analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing New York City's Flood Dataset Metadata on NYC Open Data\n",
    "Author: Mark Bauer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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",
    "from datetime import datetime, timezone\n",
    "\n",
    "sns.set_theme()\n",
    "plt.rcParams['savefig.facecolor'] = 'white'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Documention for installing watermark: https://github.com/rasbt/watermark."
   ]
  },
  {
   "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": [
    "%reload_ext watermark\n",
    "%watermark -t -d -v -p pandas,sodapy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Socrata API\n",
    "I used the Socrata API to retrieve metadata for datasets hosted on NYC Open Data. Documentation can be found here: https://dev.socrata.com/. Additionally, I used sodapy, the python client for the Socrata API, to query the metadata."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note:  \n",
    "`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": [
    "# Quering Dataset Metadata on NYC Open Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# source domain for NYC Open Data on Socrata\n",
    "socrata_domain = 'data.cityofnewyork.us'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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: (3410, 8)\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>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': '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>1</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>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': 'Civil Service List (Active)', 'id': ...   \n",
       "1  {'name': 'For Hire Vehicles (FHV) - Active', '...   \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/vx8i-nprf   \n",
       "1  https://data.cityofnewyork.us/d/8wbx-tsch   \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/City-Government/...   \n",
       "1  https://data.cityofnewyork.us/Transportation/F...   \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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# socrata method to query metadata\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",
    "\n",
    "# preview data\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3410 entries, 0 to 3409\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   resource           3410 non-null   object\n",
      " 1   classification     3410 non-null   object\n",
      " 2   metadata           3410 non-null   object\n",
      " 3   permalink          3410 non-null   object\n",
      " 4   link               3410 non-null   object\n",
      " 5   owner              3410 non-null   object\n",
      " 6   creator            3410 non-null   object\n",
      " 7   preview_image_url  138 non-null    object\n",
      "dtypes: object(8)\n",
      "memory usage: 213.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# summarize df\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (3410, 25)\n"
     ]
    },
    {
     "data": {
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       "  <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>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>2024-01-05T14:39:40.000Z</td>\n",
       "      <td>2016-06-14T21:12:15.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Calendar date, Calendar date, Calendar ...</td>\n",
       "      <td>[A candidate’s first name as it appears on the...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>59245</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-05T14:39:40.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2024-01-06T19:52:57.000Z</td>\n",
       "      <td>2015-07-16T17:33:32.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Number, Text, Calendar date, Text, Text, Text...</td>\n",
       "      <td>[Vehicle Year, Base Type, Hack Up Date, Reason...</td>\n",
       "      <td>[{'precisionStyle': 'standard', 'noCommas': 't...</td>\n",
       "      <td>428538</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>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>2024-01-06T21:06:33.000Z</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>[House Number of Property Owner, Sprinkler  Wo...</td>\n",
       "      <td>[{'align': 'right'}, {'align': 'right'}, {'ali...</td>\n",
       "      <td>53461</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>2024-01-06T23:04:04.000Z</td>\n",
       "      <td>2016-05-17T18:43:43.000Z</td>\n",
       "      <td>...</td>\n",
       "      <td>[Text, Text, Text, Number, Text, Text, Text, T...</td>\n",
       "      <td>[A NYS certified 6 hour Defensive Driving Cour...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>38609</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>2024-01-06T20:06:31.000Z</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, Expir...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>374501</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-06T20:06:31.000Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       name         id parent_fxf  \\\n",
       "0               Civil Service List (Active)  vx8i-nprf         []   \n",
       "1          For Hire Vehicles (FHV) - Active  8wbx-tsch         []   \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  A Civil Service List consists of all candidate...   \n",
       "1  <b>PLEASE NOTE:</b> This dataset, which includ...   \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  Department of Citywide Administrative Services...             None   \n",
       "1                Taxi and Limousine Commission (TLC)             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  2024-01-05T14:39:40.000Z  2016-06-14T21:12:15.000Z   \n",
       "1          None  dataset  2024-01-06T19:52:57.000Z  2015-07-16T17:33:32.000Z   \n",
       "2          None  dataset  2024-01-06T21:06:33.000Z  2013-04-18T15:18:56.000Z   \n",
       "3          None  dataset  2024-01-06T23:04:04.000Z  2016-05-17T18:43:43.000Z   \n",
       "4          None  dataset  2024-01-06T20:06:31.000Z  2015-07-16T17:24:02.000Z   \n",
       "\n",
       "   ...                                   columns_datatype  \\\n",
       "0  ...  [Text, Calendar date, Calendar date, Calendar ...   \n",
       "1  ...  [Number, Text, Calendar date, Text, Text, Text...   \n",
       "2  ...  [Text, Text, Text, Text, Text, Text, Text, Tex...   \n",
       "3  ...  [Text, Text, Text, Number, Text, Text, Text, T...   \n",
       "4  ...  [Text, Calendar date, Calendar date, Text, Tex...   \n",
       "\n",
       "                                 columns_description  \\\n",
       "0  [A candidate’s first name as it appears on the...   \n",
       "1  [Vehicle Year, Base Type, Hack Up Date, Reason...   \n",
       "2  [House Number of Property Owner, Sprinkler  Wo...   \n",
       "3  [A NYS certified 6 hour Defensive Driving Cour...   \n",
       "4  [WAV if Wheelchair Accessible Trained\\n, Expir...   \n",
       "\n",
       "                                      columns_format download_count  \\\n",
       "0  [{'displayStyle': 'plain', 'align': 'left'}, {...          59245   \n",
       "1  [{'precisionStyle': 'standard', 'noCommas': 't...         428538   \n",
       "2  [{'align': 'right'}, {'align': 'right'}, {'ali...          53461   \n",
       "3  [{'displayStyle': 'plain', 'align': 'left'}, {...          38609   \n",
       "4  [{'displayStyle': 'plain', 'align': 'left'}, {...         374501   \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  2024-01-05T14:39:40.000Z  \n",
       "1                False  2021-04-05T13:20:47.000Z  \n",
       "2                False  2020-06-22T18:23:35.000Z  \n",
       "3                False  2019-12-17T18:44:57.000Z  \n",
       "4                False  2024-01-06T20:06:31.000Z  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "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('shape of data: {}'.format(df.shape))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3410 entries, 0 to 3409\n",
      "Data columns (total 25 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   name                 3410 non-null   object\n",
      " 1   id                   3410 non-null   object\n",
      " 2   parent_fxf           3410 non-null   object\n",
      " 3   description          3410 non-null   object\n",
      " 4   attribution          3210 non-null   object\n",
      " 5   attribution_link     454 non-null    object\n",
      " 6   contact_email        0 non-null      object\n",
      " 7   type                 3410 non-null   object\n",
      " 8   updatedAt            3410 non-null   object\n",
      " 9   createdAt            3410 non-null   object\n",
      " 10  metadata_updated_at  3410 non-null   object\n",
      " 11  data_updated_at      3223 non-null   object\n",
      " 12  page_views           3410 non-null   object\n",
      " 13  columns_name         3410 non-null   object\n",
      " 14  columns_field_name   3410 non-null   object\n",
      " 15  columns_datatype     3410 non-null   object\n",
      " 16  columns_description  3410 non-null   object\n",
      " 17  columns_format       3410 non-null   object\n",
      " 18  download_count       3410 non-null   int64 \n",
      " 19  provenance           3410 non-null   object\n",
      " 20  lens_view_type       3410 non-null   object\n",
      " 21  lens_display_type    3410 non-null   object\n",
      " 22  blob_mime_type       399 non-null    object\n",
      " 23  hide_from_data_json  3410 non-null   bool  \n",
      " 24  publication_date     3407 non-null   object\n",
      "dtypes: bool(1), int64(1), object(23)\n",
      "memory usage: 642.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# summarize df\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>metadata_updated_at</th>\n",
       "      <th>data_updated_at</th>\n",
       "      <th>page_views</th>\n",
       "      <th>columns_name</th>\n",
       "      <th>columns_field_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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>2024-01-05T14:39:40.000Z</td>\n",
       "      <td>2016-06-14T21:12:15.000Z</td>\n",
       "      <td>2024-01-05T14:39:40.000Z</td>\n",
       "      <td>2024-01-05T14:24:44.000Z</td>\n",
       "      <td>{'page_views_last_week': 5170, 'page_views_las...</td>\n",
       "      <td>[First Name, Anniversary Date, Established Dat...</td>\n",
       "      <td>[first_name, anniversary_date, established_dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2024-01-06T19:52:57.000Z</td>\n",
       "      <td>2015-07-16T17:33:32.000Z</td>\n",
       "      <td>2022-09-06T21:05:32.000Z</td>\n",
       "      <td>2024-01-06T19:52:57.000Z</td>\n",
       "      <td>{'page_views_last_week': 8118, 'page_views_las...</td>\n",
       "      <td>[Vehicle Year, Base Type, Hack Up Date, Reason...</td>\n",
       "      <td>[vehicle_year, base_type, hack_up_date, reason...</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>2024-01-06T21:06:33.000Z</td>\n",
       "      <td>2013-04-18T15:18:56.000Z</td>\n",
       "      <td>2020-06-23T02:08:44.000Z</td>\n",
       "      <td>2024-01-06T21:06:33.000Z</td>\n",
       "      <td>{'page_views_last_week': 486, 'page_views_last...</td>\n",
       "      <td>[Owner's House Number, Sprinkler, Fire Suppres...</td>\n",
       "      <td>[owner_s_house_number, sprinkler, fire_suppres...</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>2024-01-06T23:04:04.000Z</td>\n",
       "      <td>2016-05-17T18:43:43.000Z</td>\n",
       "      <td>2022-05-09T22:28:03.000Z</td>\n",
       "      <td>2024-01-06T23:04:04.000Z</td>\n",
       "      <td>{'page_views_last_week': 194, 'page_views_last...</td>\n",
       "      <td>[Defensive Driving, Type, Status, App No, Othe...</td>\n",
       "      <td>[defensive_driving, type, status, app_no, othe...</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>2024-01-06T20:06:31.000Z</td>\n",
       "      <td>2015-07-16T17:24:02.000Z</td>\n",
       "      <td>2024-01-06T20:06:31.000Z</td>\n",
       "      <td>2024-01-06T20:00:24.000Z</td>\n",
       "      <td>{'page_views_last_week': 2875, 'page_views_las...</td>\n",
       "      <td>[Wheelchair Accessible Trained, Expiration Dat...</td>\n",
       "      <td>[wheelchair_accessible_trained, expiration_dat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       name         id parent_fxf  \\\n",
       "0               Civil Service List (Active)  vx8i-nprf         []   \n",
       "1          For Hire Vehicles (FHV) - Active  8wbx-tsch         []   \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  A Civil Service List consists of all candidate...   \n",
       "1  <b>PLEASE NOTE:</b> This dataset, which includ...   \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  Department of Citywide Administrative Services...             None   \n",
