docs/examples/advanced_use_cases/combining_two_datasets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IkfOIflwHsJN"
   },
   "source": [
    "## Combining geosocial and financial data to understand retail performance\n",
    "\n",
    "Geosocial data is location-based social media data that can be interpreted and analyzed as part of any location-oriented business decision. \n",
    "\n",
    "In this notebook, [Spatial.ai geosocial data](https://carto.com/spatial-data-catalog/browser/?provider=spatial_ai) is combined with [Mastercard financial data](https://carto.com/spatial-data-catalog/browser/?provider=mastercard) to quantify how much of retail performance can be explained by geosocial behavior. We compare the cities of Chicago and Los Angeles to show how retail performance is driven by different social segments depending on the city.\n",
    "\n",
    "**Note** this use case leverages premium datasets from [CARTO Data Observatory](https://carto.com/spatial-data-catalog/).\n",
    "\n",
    "The notebook is organized in the following sections:\n",
    "1. [Download data from the Data Observatory](#section1)\n",
    "2. [Analyzing geosocial drivers](#section2)\n",
    "  - [Identify where top performers are](#section21)\n",
    "  - [Characterization of top performers](#section22)\n",
    "  - [Correlation analysis](#section23)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3dhzR472HsJO"
   },
   "source": [
    "### 0. Setup\n",
    "\n",
    "#### 0.1. Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "UhohOUSPHsJO",
    "outputId": "e4d9b65d-ec5d-49da-ee4a-b43404c8561b"
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from cartoframes.auth import set_default_credentials\n",
    "from cartoframes.data.observatory import *\n",
    "from cartoframes.viz import *\n",
    "from shapely import wkt\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 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": {},
   "outputs": [],
   "source": [
    "from cartoframes.auth import set_default_credentials\n",
    "\n",
    "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": "UYz9tHunHsJY"
   },
   "source": [
    "<a id='section1'></a>\n",
    "### 1. Download data from the Data Observatory\n",
    "\n",
    "In this section, we'll download the two datasets we're interested in and combine them into a single dataframe.\n",
    "\n",
    "For more information on how to access Data Observatory datasets using CARTOframes visit the [Guides](https://carto.com/developers/cartoframes/guides/) or take a look at the <a href='https://carto.com/developers/cartoframes/examples/#example-access-premium-data-from-the-data-observatory' target='_blank'>Access Premium Data</a> template."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MxuN2Vs-zscL"
   },
   "source": [
    "#### Data loading functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function loads the census block group ids for the city of interest. This is used afterwards to filter the data downloaded from the Data Observatory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "noaWSMg6SQwB"
   },
   "outputs": [],
   "source": [
    "LA_CBG_PATH = 'https://libs.cartocdn.com/cartoframes/samples/la_cbg.csv'\n",
    "LA_BB = '-118.673619,33.553967,-117.997960,34.360425'\n",
    "CHICACO_CBG_PATH = 'https://libs.cartocdn.com/cartoframes/samples/chicago_cbg.csv'\n",
    "CHICAGO_BB = '-88.638285,41.434892,-87.487468,42.502873'\n",
    "\n",
    "def read_cbg(city):\n",
    "    cbg_list = pd.DataFrame()\n",
    "    bbox = ''\n",
    "    if city == 'la':\n",
    "        cbg_list = pd.read_csv(LA_CBG_PATH, dtype={'geoid':str})['geoid'].tolist()\n",
    "        bbox = LA_BB\n",
    "    elif city == 'chicago':\n",
    "        cbg_list = pd.read_csv(CHICACO_CBG_PATH, dtype={'geoid':str})['geoid'].tolist()\n",
    "        bbox = CHICAGO_BB\n",
    "    return cbg_list, bbox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function downloads the latest [Spatial.ai Geosocial Segments](https://carto.com/spatial-data-catalog/browser/dataset/spa_geosocial_s_d5dc42ae/) data for the specified bounding box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "DOuH4oA1ToXo"
   },
   "outputs": [],
   "source": [
    "def download_social(bbox, cbg_list):\n",
    "    dataset = Dataset.get('spa_geosocial_s_d5dc42ae')\n",
    "    sql_query = f\"SELECT * FROM $dataset$ WHERE CAST(do_date AS date) >= (SELECT MAX(CAST(do_date AS date)) FROM $dataset$) AND ST_IntersectsBox(geom, {bbox})\"\n",
    "    social = dataset.to_dataframe(sql_query=sql_query)\n",
    "    social = social[social['geoid'].isin(cbg_list)]\n",
    "    social.drop(columns=['do_label', 'do_area', 'do_perimeter', 'do_num_vertices'], inplace=True)\n",
    "    return social"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function downloads the latest [Mastercard Geographic Insights](https://carto.com/spatial-data-catalog/browser/dataset/mc_geographic__7980c5c3/) data for the specified bounding box.\n",
    "\n",
    "Note the data is also filtered by geo_type. There are three types of geo_types depending if the indices shown represent a comparison with regards to country (c), province (p) or metropolitan area (m)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "aS0Pb2C-UgzH"
   },
   "outputs": [],
   "source": [
    "def download_mastercard(bbox, cbg_list):\n",
    "    dataset = Dataset.get('mc_geographic__7980c5c3')\n",
    "    sql_query = f\"SELECT * FROM $dataset$ WHERE geo_type = 'm' AND CAST(do_date AS date) >= (SELECT MAX(CAST(do_date AS date)) FROM $dataset$) AND ST_IntersectsBox(geom, {bbox})\"\n",
    "    mrli = dataset.to_dataframe(sql_query=sql_query)\n",
    "    mrli = mrli[mrli['geoid'].isin(cbg_list)]\n",
    "    mrli.drop(columns=['do_label', 'do_area', 'do_perimeter', 'do_num_vertices'], inplace=True)\n",
    "\n",
    "    mrli = mrli.sort_values(['geoid', 'industry', 'segment', 'geo_type', 'do_date']).reset_index(drop=True)\n",
    "    return mrli"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function loads the three datasets and merges them into a single one.\n",
    "\n",
    "**Note** we can merge the dataframes because both datasets are defined at the census block group level, so the geoid's are common to both of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "0W7bJ9iEU1mx"
   },
   "outputs": [],
   "source": [
    "def download_data(city):\n",
    "    cbg_list, bbox = read_cbg(city)\n",
    "    social = download_social(bbox, cbg_list)\n",
    "    social.drop(columns='geom', inplace=True)\n",
    "    mrli = download_mastercard(bbox, cbg_list)\n",
    "    mrli = mrli[(mrli['segment'] == 'o')]\n",
    "    mrli = mrli.merge(social, on='geoid')\n",
    "    mrli = gpd.GeoDataFrame(mrli, crs='epsg:4326')\n",
    "    return mrli"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8eQ05DR1z1vL"
   },
   "source": [
    "### Download data into dataframes\n",
    "\n",
    "Here we'll load the data for Chicago and LA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "id": "VywMyAySHsJb",
    "outputId": "cf3d5c82-6548-4c6a-d070-e821530e9606"
   },
   "outputs": [
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       "      <th>EF03_awestruck</th>\n",
       "      <th>EF04_happy_place</th>\n",
       "      <th>EF05_gratitude</th>\n",
       "      <th>EF06_memory_lane</th>\n",
       "      <th>EG01_dance_devotion</th>\n",
       "      <th>EG02_artistic_appreciation</th>\n",
       "      <th>EG03_pieces_of_history</th>\n",
       "      <th>EG04_sites_to_see</th>\n",
       "      <th>EG05_hip_hop_culture</th>\n",
       "      <th>EH01_mindfulness_and_spirituality</th>\n",
       "      <th>EH02_activism</th>\n",
       "      <th>EH03_politically_engaged</th>\n",
       "      <th>EH04_praise_and_worship</th>\n",
       "      <th>EH05_conservation</th>\n",
       "      <th>EH06_civic_attentiveness</th>\n",
       "      <th>EI01_trend_trackers</th>\n",
       "      <th>EI02_all_about_hair</th>\n",
       "      <th>EI03_mens_style</th>\n",
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       "      <th>EI06_smart_chic</th>\n",
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       "      <th>EK05_functional_fitness</th>\n",
       "      <th>index_high_end_affinity</th>\n",
       "      <th>index_mid_range_consumer_affinity</th>\n",
       "      <th>index_discount_affinity</th>\n",
       "      <th>index_foodie_affinity</th>\n",
       "      <th>index_breakfast_and_brunch_affinity</th>\n",
       "      <th>index_fast_casual_affinity</th>\n",
       "      <th>index_coffee_affinity</th>\n",
       "      <th>index_fast_food_affinity</th>\n",
       "      <th>index_organic_and_local_affinity</th>\n",
       "      <th>index_fashion_affinity</th>\n",
       "      <th>index_health_conscious_affinity</th>\n",
       "      <th>index_entertainment_affinity</th>\n",
       "      <th>index_late_night_affinity</th>\n",
