damaged Tree   \n",
       "3  2020-08-04T00:00:00.000  Request Large Bulky Item Collection   \n",
       "4  2020-08-04T00:00:00.000                  Noise - Residential   \n",
       "\n",
       "                            descriptor  count  \\\n",
       "0          Entire Tree Has Fallen Down   7848   \n",
       "1       Branch or Limb Has Fallen Down   4971   \n",
       "2         Branch Cracked and Will Fall   1330   \n",
       "3  Request Large Bulky Item Collection    909   \n",
       "4                     Loud Music/Party    762   \n",
       "\n",
       "                                           type_full  \n",
       "0         Entire Tree Has Fallen Down:\\nDamaged Tree  \n",
       "1      Branch or Limb Has Fallen Down:\\nDamaged Tree  \n",
       "2        Branch Cracked and Will Fall:\\nDamaged Tree  \n",
       "3  Request Large Bulky Item Collection:\\nRequest ...  \n",
       "4             Loud Music/Party:\\nNoise - Residential  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# casting as int\n",
    "df['count'] = df['count'].astype(int)\n",
    "df['type_full'] =  df['descriptor']  + ':\\n' + df['complaint_type']\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "data = (\n",
    "    df\n",
    "    .sort_values(by='count', ascending=False)\n",
    "    .head(10)\n",
    ")\n",
    "\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='type_full',\n",
    "    x='count',\n",
    "    color='#1f77b4',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "# retrieve date string\n",
    "date = data.head(1)['day'].str.split('T').values[0][0]\n",
    "plt.title('Top 10 Count of 311 Complaints by Descriptor and Type on {}'.format(date))\n",
    "\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Descriptor and Type')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/day-first.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most probable event is Tropical Storm Isaias.\n",
    "Source: https://www.weather.gov/bgm/pastFloodAugust042020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Complaints on the Second Highest Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (100, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Sewer Backup (Use Comments) (SA)</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Water Leak</td>\n",
       "      <td>Heavy Flow</td>\n",
       "      <td>1042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>WATER LEAK</td>\n",
       "      <td>HEAVY FLOW</td>\n",
       "      <td>1038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer Maintenance</td>\n",
       "      <td>Backup</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer Maintenance</td>\n",
       "      <td>Flooding on Street</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Water Leak</td>\n",
       "      <td>Slow Leak</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>WATER LEAK</td>\n",
       "      <td>SLOW LEAK</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Catch Basin Clogged/Flooding (Use Comments) (SC)</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day       complaint_type  \\\n",
       "0  2023-09-29T00:00:00.000                Sewer   \n",
       "1  2023-09-29T00:00:00.000           Water Leak   \n",
       "2  2023-09-29T00:00:00.000           WATER LEAK   \n",
       "3  2023-09-29T00:00:00.000    Sewer Maintenance   \n",
       "4  2023-09-29T00:00:00.000                Sewer   \n",
       "5  2023-09-29T00:00:00.000    Sewer Maintenance   \n",
       "6  2023-09-29T00:00:00.000           Water Leak   \n",
       "7  2023-09-29T00:00:00.000           WATER LEAK   \n",
       "8  2023-09-29T00:00:00.000                Sewer   \n",
       "9  2023-09-29T00:00:00.000  Noise - Residential   \n",
       "\n",
       "                                         descriptor count  \n",
       "0                  Sewer Backup (Use Comments) (SA)  1050  \n",
       "1                                        Heavy Flow  1042  \n",
       "2                                        HEAVY FLOW  1038  \n",
       "3                                            Backup  1012  \n",
       "4                              Street Flooding (SJ)   623  \n",
       "5                                Flooding on Street   588  \n",
       "6                                         Slow Leak   465  \n",
       "7                                         SLOW LEAK   463  \n",
       "8  Catch Basin Clogged/Flooding (Use Comments) (SC)   394  \n",
       "9                                  Loud Music/Party   363  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    date_trunc_ymd(created_date) AS day,\n",
    "    complaint_type, \n",
    "    descriptor,\n",
    "    count(unique_key) AS count\n",
    "WHERE\n",
    "    date_trunc_ymd(created_date) == '2023-09-29T00:00:00.000'\n",
    "GROUP BY \n",
    "    day, complaint_type, descriptor\n",
    "ORDER BY \n",
    "    count DESC\n",
    "LIMIT \n",
    "    100\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "      <th>type_full</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Sewer Backup (Use Comments) (SA)</td>\n",