       "1                Taxi and Limousine Commission (TLC)             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  2024-01-05T14:39:40.000Z  2016-06-14T21:12:15.000Z   \n",
       "1          None  dataset  2024-01-06T19:52:57.000Z  2015-07-16T17:33:32.000Z   \n",
       "2          None  dataset  2024-01-06T21:06:33.000Z  2013-04-18T15:18:56.000Z   \n",
       "3          None  dataset  2024-01-06T23:04:04.000Z  2016-05-17T18:43:43.000Z   \n",
       "4          None  dataset  2024-01-06T20:06:31.000Z  2015-07-16T17:24:02.000Z   \n",
       "\n",
       "        metadata_updated_at           data_updated_at  \\\n",
       "0  2024-01-05T14:39:40.000Z  2024-01-05T14:24:44.000Z   \n",
       "1  2022-09-06T21:05:32.000Z  2024-01-06T19:52:57.000Z   \n",
       "2  2020-06-23T02:08:44.000Z  2024-01-06T21:06:33.000Z   \n",
       "3  2022-05-09T22:28:03.000Z  2024-01-06T23:04:04.000Z   \n",
       "4  2024-01-06T20:06:31.000Z  2024-01-06T20:00:24.000Z   \n",
       "\n",
       "                                          page_views  \\\n",
       "0  {'page_views_last_week': 5170, 'page_views_las...   \n",
       "1  {'page_views_last_week': 8118, 'page_views_las...   \n",
       "2  {'page_views_last_week': 486, 'page_views_last...   \n",
       "3  {'page_views_last_week': 194, 'page_views_last...   \n",
       "4  {'page_views_last_week': 2875, 'page_views_las...   \n",
       "\n",
       "                                        columns_name  \\\n",
       "0  [First Name, Anniversary Date, Established Dat...   \n",
       "1  [Vehicle Year, Base Type, Hack Up Date, Reason...   \n",
       "2  [Owner's House Number, Sprinkler, Fire Suppres...   \n",
       "3  [Defensive Driving, Type, Status, App No, Othe...   \n",
       "4  [Wheelchair Accessible Trained, Expiration Dat...   \n",
       "\n",
       "                                  columns_field_name  \n",
       "0  [first_name, anniversary_date, established_dat...  \n",
       "1  [vehicle_year, base_type, hack_up_date, reason...  \n",
       "2  [owner_s_house_number, sprinkler, fire_suppres...  \n",
       "3  [defensive_driving, type, status, app_no, othe...  \n",
       "4  [wheelchair_accessible_trained, expiration_dat...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview first 15 columns\n",
    "df.iloc[:5, :15]"
   ]
  },
  {
   "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>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>[Text, Calendar date, Calendar date, Calendar ...</td>\n",
       "      <td>[A candidate’s first name as it appears on the...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>59245</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-05T14:39:40.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Number, Text, Calendar date, Text, Text, Text...</td>\n",
       "      <td>[Vehicle Year, Base Type, Hack Up Date, Reason...</td>\n",
       "      <td>[{'precisionStyle': 'standard', 'noCommas': 't...</td>\n",
       "      <td>428538</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>2</th>\n",
       "      <td>[Text, Text, Text, Text, Text, Text, Text, Tex...</td>\n",
       "      <td>[House Number of Property Owner, Sprinkler  Wo...</td>\n",
       "      <td>[{'align': 'right'}, {'align': 'right'}, {'ali...</td>\n",
       "      <td>53461</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>[Text, Text, Text, Number, Text, Text, Text, T...</td>\n",
       "      <td>[A NYS certified 6 hour Defensive Driving Cour...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>38609</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>[Text, Calendar date, Calendar date, Text, Tex...</td>\n",
       "      <td>[WAV if Wheelchair Accessible Trained\\n, Expir...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>374501</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-06T20:06:31.000Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    columns_datatype  \\\n",
       "0  [Text, Calendar date, Calendar date, Calendar ...   \n",
       "1  [Number, Text, Calendar date, Text, Text, Text...   \n",
       "2  [Text, Text, Text, Text, Text, Text, Text, Tex...   \n",
       "3  [Text, Text, Text, Number, Text, Text, Text, T...   \n",
       "4  [Text, Calendar date, Calendar date, Text, Tex...   \n",
       "\n",
       "                                 columns_description  \\\n",
       "0  [A candidate’s first name as it appears on the...   \n",
       "1  [Vehicle Year, Base Type, Hack Up Date, Reason...   \n",
       "2  [House Number of Property Owner, Sprinkler  Wo...   \n",
       "3  [A NYS certified 6 hour Defensive Driving Cour...   \n",
       "4  [WAV if Wheelchair Accessible Trained\\n, Expir...   \n",
       "\n",
       "                                      columns_format  download_count  \\\n",
       "0  [{'displayStyle': 'plain', 'align': 'left'}, {...           59245   \n",
       "1  [{'precisionStyle': 'standard', 'noCommas': 't...          428538   \n",
       "2  [{'align': 'right'}, {'align': 'right'}, {'ali...           53461   \n",
       "3  [{'displayStyle': 'plain', 'align': 'left'}, {...           38609   \n",
       "4  [{'displayStyle': 'plain', 'align': 'left'}, {...          374501   \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  2024-01-05T14:39:40.000Z  \n",
       "1                False  2021-04-05T13:20:47.000Z  \n",
       "2                False  2020-06-22T18:23:35.000Z  \n",
       "3                False  2019-12-17T18:44:57.000Z  \n",
       "4                False  2024-01-06T20:06:31.000Z  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview columns starting at col index 15\n",
    "df.iloc[:5, 15:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>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>2024-01-05T14:39:40.000Z</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>2024-01-05T14:39:40.000Z</td>\n",
       "      <td>5170.0</td>\n",
       "      <td>23942.0</td>\n",
       "      <td>2526873.0</td>\n",
       "      <td>12.336228</td>\n",
       "      <td>14.547316</td>\n",
       "      <td>21.268922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2024-01-06T19:52:57.000Z</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>8118.0</td>\n",
       "      <td>35374.0</td>\n",
       "      <td>2510941.0</td>\n",
       "      <td>12.987086</td>\n",
       "      <td>15.110443</td>\n",
       "      <td>21.259797</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>2024-01-06T21:06:33.000Z</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>486.0</td>\n",
       "      <td>1742.0</td>\n",
       "      <td>2339278.0</td>\n",
       "      <td>8.927778</td>\n",
       "      <td>10.767357</td>\n",
       "      <td>21.157633</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>2024-01-06T23:04:04.000Z</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>194.0</td>\n",
       "      <td>940.0</td>\n",
       "      <td>1724193.0</td>\n",
       "      <td>7.607330</td>\n",
       "      <td>9.878051</td>\n",
       "      <td>20.717491</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>2024-01-06T20:06:31.000Z</td>\n",
       "      <td>2015-07-16T17:24:02.000Z</td>\n",
       "      <td>...</td>\n",
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       "      <td>2875.0</td>\n",
       "      <td>12341.0</td>\n",
       "      <td>1352271.0</td>\n",
       "      <td>11.489848</td>\n",
       "      <td>13.591289</td>\n",
       "      <td>20.366954</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
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      ],
      "text/plain": [
       "                                       name         id parent_fxf  \\\n",
       "0               Civil Service List (Active)  vx8i-nprf         []   \n",
       "1          For Hire Vehicles (FHV) - Active  8wbx-tsch         []   \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  A Civil Service List consists of all candidate...   \n",
       "1  <b>PLEASE NOTE:</b> This dataset, which includ...   \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  Department of Citywide Administrative Services...             None   \n",
       "1                Taxi and Limousine Commission (TLC)             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  2024-01-05T14:39:40.000Z  2016-06-14T21:12:15.000Z   \n",
       "1          None  dataset  2024-01-06T19:52:57.000Z  2015-07-16T17:33:32.000Z   \n",
       "2          None  dataset  2024-01-06T21:06:33.000Z  2013-04-18T15:18:56.000Z   \n",
       "3          None  dataset  2024-01-06T23:04:04.000Z  2016-05-17T18:43:43.000Z   \n",
       "4          None  dataset  2024-01-06T20:06:31.000Z  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  2024-01-05T14:39:40.000Z               5170.0               23942.0   \n",
       "1  2021-04-05T13:20:47.000Z               8118.0               35374.0   \n",
       "2  2020-06-22T18:23:35.000Z                486.0                1742.0   \n",
       "3  2019-12-17T18:44:57.000Z                194.0                 940.0   \n",
       "4  2024-01-06T20:06:31.000Z               2875.0               12341.0   \n",
       "\n",
       "  page_views_total  page_views_last_week_log page_views_last_month_log  \\\n",
       "0        2526873.0                 12.336228                 14.547316   \n",
       "1        2510941.0                 12.987086                 15.110443   \n",
       "2        2339278.0                  8.927778                 10.767357   \n",
       "3        1724193.0                  7.607330                  9.878051   \n",
       "4        1352271.0                 11.489848                 13.591289   \n",
       "\n",
       "  page_views_total_log  \n",
       "0            21.268922  \n",
       "1            21.259797  \n",
       "2            21.157633  \n",
       "3            20.717491  \n",
       "4            20.366954  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explode page_view column and expand elements inside as new columns\n",
    "df = pd.concat(\n",
    "    [df.drop(['page_views'], axis=1), df['page_views'].apply(pd.Series)],\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "# preview data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3410 entries, 0 to 3409\n",
      "Data columns (total 30 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   name                       3410 non-null   object \n",
      " 1   id                         3410 non-null   object \n",
      " 2   parent_fxf                 3410 non-null   object \n",
      " 3   description                3410 non-null   object \n",
      " 4   attribution                3210 non-null   object \n",
      " 5   attribution_link           454 non-null    object \n",
      " 6   contact_email              0 non-null      object \n",
      " 7   type                       3410 non-null   object \n",
      " 8   updatedAt                  3410 non-null   object \n",
      " 9   createdAt                  3410 non-null   object \n",
      " 10  metadata_updated_at        3410 non-null   object \n",
      " 11  data_updated_at            3223 non-null   object \n",
      " 12  columns_name               3410 non-null   object \n",
      " 13  columns_field_name         3410 non-null   object \n",
      " 14  columns_datatype           3410 non-null   object \n",
      " 15  columns_description        3410 non-null   object \n",
      " 16  columns_format             3410 non-null   object \n",
      " 17  download_count             3410 non-null   int64  \n",
      " 18  provenance                 3410 non-null   object \n",
      " 19  lens_view_type             3410 non-null   object \n",
      " 20  lens_display_type          3410 non-null   object \n",
      " 21  blob_mime_type             399 non-null    object \n",
      " 22  hide_from_data_json        3410 non-null   bool   \n",
      " 23  publication_date           3407 non-null   object \n",
      " 24  page_views_last_week       3410 non-null   float64\n",
      " 25  page_views_last_month      3410 non-null   float64\n",
      " 26  page_views_total           3410 non-null   float64\n",
      " 27  page_views_last_week_log   3410 non-null   float64\n",
      " 28  page_views_last_month_log  3410 non-null   float64\n",
      " 29  page_views_total_log       3410 non-null   float64\n",
      "dtypes: bool(1), float64(6), int64(1), object(22)\n",
      "memory usage: 776.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# summarize df\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>columns_datatype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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>2024-01-05T14:39:40.000Z</td>\n",
       "      <td>2016-06-14T21:12:15.000Z</td>\n",
       "      <td>2024-01-05T14:39:40.000Z</td>\n",
       "      <td>2024-01-05T14:24:44.000Z</td>\n",
