{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7LKLhQBzJMOz"
   },
   "source": [
    "## Data discovery: how to explore CARTO's Data Observatory catalog.\n",
    "\n",
    "This notebook shows how to use CARTOframes to discover and explore datasets from CARTO's [Data Observatory](https://carto.com/spatial-data-catalog/).\n",
    "\n",
    "If you haven't installed CARTOframes yet, please visit our [installation guide](https://carto.com/developers/cartoframes/guides/Installation/).\n",
    "\n",
    "The notebook is organized in the following sections:\n",
    "\n",
    "0. [Setup](#section0)\n",
    "\n",
    "    0.1. [Import packages](#section0.1)\n",
    "    \n",
    "    0.2. [Set CARTO default credentials](#section0.2)\n",
    "\n",
    "\n",
    "1. [Data discovery](#section1)\n",
    "     \n",
    "     1.1. [Data catalog structure](#section1.1)\n",
    "     \n",
    "     1.2. [Combining filters](#section1.2)\n",
    "     \n",
    "     1.3. [Filter datasets by the type of geography](#section1.3)\n",
    "     \n",
    "     1.4. [Get a first glimpse of a dataset](#section1.3)\n",
    "     \n",
    "     \n",
    "**Want to learn more?** Learn how to access and download datasets on the following notebooks <a href='https://carto.com/developers/cartoframes/examples/#example-access-public-data-from-the-data-observatory' target='_blank'>Access Public Data</a> and <a href='https://carto.com/developers/cartoframes/examples/#example-access-premium-data-from-the-data-observatory' target='_blank'>Access Premium Data</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V4-0hJjiVsen"
   },
   "source": [
    "<a id='section0'></a>\n",
    "### 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section0-1'></a>\n",
    "#### 0.1. Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "6vLRH4fEEkms"
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "from cartoframes.auth import set_default_credentials\n",
    "from cartoframes.data.observatory import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section0-2'></a>\n",
    "#### 0.2. Set CARTO default credentials\n",
    "\n",
    "In order to be able to use the Data Observatory via CARTOframes, you need to set your CARTO account credentials first.\n",
    "\n",
    "Please, visit the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/) for further detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "2E7lg3CDV2ge"
   },
   "outputs": [],
   "source": [
    "set_default_credentials('creds.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note about credentials**\n",
    "\n",
    "For security reasons, we recommend storing your credentials in an external file to prevent publishing them by accident when sharing your notebooks. You can get more information in the section _Setting your credentials_ of the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uwAtCjyeV3QI"
   },
   "source": [
    "<a id='section1'></a>\n",
    "### 1. Data Discovery\n",
    "\n",
    "CARTO's data catalog consists of a set of datasets organized by:\n",
    " - Country\n",
    " - Category\n",
    " - Provider\n",
    " - Geography\n",
    " \n",
    "In addition, datasets are classified in public and premium data.\n",
    "\n",
    "This classification can be used to explore the Catalog and narrow down your search for the dataset most suitable for your analysis.\n",
    "\n",
    "For example, you may start exploring by country, then filter by category and finally select a dataset based on the provider. Alternatively, you could start exploring by category or provider."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section1-1'></a>\n",
    "#### 1.1. Data catalog structure\n",
    "\n",
    "In this subsection we explore each of the four classification categories.\n",
    "\n",
    "**Note** results can be displayed in Python list format or in Pandas DataFrame format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.1.1. Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "bowL1MIBgLbX",
    "outputId": "6f897e48-0238-48bc-f9b9-12b7c0c87d6f"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>abw</td>\n",
       "      <td>Aruba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>afg</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ago</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aia</td>\n",
       "      <td>Anguilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alb</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>and</td>\n",
       "      <td>Andorra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>are</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>arg</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>arm</td>\n",
       "      <td>Armenia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>asm</td>\n",
       "      <td>American Samoa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id                  name\n",
       "0  abw                 Aruba\n",
       "1  afg           Afghanistan\n",
       "2  ago                Angola\n",
       "3  aia              Anguilla\n",
       "4  alb               Albania\n",
       "5  and               Andorra\n",
       "6  are  United Arab Emirates\n",
       "7  arg             Argentina\n",
       "8  arm               Armenia\n",
       "9  asm        American Samoa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catalog().countries.to_dataframe().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.1.2. Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "id": "rQqNGRQ_oqdr",
    "outputId": "1ceae746-0e6e-46ca-fe6c-171ee6542a2f"
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>behavioral</td>\n",
       "      <td>Behavioral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>covid19</td>\n",
       "      <td>Covid-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>demographics</td>\n",
       "      <td>Demographics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>derived</td>\n",
       "      <td>Derived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>environmental</td>\n",
       "      <td>Environmental</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>financial</td>\n",
       "      <td>Financial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>housing</td>\n",
       "      <td>Housing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>human_mobility</td>\n",
       "      <td>Human Mobility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>points_of_interest</td>\n",
       "      <td>Points of Interest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>road_traffic</td>\n",
       "      <td>Road Traffic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                name\n",
       "0          behavioral          Behavioral\n",
       "1             covid19            Covid-19\n",
       "2        demographics        Demographics\n",
       "3             derived             Derived\n",
       "4       environmental       Environmental\n",
       "5           financial           Financial\n",
       "6             housing             Housing\n",
       "7      human_mobility      Human Mobility\n",
       "8  points_of_interest  Points of Interest\n",
       "9        road_traffic        Road Traffic"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catalog().categories.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.1.3. Providers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "id": "FSYAZfjMoucY",
    "outputId": "dde40bfe-f9ab-4c47-b614-6c0b3e5a823b"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lifesight</td>\n",
       "      <td>Lifesight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tomtom</td>\n",
       "      <td>TomTom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>home_junction</td>\n",
       "      <td>Home Junction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mastercard</td>\n",
       "      <td>Mastercard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>usa_bls</td>\n",
       "      <td>Bureau of Labor Statistics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dstillery</td>\n",
       "      <td>Dstillery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>usa_acs</td>\n",
       "      <td>American Community Survey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>safegraph</td>\n",
       "      <td>SafeGraph</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>vodafone</td>\n",
       "      <td>Vodafone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>can_statistics</td>\n",
       "      <td>Statistics Canada</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id                        name\n",
       "0       lifesight                   Lifesight\n",
       "1          tomtom                      TomTom\n",
       "2   home_junction               Home Junction\n",
       "3      mastercard                  Mastercard\n",
       "4         usa_bls  Bureau of Labor Statistics\n",
       "5       dstillery                   Dstillery\n",
       "6         usa_acs   American Community Survey\n",
       "7       safegraph                   SafeGraph\n",
       "8        vodafone                    Vodafone\n",
       "9  can_statistics           Statistics Canada"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catalog().providers.to_dataframe().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.1.4. Geographies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 960
    },
    "id": "HuKsB4E5o1Th",
    "outputId": "f5ad1d4f-f36c-47ea-f03f-bd7cbb34b1bc"
   },
   "outputs": [
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       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>country_id</th>\n",
       "      <th>provider_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ne_ports_e4ee35c9</td>\n",
       "      <td>Ports - Global</td>\n",
       "      <td>Derives from High Seas, a detailed GIS compila...</td>\n",
       "      <td>glo</td>\n",
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       "      <td>POINT</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ne_parksandpro_dc9d5ce5</td>\n",
       "      <td>Parks And Protected Lands Area - United States...</td>\n",
       "      <td>Data includes the 398 authorized National Park...</td>\n",
       "      <td>usa</td>\n",
       "      <td>natural_earth</td>\n",
       "      <td>MULTIPOLYGON</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>Natural Earth</td>\n",
       "      <td>carto-do-public-data.natural_earth.geography_u...</td>\n",
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       "      <td>mbi_small_areas_f4ab4f8e</td>\n",
       "      <td>Small Areas - South Africa</td>\n",
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       "      <td>zaf</td>\n",
       "      <td>mbi</td>\n",
       "      <td>MULTIPOLYGON</td>\n",
       "      <td>None</td>\n",
       "      <td>yearly</td>\n",
       "      <td>False</td>\n",
       "      <td>eng</td>\n",
       "      <td>2020</td>\n",
       "      <td>Michael Bauer International</td>\n",
       "      <td>carto-do.mbi.geography_zaf_smallareas_2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mbi_parishes_309be8bc</td>\n",
       "      <td>Parishes - Jamaica</td>\n",
       "      <td>MBI Digital Boundaries for Jamaica at Parishes...</td>\n",
       "      <td>jam</td>\n",
       "      <td>mbi</td>\n",
       "      <td>MULTIPOLYGON</td>\n",
       "      <td>None</td>\n",
       "      <td>yearly</td>\n",
       "      <td>False</td>\n",
       "      <td>eng</td>\n",
       "      <td>2020</td>\n",
       "      <td>Michael Bauer International</td>\n",
       "      <td>carto-do.mbi.geography_jam_parishes_2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tigr_county_c781b68e</td>\n",
       "      <td>County - United States of America (2017)</td>\n",
       "      <td>County TIGER/Line Shapefiles from the United S...</td>\n",
       "      <td>usa</td>\n",
       "      <td>usa_tiger</td>\n",
       "      <td>MULTIPOLYGON</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>eng</td>\n",
       "      <td>2017</td>\n",
       "      <td>Tiger/Line geographic data from the U.S. Censu...</td>\n",
       "      <td>carto-do-public-data.usa_tiger.geography_usa_c...</td>\n",
       "    </tr>\n",
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       "                       slug  \\\n",
       "0         ne_ports_e4ee35c9   \n",
       "1   ne_parksandpro_dc9d5ce5   \n",
       "2  mbi_small_areas_f4ab4f8e   \n",
       "3     mbi_parishes_309be8bc   \n",
       "4      tigr_county_c781b68e   \n",
       "\n",
       "                                                name  \\\n",
       "0                                     Ports - Global   \n",
       "1  Parks And Protected Lands Area - United States...   \n",
       "2                         Small Areas - South Africa   \n",
       "3                                 Parishes - Jamaica   \n",
       "4           County - United States of America (2017)   \n",
       "\n",
       "                                         description country_id  \\\n",
       "0  Derives from High Seas, a detailed GIS compila...        glo   \n",
       "1  Data includes the 398 authorized National Park...        usa   \n",
       "2  MBI Digital Boundaries for South Africa at Sma...        zaf   \n",
       "3  MBI Digital Boundaries for Jamaica at Parishes...        jam   \n",
       "4  County TIGER/Line Shapefiles from the United S...        usa   \n",
       "\n",
       "     provider_id     geom_type geom_coverage update_frequency  is_public_data  \\\n",
       "0  natural_earth         POINT          None             None            True   \n",
       "1  natural_earth  MULTIPOLYGON          None             None            True   \n",
       "2            mbi  MULTIPOLYGON          None           yearly           False   \n",
       "3            mbi  MULTIPOLYGON          None           yearly           False   \n",
       "4      usa_tiger  MULTIPOLYGON          None             None            True   \n",
       "\n",
       "  lang version                                      provider_name  \\\n",
       "0  eng     410                                      Natural Earth   \n",
       "1  eng     410                                      Natural Earth   \n",
       "2  eng    2020                        Michael Bauer International   \n",
       "3  eng    2020                        Michael Bauer International   \n",
       "4  eng    2017  Tiger/Line geographic data from the U.S. Censu...   \n",
       "\n",
       "                                                  id  \n",
       "0  carto-do-public-data.natural_earth.geography_g...  \n",
       "1  carto-do-public-data.natural_earth.geography_u...  \n",
       "2         carto-do.mbi.geography_zaf_smallareas_2020  \n",
       "3           carto-do.mbi.geography_jam_parishes_2020  \n",
       "4  carto-do-public-data.usa_tiger.geography_usa_c...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catalog().geographies.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "asbQxIHQi-9Z"
   },
   "source": [
    "<a id='section1-2'></a>\n",
    "#### 1.2. Combining filters\n",
    "Let's now take a look at how to combine category and provider filters to get the public <b>demographics</b> datasets available in the <b>US</b> from <b>ACS</b> at the <b>census tract</b> level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categories available in the US."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "id": "soKtHxhso5_l",
    "outputId": "84a21fad-7a99-4548-aa7c-57f6152de6c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can find more entities with the Global country filter. To apply that filter run:\n",
      "\tCatalog().country('glo')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>behavioral</td>\n",
       "      <td>Behavioral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>covid19</td>\n",
       "      <td>Covid-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>demographics</td>\n",
       "      <td>Demographics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>derived</td>\n",
       "      <td>Derived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>environmental</td>\n",
       "      <td>Environmental</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>financial</td>\n",
       "      <td>Financial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>housing</td>\n",
       "      <td>Housing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>human_mobility</td>\n",
       "      <td>Human Mobility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>points_of_interest</td>\n",
       "      <td>Points of Interest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>road_traffic</td>\n",
       "      <td>Road Traffic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                name\n",
       "0          behavioral          Behavioral\n",
       "1             covid19            Covid-19\n",
       "2        demographics        Demographics\n",
       "3             derived             Derived\n",
       "4       environmental       Environmental\n",
       "5           financial           Financial\n",
       "6             housing             Housing\n",
       "7      human_mobility      Human Mobility\n",
       "8  points_of_interest  Points of Interest\n",
       "9        road_traffic        Road Traffic"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catalog().country('usa').categories.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Providers available in the US."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "FNlJMzn3kfIK",
    "outputId": "46cd0f10-2dad-4e1c-f84a-77b368e96c75"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can find more entities with the Global country filter. To apply that filter run:\n",
      "\tCatalog().country('glo')\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lifesight</td>\n",
       "      <td>Lifesight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tomtom</td>\n",
       "      <td>TomTom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>home_junction</td>\n",
       "      <td>Home Junction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mastercard</td>\n",
       "      <td>Mastercard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>usa_bls</td>\n",
       "      <td>Bureau of Labor Statistics</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                        name\n",
       "0      lifesight                   Lifesight\n",
       "1         tomtom                      TomTom\n",
       "2  home_junction               Home Junction\n",
       "3     mastercard                  Mastercard\n",
       "4        usa_bls  Bureau of Labor Statistics"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catalog().country('usa').providers.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of providers available in the US offering demographics data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "id": "0TGBWgPwkjDY",
    "outputId": "145812ec-eeca-4ca3-d39c-05e35484f11a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can find more entities with the Global country filter. To apply that filter run:\n",
      "\tCatalog().country('glo')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<Provider.get('usa_bls')>,\n",
       " <Provider.get('usa_acs')>,\n",
       " <Provider.get('ags')>,\n",
       " <Provider.get('experian')>,\n",
       " <Provider.get('gbr_cdrc')>,\n",
       " <Provider.get('worldpop')>,\n",
       " <Provider.get('mbi')>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catalog().country('usa').category('demographics').providers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of providers available in the US offering **public** demographics data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can find more entities with the Global country filter. To apply that filter run:\n",
      "\tCatalog().country('glo')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<Provider.get('usa_bls')>,\n",
       " <Provider.get('usa_acs')>,\n",
       " <Provider.get('gbr_cdrc')>,\n",
       " <Provider.get('worldpop')>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catalog().country('usa').category('demographics').public().providers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now take a look at all the demographics datasets provided by ACS in the US."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 926
    },
    "id": "txVEvD6ylQoT",
    "outputId": "1007d1d4-b67e-430e-a511-05e47669c300"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can find more entities with the Global country filter. To apply that filter run:\n",
      "\tCatalog().country('glo')\n"
     ]
    },
    {
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       "                       slug  \\\n",
       "0  acs_sociodemogr_a0c48b07   \n",
       "1  acs_sociodemogr_a03fb95f   \n",
       "2  acs_sociodemogr_e7b702b0   \n",
       "3  acs_sociodemogr_e1e92d8d   \n",
       "4  acs_sociodemogr_30a865f1   \n",
       "\n",
       "                                                name  \\\n",
       "0  Sociodemographics - United States of America (...   \n",
       "1  Sociodemographics - United States of America (...   \n",
       "2  Sociodemographics - United States of America (...   \n",
       "3  Sociodemographics - United States of America (...   \n",
       "4  Sociodemographics - United States of America (...   \n",
       "\n",
       "                                         description   category_id country_id  \\\n",
       "0  The American Community Survey (ACS) is an ongo...  demographics        usa   \n",
       "1  The American Community Survey (ACS) is an ongo...  demographics        usa   \n",
       "2  The American Community Survey (ACS) is an ongo...  demographics        usa   \n",
       "3  The American Community Survey (ACS) is an ongo...  demographics        usa   \n",
       "4  The American Community Survey (ACS) is an ongo...  demographics        usa   \n",
       "\n",
       "      data_source_id provider_id  \\\n",
       "0  sociodemographics     usa_acs   \n",
       "1  sociodemographics     usa_acs   \n",
       "2  sociodemographics     usa_acs   \n",
       "3  sociodemographics     usa_acs   \n",
       "4  sociodemographics     usa_acs   \n",
       "\n",
       "                                      geography_name  \\\n",
       "0           County - United States of America (2015)   \n",
       "1  Congressional District - United States of Amer...   \n",
       "2  Core-based Statistical Area - United States of...   \n",
       "3  Core-based Statistical Area - United States of...   \n",
       "4  Core-based Statistical Area - United States of...   \n",
       "\n",
       "                               geography_description temporal_aggregation  \\\n",
       "0  Shoreline clipped TIGER/Line boundaries. More ...               yearly   \n",
       "1  Shoreline clipped TIGER/Line boundaries. More ...               yearly   \n",
       "2  Shoreline clipped TIGER/Line boundaries. More ...                 3yrs   \n",
       "3  Shoreline clipped TIGER/Line boundaries. More ...               yearly   \n",
       "4  Shoreline clipped TIGER/Line boundaries. More ...                 3yrs   \n",
       "\n",
       "              time_coverage update_frequency  is_public_data lang   version  \\\n",
       "0  [2007-01-01, 2008-01-01)           yearly            True  eng      2007   \n",
       "1  [2017-01-01, 2018-01-01)           yearly            True  eng      2017   \n",
       "2  [2006-01-01, 2009-01-01)           yearly            True  eng  20062008   \n",
       "3  [2013-01-01, 2014-01-01)           yearly            True  eng      2013   \n",
       "4  [2005-01-01, 2008-01-01)           yearly            True  eng  20052007   \n",
       "\n",
       "  category_name              provider_name  \\\n",
       "0  Demographics  American Community Survey   \n",
       "1  Demographics  American Community Survey   \n",
       "2  Demographics  American Community Survey   \n",
       "3  Demographics  American Community Survey   \n",
       "4  Demographics  American Community Survey   \n",
       "\n",
       "                                        geography_id  \\\n",
       "0  carto-do-public-data.carto.geography_usa_count...   \n",
       "1  carto-do-public-data.carto.geography_usa_congr...   \n",
       "2  carto-do-public-data.carto.geography_usa_cbsa_...   \n",
       "3  carto-do-public-data.carto.geography_usa_cbsa_...   \n",
       "4  carto-do-public-data.carto.geography_usa_cbsa_...   \n",
       "\n",
       "                                                  id  \n",
       "0  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "1  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "2  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "3  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "4  carto-do-public-data.usa_acs.demographics_soci...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catalog().country('usa').category('demographics').provider('usa_acs').datasets.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KQv9beU-16bi"
   },
   "source": [
    "<a id='section1-3'></a>\n",
    "#### 1.3. Filter datasets by the type of geography\n",
    "\n",
    "We can explore the types of geographies for which the datasets are available. In order to filter by a specific type of geography, we have to apply a filter to the <i>geography_name</i> column just like we would for a string column on a Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can find more entities with the Global country filter. To apply that filter run:\n",
      "\tCatalog().country('glo')\n"
     ]
    }
   ],
   "source": [
    "datasets_acs_df = Catalog().country('usa').category('demographics').provider('usa_acs').datasets.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "id": "vI6u6tJ9q4AG",
    "outputId": "3d1dbc5d-5c04-43fa-c0b2-08fc1765f626"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['County - United States of America (2015)',\n",
       "       'Congressional District - United States of America (2019)',\n",
       "       'Core-based Statistical Area - United States of America (2019)',\n",
       "       'Census Tract - United States of America (2015)',\n",
       "       'Public Use Microdata Area - United States of America (2019)',\n",
       "       'Census Place - United States of America (2019)',\n",
       "       'State - United States of America (2015)',\n",
       "       'Census Block Group - United States of America (2015)',\n",
       "       '5-digit Zip Code Tabulation Area - United States of America (2015)',\n",
       "       'School District (secondary) - United States of America (2019)',\n",
       "       'School District (elementary) - United States of America (2019)',\n",
       "       'School District (unified) - United States of America (2019)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets_acs_df['geography_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "id": "bMin3NRkrBOz",
    "outputId": "e79d6841-3ed1-4912-e34a-8aaea416b2ef"
   },
   "outputs": [
    {
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       "      <td>demographics</td>\n",
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       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
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      "text/plain": [
       "                         slug  \\\n",
       "8     acs_sociodemogr_30d1f53   \n",
       "9    acs_sociodemogr_cfeb0968   \n",
       "35   acs_sociodemogr_9ed5d625   \n",
       "41   acs_sociodemogr_496a0675   \n",
       "42   acs_sociodemogr_d4b2cf03   \n",
       "169  acs_sociodemogr_97c32d1f   \n",
       "201  acs_sociodemogr_dda43439   \n",
       "231  acs_sociodemogr_6bf5c7f4   \n",
       "246  acs_sociodemogr_858c104e   \n",
       "\n",
       "                                                  name  \\\n",
       "8    Sociodemographics - United States of America (...   \n",
       "9    Sociodemographics - United States of America (...   \n",
       "35   Sociodemographics - United States of America (...   \n",
       "41   Sociodemographics - United States of America (...   \n",
       "42   Sociodemographics - United States of America (...   \n",
       "169  Sociodemographics - United States of America (...   \n",
       "201  Sociodemographics - United States of America (...   \n",
       "231  Sociodemographics - United States of America (...   \n",
       "246  Sociodemographics - United States of America (...   \n",
       "\n",
       "                                           description   category_id  \\\n",
       "8    The American Community Survey (ACS) is an ongo...  demographics   \n",
       "9    The American Community Survey (ACS) is an ongo...  demographics   \n",
       "35   The American Community Survey (ACS) is an ongo...  demographics   \n",
       "41   The American Community Survey (ACS) is an ongo...  demographics   \n",
       "42   The American Community Survey (ACS) is an ongo...  demographics   \n",
       "169  The American Community Survey (ACS) is an ongo...  demographics   \n",
       "201  The American Community Survey (ACS) is an ongo...  demographics   \n",
       "231  The American Community Survey (ACS) is an ongo...  demographics   \n",
       "246  The American Community Survey (ACS) is an ongo...  demographics   \n",
       "\n",
       "    country_id     data_source_id provider_id  \\\n",
       "8          usa  sociodemographics     usa_acs   \n",
       "9          usa  sociodemographics     usa_acs   \n",
       "35         usa  sociodemographics     usa_acs   \n",
       "41         usa  sociodemographics     usa_acs   \n",
       "42         usa  sociodemographics     usa_acs   \n",
       "169        usa  sociodemographics     usa_acs   \n",
       "201        usa  sociodemographics     usa_acs   \n",
       "231        usa  sociodemographics     usa_acs   \n",
       "246        usa  sociodemographics     usa_acs   \n",
       "\n",
       "                                     geography_name  \\\n",
       "8    Census Tract - United States of America (2015)   \n",
       "9    Census Tract - United States of America (2015)   \n",
       "35   Census Tract - United States of America (2015)   \n",
       "41   Census Tract - United States of America (2015)   \n",
       "42   Census Tract - United States of America (2015)   \n",
       "169  Census Tract - United States of America (2015)   \n",
       "201  Census Tract - United States of America (2015)   \n",
       "231  Census Tract - United States of America (2015)   \n",
       "246  Census Tract - United States of America (2015)   \n",
       "\n",
       "                                 geography_description temporal_aggregation  \\\n",
       "8    Shoreline clipped TIGER/Line boundaries. More ...                 5yrs   \n",
       "9    Shoreline clipped TIGER/Line boundaries. More ...                 5yrs   \n",
       "35   Shoreline clipped TIGER/Line boundaries. More ...                 5yrs   \n",
       "41   Shoreline clipped TIGER/Line boundaries. More ...                 5yrs   \n",
       "42   Shoreline clipped TIGER/Line boundaries. More ...                 5yrs   \n",
       "169  Shoreline clipped TIGER/Line boundaries. More ...                 5yrs   \n",
       "201  Shoreline clipped TIGER/Line boundaries. More ...                 5yrs   \n",
       "231  Shoreline clipped TIGER/Line boundaries. More ...                 5yrs   \n",
       "246  Shoreline clipped TIGER/Line boundaries. More ...                 5yrs   \n",
       "\n",
       "                time_coverage update_frequency  is_public_data lang   version  \\\n",
       "8    [2012-01-01, 2017-01-01)           yearly            True  eng  20122016   \n",
       "9    [2009-01-01, 2014-01-01)           yearly            True  eng  20092013   \n",
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       "41   [2013-01-01, 2018-01-01)           yearly            True  eng  20132017   \n",
       "42   [2006-01-01, 2011-01-01)           yearly            True  eng  20062010   \n",
       "169  [2010-01-01, 2015-01-01)           yearly            True  eng  20102014   \n",
       "201  [2011-01-01, 2016-01-01)           yearly            True  eng  20112015   \n",
       "231                      None           yearly            True  eng  20142018   \n",
       "246  [2008-01-01, 2013-01-01)           yearly            True  eng  20082012   \n",
       "\n",
       "    category_name              provider_name  \\\n",
       "8    Demographics  American Community Survey   \n",
       "9    Demographics  American Community Survey   \n",
       "35   Demographics  American Community Survey   \n",
       "41   Demographics  American Community Survey   \n",
       "42   Demographics  American Community Survey   \n",
       "169  Demographics  American Community Survey   \n",
       "201  Demographics  American Community Survey   \n",
       "231  Demographics  American Community Survey   \n",
       "246  Demographics  American Community Survey   \n",
       "\n",
       "                                          geography_id  \\\n",
       "8    carto-do-public-data.carto.geography_usa_censu...   \n",
       "9    carto-do-public-data.carto.geography_usa_censu...   \n",
       "35   carto-do-public-data.carto.geography_usa_censu...   \n",
       "41   carto-do-public-data.carto.geography_usa_censu...   \n",
       "42   carto-do-public-data.carto.geography_usa_censu...   \n",
       "169  carto-do-public-data.carto.geography_usa_censu...   \n",
       "201  carto-do-public-data.carto.geography_usa_censu...   \n",
       "231  carto-do-public-data.carto.geography_usa_censu...   \n",
       "246  carto-do-public-data.carto.geography_usa_censu...   \n",
       "\n",
       "                                                    id  \n",
       "8    carto-do-public-data.usa_acs.demographics_soci...  \n",
       "9    carto-do-public-data.usa_acs.demographics_soci...  \n",
       "35   carto-do-public-data.usa_acs.demographics_soci...  \n",
       "41   carto-do-public-data.usa_acs.demographics_soci...  \n",
       "42   carto-do-public-data.usa_acs.demographics_soci...  \n",
       "169  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "201  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "231  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "246  carto-do-public-data.usa_acs.demographics_soci...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets_acs_df[datasets_acs_df['geography_name'].str.contains('Census Tract')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZEzr-AfG6uSM"
   },
   "source": [
    "<a id='section1-4'></a>\n",
    "#### 1.4. Get a first glimpse of a dataset\n",
    "\n",
    "We select the dataset <i>acs_sociodemogr_496a0675</i> from the list of datasets above because it is the one with the latest data update.\n",
    "\n",
    "CARTOframes allows you to get a first glimpse of the dataset so that you can make sure it's the right dataset for your analysis. This includes:\n",
    " - Information about the dataset. This includes a description, provider, temporal aggregation, if it is public or premium, etc.\n",
    " - Information about its variables. This includes a name, description, aggregation method, etc.\n",
    " - Access to the first 10 rows of the dataset.\n",
    " - A statistical description of all numerical variables, just like the `describe()` function in Pandas.\n",
    " - A map with the geometric coverage of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "ty7maPPM66kD"
   },
   "outputs": [],
   "source": [
    "sample_ds = Dataset.get('acs_sociodemogr_496a0675')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.4.1. Information about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "A7DIGyXj9Otw",
    "outputId": "43fcb060-0f9a-42e4-c3b4-4c44cdc152fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'slug': 'acs_sociodemogr_496a0675',\n",
       " 'name': 'Sociodemographics - United States of America (Census Tract, 2017, 5yrs)',\n",
       " 'description': 'The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about the USA and its people. This dataset contains only a subset of the variables that have been deemed most relevant. More info: https://www.census.gov/programs-surveys/acs/about.html',\n",
       " 'category_id': 'demographics',\n",
       " 'country_id': 'usa',\n",
       " 'data_source_id': 'sociodemographics',\n",
       " 'provider_id': 'usa_acs',\n",
       " 'geography_name': 'Census Tract - United States of America (2015)',\n",
       " 'geography_description': 'Shoreline clipped TIGER/Line boundaries. More info: https://carto.com/blog/tiger-shoreline-clip/',\n",
       " 'temporal_aggregation': '5yrs',\n",
       " 'time_coverage': '[2013-01-01, 2018-01-01)',\n",
       " 'update_frequency': 'yearly',\n",
       " 'is_public_data': True,\n",
       " 'lang': 'eng',\n",
       " 'version': '20132017',\n",
       " 'category_name': 'Demographics',\n",
       " 'provider_name': 'American Community Survey',\n",
       " 'geography_id': 'carto-do-public-data.carto.geography_usa_censustract_2015',\n",
       " 'id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_censustract_2015_5yrs_20132017'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_ds.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.4.2. Information about the dataset variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "6eufahA97EOg",
    "outputId": "da142ec2-29b0-4efb-e45c-715b900bdf29"
   },
   "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>slug</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>db_type</th>\n",
       "      <th>agg_method</th>\n",
       "      <th>column_name</th>\n",
       "      <th>variable_group_id</th>\n",
       "      <th>dataset_id</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>geoid_e99a58c1</td>\n",
       "      <td>geoid</td>\n",
       "      <td>US Census Block Groups Geoids</td>\n",
       "      <td>STRING</td>\n",
       "      <td>None</td>\n",
       "      <td>geoid</td>\n",
       "      <td>None</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>do_date_45f076c2</td>\n",
       "      <td>do_date</td>\n",
       "      <td>First day of the year the survey was issued</td>\n",
       "      <td>DATE</td>\n",
       "      <td>None</td>\n",
       "      <td>do_date</td>\n",
       "      <td>None</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>total_pop_17ea032f</td>\n",
       "      <td>Total Population</td>\n",
       "      <td>Total Population. The total number of all peop...</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>SUM</td>\n",
       "      <td>total_pop</td>\n",
       "      <td>None</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>households_a12defd5</td>\n",
       "      <td>Number of households</td>\n",
       "      <td>Households. A count of the number of household...</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>SUM</td>\n",
       "      <td>households</td>\n",
       "      <td>None</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male_pop_eac43e5a</td>\n",
       "      <td>male_pop</td>\n",
       "      <td>Male Population. The number of people within e...</td>\n",
       "      <td>FLOAT</td>\n",
       "      <td>SUM</td>\n",
       "      <td>male_pop</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "      <td>carto-do-public-data.usa_acs.demographics_soci...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  slug                  name  \\\n",
       "0       geoid_e99a58c1                 geoid   \n",
       "1     do_date_45f076c2               do_date   \n",
       "2   total_pop_17ea032f      Total Population   \n",
       "3  households_a12defd5  Number of households   \n",
       "4    male_pop_eac43e5a              male_pop   \n",
       "\n",
       "                                         description db_type agg_method  \\\n",
       "0                      US Census Block Groups Geoids  STRING       None   \n",
       "1        First day of the year the survey was issued    DATE       None   \n",
       "2  Total Population. The total number of all peop...   FLOAT        SUM   \n",
       "3  Households. A count of the number of household...   FLOAT        SUM   \n",
       "4  Male Population. The number of people within e...   FLOAT        SUM   \n",
       "\n",
       "  column_name                                  variable_group_id  \\\n",
       "0       geoid                                               None   \n",
       "1     do_date                                               None   \n",
       "2   total_pop                                               None   \n",
       "3  households                                               None   \n",
       "4    male_pop  carto-do-public-data.usa_acs.demographics_soci...   \n",
       "\n",
       "                                          dataset_id  \\\n",
       "0  carto-do-public-data.usa_acs.demographics_soci...   \n",
       "1  carto-do-public-data.usa_acs.demographics_soci...   \n",
       "2  carto-do-public-data.usa_acs.demographics_soci...   \n",
       "3  carto-do-public-data.usa_acs.demographics_soci...   \n",
       "4  carto-do-public-data.usa_acs.demographics_soci...   \n",
       "\n",
       "                                                  id  \n",
       "0  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "1  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "2  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "3  carto-do-public-data.usa_acs.demographics_soci...  \n",
       "4  carto-do-public-data.usa_acs.demographics_soci...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_ds.variables.to_dataframe().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.4.3. Access to the ten first rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367
    },
    "id": "QJqYk6r886_J",
    "outputId": "fcd88f75-2146-4a61-b176-dc07a1c5a56c"
   },
   "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>geoid</th>\n",
       "      <th>no_car</th>\n",
       "      <th>do_date</th>\n",
       "      <th>male_20</th>\n",
       "      <th>male_21</th>\n",
       "      <th>no_cars</th>\n",
       "      <th>one_car</th>\n",
       "      <th>poverty</th>\n",
       "      <th>children</th>\n",
       "      <th>male_pop</th>\n",
       "      <th>two_cars</th>\n",
       "      <th>asian_pop</th>\n",
       "      <th>black_pop</th>\n",
       "      <th>female_20</th>\n",
       "      <th>female_21</th>\n",
       "      <th>in_school</th>\n",
       "      <th>pop_25_64</th>\n",
       "      <th>total_pop</th>\n",
       "      <th>white_pop</th>\n",
       "      <th>female_pop</th>\n",
       "      <th>gini_index</th>\n",
       "      <th>households</th>\n",
       "      <th>male_60_61</th>\n",
       "      <th>male_62_64</th>\n",
       "      <th>median_age</th>\n",
       "      <th>three_cars</th>\n",
       "      <th>male_5_to_9</th>\n",
       "      <th>median_rent</th>\n",
       "      <th>pop_16_over</th>\n",
       "      <th>pop_widowed</th>\n",
       "      <th>armed_forces</th>\n",
       "      <th>employed_pop</th>\n",
       "      <th>hispanic_pop</th>\n",
       "      <th>male_under_5</th>\n",
       "      <th>mobile_homes</th>\n",
       "      <th>pop_divorced</th>\n",
       "      <th>female_5_to_9</th>\n",
       "      <th>housing_units</th>\n",
       "      <th>male_10_to_14</th>\n",
       "      <th>male_15_to_17</th>\n",
       "      <th>male_18_to_19</th>\n",
       "      <th>male_22_to_24</th>\n",
       "      <th>male_25_to_29</th>\n",
       "      <th>male_30_to_34</th>\n",
       "      <th>male_35_to_39</th>\n",
       "      <th>male_40_to_44</th>\n",
       "      <th>male_45_to_49</th>\n",
       "      <th>male_45_to_64</th>\n",
       "      <th>male_50_to_54</th>\n",
       "      <th>male_55_to_59</th>\n",
       "      <th>male_65_to_66</th>\n",
       "      <th>male_67_to_69</th>\n",
       "      <th>male_70_to_74</th>\n",
       "      <th>male_75_to_79</th>\n",
       "      <th>male_80_to_84</th>\n",
       "      <th>median_income</th>\n",
       "      <th>pop_separated</th>\n",
       "      <th>amerindian_pop</th>\n",
       "      <th>female_under_5</th>\n",
       "      <th>four_more_cars</th>\n",
       "      <th>group_quarters</th>\n",
       "      <th>masters_degree</th>\n",
       "      <th>other_race_pop</th>\n",
       "      <th>unemployed_pop</th>\n",
       "      <th>walked_to_work</th>\n",
       "      <th>worked_at_home</th>\n",
       "      <th>female_10_to_14</th>\n",
       "      <th>female_15_to_17</th>\n",
       "      <th>female_18_to_19</th>\n",
       "      <th>female_22_to_24</th>\n",
       "      <th>female_25_to_29</th>\n",
       "      <th>female_30_to_34</th>\n",
       "      <th>female_35_to_39</th>\n",
       "      <th>female_40_to_44</th>\n",
       "      <th>female_45_to_49</th>\n",
       "      <th>female_50_to_54</th>\n",
       "      <th>female_55_to_59</th>\n",
       "      <th>female_60_to_61</th>\n",
       "      <th>female_62_to_64</th>\n",
       "      <th>female_65_to_66</th>\n",
       "      <th>female_67_to_69</th>\n",
       "      <th>female_70_to_74</th>\n",
       "      <th>female_75_to_79</th>\n",
       "      <th>female_80_to_84</th>\n",
       "      <th>pop_15_and_over</th>\n",
       "      <th>pop_now_married</th>\n",
       "      <th>asian_male_45_54</th>\n",
       "      <th>asian_male_55_64</th>\n",
       "      <th>bachelors_degree</th>\n",
       "      <th>black_male_45_54</th>\n",
       "      <th>black_male_55_64</th>\n",
       "      <th>commute_5_9_mins</th>\n",
       "      <th>commuters_by_bus</th>\n",
       "      <th>in_grades_1_to_4</th>\n",