       "      <td>1050</td>\n",
       "      <td>Sewer Backup (Use Comments) (SA):\\nSewer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Water Leak</td>\n",
       "      <td>Heavy Flow</td>\n",
       "      <td>1042</td>\n",
       "      <td>Heavy Flow:\\nWater Leak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>WATER LEAK</td>\n",
       "      <td>HEAVY FLOW</td>\n",
       "      <td>1038</td>\n",
       "      <td>HEAVY FLOW:\\nWATER LEAK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer Maintenance</td>\n",
       "      <td>Backup</td>\n",
       "      <td>1012</td>\n",
       "      <td>Backup:\\nSewer Maintenance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>623</td>\n",
       "      <td>Street Flooding (SJ):\\nSewer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day     complaint_type  \\\n",
       "0  2023-09-29T00:00:00.000              Sewer   \n",
       "1  2023-09-29T00:00:00.000         Water Leak   \n",
       "2  2023-09-29T00:00:00.000         WATER LEAK   \n",
       "3  2023-09-29T00:00:00.000  Sewer Maintenance   \n",
       "4  2023-09-29T00:00:00.000              Sewer   \n",
       "\n",
       "                         descriptor  count  \\\n",
       "0  Sewer Backup (Use Comments) (SA)   1050   \n",
       "1                        Heavy Flow   1042   \n",
       "2                        HEAVY FLOW   1038   \n",
       "3                            Backup   1012   \n",
       "4              Street Flooding (SJ)    623   \n",
       "\n",
       "                                  type_full  \n",
       "0  Sewer Backup (Use Comments) (SA):\\nSewer  \n",
       "1                   Heavy Flow:\\nWater Leak  \n",
       "2                   HEAVY FLOW:\\nWATER LEAK  \n",
       "3                Backup:\\nSewer Maintenance  \n",
       "4              Street Flooding (SJ):\\nSewer  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# castint coun as int\n",
    "df['count'] = df['count'].astype(int)\n",
    "df['type_full'] = df['descriptor'] + ':\\n' + df['complaint_type']\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "data = (\n",
    "    df\n",
    "    .sort_values(by='count', ascending=False)\n",
    "    .head(10)\n",
    ")\n",
    "\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='type_full',\n",
    "    x='count',\n",
    "    color='#1f77b4',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "# retrieve date string\n",
    "date = data.head(1)['day'].str.split('T').values[0][0]\n",
    "plt.title('Top 10 Count of 311 Complaints by Descriptor and Type on {}'.format(date))\n",
    "\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Descriptor and Type')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/day-second.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most probable event is Tropical Storm Ophelia.\n",
    "Source: https://en.wikipedia.org/wiki/September_2023_New_York_floods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Complaints on the Third Highest Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (100, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>4334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Illegal Fireworks</td>\n",
       "      <td>N/A</td>\n",
       "      <td>4199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Noise - Street/Sidewalk</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>2837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Noise - Vehicle</td>\n",
       "      <td>Car/Truck Music</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Banging/Pounding</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Non-Emergency Police Matter</td>\n",
       "      <td>Social Distancing</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Blocked Driveway</td>\n",
       "      <td>No Access</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Noise - Commercial</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Water System</td>\n",
       "      <td>Hydrant Running Full (WA4)</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day                       complaint_type  \\\n",
       "0  2020-07-05T00:00:00.000                  Noise - Residential   \n",
       "1  2020-07-05T00:00:00.000                    Illegal Fireworks   \n",
       "2  2020-07-05T00:00:00.000              Noise - Street/Sidewalk   \n",
       "3  2020-07-05T00:00:00.000  Request Large Bulky Item Collection   \n",
       "4  2020-07-05T00:00:00.000                      Noise - Vehicle   \n",
       "5  2020-07-05T00:00:00.000                  Noise - Residential   \n",
       "6  2020-07-05T00:00:00.000          Non-Emergency Police Matter   \n",
       "7  2020-07-05T00:00:00.000                     Blocked Driveway   \n",
       "8  2020-07-05T00:00:00.000                   Noise - Commercial   \n",
       "9  2020-07-05T00:00:00.000                         Water System   \n",
       "\n",
       "                            descriptor count  \n",
       "0                     Loud Music/Party  4334  \n",
       "1                                  N/A  4199  \n",
       "2                     Loud Music/Party  2837  \n",