       "      <td>[First Name, Anniversary Date, Established Dat...</td>\n",
       "      <td>[first_name, anniversary_date, established_dat...</td>\n",
       "      <td>[Text, Calendar date, Calendar date, Calendar ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2024-01-06T19:52:57.000Z</td>\n",
       "      <td>2015-07-16T17:33:32.000Z</td>\n",
       "      <td>2022-09-06T21:05:32.000Z</td>\n",
       "      <td>2024-01-06T19:52:57.000Z</td>\n",
       "      <td>[Vehicle Year, Base Type, Hack Up Date, Reason...</td>\n",
       "      <td>[vehicle_year, base_type, hack_up_date, reason...</td>\n",
       "      <td>[Number, Text, Calendar date, Text, Text, Text...</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>2024-01-06T21:06:33.000Z</td>\n",
       "      <td>2013-04-18T15:18:56.000Z</td>\n",
       "      <td>2020-06-23T02:08:44.000Z</td>\n",
       "      <td>2024-01-06T21:06:33.000Z</td>\n",
       "      <td>[Owner's House Number, Sprinkler, Fire Suppres...</td>\n",
       "      <td>[owner_s_house_number, sprinkler, fire_suppres...</td>\n",
       "      <td>[Text, Text, Text, Text, Text, Text, Text, Tex...</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>2024-01-06T23:04:04.000Z</td>\n",
       "      <td>2016-05-17T18:43:43.000Z</td>\n",
       "      <td>2022-05-09T22:28:03.000Z</td>\n",
       "      <td>2024-01-06T23:04:04.000Z</td>\n",
       "      <td>[Defensive Driving, Type, Status, App No, Othe...</td>\n",
       "      <td>[defensive_driving, type, status, app_no, othe...</td>\n",
       "      <td>[Text, Text, Text, Number, Text, Text, Text, T...</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>2024-01-06T20:06:31.000Z</td>\n",
       "      <td>2015-07-16T17:24:02.000Z</td>\n",
       "      <td>2024-01-06T20:06:31.000Z</td>\n",
       "      <td>2024-01-06T20:00:24.000Z</td>\n",
       "      <td>[Wheelchair Accessible Trained, Expiration Dat...</td>\n",
       "      <td>[wheelchair_accessible_trained, expiration_dat...</td>\n",
       "      <td>[Text, Calendar date, Calendar date, Text, Tex...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       name         id parent_fxf  \\\n",
       "0               Civil Service List (Active)  vx8i-nprf         []   \n",
       "1          For Hire Vehicles (FHV) - Active  8wbx-tsch         []   \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  A Civil Service List consists of all candidate...   \n",
       "1  <b>PLEASE NOTE:</b> This dataset, which includ...   \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  Department of Citywide Administrative Services...             None   \n",
       "1                Taxi and Limousine Commission (TLC)             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  2024-01-05T14:39:40.000Z  2016-06-14T21:12:15.000Z   \n",
       "1          None  dataset  2024-01-06T19:52:57.000Z  2015-07-16T17:33:32.000Z   \n",
       "2          None  dataset  2024-01-06T21:06:33.000Z  2013-04-18T15:18:56.000Z   \n",
       "3          None  dataset  2024-01-06T23:04:04.000Z  2016-05-17T18:43:43.000Z   \n",
       "4          None  dataset  2024-01-06T20:06:31.000Z  2015-07-16T17:24:02.000Z   \n",
       "\n",
       "        metadata_updated_at           data_updated_at  \\\n",
       "0  2024-01-05T14:39:40.000Z  2024-01-05T14:24:44.000Z   \n",
       "1  2022-09-06T21:05:32.000Z  2024-01-06T19:52:57.000Z   \n",
       "2  2020-06-23T02:08:44.000Z  2024-01-06T21:06:33.000Z   \n",
       "3  2022-05-09T22:28:03.000Z  2024-01-06T23:04:04.000Z   \n",
       "4  2024-01-06T20:06:31.000Z  2024-01-06T20:00:24.000Z   \n",
       "\n",
       "                                        columns_name  \\\n",
       "0  [First Name, Anniversary Date, Established Dat...   \n",
       "1  [Vehicle Year, Base Type, Hack Up Date, Reason...   \n",
       "2  [Owner's House Number, Sprinkler, Fire Suppres...   \n",
       "3  [Defensive Driving, Type, Status, App No, Othe...   \n",
       "4  [Wheelchair Accessible Trained, Expiration Dat...   \n",
       "\n",
       "                                  columns_field_name  \\\n",
       "0  [first_name, anniversary_date, established_dat...   \n",
       "1  [vehicle_year, base_type, hack_up_date, reason...   \n",
       "2  [owner_s_house_number, sprinkler, fire_suppres...   \n",
       "3  [defensive_driving, type, status, app_no, othe...   \n",
       "4  [wheelchair_accessible_trained, expiration_dat...   \n",
       "\n",
       "                                    columns_datatype  \n",
       "0  [Text, Calendar date, Calendar date, Calendar ...  \n",
       "1  [Number, Text, Calendar date, Text, Text, Text...  \n",
       "2  [Text, Text, Text, Text, Text, Text, Text, Tex...  \n",
       "3  [Text, Text, Text, Number, Text, Text, Text, T...  \n",
       "4  [Text, Calendar date, Calendar date, Text, Tex...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview first 15 columns\n",
    "df.iloc[:5, :15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "      <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>[A candidate’s first name as it appears on the...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>59245</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-05T14:39:40.000Z</td>\n",
       "      <td>5170.0</td>\n",
       "      <td>23942.0</td>\n",
       "      <td>2526873.0</td>\n",
       "      <td>12.336228</td>\n",
       "      <td>14.547316</td>\n",
       "      <td>21.268922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Vehicle Year, Base Type, Hack Up Date, Reason...</td>\n",
       "      <td>[{'precisionStyle': 'standard', 'noCommas': 't...</td>\n",
       "      <td>428538</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",
       "      <td>8118.0</td>\n",
       "      <td>35374.0</td>\n",
       "      <td>2510941.0</td>\n",
       "      <td>12.987086</td>\n",
       "      <td>15.110443</td>\n",
       "      <td>21.259797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[House Number of Property Owner, Sprinkler  Wo...</td>\n",
       "      <td>[{'align': 'right'}, {'align': 'right'}, {'ali...</td>\n",
       "      <td>53461</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",
       "      <td>486.0</td>\n",
       "      <td>1742.0</td>\n",
       "      <td>2339278.0</td>\n",
       "      <td>8.927778</td>\n",
       "      <td>10.767357</td>\n",
       "      <td>21.157633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[A NYS certified 6 hour Defensive Driving Cour...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>38609</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",
       "      <td>194.0</td>\n",
       "      <td>940.0</td>\n",
       "      <td>1724193.0</td>\n",
       "      <td>7.607330</td>\n",
       "      <td>9.878051</td>\n",
       "      <td>20.717491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[WAV if Wheelchair Accessible Trained\\n, Expir...</td>\n",
       "      <td>[{'displayStyle': 'plain', 'align': 'left'}, {...</td>\n",
       "      <td>374501</td>\n",
       "      <td>official</td>\n",
       "      <td>tabular</td>\n",
       "      <td>table</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-01-06T20:06:31.000Z</td>\n",
       "      <td>2875.0</td>\n",
       "      <td>12341.0</td>\n",
       "      <td>1352271.0</td>\n",
       "      <td>11.489848</td>\n",
       "      <td>13.591289</td>\n",
       "      <td>20.366954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 columns_description  \\\n",
       "0  [A candidate’s first name as it appears on the...   \n",
       "1  [Vehicle Year, Base Type, Hack Up Date, Reason...   \n",
       "2  [House Number of Property Owner, Sprinkler  Wo...   \n",
       "3  [A NYS certified 6 hour Defensive Driving Cour...   \n",
       "4  [WAV if Wheelchair Accessible Trained\\n, Expir...   \n",
       "\n",
       "                                      columns_format  download_count  \\\n",
       "0  [{'displayStyle': 'plain', 'align': 'left'}, {...           59245   \n",
       "1  [{'precisionStyle': 'standard', 'noCommas': 't...          428538   \n",
       "2  [{'align': 'right'}, {'align': 'right'}, {'ali...           53461   \n",
       "3  [{'displayStyle': 'plain', 'align': 'left'}, {...           38609   \n",
       "4  [{'displayStyle': 'plain', 'align': 'left'}, {...          374501   \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  page_views_last_week  \\\n",
       "0                False  2024-01-05T14:39:40.000Z                5170.0   \n",
       "1                False  2021-04-05T13:20:47.000Z                8118.0   \n",
       "2                False  2020-06-22T18:23:35.000Z                 486.0   \n",
       "3                False  2019-12-17T18:44:57.000Z                 194.0   \n",
       "4                False  2024-01-06T20:06:31.000Z                2875.0   \n",
       "\n",
       "   page_views_last_month  page_views_total  page_views_last_week_log  \\\n",
       "0                23942.0         2526873.0                 12.336228   \n",
       "1                35374.0         2510941.0                 12.987086   \n",
       "2                 1742.0         2339278.0                  8.927778   \n",
       "3                  940.0         1724193.0                  7.607330   \n",
       "4                12341.0         1352271.0                 11.489848   \n",
       "\n",
       "   page_views_last_month_log  page_views_total_log  \n",
       "0                  14.547316             21.268922  \n",
       "1                  15.110443             21.259797  \n",
       "2                  10.767357             21.157633  \n",
       "3                   9.878051             20.717491  \n",
       "4                  13.591289             20.366954  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview columns starting at col index 15\n",
    "df.iloc[:5, 15:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3410 entries, 0 to 3409\n",
      "Data columns (total 30 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   name                       3410 non-null   object \n",
      " 1   id                         3410 non-null   object \n",
      " 2   parent_fxf                 3410 non-null   object \n",
      " 3   description                3410 non-null   object \n",
      " 4   attribution                3210 non-null   object \n",
      " 5   attribution_link           454 non-null    object \n",
      " 6   contact_email              0 non-null      object \n",
      " 7   type                       3410 non-null   object \n",
      " 8   updatedAt                  3410 non-null   object \n",
      " 9   createdAt                  3410 non-null   object \n",
      " 10  metadata_updated_at        3410 non-null   object \n",
      " 11  data_updated_at            3223 non-null   object \n",
      " 12  columns_name               3410 non-null   object \n",
      " 13  columns_field_name         3410 non-null   object \n",
      " 14  columns_datatype           3410 non-null   object \n",
      " 15  columns_description        3410 non-null   object \n",
      " 16  columns_format             3410 non-null   object \n",
      " 17  download_count             3410 non-null   int64  \n",
      " 18  provenance                 3410 non-null   object \n",
      " 19  lens_view_type             3410 non-null   object \n",
      " 20  lens_display_type          3410 non-null   object \n",
      " 21  blob_mime_type             399 non-null    object \n",
      " 22  hide_from_data_json        3410 non-null   bool   \n",
      " 23  publication_date           3407 non-null   object \n",
      " 24  page_views_last_week       3410 non-null   int64  \n",
      " 25  page_views_last_month      3410 non-null   int64  \n",
      " 26  page_views_total           3410 non-null   int64  \n",
      " 27  page_views_last_week_log   3410 non-null   float64\n",
      " 28  page_views_last_month_log  3410 non-null   float64\n",
      " 29  page_views_total_log       3410 non-null   float64\n",
      "dtypes: bool(1), float64(3), int64(4), object(22)\n",
      "memory usage: 776.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# convert datatypes of desired columns\n",
    "cols_dict = {\n",
    "    'download_count':int,\n",
    "    'page_views_last_week':int,\n",
    "    'page_views_last_month':int,\n",
    "    'page_views_total':int, \n",
    "}\n",
    "\n",
    "df = df.astype(cols_dict)\n",
    "\n",
    "# sanity check\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>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",
       "      <th>days_elapsed</th>\n",
       "      <th>created_date</th>\n",
       "      <th>download_per_day</th>\n",
       "      <th>page_views_per_day</th>\n",
       "      <th>downloads_and_views</th>\n",