       "      <th>index_personal_care_affinity</th>\n",
       "      <th>index_younger_affinity</th>\n",
       "      <th>index_older_affinity</th>\n",
       "      <th>index_family_affinity</th>\n",
       "      <th>index_politically_liberal_affinity</th>\n",
       "      <th>index_politically_conservative_affinity</th>\n",
       "      <th>VOLUME_INDEX</th>\n",
       "      <th>do_date_y</th>\n",
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       "      <td>170310101002</td>\n",
       "      <td>2020-11-01</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>POLYGON ((-87.67710 42.02255, -87.67355 42.019...</td>\n",
       "      <td>National</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>217739.0</td>\n",
       "      <td>83.62</td>\n",
       "      <td>43.28</td>\n",
       "      <td>68.41</td>\n",
       "      <td>14.97</td>\n",
       "      <td>6.35</td>\n",
       "      <td>25.00</td>\n",
       "      <td>32.48</td>\n",
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       "      <td>29.19</td>\n",
       "      <td>52.57</td>\n",
       "      <td>67.76</td>\n",
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       "      <td>14.03</td>\n",
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       "      <td>27.18</td>\n",
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       "      <td>77.17</td>\n",
       "      <td>71.39</td>\n",
       "      <td>42.15</td>\n",
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       "      <td>40.60</td>\n",
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       "      <td>21.09</td>\n",
       "      <td>42.97</td>\n",
       "      <td>50.86</td>\n",
       "      <td>45.40</td>\n",
       "      <td>35.06</td>\n",
       "      <td>52.34</td>\n",
       "      <td>64.25</td>\n",
       "      <td>87.77</td>\n",
       "      <td>24.20</td>\n",
       "      <td>57.88</td>\n",
       "      <td>52.57</td>\n",
       "      <td>60.02</td>\n",
       "      <td>20.35</td>\n",
       "      <td>20.87</td>\n",
       "      <td>29.03</td>\n",
       "      <td>14.91</td>\n",
       "      <td>34.34</td>\n",
       "      <td>19.88</td>\n",
       "      <td>19.74</td>\n",
       "      <td>66.99</td>\n",
       "      <td>67.17</td>\n",
       "      <td>42.67</td>\n",
       "      <td>68.96</td>\n",
       "      <td>22.51</td>\n",
       "      <td>57.91</td>\n",
       "      <td>64.65</td>\n",
       "      <td>57.94</td>\n",
       "      <td>51.36</td>\n",
       "      <td>70.87</td>\n",
       "      <td>75.45</td>\n",
       "      <td>37.59</td>\n",
       "      <td>60.06</td>\n",
       "      <td>67.76</td>\n",
       "      <td>59.11</td>\n",
       "      <td>50.06</td>\n",
       "      <td>44.04</td>\n",
       "      <td>49.97</td>\n",
       "      <td>42.51</td>\n",
       "      <td>33.33</td>\n",
       "      <td>52.34</td>\n",
       "      <td>69.80</td>\n",
       "      <td>26.48</td>\n",
       "      <td>74.55</td>\n",
       "      <td>76.61</td>\n",
       "      <td>95.61</td>\n",
       "      <td>24.87</td>\n",
       "      <td>42.35</td>\n",
       "      <td>59.96</td>\n",
       "      <td>86.19</td>\n",
       "      <td>51.25</td>\n",
       "      <td>34.39</td>\n",
       "      <td>52.47</td>\n",
       "      <td>77.81</td>\n",
       "      <td>32.89</td>\n",
       "      <td>36.26</td>\n",
       "      <td>6.89</td>\n",
       "      <td>31.87</td>\n",
       "      <td>71.56</td>\n",
       "      <td>20.08</td>\n",
       "      <td>65.11</td>\n",
       "      <td>50.15</td>\n",
       "      <td>45.91</td>\n",
       "      <td>30.44</td>\n",
       "      <td>58.59</td>\n",
       "      <td>63.08</td>\n",
       "      <td>32.02</td>\n",
       "      <td>64.68</td>\n",
       "      <td>47.21</td>\n",
       "      <td>61.84</td>\n",
       "      <td>51.14</td>\n",
       "      <td>58.85</td>\n",
       "      <td>49.54</td>\n",
       "      <td>59.94</td>\n",
       "      <td>38.50</td>\n",
       "      <td>67.08</td>\n",
       "      <td>49.82</td>\n",
       "      <td>60.42</td>\n",
       "      <td>25.60</td>\n",
       "      <td>58.30</td>\n",
       "      <td>58.63</td>\n",
       "      <td>53.51</td>\n",
       "      <td>46.46</td>\n",
       "      <td>65.93</td>\n",
       "      <td>39.58</td>\n",
       "      <td>32.71</td>\n",
       "      <td>62.90</td>\n",
       "      <td>36.44</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2020-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>170310101002</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>17</td>\n",
       "      <td>app</td>\n",
       "      <td>o</td>\n",
       "      <td>m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((-87.67710 42.02255, -87.67355 42.019...</td>\n",
       "      <td>County</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>17031</td>\n",
       "      <td>3993.0</td>\n",
       "      <td>74.23</td>\n",
       "      <td>68.14</td>\n",
       "      <td>67.17</td>\n",
       "      <td>29.38</td>\n",
       "      <td>20.38</td>\n",
       "      <td>44.77</td>\n",
       "      <td>52.08</td>\n",
       "      <td>47.39</td>\n",
       "      <td>36.71</td>\n",
       "      <td>48.09</td>\n",
       "      <td>66.71</td>\n",
       "      <td>81.19</td>\n",
       "      <td>19.84</td>\n",
       "      <td>49.97</td>\n",
       "      <td>52.40</td>\n",
       "      <td>68.95</td>\n",
       "      <td>18.57</td>\n",
       "      <td>20.65</td>\n",
       "      <td>27.63</td>\n",
       "      <td>13.77</td>\n",
       "      <td>32.43</td>\n",
       "      <td>17.92</td>\n",
       "      <td>18.87</td>\n",
       "      <td>57.57</td>\n",
       "      <td>62.78</td>\n",
       "      <td>39.22</td>\n",
       "      <td>62.83</td>\n",
       "      <td>22.21</td>\n",
       "      <td>51.99</td>\n",
       "      <td>63.94</td>\n",
       "      <td>55.20</td>\n",
       "      <td>50.46</td>\n",
       "      <td>71.48</td>\n",
       "      <td>67.82</td>\n",
       "      <td>36.47</td>\n",
       "      <td>56.31</td>\n",
       "      <td>65.61</td>\n",
       "      <td>46.31</td>\n",
       "      <td>47.25</td>\n",
       "      <td>46.74</td>\n",
       "      <td>44.12</td>\n",
       "      <td>34.31</td>\n",
       "      <td>34.58</td>\n",
       "      <td>51.43</td>\n",
       "      <td>67.74</td>\n",
       "      <td>22.86</td>\n",
       "      <td>69.68</td>\n",
       "      <td>69.57</td>\n",
       "      <td>94.15</td>\n",
       "      <td>25.47</td>\n",
       "      <td>32.51</td>\n",
       "      <td>54.07</td>\n",
       "      <td>79.43</td>\n",
       "      <td>45.28</td>\n",
       "      <td>33.05</td>\n",
       "      <td>48.57</td>\n",
       "      <td>70.86</td>\n",
       "      <td>32.13</td>\n",
       "      <td>31.70</td>\n",
       "      <td>4.82</td>\n",
       "      <td>30.24</td>\n",
       "      <td>69.16</td>\n",
       "      <td>18.17</td>\n",
       "      <td>62.61</td>\n",
       "      <td>44.31</td>\n",
       "      <td>44.29</td>\n",
       "      <td>29.19</td>\n",
       "      <td>62.61</td>\n",
       "      <td>67.76</td>\n",
       "      <td>37.60</td>\n",
       "      <td>62.83</td>\n",
       "      <td>46.79</td>\n",
       "      <td>59.19</td>\n",
       "      <td>51.37</td>\n",
       "      <td>52.82</td>\n",
       "      <td>48.17</td>\n",
       "      <td>55.57</td>\n",
       "      <td>38.74</td>\n",
       "      <td>61.86</td>\n",
       "      <td>50.94</td>\n",
       "      <td>56.24</td>\n",
       "      <td>23.41</td>\n",
       "      <td>59.04</td>\n",
       "      <td>56.91</td>\n",
       "      <td>49.90</td>\n",
       "      <td>43.05</td>\n",
       "      <td>61.87</td>\n",
       "      <td>42.37</td>\n",
       "      <td>33.22</td>\n",
       "      <td>55.65</td>\n",
       "      <td>38.14</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2020-10-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          geoid   do_date_x  region_id industry segment geo_type  txn_amt  \\\n",
       "0  170310101002  2020-11-01         17      app       o        m      0.0   \n",
       "1  170310101002  2020-11-01         17      app       o        m      0.0   \n",
       "2  170310101002  2020-11-01         17      app       o        m      0.0   \n",
       "\n",
       "   txn_cnt  acct_cnt  avg_ticket  avg_freq  avg_spend_amt  \\\n",
       "0      0.0       0.0         0.0       0.0            0.0   \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",
       "\n",
       "                                                geom comparison_level  \\\n",
       "0  POLYGON ((-87.67710 42.02255, -87.67355 42.019...         National   \n",
       "1  POLYGON ((-87.67710 42.02255, -87.67355 42.019...              DMA   \n",
       "2  POLYGON ((-87.67710 42.02255, -87.67355 42.019...           County   \n",