       "3  Request Large Bulky Item Collection   752  \n",
       "4                      Car/Truck Music   412  \n",
       "5                     Banging/Pounding   304  \n",
       "6                    Social Distancing   239  \n",
       "7                            No Access   206  \n",
       "8                     Loud Music/Party   189  \n",
       "9           Hydrant Running Full (WA4)   188  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    date_trunc_ymd(created_date) AS day,\n",
    "    complaint_type, \n",
    "    descriptor,\n",
    "    count(unique_key) AS count\n",
    "WHERE\n",
    "    date_trunc_ymd(created_date) == '2020-07-05T00:00:00.000'\n",
    "GROUP BY \n",
    "    day, complaint_type, descriptor\n",
    "ORDER BY \n",
    "    count DESC\n",
    "LIMIT \n",
    "    100\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "      <th>type_full</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>4334</td>\n",
       "      <td>Loud Music/Party:\\nNoise - Residential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Illegal Fireworks</td>\n",
       "      <td>N/A</td>\n",
       "      <td>4199</td>\n",
       "      <td>N/A:\\nIllegal Fireworks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Noise - Street/Sidewalk</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>2837</td>\n",
       "      <td>Loud Music/Party:\\nNoise - Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>752</td>\n",
       "      <td>Request Large Bulky Item Collection:\\nRequest ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-05T00:00:00.000</td>\n",
       "      <td>Noise - Vehicle</td>\n",
       "      <td>Car/Truck Music</td>\n",
       "      <td>412</td>\n",
       "      <td>Car/Truck Music:\\nNoise - Vehicle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day                       complaint_type  \\\n",
       "0  2020-07-05T00:00:00.000                  Noise - Residential   \n",
       "1  2020-07-05T00:00:00.000                    Illegal Fireworks   \n",
       "2  2020-07-05T00:00:00.000              Noise - Street/Sidewalk   \n",
       "3  2020-07-05T00:00:00.000  Request Large Bulky Item Collection   \n",
       "4  2020-07-05T00:00:00.000                      Noise - Vehicle   \n",
       "\n",
       "                            descriptor  count  \\\n",
       "0                     Loud Music/Party   4334   \n",
       "1                                  N/A   4199   \n",
       "2                     Loud Music/Party   2837   \n",
       "3  Request Large Bulky Item Collection    752   \n",
       "4                      Car/Truck Music    412   \n",
       "\n",
       "                                           type_full  \n",
       "0             Loud Music/Party:\\nNoise - Residential  \n",
       "1                            N/A:\\nIllegal Fireworks  \n",
       "2         Loud Music/Party:\\nNoise - Street/Sidewalk  \n",
       "3  Request Large Bulky Item Collection:\\nRequest ...  \n",
       "4                  Car/Truck Music:\\nNoise - Vehicle  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# castint coun as int\n",
    "df['count'] = df['count'].astype(int)\n",
    "df['type_full'] = df['descriptor'] + ':\\n' + df['complaint_type']\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "data = (\n",
    "    df\n",
    "    .sort_values(by='count', ascending=False)\n",
    "    .head(10)\n",
    ")\n",
    "\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='type_full',\n",
    "    x='count',\n",
    "    color='#1f77b4',\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "# retrieve date string\n",
    "date = data.head(1)['day'].str.split('T').values[0][0]\n",
    "plt.title('Top 10 Count of 311 Complaints by Descriptor and Type on {}'.format(date))\n",
    "\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Descriptor and Type')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figures/day-third.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most probable event is July 4th celebrations.\n",
    "Source: https://abc7ny.com/nyc-shootings-2020-last-night-this-week-in/6299513/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Analyzing NYC 311 Street Flooding Complaints\n",
    "Note: `WARNING:root:Requests made without an app_token will be subject to strict throttling limits.`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Searching the dataset for the word \"flood\" in the complaint_type field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (0, 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "# SoQL query string below: select complaint_type and count of complaint_type grouped by compaint_type,\n",
    "# where the word \"flood\" is in compplaint_type, sort count in descending order and\n",
    "# limit our records to 1,000\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    complaint_type, \n",
    "    count(unique_key) AS count\n",
    "WHERE \n",
    "    LOWER(complaint_type) LIKE '%flood%'\n",
    "GROUP BY \n",
    "    complaint_type\n",
    "ORDER BY \n",
    "    count(unique_key) DESC\n",