       "      <th>downloads_and_views_per_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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>2024-01-05T14:39:40.000Z</td>\n",
       "      <td>2016-06-14 21:12:15+00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2526873</td>\n",
       "      <td>12.336228</td>\n",
       "      <td>14.547316</td>\n",
       "      <td>21.268922</td>\n",
       "      <td>2762</td>\n",
       "      <td>2016-06-14</td>\n",
       "      <td>21.45</td>\n",
       "      <td>914.87</td>\n",
       "      <td>2586118</td>\n",
       "      <td>936.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2024-01-06T19:52:57.000Z</td>\n",
       "      <td>2015-07-16 17:33:32+00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2510941</td>\n",
       "      <td>12.987086</td>\n",
       "      <td>15.110443</td>\n",
       "      <td>21.259797</td>\n",
       "      <td>3096</td>\n",
       "      <td>2015-07-16</td>\n",
       "      <td>138.42</td>\n",
       "      <td>811.03</td>\n",
       "      <td>2939479</td>\n",
       "      <td>949.44</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>2024-01-06T21:06:33.000Z</td>\n",
       "      <td>2013-04-18 15:18:56+00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2339278</td>\n",
       "      <td>8.927778</td>\n",
       "      <td>10.767357</td>\n",
       "      <td>21.157633</td>\n",
       "      <td>3915</td>\n",
       "      <td>2013-04-18</td>\n",
       "      <td>13.66</td>\n",
       "      <td>597.52</td>\n",
       "      <td>2392739</td>\n",
       "      <td>611.17</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>2024-01-06T23:04:04.000Z</td>\n",
       "      <td>2016-05-17 18:43:43+00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>1724193</td>\n",
       "      <td>7.607330</td>\n",
       "      <td>9.878051</td>\n",
       "      <td>20.717491</td>\n",
       "      <td>2790</td>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>13.84</td>\n",
       "      <td>617.99</td>\n",
       "      <td>1762802</td>\n",
       "      <td>631.83</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>2024-01-06T20:06:31.000Z</td>\n",
       "      <td>2015-07-16 17:24:02+00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>1352271</td>\n",
       "      <td>11.489848</td>\n",
       "      <td>13.591289</td>\n",
       "      <td>20.366954</td>\n",
       "      <td>3096</td>\n",
       "      <td>2015-07-16</td>\n",
       "      <td>120.96</td>\n",
       "      <td>436.78</td>\n",
       "      <td>1726772</td>\n",
       "      <td>557.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       name         id parent_fxf  \\\n",
       "0               Civil Service List (Active)  vx8i-nprf         []   \n",
       "1          For Hire Vehicles (FHV) - Active  8wbx-tsch         []   \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  A Civil Service List consists of all candidate...   \n",
       "1  <b>PLEASE NOTE:</b> This dataset, which includ...   \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  Department of Citywide Administrative Services...             None   \n",
       "1                Taxi and Limousine Commission (TLC)             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  2024-01-05T14:39:40.000Z 2016-06-14 21:12:15+00:00   \n",
       "1          None  dataset  2024-01-06T19:52:57.000Z 2015-07-16 17:33:32+00:00   \n",
       "2          None  dataset  2024-01-06T21:06:33.000Z 2013-04-18 15:18:56+00:00   \n",
       "3          None  dataset  2024-01-06T23:04:04.000Z 2016-05-17 18:43:43+00:00   \n",
       "4          None  dataset  2024-01-06T20:06:31.000Z 2015-07-16 17:24:02+00:00   \n",
       "\n",
       "   ... page_views_total page_views_last_week_log page_views_last_month_log  \\\n",
       "0  ...          2526873                12.336228                 14.547316   \n",
       "1  ...          2510941                12.987086                 15.110443   \n",
       "2  ...          2339278                 8.927778                 10.767357   \n",
       "3  ...          1724193                 7.607330                  9.878051   \n",
       "4  ...          1352271                11.489848                 13.591289   \n",
       "\n",
       "  page_views_total_log days_elapsed created_date download_per_day  \\\n",
       "0            21.268922         2762   2016-06-14            21.45   \n",
       "1            21.259797         3096   2015-07-16           138.42   \n",
       "2            21.157633         3915   2013-04-18            13.66   \n",
       "3            20.717491         2790   2016-05-17            13.84   \n",
       "4            20.366954         3096   2015-07-16           120.96   \n",
       "\n",
       "   page_views_per_day downloads_and_views downloads_and_views_per_day  \n",
       "0              914.87             2586118                      936.32  \n",
       "1              811.03             2939479                      949.44  \n",
       "2              597.52             2392739                      611.17  \n",
       "3              617.99             1762802                      631.83  \n",
       "4              436.78             1726772                      557.74  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get today's date\n",
    "today = datetime.now(timezone.utc)\n",
    "\n",
    "# convert to datetime\n",
    "df['createdAt'] = pd.to_datetime(df['createdAt'])\n",
    "\n",
    "# calculate days elapsed and format date column\n",
    "df['days_elapsed'] = (today - df['createdAt']).dt.days\n",
    "df['created_date'] = df['createdAt'].dt.strftime('%Y-%m-%d')\n",
    "\n",
    "# normalize counts\n",
    "df['download_per_day'] = round(df['download_count'] / df['days_elapsed'], 2)\n",
    "df['page_views_per_day'] = round(df['page_views_total'] / df['days_elapsed'], 2)\n",
    "\n",
    "df['downloads_and_views'] = df['download_count'] + df['page_views_total']\n",
    "df['downloads_and_views_per_day'] = round(df['downloads_and_views'] / df['days_elapsed'], 2)\n",
    "\n",
    "# preview data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CSV of Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For metadata analysis, we exclude the datasets below, as they are not flood specific and are used in many other contexts.\n",
    "- 311 Service Requests from 2010 to Present\n",
    "- Building Footprints\n",
    "- Primary Land Use Tax Lot Output - Map (MapPLUTO)\n",
    "- Emergency Response Incidents\n",
    "- NYCEM Emergency Notifications\n",
    "- Incidents Responded to by Fire Companies\n",
    "- Street Construction Permits (2013-2021)\n",
    "- Street Construction Permits (2022-Present)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're curious how to examine some of the datasets above, please visit the [searchstring-flood.ipynb](https://github.com/mebauer/nyc-flood-data/blob/main/searchstring-flood.ipynb) notebook in this repository."
   ]
  },
  {
   "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>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>uyj8-7rv5</td>\n",
       "      <td>Sandy Inundation Zone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7r5q-vr7p</td>\n",
       "      <td>NYC Stormwater Flood Map - Moderate Flood with...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5rzh-cyqd</td>\n",
       "      <td>NYC Stormwater Flood Map - Moderate Flood with...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>w8eg-8ha6</td>\n",
       "      <td>NYC Stormwater Flood Map - Extreme Flood with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6an6-9htp</td>\n",
       "      <td>Projected Sea Level Rise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ezfn-5dsb</td>\n",
       "      <td>Sea Level Rise Maps (2020s 100-year Floodplain)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ajyu-7sgg</td>\n",
       "      <td>Sea Level Rise Maps (2020s 500-year Floodplain)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hbw8-2bah</td>\n",
       "      <td>Sea Level Rise Maps (2050s 100-year Floodplain)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>qwca-zqw3</td>\n",
       "      <td>Sea Level Rise Maps (2050s 500-year Floodplain)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3vjp-ybhy</td>\n",
       "      <td>2050s Mean Sea Level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cyvg-fsk8</td>\n",
       "      <td>2080s Mean Sea Level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>q7rf-ks4h</td>\n",
       "      <td>2100s Mean Sea Level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ebsy-4b6x</td>\n",
       "      <td>2020s Mean Monthly High Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>p8e8-yh4m</td>\n",
       "      <td>2050s Mean Monthly High Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>amfa-s2y8</td>\n",
       "      <td>2080s Mean Monthly High Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mzds-2cdc</td>\n",
       "      <td>2100s Mean Monthly High Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>bsin-59hv</td>\n",
       "      <td>Building Elevation and Subgrade (BES)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>spjh-pz7h</td>\n",
       "      <td>DEP Green Infrastructure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>dpc8-z3jc</td>\n",
       "      <td>1 foot Digital Elevation Model (DEM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>uex9-rfq8</td>\n",
       "      <td>DEPs Citywide Parcel-Based Impervious Area GIS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>he6d-2qns</td>\n",
       "      <td>Land Cover Raster Data (2017) – 6in Resolution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>uihr-hn7s</td>\n",
       "      <td>Hurricane Evacuation Zones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2234-9r2y</td>\n",
       "      <td>Hurricane Inundation by Evacuation Zone (Map)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>p5md-weyf</td>\n",
       "      <td>Hurricane Evacuation Centers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ayer-cga7</td>\n",
       "      <td>Hurricane Evacuation Centers (Map)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>veqt-eu3t</td>\n",
       "      <td>Hazard Mitigation Plan - Mitigation Actions Da...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>t7k8-wj6b</td>\n",
       "      <td>Hazard Mitigation Plan – Mitigation Actions Da...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>hqhh-iv7p</td>\n",
       "      <td>Hazard Mitigation Plan - Mitigation Actions Da...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>cj2p-e3ej</td>\n",
       "      <td>Hazard Mitigation Plan - Mitigation Actions Da...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                                               name\n",
       "0   uyj8-7rv5                              Sandy Inundation Zone\n",
       "1   7r5q-vr7p  NYC Stormwater Flood Map - Moderate Flood with...\n",
       "2   5rzh-cyqd  NYC Stormwater Flood Map - Moderate Flood with...\n",
       "3   w8eg-8ha6  NYC Stormwater Flood Map - Extreme Flood with ...\n",
       "4   6an6-9htp                           Projected Sea Level Rise\n",
       "5   ezfn-5dsb    Sea Level Rise Maps (2020s 100-year Floodplain)\n",
       "6   ajyu-7sgg    Sea Level Rise Maps (2020s 500-year Floodplain)\n",
       "7   hbw8-2bah    Sea Level Rise Maps (2050s 100-year Floodplain)\n",
       "8   qwca-zqw3    Sea Level Rise Maps (2050s 500-year Floodplain)\n",
       "9   3vjp-ybhy                               2050s Mean Sea Level\n",
       "10  cyvg-fsk8                               2080s Mean Sea Level\n",
       "11  q7rf-ks4h                               2100s Mean Sea Level\n",
       "12  ebsy-4b6x                      2020s Mean Monthly High Water\n",
       "13  p8e8-yh4m                      2050s Mean Monthly High Water\n",
       "14  amfa-s2y8                      2080s Mean Monthly High Water\n",
       "15  mzds-2cdc                      2100s Mean Monthly High Water\n",
       "16  bsin-59hv              Building Elevation and Subgrade (BES)\n",
       "17  spjh-pz7h                           DEP Green Infrastructure\n",
       "18  dpc8-z3jc               1 foot Digital Elevation Model (DEM)\n",
       "19  uex9-rfq8  DEPs Citywide Parcel-Based Impervious Area GIS...\n",
       "20  he6d-2qns     Land Cover Raster Data (2017) – 6in Resolution\n",
       "21  uihr-hn7s                         Hurricane Evacuation Zones\n",
       "22  2234-9r2y      Hurricane Inundation by Evacuation Zone (Map)\n",
       "23  p5md-weyf                       Hurricane Evacuation Centers\n",
       "24  ayer-cga7                 Hurricane Evacuation Centers (Map)\n",
       "25  veqt-eu3t  Hazard Mitigation Plan - Mitigation Actions Da...\n",
       "26  t7k8-wj6b  Hazard Mitigation Plan – Mitigation Actions Da...\n",
       "27  hqhh-iv7p  Hazard Mitigation Plan - Mitigation Actions Da...\n",