       "      <th>in_grades_5_to_8</th>\n",
       "      <th>male_85_and_over</th>\n",
       "      <th>not_hispanic_pop</th>\n",
       "      <th>pop_5_years_over</th>\n",
       "      <th>white_male_45_54</th>\n",
       "      <th>white_male_55_64</th>\n",
       "      <th>associates_degree</th>\n",
       "      <th>commuters_16_over</th>\n",
       "      <th>dwellings_2_units</th>\n",
       "      <th>family_households</th>\n",
       "      <th>hispanic_any_race</th>\n",
       "      <th>in_grades_9_to_12</th>\n",
       "      <th>income_less_10000</th>\n",
       "      <th>income_per_capita</th>\n",
       "      <th>pop_25_years_over</th>\n",
       "      <th>pop_never_married</th>\n",
       "      <th>bachelors_degree_2</th>\n",
       "      <th>commute_10_14_mins</th>\n",
       "      <th>commute_15_19_mins</th>\n",
       "      <th>commute_20_24_mins</th>\n",
       "      <th>commute_25_29_mins</th>\n",
       "      <th>commute_30_34_mins</th>\n",
       "      <th>commute_35_39_mins</th>\n",
       "      <th>commute_35_44_mins</th>\n",
       "      <th>commute_40_44_mins</th>\n",
       "      <th>commute_45_59_mins</th>\n",
       "      <th>commute_60_89_mins</th>\n",
       "      <th>female_85_and_over</th>\n",
       "      <th>income_10000_14999</th>\n",
       "      <th>income_15000_19999</th>\n",
       "      <th>income_20000_24999</th>\n",
       "      <th>income_25000_29999</th>\n",
       "      <th>income_30000_34999</th>\n",
       "      <th>income_35000_39999</th>\n",
       "      <th>income_40000_44999</th>\n",
       "      <th>income_45000_49999</th>\n",
       "      <th>income_50000_59999</th>\n",
       "      <th>income_60000_74999</th>\n",
       "      <th>income_75000_99999</th>\n",
       "      <th>married_households</th>\n",
       "      <th>not_in_labor_force</th>\n",
       "      <th>not_us_citizen_pop</th>\n",
       "      <th>pop_in_labor_force</th>\n",
       "      <th>high_school_diploma</th>\n",
       "      <th>hispanic_male_45_54</th>\n",
       "      <th>hispanic_male_55_64</th>\n",
       "      <th>occupation_services</th>\n",
       "      <th>workers_16_and_over</th>\n",
       "      <th>civilian_labor_force</th>\n",
       "      <th>commute_60_more_mins</th>\n",
       "      <th>commute_90_more_mins</th>\n",
       "      <th>commute_less_10_mins</th>\n",
       "      <th>commuters_by_carpool</th>\n",
       "      <th>employed_information</th>\n",
       "      <th>in_undergrad_college</th>\n",
       "      <th>income_100000_124999</th>\n",
       "      <th>income_125000_149999</th>\n",
       "      <th>income_150000_199999</th>\n",
       "      <th>male_male_households</th>\n",
       "      <th>nonfamily_households</th>\n",
       "      <th>rent_over_50_percent</th>\n",
       "      <th>vacant_housing_units</th>\n",
       "      <th>commuters_drove_alone</th>\n",
       "      <th>employed_construction</th>\n",
       "      <th>employed_retail_trade</th>\n",
       "      <th>income_200000_or_more</th>\n",
       "      <th>less_one_year_college</th>\n",
       "      <th>male_45_64_grade_9_12</th>\n",
       "      <th>one_year_more_college</th>\n",
       "      <th>rent_10_to_15_percent</th>\n",
       "      <th>rent_15_to_20_percent</th>\n",
       "      <th>rent_20_to_25_percent</th>\n",
       "      <th>rent_25_to_30_percent</th>\n",
       "      <th>rent_30_to_35_percent</th>\n",
       "      <th>rent_35_to_40_percent</th>\n",
       "      <th>rent_40_to_50_percent</th>\n",
       "      <th>rent_under_10_percent</th>\n",
       "      <th>sales_office_employed</th>\n",
       "      <th>speak_spanish_at_home</th>\n",
       "      <th>two_or_more_races_pop</th>\n",
       "      <th>dwellings_3_to_4_units</th>\n",
       "      <th>dwellings_5_to_9_units</th>\n",
       "      <th>employed_manufacturing</th>\n",
       "      <th>male_45_64_high_school</th>\n",
       "      <th>occupied_housing_units</th>\n",
       "      <th>male_45_64_some_college</th>\n",
       "      <th>mortgaged_housing_units</th>\n",
       "      <th>occupation_sales_office</th>\n",
       "      <th>population_3_years_over</th>\n",
       "      <th>asian_including_hispanic</th>\n",
       "      <th>black_including_hispanic</th>\n",
       "      <th>dwellings_10_to_19_units</th>\n",
       "      <th>dwellings_20_to_49_units</th>\n",
       "      <th>employed_wholesale_trade</th>\n",
       "      <th>female_female_households</th>\n",
       "      <th>rent_burden_not_computed</th>\n",
       "      <th>white_including_hispanic</th>\n",
       "      <th>high_school_including_ged</th>\n",
       "      <th>commuters_by_car_truck_van</th>\n",
       "      <th>dwellings_1_units_attached</th>\n",
       "      <th>dwellings_1_units_detached</th>\n",
       "      <th>dwellings_50_or_more_units</th>\n",
       "      <th>housing_built_2000_to_2004</th>\n",
       "      <th>male_45_64_graduate_degree</th>\n",
       "      <th>occupation_management_arts</th>\n",
       "      <th>population_1_year_and_over</th>\n",
       "      <th>speak_only_english_at_home</th>\n",
       "      <th>housing_built_2005_or_later</th>\n",
       "      <th>male_45_64_bachelors_degree</th>\n",
       "      <th>median_year_structure_built</th>\n",
       "      <th>children_in_single_female_hh</th>\n",
       "      <th>families_with_young_children</th>\n",
       "      <th>graduate_professional_degree</th>\n",
       "      <th>households_retirement_income</th>\n",
       "      <th>male_45_64_associates_degree</th>\n",
       "      <th>male_45_64_less_than_9_grade</th>\n",
       "      <th>million_dollar_housing_units</th>\n",
       "      <th>owner_occupied_housing_units</th>\n",
       "      <th>percent_income_spent_on_rent</th>\n",
       "      <th>aggregate_travel_time_to_work</th>\n",
       "      <th>amerindian_including_hispanic</th>\n",
       "      <th>housing_built_1939_or_earlier</th>\n",
       "      <th>housing_units_renter_occupied</th>\n",
       "      <th>pop_determined_poverty_status</th>\n",
       "      <th>vacant_housing_units_for_rent</th>\n",
       "      <th>vacant_housing_units_for_sale</th>\n",
       "      <th>employed_public_administration</th>\n",
       "      <th>less_than_high_school_graduate</th>\n",
       "      <th>commuters_by_subway_or_elevated</th>\n",
       "      <th>bachelors_degree_or_higher_25_64</th>\n",
       "      <th>employed_education_health_social</th>\n",
       "      <th>speak_spanish_at_home_low_english</th>\n",
       "      <th>commuters_by_public_transportation</th>\n",
       "      <th>different_house_year_ago_same_city</th>\n",
       "      <th>some_college_and_associates_degree</th>\n",
       "      <th>households_public_asst_or_food_stamps</th>\n",
       "      <th>management_business_sci_arts_employed</th>\n",
       "      <th>employed_finance_insurance_real_estate</th>\n",
       "      <th>different_house_year_ago_different_city</th>\n",
       "      <th>employed_science_management_admin_waste</th>\n",
       "      <th>one_parent_families_with_young_children</th>\n",
       "      <th>two_parent_families_with_young_children</th>\n",
       "      <th>employed_other_services_not_public_admin</th>\n",
       "      <th>owner_occupied_housing_units_median_value</th>\n",
       "      <th>employed_transportation_warehousing_utilities</th>\n",
       "      <th>occupation_production_transportation_material</th>\n",
       "      <th>father_one_parent_families_with_young_children</th>\n",
       "      <th>owner_occupied_housing_units_lower_value_quartile</th>\n",
       "      <th>owner_occupied_housing_units_upper_value_quartile</th>\n",
       "      <th>employed_agriculture_forestry_fishing_hunting_mining</th>\n",
       "      <th>occupation_natural_resources_construction_maintenance</th>\n",
       "      <th>two_parents_in_labor_force_families_with_young_children</th>\n",
       "      <th>employed_arts_entertainment_recreation_accommodation_food</th>\n",
       "      <th>renter_occupied_housing_units_paying_cash_median_gross_rent</th>\n",
       "      <th>two_parents_not_in_labor_force_families_with_young_children</th>\n",
       "      <th>father_in_labor_force_one_parent_families_with_young_children</th>\n",
       "      <th>two_parents_father_in_labor_force_families_with_young_children</th>\n",
       "      <th>two_parents_mother_in_labor_force_families_with_young_children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47157005000</td>\n",
       "      <td>76.0</td>\n",
       "      <td>20132017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>552.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>0.4372</td>\n",
       "      <td>343.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>749.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>None</td>\n",
       "      <td>76.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>14410.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>None</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>8747.0</td>\n",
       "      <td>564.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>None</td>\n",
       "      <td>22.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>None</td>\n",
       "      <td>52.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>None</td>\n",
       "      <td>43.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26600.0</td>\n",
       "      <td>231300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72051990021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20132017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72071990000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20132017</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01097990000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20132017</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>None</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18127980001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20132017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47157980100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20132017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5896.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55071990000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20132017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25025990101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20132017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17785.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>71.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>None</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>None</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>23.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55025991703</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>None</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         geoid  no_car   do_date  male_20  male_21  no_cars  one_car  poverty  \\\n",
       "0  47157005000    76.0  20132017      0.0      0.0    151.0    122.0    552.0   \n",
       "1  72051990021     0.0  20132017      0.0      0.0      0.0      0.0      0.0   \n",
       "2  72071990000     0.0  20132017      0.0      0.0      0.0      0.0      0.0   \n",
       "3  01097990000     0.0  20132017      0.0      0.0      0.0      0.0      0.0   \n",
       "4  18127980001     0.0  20132017      0.0      0.0      0.0      0.0      0.0   \n",
       "5  47157980100     0.0  20132017      0.0      6.0      0.0      0.0     58.0   \n",
       "6  55071990000     0.0  20132017      0.0      0.0      0.0      0.0      0.0   \n",
       "7  25025990101     0.0  20132017      0.0      0.0      0.0      0.0      0.0   \n",
       "8  55025991703     0.0  20132017      0.0      0.0      0.0      0.0      0.0   \n",
       "9  26041990000     0.0  20132017      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   children  male_pop  two_cars  asian_pop  black_pop  female_20  female_21  \\\n",
       "0     316.0     415.0      64.0        0.0     1004.0       19.0       42.0   \n",
       "1       0.0       0.0       0.0        0.0        0.0        0.0        0.0   \n",
       "2       0.0       0.0       0.0        0.0        0.0        0.0        0.0   \n",
       "3       0.0       0.0       0.0        0.0        0.0        0.0        0.0   \n",
       "4       0.0       0.0       0.0        0.0        0.0        0.0        0.0   \n",
       "5       0.0      38.0       0.0        0.0        4.0        0.0        0.0   \n",
       "6       0.0       0.0       0.0        0.0        0.0        0.0        0.0   \n",
       "7       0.0      58.0       0.0        0.0        0.0        0.0        0.0   \n",
       "8       0.0       0.0       0.0        0.0        0.0        0.0        0.0   \n",
       "9       0.0       0.0       0.0        0.0        0.0        0.0        0.0   \n",
       "\n",
       "   in_school  pop_25_64  total_pop  white_pop  female_pop  gini_index  \\\n",
       "0      331.0      328.0     1042.0       16.0       627.0      0.4372   \n",
       "1        0.0        0.0        0.0        0.0         0.0         NaN   \n",
       "2        0.0        0.0        0.0        0.0         0.0         NaN   \n",
       "3        0.0        0.0        0.0        0.0         0.0         NaN   \n",
       "4        0.0        0.0        0.0        0.0         0.0         NaN   \n",
       "5        0.0       54.0       74.0       70.0        36.0         NaN   \n",
       "6        0.0        0.0        0.0        0.0         0.0         NaN   \n",
       "7       29.0       23.0       71.0        0.0        13.0         NaN   \n",
       "8        0.0        0.0        0.0        0.0         0.0         NaN   \n",
       "9        0.0        0.0        0.0        0.0         0.0         NaN   \n",
       "\n",
       "   households  male_60_61  male_62_64  median_age  three_cars  male_5_to_9  \\\n",
       "0       343.0         0.0         2.0        28.9         0.0         38.0   \n",
       "1         0.0         0.0         0.0         NaN         0.0          0.0   \n",
       "2         0.0         0.0         0.0         NaN         0.0          0.0   \n",
       "3         0.0         0.0         0.0         NaN         0.0          0.0   \n",
       "4         0.0         0.0         0.0         NaN         0.0          0.0   \n",
       "5         0.0         0.0         0.0        31.3         0.0          0.0   \n",
       "6         0.0         0.0         0.0         NaN         0.0          0.0   \n",
       "7        13.0         0.0         0.0        24.0        13.0          0.0   \n",
       "8         0.0         0.0         0.0         NaN         0.0          0.0   \n",
       "9         0.0         0.0         0.0         NaN         0.0          0.0   \n",
       "\n",
       "   median_rent  pop_16_over pop_widowed  armed_forces  employed_pop  \\\n",
       "0        391.0        749.0        None           0.0         233.0   \n",
       "1          NaN          0.0        None           0.0           0.0   \n",
       "2          NaN          0.0        None           0.0           0.0   \n",
       "3          NaN          0.0        None           0.0           0.0   \n",
       "4          NaN          0.0        None           0.0           0.0   \n",
       "5          NaN         74.0        None           0.0           0.0   \n",
       "6          NaN          0.0        None           0.0           0.0   \n",
       "7          NaN         71.0        None           0.0          71.0   \n",
       "8          NaN          0.0        None           0.0           0.0   \n",
       "9          NaN          0.0        None           0.0           0.0   \n",
       "\n",
       "   hispanic_pop  male_under_5  mobile_homes pop_divorced  female_5_to_9  \\\n",
       "0           0.0          44.0           5.0         None           76.0   \n",
       "1           0.0           0.0           0.0         None            0.0   \n",
       "2           0.0           0.0           0.0         None            0.0   \n",
       "3           0.0           0.0           0.0         None            0.0   \n",
       "4           0.0           0.0           0.0         None            0.0   \n",
       "5           0.0           0.0           0.0         None            0.0   \n",
       "6           0.0           0.0           0.0         None            0.0   \n",
       "7          71.0           0.0           0.0         None            0.0   \n",
       "8           0.0           0.0           0.0         None            0.0   \n",
       "9           0.0           0.0           0.0         None            0.0   \n",
       "\n",
       "   housing_units  male_10_to_14  male_15_to_17  male_18_to_19  male_22_to_24  \\\n",
       "0          465.0           27.0           26.0           30.0           22.0   \n",
       "1            0.0            0.0            0.0            0.0            0.0   \n",
       "2            0.0            0.0            0.0            0.0            0.0   \n",
       "3            0.0            0.0            0.0            0.0            0.0   \n",
       "4            0.0            0.0            0.0            0.0            0.0   \n",
       "5            0.0            0.0            0.0            6.0            0.0   \n",
       "6            0.0            0.0            0.0            0.0            0.0   \n",
       "7           13.0            0.0            0.0           16.0           19.0   \n",
       "8            0.0            0.0            0.0            0.0            0.0   \n",
       "9            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   male_25_to_29  male_30_to_34  male_35_to_39  male_40_to_44  male_45_to_49  \\\n",
       "0           11.0           13.0           31.0           11.0           15.0   \n",
       "1            0.0            0.0            0.0            0.0            0.0   \n",
       "2            0.0            0.0            0.0            0.0            0.0   \n",
       "3            0.0            0.0            0.0            0.0            0.0   \n",
       "4            0.0            0.0            0.0            0.0            0.0   \n",
       "5            0.0            6.0           12.0            4.0            0.0   \n",
       "6            0.0            0.0            0.0            0.0            0.0   \n",
       "7            0.0            0.0            0.0            0.0            0.0   \n",
       "8            0.0            0.0            0.0            0.0            0.0   \n",
       "9            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   male_45_to_64  male_50_to_54  male_55_to_59  male_65_to_66  male_67_to_69  \\\n",
       "0           79.0           21.0           41.0            0.0            5.0   \n",
       "1            0.0            0.0            0.0            0.0            0.0   \n",
       "2            0.0            0.0            0.0            0.0            0.0   \n",
       "3            0.0            0.0            0.0            0.0            0.0   \n",
       "4            0.0            0.0            0.0            0.0            0.0   \n",
       "5            0.0            0.0            0.0            0.0            0.0   \n",
       "6            0.0            0.0            0.0            0.0            0.0   \n",
       "7           23.0            0.0           23.0            0.0            0.0   \n",
       "8            0.0            0.0            0.0            0.0            0.0   \n",
       "9            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   male_70_to_74  male_75_to_79  male_80_to_84  median_income pop_separated  \\\n",
       "0           26.0            5.0           31.0        14410.0          None   \n",
       "1            0.0            0.0            0.0            NaN          None   \n",
       "2            0.0            0.0            0.0            NaN          None   \n",
       "3            0.0            0.0            0.0            NaN          None   \n",
       "4            0.0            0.0            0.0            NaN          None   \n",
       "5            0.0            4.0            0.0            NaN          None   \n",
       "6            0.0            0.0            0.0            NaN          None   \n",
       "7            0.0            0.0            0.0            NaN          None   \n",
       "8            0.0            0.0            0.0            NaN          None   \n",
       "9            0.0            0.0            0.0            NaN          None   \n",
       "\n",
       "   amerindian_pop  female_under_5  four_more_cars  group_quarters  \\\n",
       "0             0.0            24.0             6.0            45.0   \n",
       "1             0.0             0.0             0.0             0.0   \n",
       "2             0.0             0.0             0.0             0.0   \n",
       "3             0.0             0.0             0.0             0.0   \n",
       "4             0.0             0.0             0.0             0.0   \n",
       "5             0.0             0.0             0.0            74.0   \n",
       "6             0.0             0.0             0.0             0.0   \n",
       "7             0.0             0.0             0.0             0.0   \n",
       "8             0.0             0.0             0.0             0.0   \n",
       "9             0.0             0.0             0.0             0.0   \n",
       "\n",
       "   masters_degree  other_race_pop  unemployed_pop  walked_to_work  \\\n",
       "0            13.0             0.0            71.0             7.0   \n",
       "1             0.0             0.0             0.0             0.0   \n",
       "2             0.0             0.0             0.0             0.0   \n",
       "3             0.0             0.0             0.0             0.0   \n",
       "4             0.0             0.0             0.0             0.0   \n",
       "5             0.0             0.0             0.0             0.0   \n",
       "6             0.0             0.0             0.0             0.0   \n",
       "7             0.0             0.0             0.0             0.0   \n",
       "8             0.0             0.0             0.0             0.0   \n",
       "9             0.0             0.0             0.0             0.0   \n",
       "\n",
       "   worked_at_home  female_10_to_14  female_15_to_17  female_18_to_19  \\\n",
       "0             0.0             47.0             34.0             17.0   \n",
       "1             0.0              0.0              0.0              0.0   \n",
       "2             0.0              0.0              0.0              0.0   \n",
       "3             0.0              0.0              0.0              0.0   \n",
       "4             0.0              0.0              0.0              0.0   \n",
       "5             0.0              0.0              0.0              0.0   \n",
       "6             0.0              0.0              0.0              0.0   \n",
       "7             0.0              0.0              0.0              0.0   \n",
       "8             0.0              0.0              0.0              0.0   \n",
       "9             0.0              0.0              0.0              0.0   \n",
       "\n",
       "   female_22_to_24  female_25_to_29  female_30_to_34  female_35_to_39  \\\n",
       "0             32.0             34.0             13.0             38.0   \n",
       "1              0.0              0.0              0.0              0.0   \n",
       "2              0.0              0.0              0.0              0.0   \n",
       "3              0.0              0.0              0.0              0.0   \n",
       "4              0.0              0.0              0.0              0.0   \n",
       "5              4.0             18.0              6.0              0.0   \n",
       "6              0.0              0.0              0.0              0.0   \n",
       "7             13.0              0.0              0.0              0.0   \n",
       "8              0.0              0.0              0.0              0.0   \n",
       "9              0.0              0.0              0.0              0.0   \n",
       "\n",
       "   female_40_to_44  female_45_to_49  female_50_to_54  female_55_to_59  \\\n",
       "0             19.0             25.0             26.0             22.0   \n",
       "1              0.0              0.0              0.0              0.0   \n",
       "2              0.0              0.0              0.0              0.0   \n",
       "3              0.0              0.0              0.0              0.0   \n",
       "4              0.0              0.0              0.0              0.0   \n",
       "5              0.0              0.0              5.0              3.0   \n",
       "6              0.0              0.0              0.0              0.0   \n",
       "7              0.0              0.0              0.0              0.0   \n",
       "8              0.0              0.0              0.0              0.0   \n",
       "9              0.0              0.0              0.0              0.0   \n",
       "\n",
       "   female_60_to_61  female_62_to_64  female_65_to_66  female_67_to_69  \\\n",
       "0              0.0              6.0             22.0             38.0   \n",
       "1              0.0              0.0              0.0              0.0   \n",
       "2              0.0              0.0              0.0              0.0   \n",
       "3              0.0              0.0              0.0              0.0   \n",
       "4              0.0              0.0              0.0              0.0   \n",
       "5              0.0              0.0              0.0              0.0   \n",
       "6              0.0              0.0              0.0              0.0   \n",
       "7              0.0              0.0              0.0              0.0   \n",
       "8              0.0              0.0              0.0              0.0   \n",
       "9              0.0              0.0              0.0              0.0   \n",
       "\n",
       "   female_70_to_74  female_75_to_79  female_80_to_84 pop_15_and_over  \\\n",
       "0             36.0             13.0              6.0            None   \n",
       "1              0.0              0.0              0.0            None   \n",
       "2              0.0              0.0              0.0            None   \n",
       "3              0.0              0.0              0.0            None   \n",
       "4              0.0              0.0              0.0            None   \n",
       "5              0.0              0.0              0.0            None   \n",
       "6              0.0              0.0              0.0            None   \n",
       "7              0.0              0.0              0.0            None   \n",
       "8              0.0              0.0              0.0            None   \n",
       "9              0.0              0.0              0.0            None   \n",
       "\n",
       "  pop_now_married  asian_male_45_54  asian_male_55_64  bachelors_degree  \\\n",
       "0            None               0.0               0.0               0.0   \n",
       "1            None               0.0               0.0               0.0   \n",
       "2            None               0.0               0.0               0.0   \n",
       "3            None               0.0               0.0               0.0   \n",
       "4            None               0.0               0.0               0.0   \n",
       "5            None               0.0               0.0               0.0   \n",
       "6            None               0.0               0.0               0.0   \n",
       "7            None               0.0               0.0               0.0   \n",
       "8            None               0.0               0.0               0.0   \n",
       "9            None               0.0               0.0               0.0   \n",
       "\n",
       "   black_male_45_54  black_male_55_64  commute_5_9_mins  commuters_by_bus  \\\n",
       "0              23.0              35.0               7.0              52.0   \n",
       "1               0.0               0.0               0.0               0.0   \n",
       "2               0.0               0.0               0.0               0.0   \n",
       "3               0.0               0.0               0.0               0.0   \n",
       "4               0.0               0.0               0.0               0.0   \n",
       "5               0.0               0.0               0.0               0.0   \n",
       "6               0.0               0.0               0.0               0.0   \n",
       "7               0.0               0.0               0.0              71.0   \n",
       "8               0.0               0.0               0.0               0.0   \n",
       "9               0.0               0.0               0.0               0.0   \n",
       "\n",
       "   in_grades_1_to_4  in_grades_5_to_8  male_85_and_over  not_hispanic_pop  \\\n",
       "0              90.0              41.0              16.0            1042.0   \n",
       "1               0.0               0.0               0.0               0.0   \n",
       "2               0.0               0.0               0.0               0.0   \n",
       "3               0.0               0.0               0.0               0.0   \n",
       "4               0.0               0.0               0.0               0.0   \n",
       "5               0.0               0.0               0.0              74.0   \n",
       "6               0.0               0.0               0.0               0.0   \n",
       "7               0.0               0.0               0.0               0.0   \n",
       "8               0.0               0.0               0.0               0.0   \n",
       "9               0.0               0.0               0.0               0.0   \n",
       "\n",
       "  pop_5_years_over  white_male_45_54  white_male_55_64  associates_degree  \\\n",
       "0             None              13.0               3.0               28.0   \n",
       "1             None               0.0               0.0                0.0   \n",
       "2             None               0.0               0.0                0.0   \n",
       "3             None               0.0               0.0                0.0   \n",
       "4             None               0.0               0.0                0.0   \n",
       "5             None               0.0               0.0                0.0   \n",
       "6             None               0.0               0.0                0.0   \n",
       "7             None               0.0               0.0                0.0   \n",
       "8             None               0.0               0.0                0.0   \n",
       "9             None               0.0               0.0                0.0   \n",
       "\n",
       "   commuters_16_over  dwellings_2_units  family_households  hispanic_any_race  \\\n",
       "0              233.0               13.0              183.0                0.0   \n",
       "1                0.0                0.0                0.0                0.0   \n",
       "2                0.0                0.0                0.0                0.0   \n",
       "3                0.0                0.0                0.0                0.0   \n",
       "4                0.0                0.0                0.0                0.0   \n",
       "5                0.0                0.0                0.0                0.0   \n",
       "6                0.0                0.0                0.0                0.0   \n",
       "7               71.0                0.0               13.0               71.0   \n",
       "8                0.0                0.0                0.0                0.0   \n",
       "9                0.0                0.0                0.0                0.0   \n",
       "\n",
       "   in_grades_9_to_12  income_less_10000  income_per_capita  pop_25_years_over  \\\n",
       "0              111.0               79.0             8747.0              564.0   \n",
       "1                0.0                0.0                NaN                0.0   \n",
       "2                0.0                0.0                NaN                0.0   \n",
       "3                0.0                0.0                NaN                0.0   \n",
       "4                0.0                0.0                NaN                0.0   \n",
       "5                0.0                0.0             5896.0               58.0   \n",
       "6                0.0                0.0                NaN                0.0   \n",
       "7                0.0                0.0            17785.0               23.0   \n",
       "8                0.0                0.0                NaN                0.0   \n",
       "9                0.0                0.0                NaN                0.0   \n",
       "\n",
       "  pop_never_married  bachelors_degree_2  commute_10_14_mins  \\\n",
       "0              None                 0.0                 4.0   \n",
       "1              None                 NaN                 0.0   \n",
       "2              None                 NaN                 0.0   \n",
       "3              None                 0.0                 0.0   \n",
       "4              None                 0.0                 0.0   \n",
       "5              None                 0.0                 0.0   \n",
       "6              None                 0.0                 0.0   \n",
       "7              None                 0.0                 0.0   \n",
       "8              None                 0.0                 0.0   \n",
       "9              None                 0.0                 0.0   \n",
       "\n",
       "   commute_15_19_mins  commute_20_24_mins  commute_25_29_mins  \\\n",
       "0                59.0               108.0                10.0   \n",
       "1                 0.0                 0.0                 0.0   \n",
       "2                 0.0                 0.0                 0.0   \n",
       "3                 0.0                 0.0                 0.0   \n",
       "4                 0.0                 0.0                 0.0   \n",
       "5                 0.0                 0.0                 0.0   \n",
       "6                 0.0                 0.0                 0.0   \n",
       "7                 0.0                 0.0                 0.0   \n",
       "8                 0.0                 0.0                 0.0   \n",
       "9                 0.0                 0.0                 0.0   \n",
       "\n",
       "   commute_30_34_mins  commute_35_39_mins  commute_35_44_mins  \\\n",
       "0                20.0                 0.0                 7.0   \n",
       "1                 0.0                 0.0                 0.0   \n",
       "2                 0.0                 0.0                 0.0   \n",
       "3                 0.0                 0.0                 0.0   \n",
       "4                 0.0                 0.0                 0.0   \n",
       "5                 0.0                 0.0                 0.0   \n",
       "6                 0.0                 0.0                 0.0   \n",
       "7                36.0                 0.0                 0.0   \n",
       "8                 0.0                 0.0                 0.0   \n",
       "9                 0.0                 0.0                 0.0   \n",
       "\n",
       "   commute_40_44_mins  commute_45_59_mins  commute_60_89_mins  \\\n",
       "0                 7.0                13.0                 0.0   \n",
       "1                 0.0                 0.0                 0.0   \n",
       "2                 0.0                 0.0                 0.0   \n",
       "3                 0.0                 0.0                 0.0   \n",
       "4                 0.0                 0.0                 0.0   \n",
       "5                 0.0                 0.0                 0.0   \n",
       "6                 0.0                 0.0                 0.0   \n",
       "7                 0.0                 0.0                35.0   \n",
       "8                 0.0                 0.0                 0.0   \n",
       "9                 0.0                 0.0                 0.0   \n",
       "\n",
       "   female_85_and_over  income_10000_14999  income_15000_19999  \\\n",
       "0                38.0               105.0                44.0   \n",
       "1                 0.0                 0.0                 0.0   \n",
       "2                 0.0                 0.0                 0.0   \n",
       "3                 0.0                 0.0                 0.0   \n",
       "4                 0.0                 0.0                 0.0   \n",
       "5                 0.0                 0.0                 0.0   \n",
       "6                 0.0                 0.0                 0.0   \n",
       "7                 0.0                 0.0                 0.0   \n",
       "8                 0.0                 0.0                 0.0   \n",
       "9                 0.0                 0.0                 0.0   \n",
       "\n",
       "   income_20000_24999  income_25000_29999  income_30000_34999  \\\n",
       "0                38.0                13.0                18.0   \n",
       "1                 0.0                 0.0                 0.0   \n",
       "2                 0.0                 0.0                 0.0   \n",
       "3                 0.0                 0.0                 0.0   \n",
       "4                 0.0                 0.0                 0.0   \n",
       "5                 0.0                 0.0                 0.0   \n",
       "6                 0.0                 0.0                 0.0   \n",
       "7                 0.0                 0.0                 0.0   \n",
       "8                 0.0                 0.0                 0.0   \n",
       "9                 0.0                 0.0                 0.0   \n",
       "\n",
       "   income_35000_39999  income_40000_44999  income_45000_49999  \\\n",
       "0                11.0                 0.0                 0.0   \n",
       "1                 0.0                 0.0                 0.0   \n",
       "2                 0.0                 0.0                 0.0   \n",
       "3                 0.0                 0.0                 0.0   \n",
       "4                 0.0                 0.0                 0.0   \n",
       "5                 0.0                 0.0                 0.0   \n",
       "6                 0.0                 0.0                 0.0   \n",
       "7                 0.0                 0.0                 0.0   \n",
       "8                 0.0                 0.0                 0.0   \n",
       "9                 0.0                 0.0                 0.0   \n",
       "\n",
       "   income_50000_59999  income_60000_74999  income_75000_99999  \\\n",
       "0                 7.0                10.0                18.0   \n",
       "1                 0.0                 0.0                 0.0   \n",
       "2                 0.0                 0.0                 0.0   \n",
       "3                 0.0                 0.0                 0.0   \n",
       "4                 0.0                 0.0                 0.0   \n",
       "5                 0.0                 0.0                 0.0   \n",
       "6                 0.0                 0.0                 0.0   \n",
       "7                 0.0                13.0                 0.0   \n",
       "8                 0.0                 0.0                 0.0   \n",
       "9                 0.0                 0.0                 0.0   \n",
       "\n",
       "   married_households  not_in_labor_force  not_us_citizen_pop  \\\n",
       "0                26.0               445.0                 0.0   \n",
       "1                 0.0                 0.0                 NaN   \n",
       "2                 0.0                 0.0                 NaN   \n",
       "3                 0.0                 0.0                 0.0   \n",
       "4                 0.0                 0.0                 0.0   \n",
       "5                 0.0                74.0                 0.0   \n",
       "6                 0.0                 0.0                 0.0   \n",
       "7                 0.0                 0.0                71.0   \n",
       "8                 0.0                 0.0                 0.0   \n",
       "9                 0.0                 0.0                 0.0   \n",
       "\n",
       "   pop_in_labor_force  high_school_diploma  hispanic_male_45_54  \\\n",
       "0               304.0                188.0                  0.0   \n",
       "1                 0.0                  0.0                  0.0   \n",
       "2                 0.0                  0.0                  0.0   \n",
       "3                 0.0                  0.0                  0.0   \n",
       "4                 0.0                  0.0                  0.0   \n",
       "5                 0.0                  6.0                  0.0   \n",
       "6                 0.0                  0.0                  0.0   \n",
       "7                71.0                 23.0                  0.0   \n",
       "8                 0.0                  0.0                  0.0   \n",
       "9                 0.0                  0.0                  0.0   \n",
       "\n",
       "   hispanic_male_55_64  occupation_services  workers_16_and_over  \\\n",
       "0                  0.0                 58.0                233.0   \n",
       "1                  0.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "5                  0.0                  0.0                  0.0   \n",
       "6                  0.0                  0.0                  0.0   \n",
       "7                 23.0                 13.0                 71.0   \n",
       "8                  0.0                  0.0                  0.0   \n",
       "9                  0.0                  0.0                  0.0   \n",
       "\n",
       "   civilian_labor_force  commute_60_more_mins  commute_90_more_mins  \\\n",
       "0                 304.0                   5.0                   5.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   0.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "5                   0.0                   0.0                   0.0   \n",
       "6                   0.0                   0.0                   0.0   \n",
       "7                  71.0                  35.0                   0.0   \n",
       "8                   0.0                   0.0                   0.0   \n",
       "9                   0.0                   0.0                   0.0   \n",
       "\n",
       "   commute_less_10_mins  commuters_by_carpool  employed_information  \\\n",
       "0                   7.0                  21.0                   0.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   0.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "5                   0.0                   0.0                   0.0   \n",
       "6                   0.0                   0.0                   0.0   \n",
       "7                   0.0                   0.0                   0.0   \n",
       "8                   0.0                   0.0                   0.0   \n",
       "9                   0.0                   0.0                   0.0   \n",
       "\n",
       "   in_undergrad_college  income_100000_124999  income_125000_149999  \\\n",
       "0                  63.0                   0.0                   0.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   0.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "5                   0.0                   0.0                   0.0   \n",
       "6                   0.0                   0.0                   0.0   \n",
       "7                  29.0                   0.0                   0.0   \n",
       "8                   0.0                   0.0                   0.0   \n",
       "9                   0.0                   0.0                   0.0   \n",
       "\n",
       "   income_150000_199999  male_male_households  nonfamily_households  \\\n",
       "0                   0.0                   0.0                 160.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   0.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "5                   0.0                   0.0                   0.0   \n",
       "6                   0.0                   0.0                   0.0   \n",
       "7                   0.0                   0.0                   0.0   \n",
       "8                   0.0                   0.0                   0.0   \n",
       "9                   0.0                   0.0                   0.0   \n",
       "\n",
       "   rent_over_50_percent  vacant_housing_units  commuters_drove_alone  \\\n",
       "0                  38.0                 122.0                  141.0   \n",
       "1                   0.0                   0.0                    0.0   \n",
       "2                   0.0                   0.0                    0.0   \n",
       "3                   0.0                   0.0                    0.0   \n",
       "4                   0.0                   0.0                    0.0   \n",
       "5                   0.0                   0.0                    0.0   \n",
       "6                   0.0                   0.0                    0.0   \n",
       "7                   0.0                   0.0                    0.0   \n",