       "\n",
       "  comparison_geography_name comparison_geography_ID  \\\n",
       "0                       USA                     USA   \n",
       "1              Chicago (IL)                     602   \n",
       "2               Cook County                   17031   \n",
       "\n",
       "   comparison_geography_count  EA01_bookish  EA02_engine_enthusiasts  \\\n",
       "0                    217739.0         83.62                    43.28   \n",
       "1                      6735.0         79.19                    64.75   \n",
       "2                      3993.0         74.23                    68.14   \n",
       "\n",
       "   EA03_green_thumb  EA04_natural_beauty  EA05_wanderlust  EA06_handcrafted  \\\n",
       "0             68.41                14.97             6.35             25.00   \n",
       "1             71.43                29.65            21.09             42.97   \n",
       "2             67.17                29.38            20.38             44.77   \n",
       "\n",
       "   EA07_animal_advocates  EA08_dog_lovers  EA09_smoke_culture  \\\n",
       "0                  32.48            28.97               29.19   \n",
       "1                  50.86            45.40               35.06   \n",
       "2                  52.08            47.39               36.71   \n",
       "\n",
       "   EB01_daily_grind  EB02_nerd_culture  EB03_lgbtq_culture  \\\n",
       "0             52.57              67.76               93.59   \n",
       "1             52.34              64.25               87.77   \n",
       "2             48.09              66.71               81.19   \n",
       "\n",
       "   EB04_wealth_signaling  EB05_hipster  EB06_student_life  EB07_farm_culture  \\\n",
       "0                  16.93         56.80              48.33              33.18   \n",
       "1                  24.20         57.88              52.57              60.02   \n",
       "2                  19.84         49.97              52.40              68.95   \n",
       "\n",
       "   EC01_love_and_romance  EC02_happily_ever_after  EC03_dating_life  \\\n",
       "0                  14.03                     8.83             27.18   \n",
       "1                  20.35                    20.87             29.03   \n",
       "2                  18.57                    20.65             27.63   \n",
       "\n",
       "   EC04_connected_motherhood  EC05_family_time  EC06_my_crew  EC07_girl_squad  \\\n",
       "0                       5.68             30.27          9.68            21.23   \n",
       "1                      14.91             34.34         19.88            19.74   \n",
       "2                      13.77             32.43         17.92            18.87   \n",
       "\n",
       "   EC08_networking  ED01_sweet_treats  ED02_coffee_connoisseur  \\\n",
       "0            77.17              71.39                    42.15   \n",
       "1            66.99              67.17                    42.67   \n",
       "2            57.57              62.78                    39.22   \n",
       "\n",
       "   ED03_trendy_eats  ED04_whiskey_business  ED05_asian_food_and_culture  \\\n",
       "0             71.69                  13.01                        61.73   \n",
       "1             68.96                  22.51                        57.91   \n",
       "2             62.83                  22.21                        51.99   \n",
       "\n",
       "   ED06_ingredient_attentive  ED07_fueling_for_fitness  ED08_wine_lovers  \\\n",
       "0                      56.89                     51.81             38.39   \n",
       "1                      64.65                     57.94             51.36   \n",
       "2                      63.94                     55.20             50.46   \n",
       "\n",
       "   ED09_hops_and_brews  EE01_film_lovers  EE02_competitive_nature  \\\n",
       "0                68.34             78.92                    29.93   \n",
       "1                70.87             75.45                    37.59   \n",
       "2                71.48             67.82                    36.47   \n",
       "\n",
       "   EE03_live_experiences  EE04_late_night_leisure  EE05_party_life  \\\n",
       "0                  67.00                    69.64            64.63   \n",
       "1                  60.06                    67.76            59.11   \n",
       "2                  56.31                    65.61            46.31   \n",
       "\n",
       "   EE06_live_and_local_music  EE07_lighthearted_fun  EF01_deep_emotions  \\\n",
       "0                      46.62                  23.23               44.27   \n",
       "1                      50.06                  44.04               49.97   \n",
       "2                      47.25                  46.74               44.12   \n",
       "\n",
       "   EF02_heartfelt_sharing  EF03_awestruck  EF04_happy_place  EF05_gratitude  \\\n",
       "0                   49.23           18.97             49.69           67.29   \n",
       "1                   42.51           33.33             52.34           69.80   \n",
       "2                   34.31           34.58             51.43           67.74   \n",
       "\n",
       "   EF06_memory_lane  EG01_dance_devotion  EG02_artistic_appreciation  \\\n",
       "0             20.29                79.46                       79.36   \n",
       "1             26.48                74.55                       76.61   \n",
       "2             22.86                69.68                       69.57   \n",
       "\n",
       "   EG03_pieces_of_history  EG04_sites_to_see  EG05_hip_hop_culture  \\\n",
       "0                   98.01              11.81                 53.29   \n",
       "1                   95.61              24.87                 42.35   \n",
       "2                   94.15              25.47                 32.51   \n",
       "\n",
       "   EH01_mindfulness_and_spirituality  EH02_activism  EH03_politically_engaged  \\\n",
       "0                              53.82          86.23                     45.19   \n",
       "1                              59.96          86.19                     51.25   \n",
       "2                              54.07          79.43                     45.28   \n",
       "\n",
       "   EH04_praise_and_worship  EH05_conservation  EH06_civic_attentiveness  \\\n",
       "0                    23.09              40.48                     81.35   \n",
       "1                    34.39              52.47                     77.81   \n",
       "2                    33.05              48.57                     70.86   \n",
       "\n",
       "   EI01_trend_trackers  EI02_all_about_hair  EI03_mens_style  \\\n",
       "0                28.62                34.72             6.44   \n",
       "1                32.89                36.26             6.89   \n",
       "2                32.13                31.70             4.82   \n",
       "\n",
       "   EI04_fitness_fashion  EI05_body_art  EI06_smart_chic  \\\n",
       "0                 26.99          69.13            12.31   \n",
       "1                 31.87          71.56            20.08   \n",
       "2                 30.24          69.16            18.17   \n",
       "\n",
       "   EJ01_home_and_leisure  EJ02_past_reflections  EJ03_humanitarian  \\\n",
       "0                  62.56                  40.60              45.24   \n",
       "1                  65.11                  50.15              45.91   \n",
       "2                  62.61                  44.31              44.29   \n",
       "\n",
       "   EJ04_deal_seekers  EK01_organized_sports  EK02_fitness_obsession  \\\n",
       "0              23.93                  52.53                   53.74   \n",
       "1              30.44                  58.59                   63.08   \n",
       "2              29.19                  62.61                   67.76   \n",
       "\n",
       "   EK03_outdoor_adventures  EK04_yoga_advocates  EK05_functional_fitness  \\\n",
       "0                    12.33                56.89                    50.25   \n",
       "1                    32.02                64.68                    47.21   \n",
       "2                    37.60                62.83                    46.79   \n",
       "\n",
       "   index_high_end_affinity  index_mid_range_consumer_affinity  \\\n",
       "0                    54.33                              41.08   \n",
       "1                    61.84                              51.14   \n",
       "2                    59.19                              51.37   \n",
       "\n",
       "   index_discount_affinity  index_foodie_affinity  \\\n",
       "0                    60.49                  42.64   \n",
       "1                    58.85                  49.54   \n",
       "2                    52.82                  48.17   \n",