       "28  cj2p-e3ej  Hazard Mitigation Plan - Mitigation Actions Da..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in id, name of flood datasets\n",
    "datasets_df = pd.read_csv('datasets.csv')\n",
    "\n",
    "# preview flood datasets\n",
    "datasets_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (29, 36)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\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>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",
       "      <th>days_elapsed</th>\n",
       "      <th>created_date</th>\n",
       "      <th>download_per_day</th>\n",
       "      <th>page_views_per_day</th>\n",
       "      <th>downloads_and_views</th>\n",
       "      <th>downloads_and_views_per_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sandy Inundation Zone</td>\n",
       "      <td>uyj8-7rv5</td>\n",
       "      <td>[]</td>\n",
       "      <td>Areas of New York City that were flooded as a ...</td>\n",
       "      <td>Department of Small Business Services (SBS)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>map</td>\n",
       "      <td>2023-12-13T02:12:05.000Z</td>\n",
       "      <td>2015-11-09 23:21:21+00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>53576</td>\n",
       "      <td>6.169925</td>\n",
       "      <td>8.836050</td>\n",
       "      <td>15.709326</td>\n",
       "      <td>2980</td>\n",
       "      <td>2015-11-09</td>\n",
       "      <td>6.81</td>\n",
       "      <td>17.98</td>\n",
       "      <td>73865</td>\n",
       "      <td>24.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sea Level Rise Maps (2050s 500-year Floodplain)</td>\n",
       "      <td>qwca-zqw3</td>\n",
       "      <td>[]</td>\n",
       "      <td>This is the 500-Year Floodplain for the 2050s ...</td>\n",
       "      <td>Mayor's Office of Climate and Sustainability</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>map</td>\n",
       "      <td>2021-09-16T16:38:40.000Z</td>\n",
       "      <td>2013-07-23 21:56:45+00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>42653</td>\n",
       "      <td>7.357552</td>\n",
       "      <td>9.616549</td>\n",
       "      <td>15.380393</td>\n",
       "      <td>3819</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>0.63</td>\n",
       "      <td>11.17</td>\n",
       "      <td>45067</td>\n",
       "      <td>11.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 foot Digital Elevation Model (DEM)</td>\n",
       "      <td>dpc8-z3jc</td>\n",
       "      <td>[]</td>\n",
       "      <td>NYC 1foot Digital Elevation Model:\\r\\nA bare-e...</td>\n",
       "      <td>Office of Technology and Innovation (OTI)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>href</td>\n",
       "      <td>2022-09-23T19:23:09.000Z</td>\n",
       "      <td>2013-08-09 23:09:58+00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>32218</td>\n",
       "      <td>6.087463</td>\n",
       "      <td>8.308339</td>\n",
       "      <td>14.975624</td>\n",
       "      <td>3802</td>\n",
       "      <td>2013-08-09</td>\n",
       "      <td>1.72</td>\n",
       "      <td>8.47</td>\n",
       "      <td>38742</td>\n",
       "      <td>10.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sea Level Rise Maps (2020s 100-year Floodplain)</td>\n",
       "      <td>ezfn-5dsb</td>\n",
       "      <td>[]</td>\n",
       "      <td>This is the 100-Year Floodplain for the 2020s ...</td>\n",
       "      <td>Mayor's Office of Climate and Sustainability</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>map</td>\n",
       "      <td>2021-09-16T16:43:22.000Z</td>\n",
       "      <td>2013-07-23 21:05:21+00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>26065</td>\n",
       "      <td>7.044394</td>\n",
       "      <td>8.721099</td>\n",
       "      <td>14.669882</td>\n",
       "      <td>3819</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>55.42</td>\n",
       "      <td>6.83</td>\n",
       "      <td>237728</td>\n",
       "      <td>62.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEP Green Infrastructure</td>\n",
       "      <td>spjh-pz7h</td>\n",
       "      <td>[]</td>\n",
       "      <td>NYC Green Infrastructure Program initiatives. ...</td>\n",
       "      <td>Department of Environmental Protection (DEP)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>map</td>\n",
       "      <td>2023-12-13T02:19:50.000Z</td>\n",
       "      <td>2017-08-31 20:33:51+00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>14872</td>\n",
       "      <td>6.523562</td>\n",
       "      <td>8.447083</td>\n",
       "      <td>13.860408</td>\n",
       "      <td>2319</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>11.99</td>\n",
       "      <td>6.41</td>\n",
       "      <td>42668</td>\n",
       "      <td>18.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              name         id parent_fxf  \\\n",
       "0                            Sandy Inundation Zone  uyj8-7rv5         []   \n",
       "1  Sea Level Rise Maps (2050s 500-year Floodplain)  qwca-zqw3         []   \n",
       "2             1 foot Digital Elevation Model (DEM)  dpc8-z3jc         []   \n",
       "3  Sea Level Rise Maps (2020s 100-year Floodplain)  ezfn-5dsb         []   \n",
       "4                         DEP Green Infrastructure  spjh-pz7h         []   \n",
       "\n",
       "                                         description  \\\n",
       "0  Areas of New York City that were flooded as a ...   \n",
       "1  This is the 500-Year Floodplain for the 2050s ...   \n",
       "2  NYC 1foot Digital Elevation Model:\\r\\nA bare-e...   \n",
       "3  This is the 100-Year Floodplain for the 2020s ...   \n",
       "4  NYC Green Infrastructure Program initiatives. ...   \n",
       "\n",
       "                                    attribution attribution_link  \\\n",
       "0   Department of Small Business Services (SBS)             None   \n",
       "1  Mayor's Office of Climate and Sustainability             None   \n",
       "2     Office of Technology and Innovation (OTI)             None   \n",
       "3  Mayor's Office of Climate and Sustainability             None   \n",
       "4  Department of Environmental Protection (DEP)             None   \n",
       "\n",
       "  contact_email  type                 updatedAt                 createdAt  \\\n",
       "0          None   map  2023-12-13T02:12:05.000Z 2015-11-09 23:21:21+00:00   \n",
       "1          None   map  2021-09-16T16:38:40.000Z 2013-07-23 21:56:45+00:00   \n",
       "2          None  href  2022-09-23T19:23:09.000Z 2013-08-09 23:09:58+00:00   \n",
       "3          None   map  2021-09-16T16:43:22.000Z 2013-07-23 21:05:21+00:00   \n",
       "4          None   map  2023-12-13T02:19:50.000Z 2017-08-31 20:33:51+00:00   \n",
       "\n",
       "   ... page_views_total page_views_last_week_log page_views_last_month_log  \\\n",
       "0  ...            53576                 6.169925                  8.836050   \n",
       "1  ...            42653                 7.357552                  9.616549   \n",
       "2  ...            32218                 6.087463                  8.308339   \n",
       "3  ...            26065                 7.044394                  8.721099   \n",
       "4  ...            14872                 6.523562                  8.447083   \n",
       "\n",
       "  page_views_total_log days_elapsed created_date download_per_day  \\\n",
       "0            15.709326         2980   2015-11-09             6.81   \n",
       "1            15.380393         3819   2013-07-23             0.63   \n",
       "2            14.975624         3802   2013-08-09             1.72   \n",
       "3            14.669882         3819   2013-07-23            55.42   \n",
       "4            13.860408         2319   2017-08-31            11.99   \n",
       "\n",
       "   page_views_per_day downloads_and_views downloads_and_views_per_day  \n",
       "0               17.98               73865                       24.79  \n",
       "1               11.17               45067                       11.80  \n",
       "2                8.47               38742                       10.19  \n",
       "3                6.83              237728                       62.25  \n",
       "4                6.41               42668                       18.40  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign flood dataset ids as list\n",
    "ids = datasets_df['id'].values\n",
    "\n",
    "# select flood dataset ids in df\n",
    "df = (\n",
    "    df\n",
    "    .loc[df['id'].isin(ids)]\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Mayor's Office of Climate Resiliency</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NYC Emergency Management (NYCEM)</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Department of Environmental Protection (DEP)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mayor's Office of Climate and Sustainability</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Office of Technology and Innovation (OTI)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Department of City Planning (DCP)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Department of Small Business Services (SBS)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NYC Management Department (NYCEM)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    attribution  count\n",
       "0          Mayor's Office of Climate Resiliency      7\n",
       "1              NYC Emergency Management (NYCEM)      7\n",
       "2  Department of Environmental Protection (DEP)      4\n",
       "3  Mayor's Office of Climate and Sustainability      4\n",
       "4     Office of Technology and Innovation (OTI)      2\n",
       "5             Department of City Planning (DCP)      1\n",
       "6   Department of Small Business Services (SBS)      1\n",
       "7             NYC Management Department (NYCEM)      1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quick table of agencies and counts of data\n",
    "(df\n",
    " .groupby(by='attribution')['id']\n",
    " .count()\n",
    " .rename('count')\n",
    " .sort_values(ascending=False)\n",
    " .reset_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table xx: Count of datasets related to flooding by agency on NYC Open Data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Dataset Statistics\n",
    "Note: We convert table to markdown and copy/paste to README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining function to return table\n",
    "\n",
    "def table(df, sort_by, columns):\n",
    "    \"\"\"\n",
    "    Generate a table from a DataFrame.\n",
    "\n",
    "    Parameters:\n",
    "    - df (pd.DataFrame): The input DataFrame.\n",
    "    - sort_by (str): The column name to sort the DataFrame by.\n",
    "    - columns (list): A list of column names to include in the resulting table.\n",
    "\n",
    "    Returns:\n",
    "    pd.DataFrame: A table containing the top 10 rows of the input DataFrame,\n",
    "        sorted by the specified column, and including only the specified columns.\n",
    "        The index is reset and set to a range from 1 to 10.\n",
    "    \"\"\"\n",
    "    \n",
    "    table_df  = (\n",
    "        df\n",
    "        .sort_values(by=sort_by, ascending=False)\n",
    "        .loc[:, columns]\n",
    "        .head(10)\n",
    "        .reset_index(drop=True)\n",
    "        .set_index([pd.Series(range(1, 11))])\n",
    "    )\n",
    "    \n",
    "    table_df.index.names = ['rank']\n",
    "    \n",
    "    return table_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "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>created_date</th>\n",
       "      <th>download_count</th>\n",
       "      <th>download_per_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hurricane Evacuation Centers (Map)</td>\n",
       "      <td>ayer-cga7</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>230880</td>\n",
       "      <td>441.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sea Level Rise Maps (2020s 100-year Floodplain)</td>\n",
       "      <td>ezfn-5dsb</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>211663</td>\n",
       "      <td>55.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hurricane Evacuation Zones</td>\n",
       "      <td>uihr-hn7s</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>41530</td>\n",
       "      <td>13.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEP Green Infrastructure</td>\n",
       "      <td>spjh-pz7h</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>27796</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sandy Inundation Zone</td>\n",
       "      <td>uyj8-7rv5</td>\n",
       "      <td>2015-11-09</td>\n",
       "      <td>20289</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1 foot Digital Elevation Model (DEM)</td>\n",