    "LIMIT \n",
    "    1000\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Searching the dataset for the word \"flood\" in the descriptor field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (13, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sewer</td>\n",
       "      <td>Catch Basin Clogged/Flooding (Use Comments) (SC)</td>\n",
       "      <td>114680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>39512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Street Light Condition</td>\n",
       "      <td>Flood Light Lamp Out</td>\n",
       "      <td>6517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sewer</td>\n",
       "      <td>Highway Flooding (SH)</td>\n",
       "      <td>3156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Street Light Condition</td>\n",
       "      <td>Flood Light Lamp Cycling</td>\n",
       "      <td>2595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sewer Maintenance</td>\n",
       "      <td>Flooding on Street</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OEM Literature Request</td>\n",
       "      <td>Ready NY - Flooding</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Street Light Condition</td>\n",
       "      <td>Flood Light Lamp Dayburning</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Street Light Condition</td>\n",
       "      <td>Flood Light Lamp Missing</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Street Light Condition</td>\n",
       "      <td>Flood Light Lamp Dim</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sewer</td>\n",
       "      <td>RAIN GARDEN FLOODING (SRGFLD)</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Public Toilet</td>\n",
       "      <td>Flooded</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sewer Maintenance</td>\n",
       "      <td>Flooding on Highway</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            complaint_type                                        descriptor  \\\n",
       "0                    Sewer  Catch Basin Clogged/Flooding (Use Comments) (SC)   \n",
       "1                    Sewer                              Street Flooding (SJ)   \n",
       "2   Street Light Condition                              Flood Light Lamp Out   \n",
       "3                    Sewer                             Highway Flooding (SH)   \n",
       "4   Street Light Condition                          Flood Light Lamp Cycling   \n",
       "5        Sewer Maintenance                                Flooding on Street   \n",
       "6   OEM Literature Request                               Ready NY - Flooding   \n",
       "7   Street Light Condition                       Flood Light Lamp Dayburning   \n",
       "8   Street Light Condition                          Flood Light Lamp Missing   \n",
       "9   Street Light Condition                              Flood Light Lamp Dim   \n",
       "10                   Sewer                     RAIN GARDEN FLOODING (SRGFLD)   \n",
       "11           Public Toilet                                           Flooded   \n",
       "12       Sewer Maintenance                               Flooding on Highway   \n",
       "\n",
       "     count  \n",
       "0   114680  \n",
       "1    39512  \n",
       "2     6517  \n",
       "3     3156  \n",
       "4     2595  \n",
       "5      673  \n",
       "6      271  \n",
       "7      228  \n",
       "8      212  \n",
       "9      185  \n",
       "10     152  \n",
       "11      56  \n",
       "12      15  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "# SoQL query string below: select complaint_type and count of complaint_type grouped by compaint_type,\n",
    "# where the word \"flood\" is in descriptor, sort count in descending order and\n",
    "# limit our records to 1,000\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    complaint_type,\n",
    "    descriptor,\n",
    "    count(unique_key) AS count\n",
    "WHERE \n",
    "    LOWER(descriptor) LIKE '%flood%'\n",
    "GROUP BY \n",
    "    complaint_type, descriptor\n",
    "ORDER BY \n",
    "    count(unique_key) DESC\n",
    "LIMIT \n",
    "    1000\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Displaying the highest number of street flooding complaints by day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (1000, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-09-01T00:00:00.000</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-12-23T00:00:00.000</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-05T00:00:00.000</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day count\n",
       "0  2023-09-29T00:00:00.000   623\n",
       "1  2021-09-02T00:00:00.000   350\n",
       "2  2021-09-01T00:00:00.000   344\n",
       "3  2022-12-23T00:00:00.000   308\n",
       "4  2017-05-05T00:00:00.000   247"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=100\n",
    ")\n",
    "\n",
    "# SoQL query string below: select the day and the count day columns grouped by day,\n",
    "# where the descriptor is Street Flooding (SJ), sort the count in descending order and\n",