       "8                   0.0                   0.0                    0.0   \n",
       "9                   0.0                   0.0                    0.0   \n",
       "\n",
       "   employed_construction  employed_retail_trade  income_200000_or_more  \\\n",
       "0                   24.0                   19.0                    0.0   \n",
       "1                    0.0                    0.0                    0.0   \n",
       "2                    0.0                    0.0                    0.0   \n",
       "3                    0.0                    0.0                    0.0   \n",
       "4                    0.0                    0.0                    0.0   \n",
       "5                    0.0                    0.0                    0.0   \n",
       "6                    0.0                    0.0                    0.0   \n",
       "7                    0.0                    0.0                    0.0   \n",
       "8                    0.0                    0.0                    0.0   \n",
       "9                    0.0                    0.0                    0.0   \n",
       "\n",
       "   less_one_year_college  male_45_64_grade_9_12  one_year_more_college  \\\n",
       "0                   22.0                   18.0                   85.0   \n",
       "1                    0.0                    0.0                    0.0   \n",
       "2                    0.0                    0.0                    0.0   \n",
       "3                    0.0                    0.0                    0.0   \n",
       "4                    0.0                    0.0                    0.0   \n",
       "5                   18.0                    0.0                    4.0   \n",
       "6                    0.0                    0.0                    0.0   \n",
       "7                    0.0                    0.0                    0.0   \n",
       "8                    0.0                    0.0                    0.0   \n",
       "9                    0.0                    0.0                    0.0   \n",
       "\n",
       "   rent_10_to_15_percent  rent_15_to_20_percent  rent_20_to_25_percent  \\\n",
       "0                   11.0                   42.0                   39.0   \n",
       "1                    0.0                    0.0                    0.0   \n",
       "2                    0.0                    0.0                    0.0   \n",
       "3                    0.0                    0.0                    0.0   \n",
       "4                    0.0                    0.0                    0.0   \n",
       "5                    0.0                    0.0                    0.0   \n",
       "6                    0.0                    0.0                    0.0   \n",
       "7                    0.0                   13.0                    0.0   \n",
       "8                    0.0                    0.0                    0.0   \n",
       "9                    0.0                    0.0                    0.0   \n",
       "\n",
       "   rent_25_to_30_percent  rent_30_to_35_percent  rent_35_to_40_percent  \\\n",
       "0                   33.0                   47.0                    9.0   \n",
       "1                    0.0                    0.0                    0.0   \n",
       "2                    0.0                    0.0                    0.0   \n",
       "3                    0.0                    0.0                    0.0   \n",
       "4                    0.0                    0.0                    0.0   \n",
       "5                    0.0                    0.0                    0.0   \n",
       "6                    0.0                    0.0                    0.0   \n",
       "7                    0.0                    0.0                    0.0   \n",
       "8                    0.0                    0.0                    0.0   \n",
       "9                    0.0                    0.0                    0.0   \n",
       "\n",
       "   rent_40_to_50_percent  rent_under_10_percent  sales_office_employed  \\\n",
       "0                   41.0                    0.0                   30.0   \n",
       "1                    0.0                    0.0                    0.0   \n",
       "2                    0.0                    0.0                    0.0   \n",
       "3                    0.0                    0.0                    0.0   \n",
       "4                    0.0                    0.0                    0.0   \n",
       "5                    0.0                    0.0                    0.0   \n",
       "6                    0.0                    0.0                    0.0   \n",
       "7                    0.0                    0.0                    0.0   \n",
       "8                    0.0                    0.0                    0.0   \n",
       "9                    0.0                    0.0                    0.0   \n",
       "\n",
       "  speak_spanish_at_home  two_or_more_races_pop  dwellings_3_to_4_units  \\\n",
       "0                  None                   22.0                    63.0   \n",
       "1                  None                    0.0                     0.0   \n",
       "2                  None                    0.0                     0.0   \n",
       "3                  None                    0.0                     0.0   \n",
       "4                  None                    0.0                     0.0   \n",
       "5                  None                    0.0                     0.0   \n",
       "6                  None                    0.0                     0.0   \n",
       "7                  None                    0.0                    13.0   \n",
       "8                  None                    0.0                     0.0   \n",
       "9                  None                    0.0                     0.0   \n",
       "\n",
       "   dwellings_5_to_9_units  employed_manufacturing  male_45_64_high_school  \\\n",
       "0                    69.0                    16.0                    41.0   \n",
       "1                     0.0                     0.0                     0.0   \n",
       "2                     0.0                     0.0                     0.0   \n",
       "3                     0.0                     0.0                     0.0   \n",
       "4                     0.0                     0.0                     0.0   \n",
       "5                     0.0                     0.0                     0.0   \n",
       "6                     0.0                     0.0                     0.0   \n",
       "7                     0.0                     0.0                    23.0   \n",
       "8                     0.0                     0.0                     0.0   \n",
       "9                     0.0                     0.0                     0.0   \n",
       "\n",
       "   occupied_housing_units  male_45_64_some_college  mortgaged_housing_units  \\\n",
       "0                   343.0                     10.0                     20.0   \n",
       "1                     0.0                      0.0                      0.0   \n",
       "2                     0.0                      0.0                      0.0   \n",
       "3                     0.0                      0.0                      0.0   \n",
       "4                     0.0                      0.0                      0.0   \n",
       "5                     0.0                      0.0                      0.0   \n",
       "6                     0.0                      0.0                      0.0   \n",
       "7                    13.0                      0.0                      0.0   \n",
       "8                     0.0                      0.0                      0.0   \n",
       "9                     0.0                      0.0                      0.0   \n",
       "\n",
       "   occupation_sales_office  population_3_years_over  asian_including_hispanic  \\\n",
       "0                     30.0                    999.0                       0.0   \n",
       "1                      0.0                      0.0                       0.0   \n",
       "2                      0.0                      0.0                       0.0   \n",
       "3                      0.0                      0.0                       0.0   \n",
       "4                      0.0                      0.0                       0.0   \n",
       "5                      0.0                     74.0                       0.0   \n",
       "6                      0.0                      0.0                       0.0   \n",
       "7                      0.0                     71.0                       0.0   \n",
       "8                      0.0                      0.0                       0.0   \n",
       "9                      0.0                      0.0                       0.0   \n",
       "\n",
       "   black_including_hispanic  dwellings_10_to_19_units  \\\n",
       "0                    1004.0                      40.0   \n",
       "1                       0.0                       0.0   \n",
       "2                       0.0                       0.0   \n",
       "3                       0.0                       0.0   \n",
       "4                       0.0                       0.0   \n",
       "5                       4.0                       0.0   \n",
       "6                       0.0                       0.0   \n",
       "7                      19.0                       0.0   \n",
       "8                       0.0                       0.0   \n",
       "9                       0.0                       0.0   \n",
       "\n",
       "   dwellings_20_to_49_units  employed_wholesale_trade  \\\n",
       "0                      25.0                       2.0   \n",
       "1                       0.0                       0.0   \n",
       "2                       0.0                       0.0   \n",
       "3                       0.0                       0.0   \n",
       "4                       0.0                       0.0   \n",
       "5                       0.0                       0.0   \n",
       "6                       0.0                       0.0   \n",
       "7                       0.0                       0.0   \n",
       "8                       0.0                       0.0   \n",
       "9                       0.0                       0.0   \n",
       "\n",
       "   female_female_households  rent_burden_not_computed  \\\n",
       "0                       0.0                      18.0   \n",
       "1                       0.0                       0.0   \n",
       "2                       0.0                       0.0   \n",
       "3                       0.0                       0.0   \n",
       "4                       0.0                       0.0   \n",
       "5                       0.0                       0.0   \n",
       "6                       0.0                       0.0   \n",
       "7                       0.0                       0.0   \n",
       "8                       0.0                       0.0   \n",
       "9                       0.0                       0.0   \n",
       "\n",
       "   white_including_hispanic  high_school_including_ged  \\\n",
       "0                      16.0                      235.0   \n",
       "1                       0.0                        NaN   \n",
       "2                       0.0                        NaN   \n",
       "3                       0.0                        0.0   \n",
       "4                       0.0                        0.0   \n",
       "5                      70.0                       18.0   \n",
       "6                       0.0                        0.0   \n",
       "7                      52.0                       23.0   \n",
       "8                       0.0                        0.0   \n",
       "9                       0.0                        0.0   \n",
       "\n",
       "   commuters_by_car_truck_van  dwellings_1_units_attached  \\\n",
       "0                       162.0                         0.0   \n",
       "1                         0.0                         0.0   \n",
       "2                         0.0                         0.0   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "5                         0.0                         0.0   \n",
       "6                         0.0                         0.0   \n",
       "7                         0.0                         0.0   \n",
       "8                         0.0                         0.0   \n",
       "9                         0.0                         0.0   \n",
       "\n",
       "   dwellings_1_units_detached  dwellings_50_or_more_units  \\\n",
       "0                       195.0                        55.0   \n",
       "1                         0.0                         0.0   \n",
       "2                         0.0                         0.0   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "5                         0.0                         0.0   \n",
       "6                         0.0                         0.0   \n",
       "7                         0.0                         0.0   \n",
       "8                         0.0                         0.0   \n",
       "9                         0.0                         0.0   \n",
       "\n",
       "   housing_built_2000_to_2004  male_45_64_graduate_degree  \\\n",
       "0                        10.0                         1.0   \n",
       "1                         0.0                         0.0   \n",
       "2                         0.0                         0.0   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "5                         0.0                         0.0   \n",
       "6                         0.0                         0.0   \n",
       "7                         0.0                         0.0   \n",
       "8                         0.0                         0.0   \n",
       "9                         0.0                         0.0   \n",
       "\n",
       "   occupation_management_arts  population_1_year_and_over  \\\n",
       "0                        54.0                      1019.0   \n",
       "1                         0.0                         NaN   \n",
       "2                         0.0                         NaN   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "5                         0.0                        74.0   \n",
       "6                         0.0                         0.0   \n",
       "7                        16.0                        71.0   \n",
       "8                         0.0                         0.0   \n",
       "9                         0.0                         0.0   \n",
       "\n",
       "  speak_only_english_at_home  housing_built_2005_or_later  \\\n",
       "0                       None                          0.0   \n",
       "1                       None                          0.0   \n",
       "2                       None                          0.0   \n",
       "3                       None                          0.0   \n",
       "4                       None                          0.0   \n",
       "5                       None                          0.0   \n",
       "6                       None                          0.0   \n",
       "7                       None                          0.0   \n",
       "8                       None                          0.0   \n",
       "9                       None                          0.0   \n",
       "\n",
       "   male_45_64_bachelors_degree  median_year_structure_built  \\\n",
       "0                          0.0                       1981.0   \n",
       "1                          0.0                          NaN   \n",
       "2                          0.0                          NaN   \n",
       "3                          0.0                          NaN   \n",
       "4                          0.0                          NaN   \n",
       "5                          0.0                          NaN   \n",
       "6                          0.0                          NaN   \n",
       "7                          0.0                          NaN   \n",
       "8                          0.0                          NaN   \n",
       "9                          0.0                          NaN   \n",
       "\n",
       "   children_in_single_female_hh  families_with_young_children  \\\n",
       "0                         278.0                          75.0   \n",
       "1                           0.0                           0.0   \n",
       "2                           0.0                           0.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "5                           0.0                           0.0   \n",
       "6                           0.0                           0.0   \n",
       "7                           0.0                           0.0   \n",
       "8                           0.0                           0.0   \n",
       "9                           0.0                           0.0   \n",
       "\n",
       "   graduate_professional_degree  households_retirement_income  \\\n",
       "0                          13.0                          47.0   \n",
       "1                           NaN                           0.0   \n",
       "2                           NaN                           0.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "5                           0.0                           0.0   \n",
       "6                           0.0                           0.0   \n",
       "7                           0.0                           0.0   \n",
       "8                           0.0                           0.0   \n",
       "9                           0.0                           0.0   \n",
       "\n",
       "   male_45_64_associates_degree  male_45_64_less_than_9_grade  \\\n",
       "0                           7.0                           2.0   \n",
       "1                           0.0                           0.0   \n",
       "2                           0.0                           0.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "5                           0.0                           0.0   \n",
       "6                           0.0                           0.0   \n",
       "7                           0.0                           0.0   \n",
       "8                           0.0                           0.0   \n",
       "9                           0.0                           0.0   \n",
       "\n",
       "   million_dollar_housing_units  owner_occupied_housing_units  \\\n",
       "0                           0.0                          65.0   \n",
       "1                           0.0                           0.0   \n",
       "2                           0.0                           0.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "5                           0.0                           0.0   \n",
       "6                           0.0                           0.0   \n",
       "7                           0.0                           0.0   \n",
       "8                           0.0                           0.0   \n",
       "9                           0.0                           0.0   \n",
       "\n",
       "   percent_income_spent_on_rent aggregate_travel_time_to_work  \\\n",
       "0                          30.5                          None   \n",
       "1                           NaN                          None   \n",
       "2                           NaN                          None   \n",
       "3                           NaN                          None   \n",
       "4                           NaN                          None   \n",
       "5                           NaN                          None   \n",
       "6                           NaN                          None   \n",
       "7                           NaN                          None   \n",
       "8                           NaN                          None   \n",
       "9                           NaN                          None   \n",
       "\n",
       "   amerindian_including_hispanic  housing_built_1939_or_earlier  \\\n",
       "0                            0.0                           81.0   \n",
       "1                            0.0                            0.0   \n",
       "2                            0.0                            0.0   \n",
       "3                            0.0                            0.0   \n",
       "4                            0.0                            0.0   \n",
       "5                            0.0                            0.0   \n",
       "6                            0.0                            0.0   \n",
       "7                            0.0                            0.0   \n",
       "8                            0.0                            0.0   \n",
       "9                            0.0                            0.0   \n",
       "\n",
       "   housing_units_renter_occupied  pop_determined_poverty_status  \\\n",
       "0                          278.0                         1042.0   \n",
       "1                            0.0                            0.0   \n",
       "2                            0.0                            0.0   \n",
       "3                            0.0                            0.0   \n",
       "4                            0.0                            0.0   \n",
       "5                            0.0                           74.0   \n",
       "6                            0.0                            0.0   \n",
       "7                           13.0                           71.0   \n",
       "8                            0.0                            0.0   \n",
       "9                            0.0                            0.0   \n",
       "\n",
       "   vacant_housing_units_for_rent  vacant_housing_units_for_sale  \\\n",
       "0                           14.0                            0.0   \n",
       "1                            0.0                            0.0   \n",
       "2                            0.0                            0.0   \n",
       "3                            0.0                            0.0   \n",
       "4                            0.0                            0.0   \n",
       "5                            0.0                            0.0   \n",
       "6                            0.0                            0.0   \n",
       "7                            0.0                            0.0   \n",
       "8                            0.0                            0.0   \n",
       "9                            0.0                            0.0   \n",
       "\n",
       "   employed_public_administration  less_than_high_school_graduate  \\\n",
       "0                             4.0                           181.0   \n",
       "1                             0.0                             NaN   \n",
       "2                             0.0                             NaN   \n",
       "3                             0.0                             0.0   \n",
       "4                             0.0                             0.0   \n",
       "5                             0.0                            18.0   \n",
       "6                             0.0                             0.0   \n",
       "7                             0.0                             0.0   \n",
       "8                             0.0                             0.0   \n",
       "9                             0.0                             0.0   \n",
       "\n",
       "   commuters_by_subway_or_elevated  bachelors_degree_or_higher_25_64  \\\n",
       "0                              0.0                               1.0   \n",
       "1                              0.0                               0.0   \n",
       "2                              0.0                               0.0   \n",
       "3                              0.0                               0.0   \n",
       "4                              0.0                               0.0   \n",
       "5                              0.0                               0.0   \n",
       "6                              0.0                               0.0   \n",
       "7                              0.0                               0.0   \n",
       "8                              0.0                               0.0   \n",
       "9                              0.0                               0.0   \n",
       "\n",
       "   employed_education_health_social speak_spanish_at_home_low_english  \\\n",
       "0                              57.0                              None   \n",
       "1                               0.0                              None   \n",
       "2                               0.0                              None   \n",
       "3                               0.0                              None   \n",
       "4                               0.0                              None   \n",
       "5                               0.0                              None   \n",
       "6                               0.0                              None   \n",
       "7                              29.0                              None   \n",
       "8                               0.0                              None   \n",
       "9                               0.0                              None   \n",
       "\n",
       "   commuters_by_public_transportation  different_house_year_ago_same_city  \\\n",
       "0                                52.0                                38.0   \n",
       "1                                 0.0                                 NaN   \n",
       "2                                 0.0                                 NaN   \n",
       "3                                 0.0                                 0.0   \n",
       "4                                 0.0                                 0.0   \n",
       "5                                 0.0                                 0.0   \n",
       "6                                 0.0                                 0.0   \n",
       "7                                71.0                                 0.0   \n",
       "8                                 0.0                                 0.0   \n",
       "9                                 0.0                                 0.0   \n",
       "\n",
       "   some_college_and_associates_degree  households_public_asst_or_food_stamps  \\\n",
       "0                               135.0                                  147.0   \n",
       "1                                 NaN                                    0.0   \n",
       "2                                 NaN                                    0.0   \n",
       "3                                 0.0                                    0.0   \n",
       "4                                 0.0                                    0.0   \n",
       "5                                22.0                                    0.0   \n",
       "6                                 0.0                                    0.0   \n",
       "7                                 0.0                                   13.0   \n",
       "8                                 0.0                                    0.0   \n",
       "9                                 0.0                                    0.0   \n",
       "\n",
       "   management_business_sci_arts_employed  \\\n",
       "0                                   54.0   \n",
       "1                                    0.0   \n",
       "2                                    0.0   \n",
       "3                                    0.0   \n",
       "4                                    0.0   \n",
       "5                                    0.0   \n",
       "6                                    0.0   \n",
       "7                                   16.0   \n",
       "8                                    0.0   \n",
       "9                                    0.0   \n",
       "\n",
       "   employed_finance_insurance_real_estate  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                     0.0   \n",
       "3                                     0.0   \n",
       "4                                     0.0   \n",
       "5                                     0.0   \n",
       "6                                     0.0   \n",
       "7                                     0.0   \n",
       "8                                     0.0   \n",
       "9                                     0.0   \n",
       "\n",
       "   different_house_year_ago_different_city  \\\n",
       "0                                     15.0   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "5                                     63.0   \n",
       "6                                      0.0   \n",
       "7                                     19.0   \n",
       "8                                      0.0   \n",
       "9                                      0.0   \n",
       "\n",
       "   employed_science_management_admin_waste  \\\n",
       "0                                     33.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "5                                      0.0   \n",
       "6                                      0.0   \n",
       "7                                      0.0   \n",
       "8                                      0.0   \n",
       "9                                      0.0   \n",
       "\n",
       "   one_parent_families_with_young_children  \\\n",
       "0                                     75.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "5                                      0.0   \n",
       "6                                      0.0   \n",
       "7                                      0.0   \n",
       "8                                      0.0   \n",
       "9                                      0.0   \n",
       "\n",
       "   two_parent_families_with_young_children  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "5                                      0.0   \n",
       "6                                      0.0   \n",
       "7                                      0.0   \n",
       "8                                      0.0   \n",
       "9                                      0.0   \n",
       "\n",
       "   employed_other_services_not_public_admin  \\\n",
       "0                                      14.0   \n",
       "1                                       0.0   \n",
       "2                                       0.0   \n",
       "3                                       0.0   \n",
       "4                                       0.0   \n",
       "5                                       0.0   \n",
       "6                                       0.0   \n",
       "7                                      23.0   \n",
       "8                                       0.0   \n",
       "9                                       0.0   \n",
       "\n",
       "  owner_occupied_housing_units_median_value  \\\n",
       "0                                      None   \n",
       "1                                      None   \n",
       "2                                      None   \n",
       "3                                      None   \n",
       "4                                      None   \n",
       "5                                      None   \n",
       "6                                      None   \n",
       "7                                      None   \n",
       "8                                      None   \n",
       "9                                      None   \n",
       "\n",
       "   employed_transportation_warehousing_utilities  \\\n",
       "0                                           43.0   \n",
       "1                                            0.0   \n",
       "2                                            0.0   \n",
       "3                                            0.0   \n",
       "4                                            0.0   \n",
       "5                                            0.0   \n",
       "6                                            0.0   \n",
       "7                                           19.0   \n",
       "8                                            0.0   \n",
       "9                                            0.0   \n",
       "\n",
       "   occupation_production_transportation_material  \\\n",
       "0                                           80.0   \n",
       "1                                            0.0   \n",
       "2                                            0.0   \n",
       "3                                            0.0   \n",
       "4                                            0.0   \n",
       "5                                            0.0   \n",
       "6                                            0.0   \n",
       "7                                           19.0   \n",
       "8                                            0.0   \n",
       "9                                            0.0   \n",
       "\n",
       "   father_one_parent_families_with_young_children  \\\n",
       "0                                             0.0   \n",
       "1                                             0.0   \n",
       "2                                             0.0   \n",
       "3                                             0.0   \n",
       "4                                             0.0   \n",
       "5                                             0.0   \n",
       "6                                             0.0   \n",
       "7                                             0.0   \n",
       "8                                             0.0   \n",
       "9                                             0.0   \n",
       "\n",
       "   owner_occupied_housing_units_lower_value_quartile  \\\n",
       "0                                            26600.0   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "5                                                NaN   \n",
       "6                                                NaN   \n",
       "7                                                NaN   \n",
       "8                                                NaN   \n",
       "9                                                NaN   \n",
       "\n",
       "   owner_occupied_housing_units_upper_value_quartile  \\\n",
       "0                                           231300.0   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "5                                                NaN   \n",
       "6                                                NaN   \n",
       "7                                                NaN   \n",
       "8                                                NaN   \n",
       "9                                                NaN   \n",
       "\n",
       "   employed_agriculture_forestry_fishing_hunting_mining  \\\n",
       "0                                                0.0      \n",
       "1                                                0.0      \n",
       "2                                                0.0      \n",
       "3                                                0.0      \n",
       "4                                                0.0      \n",
       "5                                                0.0      \n",
       "6                                                0.0      \n",
       "7                                                0.0      \n",
       "8                                                0.0      \n",
       "9                                                0.0      \n",
       "\n",
       "   occupation_natural_resources_construction_maintenance  \\\n",
       "0                                               11.0       \n",
       "1                                                0.0       \n",
       "2                                                0.0       \n",
       "3                                                0.0       \n",
       "4                                                0.0       \n",
       "5                                                0.0       \n",
       "6                                                0.0       \n",
       "7                                               23.0       \n",
       "8                                                0.0       \n",
       "9                                                0.0       \n",
       "\n",
       "   two_parents_in_labor_force_families_with_young_children  \\\n",
       "0                                                0.0         \n",
       "1                                                0.0         \n",
       "2                                                0.0         \n",
       "3                                                0.0         \n",
       "4                                                0.0         \n",
       "5                                                0.0         \n",
       "6                                                0.0         \n",
       "7                                                0.0         \n",
       "8                                                0.0         \n",
       "9                                                0.0         \n",
       "\n",
       "   employed_arts_entertainment_recreation_accommodation_food  \\\n",
       "0                                               21.0           \n",
       "1                                                0.0           \n",
       "2                                                0.0           \n",
       "3                                                0.0           \n",
       "4                                                0.0           \n",
       "5                                                0.0           \n",
       "6                                                0.0           \n",
       "7                                                0.0           \n",
       "8                                                0.0           \n",
       "9                                                0.0           \n",
       "\n",
       "   renter_occupied_housing_units_paying_cash_median_gross_rent  \\\n",
       "0                                              507.0             \n",
       "1                                                NaN             \n",
       "2                                                NaN             \n",
       "3                                                NaN             \n",
       "4                                                NaN             \n",
       "5                                                NaN             \n",
       "6                                                NaN             \n",
       "7                                                NaN             \n",
       "8                                                NaN             \n",
       "9                                                NaN             \n",
       "\n",
       "   two_parents_not_in_labor_force_families_with_young_children  \\\n",
       "0                                                0.0             \n",
       "1                                                0.0             \n",
       "2                                                0.0             \n",
       "3                                                0.0             \n",
       "4                                                0.0             \n",
       "5                                                0.0             \n",
       "6                                                0.0             \n",
       "7                                                0.0             \n",
       "8                                                0.0             \n",
       "9                                                0.0             \n",
       "\n",
       "   father_in_labor_force_one_parent_families_with_young_children  \\\n",
       "0                                                0.0               \n",
       "1                                                0.0               \n",
       "2                                                0.0               \n",
       "3                                                0.0               \n",
       "4                                                0.0               \n",
       "5                                                0.0               \n",
       "6                                                0.0               \n",
       "7                                                0.0               \n",
       "8                                                0.0               \n",
       "9                                                0.0               \n",
       "\n",
       "   two_parents_father_in_labor_force_families_with_young_children  \\\n",
       "0                                                0.0                \n",
       "1                                                0.0                \n",
       "2                                                0.0                \n",
       "3                                                0.0                \n",
       "4                                                0.0                \n",
       "5                                                0.0                \n",
       "6                                                0.0                \n",
       "7                                                0.0                \n",
       "8                                                0.0                \n",
       "9                                                0.0                \n",
       "\n",
       "   two_parents_mother_in_labor_force_families_with_young_children  \n",
       "0                                                0.0               \n",
       "1                                                0.0               \n",
       "2                                                0.0               \n",
       "3                                                0.0               \n",
       "4                                                0.0               \n",
       "5                                                0.0               \n",
       "6                                                0.0               \n",
       "7                                                0.0               \n",
       "8                                                0.0               \n",
       "9                                                0.0               "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_ds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.4.4. Summary of different counts over the actual dataset data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rows                  7.400100e+04\n",
       "cells                 1.864825e+07\n",
       "null_cells            7.901830e+05\n",
       "null_cells_percent    4.237303e+00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_ds.counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.4.5. Fields by type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float     250\n",
       "string      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_ds.fields_by_type()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.4.6. Statistical description of numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367
    },
    "id": "oS8KjzKP89ar",
    "outputId": "f2255e8d-98c9-45c1-f7bd-1f038ac0bd78"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_pop</th>\n",
       "      <th>households</th>\n",
       "      <th>male_pop</th>\n",
       "      <th>female_pop</th>\n",
       "      <th>median_age</th>\n",
       "      <th>male_under_5</th>\n",
       "      <th>male_5_to_9</th>\n",
       "      <th>male_10_to_14</th>\n",
       "      <th>male_15_to_17</th>\n",
       "      <th>male_18_to_19</th>\n",
       "      <th>male_20</th>\n",
       "      <th>male_21</th>\n",
       "      <th>male_22_to_24</th>\n",
       "      <th>male_25_to_29</th>\n",
       "      <th>male_30_to_34</th>\n",
       "      <th>male_35_to_39</th>\n",
       "      <th>male_40_to_44</th>\n",
       "      <th>male_45_to_49</th>\n",
       "      <th>male_50_to_54</th>\n",
       "      <th>male_55_to_59</th>\n",
       "      <th>male_65_to_66</th>\n",
       "      <th>male_67_to_69</th>\n",
       "      <th>male_70_to_74</th>\n",
       "      <th>male_75_to_79</th>\n",
       "      <th>male_80_to_84</th>\n",
       "      <th>male_85_and_over</th>\n",
       "      <th>female_under_5</th>\n",
       "      <th>female_5_to_9</th>\n",
       "      <th>female_10_to_14</th>\n",
       "      <th>female_15_to_17</th>\n",
       "      <th>female_18_to_19</th>\n",
       "      <th>female_20</th>\n",
       "      <th>female_21</th>\n",
       "      <th>female_22_to_24</th>\n",
       "      <th>female_25_to_29</th>\n",
       "      <th>female_30_to_34</th>\n",
       "      <th>female_35_to_39</th>\n",
       "      <th>female_40_to_44</th>\n",
       "      <th>female_45_to_49</th>\n",
       "      <th>female_50_to_54</th>\n",
       "      <th>female_55_to_59</th>\n",
       "      <th>female_60_to_61</th>\n",
       "      <th>female_62_to_64</th>\n",
       "      <th>female_65_to_66</th>\n",
       "      <th>female_67_to_69</th>\n",
       "      <th>female_70_to_74</th>\n",
       "      <th>female_75_to_79</th>\n",
       "      <th>female_80_to_84</th>\n",
       "      <th>female_85_and_over</th>\n",
       "      <th>white_pop</th>\n",
       "      <th>population_1_year_and_over</th>\n",