       "\n",
       "   index_breakfast_and_brunch_affinity  index_fast_casual_affinity  \\\n",
       "0                                59.48                       32.71   \n",
       "1                                59.94                       38.50   \n",
       "2                                55.57                       38.74   \n",
       "\n",
       "   index_coffee_affinity  index_fast_food_affinity  \\\n",
       "0                  67.48                     36.41   \n",
       "1                  67.08                     49.82   \n",
       "2                  61.86                     50.94   \n",
       "\n",
       "   index_organic_and_local_affinity  index_fashion_affinity  \\\n",
       "0                             54.37                   21.82   \n",
       "1                             60.42                   25.60   \n",
       "2                             56.24                   23.41   \n",
       "\n",
       "   index_health_conscious_affinity  index_entertainment_affinity  \\\n",
       "0                            53.04                         59.01   \n",
       "1                            58.30                         58.63   \n",
       "2                            59.04                         56.91   \n",
       "\n",
       "   index_late_night_affinity  index_personal_care_affinity  \\\n",
       "0                      54.09                         42.52   \n",
       "1                      53.51                         46.46   \n",
       "2                      49.90                         43.05   \n",
       "\n",
       "   index_younger_affinity  index_older_affinity  index_family_affinity  \\\n",
       "0                   68.76                 22.14                  20.38   \n",
       "1                   65.93                 39.58                  32.71   \n",
       "2                   61.87                 42.37                  33.22   \n",
       "\n",
       "   index_politically_liberal_affinity  \\\n",
       "0                               64.57   \n",
       "1                               62.90   \n",
       "2                               55.65   \n",
       "\n",
       "   index_politically_conservative_affinity  VOLUME_INDEX   do_date_y  \n",
       "0                                    24.70          2.55  2020-10-01  \n",
       "1                                    36.44          2.55  2020-10-01  \n",
       "2                                    38.14          2.55  2020-10-01  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chicago = download_data('chicago')\n",
    "chicago.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "id": "NL1vy1K0HsJh",
    "outputId": "e6d94d0f-0cf7-49da-946b-5456535ebcb6"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geoid</th>\n",
       "      <th>do_date_x</th>\n",
       "      <th>region_id</th>\n",
       "      <th>industry</th>\n",
       "      <th>segment</th>\n",
       "      <th>geo_type</th>\n",
       "      <th>txn_amt</th>\n",
       "      <th>txn_cnt</th>\n",
       "      <th>acct_cnt</th>\n",
       "      <th>avg_ticket</th>\n",
       "      <th>avg_freq</th>\n",
       "      <th>avg_spend_amt</th>\n",
       "      <th>geom</th>\n",
       "      <th>comparison_level</th>\n",
       "      <th>comparison_geography_name</th>\n",
       "      <th>comparison_geography_ID</th>\n",
       "      <th>comparison_geography_count</th>\n",
       "      <th>EA01_bookish</th>\n",
       "      <th>EA02_engine_enthusiasts</th>\n",
       "      <th>EA03_green_thumb</th>\n",
       "      <th>EA04_natural_beauty</th>\n",
       "      <th>EA05_wanderlust</th>\n",
       "      <th>EA06_handcrafted</th>\n",
       "      <th>EA07_animal_advocates</th>\n",
       "      <th>EA08_dog_lovers</th>\n",
       "      <th>EA09_smoke_culture</th>\n",
       "      <th>EB01_daily_grind</th>\n",
       "      <th>EB02_nerd_culture</th>\n",
       "      <th>EB03_lgbtq_culture</th>\n",
       "      <th>EB04_wealth_signaling</th>\n",
       "      <th>EB05_hipster</th>\n",
       "      <th>EB06_student_life</th>\n",
       "      <th>EB07_farm_culture</th>\n",
       "      <th>EC01_love_and_romance</th>\n",
       "      <th>EC02_happily_ever_after</th>\n",
       "      <th>EC03_dating_life</th>\n",
       "      <th>EC04_connected_motherhood</th>\n",
       "      <th>EC05_family_time</th>\n",
       "      <th>EC06_my_crew</th>\n",
       "      <th>EC07_girl_squad</th>\n",
       "      <th>EC08_networking</th>\n",
       "      <th>ED01_sweet_treats</th>\n",
       "      <th>ED02_coffee_connoisseur</th>\n",
       "      <th>ED03_trendy_eats</th>\n",
       "      <th>ED04_whiskey_business</th>\n",
       "      <th>ED05_asian_food_and_culture</th>\n",
       "      <th>ED06_ingredient_attentive</th>\n",
       "      <th>ED07_fueling_for_fitness</th>\n",
       "      <th>ED08_wine_lovers</th>\n",
       "      <th>ED09_hops_and_brews</th>\n",
       "      <th>EE01_film_lovers</th>\n",
       "      <th>EE02_competitive_nature</th>\n",
       "      <th>EE03_live_experiences</th>\n",
       "      <th>EE04_late_night_leisure</th>\n",
       "      <th>EE05_party_life</th>\n",
       "      <th>EE06_live_and_local_music</th>\n",
       "      <th>EE07_lighthearted_fun</th>\n",
       "      <th>EF01_deep_emotions</th>\n",
       "      <th>EF02_heartfelt_sharing</th>\n",
       "      <th>EF03_awestruck</th>\n",
       "      <th>EF04_happy_place</th>\n",
       "      <th>EF05_gratitude</th>\n",
       "      <th>EF06_memory_lane</th>\n",
       "      <th>EG01_dance_devotion</th>\n",
       "      <th>EG02_artistic_appreciation</th>\n",
       "      <th>EG03_pieces_of_history</th>\n",
       "      <th>EG04_sites_to_see</th>\n",
       "      <th>EG05_hip_hop_culture</th>\n",
       "      <th>EH01_mindfulness_and_spirituality</th>\n",
       "      <th>EH02_activism</th>\n",
       "      <th>EH03_politically_engaged</th>\n",
       "      <th>EH04_praise_and_worship</th>\n",
       "      <th>EH05_conservation</th>\n",
       "      <th>EH06_civic_attentiveness</th>\n",
       "      <th>EI01_trend_trackers</th>\n",
       "      <th>EI02_all_about_hair</th>\n",
       "      <th>EI03_mens_style</th>\n",
       "      <th>EI04_fitness_fashion</th>\n",
       "      <th>EI05_body_art</th>\n",
       "      <th>EI06_smart_chic</th>\n",
       "      <th>EJ01_home_and_leisure</th>\n",
       "      <th>EJ02_past_reflections</th>\n",
       "      <th>EJ03_humanitarian</th>\n",
       "      <th>EJ04_deal_seekers</th>\n",
       "      <th>EK01_organized_sports</th>\n",
       "      <th>EK02_fitness_obsession</th>\n",
       "      <th>EK03_outdoor_adventures</th>\n",
       "      <th>EK04_yoga_advocates</th>\n",
       "      <th>EK05_functional_fitness</th>\n",
       "      <th>index_high_end_affinity</th>\n",
       "      <th>index_mid_range_consumer_affinity</th>\n",
       "      <th>index_discount_affinity</th>\n",
       "      <th>index_foodie_affinity</th>\n",
       "      <th>index_breakfast_and_brunch_affinity</th>\n",
       "      <th>index_fast_casual_affinity</th>\n",
       "      <th>index_coffee_affinity</th>\n",
       "      <th>index_fast_food_affinity</th>\n",
       "      <th>index_organic_and_local_affinity</th>\n",
       "      <th>index_fashion_affinity</th>\n",
       "      <th>index_health_conscious_affinity</th>\n",
       "      <th>index_entertainment_affinity</th>\n",
       "      <th>index_late_night_affinity</th>\n",
       "      <th>index_personal_care_affinity</th>\n",
       "      <th>index_younger_affinity</th>\n",
       "      <th>index_older_affinity</th>\n",
       "      <th>index_family_affinity</th>\n",
       "      <th>index_politically_liberal_affinity</th>\n",
       "      <th>index_politically_conservative_affinity</th>\n",
       "      <th>VOLUME_INDEX</th>\n",
       "      <th>do_date_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60371011102</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>6</td>\n",
       "      <td>aut</td>\n",
       "      <td>o</td>\n",
       "      <td>m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((-118.29945 34.25598, -118.29104 34.2...</td>\n",
       "      <td>National</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>217739.0</td>\n",
       "      <td>99.11</td>\n",
       "      <td>93.39</td>\n",
       "      <td>98.63</td>\n",
       "      <td>81.01</td>\n",
       "      <td>86.05</td>\n",
       "      <td>81.89</td>\n",
       "      <td>92.44</td>\n",
       "      <td>79.65</td>\n",
       "      <td>65.64</td>\n",
       "      <td>80.14</td>\n",
       "      <td>96.65</td>\n",
       "      <td>95.14</td>\n",
       "      <td>57.91</td>\n",
       "      <td>82.17</td>\n",
       "      <td>96.79</td>\n",
       "      <td>71.58</td>\n",
       "      <td>97.04</td>\n",
       "      <td>70.15</td>\n",
       "      <td>78.75</td>\n",