    "# limit our records to 1,000\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    date_trunc_ymd(created_date) as day, \n",
    "    count(created_date) AS count\n",
    "WHERE \n",
    "    descriptor == 'Street Flooding (SJ)'\n",
    "GROUP BY \n",
    "    day\n",
    "ORDER BY \n",
    "    count DESC\n",
    "LIMIT \n",
    "    1000\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The highest day of Street Flooding Complaints was on 2023-09-29 with 623 total number of complaints.\n"
     ]
    }
   ],
   "source": [
    "day = df.head(1)['day'][0].split('T')[0]\n",
    "count = df.head(1)['count'][0]\n",
    "\n",
    "print('The highest day of Street Flooding Complaints \\\n",
    "was on {} with {} total number of complaints.'.format(day, count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the date above to see other complaint types represented on that date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (495, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Sewer Backup (Use Comments) (SA)</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Water Leak</td>\n",
       "      <td>Heavy Flow</td>\n",
       "      <td>1042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>WATER LEAK</td>\n",
       "      <td>HEAVY FLOW</td>\n",
       "      <td>1038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer Maintenance</td>\n",
       "      <td>Backup</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer Maintenance</td>\n",
       "      <td>Flooding on Street</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Water Leak</td>\n",
       "      <td>Slow Leak</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>WATER LEAK</td>\n",
       "      <td>SLOW LEAK</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Catch Basin Clogged/Flooding (Use Comments) (SC)</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-09-29T00:00:00.000</td>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day       complaint_type  \\\n",
       "0  2023-09-29T00:00:00.000                Sewer   \n",
       "1  2023-09-29T00:00:00.000           Water Leak   \n",
       "2  2023-09-29T00:00:00.000           WATER LEAK   \n",
       "3  2023-09-29T00:00:00.000    Sewer Maintenance   \n",
       "4  2023-09-29T00:00:00.000                Sewer   \n",
       "5  2023-09-29T00:00:00.000    Sewer Maintenance   \n",
       "6  2023-09-29T00:00:00.000           Water Leak   \n",
       "7  2023-09-29T00:00:00.000           WATER LEAK   \n",
       "8  2023-09-29T00:00:00.000                Sewer   \n",
       "9  2023-09-29T00:00:00.000  Noise - Residential   \n",
       "\n",
       "                                         descriptor count  \n",
       "0                  Sewer Backup (Use Comments) (SA)  1050  \n",
       "1                                        Heavy Flow  1042  \n",
       "2                                        HEAVY FLOW  1038  \n",
       "3                                            Backup  1012  \n",
       "4                              Street Flooding (SJ)   623  \n",
       "5                                Flooding on Street   588  \n",
       "6                                         Slow Leak   465  \n",
       "7                                         SLOW LEAK   463  \n",
       "8  Catch Basin Clogged/Flooding (Use Comments) (SC)   394  \n",
       "9                                  Loud Music/Party   363  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "# SoQL query string below: select the day and count day grouped by day,\n",
    "# sort by count in descending order and limit our records to 1,000\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    date_trunc_ymd(created_date) AS day,\n",
    "    complaint_type, \n",
    "    descriptor,\n",
    "    count(unique_key) AS count\n",
    "WHERE\n",
    "    date_trunc_ymd(created_date) == '2023-09-29T00:00:00.000'\n",
    "GROUP BY \n",
    "    day, complaint_type, descriptor\n",
    "ORDER BY \n",
    "    count DESC\n",
    "LIMIT \n",
    "    1000\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the second highest event above to see other complaint types represented on that date.\n",
    "\n",
    "date of `'2021-09-02T00:00:00.000'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (480, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Sewer Backup (Use Comments) (SA)</td>\n",
       "      <td>2372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>Request Large Bulky Item Collection</td>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>WATER LEAK</td>\n",
       "      <td>HEAVY FLOW</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>Traffic Signal Condition</td>\n",
       "      <td>Controller</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>Noise - Street/Sidewalk</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>PAINT/PLASTER</td>\n",
       "      <td>CEILING</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>Blocked Driveway</td>\n",
       "      <td>No Access</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>Damaged Tree</td>\n",