       "      <th>population_3_years_over</th>\n",
       "      <th>pop_5_years_over</th>\n",
       "      <th>pop_15_and_over</th>\n",
       "      <th>pop_16_over</th>\n",
       "      <th>pop_25_years_over</th>\n",
       "      <th>pop_25_64</th>\n",
       "      <th>pop_never_married</th>\n",
       "      <th>pop_now_married</th>\n",
       "      <th>pop_separated</th>\n",
       "      <th>pop_widowed</th>\n",
       "      <th>pop_divorced</th>\n",
       "      <th>not_us_citizen_pop</th>\n",
       "      <th>black_pop</th>\n",
       "      <th>asian_pop</th>\n",
       "      <th>hispanic_pop</th>\n",
       "      <th>amerindian_pop</th>\n",
       "      <th>other_race_pop</th>\n",
       "      <th>two_or_more_races_pop</th>\n",
       "      <th>hispanic_any_race</th>\n",
       "      <th>not_hispanic_pop</th>\n",
       "      <th>asian_male_45_54</th>\n",
       "      <th>asian_male_55_64</th>\n",
       "      <th>black_male_45_54</th>\n",
       "      <th>black_male_55_64</th>\n",
       "      <th>hispanic_male_45_54</th>\n",
       "      <th>hispanic_male_55_64</th>\n",
       "      <th>white_male_45_54</th>\n",
       "      <th>white_male_55_64</th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_per_capita</th>\n",
       "      <th>income_less_10000</th>\n",
       "      <th>income_10000_14999</th>\n",
       "      <th>income_15000_19999</th>\n",
       "      <th>income_20000_24999</th>\n",
       "      <th>income_25000_29999</th>\n",
       "      <th>income_30000_34999</th>\n",
       "      <th>income_35000_39999</th>\n",
       "      <th>income_40000_44999</th>\n",
       "      <th>income_45000_49999</th>\n",
       "      <th>income_50000_59999</th>\n",
       "      <th>income_60000_74999</th>\n",
       "      <th>income_75000_99999</th>\n",
       "      <th>income_100000_124999</th>\n",
       "      <th>income_125000_149999</th>\n",
       "      <th>income_150000_199999</th>\n",
       "      <th>income_200000_or_more</th>\n",
       "      <th>pop_determined_poverty_status</th>\n",
       "      <th>poverty</th>\n",
       "      <th>gini_index</th>\n",
       "      <th>housing_units</th>\n",
       "      <th>renter_occupied_housing_units_paying_cash_median_gross_rent</th>\n",
       "      <th>owner_occupied_housing_units_lower_value_quartile</th>\n",
       "      <th>owner_occupied_housing_units_median_value</th>\n",
       "      <th>owner_occupied_housing_units_upper_value_quartile</th>\n",
       "      <th>occupied_housing_units</th>\n",
       "      <th>housing_units_renter_occupied</th>\n",
       "      <th>vacant_housing_units</th>\n",
       "      <th>vacant_housing_units_for_rent</th>\n",
       "      <th>vacant_housing_units_for_sale</th>\n",
       "      <th>dwellings_1_units_detached</th>\n",
       "      <th>dwellings_1_units_attached</th>\n",
       "      <th>dwellings_2_units</th>\n",
       "      <th>dwellings_3_to_4_units</th>\n",
       "      <th>dwellings_5_to_9_units</th>\n",
       "      <th>dwellings_10_to_19_units</th>\n",
       "      <th>dwellings_20_to_49_units</th>\n",
       "      <th>dwellings_50_or_more_units</th>\n",
       "      <th>mobile_homes</th>\n",
       "      <th>housing_built_2005_or_later</th>\n",
       "      <th>housing_built_2000_to_2004</th>\n",
       "      <th>housing_built_1939_or_earlier</th>\n",
       "      <th>median_year_structure_built</th>\n",
       "      <th>married_households</th>\n",
       "      <th>nonfamily_households</th>\n",
       "      <th>family_households</th>\n",
       "      <th>households_public_asst_or_food_stamps</th>\n",
       "      <th>male_male_households</th>\n",
       "      <th>female_female_households</th>\n",
       "      <th>children</th>\n",
       "      <th>children_in_single_female_hh</th>\n",
       "      <th>median_rent</th>\n",
       "      <th>percent_income_spent_on_rent</th>\n",
       "      <th>rent_burden_not_computed</th>\n",
       "      <th>rent_over_50_percent</th>\n",
       "      <th>rent_40_to_50_percent</th>\n",
       "      <th>rent_35_to_40_percent</th>\n",
       "      <th>rent_30_to_35_percent</th>\n",
       "      <th>rent_25_to_30_percent</th>\n",
       "      <th>rent_20_to_25_percent</th>\n",
       "      <th>rent_15_to_20_percent</th>\n",
       "      <th>rent_10_to_15_percent</th>\n",
       "      <th>rent_under_10_percent</th>\n",
       "      <th>owner_occupied_housing_units</th>\n",
       "      <th>million_dollar_housing_units</th>\n",
       "      <th>mortgaged_housing_units</th>\n",
       "      <th>different_house_year_ago_different_city</th>\n",
       "      <th>different_house_year_ago_same_city</th>\n",
       "      <th>families_with_young_children</th>\n",
       "      <th>two_parent_families_with_young_children</th>\n",
       "      <th>two_parents_in_labor_force_families_with_young_children</th>\n",
       "      <th>two_parents_father_in_labor_force_families_with_young_children</th>\n",
       "      <th>two_parents_mother_in_labor_force_families_with_young_children</th>\n",
       "      <th>two_parents_not_in_labor_force_families_with_young_children</th>\n",
       "      <th>one_parent_families_with_young_children</th>\n",
       "      <th>father_one_parent_families_with_young_children</th>\n",
       "      <th>father_in_labor_force_one_parent_families_with_young_children</th>\n",
       "      <th>commute_less_10_mins</th>\n",
       "      <th>commute_10_14_mins</th>\n",
       "      <th>commute_15_19_mins</th>\n",
       "      <th>commute_20_24_mins</th>\n",
       "      <th>commute_25_29_mins</th>\n",
       "      <th>commute_30_34_mins</th>\n",
       "      <th>commute_35_44_mins</th>\n",
       "      <th>commute_45_59_mins</th>\n",
       "      <th>commute_60_more_mins</th>\n",
       "      <th>commuters_16_over</th>\n",
       "      <th>walked_to_work</th>\n",
       "      <th>worked_at_home</th>\n",
       "      <th>no_car</th>\n",
       "      <th>no_cars</th>\n",
       "      <th>one_car</th>\n",
       "      <th>two_cars</th>\n",
       "      <th>three_cars</th>\n",
       "      <th>four_more_cars</th>\n",
       "      <th>aggregate_travel_time_to_work</th>\n",
       "      <th>commuters_by_public_transportation</th>\n",
       "      <th>commuters_by_bus</th>\n",
       "      <th>commuters_by_car_truck_van</th>\n",
       "      <th>commuters_by_carpool</th>\n",
       "      <th>commuters_by_subway_or_elevated</th>\n",
       "      <th>commuters_drove_alone</th>\n",
       "      <th>group_quarters</th>\n",
       "      <th>associates_degree</th>\n",
       "      <th>bachelors_degree</th>\n",
       "      <th>high_school_diploma</th>\n",
       "      <th>less_one_year_college</th>\n",
       "      <th>masters_degree</th>\n",
       "      <th>one_year_more_college</th>\n",
       "      <th>less_than_high_school_graduate</th>\n",
       "      <th>high_school_including_ged</th>\n",
       "      <th>bachelors_degree_2</th>\n",
       "      <th>bachelors_degree_or_higher_25_64</th>\n",
       "      <th>graduate_professional_degree</th>\n",
       "      <th>some_college_and_associates_degree</th>\n",
       "      <th>male_45_64_associates_degree</th>\n",
       "      <th>male_45_64_bachelors_degree</th>\n",
       "      <th>male_45_64_graduate_degree</th>\n",
       "      <th>male_45_64_less_than_9_grade</th>\n",
       "      <th>male_45_64_grade_9_12</th>\n",
       "      <th>male_45_64_high_school</th>\n",
       "      <th>male_45_64_some_college</th>\n",
       "      <th>male_45_to_64</th>\n",
       "      <th>employed_pop</th>\n",
       "      <th>unemployed_pop</th>\n",
       "      <th>pop_in_labor_force</th>\n",
       "      <th>not_in_labor_force</th>\n",
       "      <th>workers_16_and_over</th>\n",
       "      <th>armed_forces</th>\n",
       "      <th>civilian_labor_force</th>\n",
       "      <th>employed_agriculture_forestry_fishing_hunting_mining</th>\n",
       "      <th>employed_arts_entertainment_recreation_accommodation_food</th>\n",
       "      <th>employed_construction</th>\n",
       "      <th>employed_education_health_social</th>\n",
       "      <th>employed_finance_insurance_real_estate</th>\n",
       "      <th>employed_information</th>\n",
       "      <th>employed_manufacturing</th>\n",
       "      <th>employed_other_services_not_public_admin</th>\n",
       "      <th>employed_public_administration</th>\n",
       "      <th>employed_retail_trade</th>\n",
       "      <th>employed_science_management_admin_waste</th>\n",
       "      <th>employed_transportation_warehousing_utilities</th>\n",
       "      <th>employed_wholesale_trade</th>\n",
       "      <th>occupation_management_arts</th>\n",
       "      <th>occupation_natural_resources_construction_maintenance</th>\n",
       "      <th>occupation_production_transportation_material</th>\n",
       "      <th>occupation_sales_office</th>\n",
       "      <th>occupation_services</th>\n",
       "      <th>management_business_sci_arts_employed</th>\n",
       "      <th>sales_office_employed</th>\n",
       "      <th>in_grades_1_to_4</th>\n",
       "      <th>in_grades_5_to_8</th>\n",
       "      <th>in_grades_9_to_12</th>\n",
       "      <th>in_school</th>\n",
       "      <th>in_undergrad_college</th>\n",
       "      <th>speak_only_english_at_home</th>\n",
       "      <th>speak_spanish_at_home</th>\n",
       "      <th>speak_spanish_at_home_low_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>avg</th>\n",
       "      <td>4.384716e+03</td>\n",
       "      <td>1.622255e+03</td>\n",
       "      <td>2.157711e+03</td>\n",
       "      <td>2.227005e+03</td>\n",
       "      <td>3.930112e+01</td>\n",
       "      <td>1.383831e+02</td>\n",
       "      <td>1.423078e+02</td>\n",
       "      <td>1.445751e+02</td>\n",
       "      <td>8.804836e+01</td>\n",
       "      <td>6.037216e+01</td>\n",
       "      <td>3.276450e+01</td>\n",
       "      <td>3.217450e+01</td>\n",
       "      <td>9.285125e+01</td>\n",
       "      <td>1.552524e+02</td>\n",
       "      <td>1.483501e+02</td>\n",
       "      <td>1.388303e+02</td>\n",
       "      <td>1.375463e+02</td>\n",
       "      <td>1.416246e+02</td>\n",
       "      <td>1.482727e+02</td>\n",
       "      <td>1.423548e+02</td>\n",
       "      <td>4.490084e+01</td>\n",
       "      <td>5.796503e+01</td>\n",
       "      <td>7.306170e+01</td>\n",
       "      <td>5.008765e+01</td>\n",
       "      <td>3.334291e+01</td>\n",
       "      <td>2.886338e+01</td>\n",
       "      <td>1.322310e+02</td>\n",
       "      <td>1.365999e+02</td>\n",
       "      <td>1.383163e+02</td>\n",
       "      <td>8.401951e+01</td>\n",
       "      <td>5.756660e+01</td>\n",
       "      <td>3.088993e+01</td>\n",
       "      <td>3.041858e+01</td>\n",
       "      <td>8.833956e+01</td>\n",
       "      <td>1.505691e+02</td>\n",
       "      <td>1.468783e+02</td>\n",
       "      <td>1.396495e+02</td>\n",
       "      <td>1.393065e+02</td>\n",
       "      <td>1.446716e+02</td>\n",
       "      <td>1.538896e+02</td>\n",
       "      <td>1.514543e+02</td>\n",
       "      <td>5.749201e+01</td>\n",
       "      <td>7.940259e+01</td>\n",
       "      <td>4.982987e+01</td>\n",
       "      <td>6.520221e+01</td>\n",
       "      <td>8.553553e+01</td>\n",
       "      <td>6.253888e+01</td>\n",
       "      <td>4.708153e+01</td>\n",
       "      <td>5.512231e+01</td>\n",
       "      <td>2.666237e+03</td>\n",
       "      <td>4.342539e+03</td>\n",
       "      <td>4.225816e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.495006e+03</td>\n",
       "      <td>2.954858e+03</td>\n",
       "      <td>2.301326e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.057622e+02</td>\n",
       "      <td>5.330768e+02</td>\n",
       "      <td>2.296153e+02</td>\n",
       "      <td>8.100321e+02</td>\n",
       "      <td>2.836241e+01</td>\n",
       "      <td>9.694369</td>\n",
       "      <td>1.007309e+02</td>\n",
       "      <td>8.100321e+02</td>\n",
       "      <td>3.574684e+03</td>\n",
       "      <td>1.491637e+01</td>\n",
       "      <td>11.693788</td>\n",
       "      <td>3.440143e+01</td>\n",
       "      <td>2.904223e+01</td>\n",
       "      <td>4.700662e+01</td>\n",
       "      <td>3.048543e+01</td>\n",
       "      <td>1.883150e+02</td>\n",
       "      <td>1.926332e+02</td>\n",
       "      <td>6.120650e+04</td>\n",
       "      <td>3.065180e+04</td>\n",
       "      <td>1.120286e+02</td>\n",
       "      <td>7.990310e+01</td>\n",
       "      <td>7.906576e+01</td>\n",
       "      <td>8.121069e+01</td>\n",
       "      <td>7.699035e+01</td>\n",
       "      <td>7.811121e+01</td>\n",
       "      <td>7.282273e+01</td>\n",
       "      <td>7.298924e+01</td>\n",
       "      <td>6.437017e+01</td>\n",
       "      <td>1.248865e+02</td>\n",
       "      <td>1.605002e+02</td>\n",
       "      <td>1.984182e+02</td>\n",
       "      <td>1.391279e+02</td>\n",
       "      <td>8.701426e+01</td>\n",
       "      <td>9.380035e+01</td>\n",
       "      <td>1.010161e+02</td>\n",
       "      <td>4.276775e+03</td>\n",
       "      <td>6.377423e+02</td>\n",
       "      <td>0.425911</td>\n",
       "      <td>1.850797e+03</td>\n",
       "      <td>1.058678e+03</td>\n",
       "      <td>1.765685e+05</td>\n",
       "      <td>2.443280e+05</td>\n",
       "      <td>3.329604e+05</td>\n",
       "      <td>1.622255e+03</td>\n",
       "      <td>5.861794e+02</td>\n",
       "      <td>2.285420e+02</td>\n",
       "      <td>3.884688e+01</td>\n",
       "      <td>1.863187e+01</td>\n",
       "      <td>1.143346e+03</td>\n",
       "      <td>1.092662e+02</td>\n",
       "      <td>6.762999e+01</td>\n",
       "      <td>8.112938e+01</td>\n",
       "      <td>8.797462e+01</td>\n",
       "      <td>8.248062e+01</td>\n",
       "      <td>6.591754e+01</td>\n",
       "      <td>9.643179e+01</td>\n",
       "      <td>1.150671e+02</td>\n",
       "      <td>1.610305e+01</td>\n",
       "      <td>4.221539e+01</td>\n",
       "      <td>9.403019e+01</td>\n",
       "      <td>1.971950e+03</td>\n",
       "      <td>7.829447e+02</td>\n",
       "      <td>5.526839e+02</td>\n",
       "      <td>1.069572e+03</td>\n",
       "      <td>2.201474e+02</td>\n",
       "      <td>2.847543</td>\n",
       "      <td>2.909866</td>\n",
       "      <td>1.004481e+03</td>\n",
       "      <td>2.549481e+02</td>\n",
       "      <td>8.960611e+02</td>\n",
       "      <td>3.075703e+01</td>\n",
       "      <td>4.549625e+01</td>\n",
       "      <td>1.383322e+02</td>\n",
       "      <td>4.980808e+01</td>\n",
       "      <td>3.622229e+01</td>\n",
       "      <td>4.936739e+01</td>\n",
       "      <td>6.223202e+01</td>\n",
       "      <td>6.874649e+01</td>\n",
       "      <td>6.792566e+01</td>\n",
       "      <td>4.691335e+01</td>\n",
       "      <td>2.113569e+01</td>\n",
       "      <td>1.036076e+03</td>\n",
       "      <td>1.498115e+01</td>\n",
       "      <td>6.556553e+02</td>\n",
       "      <td>4.115417e+02</td>\n",
       "      <td>1.951105e+02</td>\n",
       "      <td>3.127425e+02</td>\n",
       "      <td>2.018102e+02</td>\n",
       "      <td>1.182977e+02</td>\n",
       "      <td>7.417954e+01</td>\n",
       "      <td>6.340941</td>\n",
       "      <td>2.991987</td>\n",
       "      <td>1.109323e+02</td>\n",
       "      <td>2.560057e+01</td>\n",
       "      <td>2.281514e+01</td>\n",
       "      <td>2.433403e+02</td>\n",
       "      <td>2.615797e+02</td>\n",
       "      <td>2.943585e+02</td>\n",
       "      <td>2.800684e+02</td>\n",
       "      <td>1.222793e+02</td>\n",
       "      <td>2.636407e+02</td>\n",
       "      <td>1.311400e+02</td>\n",
       "      <td>1.560668e+02</td>\n",
       "      <td>1.717179e+02</td>\n",
       "      <td>1.924192e+03</td>\n",
       "      <td>5.515674e+01</td>\n",
       "      <td>9.526220e+01</td>\n",
       "      <td>8.728505e+01</td>\n",
       "      <td>1.442299e+02</td>\n",
       "      <td>5.405044e+02</td>\n",
       "      <td>6.048036e+02</td>\n",
       "      <td>2.295713e+02</td>\n",
       "      <td>1.031463e+02</td>\n",
       "      <td>5.934150e+04</td>\n",
       "      <td>1.031013e+02</td>\n",
       "      <td>5.114798e+01</td>\n",
       "      <td>1.729272e+03</td>\n",
       "      <td>1.847843e+02</td>\n",
       "      <td>3.818269e+01</td>\n",
       "      <td>1.544488e+03</td>\n",
       "      <td>1.098196e+02</td>\n",
       "      <td>2.452862e+02</td>\n",
       "      <td>5.649029e+02</td>\n",
       "      <td>6.904443e+02</td>\n",
       "      <td>1.821747e+02</td>\n",
       "      <td>2.468936e+02</td>\n",
       "      <td>4.290593e+02</td>\n",
       "      <td>3.755628e+02</td>\n",
       "      <td>8.088810e+02</td>\n",
       "      <td>5.663747e+02</td>\n",
       "      <td>7.435482e+02</td>\n",
       "      <td>3.491915e+02</td>\n",
       "      <td>8.603443e+02</td>\n",
       "      <td>4.302832e+01</td>\n",
       "      <td>1.000650e+02</td>\n",
       "      <td>6.498545e+01</td>\n",
       "      <td>3.009663e+01</td>\n",
       "      <td>4.223971e+01</td>\n",
       "      <td>1.648105e+02</td>\n",
       "      <td>1.128077e+02</td>\n",
       "      <td>5.580332e+02</td>\n",
       "      <td>2.049152e+03</td>\n",
       "      <td>1.456859e+02</td>\n",
       "      <td>2.208707e+03</td>\n",
       "      <td>1.286299e+03</td>\n",
       "      <td>2.019454e+03</td>\n",
       "      <td>1.386930e+01</td>\n",
       "      <td>2.194838e+03</td>\n",
       "      <td>3.827483e+01</td>\n",
       "      <td>1.984437e+02</td>\n",
       "      <td>1.299722e+02</td>\n",
       "      <td>4.733737e+02</td>\n",
       "      <td>1.346475e+02</td>\n",
       "      <td>4.315308e+01</td>\n",
       "      <td>2.104029e+02</td>\n",
       "      <td>1.003838e+02</td>\n",
       "      <td>9.616204e+01</td>\n",
       "      <td>2.338625e+02</td>\n",
       "      <td>2.311254e+02</td>\n",
       "      <td>1.043314e+02</td>\n",
       "      <td>5.501904e+01</td>\n",
       "      <td>7.665854e+02</td>\n",
       "      <td>1.820007e+02</td>\n",
       "      <td>2.491306e+02</td>\n",
       "      <td>4.828023e+02</td>\n",
       "      <td>3.686331e+02</td>\n",
       "      <td>7.665854e+02</td>\n",
       "      <td>4.828023e+02</td>\n",
       "      <td>2.228999e+02</td>\n",
       "      <td>2.253273e+02</td>\n",
       "      <td>2.322261e+02</td>\n",
       "      <td>1.116528e+03</td>\n",
       "      <td>2.557242e+02</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.552800e+04</td>\n",
       "      <td>2.142900e+04</td>\n",
       "      <td>3.226600e+04</td>\n",
       "      <td>3.326200e+04</td>\n",
       "      <td>8.570000e+01</td>\n",
       "      <td>3.297000e+03</td>\n",
       "      <td>3.464000e+03</td>\n",
       "      <td>3.137000e+03</td>\n",
       "      <td>2.416000e+03</td>\n",
       "      <td>3.909000e+03</td>\n",
       "      <td>2.845000e+03</td>\n",
       "      <td>3.025000e+03</td>\n",
       "      <td>6.083000e+03</td>\n",
       "      <td>3.956000e+03</td>\n",
       "      <td>2.143000e+03</td>\n",
       "      <td>3.146000e+03</td>\n",
       "      <td>3.718000e+03</td>\n",
       "      <td>2.652000e+03</td>\n",
       "      <td>2.300000e+03</td>\n",
       "      <td>1.741000e+03</td>\n",
       "      <td>1.244000e+03</td>\n",
       "      <td>3.225000e+03</td>\n",
       "      <td>4.708000e+03</td>\n",
       "      <td>3.287000e+03</td>\n",
       "      <td>1.666000e+03</td>\n",
       "      <td>6.670000e+02</td>\n",
       "      <td>2.829000e+03</td>\n",
       "      <td>3.509000e+03</td>\n",
       "      <td>3.667000e+03</td>\n",
       "      <td>1.601000e+03</td>\n",
       "      <td>4.815000e+03</td>\n",
       "      <td>1.586000e+03</td>\n",
       "      <td>1.330000e+03</td>\n",
       "      <td>2.045000e+03</td>\n",
       "      <td>2.410000e+03</td>\n",
       "      <td>2.840000e+03</td>\n",
       "      <td>3.450000e+03</td>\n",
       "      <td>3.586000e+03</td>\n",
       "      <td>3.628000e+03</td>\n",
       "      <td>1.756000e+03</td>\n",
       "      <td>1.445000e+03</td>\n",
       "      <td>8.730000e+02</td>\n",
       "      <td>2.105000e+03</td>\n",
       "      <td>2.147000e+03</td>\n",
       "      <td>3.661000e+03</td>\n",
       "      <td>5.268000e+03</td>\n",
       "      <td>2.481000e+03</td>\n",
       "      <td>1.166000e+03</td>\n",
       "      <td>1.337000e+03</td>\n",
       "      <td>3.868400e+04</td>\n",
       "      <td>6.480300e+04</td>\n",
       "      <td>6.243800e+04</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4.464200e+04</td>\n",
       "      <td>3.948600e+04</td>\n",
       "      <td>3.425800e+04</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.223200e+04</td>\n",
       "      <td>1.665500e+04</td>\n",
       "      <td>1.371200e+04</td>\n",
       "      <td>2.868300e+04</td>\n",
       "      <td>9.644000e+03</td>\n",
       "      <td>1385.000000</td>\n",
       "      <td>3.611000e+03</td>\n",
       "      <td>2.868300e+04</td>\n",
       "      <td>5.047700e+04</td>\n",
       "      <td>1.301000e+03</td>\n",
       "      <td>745.000000</td>\n",
       "      <td>1.094000e+03</td>\n",
       "      <td>1.122000e+03</td>\n",
       "      <td>1.464000e+03</td>\n",
       "      <td>7.770000e+02</td>\n",
       "      <td>2.189000e+03</td>\n",
       "      <td>2.545000e+03</td>\n",
       "      <td>2.500010e+05</td>\n",
       "      <td>2.202530e+05</td>\n",
       "      <td>1.550000e+03</td>\n",
       "      <td>9.300000e+02</td>\n",
       "      <td>7.610000e+02</td>\n",
       "      <td>1.009000e+03</td>\n",
       "      <td>8.760000e+02</td>\n",
       "      <td>8.640000e+02</td>\n",
       "      <td>1.530000e+03</td>\n",
       "      <td>1.118000e+03</td>\n",
       "      <td>8.610000e+02</td>\n",
       "      <td>2.054000e+03</td>\n",
       "      <td>2.789000e+03</td>\n",
       "      <td>3.368000e+03</td>\n",
       "      <td>2.704000e+03</td>\n",
       "      <td>2.157000e+03</td>\n",
       "      <td>3.878000e+03</td>\n",
       "      <td>5.386000e+03</td>\n",
       "      <td>6.533200e+04</td>\n",
       "      <td>9.440000e+03</td>\n",
       "      <td>0.829000</td>\n",
       "      <td>2.652600e+04</td>\n",
       "      <td>3.501000e+03</td>\n",
       "      <td>2.000001e+06</td>\n",
       "      <td>2.000001e+06</td>\n",
       "      <td>2.000001e+06</td>\n",
       "      <td>2.142900e+04</td>\n",
       "      <td>8.039000e+03</td>\n",
       "      <td>1.192400e+04</td>\n",
       "      <td>4.472000e+03</td>\n",
       "      <td>5.470000e+02</td>\n",
       "      <td>2.552700e+04</td>\n",
       "      <td>4.280000e+03</td>\n",
       "      <td>1.999000e+03</td>\n",
       "      <td>1.629000e+03</td>\n",
       "      <td>1.863000e+03</td>\n",
       "      <td>5.280000e+03</td>\n",
       "      <td>4.283000e+03</td>\n",
       "      <td>1.151800e+04</td>\n",
       "      <td>3.314000e+03</td>\n",
       "      <td>2.502000e+03</td>\n",
       "      <td>7.206000e+03</td>\n",
       "      <td>6.177000e+03</td>\n",
       "      <td>2.016000e+03</td>\n",
       "      <td>1.510500e+04</td>\n",
       "      <td>6.883000e+03</td>\n",
       "      <td>1.681300e+04</td>\n",
       "      <td>2.325000e+03</td>\n",
       "      <td>355.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>2.360800e+04</td>\n",
       "      <td>2.863000e+03</td>\n",
       "      <td>3.501000e+03</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>1.100000e+03</td>\n",
       "      <td>3.487000e+03</td>\n",
       "      <td>9.720000e+02</td>\n",
       "      <td>6.410000e+02</td>\n",
       "      <td>8.600000e+02</td>\n",
       "      <td>9.990000e+02</td>\n",
       "      <td>1.382000e+03</td>\n",
       "      <td>1.879000e+03</td>\n",
       "      <td>1.098000e+03</td>\n",
       "      <td>9.120000e+02</td>\n",
       "      <td>2.047300e+04</td>\n",
       "      <td>1.453000e+03</td>\n",
       "      <td>1.267200e+04</td>\n",
       "      <td>1.306400e+04</td>\n",
       "      <td>5.347000e+03</td>\n",
       "      <td>6.700000e+03</td>\n",
       "      <td>6.517000e+03</td>\n",
       "      <td>3.989000e+03</td>\n",
       "      <td>3.888000e+03</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>285.000000</td>\n",
       "      <td>1.418000e+03</td>\n",
       "      <td>5.020000e+02</td>\n",
       "      <td>5.020000e+02</td>\n",
       "      <td>1.162100e+04</td>\n",
       "      <td>5.838000e+03</td>\n",
       "      <td>7.095000e+03</td>\n",
       "      <td>4.886000e+03</td>\n",
       "      <td>2.205000e+03</td>\n",
       "      <td>4.880000e+03</td>\n",
       "      <td>3.178000e+03</td>\n",
       "      <td>5.390000e+03</td>\n",
       "      <td>6.070000e+03</td>\n",
       "      <td>2.648200e+04</td>\n",
       "      <td>1.162100e+04</td>\n",
       "      <td>7.043000e+03</td>\n",
       "      <td>8.111000e+03</td>\n",
       "      <td>5.847000e+03</td>\n",
       "      <td>1.554800e+04</td>\n",
       "      <td>1.201200e+04</td>\n",
       "      <td>3.231000e+03</td>\n",
       "      <td>1.107000e+03</td>\n",
       "      <td>9.731600e+05</td>\n",
       "      <td>7.759000e+03</td>\n",
       "      <td>3.940000e+03</td>\n",
       "      <td>2.501200e+04</td>\n",
       "      <td>3.023000e+03</td>\n",
       "      <td>5.945000e+03</td>\n",
       "      <td>2.198900e+04</td>\n",
       "      <td>1.642100e+04</td>\n",
       "      <td>4.070000e+03</td>\n",
       "      <td>1.376700e+04</td>\n",
       "      <td>8.215000e+03</td>\n",
       "      <td>4.037000e+03</td>\n",
       "      <td>7.767000e+03</td>\n",
       "      <td>5.621000e+03</td>\n",
       "      <td>5.363000e+03</td>\n",
       "      <td>8.929000e+03</td>\n",
       "      <td>1.376700e+04</td>\n",
       "      <td>2.285600e+04</td>\n",
       "      <td>1.037400e+04</td>\n",
       "      <td>1.274500e+04</td>\n",
       "      <td>8.550000e+02</td>\n",
       "      <td>2.547000e+03</td>\n",
       "      <td>2.448000e+03</td>\n",
       "      <td>9.000000e+02</td>\n",
       "      <td>1.626000e+03</td>\n",
       "      <td>2.084000e+03</td>\n",
       "      <td>1.628000e+03</td>\n",
       "      <td>6.993000e+03</td>\n",
       "      <td>2.894500e+04</td>\n",
       "      <td>1.607000e+03</td>\n",
       "      <td>3.055200e+04</td>\n",
       "      <td>3.414200e+04</td>\n",
       "      <td>2.825200e+04</td>\n",
       "      <td>2.121400e+04</td>\n",
       "      <td>3.055200e+04</td>\n",
       "      <td>4.197000e+03</td>\n",
       "      <td>5.211000e+03</td>\n",
       "      <td>1.950000e+03</td>\n",
       "      <td>9.127000e+03</td>\n",
       "      <td>2.853000e+03</td>\n",
       "      <td>1.141000e+03</td>\n",
       "      <td>3.993000e+03</td>\n",
       "      <td>1.175000e+03</td>\n",
       "      <td>1.816000e+03</td>\n",
       "      <td>2.808000e+03</td>\n",
       "      <td>5.115000e+03</td>\n",
       "      <td>1.465000e+03</td>\n",
       "      <td>1.294000e+03</td>\n",
       "      <td>1.909700e+04</td>\n",
       "      <td>3.973000e+03</td>\n",
       "      <td>2.495000e+03</td>\n",
       "      <td>6.162000e+03</td>\n",
       "      <td>3.948000e+03</td>\n",
       "      <td>1.909700e+04</td>\n",
       "      <td>6.162000e+03</td>\n",
       "      <td>5.927000e+03</td>\n",
       "      <td>5.674000e+03</td>\n",
       "      <td>4.965000e+03</td>\n",
       "      <td>2.398900e+04</td>\n",
       "      <td>1.298500e+04</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.700000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.499000e+03</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>9.999000e+03</td>\n",
       "      <td>9.999000e+03</td>\n",
       "      <td>9.999000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.939000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.500000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>3.244734e+08</td>\n",
       "      <td>1.200485e+08</td>\n",
       "      <td>1.596728e+08</td>\n",
       "      <td>1.648006e+08</td>\n",
       "      <td>2.879751e+06</td>\n",
       "      <td>1.024049e+07</td>\n",
       "      <td>1.053092e+07</td>\n",
       "      <td>1.069870e+07</td>\n",
       "      <td>6.515667e+06</td>\n",
       "      <td>4.467600e+06</td>\n",
       "      <td>2.424606e+06</td>\n",
       "      <td>2.380945e+06</td>\n",
       "      <td>6.871085e+06</td>\n",
       "      <td>1.148883e+07</td>\n",
       "      <td>1.097806e+07</td>\n",
       "      <td>1.027358e+07</td>\n",
       "      <td>1.017856e+07</td>\n",
       "      <td>1.048036e+07</td>\n",
       "      <td>1.097233e+07</td>\n",
       "      <td>1.053440e+07</td>\n",
       "      <td>3.322707e+06</td>\n",
       "      <td>4.289470e+06</td>\n",
       "      <td>5.406639e+06</td>\n",
       "      <td>3.706536e+06</td>\n",
       "      <td>2.467409e+06</td>\n",
       "      <td>2.135919e+06</td>\n",
       "      <td>9.785223e+06</td>\n",
       "      <td>1.010853e+07</td>\n",
       "      <td>1.023555e+07</td>\n",
       "      <td>6.217528e+06</td>\n",
       "      <td>4.259986e+06</td>\n",
       "      <td>2.285886e+06</td>\n",
       "      <td>2.251005e+06</td>\n",
       "      <td>6.537216e+06</td>\n",
       "      <td>1.114227e+07</td>\n",
       "      <td>1.086914e+07</td>\n",
       "      <td>1.033420e+07</td>\n",
       "      <td>1.030882e+07</td>\n",
       "      <td>1.070584e+07</td>\n",
       "      <td>1.138799e+07</td>\n",
       "      <td>1.120777e+07</td>\n",
       "      <td>4.254466e+06</td>\n",
       "      <td>5.875871e+06</td>\n",
       "      <td>3.687460e+06</td>\n",
       "      <td>4.825029e+06</td>\n",
       "      <td>6.329715e+06</td>\n",
       "      <td>4.627940e+06</td>\n",
       "      <td>3.484080e+06</td>\n",
       "      <td>4.079106e+06</td>\n",
       "      <td>1.973042e+08</td>\n",
       "      <td>3.172486e+08</td>\n",
       "      <td>3.127146e+08</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.586340e+08</td>\n",
       "      <td>2.186624e+08</td>\n",
       "      <td>1.703004e+08</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.233776e+07</td>\n",
       "      <td>3.944822e+07</td>\n",
       "      <td>1.699176e+07</td>\n",
       "      <td>5.994318e+07</td>\n",
       "      <td>2.098847e+06</td>\n",
       "      <td>717393.000000</td>\n",
       "      <td>7.454186e+06</td>\n",
       "      <td>5.994318e+07</td>\n",
       "      <td>2.645302e+08</td>\n",
       "      <td>1.103826e+06</td>\n",
       "      <td>865352.000000</td>\n",
       "      <td>2.545740e+06</td>\n",
       "      <td>2.149154e+06</td>\n",
       "      <td>3.478537e+06</td>\n",
       "      <td>2.255952e+06</td>\n",
       "      <td>1.393550e+07</td>\n",
       "      <td>1.425505e+07</td>\n",
       "      <td>4.464586e+09</td>\n",
       "      <td>2.245429e+09</td>\n",
       "      <td>8.290228e+06</td>\n",
       "      <td>5.912909e+06</td>\n",
       "      <td>5.850945e+06</td>\n",
       "      <td>6.009672e+06</td>\n",
       "      <td>5.697363e+06</td>\n",
       "      <td>5.780308e+06</td>\n",
       "      <td>5.388955e+06</td>\n",
       "      <td>5.401277e+06</td>\n",
       "      <td>4.763457e+06</td>\n",
       "      <td>9.241728e+06</td>\n",
       "      <td>1.187718e+07</td>\n",
       "      <td>1.468315e+07</td>\n",
       "      <td>1.029560e+07</td>\n",
       "      <td>6.439142e+06</td>\n",
       "      <td>6.941320e+06</td>\n",
       "      <td>7.475296e+06</td>\n",
       "      <td>3.164856e+08</td>\n",
       "      <td>4.719356e+07</td>\n",
       "      <td>31111.100500</td>\n",
       "      <td>1.369609e+08</td>\n",
       "      <td>7.612849e+07</td>\n",
       "      <td>1.258933e+10</td>\n",
       "      <td>1.758746e+10</td>\n",
       "      <td>2.396882e+10</td>\n",
       "      <td>1.200485e+08</td>\n",
       "      <td>4.337786e+07</td>\n",
       "      <td>1.691234e+07</td>\n",
       "      <td>2.874708e+06</td>\n",
       "      <td>1.378777e+06</td>\n",
       "      <td>8.460878e+07</td>\n",
       "      <td>8.085807e+06</td>\n",
       "      <td>5.004687e+06</td>\n",
       "      <td>6.003655e+06</td>\n",
       "      <td>6.510210e+06</td>\n",
       "      <td>6.103648e+06</td>\n",
       "      <td>4.877964e+06</td>\n",
       "      <td>7.136049e+06</td>\n",
       "      <td>8.515077e+06</td>\n",
       "      <td>1.191642e+06</td>\n",
       "      <td>3.123981e+06</td>\n",
       "      <td>6.958328e+06</td>\n",
       "      <td>1.429487e+08</td>\n",
       "      <td>5.793869e+07</td>\n",
       "      <td>4.089916e+07</td>\n",
       "      <td>7.914937e+07</td>\n",
       "      <td>1.629113e+07</td>\n",
       "      <td>210721.000000</td>\n",
       "      <td>215333.000000</td>\n",
       "      <td>7.433261e+07</td>\n",
       "      <td>1.886641e+07</td>\n",
       "      <td>6.427357e+07</td>\n",
       "      <td>2.225702e+06</td>\n",
       "      <td>3.366768e+06</td>\n",
       "      <td>1.023672e+07</td>\n",
       "      <td>3.685848e+06</td>\n",
       "      <td>2.680486e+06</td>\n",
       "      <td>3.653236e+06</td>\n",
       "      <td>4.605232e+06</td>\n",
       "      <td>5.087309e+06</td>\n",
       "      <td>5.026567e+06</td>\n",
       "      <td>3.471635e+06</td>\n",
       "      <td>1.564062e+06</td>\n",
       "      <td>7.667066e+07</td>\n",
       "      <td>1.108620e+06</td>\n",
       "      <td>4.851915e+07</td>\n",
       "      <td>3.006559e+07</td>\n",
       "      <td>1.425400e+07</td>\n",
       "      <td>2.314326e+07</td>\n",
       "      <td>1.493416e+07</td>\n",
       "      <td>8.754150e+06</td>\n",
       "      <td>5.489360e+06</td>\n",
       "      <td>469236.000000</td>\n",
       "      <td>221410.000000</td>\n",
       "      <td>8.209099e+06</td>\n",
       "      <td>1.894468e+06</td>\n",
       "      <td>1.688343e+06</td>\n",
       "      <td>1.800742e+07</td>\n",
       "      <td>1.935716e+07</td>\n",
       "      <td>2.178282e+07</td>\n",
       "      <td>2.072534e+07</td>\n",
       "      <td>9.048793e+06</td>\n",
       "      <td>1.950968e+07</td>\n",
       "      <td>9.704493e+06</td>\n",
       "      <td>1.154910e+07</td>\n",
       "      <td>1.270730e+07</td>\n",
       "      <td>1.423921e+08</td>\n",
       "      <td>4.081654e+06</td>\n",
       "      <td>7.049498e+06</td>\n",
       "      <td>6.459181e+06</td>\n",
       "      <td>1.067316e+07</td>\n",
       "      <td>3.999786e+07</td>\n",
       "      <td>4.475607e+07</td>\n",
       "      <td>1.698850e+07</td>\n",
       "      <td>7.632928e+06</td>\n",
       "      <td>2.970754e+09</td>\n",
       "      <td>7.629599e+06</td>\n",
       "      <td>3.785002e+06</td>\n",
       "      <td>1.279679e+08</td>\n",
       "      <td>1.367422e+07</td>\n",
       "      <td>2.825557e+06</td>\n",
       "      <td>1.142936e+08</td>\n",
       "      <td>8.126758e+06</td>\n",
       "      <td>1.815143e+07</td>\n",
       "      <td>4.180338e+07</td>\n",
       "      <td>5.109357e+07</td>\n",
       "      <td>1.348111e+07</td>\n",
       "      <td>1.827037e+07</td>\n",
       "      <td>3.175082e+07</td>\n",
       "      <td>2.743711e+07</td>\n",
       "      <td>5.909361e+07</td>\n",
       "      <td>4.137707e+07</td>\n",
       "      <td>5.502331e+07</td>\n",
       "      <td>2.551054e+07</td>\n",
       "      <td>6.285332e+07</td>\n",
       "      <td>3.184139e+06</td>\n",
       "      <td>7.404907e+06</td>\n",
       "      <td>4.808988e+06</td>\n",
       "      <td>2.227181e+06</td>\n",
       "      <td>3.125781e+06</td>\n",
       "      <td>1.219614e+07</td>\n",
       "      <td>8.347879e+06</td>\n",
       "      <td>4.129502e+07</td>\n",
       "      <td>1.516393e+08</td>\n",
       "      <td>1.078090e+07</td>\n",
       "      <td>1.634465e+08</td>\n",
       "      <td>9.518743e+07</td>\n",
       "      <td>1.494416e+08</td>\n",
       "      <td>1.026342e+06</td>\n",
       "      <td>1.624202e+08</td>\n",
       "      <td>2.832376e+06</td>\n",
       "      <td>1.468503e+07</td>\n",
       "      <td>9.618071e+06</td>\n",
       "      <td>3.503013e+07</td>\n",
       "      <td>9.964050e+06</td>\n",
       "      <td>3.193371e+06</td>\n",
       "      <td>1.557002e+07</td>\n",
       "      <td>7.428501e+06</td>\n",
       "      <td>7.116087e+06</td>\n",
       "      <td>1.730606e+07</td>\n",
       "      <td>1.710351e+07</td>\n",
       "      <td>7.720629e+06</td>\n",
       "      <td>4.071464e+06</td>\n",
       "      <td>5.672809e+07</td>\n",
       "      <td>1.346823e+07</td>\n",
       "      <td>1.843592e+07</td>\n",
       "      <td>3.572785e+07</td>\n",
       "      <td>2.727922e+07</td>\n",
       "      <td>5.672809e+07</td>\n",
       "      <td>3.572785e+07</td>\n",
       "      <td>1.649482e+07</td>\n",
       "      <td>1.667444e+07</td>\n",
       "      <td>1.718497e+07</td>\n",
       "      <td>8.262417e+07</td>\n",
       "      <td>1.892385e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>range</th>\n",
       "      <td>6.552800e+04</td>\n",
       "      <td>2.142900e+04</td>\n",
       "      <td>3.226600e+04</td>\n",
       "      <td>3.326200e+04</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>3.297000e+03</td>\n",
       "      <td>3.464000e+03</td>\n",
       "      <td>3.137000e+03</td>\n",
       "      <td>2.416000e+03</td>\n",
       "      <td>3.909000e+03</td>\n",
       "      <td>2.845000e+03</td>\n",
       "      <td>3.025000e+03</td>\n",
       "      <td>6.083000e+03</td>\n",
       "      <td>3.956000e+03</td>\n",
       "      <td>2.143000e+03</td>\n",
       "      <td>3.146000e+03</td>\n",
       "      <td>3.718000e+03</td>\n",
       "      <td>2.652000e+03</td>\n",
       "      <td>2.300000e+03</td>\n",
       "      <td>1.741000e+03</td>\n",
       "      <td>1.244000e+03</td>\n",
       "      <td>3.225000e+03</td>\n",
       "      <td>4.708000e+03</td>\n",
       "      <td>3.287000e+03</td>\n",
       "      <td>1.666000e+03</td>\n",
       "      <td>6.670000e+02</td>\n",
       "      <td>2.829000e+03</td>\n",
       "      <td>3.509000e+03</td>\n",
       "      <td>3.667000e+03</td>\n",
       "      <td>1.601000e+03</td>\n",
       "      <td>4.815000e+03</td>\n",
       "      <td>1.586000e+03</td>\n",
       "      <td>1.330000e+03</td>\n",
       "      <td>2.045000e+03</td>\n",
       "      <td>2.410000e+03</td>\n",
       "      <td>2.840000e+03</td>\n",
       "      <td>3.450000e+03</td>\n",
       "      <td>3.586000e+03</td>\n",
       "      <td>3.628000e+03</td>\n",
       "      <td>1.756000e+03</td>\n",
       "      <td>1.445000e+03</td>\n",
       "      <td>8.730000e+02</td>\n",
       "      <td>2.105000e+03</td>\n",
       "      <td>2.147000e+03</td>\n",
       "      <td>3.661000e+03</td>\n",
       "      <td>5.268000e+03</td>\n",
       "      <td>2.481000e+03</td>\n",
       "      <td>1.166000e+03</td>\n",
       "      <td>1.337000e+03</td>\n",
       "      <td>3.868400e+04</td>\n",
       "      <td>6.480300e+04</td>\n",
       "      <td>6.243800e+04</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4.464200e+04</td>\n",
       "      <td>3.948600e+04</td>\n",
       "      <td>3.425800e+04</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.223200e+04</td>\n",
       "      <td>1.665500e+04</td>\n",
       "      <td>1.371200e+04</td>\n",
       "      <td>2.868300e+04</td>\n",
       "      <td>9.644000e+03</td>\n",
       "      <td>1385.000000</td>\n",
       "      <td>3.611000e+03</td>\n",
       "      <td>2.868300e+04</td>\n",
       "      <td>5.047700e+04</td>\n",
       "      <td>1.301000e+03</td>\n",
       "      <td>745.000000</td>\n",
       "      <td>1.094000e+03</td>\n",
       "      <td>1.122000e+03</td>\n",
       "      <td>1.464000e+03</td>\n",
       "      <td>7.770000e+02</td>\n",
       "      <td>2.189000e+03</td>\n",
       "      <td>2.545000e+03</td>\n",
       "      <td>2.475020e+05</td>\n",
       "      <td>2.202210e+05</td>\n",
       "      <td>1.550000e+03</td>\n",
       "      <td>9.300000e+02</td>\n",
       "      <td>7.610000e+02</td>\n",
       "      <td>1.009000e+03</td>\n",
       "      <td>8.760000e+02</td>\n",
       "      <td>8.640000e+02</td>\n",
       "      <td>1.530000e+03</td>\n",
       "      <td>1.118000e+03</td>\n",
       "      <td>8.610000e+02</td>\n",
       "      <td>2.054000e+03</td>\n",
       "      <td>2.789000e+03</td>\n",
       "      <td>3.368000e+03</td>\n",
       "      <td>2.704000e+03</td>\n",
       "      <td>2.157000e+03</td>\n",
       "      <td>3.878000e+03</td>\n",
       "      <td>5.386000e+03</td>\n",
       "      <td>6.533200e+04</td>\n",
       "      <td>9.440000e+03</td>\n",
       "      <td>0.828300</td>\n",
       "      <td>2.652600e+04</td>\n",
       "      <td>3.402000e+03</td>\n",
       "      <td>1.990002e+06</td>\n",
       "      <td>1.990002e+06</td>\n",
       "      <td>1.990002e+06</td>\n",
       "      <td>2.142900e+04</td>\n",
       "      <td>8.039000e+03</td>\n",
       "      <td>1.192400e+04</td>\n",
       "      <td>4.472000e+03</td>\n",
       "      <td>5.470000e+02</td>\n",
       "      <td>2.552700e+04</td>\n",
       "      <td>4.280000e+03</td>\n",
       "      <td>1.999000e+03</td>\n",
       "      <td>1.629000e+03</td>\n",
       "      <td>1.863000e+03</td>\n",
       "      <td>5.280000e+03</td>\n",
       "      <td>4.283000e+03</td>\n",
       "      <td>1.151800e+04</td>\n",
       "      <td>3.314000e+03</td>\n",
       "      <td>2.502000e+03</td>\n",
       "      <td>7.206000e+03</td>\n",
       "      <td>6.177000e+03</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>1.510500e+04</td>\n",
       "      <td>6.883000e+03</td>\n",
       "      <td>1.681300e+04</td>\n",
       "      <td>2.325000e+03</td>\n",
       "      <td>355.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>2.360800e+04</td>\n",
       "      <td>2.863000e+03</td>\n",
       "      <td>3.402000e+03</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>1.100000e+03</td>\n",
       "      <td>3.487000e+03</td>\n",
       "      <td>9.720000e+02</td>\n",
       "      <td>6.410000e+02</td>\n",
       "      <td>8.600000e+02</td>\n",
       "      <td>9.990000e+02</td>\n",
       "      <td>1.382000e+03</td>\n",
       "      <td>1.879000e+03</td>\n",
       "      <td>1.098000e+03</td>\n",
       "      <td>9.120000e+02</td>\n",
       "      <td>2.047300e+04</td>\n",
       "      <td>1.453000e+03</td>\n",
       "      <td>1.267200e+04</td>\n",
       "      <td>1.306400e+04</td>\n",
       "      <td>5.347000e+03</td>\n",
       "      <td>6.700000e+03</td>\n",
       "      <td>6.517000e+03</td>\n",
       "      <td>3.989000e+03</td>\n",
       "      <td>3.888000e+03</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>285.000000</td>\n",
       "      <td>1.418000e+03</td>\n",
       "      <td>5.020000e+02</td>\n",
       "      <td>5.020000e+02</td>\n",
       "      <td>1.162100e+04</td>\n",
       "      <td>5.838000e+03</td>\n",
       "      <td>7.095000e+03</td>\n",
       "      <td>4.886000e+03</td>\n",
       "      <td>2.205000e+03</td>\n",
       "      <td>4.880000e+03</td>\n",
       "      <td>3.178000e+03</td>\n",
       "      <td>5.390000e+03</td>\n",
       "      <td>6.070000e+03</td>\n",
       "      <td>2.648200e+04</td>\n",
       "      <td>1.162100e+04</td>\n",
       "      <td>7.043000e+03</td>\n",
       "      <td>8.111000e+03</td>\n",
       "      <td>5.847000e+03</td>\n",
       "      <td>1.554800e+04</td>\n",
       "      <td>1.201200e+04</td>\n",
       "      <td>3.231000e+03</td>\n",
       "      <td>1.107000e+03</td>\n",
       "      <td>9.730950e+05</td>\n",
       "      <td>7.759000e+03</td>\n",
       "      <td>3.940000e+03</td>\n",
       "      <td>2.501200e+04</td>\n",
       "      <td>3.023000e+03</td>\n",
       "      <td>5.945000e+03</td>\n",
       "      <td>2.198900e+04</td>\n",
       "      <td>1.642100e+04</td>\n",
       "      <td>4.070000e+03</td>\n",
       "      <td>1.376700e+04</td>\n",
       "      <td>8.215000e+03</td>\n",
       "      <td>4.037000e+03</td>\n",
       "      <td>7.767000e+03</td>\n",
       "      <td>5.621000e+03</td>\n",
       "      <td>5.363000e+03</td>\n",
       "      <td>8.929000e+03</td>\n",
       "      <td>1.376700e+04</td>\n",
       "      <td>2.285600e+04</td>\n",
       "      <td>1.037400e+04</td>\n",
       "      <td>1.274500e+04</td>\n",
       "      <td>8.550000e+02</td>\n",
       "      <td>2.547000e+03</td>\n",
       "      <td>2.448000e+03</td>\n",
       "      <td>9.000000e+02</td>\n",
       "      <td>1.626000e+03</td>\n",
       "      <td>2.084000e+03</td>\n",
       "      <td>1.628000e+03</td>\n",
       "      <td>6.993000e+03</td>\n",
       "      <td>2.894500e+04</td>\n",
       "      <td>1.607000e+03</td>\n",
       "      <td>3.055200e+04</td>\n",
       "      <td>3.414200e+04</td>\n",
       "      <td>2.825200e+04</td>\n",
       "      <td>2.121400e+04</td>\n",
       "      <td>3.055200e+04</td>\n",
       "      <td>4.197000e+03</td>\n",
       "      <td>5.211000e+03</td>\n",
       "      <td>1.950000e+03</td>\n",
       "      <td>9.127000e+03</td>\n",
       "      <td>2.853000e+03</td>\n",
       "      <td>1.141000e+03</td>\n",
       "      <td>3.993000e+03</td>\n",
       "      <td>1.175000e+03</td>\n",
       "      <td>1.816000e+03</td>\n",
       "      <td>2.808000e+03</td>\n",
       "      <td>5.115000e+03</td>\n",
       "      <td>1.465000e+03</td>\n",
       "      <td>1.294000e+03</td>\n",