       "      <td>92.92</td>\n",
       "      <td>98.63</td>\n",
       "      <td>98.44</td>\n",
       "      <td>63.37</td>\n",
       "      <td>65.62</td>\n",
       "      <td>90.24</td>\n",
       "      <td>94.63</td>\n",
       "      <td>92.97</td>\n",
       "      <td>84.73</td>\n",
       "      <td>91.36</td>\n",
       "      <td>56.71</td>\n",
       "      <td>73.29</td>\n",
       "      <td>89.60</td>\n",
       "      <td>92.81</td>\n",
       "      <td>99.06</td>\n",
       "      <td>61.66</td>\n",
       "      <td>98.07</td>\n",
       "      <td>96.98</td>\n",
       "      <td>86.23</td>\n",
       "      <td>97.13</td>\n",
       "      <td>98.71</td>\n",
       "      <td>98.55</td>\n",
       "      <td>96.21</td>\n",
       "      <td>98.83</td>\n",
       "      <td>80.65</td>\n",
       "      <td>90.82</td>\n",
       "      <td>92.39</td>\n",
       "      <td>99.49</td>\n",
       "      <td>96.52</td>\n",
       "      <td>89.71</td>\n",
       "      <td>95.96</td>\n",
       "      <td>95.53</td>\n",
       "      <td>96.29</td>\n",
       "      <td>88.59</td>\n",
       "      <td>78.29</td>\n",
       "      <td>98.69</td>\n",
       "      <td>77.74</td>\n",
       "      <td>83.34</td>\n",
       "      <td>52.02</td>\n",
       "      <td>86.27</td>\n",
       "      <td>58.59</td>\n",
       "      <td>68.67</td>\n",
       "      <td>95.61</td>\n",
       "      <td>67.51</td>\n",
       "      <td>92.95</td>\n",
       "      <td>98.59</td>\n",
       "      <td>99.44</td>\n",
       "      <td>77.21</td>\n",
       "      <td>86.57</td>\n",
       "      <td>75.94</td>\n",
       "      <td>44.97</td>\n",
       "      <td>88.27</td>\n",
       "      <td>64.15</td>\n",
       "      <td>89.79</td>\n",
       "      <td>85.64</td>\n",
       "      <td>86.32</td>\n",
       "      <td>76.91</td>\n",
       "      <td>93.58</td>\n",
       "      <td>79.28</td>\n",
       "      <td>91.15</td>\n",
       "      <td>81.09</td>\n",
       "      <td>89.03</td>\n",
       "      <td>66.61</td>\n",
       "      <td>77.64</td>\n",
       "      <td>94.26</td>\n",
       "      <td>87.30</td>\n",
       "      <td>78.58</td>\n",
       "      <td>95.65</td>\n",
       "      <td>76.62</td>\n",
       "      <td>92.05</td>\n",
       "      <td>93.62</td>\n",
       "      <td>75.81</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2020-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60371011102</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>6</td>\n",
       "      <td>aut</td>\n",
       "      <td>o</td>\n",
       "      <td>m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((-118.29945 34.25598, -118.29104 34.2...</td>\n",
       "      <td>DMA</td>\n",
       "      <td>Los Angeles (CA)</td>\n",
       "      <td>803</td>\n",
       "      <td>10816.0</td>\n",
       "      <td>99.52</td>\n",
       "      <td>88.67</td>\n",
       "      <td>98.91</td>\n",
       "      <td>87.12</td>\n",
       "      <td>88.63</td>\n",
       "      <td>83.45</td>\n",
       "      <td>91.40</td>\n",
       "      <td>83.68</td>\n",
       "      <td>56.52</td>\n",
       "      <td>81.82</td>\n",
       "      <td>97.66</td>\n",
       "      <td>88.44</td>\n",
       "      <td>56.34</td>\n",
       "      <td>75.89</td>\n",
       "      <td>97.49</td>\n",
       "      <td>91.51</td>\n",
       "      <td>97.05</td>\n",
       "      <td>80.63</td>\n",
       "      <td>80.34</td>\n",
       "      <td>94.37</td>\n",
       "      <td>98.85</td>\n",
       "      <td>98.60</td>\n",
       "      <td>75.50</td>\n",
       "      <td>70.75</td>\n",
       "      <td>88.76</td>\n",
       "      <td>92.90</td>\n",
       "      <td>89.00</td>\n",
       "      <td>90.99</td>\n",
       "      <td>89.55</td>\n",
       "      <td>65.53</td>\n",
       "      <td>73.97</td>\n",
       "      <td>90.48</td>\n",
       "      <td>96.05</td>\n",
       "      <td>94.90</td>\n",
       "      <td>63.92</td>\n",
       "      <td>96.09</td>\n",
       "      <td>97.33</td>\n",
       "      <td>84.31</td>\n",
       "      <td>96.26</td>\n",
       "      <td>99.22</td>\n",
       "      <td>96.09</td>\n",
       "      <td>92.04</td>\n",
       "      <td>98.99</td>\n",
       "      <td>84.75</td>\n",
       "      <td>81.49</td>\n",
       "      <td>91.32</td>\n",
       "      <td>98.72</td>\n",
       "      <td>92.87</td>\n",
       "      <td>90.35</td>\n",
       "      <td>96.50</td>\n",
       "      <td>93.83</td>\n",
       "      <td>91.29</td>\n",
       "      <td>82.88</td>\n",
       "      <td>88.66</td>\n",
       "      <td>98.60</td>\n",
       "      <td>80.79</td>\n",
       "      <td>78.17</td>\n",
       "      <td>73.71</td>\n",
       "      <td>84.72</td>\n",
       "      <td>54.19</td>\n",
       "      <td>70.00</td>\n",
       "      <td>92.82</td>\n",
       "      <td>64.82</td>\n",
       "      <td>92.87</td>\n",
       "      <td>98.71</td>\n",
       "      <td>99.51</td>\n",
       "      <td>67.67</td>\n",
       "      <td>89.37</td>\n",
       "      <td>77.37</td>\n",
       "      <td>51.53</td>\n",
       "      <td>86.51</td>\n",
       "      <td>61.55</td>\n",
       "      <td>88.08</td>\n",
       "      <td>86.62</td>\n",
       "      <td>82.07</td>\n",
       "      <td>80.79</td>\n",
       "      <td>91.70</td>\n",
       "      <td>84.66</td>\n",
       "      <td>88.42</td>\n",
       "      <td>84.90</td>\n",
       "      <td>86.00</td>\n",
       "      <td>69.49</td>\n",
       "      <td>77.76</td>\n",
       "      <td>95.14</td>\n",
       "      <td>89.89</td>\n",
       "      <td>76.08</td>\n",
       "      <td>95.54</td>\n",
       "      <td>83.43</td>\n",
       "      <td>96.39</td>\n",
       "      <td>89.17</td>\n",
       "      <td>83.32</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2020-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60371011102</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>6</td>\n",
       "      <td>aut</td>\n",
       "      <td>o</td>\n",
       "      <td>m</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((-118.29945 34.25598, -118.29104 34.2...</td>\n",
       "      <td>County</td>\n",
       "      <td>Los Angeles County</td>\n",
       "      <td>6037</td>\n",
       "      <td>6425.0</td>\n",
       "      <td>99.42</td>\n",
       "      <td>90.01</td>\n",
       "      <td>98.62</td>\n",
       "      <td>88.70</td>\n",
       "      <td>89.88</td>\n",
       "      <td>84.85</td>\n",
       "      <td>91.16</td>\n",
       "      <td>83.57</td>\n",
       "      <td>56.72</td>\n",
       "      <td>79.97</td>\n",
       "      <td>97.75</td>\n",
       "      <td>83.02</td>\n",
       "      <td>52.28</td>\n",
       "      <td>70.08</td>\n",
       "      <td>97.65</td>\n",
       "      <td>94.57</td>\n",
       "      <td>97.00</td>\n",
       "      <td>83.31</td>\n",
       "      <td>78.01</td>\n",
       "      <td>95.43</td>\n",
       "      <td>99.12</td>\n",
       "      <td>98.85</td>\n",
       "      <td>76.10</td>\n",
       "      <td>66.60</td>\n",
       "      <td>87.42</td>\n",
       "      <td>92.62</td>\n",
       "      <td>87.48</td>\n",
       "      <td>91.13</td>\n",
       "      <td>88.70</td>\n",
       "      <td>62.53</td>\n",
       "      <td>73.89</td>\n",
       "      <td>89.23</td>\n",
       "      <td>96.30</td>\n",
       "      <td>92.49</td>\n",
       "      <td>63.11</td>\n",
       "      <td>94.68</td>\n",
       "      <td>97.15</td>\n",
       "      <td>78.82</td>\n",
       "      <td>95.61</td>\n",
       "      <td>99.48</td>\n",
       "      <td>94.50</td>\n",
       "      <td>88.63</td>\n",
       "      <td>99.08</td>\n",
       "      <td>83.88</td>\n",
       "      <td>79.34</td>\n",
       "      <td>90.58</td>\n",
       "      <td>98.20</td>\n",
       "      <td>89.67</td>\n",
       "      <td>87.81</td>\n",
       "      <td>96.71</td>\n",
       "      <td>91.23</td>\n",
       "      <td>88.71</td>\n",
       "      <td>75.03</td>\n",
       "      <td>85.88</td>\n",
       "      <td>98.93</td>\n",
       "      <td>77.62</td>\n",
       "      <td>73.29</td>\n",
       "      <td>74.88</td>\n",
       "      <td>84.98</td>\n",
       "      <td>50.35</td>\n",
       "      <td>69.03</td>\n",
       "      <td>93.42</td>\n",
       "      <td>62.72</td>\n",
       "      <td>91.13</td>\n",
       "      <td>98.15</td>\n",
       "      <td>99.46</td>\n",
       "      <td>67.39</td>\n",
       "      <td>91.23</td>\n",
       "      <td>79.47</td>\n",
       "      <td>55.72</td>\n",
       "      <td>84.38</td>\n",
       "      <td>61.75</td>\n",
       "      <td>85.79</td>\n",
       "      <td>86.98</td>\n",
       "      <td>79.04</td>\n",
       "      <td>80.04</td>\n",
       "      <td>90.48</td>\n",
       "      <td>85.09</td>\n",