       "      <td>dpc8-z3jc</td>\n",
       "      <td>2013-08-09</td>\n",
       "      <td>6524</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Land Cover Raster Data (2017) – 6in Resolution</td>\n",
       "      <td>he6d-2qns</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>3522</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NYC Stormwater Flood Map - Extreme Flood with ...</td>\n",
       "      <td>w8eg-8ha6</td>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>3003</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sea Level Rise Maps (2050s 100-year Floodplain)</td>\n",
       "      <td>hbw8-2bah</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>2541</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sea Level Rise Maps (2050s 500-year Floodplain)</td>\n",
       "      <td>qwca-zqw3</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>2414</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   name         id  \\\n",
       "rank                                                                 \n",
       "1                    Hurricane Evacuation Centers (Map)  ayer-cga7   \n",
       "2       Sea Level Rise Maps (2020s 100-year Floodplain)  ezfn-5dsb   \n",
       "3                            Hurricane Evacuation Zones  uihr-hn7s   \n",
       "4                              DEP Green Infrastructure  spjh-pz7h   \n",
       "5                                 Sandy Inundation Zone  uyj8-7rv5   \n",
       "6                  1 foot Digital Elevation Model (DEM)  dpc8-z3jc   \n",
       "7        Land Cover Raster Data (2017) – 6in Resolution  he6d-2qns   \n",
       "8     NYC Stormwater Flood Map - Extreme Flood with ...  w8eg-8ha6   \n",
       "9       Sea Level Rise Maps (2050s 100-year Floodplain)  hbw8-2bah   \n",
       "10      Sea Level Rise Maps (2050s 500-year Floodplain)  qwca-zqw3   \n",
       "\n",
       "     created_date  download_count  download_per_day  \n",
       "rank                                                 \n",
       "1      2022-08-01          230880            441.45  \n",
       "2      2013-07-23          211663             55.42  \n",
       "3      2015-07-14           41530             13.41  \n",
       "4      2017-08-31           27796             11.99  \n",
       "5      2015-11-09           20289              6.81  \n",
       "6      2013-08-09            6524              1.72  \n",
       "7      2018-12-07            3522              1.90  \n",
       "8      2021-06-07            3003              3.18  \n",
       "9      2013-07-23            2541              0.67  \n",
       "10     2013-07-23            2414              0.63  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\n",
    "    'name', 'id', 'created_date',\n",
    "    'download_count', 'download_per_day'\n",
    "]\n",
    "\n",
    "downloads_df = table(df, sort_by='download_count', columns=cols)\n",
    "\n",
    "downloads_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table xx: Top 10 flood datasets by total downloads on NYC Open Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   rank | name                                                              | id        | created_date   |   download_count |   download_per_day |\n",
      "|-------:|:------------------------------------------------------------------|:----------|:---------------|-----------------:|-------------------:|\n",
      "|      1 | Hurricane Evacuation Centers (Map)                                | ayer-cga7 | 2022-08-01     |           230880 |             441.45 |\n",
      "|      2 | Sea Level Rise Maps (2020s 100-year Floodplain)                   | ezfn-5dsb | 2013-07-23     |           211663 |              55.42 |\n",
      "|      3 | Hurricane Evacuation Zones                                        | uihr-hn7s | 2015-07-14     |            41530 |              13.41 |\n",
      "|      4 | DEP Green Infrastructure                                          | spjh-pz7h | 2017-08-31     |            27796 |              11.99 |\n",
      "|      5 | Sandy Inundation Zone                                             | uyj8-7rv5 | 2015-11-09     |            20289 |               6.81 |\n",
      "|      6 | 1 foot Digital Elevation Model (DEM)                              | dpc8-z3jc | 2013-08-09     |             6524 |               1.72 |\n",
      "|      7 | Land Cover Raster Data (2017) – 6in Resolution                    | he6d-2qns | 2018-12-07     |             3522 |               1.9  |\n",
      "|      8 | NYC Stormwater Flood Map - Extreme Flood with 2080 Sea Level Rise | w8eg-8ha6 | 2021-06-07     |             3003 |               3.18 |\n",
      "|      9 | Sea Level Rise Maps (2050s 100-year Floodplain)                   | hbw8-2bah | 2013-07-23     |             2541 |               0.67 |\n",
      "|     10 | Sea Level Rise Maps (2050s 500-year Floodplain)                   | qwca-zqw3 | 2013-07-23     |             2414 |               0.63 |\n"
     ]
    }
   ],
   "source": [
    "print(downloads_df.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>created_date</th>\n",
       "      <th>download_count</th>\n",
       "      <th>download_per_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hurricane Evacuation Centers (Map)</td>\n",
       "      <td>ayer-cga7</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>230880</td>\n",
       "      <td>441.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sea Level Rise Maps (2020s 100-year Floodplain)</td>\n",
       "      <td>ezfn-5dsb</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>211663</td>\n",
       "      <td>55.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hurricane Evacuation Zones</td>\n",
       "      <td>uihr-hn7s</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>41530</td>\n",
       "      <td>13.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEP Green Infrastructure</td>\n",
       "      <td>spjh-pz7h</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>27796</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sandy Inundation Zone</td>\n",
       "      <td>uyj8-7rv5</td>\n",
       "      <td>2015-11-09</td>\n",
       "      <td>20289</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NYC Stormwater Flood Map - Moderate Flood with...</td>\n",
       "      <td>7r5q-vr7p</td>\n",
       "      <td>2022-08-17</td>\n",
       "      <td>2089</td>\n",
       "      <td>4.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NYC Stormwater Flood Map - Extreme Flood with ...</td>\n",
       "      <td>w8eg-8ha6</td>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>3003</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Building Elevation and Subgrade (BES)</td>\n",
       "      <td>bsin-59hv</td>\n",
       "      <td>2023-09-12</td>\n",
       "      <td>258</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NYC Stormwater Flood Map - Moderate Flood with...</td>\n",
       "      <td>5rzh-cyqd</td>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>1847</td>\n",
       "      <td>1.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Land Cover Raster Data (2017) – 6in Resolution</td>\n",
       "      <td>he6d-2qns</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>3522</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   name         id  \\\n",
       "rank                                                                 \n",
       "1                    Hurricane Evacuation Centers (Map)  ayer-cga7   \n",
       "2       Sea Level Rise Maps (2020s 100-year Floodplain)  ezfn-5dsb   \n",
       "3                            Hurricane Evacuation Zones  uihr-hn7s   \n",
       "4                              DEP Green Infrastructure  spjh-pz7h   \n",
       "5                                 Sandy Inundation Zone  uyj8-7rv5   \n",
       "6     NYC Stormwater Flood Map - Moderate Flood with...  7r5q-vr7p   \n",
       "7     NYC Stormwater Flood Map - Extreme Flood with ...  w8eg-8ha6   \n",
       "8                 Building Elevation and Subgrade (BES)  bsin-59hv   \n",
       "9     NYC Stormwater Flood Map - Moderate Flood with...  5rzh-cyqd   \n",
       "10       Land Cover Raster Data (2017) – 6in Resolution  he6d-2qns   \n",
       "\n",
       "     created_date  download_count  download_per_day  \n",
       "rank                                                 \n",
       "1      2022-08-01          230880            441.45  \n",
       "2      2013-07-23          211663             55.42  \n",
       "3      2015-07-14           41530             13.41  \n",
       "4      2017-08-31           27796             11.99  \n",
       "5      2015-11-09           20289              6.81  \n",
       "6      2022-08-17            2089              4.12  \n",
       "7      2021-06-07            3003              3.18  \n",
       "8      2023-09-12             258              2.22  \n",
       "9      2021-06-07            1847              1.96  \n",
       "10     2018-12-07            3522              1.90  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\n",
    "    'name', 'id', 'created_date',\n",
    "    'download_count', 'download_per_day'\n",
    "]\n",
    "\n",
    "downloads_day = table(df, sort_by='download_per_day', columns=cols)\n",
    "\n",
    "downloads_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table xx: Top 10 flood datasets by total downloads per day on NYC Open Data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Page Views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>created_date</th>\n",
       "      <th>page_views_per_day</th>\n",
       "      <th>page_views_last_week</th>\n",
       "      <th>page_views_last_month</th>\n",
       "      <th>page_views_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sandy Inundation Zone</td>\n",
       "      <td>uyj8-7rv5</td>\n",
       "      <td>2015-11-09</td>\n",
       "      <td>17.98</td>\n",
       "      <td>71</td>\n",
       "      <td>456</td>\n",
       "      <td>53576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sea Level Rise Maps (2050s 500-year Floodplain)</td>\n",
       "      <td>qwca-zqw3</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>11.17</td>\n",
       "      <td>163</td>\n",
       "      <td>784</td>\n",
       "      <td>42653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1 foot Digital Elevation Model (DEM)</td>\n",
       "      <td>dpc8-z3jc</td>\n",
       "      <td>2013-08-09</td>\n",
       "      <td>8.47</td>\n",
       "      <td>67</td>\n",
       "      <td>316</td>\n",
       "      <td>32218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sea Level Rise Maps (2020s 100-year Floodplain)</td>\n",
       "      <td>ezfn-5dsb</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>6.83</td>\n",
       "      <td>131</td>\n",
       "      <td>421</td>\n",
       "      <td>26065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DEP Green Infrastructure</td>\n",
       "      <td>spjh-pz7h</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>6.41</td>\n",
       "      <td>91</td>\n",
       "      <td>348</td>\n",
       "      <td>14872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hurricane Evacuation Zones</td>\n",
       "      <td>uihr-hn7s</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>3.32</td>\n",
       "      <td>37</td>\n",
       "      <td>100</td>\n",
       "      <td>10288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sea Level Rise Maps (2050s 100-year Floodplain)</td>\n",
       "      <td>hbw8-2bah</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>2.68</td>\n",
       "      <td>105</td>\n",
       "      <td>216</td>\n",
       "      <td>10246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Land Cover Raster Data (2017) – 6in Resolution</td>\n",
       "      <td>he6d-2qns</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>5.00</td>\n",
       "      <td>21</td>\n",
       "      <td>211</td>\n",
       "      <td>9273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hurricane Evacuation Centers (Map)</td>\n",
       "      <td>ayer-cga7</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>16.37</td>\n",
       "      <td>19</td>\n",
       "      <td>87</td>\n",
       "      <td>8562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NYC Stormwater Flood Map - Extreme Flood with ...</td>\n",
       "      <td>w8eg-8ha6</td>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>7.56</td>\n",
       "      <td>48</td>\n",
       "      <td>167</td>\n",
       "      <td>7132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   name         id  \\\n",
       "rank                                                                 \n",
       "1                                 Sandy Inundation Zone  uyj8-7rv5   \n",