       "      <td>1.909700e+04</td>\n",
       "      <td>3.973000e+03</td>\n",
       "      <td>2.495000e+03</td>\n",
       "      <td>6.162000e+03</td>\n",
       "      <td>3.948000e+03</td>\n",
       "      <td>1.909700e+04</td>\n",
       "      <td>6.162000e+03</td>\n",
       "      <td>5.927000e+03</td>\n",
       "      <td>5.674000e+03</td>\n",
       "      <td>4.965000e+03</td>\n",
       "      <td>2.398900e+04</td>\n",
       "      <td>1.298500e+04</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stdev</th>\n",
       "      <td>2.228937e+03</td>\n",
       "      <td>7.936172e+02</td>\n",
       "      <td>1.120561e+03</td>\n",
       "      <td>1.146240e+03</td>\n",
       "      <td>7.750633e+00</td>\n",
       "      <td>1.104914e+02</td>\n",
       "      <td>1.121643e+02</td>\n",
       "      <td>1.132003e+02</td>\n",
       "      <td>7.188730e+01</td>\n",
       "      <td>1.075927e+02</td>\n",
       "      <td>5.489900e+01</td>\n",
       "      <td>5.251894e+01</td>\n",
       "      <td>9.475956e+01</td>\n",
       "      <td>1.251577e+02</td>\n",
       "      <td>1.155036e+02</td>\n",
       "      <td>1.057082e+02</td>\n",
       "      <td>1.034058e+02</td>\n",
       "      <td>9.913924e+01</td>\n",
       "      <td>9.575753e+01</td>\n",
       "      <td>8.738622e+01</td>\n",
       "      <td>3.685899e+01</td>\n",
       "      <td>4.708550e+01</td>\n",
       "      <td>5.993755e+01</td>\n",
       "      <td>4.641712e+01</td>\n",
       "      <td>3.390149e+01</td>\n",
       "      <td>3.303075e+01</td>\n",
       "      <td>1.062496e+02</td>\n",
       "      <td>1.096859e+02</td>\n",
       "      <td>1.100421e+02</td>\n",
       "      <td>6.923133e+01</td>\n",
       "      <td>1.222020e+02</td>\n",
       "      <td>5.400368e+01</td>\n",
       "      <td>5.017502e+01</td>\n",
       "      <td>7.957206e+01</td>\n",
       "      <td>1.181003e+02</td>\n",
       "      <td>1.097178e+02</td>\n",
       "      <td>1.040098e+02</td>\n",
       "      <td>1.022617e+02</td>\n",
       "      <td>9.984035e+01</td>\n",
       "      <td>9.595609e+01</td>\n",
       "      <td>9.064753e+01</td>\n",
       "      <td>4.279481e+01</td>\n",
       "      <td>5.533706e+01</td>\n",
       "      <td>4.018353e+01</td>\n",
       "      <td>5.125616e+01</td>\n",
       "      <td>6.700952e+01</td>\n",
       "      <td>5.231319e+01</td>\n",
       "      <td>4.266777e+01</td>\n",
       "      <td>5.793785e+01</td>\n",
       "      <td>1.907504e+03</td>\n",
       "      <td>2.201732e+03</td>\n",
       "      <td>2.139467e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.719905e+03</td>\n",
       "      <td>1.474748e+03</td>\n",
       "      <td>1.216138e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4.535513e+02</td>\n",
       "      <td>9.093226e+02</td>\n",
       "      <td>5.036082e+02</td>\n",
       "      <td>1.282778e+03</td>\n",
       "      <td>1.789563e+02</td>\n",
       "      <td>34.687847</td>\n",
       "      <td>1.270444e+02</td>\n",
       "      <td>1.282778e+03</td>\n",
       "      <td>2.056417e+03</td>\n",
       "      <td>3.975201e+01</td>\n",
       "      <td>31.827724</td>\n",
       "      <td>6.303262e+01</td>\n",
       "      <td>5.395722e+01</td>\n",
       "      <td>8.368872e+01</td>\n",
       "      <td>5.878341e+01</td>\n",
       "      <td>1.523871e+02</td>\n",
       "      <td>1.455158e+02</td>\n",
       "      <td>3.090106e+04</td>\n",
       "      <td>1.612694e+04</td>\n",
       "      <td>1.063003e+02</td>\n",
       "      <td>6.984943e+01</td>\n",
       "      <td>6.359047e+01</td>\n",
       "      <td>6.205942e+01</td>\n",
       "      <td>5.829994e+01</td>\n",
       "      <td>5.862582e+01</td>\n",
       "      <td>5.505314e+01</td>\n",
       "      <td>5.498863e+01</td>\n",
       "      <td>4.950700e+01</td>\n",
       "      <td>8.417410e+01</td>\n",
       "      <td>1.059809e+02</td>\n",
       "      <td>1.372959e+02</td>\n",
       "      <td>1.147172e+02</td>\n",
       "      <td>8.513799e+01</td>\n",
       "      <td>1.078177e+02</td>\n",
       "      <td>1.647825e+02</td>\n",
       "      <td>2.216522e+03</td>\n",
       "      <td>5.618142e+02</td>\n",
       "      <td>0.063577</td>\n",
       "      <td>8.958362e+02</td>\n",
       "      <td>4.642559e+02</td>\n",
       "      <td>1.648026e+05</td>\n",
       "      <td>2.189567e+05</td>\n",
       "      <td>2.866141e+05</td>\n",
       "      <td>7.936172e+02</td>\n",
       "      <td>4.791495e+02</td>\n",
       "      <td>3.131224e+02</td>\n",
       "      <td>6.702031e+01</td>\n",
       "      <td>2.830720e+01</td>\n",
       "      <td>7.543086e+02</td>\n",
       "      <td>1.952911e+02</td>\n",
       "      <td>1.133588e+02</td>\n",
       "      <td>1.165817e+02</td>\n",
       "      <td>1.324159e+02</td>\n",
       "      <td>1.511113e+02</td>\n",
       "      <td>1.440865e+02</td>\n",
       "      <td>2.991683e+02</td>\n",
       "      <td>2.250775e+02</td>\n",
       "      <td>4.732752e+01</td>\n",
       "      <td>1.038676e+02</td>\n",
       "      <td>1.166008e+02</td>\n",
       "      <td>1.755339e+01</td>\n",
       "      <td>5.050110e+02</td>\n",
       "      <td>3.637558e+02</td>\n",
       "      <td>5.859720e+02</td>\n",
       "      <td>1.966127e+02</td>\n",
       "      <td>8.899265</td>\n",
       "      <td>7.652833</td>\n",
       "      <td>6.760134e+02</td>\n",
       "      <td>2.402736e+02</td>\n",
       "      <td>4.587204e+02</td>\n",
       "      <td>7.795312e+00</td>\n",
       "      <td>4.974370e+01</td>\n",
       "      <td>1.432986e+02</td>\n",
       "      <td>5.574670e+01</td>\n",
       "      <td>4.303120e+01</td>\n",
       "      <td>5.598719e+01</td>\n",
       "      <td>6.694950e+01</td>\n",
       "      <td>7.252401e+01</td>\n",
       "      <td>7.206055e+01</td>\n",
       "      <td>5.312190e+01</td>\n",
       "      <td>3.129274e+01</td>\n",
       "      <td>6.543429e+02</td>\n",
       "      <td>5.537075e+01</td>\n",
       "      <td>4.790928e+02</td>\n",
       "      <td>3.932843e+02</td>\n",
       "      <td>2.492432e+02</td>\n",
       "      <td>2.316744e+02</td>\n",
       "      <td>1.829281e+02</td>\n",
       "      <td>1.148169e+02</td>\n",
       "      <td>9.050640e+01</td>\n",
       "      <td>18.145011</td>\n",
       "      <td>11.523613</td>\n",
       "      <td>1.141550e+02</td>\n",
       "      <td>3.774419e+01</td>\n",
       "      <td>3.534531e+01</td>\n",
       "      <td>2.257531e+02</td>\n",
       "      <td>1.967251e+02</td>\n",
       "      <td>2.132096e+02</td>\n",
       "      <td>2.089584e+02</td>\n",
       "      <td>1.102167e+02</td>\n",
       "      <td>2.155892e+02</td>\n",
       "      <td>1.292811e+02</td>\n",
       "      <td>1.602168e+02</td>\n",
       "      <td>1.904055e+02</td>\n",
       "      <td>1.072317e+03</td>\n",
       "      <td>1.404476e+02</td>\n",
       "      <td>1.047340e+02</td>\n",
       "      <td>2.315210e+02</td>\n",
       "      <td>2.298423e+02</td>\n",
       "      <td>3.314566e+02</td>\n",
       "      <td>3.799984e+02</td>\n",
       "      <td>1.711299e+02</td>\n",
       "      <td>9.448405e+01</td>\n",
       "      <td>3.422968e+04</td>\n",
       "      <td>2.647627e+02</td>\n",
       "      <td>1.086306e+02</td>\n",
       "      <td>1.041140e+03</td>\n",
       "      <td>1.447731e+02</td>\n",
       "      <td>1.967409e+02</td>\n",
       "      <td>9.474669e+02</td>\n",
       "      <td>4.446641e+02</td>\n",
       "      <td>1.694405e+02</td>\n",
       "      <td>4.828823e+02</td>\n",
       "      <td>4.093667e+02</td>\n",
       "      <td>1.267778e+02</td>\n",
       "      <td>2.557686e+02</td>\n",
       "      <td>2.629695e+02</td>\n",
       "      <td>3.540041e+02</td>\n",
       "      <td>4.791437e+02</td>\n",
       "      <td>4.842803e+02</td>\n",
       "      <td>7.062823e+02</td>\n",
       "      <td>3.801689e+02</td>\n",
       "      <td>5.062105e+02</td>\n",
       "      <td>4.047851e+01</td>\n",
       "      <td>1.009911e+02</td>\n",
       "      <td>8.698075e+01</td>\n",
       "      <td>4.965424e+01</td>\n",
       "      <td>4.571015e+01</td>\n",
       "      <td>1.159105e+02</td>\n",
       "      <td>8.115282e+01</td>\n",
       "      <td>3.033556e+02</td>\n",
       "      <td>1.138865e+03</td>\n",
       "      <td>1.092197e+02</td>\n",
       "      <td>1.203354e+03</td>\n",
       "      <td>7.150048e+02</td>\n",
       "      <td>1.129133e+03</td>\n",
       "      <td>1.616169e+02</td>\n",
       "      <td>1.192208e+03</td>\n",
       "      <td>8.874869e+01</td>\n",
       "      <td>1.600300e+02</td>\n",
       "      <td>1.116914e+02</td>\n",
       "      <td>3.015791e+02</td>\n",
       "      <td>1.305728e+02</td>\n",
       "      <td>5.400275e+01</td>\n",
       "      <td>1.826280e+02</td>\n",
       "      <td>7.619403e+01</td>\n",
       "      <td>9.987631e+01</td>\n",
       "      <td>1.551951e+02</td>\n",
       "      <td>2.015002e+02</td>\n",
       "      <td>9.018655e+01</td>\n",
       "      <td>5.451432e+01</td>\n",
       "      <td>6.156759e+02</td>\n",
       "      <td>1.512020e+02</td>\n",
       "      <td>1.862018e+02</td>\n",
       "      <td>2.988008e+02</td>\n",
       "      <td>2.366780e+02</td>\n",
       "      <td>6.156759e+02</td>\n",
       "      <td>2.988008e+02</td>\n",
       "      <td>1.687454e+02</td>\n",
       "      <td>1.692225e+02</td>\n",
       "      <td>1.687234e+02</td>\n",
       "      <td>7.662855e+02</td>\n",
       "      <td>4.083119e+02</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q1</th>\n",
       "      <td>2.627000e+03</td>\n",
       "      <td>9.980000e+02</td>\n",
       "      <td>1.276000e+03</td>\n",
       "      <td>1.318000e+03</td>\n",
       "      <td>3.280000e+01</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>5.700000e+01</td>\n",
       "      <td>5.900000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>6.600000e+01</td>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>6.800000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>3.600000e+01</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>9.510000e+02</td>\n",
       "      <td>2.604000e+03</td>\n",
       "      <td>2.519000e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.125000e+03</td>\n",
       "      <td>1.782000e+03</td>\n",
       "      <td>1.328000e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.600000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>6.600000e+01</td>\n",
       "      <td>1.943000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>3.697400e+04</td>\n",
       "      <td>1.902300e+04</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>5.800000e+01</td>\n",
       "      <td>7.600000e+01</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>2.559000e+03</td>\n",
       "      <td>2.120000e+02</td>\n",
       "      <td>0.373800</td>\n",
       "      <td>1.153000e+03</td>\n",
       "      <td>6.920000e+02</td>\n",
       "      <td>6.570000e+04</td>\n",
       "      <td>9.880000e+04</td>\n",
       "      <td>1.491000e+05</td>\n",
       "      <td>9.980000e+02</td>\n",
       "      <td>2.090000e+02</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.220000e+02</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.956000e+03</td>\n",
       "      <td>3.810000e+02</td>\n",
       "      <td>2.730000e+02</td>\n",
       "      <td>6.120000e+02</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.080000e+02</td>\n",
       "      <td>7.300000e+01</td>\n",
       "      <td>5.290000e+02</td>\n",
       "      <td>2.420000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.960000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.740000e+02</td>\n",
       "      <td>1.400000e+02</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.410000e+02</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.400000e+01</td>\n",
       "      <td>1.040000e+02</td>\n",
       "      <td>1.260000e+02</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>3.600000e+01</td>\n",
       "      <td>9.500000e+01</td>\n",
       "      <td>3.300000e+01</td>\n",
       "      <td>3.800000e+01</td>\n",
       "      <td>4.200000e+01</td>\n",
       "      <td>1.072000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>2.780000e+02</td>\n",
       "      <td>3.010000e+02</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>3.315000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.940000e+02</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.800000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>1.910000e+02</td>\n",
       "      <td>3.420000e+02</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>2.220000e+02</td>\n",
       "      <td>1.150000e+02</td>\n",
       "      <td>4.000000e+02</td>\n",
       "      <td>1.920000e+02</td>\n",
       "      <td>2.200000e+02</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>4.570000e+02</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>6.700000e+01</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>3.160000e+02</td>\n",
       "      <td>1.143000e+03</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>1.245000e+03</td>\n",
       "      <td>7.460000e+02</td>\n",
       "      <td>1.115000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.235000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>2.340000e+02</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>6.500000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>1.110000e+02</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>2.900000e+02</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>9.600000e+01</td>\n",
       "      <td>2.430000e+02</td>\n",
       "      <td>1.860000e+02</td>\n",
       "      <td>2.900000e+02</td>\n",
       "      <td>2.430000e+02</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>1.020000e+02</td>\n",
       "      <td>5.600000e+02</td>\n",
       "      <td>8.400000e+01</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q3</th>\n",
       "      <td>4.606000e+03</td>\n",
       "      <td>1.714000e+03</td>\n",
       "      <td>2.254000e+03</td>\n",
       "      <td>2.334000e+03</td>\n",
       "      <td>4.110000e+01</td>\n",
       "      <td>1.380000e+02</td>\n",
       "      <td>1.420000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>8.800000e+01</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>1.510000e+02</td>\n",
       "      <td>1.460000e+02</td>\n",
       "      <td>1.380000e+02</td>\n",
       "      <td>1.380000e+02</td>\n",
       "      <td>1.440000e+02</td>\n",
       "      <td>1.520000e+02</td>\n",
       "      <td>1.480000e+02</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>5.900000e+01</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>3.300000e+01</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>1.320000e+02</td>\n",
       "      <td>1.370000e+02</td>\n",
       "      <td>1.390000e+02</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>8.400000e+01</td>\n",
       "      <td>1.490000e+02</td>\n",
       "      <td>1.460000e+02</td>\n",
       "      <td>1.400000e+02</td>\n",
       "      <td>1.410000e+02</td>\n",
       "      <td>1.480000e+02</td>\n",
       "      <td>1.590000e+02</td>\n",
       "      <td>1.590000e+02</td>\n",
       "      <td>5.800000e+01</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>6.600000e+01</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>5.100000e+01</td>\n",
       "      <td>2.942000e+03</td>\n",
       "      <td>4.525000e+03</td>\n",
       "      <td>4.421000e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.666000e+03</td>\n",
       "      <td>3.115000e+03</td>\n",
       "      <td>2.398000e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.980000e+02</td>\n",
       "      <td>2.690000e+02</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>4.480000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.800000e+01</td>\n",
       "      <td>4.480000e+02</td>\n",
       "      <td>3.809000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>2.100000e+02</td>\n",
       "      <td>6.082500e+04</td>\n",
       "      <td>3.022100e+04</td>\n",
       "      <td>1.040000e+02</td>\n",
       "      <td>7.900000e+01</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>6.500000e+01</td>\n",
       "      <td>1.280000e+02</td>\n",
       "      <td>1.670000e+02</td>\n",
       "      <td>2.070000e+02</td>\n",
       "      <td>1.410000e+02</td>\n",
       "      <td>8.300000e+01</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>4.496000e+03</td>\n",
       "      <td>6.050000e+02</td>\n",
       "      <td>0.434700</td>\n",
       "      <td>1.949000e+03</td>\n",
       "      <td>1.055000e+03</td>\n",
       "      <td>1.533000e+05</td>\n",
       "      <td>2.108000e+05</td>\n",
       "      <td>2.867000e+05</td>\n",
       "      <td>1.714000e+03</td>\n",
       "      <td>5.700000e+02</td>\n",
       "      <td>1.880000e+02</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>1.248000e+03</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>5.900000e+01</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>4.300000e+01</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>1.978000e+03</td>\n",
       "      <td>8.130000e+02</td>\n",
       "      <td>5.550000e+02</td>\n",
       "      <td>1.126000e+03</td>\n",
       "      <td>2.100000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.019000e+03</td>\n",
       "      <td>2.330000e+02</td>\n",
       "      <td>8.890000e+02</td>\n",
       "      <td>3.140000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>1.260000e+02</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>5.700000e+01</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>4.200000e+01</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>1.105000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.790000e+02</td>\n",
       "      <td>3.830000e+02</td>\n",
       "      <td>1.660000e+02</td>\n",
       "      <td>3.130000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.130000e+02</td>\n",
       "      <td>6.600000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>2.310000e+02</td>\n",
       "      <td>2.610000e+02</td>\n",
       "      <td>2.970000e+02</td>\n",
       "      <td>2.830000e+02</td>\n",
       "      <td>1.210000e+02</td>\n",
       "      <td>2.610000e+02</td>\n",
       "      <td>1.240000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>2.014000e+03</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>5.560000e+02</td>\n",
       "      <td>6.360000e+02</td>\n",
       "      <td>2.400000e+02</td>\n",
       "      <td>1.020000e+02</td>\n",
       "      <td>6.035000e+04</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>1.824000e+03</td>\n",
       "      <td>1.830000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.637000e+03</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>2.520000e+02</td>\n",
       "      <td>5.500000e+02</td>\n",
       "      <td>7.280000e+02</td>\n",
       "      <td>1.890000e+02</td>\n",
       "      <td>2.160000e+02</td>\n",
       "      <td>4.380000e+02</td>\n",
       "      <td>3.390000e+02</td>\n",
       "      <td>8.500000e+02</td>\n",
       "      <td>5.470000e+02</td>\n",
       "      <td>6.830000e+02</td>\n",
       "      <td>2.950000e+02</td>\n",
       "      <td>9.020000e+02</td>\n",
       "      <td>4.200000e+01</td>\n",
       "      <td>9.200000e+01</td>\n",
       "      <td>4.900000e+01</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>3.900000e+01</td>\n",
       "      <td>1.720000e+02</td>\n",
       "      <td>1.150000e+02</td>\n",
       "      <td>5.820000e+02</td>\n",
       "      <td>2.156000e+03</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>2.319000e+03</td>\n",
       "      <td>1.325000e+03</td>\n",
       "      <td>2.115000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.300000e+03</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>1.940000e+02</td>\n",
       "      <td>1.260000e+02</td>\n",
       "      <td>4.870000e+02</td>\n",
       "      <td>1.250000e+02</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>2.040000e+02</td>\n",
       "      <td>1.010000e+02</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>2.380000e+02</td>\n",
       "      <td>2.200000e+02</td>\n",
       "      <td>1.010000e+02</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>7.470000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>2.530000e+02</td>\n",
       "      <td>5.010000e+02</td>\n",
       "      <td>3.740000e+02</td>\n",
       "      <td>7.470000e+02</td>\n",
       "      <td>5.010000e+02</td>\n",
       "      <td>2.240000e+02</td>\n",
       "      <td>2.260000e+02</td>\n",
       "      <td>2.360000e+02</td>\n",
       "      <td>1.120000e+03</td>\n",
       "      <td>2.120000e+02</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>3.627000e+03</td>\n",
       "      <td>1.357000e+03</td>\n",
       "      <td>1.773000e+03</td>\n",
       "      <td>1.842000e+03</td>\n",
       "      <td>3.710000e+01</td>\n",
       "      <td>9.400000e+01</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>5.900000e+01</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>1.030000e+02</td>\n",
       "      <td>1.010000e+02</td>\n",
       "      <td>9.600000e+01</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>1.030000e+02</td>\n",
       "      <td>1.110000e+02</td>\n",
       "      <td>1.080000e+02</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>3.900000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>9.400000e+01</td>\n",
       "      <td>9.400000e+01</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>1.020000e+02</td>\n",
       "      <td>1.020000e+02</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>1.060000e+02</td>\n",
       "      <td>1.180000e+02</td>\n",
       "      <td>1.170000e+02</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>5.800000e+01</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.989000e+03</td>\n",
       "      <td>3.590000e+03</td>\n",
       "      <td>3.499000e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.921000e+03</td>\n",
       "      <td>2.453000e+03</td>\n",
       "      <td>1.880000e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>1.870000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>1.870000e+02</td>\n",
       "      <td>2.906000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.270000e+02</td>\n",
       "      <td>1.350000e+02</td>\n",
       "      <td>4.881100e+04</td>\n",
       "      <td>2.455200e+04</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>5.100000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>4.900000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>9.200000e+01</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>9.200000e+01</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>3.523000e+03</td>\n",
       "      <td>3.830000e+02</td>\n",
       "      <td>0.405000</td>\n",
       "      <td>1.547000e+03</td>\n",
       "      <td>8.510000e+02</td>\n",
       "      <td>1.011000e+05</td>\n",
       "      <td>1.479000e+05</td>\n",
       "      <td>2.031000e+05</td>\n",
       "      <td>1.357000e+03</td>\n",
       "      <td>3.740000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.150000e+02</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>1.969000e+03</td>\n",
       "      <td>5.990000e+02</td>\n",
       "      <td>4.090000e+02</td>\n",
       "      <td>8.700000e+02</td>\n",
       "      <td>1.290000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.650000e+02</td>\n",
       "      <td>1.440000e+02</td>\n",
       "      <td>6.910000e+02</td>\n",
       "      <td>2.810000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>8.120000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.720000e+02</td>\n",
       "      <td>2.510000e+02</td>\n",
       "      <td>6.700000e+01</td>\n",
       "      <td>2.230000e+02</td>\n",
       "      <td>1.300000e+02</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.700000e+01</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.500000e+02</td>\n",
       "      <td>1.800000e+02</td>\n",
       "      <td>2.070000e+02</td>\n",
       "      <td>1.930000e+02</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>1.740000e+02</td>\n",
       "      <td>7.300000e+01</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>8.400000e+01</td>\n",
       "      <td>1.548000e+03</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>4.110000e+02</td>\n",
       "      <td>4.710000e+02</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>4.599500e+04</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.362000e+03</td>\n",
       "      <td>1.240000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.211000e+03</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.780000e+02</td>\n",
       "      <td>3.420000e+02</td>\n",
       "      <td>5.340000e+02</td>\n",
       "      <td>1.320000e+02</td>\n",
       "      <td>1.260000e+02</td>\n",
       "      <td>3.250000e+02</td>\n",
       "      <td>2.140000e+02</td>\n",
       "      <td>6.300000e+02</td>\n",
       "      <td>3.450000e+02</td>\n",
       "      <td>4.080000e+02</td>\n",
       "      <td>1.650000e+02</td>\n",
       "      <td>6.720000e+02</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>1.180000e+02</td>\n",
       "      <td>7.900000e+01</td>\n",
       "      <td>4.500000e+02</td>\n",
       "      <td>1.651000e+03</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.793000e+03</td>\n",
       "      <td>1.026000e+03</td>\n",
       "      <td>1.622000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.780000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.330000e+02</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>3.580000e+02</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>1.290000e+02</td>\n",
       "      <td>6.900000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>1.720000e+02</td>\n",
       "      <td>1.410000e+02</td>\n",
       "      <td>6.600000e+01</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>4.950000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.680000e+02</td>\n",
       "      <td>3.690000e+02</td>\n",
       "      <td>2.740000e+02</td>\n",
       "      <td>4.950000e+02</td>\n",
       "      <td>3.690000e+02</td>\n",
       "      <td>1.580000e+02</td>\n",
       "      <td>1.610000e+02</td>\n",
       "      <td>1.650000e+02</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>1.420000e+02</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interquartile_range</th>\n",
       "      <td>1.979000e+03</td>\n",
       "      <td>7.160000e+02</td>\n",
       "      <td>9.780000e+02</td>\n",
       "      <td>1.016000e+03</td>\n",
       "      <td>8.300000e+00</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>3.800000e+01</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>7.900000e+01</td>\n",
       "      <td>8.300000e+01</td>\n",
       "      <td>8.400000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>8.900000e+01</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.900000e+01</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>5.100000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>1.991000e+03</td>\n",
       "      <td>1.921000e+03</td>\n",
       "      <td>1.902000e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.541000e+03</td>\n",
       "      <td>1.333000e+03</td>\n",
       "      <td>1.070000e+03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.760000e+02</td>\n",
       "      <td>2.530000e+02</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>3.820000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>3.820000e+02</td>\n",
       "      <td>1.866000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>1.470000e+02</td>\n",
       "      <td>1.500000e+02</td>\n",
       "      <td>2.385100e+04</td>\n",
       "      <td>1.119800e+04</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>4.900000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>9.100000e+01</td>\n",
       "      <td>1.210000e+02</td>\n",
       "      <td>9.400000e+01</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>6.700000e+01</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>1.937000e+03</td>\n",
       "      <td>3.930000e+02</td>\n",
       "      <td>0.060900</td>\n",
       "      <td>7.960000e+02</td>\n",
       "      <td>3.630000e+02</td>\n",
       "      <td>8.760000e+04</td>\n",
       "      <td>1.120000e+05</td>\n",
       "      <td>1.376000e+05</td>\n",
       "      <td>7.160000e+02</td>\n",
       "      <td>3.610000e+02</td>\n",
       "      <td>1.240000e+02</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>7.260000e+02</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>5.900000e+01</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>4.300000e+01</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>4.320000e+02</td>\n",
       "      <td>2.820000e+02</td>\n",
       "      <td>5.140000e+02</td>\n",
       "      <td>1.480000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.110000e+02</td>\n",
       "      <td>1.600000e+02</td>\n",
       "      <td>3.600000e+02</td>\n",
       "      <td>7.200000e+00</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>3.800000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>4.900000e+01</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>6.090000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.050000e+02</td>\n",
       "      <td>2.430000e+02</td>\n",
       "      <td>1.600000e+02</td>\n",
       "      <td>1.720000e+02</td>\n",
       "      <td>1.270000e+02</td>\n",
       "      <td>7.900000e+01</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>1.470000e+02</td>\n",
       "      <td>1.570000e+02</td>\n",
       "      <td>1.710000e+02</td>\n",
       "      <td>1.730000e+02</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>1.660000e+02</td>\n",
       "      <td>9.100000e+01</td>\n",
       "      <td>1.070000e+02</td>\n",
       "      <td>1.030000e+02</td>\n",
       "      <td>9.420000e+02</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>2.780000e+02</td>\n",
       "      <td>3.350000e+02</td>\n",
       "      <td>1.550000e+02</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>2.720000e+04</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>9.300000e+02</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.570000e+02</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>1.430000e+02</td>\n",
       "      <td>3.590000e+02</td>\n",
       "      <td>3.860000e+02</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>1.540000e+02</td>\n",
       "      <td>2.160000e+02</td>\n",
       "      <td>2.240000e+02</td>\n",
       "      <td>4.500000e+02</td>\n",
       "      <td>3.550000e+02</td>\n",
       "      <td>4.630000e+02</td>\n",
       "      <td>2.140000e+02</td>\n",
       "      <td>4.450000e+02</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>6.800000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>6.800000e+01</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>1.013000e+03</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>1.074000e+03</td>\n",
       "      <td>5.790000e+02</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.065000e+03</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>1.120000e+02</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>2.530000e+02</td>\n",
       "      <td>8.400000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.390000e+02</td>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>5.800000e+01</td>\n",
       "      <td>1.270000e+02</td>\n",
       "      <td>1.390000e+02</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>4.570000e+02</td>\n",
       "      <td>1.180000e+02</td>\n",
       "      <td>1.570000e+02</td>\n",
       "      <td>2.580000e+02</td>\n",
       "      <td>1.880000e+02</td>\n",
       "      <td>4.570000e+02</td>\n",
       "      <td>2.580000e+02</td>\n",
       "      <td>1.270000e+02</td>\n",
       "      <td>1.290000e+02</td>\n",
       "      <td>1.340000e+02</td>\n",
       "      <td>5.600000e+02</td>\n",
       "      <td>1.280000e+02</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        total_pop    households      male_pop    female_pop  \\\n",
       "avg                  4.384716e+03  1.622255e+03  2.157711e+03  2.227005e+03   \n",
       "max                  6.552800e+04  2.142900e+04  3.226600e+04  3.326200e+04   \n",
       "min                  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "sum                  3.244734e+08  1.200485e+08  1.596728e+08  1.648006e+08   \n",
       "range                6.552800e+04  2.142900e+04  3.226600e+04  3.326200e+04   \n",
       "stdev                2.228937e+03  7.936172e+02  1.120561e+03  1.146240e+03   \n",
       "q1                   2.627000e+03  9.980000e+02  1.276000e+03  1.318000e+03   \n",
       "q3                   4.606000e+03  1.714000e+03  2.254000e+03  2.334000e+03   \n",
       "median               3.627000e+03  1.357000e+03  1.773000e+03  1.842000e+03   \n",
       "interquartile_range  1.979000e+03  7.160000e+02  9.780000e+02  1.016000e+03   \n",
       "\n",
       "                       median_age  male_under_5   male_5_to_9  male_10_to_14  \\\n",
       "avg                  3.930112e+01  1.383831e+02  1.423078e+02   1.445751e+02   \n",
       "max                  8.570000e+01  3.297000e+03  3.464000e+03   3.137000e+03   \n",
       "min                  7.700000e+00  0.000000e+00  0.000000e+00   0.000000e+00   \n",
       "sum                  2.879751e+06  1.024049e+07  1.053092e+07   1.069870e+07   \n",
       "range                7.800000e+01  3.297000e+03  3.464000e+03   3.137000e+03   \n",
       "stdev                7.750633e+00  1.104914e+02  1.121643e+02   1.132003e+02   \n",
       "q1                   3.280000e+01  5.600000e+01  5.700000e+01   5.900000e+01   \n",
       "q3                   4.110000e+01  1.380000e+02  1.420000e+02   1.450000e+02   \n",
       "median               3.710000e+01  9.400000e+01  9.800000e+01   9.900000e+01   \n",
       "interquartile_range  8.300000e+00  8.200000e+01  8.500000e+01   8.600000e+01   \n",
       "\n",
       "                     male_15_to_17  male_18_to_19       male_20       male_21  \\\n",
       "avg                   8.804836e+01   6.037216e+01  3.276450e+01  3.217450e+01   \n",
       "max                   2.416000e+03   3.909000e+03  2.845000e+03  3.025000e+03   \n",
       "min                   0.000000e+00   0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "sum                   6.515667e+06   4.467600e+06  2.424606e+06  2.380945e+06   \n",
       "range                 2.416000e+03   3.909000e+03  2.845000e+03  3.025000e+03   \n",
       "stdev                 7.188730e+01   1.075927e+02  5.489900e+01  5.251894e+01   \n",
       "q1                    3.200000e+01   1.400000e+01  0.000000e+00  0.000000e+00   \n",
       "q3                    8.800000e+01   5.200000e+01  2.600000e+01  2.600000e+01   \n",
       "median                5.900000e+01   3.100000e+01  1.300000e+01  1.300000e+01   \n",
       "interquartile_range   5.600000e+01   3.800000e+01  2.600000e+01  2.600000e+01   \n",
       "\n",
       "                     male_22_to_24  male_25_to_29  male_30_to_34  \\\n",
       "avg                   9.285125e+01   1.552524e+02   1.483501e+02   \n",
       "max                   6.083000e+03   3.956000e+03   2.143000e+03   \n",
       "min                   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "sum                   6.871085e+06   1.148883e+07   1.097806e+07   \n",
       "range                 6.083000e+03   3.956000e+03   2.143000e+03   \n",
       "stdev                 9.475956e+01   1.251577e+02   1.155036e+02   \n",
       "q1                    3.100000e+01   6.100000e+01   6.100000e+01   \n",
       "q3                    8.700000e+01   1.510000e+02   1.460000e+02   \n",
       "median                5.600000e+01   1.030000e+02   1.010000e+02   \n",
       "interquartile_range   5.600000e+01   9.000000e+01   8.500000e+01   \n",
       "\n",
       "                     male_35_to_39  male_40_to_44  male_45_to_49  \\\n",
       "avg                   1.388303e+02   1.375463e+02   1.416246e+02   \n",
       "max                   3.146000e+03   3.718000e+03   2.652000e+03   \n",
       "min                   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "sum                   1.027358e+07   1.017856e+07   1.048036e+07   \n",
       "range                 3.146000e+03   3.718000e+03   2.652000e+03   \n",
       "stdev                 1.057082e+02   1.034058e+02   9.913924e+01   \n",
       "q1                    6.000000e+01   6.000000e+01   6.600000e+01   \n",
       "q3                    1.380000e+02   1.380000e+02   1.440000e+02   \n",
       "median                9.600000e+01   9.700000e+01   1.030000e+02   \n",
       "interquartile_range   7.800000e+01   7.800000e+01   7.800000e+01   \n",
       "\n",
       "                     male_50_to_54  male_55_to_59  male_65_to_66  \\\n",
       "avg                   1.482727e+02   1.423548e+02   4.490084e+01   \n",
       "max                   2.300000e+03   1.741000e+03   1.244000e+03   \n",
       "min                   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "sum                   1.097233e+07   1.053440e+07   3.322707e+06   \n",
       "range                 2.300000e+03   1.741000e+03   1.244000e+03   \n",
       "stdev                 9.575753e+01   8.738622e+01   3.685899e+01   \n",
       "q1                    7.200000e+01   7.100000e+01   1.500000e+01   \n",
       "q3                    1.520000e+02   1.480000e+02   4.500000e+01   \n",
       "median                1.110000e+02   1.080000e+02   2.900000e+01   \n",
       "interquartile_range   8.000000e+01   7.700000e+01   3.000000e+01   \n",
       "\n",
       "                     male_67_to_69  male_70_to_74  male_75_to_79  \\\n",
       "avg                   5.796503e+01   7.306170e+01   5.008765e+01   \n",
       "max                   3.225000e+03   4.708000e+03   3.287000e+03   \n",
       "min                   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "sum                   4.289470e+06   5.406639e+06   3.706536e+06   \n",
       "range                 3.225000e+03   4.708000e+03   3.287000e+03   \n",
       "stdev                 4.708550e+01   5.993755e+01   4.641712e+01   \n",
       "q1                    2.200000e+01   2.800000e+01   1.600000e+01   \n",
       "q3                    5.900000e+01   7.400000e+01   5.000000e+01   \n",
       "median                3.900000e+01   5.000000e+01   3.200000e+01   \n",
       "interquartile_range   3.700000e+01   4.600000e+01   3.400000e+01   \n",
       "\n",
       "                     male_80_to_84  male_85_and_over  female_under_5  \\\n",
       "avg                   3.334291e+01      2.886338e+01    1.322310e+02   \n",
       "max                   1.666000e+03      6.670000e+02    2.829000e+03   \n",
       "min                   0.000000e+00      0.000000e+00    0.000000e+00   \n",
       "sum                   2.467409e+06      2.135919e+06    9.785223e+06   \n",
       "range                 1.666000e+03      6.670000e+02    2.829000e+03   \n",
       "stdev                 3.390149e+01      3.303075e+01    1.062496e+02   \n",
       "q1                    8.000000e+00      4.000000e+00    5.300000e+01   \n",
       "q3                    3.300000e+01      2.700000e+01    1.320000e+02   \n",
       "median                1.900000e+01      1.500000e+01    9.000000e+01   \n",
       "interquartile_range   2.500000e+01      2.300000e+01    7.900000e+01   \n",
       "\n",
       "                     female_5_to_9  female_10_to_14  female_15_to_17  \\\n",
       "avg                   1.365999e+02     1.383163e+02     8.401951e+01   \n",
       "max                   3.509000e+03     3.667000e+03     1.601000e+03   \n",
       "min                   0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "sum                   1.010853e+07     1.023555e+07     6.217528e+06   \n",
       "range                 3.509000e+03     3.667000e+03     1.601000e+03   \n",
       "stdev                 1.096859e+02     1.100421e+02     6.923133e+01   \n",
       "q1                    5.400000e+01     5.500000e+01     3.000000e+01   \n",
       "q3                    1.370000e+02     1.390000e+02     8.500000e+01   \n",
       "median                9.400000e+01     9.400000e+01     5.600000e+01   \n",
       "interquartile_range   8.300000e+01     8.400000e+01     5.500000e+01   \n",
       "\n",
       "                     female_18_to_19     female_20     female_21  \\\n",
       "avg                     5.756660e+01  3.088993e+01  3.041858e+01   \n",
       "max                     4.815000e+03  1.586000e+03  1.330000e+03   \n",
       "min                     0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "sum                     4.259986e+06  2.285886e+06  2.251005e+06   \n",
       "range                   4.815000e+03  1.586000e+03  1.330000e+03   \n",
       "stdev                   1.222020e+02  5.400368e+01  5.017502e+01   \n",
       "q1                      1.200000e+01  0.000000e+00  0.000000e+00   \n",
       "q3                      4.600000e+01  2.300000e+01  2.400000e+01   \n",
       "median                  2.700000e+01  1.100000e+01  1.100000e+01   \n",
       "interquartile_range     3.400000e+01  2.300000e+01  2.400000e+01   \n",
       "\n",
       "                     female_22_to_24  female_25_to_29  female_30_to_34  \\\n",
       "avg                     8.833956e+01     1.505691e+02     1.468783e+02   \n",
       "max                     2.045000e+03     2.410000e+03     2.840000e+03   \n",
       "min                     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "sum                     6.537216e+06     1.114227e+07     1.086914e+07   \n",
       "range                   2.045000e+03     2.410000e+03     2.840000e+03   \n",
       "stdev                   7.957206e+01     1.181003e+02     1.097178e+02   \n",
       "q1                      3.000000e+01     6.000000e+01     6.400000e+01   \n",
       "q3                      8.400000e+01     1.490000e+02     1.460000e+02   \n",
       "median                  5.600000e+01     1.020000e+02     1.020000e+02   \n",
       "interquartile_range     5.400000e+01     8.900000e+01     8.200000e+01   \n",
       "\n",
       "                     female_35_to_39  female_40_to_44  female_45_to_49  \\\n",
       "avg                     1.396495e+02     1.393065e+02     1.446716e+02   \n",
       "max                     3.450000e+03     3.586000e+03     3.628000e+03   \n",
       "min                     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "sum                     1.033420e+07     1.030882e+07     1.070584e+07   \n",
       "range                   3.450000e+03     3.586000e+03     3.628000e+03   \n",
       "stdev                   1.040098e+02     1.022617e+02     9.984035e+01   \n",
       "q1                      6.200000e+01     6.200000e+01     6.800000e+01   \n",
       "q3                      1.400000e+02     1.410000e+02     1.480000e+02   \n",
       "median                  1.000000e+02     9.900000e+01     1.060000e+02   \n",
       "interquartile_range     7.800000e+01     7.900000e+01     8.000000e+01   \n",
       "\n",
       "                     female_50_to_54  female_55_to_59  female_60_to_61  \\\n",
       "avg                     1.538896e+02     1.514543e+02     5.749201e+01   \n",
       "max                     1.756000e+03     1.445000e+03     8.730000e+02   \n",
       "min                     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "sum                     1.138799e+07     1.120777e+07     4.254466e+06   \n",
       "range                   1.756000e+03     1.445000e+03     8.730000e+02   \n",
       "stdev                   9.595609e+01     9.064753e+01     4.279481e+01   \n",
       "q1                      7.700000e+01     7.700000e+01     2.300000e+01   \n",
       "q3                      1.590000e+02     1.590000e+02     5.800000e+01   \n",
       "median                  1.180000e+02     1.170000e+02     4.000000e+01   \n",
       "interquartile_range     8.200000e+01     8.200000e+01     3.500000e+01   \n",
       "\n",
       "                     female_62_to_64  female_65_to_66  female_67_to_69  \\\n",
       "avg                     7.940259e+01     4.982987e+01     6.520221e+01   \n",
       "max                     2.105000e+03     2.147000e+03     3.661000e+03   \n",
       "min                     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "sum                     5.875871e+06     3.687460e+06     4.825029e+06   \n",
       "range                   2.105000e+03     2.147000e+03     3.661000e+03   \n",
       "stdev                   5.533706e+01     4.018353e+01     5.125616e+01   \n",
       "q1                      3.500000e+01     1.800000e+01     2.600000e+01   \n",
       "q3                      8.100000e+01     5.000000e+01     6.600000e+01   \n",
       "median                  5.800000e+01     3.400000e+01     4.500000e+01   \n",
       "interquartile_range     4.600000e+01     3.200000e+01     4.000000e+01   \n",
       "\n",
       "                     female_70_to_74  female_75_to_79  female_80_to_84  \\\n",
       "avg                     8.553553e+01     6.253888e+01     4.708153e+01   \n",
       "max                     5.268000e+03     2.481000e+03     1.166000e+03   \n",
       "min                     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "sum                     6.329715e+06     4.627940e+06     3.484080e+06   \n",