       "      <td>85.97</td>\n",
       "      <td>85.32</td>\n",
       "      <td>83.54</td>\n",
       "      <td>68.39</td>\n",
       "      <td>78.14</td>\n",
       "      <td>94.47</td>\n",
       "      <td>88.80</td>\n",
       "      <td>74.42</td>\n",
       "      <td>94.37</td>\n",
       "      <td>84.98</td>\n",
       "      <td>97.38</td>\n",
       "      <td>85.11</td>\n",
       "      <td>84.68</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2020-10-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         geoid   do_date_x  region_id industry segment geo_type  txn_amt  \\\n",
       "0  60371011102  2020-11-01          6      aut       o        m      0.0   \n",
       "1  60371011102  2020-11-01          6      aut       o        m      0.0   \n",
       "2  60371011102  2020-11-01          6      aut       o        m      0.0   \n",
       "\n",
       "   txn_cnt  acct_cnt  avg_ticket  avg_freq  avg_spend_amt  \\\n",
       "0      0.0       0.0         0.0       0.0            0.0   \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",
       "\n",
       "                                                geom comparison_level  \\\n",
       "0  POLYGON ((-118.29945 34.25598, -118.29104 34.2...         National   \n",
       "1  POLYGON ((-118.29945 34.25598, -118.29104 34.2...              DMA   \n",
       "2  POLYGON ((-118.29945 34.25598, -118.29104 34.2...           County   \n",
       "\n",
       "  comparison_geography_name comparison_geography_ID  \\\n",
       "0                       USA                     USA   \n",
       "1          Los Angeles (CA)                     803   \n",
       "2        Los Angeles County                    6037   \n",
       "\n",
       "   comparison_geography_count  EA01_bookish  EA02_engine_enthusiasts  \\\n",
       "0                    217739.0         99.11                    93.39   \n",
       "1                     10816.0         99.52                    88.67   \n",
       "2                      6425.0         99.42                    90.01   \n",
       "\n",
       "   EA03_green_thumb  EA04_natural_beauty  EA05_wanderlust  EA06_handcrafted  \\\n",
       "0             98.63                81.01            86.05             81.89   \n",
       "1             98.91                87.12            88.63             83.45   \n",
       "2             98.62                88.70            89.88             84.85   \n",
       "\n",
       "   EA07_animal_advocates  EA08_dog_lovers  EA09_smoke_culture  \\\n",
       "0                  92.44            79.65               65.64   \n",
       "1                  91.40            83.68               56.52   \n",
       "2                  91.16            83.57               56.72   \n",
       "\n",
       "   EB01_daily_grind  EB02_nerd_culture  EB03_lgbtq_culture  \\\n",
       "0             80.14              96.65               95.14   \n",
       "1             81.82              97.66               88.44   \n",
       "2             79.97              97.75               83.02   \n",
       "\n",
       "   EB04_wealth_signaling  EB05_hipster  EB06_student_life  EB07_farm_culture  \\\n",
       "0                  57.91         82.17              96.79              71.58   \n",
       "1                  56.34         75.89              97.49              91.51   \n",
       "2                  52.28         70.08              97.65              94.57   \n",
       "\n",
       "   EC01_love_and_romance  EC02_happily_ever_after  EC03_dating_life  \\\n",
       "0                  97.04                    70.15             78.75   \n",
       "1                  97.05                    80.63             80.34   \n",
       "2                  97.00                    83.31             78.01   \n",
       "\n",
       "   EC04_connected_motherhood  EC05_family_time  EC06_my_crew  EC07_girl_squad  \\\n",
       "0                      92.92             98.63         98.44            63.37   \n",
       "1                      94.37             98.85         98.60            75.50   \n",
       "2                      95.43             99.12         98.85            76.10   \n",
       "\n",
       "   EC08_networking  ED01_sweet_treats  ED02_coffee_connoisseur  \\\n",
       "0            65.62              90.24                    94.63   \n",
       "1            70.75              88.76                    92.90   \n",
       "2            66.60              87.42                    92.62   \n",
       "\n",
       "   ED03_trendy_eats  ED04_whiskey_business  ED05_asian_food_and_culture  \\\n",
       "0             92.97                  84.73                        91.36   \n",
       "1             89.00                  90.99                        89.55   \n",
       "2             87.48                  91.13                        88.70   \n",
       "\n",
       "   ED06_ingredient_attentive  ED07_fueling_for_fitness  ED08_wine_lovers  \\\n",
       "0                      56.71                     73.29             89.60   \n",
       "1                      65.53                     73.97             90.48   \n",
       "2                      62.53                     73.89             89.23   \n",
       "\n",
       "   ED09_hops_and_brews  EE01_film_lovers  EE02_competitive_nature  \\\n",
       "0                92.81             99.06                    61.66   \n",
       "1                96.05             94.90                    63.92   \n",
       "2                96.30             92.49                    63.11   \n",
       "\n",
       "   EE03_live_experiences  EE04_late_night_leisure  EE05_party_life  \\\n",
       "0                  98.07                    96.98            86.23   \n",
       "1                  96.09                    97.33            84.31   \n",
       "2                  94.68                    97.15            78.82   \n",
       "\n",
       "   EE06_live_and_local_music  EE07_lighthearted_fun  EF01_deep_emotions  \\\n",
       "0                      97.13                  98.71               98.55   \n",
       "1                      96.26                  99.22               96.09   \n",
       "2                      95.61                  99.48               94.50   \n",
       "\n",
       "   EF02_heartfelt_sharing  EF03_awestruck  EF04_happy_place  EF05_gratitude  \\\n",
       "0                   96.21           98.83             80.65           90.82   \n",
       "1                   92.04           98.99             84.75           81.49   \n",
       "2                   88.63           99.08             83.88           79.34   \n",
       "\n",
       "   EF06_memory_lane  EG01_dance_devotion  EG02_artistic_appreciation  \\\n",
       "0             92.39                99.49                       96.52   \n",
       "1             91.32                98.72                       92.87   \n",
       "2             90.58                98.20                       89.67   \n",
       "\n",
       "   EG03_pieces_of_history  EG04_sites_to_see  EG05_hip_hop_culture  \\\n",
       "0                   89.71              95.96                 95.53   \n",
       "1                   90.35              96.50                 93.83   \n",
       "2                   87.81              96.71                 91.23   \n",
       "\n",
       "   EH01_mindfulness_and_spirituality  EH02_activism  EH03_politically_engaged  \\\n",
       "0                              96.29          88.59                     78.29   \n",
       "1                              91.29          82.88                     88.66   \n",
       "2                              88.71          75.03                     85.88   \n",
       "\n",
       "   EH04_praise_and_worship  EH05_conservation  EH06_civic_attentiveness  \\\n",
       "0                    98.69              77.74                     83.34   \n",
       "1                    98.60              80.79                     78.17   \n",
       "2                    98.93              77.62                     73.29   \n",
       "\n",
       "   EI01_trend_trackers  EI02_all_about_hair  EI03_mens_style  \\\n",
       "0                52.02                86.27            58.59   \n",
       "1                73.71                84.72            54.19   \n",
       "2                74.88                84.98            50.35   \n",
       "\n",
       "   EI04_fitness_fashion  EI05_body_art  EI06_smart_chic  \\\n",
       "0                 68.67          95.61            67.51   \n",
       "1                 70.00          92.82            64.82   \n",
       "2                 69.03          93.42            62.72   \n",
       "\n",
       "   EJ01_home_and_leisure  EJ02_past_reflections  EJ03_humanitarian  \\\n",
       "0                  92.95                  98.59              99.44   \n",
       "1                  92.87                  98.71              99.51   \n",