       "2       Sea Level Rise Maps (2050s 500-year Floodplain)  qwca-zqw3   \n",
       "3                  1 foot Digital Elevation Model (DEM)  dpc8-z3jc   \n",
       "4       Sea Level Rise Maps (2020s 100-year Floodplain)  ezfn-5dsb   \n",
       "5                              DEP Green Infrastructure  spjh-pz7h   \n",
       "6                            Hurricane Evacuation Zones  uihr-hn7s   \n",
       "7       Sea Level Rise Maps (2050s 100-year Floodplain)  hbw8-2bah   \n",
       "8        Land Cover Raster Data (2017) – 6in Resolution  he6d-2qns   \n",
       "9                    Hurricane Evacuation Centers (Map)  ayer-cga7   \n",
       "10    NYC Stormwater Flood Map - Extreme Flood with ...  w8eg-8ha6   \n",
       "\n",
       "     created_date  page_views_per_day  page_views_last_week  \\\n",
       "rank                                                          \n",
       "1      2015-11-09               17.98                    71   \n",
       "2      2013-07-23               11.17                   163   \n",
       "3      2013-08-09                8.47                    67   \n",
       "4      2013-07-23                6.83                   131   \n",
       "5      2017-08-31                6.41                    91   \n",
       "6      2015-07-14                3.32                    37   \n",
       "7      2013-07-23                2.68                   105   \n",
       "8      2018-12-07                5.00                    21   \n",
       "9      2022-08-01               16.37                    19   \n",
       "10     2021-06-07                7.56                    48   \n",
       "\n",
       "      page_views_last_month  page_views_total  \n",
       "rank                                           \n",
       "1                       456             53576  \n",
       "2                       784             42653  \n",
       "3                       316             32218  \n",
       "4                       421             26065  \n",
       "5                       348             14872  \n",
       "6                       100             10288  \n",
       "7                       216             10246  \n",
       "8                       211              9273  \n",
       "9                        87              8562  \n",
       "10                      167              7132  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols  = [\n",
    "    'name', 'id', 'created_date',\n",
    "    'page_views_per_day', 'page_views_last_week',\n",
    "    'page_views_last_month', 'page_views_total'\n",
    "]\n",
    "\n",
    "views_df = table(df, sort_by='page_views_total', columns=cols)\n",
    "\n",
    "views_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table xx: Top 10 flood datasets by total page views on NYC Open Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   rank | name                                                              | id        | created_date   |   page_views_per_day |   page_views_last_week |   page_views_last_month |   page_views_total |\n",
      "|-------:|:------------------------------------------------------------------|:----------|:---------------|---------------------:|-----------------------:|------------------------:|-------------------:|\n",
      "|      1 | Sandy Inundation Zone                                             | uyj8-7rv5 | 2015-11-09     |                17.98 |                     71 |                     456 |              53576 |\n",
      "|      2 | Sea Level Rise Maps (2050s 500-year Floodplain)                   | qwca-zqw3 | 2013-07-23     |                11.17 |                    163 |                     784 |              42653 |\n",
      "|      3 | 1 foot Digital Elevation Model (DEM)                              | dpc8-z3jc | 2013-08-09     |                 8.47 |                     67 |                     316 |              32218 |\n",
      "|      4 | Sea Level Rise Maps (2020s 100-year Floodplain)                   | ezfn-5dsb | 2013-07-23     |                 6.83 |                    131 |                     421 |              26065 |\n",
      "|      5 | DEP Green Infrastructure                                          | spjh-pz7h | 2017-08-31     |                 6.41 |                     91 |                     348 |              14872 |\n",
      "|      6 | Hurricane Evacuation Zones                                        | uihr-hn7s | 2015-07-14     |                 3.32 |                     37 |                     100 |              10288 |\n",
      "|      7 | Sea Level Rise Maps (2050s 100-year Floodplain)                   | hbw8-2bah | 2013-07-23     |                 2.68 |                    105 |                     216 |              10246 |\n",
      "|      8 | Land Cover Raster Data (2017) – 6in Resolution                    | he6d-2qns | 2018-12-07     |                 5    |                     21 |                     211 |               9273 |\n",
      "|      9 | Hurricane Evacuation Centers (Map)                                | ayer-cga7 | 2022-08-01     |                16.37 |                     19 |                      87 |               8562 |\n",
      "|     10 | NYC Stormwater Flood Map - Extreme Flood with 2080 Sea Level Rise | w8eg-8ha6 | 2021-06-07     |                 7.56 |                     48 |                     167 |               7132 |\n"
     ]
    }
   ],
   "source": [
    "print(views_df.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>created_date</th>\n",
       "      <th>page_views_per_day</th>\n",
       "      <th>page_views_last_week</th>\n",
       "      <th>page_views_last_month</th>\n",
       "      <th>page_views_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sandy Inundation Zone</td>\n",
       "      <td>uyj8-7rv5</td>\n",
       "      <td>2015-11-09</td>\n",
       "      <td>17.98</td>\n",
       "      <td>71</td>\n",
       "      <td>456</td>\n",
       "      <td>53576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hurricane Evacuation Centers (Map)</td>\n",
       "      <td>ayer-cga7</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>16.37</td>\n",
       "      <td>19</td>\n",
       "      <td>87</td>\n",
       "      <td>8562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sea Level Rise Maps (2050s 500-year Floodplain)</td>\n",
       "      <td>qwca-zqw3</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>11.17</td>\n",
       "      <td>163</td>\n",
       "      <td>784</td>\n",
       "      <td>42653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Building Elevation and Subgrade (BES)</td>\n",
       "      <td>bsin-59hv</td>\n",
       "      <td>2023-09-12</td>\n",
       "      <td>9.24</td>\n",
       "      <td>18</td>\n",
       "      <td>78</td>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NYC Stormwater Flood Map - Moderate Flood with...</td>\n",
       "      <td>7r5q-vr7p</td>\n",
       "      <td>2022-08-17</td>\n",
       "      <td>8.63</td>\n",
       "      <td>23</td>\n",
       "      <td>199</td>\n",
       "      <td>4376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1 foot Digital Elevation Model (DEM)</td>\n",
       "      <td>dpc8-z3jc</td>\n",
       "      <td>2013-08-09</td>\n",
       "      <td>8.47</td>\n",
       "      <td>67</td>\n",
       "      <td>316</td>\n",
       "      <td>32218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NYC Stormwater Flood Map - Extreme Flood with ...</td>\n",
       "      <td>w8eg-8ha6</td>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>7.56</td>\n",
       "      <td>48</td>\n",
       "      <td>167</td>\n",
       "      <td>7132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sea Level Rise Maps (2020s 100-year Floodplain)</td>\n",
       "      <td>ezfn-5dsb</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>6.83</td>\n",
       "      <td>131</td>\n",
       "      <td>421</td>\n",
       "      <td>26065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DEP Green Infrastructure</td>\n",
       "      <td>spjh-pz7h</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>6.41</td>\n",
       "      <td>91</td>\n",
       "      <td>348</td>\n",
       "      <td>14872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DEP's Citywide Parcel-Based Impervious Area GI...</td>\n",
       "      <td>uex9-rfq8</td>\n",
       "      <td>2020-07-13</td>\n",
       "      <td>5.34</td>\n",
       "      <td>21</td>\n",
       "      <td>132</td>\n",
       "      <td>6788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   name         id  \\\n",
       "rank                                                                 \n",
       "1                                 Sandy Inundation Zone  uyj8-7rv5   \n",
       "2                    Hurricane Evacuation Centers (Map)  ayer-cga7   \n",
       "3       Sea Level Rise Maps (2050s 500-year Floodplain)  qwca-zqw3   \n",
       "4                 Building Elevation and Subgrade (BES)  bsin-59hv   \n",
       "5     NYC Stormwater Flood Map - Moderate Flood with...  7r5q-vr7p   \n",
       "6                  1 foot Digital Elevation Model (DEM)  dpc8-z3jc   \n",
       "7     NYC Stormwater Flood Map - Extreme Flood with ...  w8eg-8ha6   \n",
       "8       Sea Level Rise Maps (2020s 100-year Floodplain)  ezfn-5dsb   \n",
       "9                              DEP Green Infrastructure  spjh-pz7h   \n",
       "10    DEP's Citywide Parcel-Based Impervious Area GI...  uex9-rfq8   \n",
       "\n",
       "     created_date  page_views_per_day  page_views_last_week  \\\n",
       "rank                                                          \n",
       "1      2015-11-09               17.98                    71   \n",
       "2      2022-08-01               16.37                    19   \n",
       "3      2013-07-23               11.17                   163   \n",
       "4      2023-09-12                9.24                    18   \n",
       "5      2022-08-17                8.63                    23   \n",
       "6      2013-08-09                8.47                    67   \n",
       "7      2021-06-07                7.56                    48   \n",
       "8      2013-07-23                6.83                   131   \n",
       "9      2017-08-31                6.41                    91   \n",
       "10     2020-07-13                5.34                    21   \n",
       "\n",
       "      page_views_last_month  page_views_total  \n",
       "rank                                           \n",
       "1                       456             53576  \n",
       "2                        87              8562  \n",
       "3                       784             42653  \n",
       "4                        78              1072  \n",
       "5                       199              4376  \n",
       "6                       316             32218  \n",
       "7                       167              7132  \n",
       "8                       421             26065  \n",
       "9                       348             14872  \n",
       "10                      132              6788  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\n",
    "    'name', 'id', 'created_date',\n",
    "    'page_views_per_day', 'page_views_last_week',\n",
    "    'page_views_last_month', 'page_views_total'\n",
    "]\n",
    "\n",
    "views_day = table(df, sort_by='page_views_per_day', columns=cols)\n",
    "\n",
    "views_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table xx: Top 10 flood datasets by total page views per day on NYC Open Data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloads and Page Views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>name</th>\n",
       "      <th>id</th>\n",
       "      <th>created_date</th>\n",
       "      <th>download_count</th>\n",
       "      <th>page_views_total</th>\n",
       "      <th>downloads_and_views</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hurricane Evacuation Centers (Map)</td>\n",
       "      <td>ayer-cga7</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>230880</td>\n",
       "      <td>8562</td>\n",
       "      <td>239442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sea Level Rise Maps (2020s 100-year Floodplain)</td>\n",
       "      <td>ezfn-5dsb</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>211663</td>\n",
       "      <td>26065</td>\n",
       "      <td>237728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sandy Inundation Zone</td>\n",
       "      <td>uyj8-7rv5</td>\n",
       "      <td>2015-11-09</td>\n",
       "      <td>20289</td>\n",
       "      <td>53576</td>\n",
       "      <td>73865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hurricane Evacuation Zones</td>\n",
       "      <td>uihr-hn7s</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>41530</td>\n",