       "range                   5.268000e+03     2.481000e+03     1.166000e+03   \n",
       "stdev                   6.700952e+01     5.231319e+01     4.266777e+01   \n",
       "q1                      3.600000e+01     2.200000e+01     1.400000e+01   \n",
       "q3                      8.700000e+01     6.300000e+01     4.600000e+01   \n",
       "median                  6.000000e+01     4.100000e+01     2.900000e+01   \n",
       "interquartile_range     5.100000e+01     4.100000e+01     3.200000e+01   \n",
       "\n",
       "                     female_85_and_over     white_pop  \\\n",
       "avg                        5.512231e+01  2.666237e+03   \n",
       "max                        1.337000e+03  3.868400e+04   \n",
       "min                        0.000000e+00  0.000000e+00   \n",
       "sum                        4.079106e+06  1.973042e+08   \n",
       "range                      1.337000e+03  3.868400e+04   \n",
       "stdev                      5.793785e+01  1.907504e+03   \n",
       "q1                         1.400000e+01  9.510000e+02   \n",
       "q3                         5.100000e+01  2.942000e+03   \n",
       "median                     3.000000e+01  1.989000e+03   \n",
       "interquartile_range        3.700000e+01  1.991000e+03   \n",
       "\n",
       "                     population_1_year_and_over  population_3_years_over  \\\n",
       "avg                                4.342539e+03             4.225816e+03   \n",
       "max                                6.480300e+04             6.243800e+04   \n",
       "min                                0.000000e+00             0.000000e+00   \n",
       "sum                                3.172486e+08             3.127146e+08   \n",
       "range                              6.480300e+04             6.243800e+04   \n",
       "stdev                              2.201732e+03             2.139467e+03   \n",
       "q1                                 2.604000e+03             2.519000e+03   \n",
       "q3                                 4.525000e+03             4.421000e+03   \n",
       "median                             3.590000e+03             3.499000e+03   \n",
       "interquartile_range                1.921000e+03             1.902000e+03   \n",
       "\n",
       "                    pop_5_years_over pop_15_and_over   pop_16_over  \\\n",
       "avg                             None            None  3.495006e+03   \n",
       "max                             None            None  4.464200e+04   \n",
       "min                             None            None  0.000000e+00   \n",
       "sum                             None            None  2.586340e+08   \n",
       "range                           None            None  4.464200e+04   \n",
       "stdev                           None            None  1.719905e+03   \n",
       "q1                              None            None  2.125000e+03   \n",
       "q3                              None            None  3.666000e+03   \n",
       "median                          None            None  2.921000e+03   \n",
       "interquartile_range             None            None  1.541000e+03   \n",
       "\n",
       "                     pop_25_years_over     pop_25_64 pop_never_married  \\\n",
       "avg                       2.954858e+03  2.301326e+03              None   \n",
       "max                       3.948600e+04  3.425800e+04              None   \n",
       "min                       0.000000e+00  0.000000e+00              None   \n",
       "sum                       2.186624e+08  1.703004e+08              None   \n",
       "range                     3.948600e+04  3.425800e+04              None   \n",
       "stdev                     1.474748e+03  1.216138e+03              None   \n",
       "q1                        1.782000e+03  1.328000e+03              None   \n",
       "q3                        3.115000e+03  2.398000e+03              None   \n",
       "median                    2.453000e+03  1.880000e+03              None   \n",
       "interquartile_range       1.333000e+03  1.070000e+03              None   \n",
       "\n",
       "                    pop_now_married pop_separated pop_widowed pop_divorced  \\\n",
       "avg                            None          None        None         None   \n",
       "max                            None          None        None         None   \n",
       "min                            None          None        None         None   \n",
       "sum                            None          None        None         None   \n",
       "range                          None          None        None         None   \n",
       "stdev                          None          None        None         None   \n",
       "q1                             None          None        None         None   \n",
       "q3                             None          None        None         None   \n",
       "median                         None          None        None         None   \n",
       "interquartile_range            None          None        None         None   \n",
       "\n",
       "                     not_us_citizen_pop     black_pop     asian_pop  \\\n",
       "avg                        3.057622e+02  5.330768e+02  2.296153e+02   \n",
       "max                        1.223200e+04  1.665500e+04  1.371200e+04   \n",
       "min                        0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "sum                        2.233776e+07  3.944822e+07  1.699176e+07   \n",
       "range                      1.223200e+04  1.665500e+04  1.371200e+04   \n",
       "stdev                      4.535513e+02  9.093226e+02  5.036082e+02   \n",
       "q1                         2.200000e+01  1.600000e+01  0.000000e+00   \n",
       "q3                         1.980000e+02  2.690000e+02  9.700000e+01   \n",
       "median                     8.200000e+01  8.200000e+01  2.800000e+01   \n",
       "interquartile_range        1.760000e+02  2.530000e+02  9.700000e+01   \n",
       "\n",
       "                     hispanic_pop  amerindian_pop  other_race_pop  \\\n",
       "avg                  8.100321e+02    2.836241e+01        9.694369   \n",
       "max                  2.868300e+04    9.644000e+03     1385.000000   \n",
       "min                  0.000000e+00    0.000000e+00        0.000000   \n",
       "sum                  5.994318e+07    2.098847e+06   717393.000000   \n",
       "range                2.868300e+04    9.644000e+03     1385.000000   \n",
       "stdev                1.282778e+03    1.789563e+02       34.687847   \n",
       "q1                   6.600000e+01    0.000000e+00        0.000000   \n",
       "q3                   4.480000e+02    2.000000e+00        0.000000   \n",
       "median               1.870000e+02    0.000000e+00        0.000000   \n",
       "interquartile_range  3.820000e+02    2.000000e+00        0.000000   \n",
       "\n",
       "                     two_or_more_races_pop  hispanic_any_race  \\\n",
       "avg                           1.007309e+02       8.100321e+02   \n",
       "max                           3.611000e+03       2.868300e+04   \n",
       "min                           0.000000e+00       0.000000e+00   \n",
       "sum                           7.454186e+06       5.994318e+07   \n",
       "range                         3.611000e+03       2.868300e+04   \n",
       "stdev                         1.270444e+02       1.282778e+03   \n",
       "q1                            1.600000e+01       6.600000e+01   \n",
       "q3                            8.800000e+01       4.480000e+02   \n",
       "median                        4.700000e+01       1.870000e+02   \n",
       "interquartile_range           7.200000e+01       3.820000e+02   \n",
       "\n",
       "                     not_hispanic_pop  asian_male_45_54  asian_male_55_64  \\\n",
       "avg                      3.574684e+03      1.491637e+01         11.693788   \n",
       "max                      5.047700e+04      1.301000e+03        745.000000   \n",
       "min                      0.000000e+00      0.000000e+00          0.000000   \n",
       "sum                      2.645302e+08      1.103826e+06     865352.000000   \n",
       "range                    5.047700e+04      1.301000e+03        745.000000   \n",
       "stdev                    2.056417e+03      3.975201e+01         31.827724   \n",
       "q1                       1.943000e+03      0.000000e+00          0.000000   \n",
       "q3                       3.809000e+03      0.000000e+00          0.000000   \n",
       "median                   2.906000e+03      0.000000e+00          0.000000   \n",
       "interquartile_range      1.866000e+03      0.000000e+00          0.000000   \n",
       "\n",
       "                     black_male_45_54  black_male_55_64  hispanic_male_45_54  \\\n",
       "avg                      3.440143e+01      2.904223e+01         4.700662e+01   \n",
       "max                      1.094000e+03      1.122000e+03         1.464000e+03   \n",
       "min                      0.000000e+00      0.000000e+00         0.000000e+00   \n",
       "sum                      2.545740e+06      2.149154e+06         3.478537e+06   \n",
       "range                    1.094000e+03      1.122000e+03         1.464000e+03   \n",
       "stdev                    6.303262e+01      5.395722e+01         8.368872e+01   \n",
       "q1                       0.000000e+00      0.000000e+00         0.000000e+00   \n",
       "q3                       1.600000e+01      1.300000e+01         2.300000e+01   \n",
       "median                   0.000000e+00      0.000000e+00         5.000000e+00   \n",
       "interquartile_range      1.600000e+01      1.300000e+01         2.300000e+01   \n",
       "\n",
       "                     hispanic_male_55_64  white_male_45_54  white_male_55_64  \\\n",
       "avg                         3.048543e+01      1.883150e+02      1.926332e+02   \n",
       "max                         7.770000e+02      2.189000e+03      2.545000e+03   \n",
       "min                         0.000000e+00      0.000000e+00      0.000000e+00   \n",
       "sum                         2.255952e+06      1.393550e+07      1.425505e+07   \n",
       "range                       7.770000e+02      2.189000e+03      2.545000e+03   \n",
       "stdev                       5.878341e+01      1.523871e+02      1.455158e+02   \n",
       "q1                          0.000000e+00      5.300000e+01      6.000000e+01   \n",
       "q3                          1.300000e+01      2.000000e+02      2.100000e+02   \n",
       "median                      0.000000e+00      1.270000e+02      1.350000e+02   \n",
       "interquartile_range         1.300000e+01      1.470000e+02      1.500000e+02   \n",
       "\n",
       "                     median_income  income_per_capita  income_less_10000  \\\n",
       "avg                   6.120650e+04       3.065180e+04       1.120286e+02   \n",
       "max                   2.500010e+05       2.202530e+05       1.550000e+03   \n",
       "min                   2.499000e+03       3.200000e+01       0.000000e+00   \n",
       "sum                   4.464586e+09       2.245429e+09       8.290228e+06   \n",
       "range                 2.475020e+05       2.202210e+05       1.550000e+03   \n",
       "stdev                 3.090106e+04       1.612694e+04       1.063003e+02   \n",
       "q1                    3.697400e+04       1.902300e+04       3.400000e+01   \n",
       "q3                    6.082500e+04       3.022100e+04       1.040000e+02   \n",
       "median                4.881100e+04       2.455200e+04       6.400000e+01   \n",
       "interquartile_range   2.385100e+04       1.119800e+04       7.000000e+01   \n",
       "\n",
       "                     income_10000_14999  income_15000_19999  \\\n",
       "avg                        7.990310e+01        7.906576e+01   \n",
       "max                        9.300000e+02        7.610000e+02   \n",
       "min                        0.000000e+00        0.000000e+00   \n",
       "sum                        5.912909e+06        5.850945e+06   \n",
       "range                      9.300000e+02        7.610000e+02   \n",
       "stdev                      6.984943e+01        6.359047e+01   \n",
       "q1                         2.300000e+01        2.600000e+01   \n",
       "q3                         7.900000e+01        8.000000e+01   \n",
       "median                     4.800000e+01        5.100000e+01   \n",
       "interquartile_range        5.600000e+01        5.400000e+01   \n",
       "\n",
       "                     income_20000_24999  income_25000_29999  \\\n",
       "avg                        8.121069e+01        7.699035e+01   \n",
       "max                        1.009000e+03        8.760000e+02   \n",
       "min                        0.000000e+00        0.000000e+00   \n",
       "sum                        6.009672e+06        5.697363e+06   \n",
       "range                      1.009000e+03        8.760000e+02   \n",
       "stdev                      6.205942e+01        5.829994e+01   \n",
       "q1                         3.000000e+01        2.900000e+01   \n",
       "q3                         8.200000e+01        7.800000e+01   \n",
       "median                     5.500000e+01        5.200000e+01   \n",
       "interquartile_range        5.200000e+01        4.900000e+01   \n",
       "\n",
       "                     income_30000_34999  income_35000_39999  \\\n",
       "avg                        7.811121e+01        7.282273e+01   \n",
       "max                        8.640000e+02        1.530000e+03   \n",
       "min                        0.000000e+00        0.000000e+00   \n",
       "sum                        5.780308e+06        5.388955e+06   \n",
       "range                      8.640000e+02        1.530000e+03   \n",
       "stdev                      5.862582e+01        5.505314e+01   \n",
       "q1                         3.000000e+01        2.800000e+01   \n",
       "q3                         8.000000e+01        7.400000e+01   \n",
       "median                     5.300000e+01        4.900000e+01   \n",
       "interquartile_range        5.000000e+01        4.600000e+01   \n",
       "\n",
       "                     income_40000_44999  income_45000_49999  \\\n",
       "avg                        7.298924e+01        6.437017e+01   \n",
       "max                        1.118000e+03        8.610000e+02   \n",
       "min                        0.000000e+00        0.000000e+00   \n",
       "sum                        5.401277e+06        4.763457e+06   \n",
       "range                      1.118000e+03        8.610000e+02   \n",
       "stdev                      5.498863e+01        4.950700e+01   \n",
       "q1                         2.900000e+01        2.400000e+01   \n",
       "q3                         7.400000e+01        6.500000e+01   \n",
       "median                     5.000000e+01        4.400000e+01   \n",
       "interquartile_range        4.500000e+01        4.100000e+01   \n",
       "\n",
       "                     income_50000_59999  income_60000_74999  \\\n",
       "avg                        1.248865e+02        1.605002e+02   \n",
       "max                        2.054000e+03        2.789000e+03   \n",
       "min                        0.000000e+00        0.000000e+00   \n",
       "sum                        9.241728e+06        1.187718e+07   \n",
       "range                      2.054000e+03        2.789000e+03   \n",
       "stdev                      8.417410e+01        1.059809e+02   \n",
       "q1                         5.800000e+01        7.600000e+01   \n",
       "q3                         1.280000e+02        1.670000e+02   \n",
       "median                     9.200000e+01        1.200000e+02   \n",
       "interquartile_range        7.000000e+01        9.100000e+01   \n",
       "\n",
       "                     income_75000_99999  income_100000_124999  \\\n",
       "avg                        1.984182e+02          1.391279e+02   \n",
       "max                        3.368000e+03          2.704000e+03   \n",
       "min                        0.000000e+00          0.000000e+00   \n",
       "sum                        1.468315e+07          1.029560e+07   \n",
       "range                      3.368000e+03          2.704000e+03   \n",
       "stdev                      1.372959e+02          1.147172e+02   \n",
       "q1                         8.600000e+01          4.700000e+01   \n",
       "q3                         2.070000e+02          1.410000e+02   \n",
       "median                     1.450000e+02          9.200000e+01   \n",
       "interquartile_range        1.210000e+02          9.400000e+01   \n",
       "\n",
       "                     income_125000_149999  income_150000_199999  \\\n",
       "avg                          8.701426e+01          9.380035e+01   \n",
       "max                          2.157000e+03          3.878000e+03   \n",
       "min                          0.000000e+00          0.000000e+00   \n",
       "sum                          6.439142e+06          6.941320e+06   \n",
       "range                        2.157000e+03          3.878000e+03   \n",
       "stdev                        8.513799e+01          1.078177e+02   \n",
       "q1                           1.900000e+01          1.400000e+01   \n",
       "q3                           8.300000e+01          8.100000e+01   \n",
       "median                       4.800000e+01          4.000000e+01   \n",
       "interquartile_range          6.400000e+01          6.700000e+01   \n",
       "\n",
       "                     income_200000_or_more  pop_determined_poverty_status  \\\n",
       "avg                           1.010161e+02                   4.276775e+03   \n",
       "max                           5.386000e+03                   6.533200e+04   \n",
       "min                           0.000000e+00                   0.000000e+00   \n",
       "sum                           7.475296e+06                   3.164856e+08   \n",
       "range                         5.386000e+03                   6.533200e+04   \n",
       "stdev                         1.647825e+02                   2.216522e+03   \n",
       "q1                            8.000000e+00                   2.559000e+03   \n",
       "q3                            6.000000e+01                   4.496000e+03   \n",
       "median                        2.600000e+01                   3.523000e+03   \n",
       "interquartile_range           5.200000e+01                   1.937000e+03   \n",
       "\n",
       "                          poverty    gini_index  housing_units  \\\n",
       "avg                  6.377423e+02      0.425911   1.850797e+03   \n",
       "max                  9.440000e+03      0.829000   2.652600e+04   \n",
       "min                  0.000000e+00      0.000700   0.000000e+00   \n",
       "sum                  4.719356e+07  31111.100500   1.369609e+08   \n",
       "range                9.440000e+03      0.828300   2.652600e+04   \n",
       "stdev                5.618142e+02      0.063577   8.958362e+02   \n",
       "q1                   2.120000e+02      0.373800   1.153000e+03   \n",
       "q3                   6.050000e+02      0.434700   1.949000e+03   \n",
       "median               3.830000e+02      0.405000   1.547000e+03   \n",
       "interquartile_range  3.930000e+02      0.060900   7.960000e+02   \n",
       "\n",
       "                     renter_occupied_housing_units_paying_cash_median_gross_rent  \\\n",
       "avg                                                       1.058678e+03             \n",
       "max                                                       3.501000e+03             \n",
       "min                                                       9.900000e+01             \n",
       "sum                                                       7.612849e+07             \n",
       "range                                                     3.402000e+03             \n",
       "stdev                                                     4.642559e+02             \n",
       "q1                                                        6.920000e+02             \n",
       "q3                                                        1.055000e+03             \n",
       "median                                                    8.510000e+02             \n",
       "interquartile_range                                       3.630000e+02             \n",
       "\n",
       "                     owner_occupied_housing_units_lower_value_quartile  \\\n",
       "avg                                                       1.765685e+05   \n",
       "max                                                       2.000001e+06   \n",
       "min                                                       9.999000e+03   \n",
       "sum                                                       1.258933e+10   \n",
       "range                                                     1.990002e+06   \n",
       "stdev                                                     1.648026e+05   \n",
       "q1                                                        6.570000e+04   \n",
       "q3                                                        1.533000e+05   \n",
       "median                                                    1.011000e+05   \n",
       "interquartile_range                                       8.760000e+04   \n",
       "\n",
       "                     owner_occupied_housing_units_median_value  \\\n",
       "avg                                               2.443280e+05   \n",
       "max                                               2.000001e+06   \n",
       "min                                               9.999000e+03   \n",
       "sum                                               1.758746e+10   \n",
       "range                                             1.990002e+06   \n",
       "stdev                                             2.189567e+05   \n",
       "q1                                                9.880000e+04   \n",
       "q3                                                2.108000e+05   \n",
       "median                                            1.479000e+05   \n",
       "interquartile_range                               1.120000e+05   \n",
       "\n",
       "                     owner_occupied_housing_units_upper_value_quartile  \\\n",
       "avg                                                       3.329604e+05   \n",
       "max                                                       2.000001e+06   \n",
       "min                                                       9.999000e+03   \n",
       "sum                                                       2.396882e+10   \n",
       "range                                                     1.990002e+06   \n",
       "stdev                                                     2.866141e+05   \n",
       "q1                                                        1.491000e+05   \n",
       "q3                                                        2.867000e+05   \n",
       "median                                                    2.031000e+05   \n",
       "interquartile_range                                       1.376000e+05   \n",
       "\n",
       "                     occupied_housing_units  housing_units_renter_occupied  \\\n",
       "avg                            1.622255e+03                   5.861794e+02   \n",
       "max                            2.142900e+04                   8.039000e+03   \n",
       "min                            0.000000e+00                   0.000000e+00   \n",
       "sum                            1.200485e+08                   4.337786e+07   \n",
       "range                          2.142900e+04                   8.039000e+03   \n",
       "stdev                          7.936172e+02                   4.791495e+02   \n",
       "q1                             9.980000e+02                   2.090000e+02   \n",
       "q3                             1.714000e+03                   5.700000e+02   \n",
       "median                         1.357000e+03                   3.740000e+02   \n",
       "interquartile_range            7.160000e+02                   3.610000e+02   \n",
       "\n",
       "                     vacant_housing_units  vacant_housing_units_for_rent  \\\n",
       "avg                          2.285420e+02                   3.884688e+01   \n",
       "max                          1.192400e+04                   4.472000e+03   \n",
       "min                          0.000000e+00                   0.000000e+00   \n",
       "sum                          1.691234e+07                   2.874708e+06   \n",
       "range                        1.192400e+04                   4.472000e+03   \n",
       "stdev                        3.131224e+02                   6.702031e+01   \n",
       "q1                           6.400000e+01                   0.000000e+00   \n",
       "q3                           1.880000e+02                   3.100000e+01   \n",
       "median                       1.200000e+02                   1.000000e+01   \n",
       "interquartile_range          1.240000e+02                   3.100000e+01   \n",
       "\n",
       "                     vacant_housing_units_for_sale  \\\n",
       "avg                                   1.863187e+01   \n",
       "max                                   5.470000e+02   \n",
       "min                                   0.000000e+00   \n",
       "sum                                   1.378777e+06   \n",
       "range                                 5.470000e+02   \n",
       "stdev                                 2.830720e+01   \n",
       "q1                                    0.000000e+00   \n",
       "q3                                    1.500000e+01   \n",
       "median                                0.000000e+00   \n",
       "interquartile_range                   1.500000e+01   \n",
       "\n",
       "                     dwellings_1_units_detached  dwellings_1_units_attached  \\\n",
       "avg                                1.143346e+03                1.092662e+02   \n",
       "max                                2.552700e+04                4.280000e+03   \n",
       "min                                0.000000e+00                0.000000e+00   \n",
       "sum                                8.460878e+07                8.085807e+06   \n",
       "range                              2.552700e+04                4.280000e+03   \n",
       "stdev                              7.543086e+02                1.952911e+02   \n",
       "q1                                 5.220000e+02                7.000000e+00   \n",
       "q3                                 1.248000e+03                6.200000e+01   \n",
       "median                             9.150000e+02                2.600000e+01   \n",
       "interquartile_range                7.260000e+02                5.500000e+01   \n",
       "\n",
       "                     dwellings_2_units  dwellings_3_to_4_units  \\\n",
       "avg                       6.762999e+01            8.112938e+01   \n",
       "max                       1.999000e+03            1.629000e+03   \n",
       "min                       0.000000e+00            0.000000e+00   \n",
       "sum                       5.004687e+06            6.003655e+06   \n",
       "range                     1.999000e+03            1.629000e+03   \n",
       "stdev                     1.133588e+02            1.165817e+02   \n",
       "q1                        0.000000e+00            0.000000e+00   \n",
       "q3                        4.100000e+01            5.900000e+01   \n",
       "median                    1.500000e+01            2.300000e+01   \n",
       "interquartile_range       4.100000e+01            5.900000e+01   \n",
       "\n",
       "                     dwellings_5_to_9_units  dwellings_10_to_19_units  \\\n",
       "avg                            8.797462e+01              8.248062e+01   \n",
       "max                            1.863000e+03              5.280000e+03   \n",
       "min                            0.000000e+00              0.000000e+00   \n",
       "sum                            6.510210e+06              6.103648e+06   \n",
       "range                          1.863000e+03              5.280000e+03   \n",
       "stdev                          1.324159e+02              1.511113e+02   \n",
       "q1                             0.000000e+00              0.000000e+00   \n",
       "q3                             6.000000e+01              4.300000e+01   \n",
       "median                         1.900000e+01              9.000000e+00   \n",
       "interquartile_range            6.000000e+01              4.300000e+01   \n",
       "\n",
       "                     dwellings_20_to_49_units  dwellings_50_or_more_units  \\\n",
       "avg                              6.591754e+01                9.643179e+01   \n",
       "max                              4.283000e+03                1.151800e+04   \n",
       "min                              0.000000e+00                0.000000e+00   \n",
       "sum                              4.877964e+06                7.136049e+06   \n",
       "range                            4.283000e+03                1.151800e+04   \n",
       "stdev                            1.440865e+02                2.991683e+02   \n",
       "q1                               0.000000e+00                0.000000e+00   \n",
       "q3                               2.700000e+01                2.000000e+01   \n",
       "median                           0.000000e+00                0.000000e+00   \n",
       "interquartile_range              2.700000e+01                2.000000e+01   \n",
       "\n",
       "                     mobile_homes  housing_built_2005_or_later  \\\n",
       "avg                  1.150671e+02                 1.610305e+01   \n",
       "max                  3.314000e+03                 2.502000e+03   \n",
       "min                  0.000000e+00                 0.000000e+00   \n",
       "sum                  8.515077e+06                 1.191642e+06   \n",
       "range                3.314000e+03                 2.502000e+03   \n",
       "stdev                2.250775e+02                 4.732752e+01   \n",
       "q1                   0.000000e+00                 0.000000e+00   \n",
       "q3                   2.700000e+01                 0.000000e+00   \n",
       "median               0.000000e+00                 0.000000e+00   \n",
       "interquartile_range  2.700000e+01                 0.000000e+00   \n",
       "\n",
       "                     housing_built_2000_to_2004  \\\n",
       "avg                                4.221539e+01   \n",
       "max                                7.206000e+03   \n",
       "min                                0.000000e+00   \n",
       "sum                                3.123981e+06   \n",
       "range                              7.206000e+03   \n",
       "stdev                              1.038676e+02   \n",
       "q1                                 0.000000e+00   \n",
       "q3                                 2.100000e+01   \n",
       "median                             4.000000e+00   \n",
       "interquartile_range                2.100000e+01   \n",
       "\n",
       "                     housing_built_1939_or_earlier  \\\n",
       "avg                                   9.403019e+01   \n",
       "max                                   6.177000e+03   \n",
       "min                                   0.000000e+00   \n",
       "sum                                   6.958328e+06   \n",
       "range                                 6.177000e+03   \n",
       "stdev                                 1.166008e+02   \n",
       "q1                                    9.000000e+00   \n",
       "q3                                    8.100000e+01   \n",
       "median                                4.000000e+01   \n",
       "interquartile_range                   7.200000e+01   \n",
       "\n",
       "                     median_year_structure_built  married_households  \\\n",
       "avg                                 1.971950e+03        7.829447e+02   \n",
       "max                                 2.016000e+03        1.510500e+04   \n",
       "min                                 1.939000e+03        0.000000e+00   \n",
       "sum                                 1.429487e+08        5.793869e+07   \n",
       "range                               7.700000e+01        1.510500e+04   \n",
       "stdev                               1.755339e+01        5.050110e+02   \n",
       "q1                                  1.956000e+03        3.810000e+02   \n",
       "q3                                  1.978000e+03        8.130000e+02   \n",
       "median                              1.969000e+03        5.990000e+02   \n",
       "interquartile_range                 2.200000e+01        4.320000e+02   \n",
       "\n",
       "                     nonfamily_households  family_households  \\\n",
       "avg                          5.526839e+02       1.069572e+03   \n",
       "max                          6.883000e+03       1.681300e+04   \n",
       "min                          0.000000e+00       0.000000e+00   \n",
       "sum                          4.089916e+07       7.914937e+07   \n",
       "range                        6.883000e+03       1.681300e+04   \n",
       "stdev                        3.637558e+02       5.859720e+02   \n",
       "q1                           2.730000e+02       6.120000e+02   \n",
       "q3                           5.550000e+02       1.126000e+03   \n",
       "median                       4.090000e+02       8.700000e+02   \n",
       "interquartile_range          2.820000e+02       5.140000e+02   \n",
       "\n",
       "                     households_public_asst_or_food_stamps  \\\n",
       "avg                                           2.201474e+02   \n",
       "max                                           2.325000e+03   \n",
       "min                                           0.000000e+00   \n",
       "sum                                           1.629113e+07   \n",
       "range                                         2.325000e+03   \n",
       "stdev                                         1.966127e+02   \n",
       "q1                                            6.200000e+01   \n",
       "q3                                            2.100000e+02   \n",
       "median                                        1.290000e+02   \n",
       "interquartile_range                           1.480000e+02   \n",
       "\n",
       "                     male_male_households  female_female_households  \\\n",
       "avg                              2.847543                  2.909866   \n",
       "max                            355.000000                174.000000   \n",
       "min                              0.000000                  0.000000   \n",
       "sum                         210721.000000             215333.000000   \n",
       "range                          355.000000                174.000000   \n",
       "stdev                            8.899265                  7.652833   \n",
       "q1                               0.000000                  0.000000   \n",
       "q3                               0.000000                  0.000000   \n",
       "median                           0.000000                  0.000000   \n",
       "interquartile_range              0.000000                  0.000000   \n",
       "\n",
       "                         children  children_in_single_female_hh   median_rent  \\\n",
       "avg                  1.004481e+03                  2.549481e+02  8.960611e+02   \n",
       "max                  2.360800e+04                  2.863000e+03  3.501000e+03   \n",
       "min                  0.000000e+00                  0.000000e+00  9.900000e+01   \n",
       "sum                  7.433261e+07                  1.886641e+07  6.427357e+07   \n",
       "range                2.360800e+04                  2.863000e+03  3.402000e+03   \n",
       "stdev                6.760134e+02                  2.402736e+02  4.587204e+02   \n",
       "q1                   5.080000e+02                  7.300000e+01  5.290000e+02   \n",
       "q3                   1.019000e+03                  2.330000e+02  8.890000e+02   \n",
       "median               7.650000e+02                  1.440000e+02  6.910000e+02   \n",
       "interquartile_range  5.110000e+02                  1.600000e+02  3.600000e+02   \n",
       "\n",
       "                     percent_income_spent_on_rent  rent_burden_not_computed  \\\n",
       "avg                                  3.075703e+01              4.549625e+01   \n",
       "max                                  5.000000e+01              1.100000e+03   \n",
       "min                                  1.000000e+01              0.000000e+00   \n",
       "sum                                  2.225702e+06              3.366768e+06   \n",
       "range                                4.000000e+01              1.100000e+03   \n",
       "stdev                                7.795312e+00              4.974370e+01   \n",
       "q1                                   2.420000e+01              9.000000e+00   \n",
       "q3                                   3.140000e+01              4.100000e+01   \n",
       "median                               2.810000e+01              2.400000e+01   \n",
       "interquartile_range                  7.200000e+00              3.200000e+01   \n",
       "\n",
       "                     rent_over_50_percent  rent_40_to_50_percent  \\\n",
       "avg                          1.383322e+02           4.980808e+01   \n",
       "max                          3.487000e+03           9.720000e+02   \n",
       "min                          0.000000e+00           0.000000e+00   \n",
       "sum                          1.023672e+07           3.685848e+06   \n",
       "range                        3.487000e+03           9.720000e+02   \n",
       "stdev                        1.432986e+02           5.574670e+01   \n",
       "q1                           2.900000e+01           7.000000e+00   \n",
       "q3                           1.260000e+02           4.500000e+01   \n",
       "median                       7.000000e+01           2.200000e+01   \n",
       "interquartile_range          9.700000e+01           3.800000e+01   \n",
       "\n",
       "                     rent_35_to_40_percent  rent_30_to_35_percent  \\\n",
       "avg                           3.622229e+01           4.936739e+01   \n",
       "max                           6.410000e+02           8.600000e+02   \n",
       "min                           0.000000e+00           0.000000e+00   \n",
       "sum                           2.680486e+06           3.653236e+06   \n",
       "range                         6.410000e+02           8.600000e+02   \n",
       "stdev                         4.303120e+01           5.598719e+01   \n",
       "q1                            0.000000e+00           7.000000e+00   \n",
       "q3                            3.200000e+01           4.400000e+01   \n",
       "median                        1.500000e+01           2.200000e+01   \n",
       "interquartile_range           3.200000e+01           3.700000e+01   \n",
       "\n",
       "                     rent_25_to_30_percent  rent_20_to_25_percent  \\\n",
       "avg                           6.223202e+01           6.874649e+01   \n",
       "max                           9.990000e+02           1.382000e+03   \n",
       "min                           0.000000e+00           0.000000e+00   \n",
       "sum                           4.605232e+06           5.087309e+06   \n",
       "range                         9.990000e+02           1.382000e+03   \n",
       "stdev                         6.694950e+01           7.252401e+01   \n",
       "q1                            1.100000e+01           1.400000e+01   \n",
       "q3                            5.700000e+01           6.300000e+01   \n",
       "median                        3.000000e+01           3.500000e+01   \n",
       "interquartile_range           4.600000e+01           4.900000e+01   \n",
       "\n",
       "                     rent_15_to_20_percent  rent_10_to_15_percent  \\\n",
       "avg                           6.792566e+01           4.691335e+01   \n",
       "max                           1.879000e+03           1.098000e+03   \n",
       "min                           0.000000e+00           0.000000e+00   \n",
       "sum                           5.026567e+06           3.471635e+06   \n",
       "range                         1.879000e+03           1.098000e+03   \n",
       "stdev                         7.206055e+01           5.312190e+01   \n",
       "q1                            1.500000e+01           8.000000e+00   \n",
       "q3                            6.200000e+01           4.200000e+01   \n",
       "median                        3.500000e+01           2.300000e+01   \n",
       "interquartile_range           4.700000e+01           3.400000e+01   \n",
       "\n",
       "                     rent_under_10_percent  owner_occupied_housing_units  \\\n",
       "avg                           2.113569e+01                  1.036076e+03   \n",
       "max                           9.120000e+02                  2.047300e+04   \n",
       "min                           0.000000e+00                  0.000000e+00   \n",
       "sum                           1.564062e+06                  7.667066e+07   \n",
       "range                         9.120000e+02                  2.047300e+04   \n",
       "stdev                         3.129274e+01                  6.543429e+02   \n",
       "q1                            0.000000e+00                  4.960000e+02   \n",
       "q3                            1.700000e+01                  1.105000e+03   \n",
       "median                        7.000000e+00                  8.120000e+02   \n",
       "interquartile_range           1.700000e+01                  6.090000e+02   \n",
       "\n",
       "                     million_dollar_housing_units  mortgaged_housing_units  \\\n",
       "avg                                  1.498115e+01             6.556553e+02   \n",
       "max                                  1.453000e+03             1.267200e+04   \n",
       "min                                  0.000000e+00             0.000000e+00   \n",
       "sum                                  1.108620e+06             4.851915e+07   \n",
       "range                                1.453000e+03             1.267200e+04   \n",
       "stdev                                5.537075e+01             4.790928e+02   \n",
       "q1                                   0.000000e+00             2.740000e+02   \n",
       "q3                                   0.000000e+00             6.790000e+02   \n",
       "median                               0.000000e+00             4.720000e+02   \n",
       "interquartile_range                  0.000000e+00             4.050000e+02   \n",
       "\n",
       "                     different_house_year_ago_different_city  \\\n",
       "avg                                             4.115417e+02   \n",
       "max                                             1.306400e+04   \n",
       "min                                             0.000000e+00   \n",
       "sum                                             3.006559e+07   \n",
       "range                                           1.306400e+04   \n",
       "stdev                                           3.932843e+02   \n",
       "q1                                              1.400000e+02   \n",
       "q3                                              3.830000e+02   \n",
       "median                                          2.510000e+02   \n",
       "interquartile_range                             2.430000e+02   \n",
       "\n",
       "                     different_house_year_ago_same_city  \\\n",
       "avg                                        1.951105e+02   \n",
       "max                                        5.347000e+03   \n",
       "min                                        0.000000e+00   \n",
       "sum                                        1.425400e+07   \n",
       "range                                      5.347000e+03   \n",
       "stdev                                      2.492432e+02   \n",
       "q1                                         6.000000e+00   \n",
       "q3                                         1.660000e+02   \n",
       "median                                     6.700000e+01   \n",
       "interquartile_range                        1.600000e+02   \n",
       "\n",
       "                     families_with_young_children  \\\n",
       "avg                                  3.127425e+02   \n",
       "max                                  6.700000e+03   \n",
       "min                                  0.000000e+00   \n",
       "sum                                  2.314326e+07   \n",
       "range                                6.700000e+03   \n",
       "stdev                                2.316744e+02   \n",
       "q1                                   1.410000e+02   \n",
       "q3                                   3.130000e+02   \n",
       "median                               2.230000e+02   \n",
       "interquartile_range                  1.720000e+02   \n",
       "\n",
       "                     two_parent_families_with_young_children  \\\n",
       "avg                                             2.018102e+02   \n",