       "      <td>Branch or Limb Has Fallen Down</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-09-02T00:00:00.000</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Catch Basin Clogged/Flooding (Use Comments) (SC)</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       day                       complaint_type  \\\n",
       "0  2021-09-02T00:00:00.000                                Sewer   \n",
       "1  2021-09-02T00:00:00.000  Request Large Bulky Item Collection   \n",
       "2  2021-09-02T00:00:00.000                           WATER LEAK   \n",
       "3  2021-09-02T00:00:00.000             Traffic Signal Condition   \n",
       "4  2021-09-02T00:00:00.000              Noise - Street/Sidewalk   \n",
       "5  2021-09-02T00:00:00.000                                Sewer   \n",
       "6  2021-09-02T00:00:00.000                        PAINT/PLASTER   \n",
       "7  2021-09-02T00:00:00.000                     Blocked Driveway   \n",
       "8  2021-09-02T00:00:00.000                         Damaged Tree   \n",
       "9  2021-09-02T00:00:00.000                                Sewer   \n",
       "\n",
       "                                         descriptor count  \n",
       "0                  Sewer Backup (Use Comments) (SA)  2372  \n",
       "1               Request Large Bulky Item Collection  1350  \n",
       "2                                        HEAVY FLOW   564  \n",
       "3                                        Controller   391  \n",
       "4                                  Loud Music/Party   358  \n",
       "5                              Street Flooding (SJ)   350  \n",
       "6                                           CEILING   321  \n",
       "7                                         No Access   313  \n",
       "8                    Branch or Limb Has Fallen Down   303  \n",
       "9  Catch Basin Clogged/Flooding (Use Comments) (SC)   295  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=1000\n",
    ")\n",
    "\n",
    "# SoQL query string below: select the day and count day grouped by day,\n",
    "# sort by count in descending order and limit our records to 1,000\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    date_trunc_ymd(created_date) AS day,\n",
    "    complaint_type, \n",
    "    descriptor,\n",
    "    count(unique_key) AS count\n",
    "WHERE\n",
    "    date_trunc_ymd(created_date) == '2021-09-02T00:00:00.000'\n",
    "GROUP BY \n",
    "    day, complaint_type, descriptor\n",
    "ORDER BY \n",
    "    count DESC\n",
    "LIMIT \n",
    "    1000\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Selecting the rows and columns where the descriptor field = 'Street Flooding'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (1000, 30)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_key</th>\n",
       "      <th>created_date</th>\n",
       "      <th>agency</th>\n",
       "      <th>agency_name</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>descriptor</th>\n",
       "      <th>incident_zip</th>\n",
       "      <th>intersection_street_1</th>\n",
       "      <th>intersection_street_2</th>\n",
       "      <th>address_type</th>\n",
       "      <th>...</th>\n",
       "      <th>longitude</th>\n",
       "      <th>location</th>\n",
       "      <th>incident_address</th>\n",
       "      <th>street_name</th>\n",
       "      <th>cross_street_1</th>\n",
       "      <th>cross_street_2</th>\n",
       "      <th>bbl</th>\n",
       "      <th>closed_date</th>\n",
       "      <th>resolution_description</th>\n",
       "      <th>resolution_action_updated_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60937647</td>\n",
       "      <td>2024-04-21T22:56:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>10036</td>\n",
       "      <td>7 AVENUE</td>\n",
       "      <td>WEST 47 STREET</td>\n",
       "      <td>INTERSECTION</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.98461925002192</td>\n",
       "      <td>{'latitude': '40.7591997120635', 'longitude': ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60937648</td>\n",
       "      <td>2024-04-21T21:59:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11217</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.97650143173598</td>\n",
       "      <td>{'latitude': '40.68672300881048', 'longitude':...</td>\n",
       "      <td>92 FORT GREENE PLACE</td>\n",
       "      <td>FORT GREENE PLACE</td>\n",
       "      <td>LAFAYETTE AVE</td>\n",
       "      <td>HANSON PL</td>\n",
       "      <td>3021120032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60937649</td>\n",
       "      <td>2024-04-21T13:56:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>-74.00949923604392</td>\n",
       "      <td>{'latitude': '40.64826519646832', 'longitude':...</td>\n",
       "      <td>423 46 STREET</td>\n",
       "      <td>46 STREET</td>\n",
       "      <td>4 AVE</td>\n",
       "      <td>5 AVE</td>\n",
       "      <td>3007470067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60938666</td>\n",
       "      <td>2024-04-21T11:14:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.90441538293082</td>\n",