       "      <td>10288</td>\n",
       "      <td>51818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sea Level Rise Maps (2050s 500-year Floodplain)</td>\n",
       "      <td>qwca-zqw3</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>2414</td>\n",
       "      <td>42653</td>\n",
       "      <td>45067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DEP Green Infrastructure</td>\n",
       "      <td>spjh-pz7h</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>27796</td>\n",
       "      <td>14872</td>\n",
       "      <td>42668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1 foot Digital Elevation Model (DEM)</td>\n",
       "      <td>dpc8-z3jc</td>\n",
       "      <td>2013-08-09</td>\n",
       "      <td>6524</td>\n",
       "      <td>32218</td>\n",
       "      <td>38742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Land Cover Raster Data (2017) – 6in Resolution</td>\n",
       "      <td>he6d-2qns</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>3522</td>\n",
       "      <td>9273</td>\n",
       "      <td>12795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sea Level Rise Maps (2050s 100-year Floodplain)</td>\n",
       "      <td>hbw8-2bah</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>2541</td>\n",
       "      <td>10246</td>\n",
       "      <td>12787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NYC Stormwater Flood Map - Extreme Flood with ...</td>\n",
       "      <td>w8eg-8ha6</td>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>3003</td>\n",
       "      <td>7132</td>\n",
       "      <td>10135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   name         id  \\\n",
       "rank                                                                 \n",
       "1                    Hurricane Evacuation Centers (Map)  ayer-cga7   \n",
       "2       Sea Level Rise Maps (2020s 100-year Floodplain)  ezfn-5dsb   \n",
       "3                                 Sandy Inundation Zone  uyj8-7rv5   \n",
       "4                            Hurricane Evacuation Zones  uihr-hn7s   \n",
       "5       Sea Level Rise Maps (2050s 500-year Floodplain)  qwca-zqw3   \n",
       "6                              DEP Green Infrastructure  spjh-pz7h   \n",
       "7                  1 foot Digital Elevation Model (DEM)  dpc8-z3jc   \n",
       "8        Land Cover Raster Data (2017) – 6in Resolution  he6d-2qns   \n",
       "9       Sea Level Rise Maps (2050s 100-year Floodplain)  hbw8-2bah   \n",
       "10    NYC Stormwater Flood Map - Extreme Flood with ...  w8eg-8ha6   \n",
       "\n",
       "     created_date  download_count  page_views_total  downloads_and_views  \n",
       "rank                                                                      \n",
       "1      2022-08-01          230880              8562               239442  \n",
       "2      2013-07-23          211663             26065               237728  \n",
       "3      2015-11-09           20289             53576                73865  \n",
       "4      2015-07-14           41530             10288                51818  \n",
       "5      2013-07-23            2414             42653                45067  \n",
       "6      2017-08-31           27796             14872                42668  \n",
       "7      2013-08-09            6524             32218                38742  \n",
       "8      2018-12-07            3522              9273                12795  \n",
       "9      2013-07-23            2541             10246                12787  \n",
       "10     2021-06-07            3003              7132                10135  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\n",
    "    'name', 'id', 'created_date',\n",
    "    'download_count', 'page_views_total', 'downloads_and_views'\n",
    "]\n",
    "\n",
    "total = table(df, sort_by='downloads_and_views', columns=cols)\n",
    "\n",
    "total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table xx: Top 10 flood datasets by total downloads and page views on NYC Open Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   rank | name                                                              | id        | created_date   |   download_count |   page_views_total |   downloads_and_views |\n",
      "|-------:|:------------------------------------------------------------------|:----------|:---------------|-----------------:|-------------------:|----------------------:|\n",
      "|      1 | Hurricane Evacuation Centers (Map)                                | ayer-cga7 | 2022-08-01     |           230880 |               8562 |                239442 |\n",
      "|      2 | Sea Level Rise Maps (2020s 100-year Floodplain)                   | ezfn-5dsb | 2013-07-23     |           211663 |              26065 |                237728 |\n",
      "|      3 | Sandy Inundation Zone                                             | uyj8-7rv5 | 2015-11-09     |            20289 |              53576 |                 73865 |\n",
      "|      4 | Hurricane Evacuation Zones                                        | uihr-hn7s | 2015-07-14     |            41530 |              10288 |                 51818 |\n",
      "|      5 | Sea Level Rise Maps (2050s 500-year Floodplain)                   | qwca-zqw3 | 2013-07-23     |             2414 |              42653 |                 45067 |\n",
      "|      6 | DEP Green Infrastructure                                          | spjh-pz7h | 2017-08-31     |            27796 |              14872 |                 42668 |\n",
      "|      7 | 1 foot Digital Elevation Model (DEM)                              | dpc8-z3jc | 2013-08-09     |             6524 |              32218 |                 38742 |\n",
      "|      8 | Land Cover Raster Data (2017) – 6in Resolution                    | he6d-2qns | 2018-12-07     |             3522 |               9273 |                 12795 |\n",
      "|      9 | Sea Level Rise Maps (2050s 100-year Floodplain)                   | hbw8-2bah | 2013-07-23     |             2541 |              10246 |                 12787 |\n",
      "|     10 | NYC Stormwater Flood Map - Extreme Flood with 2080 Sea Level Rise | w8eg-8ha6 | 2021-06-07     |             3003 |               7132 |                 10135 |\n"
     ]
    }
   ],
   "source": [
    "print(total.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>created_date</th>\n",
       "      <th>downloads_and_views</th>\n",
       "      <th>downloads_and_views_per_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hurricane Evacuation Centers (Map)</td>\n",
       "      <td>ayer-cga7</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>239442</td>\n",
       "      <td>457.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sea Level Rise Maps (2020s 100-year Floodplain)</td>\n",
       "      <td>ezfn-5dsb</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>237728</td>\n",
       "      <td>62.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sandy Inundation Zone</td>\n",
       "      <td>uyj8-7rv5</td>\n",
       "      <td>2015-11-09</td>\n",
       "      <td>73865</td>\n",
       "      <td>24.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEP Green Infrastructure</td>\n",
       "      <td>spjh-pz7h</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>42668</td>\n",
       "      <td>18.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hurricane Evacuation Zones</td>\n",
       "      <td>uihr-hn7s</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>51818</td>\n",
       "      <td>16.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NYC Stormwater Flood Map - Moderate Flood with...</td>\n",
       "      <td>7r5q-vr7p</td>\n",
       "      <td>2022-08-17</td>\n",
       "      <td>6465</td>\n",
       "      <td>12.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sea Level Rise Maps (2050s 500-year Floodplain)</td>\n",
       "      <td>qwca-zqw3</td>\n",
       "      <td>2013-07-23</td>\n",
       "      <td>45067</td>\n",
       "      <td>11.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Building Elevation and Subgrade (BES)</td>\n",
       "      <td>bsin-59hv</td>\n",
       "      <td>2023-09-12</td>\n",
       "      <td>1330</td>\n",
       "      <td>11.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NYC Stormwater Flood Map - Extreme Flood with ...</td>\n",
       "      <td>w8eg-8ha6</td>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>10135</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1 foot Digital Elevation Model (DEM)</td>\n",
       "      <td>dpc8-z3jc</td>\n",
       "      <td>2013-08-09</td>\n",
       "      <td>38742</td>\n",
       "      <td>10.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   name         id  \\\n",
       "rank                                                                 \n",
       "1                    Hurricane Evacuation Centers (Map)  ayer-cga7   \n",
       "2       Sea Level Rise Maps (2020s 100-year Floodplain)  ezfn-5dsb   \n",
       "3                                 Sandy Inundation Zone  uyj8-7rv5   \n",
       "4                              DEP Green Infrastructure  spjh-pz7h   \n",
       "5                            Hurricane Evacuation Zones  uihr-hn7s   \n",
       "6     NYC Stormwater Flood Map - Moderate Flood with...  7r5q-vr7p   \n",
       "7       Sea Level Rise Maps (2050s 500-year Floodplain)  qwca-zqw3   \n",
       "8                 Building Elevation and Subgrade (BES)  bsin-59hv   \n",
       "9     NYC Stormwater Flood Map - Extreme Flood with ...  w8eg-8ha6   \n",
       "10                 1 foot Digital Elevation Model (DEM)  dpc8-z3jc   \n",
       "\n",
       "     created_date  downloads_and_views  downloads_and_views_per_day  \n",
       "rank                                                                 \n",
       "1      2022-08-01               239442                       457.82  \n",
       "2      2013-07-23               237728                        62.25  \n",
       "3      2015-11-09                73865                        24.79  \n",
       "4      2017-08-31                42668                        18.40  \n",
       "5      2015-07-14                51818                        16.73  \n",
       "6      2022-08-17                 6465                        12.75  \n",
       "7      2013-07-23                45067                        11.80  \n",
       "8      2023-09-12                 1330                        11.47  \n",
       "9      2021-06-07                10135                        10.75  \n",
       "10     2013-08-09                38742                        10.19  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\n",
    "    'name', 'id', 'created_date',\n",
    "    'downloads_and_views', 'downloads_and_views_per_day'\n",
    "]\n",
    "\n",
    "total_per_day = table(df, sort_by='downloads_and_views_per_day', columns=cols)\n",
    "\n",
    "total_per_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table xx: Top 10 flood datasets by total downloads and page views per day on NYC Open Data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relationship Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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.scatterplot(\n",
    "    x=df['page_views_total'],\n",
    "    y=df['download_count'],\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Total Download Count vs. Page Views of Flood Datasets on NYC Open Data')\n",
    "plt.xlabel('Total Page Views')\n",
    "plt.ylabel('Total Download Count')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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.scatterplot(\n",
    "    x=df['page_views_per_day'],\n",
    "    y=df['download_per_day'],\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Downloads per Day vs. Page Views per Day of Flood Datasets on NYC Open Data')\n",
    "plt.xlabel('Total Page Views per Day')\n",
    "plt.ylabel('Total Download Count per Day')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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.scatterplot(\n",
    "    x=df['download_per_day'],\n",
    "    y=df['download_count'],\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Total Downloads vs. Total Downloads per Day of Flood Datasets on NYC Open Data')\n",
    "plt.xlabel('Total Downloads per Day')\n",
    "plt.ylabel('Total Downloads')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "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",
    "sns.scatterplot(\n",
    "    x=df['page_views_per_day'],\n",
    "    y=df['page_views_total'],\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Total Page Views vs. Total Page Views per Day of Flood Datasets on NYC Open Data')\n",
    "plt.xlabel('Total Page Views per Day')\n",
    "plt.ylabel('Total Page Views')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}