       "2                  91.13                  98.15              99.46   \n",
       "\n",
       "   EJ04_deal_seekers  EK01_organized_sports  EK02_fitness_obsession  \\\n",
       "0              77.21                  86.57                   75.94   \n",
       "1              67.67                  89.37                   77.37   \n",
       "2              67.39                  91.23                   79.47   \n",
       "\n",
       "   EK03_outdoor_adventures  EK04_yoga_advocates  EK05_functional_fitness  \\\n",
       "0                    44.97                88.27                    64.15   \n",
       "1                    51.53                86.51                    61.55   \n",
       "2                    55.72                84.38                    61.75   \n",
       "\n",
       "   index_high_end_affinity  index_mid_range_consumer_affinity  \\\n",
       "0                    89.79                              85.64   \n",
       "1                    88.08                              86.62   \n",
       "2                    85.79                              86.98   \n",
       "\n",
       "   index_discount_affinity  index_foodie_affinity  \\\n",
       "0                    86.32                  76.91   \n",
       "1                    82.07                  80.79   \n",
       "2                    79.04                  80.04   \n",
       "\n",
       "   index_breakfast_and_brunch_affinity  index_fast_casual_affinity  \\\n",
       "0                                93.58                       79.28   \n",
       "1                                91.70                       84.66   \n",
       "2                                90.48                       85.09   \n",
       "\n",
       "   index_coffee_affinity  index_fast_food_affinity  \\\n",
       "0                  91.15                     81.09   \n",
       "1                  88.42                     84.90   \n",
       "2                  85.97                     85.32   \n",
       "\n",
       "   index_organic_and_local_affinity  index_fashion_affinity  \\\n",
       "0                             89.03                   66.61   \n",
       "1                             86.00                   69.49   \n",
       "2                             83.54                   68.39   \n",
       "\n",
       "   index_health_conscious_affinity  index_entertainment_affinity  \\\n",
       "0                            77.64                         94.26   \n",
       "1                            77.76                         95.14   \n",
       "2                            78.14                         94.47   \n",
       "\n",
       "   index_late_night_affinity  index_personal_care_affinity  \\\n",
       "0                      87.30                         78.58   \n",
       "1                      89.89                         76.08   \n",
       "2                      88.80                         74.42   \n",
       "\n",
       "   index_younger_affinity  index_older_affinity  index_family_affinity  \\\n",
       "0                   95.65                 76.62                  92.05   \n",
       "1                   95.54                 83.43                  96.39   \n",
       "2                   94.37                 84.98                  97.38   \n",
       "\n",
       "   index_politically_liberal_affinity  \\\n",
       "0                               93.62   \n",
       "1                               89.17   \n",
       "2                               85.11   \n",
       "\n",
       "   index_politically_conservative_affinity  VOLUME_INDEX   do_date_y  \n",
       "0                                    75.81          0.73  2020-10-01  \n",
       "1                                    83.32          0.73  2020-10-01  \n",
       "2                                    84.68          0.73  2020-10-01  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la = download_data('la')\n",
    "la.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z8-bhK-lHsJj"
   },
   "source": [
    "<a id='section2'></a>\n",
    "### 2. Analysis\n",
    "\n",
    "Once we have the data ready for analysis, we will start by selecting a target variable out of the Mastercard financial variables. This is the variable we are interested in explaining. We'll select the variable `txn_amt` which is the total transaction amount by census block group. In addition, we can select an industry. Mastercard provides data for 5 different industries: eating places, groceries, apparel, automotive fuel, and accomodation. They also provide the industry retail which is the sum of all the latter. For this use case, we'll select eating places, `eap`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "inWqXwQkHsJk"
   },
   "outputs": [],
   "source": [
    "target_var = 'txn_amt'  # Total transaction amount\n",
    "industry = 'eap'  # Eating places"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch_ret = chicago[(chicago['industry'] == industry) & (chicago['comparison_level'] == 'DMA')]\n",
    "la_ret = la[(la['industry'] == industry) & (la['comparison_level'] == 'DMA')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nnZ6HzJ9HsJm"
   },
   "source": [
    "<a id='section21'></a>\n",
    "#### 2.1 Top performers. Location visualization.\n",
    "\n",
    "Here we identify the top 5% performers and visualize them. \n",
    "\n",
    "An interesting insight here is how in Chicago top performers tend to concentrate in the downtown area, while in LA they are more spread throughout the city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "kMgpD-ppHsJn"
   },
   "outputs": [],
   "source": [
    "def classify_finance(value, thres_95):\n",
    "    if value >= thres_95:\n",
    "        return 'Top 5%'\n",
    "    else:\n",
    "        return 'Rest'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 230
    },
    "id": "kza5n_vtHsJr",
    "outputId": "ef5593b3-2b88-4c4c-c238-027401093b91"
   },
   "outputs": [],
   "source": [
    "ch_ret.loc[:, f'{target_var}_class'] = list(map(classify_finance, ch_ret[target_var], [ch_ret[target_var].quantile(0.95)]*ch_ret.shape[0]))\n",
    "la_ret.loc[:, f'{target_var}_class'] = list(map(classify_finance, la_ret[target_var], [la_ret[target_var].quantile(0.95)]*la_ret.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462
    },
    "id": "LGlbGpkuHsJ1",
    "outputId": "7e666c83-47bc-4894-dab6-e0eee74fce90"
   },
   "outputs": [
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       "              \n",
       "              <div class=&quot;as-map-panels&quot; data-name=&quot;Legends&quot;>\n",
       "                <div class=&quot;as-panel as-panel--vertical as-panel--top as-panel--left&quot;>\n",
       "                  \n",
       "\n",
       "<div class=&quot;as-panel__element&quot; id=&quot;legends-1&quot;>\n",
       "  <as-layer-selector id=&quot;layer-selector-1&quot; class=&quot;layer-selector&quot;>\n",
       "    \n",
       "      \n",
       "        \n",
       "        \n",
       "        <div slot=&quot;as-checkbox-layer-0-slot&quot;>\n",
       "          \n",
       "            \n",
       "              <as-legend\n",
       "                heading=&quot;Performers&quot;\n",
       "                description=&quot;&quot;>\n",
       "                <as-legend-color-category id=&quot;layer0_map1_legend0&quot; slot=&quot;legends&quot;></as-legend-color-category>\n",
       "                \n",
       "              </as-legend>\n",
       "            \n",
       "          \n",
       "        </div>\n",
       "      \n",
       "    \n",
       "  </as-layer-selector>\n",
       "</div>\n",
       "                </div>\n",
       "              </div>\n",
       "            \n",
       "\n",
       "          </as-responsive-content>\n",
       "        </section>\n",
       "      </div>\n",
       "    \n",
       "    </div>\n",
       "  \n",
       "  </main>\n",
       "\n",
       "  \n",
       "\n",
       "  <div id=&quot;error-container&quot; class=&quot;error&quot;>\n",
       "  <section class=&quot;error-section&quot;>\n",
       "    <span class=&quot;errors&quot; id=&quot;error-name&quot;></span>:\n",
       "    <section id=&quot;error-content&quot;>\n",
       "      <span class=&quot;errors&quot; id=&quot;error-type&quot;></span>\n",
       "      <span class=&quot;errors&quot; id=&quot;error-message&quot;></span>\n",
       "    </section>\n",
       "  </section>\n",
       "\n",
       "  <details class=&quot;error-details&quot;>\n",
       "    <summary>StackTrace</summary>\n",
       "    <ul id=&quot;error-stacktrace&quot;></ul>\n",
       "  </details>\n",
       "</div>\n",
       "</body>\n",
       "<script>\n",
       "  var init = (function () {\n",
       "  'use strict';\n",
       "\n",
       "  const BASEMAPS = {\n",
       "    DarkMatter: carto.basemaps.darkmatter,\n",