       "      <td>{'latitude': '40.75729191233922', 'longitude':...</td>\n",
       "      <td>57-06 31 AVENUE</td>\n",
       "      <td>31 AVENUE</td>\n",
       "      <td>AMTRAK-NY/CONN RAIL LINE</td>\n",
       "      <td>58 ST</td>\n",
       "      <td>4011340019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60929521</td>\n",
       "      <td>2024-04-20T15:32:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>11369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.87361408066782</td>\n",
       "      <td>{'latitude': '40.75969682793435', 'longitude':...</td>\n",
       "      <td>31-33 95 STREET</td>\n",
       "      <td>95 STREET</td>\n",
       "      <td>31 AVE</td>\n",
       "      <td>JACKSON MILL RD</td>\n",
       "      <td>4014070054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_key             created_date agency  \\\n",
       "0   60937647  2024-04-21T22:56:00.000    DEP   \n",
       "1   60937648  2024-04-21T21:59:00.000    DEP   \n",
       "2   60937649  2024-04-21T13:56:00.000    DEP   \n",
       "3   60938666  2024-04-21T11:14:00.000    DEP   \n",
       "4   60929521  2024-04-20T15:32:00.000    DEP   \n",
       "\n",
       "                              agency_name complaint_type  \\\n",
       "0  Department of Environmental Protection          Sewer   \n",
       "1  Department of Environmental Protection          Sewer   \n",
       "2  Department of Environmental Protection          Sewer   \n",
       "3  Department of Environmental Protection          Sewer   \n",
       "4  Department of Environmental Protection          Sewer   \n",
       "\n",
       "             descriptor incident_zip intersection_street_1  \\\n",
       "0  Street Flooding (SJ)        10036              7 AVENUE   \n",
       "1  Street Flooding (SJ)        11217                   NaN   \n",
       "2  Street Flooding (SJ)        11220                   NaN   \n",
       "3  Street Flooding (SJ)        11377                   NaN   \n",
       "4  Street Flooding (SJ)        11369                   NaN   \n",
       "\n",
       "  intersection_street_2  address_type  ...           longitude  \\\n",
       "0        WEST 47 STREET  INTERSECTION  ...  -73.98461925002192   \n",
       "1                   NaN       ADDRESS  ...  -73.97650143173598   \n",
       "2                   NaN       ADDRESS  ...  -74.00949923604392   \n",
       "3                   NaN       ADDRESS  ...  -73.90441538293082   \n",
       "4                   NaN       ADDRESS  ...  -73.87361408066782   \n",
       "\n",
       "                                            location      incident_address  \\\n",
       "0  {'latitude': '40.7591997120635', 'longitude': ...                   NaN   \n",
       "1  {'latitude': '40.68672300881048', 'longitude':...  92 FORT GREENE PLACE   \n",
       "2  {'latitude': '40.64826519646832', 'longitude':...         423 46 STREET   \n",
       "3  {'latitude': '40.75729191233922', 'longitude':...       57-06 31 AVENUE   \n",
       "4  {'latitude': '40.75969682793435', 'longitude':...       31-33 95 STREET   \n",
       "\n",
       "         street_name            cross_street_1   cross_street_2         bbl  \\\n",
       "0                NaN                       NaN              NaN         NaN   \n",
       "1  FORT GREENE PLACE             LAFAYETTE AVE        HANSON PL  3021120032   \n",
       "2          46 STREET                     4 AVE            5 AVE  3007470067   \n",
       "3          31 AVENUE  AMTRAK-NY/CONN RAIL LINE            58 ST  4011340019   \n",
       "4          95 STREET                    31 AVE  JACKSON MILL RD  4014070054   \n",
       "\n",
       "  closed_date resolution_description resolution_action_updated_date  \n",
       "0         NaN                    NaN                            NaN  \n",
       "1         NaN                    NaN                            NaN  \n",
       "2         NaN                    NaN                            NaN  \n",
       "3         NaN                    NaN                            NaN  \n",
       "4         NaN                    NaN                            NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "socrata_domain = 'data.cityofnewyork.us'\n",
    "socrata_dataset_identifier = 'erm2-nwe9'\n",
    "\n",
    "client = Socrata(\n",
    "    socrata_domain,\n",
    "    app_token=None,\n",
    "    timeout=100\n",
    ")\n",
    "\n",
    "# SoQL query string below: select all columns where the descriptor is Street Flooding(SJ),\n",
    "# sort the created date field in descending order and limit our records to 1,000\n",
    "\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    *\n",
    "WHERE \n",
    "    descriptor == 'Street Flooding (SJ)'\n",
    "ORDER BY \n",
    "    created_date DESC\n",
    "LIMIT \n",
    "    1000\n",
    "\"\"\"\n",
    "\n",
    "results = client.get(\n",
    "    socrata_dataset_identifier,\n",
    "    query=query\n",
    ")\n",
    "client.close()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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