       "max                                             6.517000e+03   \n",
       "min                                             0.000000e+00   \n",
       "sum                                             1.493416e+07   \n",
       "range                                           6.517000e+03   \n",
       "stdev                                           1.829281e+02   \n",
       "q1                                              7.000000e+01   \n",
       "q3                                              1.970000e+02   \n",
       "median                                          1.300000e+02   \n",
       "interquartile_range                             1.270000e+02   \n",
       "\n",
       "                     two_parents_in_labor_force_families_with_young_children  \\\n",
       "avg                                                       1.182977e+02         \n",
       "max                                                       3.989000e+03         \n",
       "min                                                       0.000000e+00         \n",
       "sum                                                       8.754150e+06         \n",
       "range                                                     3.989000e+03         \n",
       "stdev                                                     1.148169e+02         \n",
       "q1                                                        3.400000e+01         \n",
       "q3                                                        1.130000e+02         \n",
       "median                                                    7.100000e+01         \n",
       "interquartile_range                                       7.900000e+01         \n",
       "\n",
       "                     two_parents_father_in_labor_force_families_with_young_children  \\\n",
       "avg                                                       7.417954e+01                \n",
       "max                                                       3.888000e+03                \n",
       "min                                                       0.000000e+00                \n",
       "sum                                                       5.489360e+06                \n",
       "range                                                     3.888000e+03                \n",
       "stdev                                                     9.050640e+01                \n",
       "q1                                                        1.200000e+01                \n",
       "q3                                                        6.600000e+01                \n",
       "median                                                    3.500000e+01                \n",
       "interquartile_range                                       5.400000e+01                \n",
       "\n",
       "                     two_parents_mother_in_labor_force_families_with_young_children  \\\n",
       "avg                                                           6.340941                \n",
       "max                                                        1308.000000                \n",
       "min                                                           0.000000                \n",
       "sum                                                      469236.000000                \n",
       "range                                                      1308.000000                \n",
       "stdev                                                        18.145011                \n",
       "q1                                                            0.000000                \n",
       "q3                                                            0.000000                \n",
       "median                                                        0.000000                \n",
       "interquartile_range                                           0.000000                \n",
       "\n",
       "                     two_parents_not_in_labor_force_families_with_young_children  \\\n",
       "avg                                                           2.991987             \n",
       "max                                                         285.000000             \n",
       "min                                                           0.000000             \n",
       "sum                                                      221410.000000             \n",
       "range                                                       285.000000             \n",
       "stdev                                                        11.523613             \n",
       "q1                                                            0.000000             \n",
       "q3                                                            0.000000             \n",
       "median                                                        0.000000             \n",
       "interquartile_range                                           0.000000             \n",
       "\n",
       "                     one_parent_families_with_young_children  \\\n",
       "avg                                             1.109323e+02   \n",
       "max                                             1.418000e+03   \n",
       "min                                             0.000000e+00   \n",
       "sum                                             8.209099e+06   \n",
       "range                                           1.418000e+03   \n",
       "stdev                                           1.141550e+02   \n",
       "q1                                              2.300000e+01   \n",
       "q3                                              1.000000e+02   \n",
       "median                                          5.700000e+01   \n",
       "interquartile_range                             7.700000e+01   \n",
       "\n",
       "                     father_one_parent_families_with_young_children  \\\n",
       "avg                                                    2.560057e+01   \n",
       "max                                                    5.020000e+02   \n",
       "min                                                    0.000000e+00   \n",
       "sum                                                    1.894468e+06   \n",
       "range                                                  5.020000e+02   \n",
       "stdev                                                  3.774419e+01   \n",
       "q1                                                     0.000000e+00   \n",
       "q3                                                     1.900000e+01   \n",
       "median                                                 5.000000e+00   \n",
       "interquartile_range                                    1.900000e+01   \n",
       "\n",
       "                     father_in_labor_force_one_parent_families_with_young_children  \\\n",
       "avg                                                       2.281514e+01               \n",
       "max                                                       5.020000e+02               \n",
       "min                                                       0.000000e+00               \n",
       "sum                                                       1.688343e+06               \n",
       "range                                                     5.020000e+02               \n",
       "stdev                                                     3.534531e+01               \n",
       "q1                                                        0.000000e+00               \n",
       "q3                                                        1.700000e+01               \n",
       "median                                                    0.000000e+00               \n",
       "interquartile_range                                       1.700000e+01               \n",
       "\n",
       "                     commute_less_10_mins  commute_10_14_mins  \\\n",
       "avg                          2.433403e+02        2.615797e+02   \n",
       "max                          1.162100e+04        5.838000e+03   \n",
       "min                          0.000000e+00        0.000000e+00   \n",
       "sum                          1.800742e+07        1.935716e+07   \n",
       "range                        1.162100e+04        5.838000e+03   \n",
       "stdev                        2.257531e+02        1.967251e+02   \n",
       "q1                           8.400000e+01        1.040000e+02   \n",
       "q3                           2.310000e+02        2.610000e+02   \n",
       "median                       1.500000e+02        1.800000e+02   \n",
       "interquartile_range          1.470000e+02        1.570000e+02   \n",
       "\n",
       "                     commute_15_19_mins  commute_20_24_mins  \\\n",
       "avg                        2.943585e+02        2.800684e+02   \n",
       "max                        7.095000e+03        4.886000e+03   \n",
       "min                        0.000000e+00        0.000000e+00   \n",
       "sum                        2.178282e+07        2.072534e+07   \n",
       "range                      7.095000e+03        4.886000e+03   \n",
       "stdev                      2.132096e+02        2.089584e+02   \n",
       "q1                         1.260000e+02        1.100000e+02   \n",
       "q3                         2.970000e+02        2.830000e+02   \n",
       "median                     2.070000e+02        1.930000e+02   \n",
       "interquartile_range        1.710000e+02        1.730000e+02   \n",
       "\n",
       "                     commute_25_29_mins  commute_30_34_mins  \\\n",
       "avg                        1.222793e+02        2.636407e+02   \n",
       "max                        2.205000e+03        4.880000e+03   \n",
       "min                        0.000000e+00        0.000000e+00   \n",
       "sum                        9.048793e+06        1.950968e+07   \n",
       "range                      2.205000e+03        4.880000e+03   \n",
       "stdev                      1.102167e+02        2.155892e+02   \n",
       "q1                         3.600000e+01        9.500000e+01   \n",
       "q3                         1.210000e+02        2.610000e+02   \n",
       "median                     7.400000e+01        1.740000e+02   \n",
       "interquartile_range        8.500000e+01        1.660000e+02   \n",
       "\n",
       "                     commute_35_44_mins  commute_45_59_mins  \\\n",
       "avg                        1.311400e+02        1.560668e+02   \n",
       "max                        3.178000e+03        5.390000e+03   \n",
       "min                        0.000000e+00        0.000000e+00   \n",
       "sum                        9.704493e+06        1.154910e+07   \n",
       "range                      3.178000e+03        5.390000e+03   \n",
       "stdev                      1.292811e+02        1.602168e+02   \n",
       "q1                         3.300000e+01        3.800000e+01   \n",
       "q3                         1.240000e+02        1.450000e+02   \n",
       "median                     7.300000e+01        8.200000e+01   \n",
       "interquartile_range        9.100000e+01        1.070000e+02   \n",
       "\n",
       "                     commute_60_more_mins  commuters_16_over  walked_to_work  \\\n",
       "avg                          1.717179e+02       1.924192e+03    5.515674e+01   \n",
       "max                          6.070000e+03       2.648200e+04    1.162100e+04   \n",
       "min                          0.000000e+00       0.000000e+00    0.000000e+00   \n",
       "sum                          1.270730e+07       1.423921e+08    4.081654e+06   \n",
       "range                        6.070000e+03       2.648200e+04    1.162100e+04   \n",
       "stdev                        1.904055e+02       1.072317e+03    1.404476e+02   \n",
       "q1                           4.200000e+01       1.072000e+03    0.000000e+00   \n",
       "q3                           1.450000e+02       2.014000e+03    3.400000e+01   \n",
       "median                       8.400000e+01       1.548000e+03    1.600000e+01   \n",
       "interquartile_range          1.030000e+02       9.420000e+02    3.400000e+01   \n",
       "\n",
       "                     worked_at_home        no_car       no_cars       one_car  \\\n",
       "avg                    9.526220e+01  8.728505e+01  1.442299e+02  5.405044e+02   \n",
       "max                    7.043000e+03  8.111000e+03  5.847000e+03  1.554800e+04   \n",
       "min                    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "sum                    7.049498e+06  6.459181e+06  1.067316e+07  3.999786e+07   \n",
       "range                  7.043000e+03  8.111000e+03  5.847000e+03  1.554800e+04   \n",
       "stdev                  1.047340e+02  2.315210e+02  2.298423e+02  3.314566e+02   \n",
       "q1                     2.300000e+01  7.000000e+00  2.900000e+01  2.780000e+02   \n",
       "q3                     8.700000e+01  4.800000e+01  1.090000e+02  5.560000e+02   \n",
       "median                 5.200000e+01  2.300000e+01  6.200000e+01  4.110000e+02   \n",
       "interquartile_range    6.400000e+01  4.100000e+01  8.000000e+01  2.780000e+02   \n",
       "\n",
       "                         two_cars    three_cars  four_more_cars  \\\n",
       "avg                  6.048036e+02  2.295713e+02    1.031463e+02   \n",
       "max                  1.201200e+04  3.231000e+03    1.107000e+03   \n",
       "min                  0.000000e+00  0.000000e+00    0.000000e+00   \n",
       "sum                  4.475607e+07  1.698850e+07    7.632928e+06   \n",
       "range                1.201200e+04  3.231000e+03    1.107000e+03   \n",
       "stdev                3.799984e+02  1.711299e+02    9.448405e+01   \n",
       "q1                   3.010000e+02  8.500000e+01    2.400000e+01   \n",
       "q3                   6.360000e+02  2.400000e+02    1.020000e+02   \n",
       "median               4.710000e+02  1.620000e+02    6.000000e+01   \n",
       "interquartile_range  3.350000e+02  1.550000e+02    7.800000e+01   \n",
       "\n",
       "                     aggregate_travel_time_to_work  \\\n",
       "avg                                   5.934150e+04   \n",
       "max                                   9.731600e+05   \n",
       "min                                   6.500000e+01   \n",
       "sum                                   2.970754e+09   \n",
       "range                                 9.730950e+05   \n",
       "stdev                                 3.422968e+04   \n",
       "q1                                    3.315000e+04   \n",
       "q3                                    6.035000e+04   \n",
       "median                                4.599500e+04   \n",
       "interquartile_range                   2.720000e+04   \n",
       "\n",
       "                     commuters_by_public_transportation  commuters_by_bus  \\\n",
       "avg                                        1.031013e+02      5.114798e+01   \n",
       "max                                        7.759000e+03      3.940000e+03   \n",
       "min                                        0.000000e+00      0.000000e+00   \n",
       "sum                                        7.629599e+06      3.785002e+06   \n",
       "range                                      7.759000e+03      3.940000e+03   \n",
       "stdev                                      2.647627e+02      1.086306e+02   \n",
       "q1                                         0.000000e+00      0.000000e+00   \n",
       "q3                                         3.400000e+01      2.000000e+01   \n",
       "median                                     8.000000e+00      2.000000e+00   \n",
       "interquartile_range                        3.400000e+01      2.000000e+01   \n",
       "\n",
       "                     commuters_by_car_truck_van  commuters_by_carpool  \\\n",
       "avg                                1.729272e+03          1.847843e+02   \n",
       "max                                2.501200e+04          3.023000e+03   \n",
       "min                                0.000000e+00          0.000000e+00   \n",
       "sum                                1.279679e+08          1.367422e+07   \n",
       "range                              2.501200e+04          3.023000e+03   \n",
       "stdev                              1.041140e+03          1.447731e+02   \n",
       "q1                                 8.940000e+02          7.400000e+01   \n",
       "q3                                 1.824000e+03          1.830000e+02   \n",
       "median                             1.362000e+03          1.240000e+02   \n",
       "interquartile_range                9.300000e+02          1.090000e+02   \n",
       "\n",
       "                     commuters_by_subway_or_elevated  commuters_drove_alone  \\\n",
       "avg                                     3.818269e+01           1.544488e+03   \n",
       "max                                     5.945000e+03           2.198900e+04   \n",
       "min                                     0.000000e+00           0.000000e+00   \n",
       "sum                                     2.825557e+06           1.142936e+08   \n",
       "range                                   5.945000e+03           2.198900e+04   \n",
       "stdev                                   1.967409e+02           9.474669e+02   \n",
       "q1                                      0.000000e+00           7.800000e+02   \n",
       "q3                                      0.000000e+00           1.637000e+03   \n",
       "median                                  0.000000e+00           1.211000e+03   \n",
       "interquartile_range                     0.000000e+00           8.570000e+02   \n",
       "\n",
       "                     group_quarters  associates_degree  bachelors_degree  \\\n",
       "avg                    1.098196e+02       2.452862e+02      5.649029e+02   \n",
       "max                    1.642100e+04       4.070000e+03      1.376700e+04   \n",
       "min                    0.000000e+00       0.000000e+00      0.000000e+00   \n",
       "sum                    8.126758e+06       1.815143e+07      4.180338e+07   \n",
       "range                  1.642100e+04       4.070000e+03      1.376700e+04   \n",
       "stdev                  4.446641e+02       1.694405e+02      4.828823e+02   \n",
       "q1                     0.000000e+00       1.090000e+02      1.910000e+02   \n",
       "q3                     2.000000e+01       2.520000e+02      5.500000e+02   \n",
       "median                 5.000000e+00       1.780000e+02      3.420000e+02   \n",
       "interquartile_range    2.000000e+01       1.430000e+02      3.590000e+02   \n",
       "\n",
       "                     high_school_diploma  less_one_year_college  \\\n",
       "avg                         6.904443e+02           1.821747e+02   \n",
       "max                         8.215000e+03           4.037000e+03   \n",
       "min                         0.000000e+00           0.000000e+00   \n",
       "sum                         5.109357e+07           1.348111e+07   \n",
       "range                       8.215000e+03           4.037000e+03   \n",
       "stdev                       4.093667e+02           1.267778e+02   \n",
       "q1                          3.420000e+02           8.000000e+01   \n",
       "q3                          7.280000e+02           1.890000e+02   \n",
       "median                      5.340000e+02           1.320000e+02   \n",
       "interquartile_range         3.860000e+02           1.090000e+02   \n",
       "\n",
       "                     masters_degree  one_year_more_college  \\\n",
       "avg                    2.468936e+02           4.290593e+02   \n",
       "max                    7.767000e+03           5.621000e+03   \n",
       "min                    0.000000e+00           0.000000e+00   \n",
       "sum                    1.827037e+07           3.175082e+07   \n",
       "range                  7.767000e+03           5.621000e+03   \n",
       "stdev                  2.557686e+02           2.629695e+02   \n",
       "q1                     6.200000e+01           2.220000e+02   \n",
       "q3                     2.160000e+02           4.380000e+02   \n",
       "median                 1.260000e+02           3.250000e+02   \n",
       "interquartile_range    1.540000e+02           2.160000e+02   \n",
       "\n",
       "                     less_than_high_school_graduate  \\\n",
       "avg                                    3.755628e+02   \n",
       "max                                    5.363000e+03   \n",
       "min                                    0.000000e+00   \n",
       "sum                                    2.743711e+07   \n",
       "range                                  5.363000e+03   \n",
       "stdev                                  3.540041e+02   \n",
       "q1                                     1.150000e+02   \n",
       "q3                                     3.390000e+02   \n",
       "median                                 2.140000e+02   \n",
       "interquartile_range                    2.240000e+02   \n",
       "\n",
       "                     high_school_including_ged  bachelors_degree_2  \\\n",
       "avg                               8.088810e+02        5.663747e+02   \n",
       "max                               8.929000e+03        1.376700e+04   \n",
       "min                               0.000000e+00        0.000000e+00   \n",
       "sum                               5.909361e+07        4.137707e+07   \n",
       "range                             8.929000e+03        1.376700e+04   \n",
       "stdev                             4.791437e+02        4.842803e+02   \n",
       "q1                                4.000000e+02        1.920000e+02   \n",
       "q3                                8.500000e+02        5.470000e+02   \n",
       "median                            6.300000e+02        3.450000e+02   \n",
       "interquartile_range               4.500000e+02        3.550000e+02   \n",
       "\n",
       "                     bachelors_degree_or_higher_25_64  \\\n",
       "avg                                      7.435482e+02   \n",
       "max                                      2.285600e+04   \n",
       "min                                      0.000000e+00   \n",
       "sum                                      5.502331e+07   \n",
       "range                                    2.285600e+04   \n",
       "stdev                                    7.062823e+02   \n",
       "q1                                       2.200000e+02   \n",
       "q3                                       6.830000e+02   \n",
       "median                                   4.080000e+02   \n",
       "interquartile_range                      4.630000e+02   \n",
       "\n",
       "                     graduate_professional_degree  \\\n",
       "avg                                  3.491915e+02   \n",
       "max                                  1.037400e+04   \n",
       "min                                  0.000000e+00   \n",
       "sum                                  2.551054e+07   \n",
       "range                                1.037400e+04   \n",
       "stdev                                3.801689e+02   \n",
       "q1                                   8.100000e+01   \n",
       "q3                                   2.950000e+02   \n",
       "median                               1.650000e+02   \n",
       "interquartile_range                  2.140000e+02   \n",
       "\n",
       "                     some_college_and_associates_degree  \\\n",
       "avg                                        8.603443e+02   \n",
       "max                                        1.274500e+04   \n",
       "min                                        0.000000e+00   \n",
       "sum                                        6.285332e+07   \n",
       "range                                      1.274500e+04   \n",
       "stdev                                      5.062105e+02   \n",
       "q1                                         4.570000e+02   \n",
       "q3                                         9.020000e+02   \n",
       "median                                     6.720000e+02   \n",
       "interquartile_range                        4.450000e+02   \n",
       "\n",
       "                     male_45_64_associates_degree  \\\n",
       "avg                                  4.302832e+01   \n",
       "max                                  8.550000e+02   \n",
       "min                                  0.000000e+00   \n",
       "sum                                  3.184139e+06   \n",
       "range                                8.550000e+02   \n",
       "stdev                                4.047851e+01   \n",
       "q1                                   1.100000e+01   \n",
       "q3                                   4.200000e+01   \n",
       "median                               2.500000e+01   \n",
       "interquartile_range                  3.100000e+01   \n",
       "\n",
       "                     male_45_64_bachelors_degree  male_45_64_graduate_degree  \\\n",
       "avg                                 1.000650e+02                6.498545e+01   \n",
       "max                                 2.547000e+03                2.448000e+03   \n",
       "min                                 0.000000e+00                0.000000e+00   \n",
       "sum                                 7.404907e+06                4.808988e+06   \n",
       "range                               2.547000e+03                2.448000e+03   \n",
       "stdev                               1.009911e+02                8.698075e+01   \n",
       "q1                                  2.400000e+01                8.000000e+00   \n",
       "q3                                  9.200000e+01                4.900000e+01   \n",
       "median                              5.200000e+01                2.400000e+01   \n",
       "interquartile_range                 6.800000e+01                4.100000e+01   \n",
       "\n",
       "                     male_45_64_less_than_9_grade  male_45_64_grade_9_12  \\\n",
       "avg                                  3.009663e+01           4.223971e+01   \n",
       "max                                  9.000000e+02           1.626000e+03   \n",
       "min                                  0.000000e+00           0.000000e+00   \n",
       "sum                                  2.227181e+06           3.125781e+06   \n",
       "range                                9.000000e+02           1.626000e+03   \n",
       "stdev                                4.965424e+01           4.571015e+01   \n",
       "q1                                   0.000000e+00           7.000000e+00   \n",
       "q3                                   1.900000e+01           3.900000e+01   \n",
       "median                               6.000000e+00           2.100000e+01   \n",
       "interquartile_range                  1.900000e+01           3.200000e+01   \n",
       "\n",
       "                     male_45_64_high_school  male_45_64_some_college  \\\n",
       "avg                            1.648105e+02             1.128077e+02   \n",
       "max                            2.084000e+03             1.628000e+03   \n",
       "min                            0.000000e+00             0.000000e+00   \n",
       "sum                            1.219614e+07             8.347879e+06   \n",
       "range                          2.084000e+03             1.628000e+03   \n",
       "stdev                          1.159105e+02             8.115282e+01   \n",
       "q1                             6.700000e+01             4.700000e+01   \n",
       "q3                             1.720000e+02             1.150000e+02   \n",
       "median                         1.180000e+02             7.900000e+01   \n",
       "interquartile_range            1.050000e+02             6.800000e+01   \n",
       "\n",
       "                     male_45_to_64  employed_pop  unemployed_pop  \\\n",
       "avg                   5.580332e+02  2.049152e+03    1.456859e+02   \n",
       "max                   6.993000e+03  2.894500e+04    1.607000e+03   \n",
       "min                   0.000000e+00  0.000000e+00    0.000000e+00   \n",
       "sum                   4.129502e+07  1.516393e+08    1.078090e+07   \n",
       "range                 6.993000e+03  2.894500e+04    1.607000e+03   \n",
       "stdev                 3.033556e+02  1.138865e+03    1.092197e+02   \n",
       "q1                    3.160000e+02  1.143000e+03    6.000000e+01   \n",
       "q3                    5.820000e+02  2.156000e+03    1.450000e+02   \n",
       "median                4.500000e+02  1.651000e+03    1.000000e+02   \n",
       "interquartile_range   2.660000e+02  1.013000e+03    8.500000e+01   \n",
       "\n",
       "                     pop_in_labor_force  not_in_labor_force  \\\n",
       "avg                        2.208707e+03        1.286299e+03   \n",
       "max                        3.055200e+04        3.414200e+04   \n",
       "min                        0.000000e+00        0.000000e+00   \n",
       "sum                        1.634465e+08        9.518743e+07   \n",
       "range                      3.055200e+04        3.414200e+04   \n",
       "stdev                      1.203354e+03        7.150048e+02   \n",
       "q1                         1.245000e+03        7.460000e+02   \n",
       "q3                         2.319000e+03        1.325000e+03   \n",
       "median                     1.793000e+03        1.026000e+03   \n",
       "interquartile_range        1.074000e+03        5.790000e+02   \n",
       "\n",
       "                     workers_16_and_over  armed_forces  civilian_labor_force  \\\n",
       "avg                         2.019454e+03  1.386930e+01          2.194838e+03   \n",
       "max                         2.825200e+04  2.121400e+04          3.055200e+04   \n",
       "min                         0.000000e+00  0.000000e+00          0.000000e+00   \n",
       "sum                         1.494416e+08  1.026342e+06          1.624202e+08   \n",
       "range                       2.825200e+04  2.121400e+04          3.055200e+04   \n",
       "stdev                       1.129133e+03  1.616169e+02          1.192208e+03   \n",
       "q1                          1.115000e+03  0.000000e+00          1.235000e+03   \n",
       "q3                          2.115000e+03  0.000000e+00          2.300000e+03   \n",
       "median                      1.622000e+03  0.000000e+00          1.780000e+03   \n",
       "interquartile_range         1.000000e+03  0.000000e+00          1.065000e+03   \n",
       "\n",
       "                     employed_agriculture_forestry_fishing_hunting_mining  \\\n",
       "avg                                                       3.827483e+01      \n",
       "max                                                       4.197000e+03      \n",
       "min                                                       0.000000e+00      \n",
       "sum                                                       2.832376e+06      \n",
       "range                                                     4.197000e+03      \n",
       "stdev                                                     8.874869e+01      \n",
       "q1                                                        0.000000e+00      \n",
       "q3                                                        1.700000e+01      \n",
       "median                                                    0.000000e+00      \n",
       "interquartile_range                                       1.700000e+01      \n",
       "\n",
       "                     employed_arts_entertainment_recreation_accommodation_food  \\\n",
       "avg                                                       1.984437e+02           \n",
       "max                                                       5.211000e+03           \n",
       "min                                                       0.000000e+00           \n",
       "sum                                                       1.468503e+07           \n",
       "range                                                     5.211000e+03           \n",
       "stdev                                                     1.600300e+02           \n",
       "q1                                                        8.200000e+01           \n",
       "q3                                                        1.940000e+02           \n",
       "median                                                    1.330000e+02           \n",
       "interquartile_range                                       1.120000e+02           \n",
       "\n",
       "                     employed_construction  employed_education_health_social  \\\n",
       "avg                           1.299722e+02                      4.733737e+02   \n",
       "max                           1.950000e+03                      9.127000e+03   \n",
       "min                           0.000000e+00                      0.000000e+00   \n",
       "sum                           9.618071e+06                      3.503013e+07   \n",
       "range                         1.950000e+03                      9.127000e+03   \n",
       "stdev                         1.116914e+02                      3.015791e+02   \n",
       "q1                            4.500000e+01                      2.340000e+02   \n",
       "q3                            1.260000e+02                      4.870000e+02   \n",
       "median                        8.200000e+01                      3.580000e+02   \n",
       "interquartile_range           8.100000e+01                      2.530000e+02   \n",
       "\n",
       "                     employed_finance_insurance_real_estate  \\\n",
       "avg                                            1.346475e+02   \n",
       "max                                            2.853000e+03   \n",
       "min                                            0.000000e+00   \n",
       "sum                                            9.964050e+06   \n",
       "range                                          2.853000e+03   \n",
       "stdev                                          1.305728e+02   \n",
       "q1                                             4.100000e+01   \n",
       "q3                                             1.250000e+02   \n",
       "median                                         7.800000e+01   \n",
       "interquartile_range                            8.400000e+01   \n",
       "\n",
       "                     employed_information  employed_manufacturing  \\\n",
       "avg                          4.315308e+01            2.104029e+02   \n",
       "max                          1.141000e+03            3.993000e+03   \n",
       "min                          0.000000e+00            0.000000e+00   \n",
       "sum                          3.193371e+06            1.557002e+07   \n",
       "range                        1.141000e+03            3.993000e+03   \n",
       "stdev                        5.400275e+01            1.826280e+02   \n",
       "q1                           7.000000e+00            6.500000e+01   \n",
       "q3                           3.700000e+01            2.040000e+02   \n",
       "median                       1.900000e+01            1.290000e+02   \n",
       "interquartile_range          3.000000e+01            1.390000e+02   \n",
       "\n",
       "                     employed_other_services_not_public_admin  \\\n",
       "avg                                              1.003838e+02   \n",
       "max                                              1.175000e+03   \n",
       "min                                              0.000000e+00   \n",
       "sum                                              7.428501e+06   \n",
       "range                                            1.175000e+03   \n",
       "stdev                                            7.619403e+01   \n",
       "q1                                               4.000000e+01   \n",
       "q3                                               1.010000e+02   \n",
       "median                                           6.900000e+01   \n",
       "interquartile_range                              6.100000e+01   \n",
       "\n",
       "                     employed_public_administration  employed_retail_trade  \\\n",
       "avg                                    9.616204e+01           2.338625e+02   \n",
       "max                                    1.816000e+03           2.808000e+03   \n",
       "min                                    0.000000e+00           0.000000e+00   \n",
       "sum                                    7.116087e+06           1.730606e+07   \n",
       "range                                  1.816000e+03           2.808000e+03   \n",
       "stdev                                  9.987631e+01           1.551951e+02   \n",
       "q1                                     2.900000e+01           1.110000e+02   \n",
       "q3                                     8.700000e+01           2.380000e+02   \n",
       "median                                 5.500000e+01           1.720000e+02   \n",
       "interquartile_range                    5.800000e+01           1.270000e+02   \n",
       "\n",
       "                     employed_science_management_admin_waste  \\\n",
       "avg                                             2.311254e+02   \n",
       "max                                             5.115000e+03   \n",
       "min                                             0.000000e+00   \n",
       "sum                                             1.710351e+07   \n",
       "range                                           5.115000e+03   \n",
       "stdev                                           2.015002e+02   \n",
       "q1                                              8.100000e+01   \n",
       "q3                                              2.200000e+02   \n",
       "median                                          1.410000e+02   \n",
       "interquartile_range                             1.390000e+02   \n",
       "\n",
       "                     employed_transportation_warehousing_utilities  \\\n",
       "avg                                                   1.043314e+02   \n",
       "max                                                   1.465000e+03   \n",
       "min                                                   0.000000e+00   \n",
       "sum                                                   7.720629e+06   \n",
       "range                                                 1.465000e+03   \n",
       "stdev                                                 9.018655e+01   \n",
       "q1                                                    3.700000e+01   \n",
       "q3                                                    1.010000e+02   \n",
       "median                                                6.600000e+01   \n",
       "interquartile_range                                   6.400000e+01   \n",
       "\n",
       "                     employed_wholesale_trade  occupation_management_arts  \\\n",
       "avg                              5.501904e+01                7.665854e+02   \n",
       "max                              1.294000e+03                1.909700e+04   \n",
       "min                              0.000000e+00                0.000000e+00   \n",
       "sum                              4.071464e+06                5.672809e+07   \n",
       "range                            1.294000e+03                1.909700e+04   \n",
       "stdev                            5.451432e+01                6.156759e+02   \n",
       "q1                               1.300000e+01                2.900000e+02   \n",
       "q3                               5.300000e+01                7.470000e+02   \n",
       "median                           3.100000e+01                4.950000e+02   \n",
       "interquartile_range              4.000000e+01                4.570000e+02   \n",
       "\n",
       "                     occupation_natural_resources_construction_maintenance  \\\n",
       "avg                                                       1.820007e+02       \n",
       "max                                                       3.973000e+03       \n",
       "min                                                       0.000000e+00       \n",
       "sum                                                       1.346823e+07       \n",
       "range                                                     3.973000e+03       \n",
       "stdev                                                     1.512020e+02       \n",
       "q1                                                        6.300000e+01       \n",
       "q3                                                        1.810000e+02       \n",
       "median                                                    1.200000e+02       \n",
       "interquartile_range                                       1.180000e+02       \n",
       "\n",
       "                     occupation_production_transportation_material  \\\n",
       "avg                                                   2.491306e+02   \n",
       "max                                                   2.495000e+03   \n",
       "min                                                   0.000000e+00   \n",
       "sum                                                   1.843592e+07   \n",
       "range                                                 2.495000e+03   \n",
       "stdev                                                 1.862018e+02   \n",
       "q1                                                    9.600000e+01   \n",
       "q3                                                    2.530000e+02   \n",
       "median                                                1.680000e+02   \n",
       "interquartile_range                                   1.570000e+02   \n",
       "\n",
       "                     occupation_sales_office  occupation_services  \\\n",
       "avg                             4.828023e+02         3.686331e+02   \n",
       "max                             6.162000e+03         3.948000e+03   \n",
       "min                             0.000000e+00         0.000000e+00   \n",
       "sum                             3.572785e+07         2.727922e+07   \n",
       "range                           6.162000e+03         3.948000e+03   \n",
       "stdev                           2.988008e+02         2.366780e+02   \n",
       "q1                              2.430000e+02         1.860000e+02   \n",
       "q3                              5.010000e+02         3.740000e+02   \n",
       "median                          3.690000e+02         2.740000e+02   \n",
       "interquartile_range             2.580000e+02         1.880000e+02   \n",
       "\n",
       "                     management_business_sci_arts_employed  \\\n",
       "avg                                           7.665854e+02   \n",
       "max                                           1.909700e+04   \n",
       "min                                           0.000000e+00   \n",
       "sum                                           5.672809e+07   \n",
       "range                                         1.909700e+04   \n",
       "stdev                                         6.156759e+02   \n",
       "q1                                            2.900000e+02   \n",
       "q3                                            7.470000e+02   \n",
       "median                                        4.950000e+02   \n",
       "interquartile_range                           4.570000e+02   \n",
       "\n",
       "                     sales_office_employed  in_grades_1_to_4  \\\n",
       "avg                           4.828023e+02      2.228999e+02   \n",
       "max                           6.162000e+03      5.927000e+03   \n",
       "min                           0.000000e+00      0.000000e+00   \n",
       "sum                           3.572785e+07      1.649482e+07   \n",
       "range                         6.162000e+03      5.927000e+03   \n",
       "stdev                         2.988008e+02      1.687454e+02   \n",
       "q1                            2.430000e+02      9.700000e+01   \n",
       "q3                            5.010000e+02      2.240000e+02   \n",
       "median                        3.690000e+02      1.580000e+02   \n",
       "interquartile_range           2.580000e+02      1.270000e+02   \n",
       "\n",
       "                     in_grades_5_to_8  in_grades_9_to_12     in_school  \\\n",
       "avg                      2.253273e+02       2.322261e+02  1.116528e+03   \n",
       "max                      5.674000e+03       4.965000e+03  2.398900e+04   \n",
       "min                      0.000000e+00       0.000000e+00  0.000000e+00   \n",
       "sum                      1.667444e+07       1.718497e+07  8.262417e+07   \n",
       "range                    5.674000e+03       4.965000e+03  2.398900e+04   \n",
       "stdev                    1.692225e+02       1.687234e+02  7.662855e+02   \n",
       "q1                       9.700000e+01       1.020000e+02  5.600000e+02   \n",
       "q3                       2.260000e+02       2.360000e+02  1.120000e+03   \n",
       "median                   1.610000e+02       1.650000e+02  8.330000e+02   \n",
       "interquartile_range      1.290000e+02       1.340000e+02  5.600000e+02   \n",
       "\n",
       "                     in_undergrad_college speak_only_english_at_home  \\\n",
       "avg                          2.557242e+02                       None   \n",
       "max                          1.298500e+04                       None   \n",
       "min                          0.000000e+00                       None   \n",
       "sum                          1.892385e+07                       None   \n",
       "range                        1.298500e+04                       None   \n",
       "stdev                        4.083119e+02                       None   \n",
       "q1                           8.400000e+01                       None   \n",
       "q3                           2.120000e+02                       None   \n",
       "median                       1.420000e+02                       None   \n",
       "interquartile_range          1.280000e+02                       None   \n",
       "\n",
       "                    speak_spanish_at_home speak_spanish_at_home_low_english  \n",
       "avg                                  None                              None  \n",
       "max                                  None                              None  \n",
       "min                                  None                              None  \n",
       "sum                                  None                              None  \n",
       "range                                None                              None  \n",
       "stdev                                None                              None  \n",
       "q1                                   None                              None  \n",
       "q3                                   None                              None  \n",
       "median                               None                              None  \n",
       "interquartile_range                  None                              None  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_ds.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.4.5. Visualization of dataset coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 674
    },
    "id": "vLWOeAhn9Wf1",
    "outputId": "e4e46f9f-c9e2-4eb4-cd24-9ab08be22a71"
   },
   "outputs": [
    {
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       "  function parseChrome(line) {\n",
       "    const parts = chromeRe.exec(line);\n",
       "\n",
       "    if (!parts) {\n",
       "      return null;\n",
       "    }\n",
       "\n",
       "    const isNative = parts[2] && parts[2].indexOf('native') === 0; // start of line\n",
       "    const isEval = parts[2] && parts[2].indexOf('eval') === 0; // start of line\n",
       "\n",
       "    const submatch = chromeEvalRe.exec(parts[2]);\n",
       "    if (isEval && submatch != null) {\n",
       "      // throw out eval line/column and use top-most line/column number\n",
       "      parts[2] = submatch[1]; // url\n",
       "      parts[3] = submatch[2]; // line\n",
       "      parts[4] = submatch[3]; // column\n",
       "    }\n",
       "\n",
       "    return {\n",
       "      file: !isNative ? parts[2] : null,\n",
       "      methodName: parts[1] || UNKNOWN_FUNCTION,\n",
       "      arguments: isNative ? [parts[2]] : [],\n",
       "      lineNumber: parts[3] ? +parts[3] : null,\n",
       "      column: parts[4] ? +parts[4] : null,\n",
       "    };\n",
       "  }\n",
       "\n",
       "  function parseWinjs(line) {\n",
       "    const parts = winjsRe.exec(line);\n",
       "\n",
       "    if (!parts) {\n",
       "      return null;\n",
       "    }\n",
       "\n",
       "    return {\n",
       "      file: parts[2],\n",
       "      methodName: parts[1] || UNKNOWN_FUNCTION,\n",
       "      arguments: [],\n",
       "      lineNumber: +parts[3],\n",
       "      column: parts[4] ? +parts[4] : null,\n",
       "    };\n",
       "  }\n",
       "\n",
       "  function parseGecko(line) {\n",
       "    const parts = geckoRe.exec(line);\n",
       "\n",
       "    if (!parts) {\n",
       "      return null;\n",
       "    }\n",
       "\n",
       "    const isEval = parts[3] && parts[3].indexOf(' > eval') > -1;\n",
       "\n",
       "    const submatch = geckoEvalRe.exec(parts[3]);\n",
       "    if (isEval && submatch != null) {\n",
       "      // throw out eval line/column and use top-most line number\n",
       "      parts[3] = submatch[1];\n",
       "      parts[4] = submatch[2];\n",
       "      parts[5] = null; // no column when eval\n",
       "    }\n",
       "\n",
       "    return {\n",
       "      file: parts[3],\n",
       "      methodName: parts[1] || UNKNOWN_FUNCTION,\n",
       "      arguments: parts[2] ? parts[2].split(',') : [],\n",
       "      lineNumber: parts[4] ? +parts[4] : null,\n",
       "      column: parts[5] ? +parts[5] : null,\n",
       "    };\n",
       "  }\n",
       "\n",
       "  function displayError(e) {\n",
       "    const error$ = document.getElementById('error-container');\n",
       "    const errors$ = error$.getElementsByClassName('errors');\n",
       "    const stacktrace$ = document.getElementById('error-stacktrace');\n",
       "\n",
       "    errors$[0].innerHTML = e.name;\n",
       "    errors$[1].innerHTML = e.type;\n",
       "    errors$[2].innerHTML = e.message.replace(e.type, '');\n",
       "\n",
       "    error$.style.visibility = 'visible';\n",
       "\n",
       "    const stack = parse(e.stack);\n",
       "    const list = stack.map(item => {\n",
       "      return `<li>\n",
       "      at <span class=&quot;stacktrace-method&quot;>${item.methodName}:</span>\n",
       "      (${item.file}:${item.lineNumber}:${item.column})\n",
       "    </li>`;\n",
       "    });\n",
       "\n",
       "    stacktrace$.innerHTML = list.join('\\n');\n",
       "  }\n",
       "\n",
       "  // Computes the decimal coefficient and exponent of the specified number x with\n",
       "  // significant digits p, where x is positive and p is in [1, 21] or undefined.\n",
       "  // For example, formatDecimal(1.23) returns [&quot;123&quot;, 0].\n",
       "  function formatDecimal(x, p) {\n",
       "    if ((i = (x = p ? x.toExponential(p - 1) : x.toExponential()).indexOf(&quot;e&quot;)) < 0) return null; // NaN, ±Infinity\n",
       "    var i, coefficient = x.slice(0, i);\n",
       "\n",
       "    // The string returned by toExponential either has the form \\d\\.\\d+e[-+]\\d+\n",
       "    // (e.g., 1.2e+3) or the form \\de[-+]\\d+ (e.g., 1e+3).\n",
       "    return [\n",
       "      coefficient.length > 1 ? coefficient[0] + coefficient.slice(2) : coefficient,\n",
       "      +x.slice(i + 1)\n",
       "    ];\n",
       "  }\n",
       "\n",
       "  function exponent(x) {\n",
       "    return x = formatDecimal(Math.abs(x)), x ? x[1] : NaN;\n",
       "  }\n",
       "\n",
       "  function formatGroup(grouping, thousands) {\n",
       "    return function(value, width) {\n",
       "      var i = value.length,\n",
       "          t = [],\n",
       "          j = 0,\n",
       "          g = grouping[0],\n",
       "          length = 0;\n",
       "\n",
       "      while (i > 0 && g > 0) {\n",
       "        if (length + g + 1 > width) g = Math.max(1, width - length);\n",
       "        t.push(value.substring(i -= g, i + g));\n",
       "        if ((length += g + 1) > width) break;\n",
       "        g = grouping[j = (j + 1) % grouping.length];\n",
       "      }\n",
       "\n",
       "      return t.reverse().join(thousands);\n",
       "    };\n",
       "  }\n",
       "\n",
       "  function formatNumerals(numerals) {\n",
       "    return function(value) {\n",
       "      return value.replace(/[0-9]/g, function(i) {\n",
       "        return numerals[+i];\n",
       "      });\n",
       "    };\n",
       "  }\n",
       "\n",
       "  // [[fill]align][sign][symbol][0][width][,][.precision][~][type]\n",
       "  var re = /^(?:(.)?([<>=^]))?([+\\-( ])?([$#])?(0)?(\\d+)?(,)?(\\.\\d+)?(~)?([a-z%])?$/i;\n",
       "\n",
       "  function formatSpecifier(specifier) {\n",
       "    if (!(match = re.exec(specifier))) throw new Error(&quot;invalid format: &quot; + specifier);\n",
       "    var match;\n",
       "    return new FormatSpecifier({\n",
       "      fill: match[1],\n",
       "      align: match[2],\n",
       "      sign: match[3],\n",
       "      symbol: match[4],\n",
       "      zero: match[5],\n",
       "      width: match[6],\n",
       "      comma: match[7],\n",
       "      precision: match[8] && match[8].slice(1),\n",
       "      trim: match[9],\n",
       "      type: match[10]\n",
       "    });\n",
       "  }\n",
       "\n",
       "  formatSpecifier.prototype = FormatSpecifier.prototype; // instanceof\n",
       "\n",
       "  function FormatSpecifier(specifier) {\n",
       "    this.fill = specifier.fill === undefined ? &quot; &quot; : specifier.fill + &quot;&quot;;\n",
       "    this.align = specifier.align === undefined ? &quot;>&quot; : specifier.align + &quot;&quot;;\n",
       "    this.sign = specifier.sign === undefined ? &quot;-&quot; : specifier.sign + &quot;&quot;;\n",
       "    this.symbol = specifier.symbol === undefined ? &quot;&quot; : specifier.symbol + &quot;&quot;;\n",
       "    this.zero = !!specifier.zero;\n",
       "    this.width = specifier.width === undefined ? undefined : +specifier.width;\n",
       "    this.comma = !!specifier.comma;\n",
       "    this.precision = specifier.precision === undefined ? undefined : +specifier.precision;\n",
       "    this.trim = !!specifier.trim;\n",
       "    this.type = specifier.type === undefined ? &quot;&quot; : specifier.type + &quot;&quot;;\n",
       "  }\n",
       "\n",
       "  FormatSpecifier.prototype.toString = function() {\n",
       "    return this.fill\n",
       "        + this.align\n",
       "        + this.sign\n",
       "        + this.symbol\n",
       "        + (this.zero ? &quot;0&quot; : &quot;&quot;)\n",
       "        + (this.width === undefined ? &quot;&quot; : Math.max(1, this.width | 0))\n",
       "        + (this.comma ? &quot;,&quot; : &quot;&quot;)\n",
       "        + (this.precision === undefined ? &quot;&quot; : &quot;.&quot; + Math.max(0, this.precision | 0))\n",
       "        + (this.trim ? &quot;~&quot; : &quot;&quot;)\n",
       "        + this.type;\n",
       "  };\n",
       "\n",
       "  // Trims insignificant zeros, e.g., replaces 1.2000k with 1.2k.\n",
       "  function formatTrim(s) {\n",
       "    out: for (var n = s.length, i = 1, i0 = -1, i1; i < n; ++i) {\n",
       "      switch (s[i]) {\n",
       "        case &quot;.&quot;: i0 = i1 = i; break;\n",
       "        case &quot;0&quot;: if (i0 === 0) i0 = i; i1 = i; break;\n",
       "        default: if (!+s[i]) break out; if (i0 > 0) i0 = 0; break;\n",
       "      }\n",
       "    }\n",
       "    return i0 > 0 ? s.slice(0, i0) + s.slice(i1 + 1) : s;\n",
       "  }\n",
       "\n",
       "  var prefixExponent;\n",
       "\n",
       "  function formatPrefixAuto(x, p) {\n",
       "    var d = formatDecimal(x, p);\n",
       "    if (!d) return x + &quot;&quot;;\n",
       "    var coefficient = d[0],\n",
       "        exponent = d[1],\n",
       "        i = exponent - (prefixExponent = Math.max(-8, Math.min(8, Math.floor(exponent / 3))) * 3) + 1,\n",
       "        n = coefficient.length;\n",
       "    return i === n ? coefficient\n",
       "        : i > n ? coefficient + new Array(i - n + 1).join(&quot;0&quot;)\n",
       "        : i > 0 ? coefficient.slice(0, i) + &quot;.&quot; + coefficient.slice(i)\n",
       "        : &quot;0.&quot; + new Array(1 - i).join(&quot;0&quot;) + formatDecimal(x, Math.max(0, p + i - 1))[0]; // less than 1y!\n",
       "  }\n",
       "\n",
       "  function formatRounded(x, p) {\n",
       "    var d = formatDecimal(x, p);\n",
       "    if (!d) return x + &quot;&quot;;\n",
       "    var coefficient = d[0],\n",
       "        exponent = d[1];\n",
       "    return exponent < 0 ? &quot;0.&quot; + new Array(-exponent).join(&quot;0&quot;) + coefficient\n",
       "        : coefficient.length > exponent + 1 ? coefficient.slice(0, exponent + 1) + &quot;.&quot; + coefficient.slice(exponent + 1)\n",
       "        : coefficient + new Array(exponent - coefficient.length + 2).join(&quot;0&quot;);\n",
       "  }\n",
       "\n",
       "  var formatTypes = {\n",
       "    &quot;%&quot;: function(x, p) { return (x * 100).toFixed(p); },\n",
       "    &quot;b&quot;: function(x) { return Math.round(x).toString(2); },\n",
       "    &quot;c&quot;: function(x) { return x + &quot;&quot;; },\n",
       "    &quot;d&quot;: function(x) { return Math.round(x).toString(10); },\n",
       "    &quot;e&quot;: function(x, p) { return x.toExponential(p); },\n",
       "    &quot;f&quot;: function(x, p) { return x.toFixed(p); },\n",
       "    &quot;g&quot;: function(x, p) { return x.toPrecision(p); },\n",
       "    &quot;o&quot;: function(x) { return Math.round(x).toString(8); },\n",
       "    &quot;p&quot;: function(x, p) { return formatRounded(x * 100, p); },\n",
       "    &quot;r&quot;: formatRounded,\n",
       "    &quot;s&quot;: formatPrefixAuto,\n",
       "    &quot;X&quot;: function(x) { return Math.round(x).toString(16).toUpperCase(); },\n",
       "    &quot;x&quot;: function(x) { return Math.round(x).toString(16); }\n",
       "  };\n",
       "\n",
       "  function identity(x) {\n",
       "    return x;\n",
       "  }\n",
       "\n",
       "  var map = Array.prototype.map,\n",
       "      prefixes = [&quot;y&quot;,&quot;z&quot;,&quot;a&quot;,&quot;f&quot;,&quot;p&quot;,&quot;n&quot;,&quot;µ&quot;,&quot;m&quot;,&quot;&quot;,&quot;k&quot;,&quot;M&quot;,&quot;G&quot;,&quot;T&quot;,&quot;P&quot;,&quot;E&quot;,&quot;Z&quot;,&quot;Y&quot;];\n",
       "\n",
       "  function formatLocale(locale) {\n",
       "    var group = locale.grouping === undefined || locale.thousands === undefined ? identity : formatGroup(map.call(locale.grouping, Number), locale.thousands + &quot;&quot;),\n",
       "        currencyPrefix = locale.currency === undefined ? &quot;&quot; : locale.currency[0] + &quot;&quot;,\n",
       "        currencySuffix = locale.currency === undefined ? &quot;&quot; : locale.currency[1] + &quot;&quot;,\n",
       "        decimal = locale.decimal === undefined ? &quot;.&quot; : locale.decimal + &quot;&quot;,\n",
       "        numerals = locale.numerals === undefined ? identity : formatNumerals(map.call(locale.numerals, String)),\n",
       "        percent = locale.percent === undefined ? &quot;%&quot; : locale.percent + &quot;&quot;,\n",
       "        minus = locale.minus === undefined ? &quot;-&quot; : locale.minus + &quot;&quot;,\n",
       "        nan = locale.nan === undefined ? &quot;NaN&quot; : locale.nan + &quot;&quot;;\n",
       "\n",
       "    function newFormat(specifier) {\n",
       "      specifier = formatSpecifier(specifier);\n",
       "\n",
       "      var fill = specifier.fill,\n",
       "          align = specifier.align,\n",
       "          sign = specifier.sign,\n",
       "          symbol = specifier.symbol,\n",
       "          zero = specifier.zero,\n",
       "          width = specifier.width,\n",
       "          comma = specifier.comma,\n",
       "          precision = specifier.precision,\n",
       "          trim = specifier.trim,\n",
       "          type = specifier.type;\n",
       "\n",
       "      // The &quot;n&quot; type is an alias for &quot;,g&quot;.\n",
       "      if (type === &quot;n&quot;) comma = true, type = &quot;g&quot;;\n",
       "\n",
       "      // The &quot;&quot; type, and any invalid type, is an alias for &quot;.12~g&quot;.\n",
       "      else if (!formatTypes[type]) precision === undefined && (precision = 12), trim = true, type = &quot;g&quot;;\n",
       "\n",
       "      // If zero fill is specified, padding goes after sign and before digits.\n",
       "      if (zero || (fill === &quot;0&quot; && align === &quot;=&quot;)) zero = true, fill = &quot;0&quot;, align = &quot;=&quot;;\n",
       "\n",
       "      // Compute the prefix and suffix.\n",
       "      // For SI-prefix, the suffix is lazily computed.\n",
       "      var prefix = symbol === &quot;$&quot; ? currencyPrefix : symbol === &quot;#&quot; && /[boxX]/.test(type) ? &quot;0&quot; + type.toLowerCase() : &quot;&quot;,\n",
       "          suffix = symbol === &quot;$&quot; ? currencySuffix : /[%p]/.test(type) ? percent : &quot;&quot;;\n",
       "\n",
       "      // What format function should we use?\n",
       "      // Is this an integer type?\n",
       "      // Can this type generate exponential notation?\n",
       "      var formatType = formatTypes[type],\n",
       "          maybeSuffix = /[defgprs%]/.test(type);\n",
       "\n",
       "      // Set the default precision if not specified,\n",
       "      // or clamp the specified precision to the supported range.\n",
       "      // For significant precision, it must be in [1, 21].\n",
       "      // For fixed precision, it must be in [0, 20].\n",
       "      precision = precision === undefined ? 6\n",
       "          : /[gprs]/.test(type) ? Math.max(1, Math.min(21, precision))\n",
       "          : Math.max(0, Math.min(20, precision));\n",
       "\n",
       "      function format(value) {\n",
       "        var valuePrefix = prefix,\n",
       "            valueSuffix = suffix,\n",
       "            i, n, c;\n",
       "\n",
       "        if (type === &quot;c&quot;) {\n",
       "          valueSuffix = formatType(value) + valueSuffix;\n",
       "          value = &quot;&quot;;\n",
       "        } else {\n",
       "          value = +value;\n",
       "\n",
       "          // Determine the sign. -0 is not less than 0, but 1 / -0 is!\n",
       "          var valueNegative = value < 0 || 1 / value < 0;\n",
       "\n",
       "          // Perform the initial formatting.\n",
       "          value = isNaN(value) ? nan : formatType(Math.abs(value), precision);\n",
       "\n",
       "          // Trim insignificant zeros.\n",
       "          if (trim) value = formatTrim(value);\n",
       "\n",
       "          // If a negative value rounds to zero after formatting, and no explicit positive sign is requested, hide the sign.\n",
       "          if (valueNegative && +value === 0 && sign !== &quot;+&quot;) valueNegative = false;\n",
       "\n",
       "          // Compute the prefix and suffix.\n",
       "          valuePrefix = (valueNegative ? (sign === &quot;(&quot; ? sign : minus) : sign === &quot;-&quot; || sign === &quot;(&quot; ? &quot;&quot; : sign) + valuePrefix;\n",
       "          valueSuffix = (type === &quot;s&quot; ? prefixes[8 + prefixExponent / 3] : &quot;&quot;) + valueSuffix + (valueNegative && sign === &quot;(&quot; ? &quot;)&quot; : &quot;&quot;);\n",
       "\n",
       "          // Break the formatted value into the integer “value” part that can be\n",
       "          // grouped, and fractional or exponential “suffix” part that is not.\n",
       "          if (maybeSuffix) {\n",
       "            i = -1, n = value.length;\n",
       "            while (++i < n) {\n",
       "              if (c = value.charCodeAt(i), 48 > c || c > 57) {\n",
       "                valueSuffix = (c === 46 ? decimal + value.slice(i + 1) : value.slice(i)) + valueSuffix;\n",
       "                value = value.slice(0, i);\n",
       "                break;\n",
       "              }\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "\n",
       "        // If the fill character is not &quot;0&quot;, grouping is applied before padding.\n",
       "        if (comma && !zero) value = group(value, Infinity);\n",
       "\n",
       "        // Compute the padding.\n",
       "        var length = valuePrefix.length + value.length + valueSuffix.length,\n",
       "            padding = length < width ? new Array(width - length + 1).join(fill) : &quot;&quot;;\n",
       "\n",
       "        // If the fill character is &quot;0&quot;, grouping is applied after padding.\n",
       "        if (comma && zero) value = group(padding + value, padding.length ? width - valueSuffix.length : Infinity), padding = &quot;&quot;;\n",
       "\n",
       "        // Reconstruct the final output based on the desired alignment.\n",
       "        switch (align) {\n",
       "          case &quot;<&quot;: value = valuePrefix + value + valueSuffix + padding; break;\n",
       "          case &quot;=&quot;: value = valuePrefix + padding + value + valueSuffix; break;\n",
       "          case &quot;^&quot;: value = padding.slice(0, length = padding.length >> 1) + valuePrefix + value + valueSuffix + padding.slice(length); break;\n",
       "          default: value = padding + valuePrefix + value + valueSuffix; break;\n",
       "        }\n",
       "\n",
       "        return numerals(value);\n",
       "      }\n",
       "\n",
       "      format.toString = function() {\n",
       "        return specifier + &quot;&quot;;\n",
       "      };\n",
       "\n",
       "      return format;\n",
       "    }\n",
       "\n",
       "    function formatPrefix(specifier, value) {\n",
       "      var f = newFormat((specifier = formatSpecifier(specifier), specifier.type = &quot;f&quot;, specifier)),\n",
       "          e = Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3,\n",
       "          k = Math.pow(10, -e),\n",
       "          prefix = prefixes[8 + e / 3];\n",
       "      return function(value) {\n",
       "        return f(k * value) + prefix;\n",
       "      };\n",
       "    }\n",
       "\n",
       "    return {\n",
       "      format: newFormat,\n",
       "      formatPrefix: formatPrefix\n",
       "    };\n",
       "  }\n",
       "\n",
       "  var locale;\n",
       "  var format;\n",
       "  var formatPrefix;\n",
       "\n",
       "  defaultLocale({\n",
       "    decimal: &quot;.&quot;,\n",
       "    thousands: &quot;,&quot;,\n",
       "    grouping: [3],\n",
       "    currency: [&quot;$&quot;, &quot;&quot;],\n",
       "    minus: &quot;-&quot;\n",
       "  });\n",
       "\n",
       "  function defaultLocale(definition) {\n",
       "    locale = formatLocale(definition);\n",
       "    format = locale.format;\n",
       "    formatPrefix = locale.formatPrefix;\n",
       "    return locale;\n",
       "  }\n",
       "\n",
       "  function formatter(value, specifier) {\n",
       "    const formatFunc = specifier ? format(specifier) : formatValue;\n",
       "\n",
       "    if (Array.isArray(value)) {\n",
       "      const [first, second] = value;\n",
       "      if (first === -Infinity) {\n",
       "        return `< ${formatFunc(second)}`;\n",
       "      }\n",
       "      if (second === Infinity) {\n",
       "        return `> ${formatFunc(first)}`;\n",
       "      }\n",
       "      return `${formatFunc(first)} - ${formatFunc(second)}`;\n",
       "    }\n",
       "    return formatFunc(value);\n",
       "  }\n",
       "\n",
       "  function formatValue(value) {\n",
       "    if (typeof value === 'number') {\n",
       "      return formatNumber(value);\n",
       "    }\n",
       "    return value;\n",
       "  }\n",
       "\n",
       "  function formatNumber(value) {\n",
       "    if (!Number.isInteger(value)) {\n",
       "      return value.toLocaleString(undefined, {\n",
       "        minimumFractionDigits: 2,\n",
       "        maximumFractionDigits: 3\n",
       "      });\n",
       "    }\n",
       "    return value.toLocaleString();\n",
       "  }\n",
       "\n",
       "  function updateViewport(id, map) {\n",
       "    function updateMapInfo() {\n",
       "      const mapInfo$ = document.getElementById(id);\n",
       "      const center = map.getCenter();\n",
       "      const lat = center.lat.toFixed(6);\n",
       "      const lng = center.lng.toFixed(6);\n",
       "      const zoom = map.getZoom().toFixed(2);\n",
       "\n",
       "      mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n",
       "    }\n",
       "\n",
       "    updateMapInfo();\n",
       "\n",
       "    map.on('zoom', updateMapInfo);\n",
       "    map.on('move', updateMapInfo);\n",
       "  }\n",
       "\n",
       "  function getBasecolorSettings(basecolor) {\n",
       "    return {\n",
       "      'version': 8,\n",
       "      'sources': {},\n",
       "      'layers': [{\n",
       "          'id': 'background',\n",
       "          'type': 'background',\n",
       "          'paint': {\n",
       "              'background-color': basecolor\n",
       "          }\n",
       "      }]\n",
       "    };\n",
       "  }\n",
       "\n",
       "  function getImageElement(mapIndex) {\n",
       "    const id = mapIndex !== undefined ? `map-image-${mapIndex}` : 'map-image';\n",
       "    return document.getElementById(id);\n",
       "  }\n",
       "\n",
       "  function getContainerElement(mapIndex) {\n",
       "    const id = mapIndex !== undefined ? `main-container-${mapIndex}` : 'main-container';\n",
       "    return document.getElementById(id);\n",
       "  }\n",
       "\n",
       "  function saveImage(mapIndex) {\n",
       "    const img = getImageElement(mapIndex);\n",
       "    const container = getContainerElement(mapIndex);\n",
       "\n",
       "    html2canvas(container)\n",
       "      .then((canvas) => setMapImage(canvas, img, container));\n",
       "  }\n",
       "\n",
       "  function setMapImage(canvas, img, container) {\n",
       "    const src = canvas.toDataURL();\n",
       "    img.setAttribute('src', src);\n",
       "    img.style.display = 'block';\n",
       "    container.style.display = 'none';\n",
       "  }\n",
       "\n",
       "  function resetPopupClick(interactivity) {\n",
       "    interactivity.off('featureClick');\n",
       "  }\n",
       "\n",
       "  function resetPopupHover(interactivity) {\n",
       "    interactivity.off('featureHover');\n",
       "  }\n",
       "\n",
       "  function setPopupsClick(map, clickPopup, hoverPopup, interactivity, attrs) {\n",
       "    interactivity.on('featureClick', (event) => {\n",
       "      updatePopup(map, clickPopup, event, attrs);\n",
       "      hoverPopup.remove();\n",
       "    });\n",
       "  }\n",
       "\n",
       "  function setPopupsHover(map, hoverPopup, interactivity, attrs) {\n",
       "    interactivity.on('featureHover', (event) => {\n",
       "      updatePopup(map, hoverPopup, event, attrs);\n",
       "    });\n",
       "  }\n",
       "\n",
       "  function updatePopup(map, popup, event, attrs) {\n",
       "    if (event.features.length > 0) {\n",
       "      let popupHTML = '';\n",
       "      const layerIDs = [];\n",
       "\n",
       "      for (const feature of event.features) {\n",
       "        if (layerIDs.includes(feature.layerId)) {\n",
       "          continue;\n",
       "        }\n",
       "        // Track layers to add only one feature per layer\n",
       "        layerIDs.push(feature.layerId);\n",
       "\n",
       "        for (const item of attrs) {\n",
       "          const variable = feature.variables[item.name];\n",
       "          if (variable) {\n",
       "            let value = variable.value;\n",
       "            value = formatter(value, item.format);\n",
       "\n",
       "            popupHTML = `\n",
       "            <span class=&quot;popup-name&quot;>${item.title}</span>\n",
       "            <span class=&quot;popup-value&quot;>${value}</span>\n",
       "          ` + popupHTML;\n",
       "          }\n",
       "        }\n",
       "      }\n",
       "\n",
       "      if (popupHTML) {\n",
       "        popup\n",
       "            .setLngLat([event.coordinates.lng, event.coordinates.lat])\n",
       "            .setHTML(`<div class=&quot;popup-content&quot;>${popupHTML}</div>`);\n",
       "\n",
       "        if (!popup.isOpen()) {\n",
       "          popup.addTo(map);\n",
       "        }\n",
       "      } else {\n",
       "        popup.remove();\n",
       "      }\n",
       "    } else {\n",
       "      popup.remove();\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function setInteractivity(map, interactiveLayers, interactiveMapLayers) {\n",
       "    const interactivity = new carto.Interactivity(interactiveMapLayers);\n",
       "\n",
       "    const clickPopup = new mapboxgl.Popup({\n",
       "      closeButton: true,\n",
       "      closeOnClick: false\n",
       "    });\n",
       "\n",
       "    const hoverPopup = new mapboxgl.Popup({\n",
       "      closeButton: false,\n",
       "      closeOnClick: false\n",
       "    });\n",
       "\n",
       "    const { clickAttrs, hoverAttrs } = _setInteractivityAttrs(interactiveLayers);\n",
       "\n",
       "    resetPopupClick(map);\n",
       "    resetPopupHover(map);\n",
       "\n",
       "    if (clickAttrs.length > 0) {\n",
       "      setPopupsClick(map, clickPopup, hoverPopup, interactivity, clickAttrs);\n",
       "    }\n",
       "\n",
       "    if (hoverAttrs.length > 0) {\n",
       "      setPopupsHover(map, hoverPopup, interactivity, hoverAttrs);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function _setInteractivityAttrs(interactiveLayers) {\n",
       "    let clickAttrs = [];\n",
       "    let hoverAttrs = [];\n",
       "\n",
       "    interactiveLayers.forEach((interactiveLayer) => {\n",
       "      interactiveLayer.interactivity.forEach((interactivityDef) => {\n",
       "        if (interactivityDef.event === 'click') {\n",
       "          clickAttrs = clickAttrs.concat(interactivityDef.attrs);\n",
       "        } else if (interactivityDef.event === 'hover') {\n",
       "          hoverAttrs = hoverAttrs.concat(interactivityDef.attrs);\n",
       "        }\n",
       "      });\n",
       "    });\n",
       "\n",
       "    return { clickAttrs, hoverAttrs };\n",
       "  }\n",
       "\n",
       "  function renderWidget(widget, value) {\n",
       "    widget.element = widget.element || document.querySelector(`#${widget.id}-value`);\n",
       "\n",
       "    if (value && widget.element) {\n",
       "      widget.element.innerText = typeof value === 'number' ? formatter(value, widget.options.format) : value;\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function renderBridge(bridge, widget, mapLayer) {\n",
       "    widget.element = widget.element || document.querySelector(`#${widget.id}`);\n",
       "\n",
       "    switch (widget.type) {\n",
       "      case 'histogram':\n",
       "        const type = _getWidgetType(mapLayer, widget.value, widget.prop);\n",
       "        const histogram = type === 'category' ? 'categoricalHistogram' : 'numericalHistogram';\n",
       "        bridge[histogram](widget.element, widget.value, widget.options);\n",
       "        break;\n",
       "      case 'category':\n",
       "        bridge.category(widget.element, widget.value, widget.options);\n",
       "        break;\n",
       "      case 'animation':\n",
       "        widget.options.propertyName = widget.prop;\n",
       "        bridge.animationControls(widget.element, widget.value, widget.options);\n",
       "        break;\n",
       "      case 'time-series':\n",
       "        widget.options.propertyName = widget.prop;\n",
       "        bridge.timeSeries(widget.element, widget.value, widget.options);\n",
       "        break;\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function bridgeLayerWidgets(map, mapLayer, mapSource, widgets) {\n",
       "    const bridge = new AsBridge.VL.Bridge({\n",
       "      carto: carto,\n",
       "      layer: mapLayer,\n",
       "      source: mapSource,\n",
       "      map: map\n",
       "    });\n",
       "\n",
       "    widgets\n",
       "      .filter((widget) => widget.has_bridge)\n",
       "      .forEach((widget) => renderBridge(bridge, widget, mapLayer));\n",
       "\n",
       "    bridge.build();\n",
       "  }\n",
       "\n",
       "  function _getWidgetType(layer, property, value) {\n",
       "    return layer.metadata && layer.metadata.properties[value] ?\n",
       "      layer.metadata.properties[value].type\n",
       "      : _getWidgetPropertyType(layer, property);\n",
       "  }\n",
       "\n",
       "  function _getWidgetPropertyType(layer, property) {\n",
       "    return layer.metadata && layer.metadata.properties[property] ?\n",
       "      layer.metadata.properties[property].type\n",
       "      : null;\n",
       "  }\n",
       "\n",
       "  function createLegends(layer, legends, layerIndex, mapIndex=0) {\n",
       "    if (legends.length) {\n",
       "      legends.forEach((legend, legendIndex) => _createLegend(layer, legend, layerIndex, legendIndex, mapIndex));\n",
       "    } else {\n",
       "      _createLegend(layer, legends, layerIndex, 0, mapIndex);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function _createLegend(layer, legend, layerIndex, legendIndex, mapIndex=0) {\n",
       "    const element = document.querySelector(`#layer${layerIndex}_map${mapIndex}_legend${legendIndex}`);\n",
       "\n",
       "    if (legend.prop) {\n",
       "      const othersLabel = 'Others';   // TODO: i18n\n",
       "      const prop = legend.prop;\n",
       "      const dynamic = legend.dynamic;\n",
       "      const order = legend.ascending ? 'ASC' : 'DESC';\n",
       "      const variable = legend.variable;\n",
       "      const config = { othersLabel, variable, order };\n",
       "      const formatFunc = (value) => formatter(value, legend.format);\n",
       "      const options = { format: formatFunc, config, dynamic };\n",
       "\n",
       "      if (legend.type.startsWith('size-continuous')) {\n",
       "        config.samples = 4;\n",
       "      }\n",
       "\n",
       "      AsBridge.VL.Legends.rampLegend(element, layer, prop, options);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function SourceFactory() {\n",
       "    const sourceTypes = { GeoJSON, Query, MVT };\n",
       "\n",
       "    this.createSource = (layer) => {\n",
       "      return sourceTypes[layer.type](layer);\n",
       "    };\n",
       "  }\n",
       "\n",
       "  function GeoJSON(layer) {\n",
       "    const options = JSON.parse(JSON.stringify(layer.options));\n",
       "    const data = _decodeJSONData(layer.data, layer.encode_data);\n",
       "\n",
       "    return new carto.source.GeoJSON(data, options);\n",
       "  }\n",
       "\n",
       "  function Query(layer) {\n",
       "    const auth = {\n",
       "      username: layer.credentials.username,\n",
       "      apiKey: layer.credentials.api_key || 'default_public'\n",
       "    };\n",
       "\n",
       "    const config = {\n",
       "      serverURL: layer.credentials.base_url || `https://${layer.credentials.username}.carto.com/`\n",
       "    };\n",
       "\n",
       "    return new carto.source.SQL(layer.data, auth, config);\n",
       "  }\n",
       "\n",
       "  function MVT(layer) {\n",
       "    return new carto.source.MVT(layer.data.file, JSON.parse(layer.data.metadata));\n",
       "  }\n",
       "\n",
       "  function _decodeJSONData(data, encodeData) {\n",
       "    try {\n",
       "      if (encodeData) {\n",
       "        const decodedJSON = pako.inflate(atob(data), { to: 'string' });\n",
       "        return JSON.parse(decodedJSON);\n",
       "      } else {\n",
       "        return JSON.parse(data);\n",
       "      }\n",
       "    } catch(error) {\n",
       "      throw new Error(`\n",
       "      Error: &quot;${error}&quot;. CARTOframes is not able to parse your local data because it is too large.\n",
       "      Please, disable the data compresion with encode_data=False in your Layer class.\n",
       "    `);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  const factory = new SourceFactory();\n",
       "\n",
       "  function initMapLayer(layer, layerIndex, numLayers, hasLegends, map, mapIndex) {\n",
       "    const mapSource = factory.createSource(layer);\n",
       "    const mapViz = new carto.Viz(layer.viz);\n",
       "    const mapLayer = new carto.Layer(`layer${layerIndex}`, mapSource, mapViz);\n",
       "    const mapLayerIndex = numLayers - layerIndex - 1;\n",
       "\n",
       "    try {\n",
       "      mapLayer._updateLayer.catch(displayError);\n",
       "    } catch (e) {\n",
       "      throw e;\n",
       "    }\n",
       "\n",
       "    mapLayer.addTo(map);\n",
       "\n",
       "    setLayerLegend(layer, mapLayerIndex, mapLayer, mapIndex, hasLegends);\n",
       "    setLayerWidgets(map, layer, mapLayer, mapLayerIndex, mapSource);\n",
       "\n",
       "    return mapLayer;\n",
       "  }\n",
       "\n",
       "  function getInteractiveLayers(layers, mapLayers) {\n",
       "    const interactiveLayers = [];\n",
       "    const interactiveMapLayers = [];\n",
       "\n",
       "    layers.forEach((layer, index) => {\n",
       "      if (layer.interactivity) {\n",
       "        interactiveLayers.push(layer);\n",
       "        interactiveMapLayers.push(mapLayers[index]);\n",
       "      }\n",
       "    });\n",
       "\n",
       "    return { interactiveLayers, interactiveMapLayers };\n",
       "  }\n",
       "\n",
       "  function setLayerLegend(layer, mapLayerIndex, mapLayer, mapIndex, hasLegends) {\n",
       "    if (hasLegends && layer.legends) {\n",
       "      createLegends(mapLayer, layer.legends, mapLayerIndex, mapIndex);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function setLayerWidgets(map, layer, mapLayer, mapLayerIndex, mapSource) {\n",
       "    if (layer.widgets.length) {\n",
       "      initLayerWidgets(layer.widgets, mapLayerIndex);\n",
       "      updateLayerWidgets(layer.widgets, mapLayer);\n",
       "      bridgeLayerWidgets(map, mapLayer, mapSource, layer.widgets);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function initLayerWidgets(widgets, mapLayerIndex) {\n",
       "    widgets.forEach((widget, widgetIndex) => {\n",
       "      const id = `layer${mapLayerIndex}_widget${widgetIndex}`;\n",
       "      widget.id = id;\n",
       "    });\n",
       "  }\n",
       "\n",
       "  function updateLayerWidgets(widgets, mapLayer) {\n",
       "    mapLayer.on('updated', () => renderLayerWidgets(widgets, mapLayer));\n",
       "  }\n",
       "\n",
       "  function renderLayerWidgets(widgets, mapLayer) {\n",
       "    const variables = mapLayer.viz.variables;\n",
       "\n",
       "    widgets\n",
       "      .filter((widget) => !widget.has_bridge)\n",
       "      .forEach((widget) => {\n",
       "        const name = widget.variable_name;\n",
       "        const value = getWidgetValue(name, variables);\n",
       "        renderWidget(widget, value);\n",
       "      });\n",
       "  }\n",
       "\n",
       "  function getWidgetValue(name, variables) {\n",
       "    return name && variables[name] ? variables[name].value : null;\n",
       "  }\n",
       "\n",
       "  function setReady(settings) {\n",
       "    try {\n",
       "      return settings.maps ? initMaps(settings.maps) : initMap(settings);\n",
       "    } catch (e) {\n",
       "      displayError(e);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function initMaps(maps) {\n",
       "    return maps.map((mapSettings, mapIndex) => {\n",
       "      return initMap(mapSettings, mapIndex);\n",
       "    });\n",
       "  }\n",
       "\n",
       "  function initMap(settings, mapIndex) {\n",
       "    const basecolor = getBasecolorSettings(settings.basecolor);\n",
       "    const basemapStyle =  BASEMAPS[settings.basemap] || settings.basemap || basecolor;\n",
       "    const container = mapIndex !== undefined ? `map-${mapIndex}` : 'map';\n",
       "    const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n",
       "\n",
       "    if (settings.show_info) {\n",
       "      const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n",
       "      updateViewport(id, map);\n",
       "    }\n",
       "\n",
       "    if (settings.camera) {\n",
       "      map.flyTo(settings.camera);\n",
       "    }\n",
       "\n",
       "    return initLayers(map, settings, mapIndex);\n",
       "  }\n",
       "\n",
       "  function initLayers(map, settings, mapIndex) {\n",
       "    const numLayers = settings.layers.length;\n",
       "    const hasLegends = settings.has_legends;\n",
       "    const isStatic = settings.is_static;\n",
       "    const layers = settings.layers;\n",
       "    const mapLayers = getMapLayers(\n",
       "      layers,\n",
       "      numLayers,\n",
       "      hasLegends,\n",
       "      map,\n",
       "      mapIndex\n",
       "    );\n",
       "\n",
       "    if (settings.layer_selector) {\n",
       "      addLayersSelector(layers.reverse(), mapLayers.reverse(), mapIndex);\n",
       "    }\n",
       "\n",
       "    setInteractiveLayers(map, layers, mapLayers);\n",
       "\n",
       "    return waitForMapLayersLoad(isStatic, mapIndex, mapLayers);\n",
       "  }\n",
       "\n",
       "  function waitForMapLayersLoad(isStatic, mapIndex, mapLayers) {\n",
       "    return new Promise((resolve) => {\n",
       "      carto.on('loaded', mapLayers, onMapLayersLoaded.bind(\n",
       "        this, isStatic, mapIndex, mapLayers, resolve)\n",
       "      );\n",
       "    });\n",
       "  }\n",
       "\n",
       "  function onMapLayersLoaded(isStatic, mapIndex, mapLayers, resolve) {\n",
       "    if (isStatic) {\n",
       "      saveImage(mapIndex);\n",
       "    }\n",
       "\n",
       "    resolve(mapLayers);\n",
       "  }\n",
       "\n",
       "  function getMapLayers(layers, numLayers, hasLegends, map, mapIndex) {\n",
       "    return layers.map((layer, layerIndex) => {\n",
       "      return initMapLayer(layer, layerIndex, numLayers, hasLegends, map, mapIndex);\n",
       "    });\n",
       "  }\n",
       "\n",
       "  function setInteractiveLayers(map, layers, mapLayers) {\n",
       "    const { interactiveLayers, interactiveMapLayers } = getInteractiveLayers(layers, mapLayers);\n",
       "\n",
       "    if (interactiveLayers && interactiveLayers.length > 0) {\n",
       "      setInteractivity(map, interactiveLayers, interactiveMapLayers);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function addLayersSelector(layers, mapLayers, mapIndex) {\n",
       "    const layerSelectorId = mapIndex !== undefined ? `#layer-selector-${mapIndex}` : '#layer-selector';\n",
       "    const layerSelector$ = document.querySelector(layerSelectorId);\n",
       "    const layersInfo = mapLayers.map((layer, index) => {\n",
       "      return {\n",
       "        title: layers[index].title || `Layer ${index}`,\n",
       "        id: layer.id,\n",
       "        checked: true\n",
       "      };\n",
       "    });\n",
       "\n",
       "    const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n",
       "\n",
       "    layerSelector.build();\n",
       "  }\n",
       "\n",
       "  function createMap(container, basemapStyle, bounds, accessToken) {\n",
       "    const map = createMapboxGLMap(container, basemapStyle, accessToken);\n",
       "\n",
       "    map.addControl(attributionControl);\n",
       "    map.fitBounds(bounds, FIT_BOUNDS_SETTINGS);\n",
       "\n",
       "    return map;\n",
       "  }\n",
       "\n",
       "  function createMapboxGLMap(container, style, accessToken) {\n",
       "    if (accessToken) {\n",
       "      mapboxgl.accessToken = accessToken;\n",
       "    }\n",
       "\n",
       "    return new mapboxgl.Map({\n",
       "      container,\n",
       "      style,\n",
       "      zoom: 9,\n",
       "      dragRotate: false,\n",
       "      attributionControl: false\n",
       "    });\n",
       "  }\n",
       "\n",
       "  function init(settings) {\n",
       "    setReady(settings);\n",
       "  }\n",
       "\n",
       "  return init;\n",
       "\n",
       "}());\n",
       "</script>\n",
       "<script>\n",
       "  document\n",
       "  .querySelector('as-responsive-content')\n",
       "  .addEventListener('ready', () => {\n",
       "    const basecolor = '';\n",
       "    const basemap = 'Positron';\n",
       "    const bounds = [[-179.143503384, 18.9061171430001], [179.780935092, 71.4125023460001]];\n",
       "    const camera = null;\n",
       "    const has_legends = 'False' === 'true';\n",
       "    const is_static = 'None' === 'true';\n",
       "    const layer_selector = 'False' === 'true';\n",
       "    const layers = [{&quot;credentials&quot;: null, &quot;data&quot;: &quot;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&quot;, &quot;encode_data&quot;: true, &quot;has_legend_list&quot;: true, &quot;interactivity&quot;: [], &quot;legends&quot;: [], &quot;map_index&quot;: 0, &quot;options&quot;: {&quot;dateColumns&quot;: []}, &quot;source&quot;: 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&quot;, &quot;title&quot;: null, &quot;type&quot;: &quot;GeoJSON&quot;, &quot;viz&quot;: &quot;color: hex(\\&quot;#826DBA\\&quot;)\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n&quot;, &quot;widgets&quot;: []}];\n",
       "    const mapboxtoken = '';\n",
       "    const show_info = 'None' === 'true';\n",
       "\n",
       "    init({\n",
       "      basecolor,\n",
       "      basemap,\n",
       "      bounds,\n",
       "      camera,\n",
       "      has_legends,\n",
       "      is_static,\n",
       "      layer_selector,\n",
       "      layers,\n",
       "      mapboxtoken,\n",
       "      show_info\n",
       "    });\n",
       "});\n",
       "</script>\n",
       "</html>\n",
       "\">\n",
       "\n",
       "</iframe>"
      ],
      "text/plain": [
       "<cartoframes.viz.map.Map at 0x7f5b460e43c8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_ds.geom_coverage()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "CARTO | Data Observatory v2.0 - Access to Data.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}