       "    Voyager: carto.basemaps.voyager,\n",
       "    Positron: carto.basemaps.positron\n",
       "  };\n",
       "\n",
       "  const attributionControl = new mapboxgl.AttributionControl({\n",
       "    compact: false\n",
       "  });\n",
       "\n",
       "  const FIT_BOUNDS_SETTINGS = { animate: false, padding: 50, maxZoom: 16 };\n",
       "\n",
       "  /** From https://github.com/errwischt/stacktrace-parser/blob/master/src/stack-trace-parser.js */\n",
       "\n",
       "  /**\n",
       "   * This parses the different stack traces and puts them into one format\n",
       "   * This borrows heavily from TraceKit (https://github.com/csnover/TraceKit)\n",
       "   */\n",
       "\n",
       "  const UNKNOWN_FUNCTION = '<unknown>';\n",
       "  const chromeRe = /^\\s*at (.*?) ?\\(((?:file|https?|blob|chrome-extension|native|eval|webpack|<anonymous>|\\/).*?)(?::(\\d+))?(?::(\\d+))?\\)?\\s*$/i;\n",
       "  const chromeEvalRe = /\\((\\S*)(?::(\\d+))(?::(\\d+))\\)/;\n",
       "  const winjsRe = /^\\s*at (?:((?:\\[object object\\])?.+) )?\\(?((?:file|ms-appx|https?|webpack|blob):.*?):(\\d+)(?::(\\d+))?\\)?\\s*$/i;\n",
       "  const geckoRe = /^\\s*(.*?)(?:\\((.*?)\\))?(?:^|@)((?:file|https?|blob|chrome|webpack|resource|\\[native).*?|[^@]*bundle)(?::(\\d+))?(?::(\\d+))?\\s*$/i;\n",
       "  const geckoEvalRe = /(\\S+) line (\\d+)(?: > eval line \\d+)* > eval/i;\n",
       "\n",
       "  function parse(stackString) {\n",
       "    const lines = stackString.split('\\n');\n",
       "\n",
       "    return lines.reduce((stack, line) => {\n",
       "      const parseResult =\n",
       "        parseChrome(line) ||\n",
       "        parseWinjs(line) ||\n",
       "        parseGecko(line);\n",
       "\n",
       "      if (parseResult) {\n",
       "        stack.push(parseResult);\n",
       "      }\n",
       "\n",
       "      return stack;\n",
       "    }, []);\n",
       "  }\n",
       "\n",
       "  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",
       "  const maps = [{&quot;_airship_path&quot;: null, &quot;_carto_vl_path&quot;: null, &quot;basecolor&quot;: &quot;&quot;, &quot;basemap&quot;: &quot;Positron&quot;, &quot;bounds&quot;: [[-87.940247, 41.691727], [-87.587021, 42.011456]], &quot;camera&quot;: null, &quot;description&quot;: null, &quot;has_legends&quot;: true, &quot;has_widgets&quot;: false, &quot;is_static&quot;: false, &quot;layer_selector&quot;: false, &quot;layers&quot;: [{&quot;credentials&quot;: null, &quot;data&quot;: &quot;H4sIAGMcyl8C/71dTa8ut23+K8UBunMNfZPKtkB3BbrozjACI7kNDDi+hnMD1DD83/vwHVKkxjN9swjmrs59jo5G4pdIipJ+/fjyy0+fPv7w8R+fvvvy958//fvnH3749Kcv33/+8eOrj/85sL99/OGbXz++/zNa5dQbfrH/DYCffv7806efv3wvbX/9+PK/P/7xu79++eOffvjubwA+/vvzT//S//Xjt68+/vLp818/ffn5l1ero5P/+vzDL395fe5Pnz///Ofvf/zuy+uL33zzb0xfjz7n4K9a/nrSnJS//Uphmrm/YE4pNYd5HDBxJl7waKMpXFJZcCeHs/fdUy0GlxpgbjaSPiPMNpIeBlgyGZx7gMc44Mxt9T3QRjvJNH3cp8l/++1vv321WDHqw6zolOsxmlHZp9TmXPAcDhcj12iJFlxyLwY733IZBnoPaRofxvpaA+lJQS7kcKJ5wARSLphq1bERfnSYp9KVuDsMmdJh0KwjwMQmVMX7no1MTEaQHsijjmQGwUwtm/C0OMXSrTE1F8zUuajw1B5gWkJPNXyRTAJzSTXAJZuocRgJqYpwmUHU0Fo7qbXFcevcueXicCk2ksrk06lplAW3ICG8WtegUHmu1lErs7Kd88gON0rV4MRXcO3hk/iFkiqKVKuLDbPye9W+NgQ3ZmNXj01ZmfuzyspM9ZDlUXNdU4JMlqlwcQ4B5mPsonWLXKCd6onIxyIAV0zVYOcnQ+MPmkM36tJYjGRq68aDA7wNcCPXrPVpco1j/n22uWSOB1YLhbtLP9RdhavD2njrqvYAaCVHjbI5GE18z+FcAxyHEWlSeh0P2/tWjvk04bfr+EgLHq5ZhcpBk1Yaz2AoWlV4jOm2s3ZWmFKEqWvfIwcD3NKhcK31MqOl7Qv2xpOnosnFrdeqKyzgHCwWrIPCbdSweLW+4LimRYrs7OH8MHsw+IPidW7GFp6BwjOaz0tm3s3Tum6Nmq/dvSgv+0i+/mOt0j5ATe8a7J4GB7/oNOqNhlwe9i4bqHFMqXKQzpRnV5RcsursKRvssgJYu8BM3U9JtbHBvlYLrH1MLAoOs31x5rl4ieGNabCvYqdR7yR82ko0aLCOEW6c+1KZVcMB+7oMWO1epTHD/HMrCsNDfsOIG66dBrKRBUbhYbJAzXT+MDcuFvCtVDvh8jtZyiyHhStzDJ9oYY0WABcXIjgjB7UKvNdI8nSoVoGjGuSzqGcnvfUAqx+Ivmcg+ag2kuIeKbzXoWqbxggjgW1WuAa+wbfQ1nlQiRzS1rm693UmVWQcrPbDnkDjrv575i2QyMXQoKB1KFkyjKBzopJ6QgXy7p2Ah83gIM6jkHIZ3XlryDYZ7F3vw9tpVelpIWe18SXa+Lvp3xALccUwmEeQOFOJGtwpwEYshBWuV7mrV1J6DzYBUk4GV6f4adw7FZ921esg1RbMjVxBx1BnMGe3FNAVXYSAettGeaEjoBqoIGz1CBALk67jucHD956bRuanYZSRDQ6dnAa9kRDceJiE1/OvZRFw+MhrUgkSeAktLJXG3IA5wKyxK8g93ajC8ipcM4W+SzGKz2CC7xixjXon4UhP271mM6LUXQ256chHnq6FcDOM+y1vsE6oxuQMflbSJg+uELgbH9IMCk5m3zKC8Xf299pYn6ZyIuzDDhJsmi5vCa7gmj6cFVUgwO4gAdYVNbWQTStYoofCzQWrkOUzJGO0qIU4XKMuwDnA+0g2uvT+sMBBDfOw0biBE1dtwZ50uaPi3UxJtTZRkKIi0c+Clxomi5gS/oocTuqTomtPq2HYxrdOLs3n2ZxoOx+mLTRAyYJFxA2O5Q+BuooDtXkODsbTcnmYp8c1cMg0qwjtnWHxsLSspBfdenLTxBrgFOFteDuxnnbV78Z+M9PrReV6AbperE/f22ZP6Wk1LFMjslRzDvI8NPQC3IL0Dw3rElzYqLSaAwA83unyjeZD4tSHSwj3XA9PA9zJ9bSngcFoeilBL9w8TT4cTZ5YMFuwWoejKbCbFkTpB10Ahxya5D/YWo9gzDRSg9Ma7CRX3WBKiSKqWdWUe+cAt8Uffs+2fY47xWt6muJEOv1otQrM/TSY6C0Nbyh+zZ8bbp5GstOlPS6J18y7YTVb4imVGuRiFFtVM79ba29W5tM4dqp0etrbulQWBJnK0Ol5FKDqrib8c6FoxSaU2I156eZqJXJPGKjuMCZYLe8aymfaWd3VADzn6qS80+VtKjtdqT9O12u9utbCwUtRPEgHYQ9nDTDVwJw0tTVxaJyTNhbdfcPJGxO5j3mnID8umZdMvpGIO/m5kbZL0byR42abWim34DK3oiFdyqP8AwtKnMxO2Tkfp+zlGG9m1LqmSRPEkd8ZyDtrun1xmz/n9niEeWXFG5l1qyUsYj0lCzCTu+R34ehd2LV9cZ9+e3r68ERtbY8Cnc0GUZyQ8NFgn08qySwFM4UujhhI4Max5/jBffrjact8t7Zdms9bWt1MdDmCqfW36+Y2kJ0q/LTXCD7rjCg4R5nrNNQjMoGzLUxhcUuVDPWFKVOyVUW2/Bc8NBWz95HHZIWJfCskU2n6RSmycHhkk0PyLdbMzcXTdTYz1UXwMGqtzRF00DsRv9GHjXo7K2d5XL8vhfNuQnfTr7ZGhlKWOwk/fXIjAML2pzX82kG69qZuZPlG8q/V5Foorg3qjUW5c9+2qex0LU8LViumyLlERyjryGutgVLFgr/agtG7tBF3TLhzgLdx7ESp+WnDeTnPTFTZYC8JyY0pG+wFt7kNI2F3mciYp3XdKfSRV9dempKbZslYaoTqOzN7bZRvuHOa4k7w8TDBQVkzWGW4iwVi5WmwbzwBtkin8MzvCH7NnRtengayk4X/2cr5n3//4cv397RZZkTtBexIMFCtdoXZaVOK+rAAozFPWv2Nxj2s43Na19xmkCSt1BXYzRyEVEnJMKROM0Q7xVp7yXAGF6rCI/gIRdPhMmyPJjLWKWVTykEDChVlKpa6qEXmZqVSorxU5V7qPTgaoxeDc+D1mNo3nIsYMlrrHCqMz1z4Fr8w6e1af8sItqP0LiqW7pYu11GtdVhvck2sjKvZd7dAgK6taxw76DIUhlhGWAcJngQONRV2puAmSAGKwqMFmsMwKT9H7YGfzWY5krsg6EQXVayBQYRqUlHpsY8yu34RKhq6zkPn3oMzja6Vy9y8aAfi1pSsIHCgdlZXCEIY5MoqkkHt6Tv6eSTN2XApvgF+5uSm/wgV0+Nhjdl/GLoSg3q1XSXUlZWq5ype8J1F+//t390Kf+MPnMa3Uyvz467MleLeqPkNXa6JeEfx0xf3+dfHN9mzWb+WKOQpkprnWWLWclAz3rW4yX6ZtbzbaNi/uM+/P54CSkMHGSvu4CvMbPAWOlyb+RtO32nitV7cONWXnu81xW/Yc8PM09R3RtDTMQVNcwhgRYIQpa42d+YNTmrPqQeTQ3XYUhZy6tWWPRh/D4O7rajixrxxkO68qWtxuBGeG1E7TXxnw3x801DLzF+E5Xf0vuHOLS8vCXAnnteWaVK28Cb1AO/j3qiIRf7xrdeiwVbbdvuvx35ng6/t58227umTOwHKeLquMKsHOhsFPbqly9WUatZDFgLXUJ1ZLEMCRQvw/smdAE/n1uEINWVeChE04LI0I1TMmF8phQMBtciJwgGBOw2904tLit/w5zzqnYb89AmJvPIbOcSfLWkJKeDcwikTS9RJapgdrppmw5xCeaoEe7Yo+LGryovksPmOjqp9UPLDFxX2TPuQ45ShwtdijRHL4jpbUEHVcwq10eJmLOm55P2NoFwL1Y0EamWVoC0UuBk5pGo8wNX6EEckTHyavxNSl1LcsNyPMEP5nznNfuoPpq4up4nDgYm5XOlw1OEkCptglvqscsvxWhCrfJ0Q+KxDvJzpdaBdYKhKXjAYf2w9ywE7WnC26ixBVydpsBbmzpHW8VPAmi6ZshuSA6y113BLYmvWTfDJ7oCwFGKTDrD3MJKRDfZ6PYFlyRe4eVTDubzi6wNeX8Tin1fj7H3shNq51p49oMIlF7F5GE2WbIWNEc54VraRH4SWA8pSa/eCS+kOd+0ERG49djIVHjP2Xfqi4eo78yKLn2m/lZ9rYbsjeCP7YETrtIkvTZO2OkOY0e6NJ0kKXlsHeuzUi7wk5mejBUqU9cx4yX6GlcCTYeiiIME6aqEuuLcKkgXudlAmrYkCtgOvUgjgrUnTygIv0v5uIJEsDGv37IoJi1pW5cIy+VQqr4IG39kbc+oRhSQ1ywumtgpYawuwZmal/MFzipSrVTtD9ByGdlt9fXfXjaQ+1WBfo0/D3mmYH95WIKsxrtnXdKLSV92G7x7c0PtmlhAn7aOHs/IE90NLYnqoWAJsRYM9hXsIqOr6kHq4v0UOzlu9ePCJaLIVrsNx8dlsU9zIDefyYXLDXKqDWrNvV1FJ6nhAZH0+BU0OuM9AQngvxWDP7p67jhOFuD68Gd2pWuVCD9EvYEvl9OLZ9z661d22Ec6dd8tlzMrhJhNY5JWXjLeNpNksP1HCdSjTUk0pHLaCPFleCpo/w1Eh83KDd9lW4aBsGYWzSWO5oqmFU0h9/t5DlcNdK33Scmys4ygu4VKUZa5o2I4AvHLPJYdxkNWDtHAvkMBK63hwilaYWcPJKVh024qu4Yg/7KCixe+0OTN3v3Do8SwOSEiWB2scru/pSeOYEeodG1l0k4nCoducyGAPEdvQwwaAw/ZqW9s/JYXzzF1PGclultu21pnYdrO8LKfZKNjJjc/Zzl+L/B1Z1QPSGYIHuEjauocyoJbL2lgK202IGPtqHcIYeNi23xS0BjHbsI2vGHbeBIF3IeNlfHkTjN6FrqzqKzoWQ6S0WlM48l+boTVekGB6yvGWAYiNBpjhYiGgttPGIzAXzg+vvdqovdZFYONJIHf9gAP7rH4MOUn1Gs5AfLTkBDPVuBawD/61H6jwzAEeqvPQA7eX0ov2DWPsN5WdPnmiQOGnKXA9yps5ZdJdacrhPokBV3koPPxw6Rh634nAbiPPn9wpAOl8fMNh2I51uFEDsJp9bm2GLR7STAiivhQ2BvTst7T2ZbpUm6rsODhcim3X489C0SCicYNDFX4aZitGn2+KMu5KOK63M663Pm72SS73VO72X3aabizOiR9mMYRSPQkEOGEbe0y1ZzWHQiArMYmn+WBS1ScC6tV4GRZBe6gjFBNBZdjgWLOa1HWp8Xam8+h2YuXUniYWq29dQ7wB9IidsVKH+o1mm5UlpicFPmhYuISJXpP2kgv7IHaSINJ8lCSIJXT/NsGSmU4JqlYvjbTWTsCspiDJDq/BLJccGrxqSTq8HE3XI3RcSy3gg6xY11eup0Ojdd8Mn+MA61kC8UCWdHc54XJQO1HoWSJAhdnjLMBTlR5+VvXJFPX9ksQZYeaRHjt3WktPc4eLUjbNQC0Yah0l1tXpcNVKP/ivJbQ+6k3l/sK1sQs0FYdD40T2xVycafDllD/Q2/BFM+vC+flGIG7E5zzJneY9PU5zOspFEVSlNiIFisEzwmRkzOt80S3Nbxh0yc4b5p/Ht5FLLn162Meo6gmJ4IRqNB7N4BJsLetVgISl0qM2dHIk5AQOa+2p722qtf/TrzT6R8pOEe0NDY+zF84BrbrBncMtXYCL+iGyF8ILLrp9hqBtGSopLlVtgge00gIkW9tD4b5SC9Qk/62w59JJDhkdX0QsuULdF6yflIx96HvBJcDVG69sKsFZVCNQMBsfCJxFG1+h2FprGvOsywU7k8/LRV9/cHwVDngYC7rXfsgt0qv18VWMMBBLLgFW2MNPfLWRwVRqgLUWP7dZOMCaAMg1zcjjop3kEtgGVNeX6SfCBdb4M9HIFFhvYPfhwSmYBjfn2VqiaHCArcwd/kINsPnpueaVGBBYXbAslzE73E1euVOL8DAx7kF69NJhEUFvjJB9Md63EeRyZ5P5xFcKUgJJWWvzs5Q6OBc1uspSh3rBcgTpm6BFuTmZCXrYpYIRP3JtNJNrGtDDEBAWgeq7m7kcjhZxG+s6NJLbpLV18y1suQda1yDAw1Etbia4NEuIJXo5/ExEe0uG8cFe2NCwnbqP+URBenZ/AprBh5YQQroVtwIeU4nVA6hX1FC475Ak40mKNkerbhoD7SsnAgFJyeC0nBuBu35OrlV0WMc2U2hakzoHEkv62ObQtjDhzeF9emdqP5u1l52Fw2Ri+u6VyODVzwBhA1Wu6X3Nmks2krnpJBH7dHgfxkYV0fyHqWL1kNC1shK4UAWtfBHYbTLgpPNHAOxTlaosg4ujibTr3NjFUDRS4UBErFCGDvc35J5C7cNvV5T9A+NZ7X28E/AbdbjWnRu+XwrJjUiRXjQMNPsycqb0xviOmPxZxkO9D79KJre8cYGrwX5EA7Bmn0C2xYaS9UAQvap6AqxskH2VxWH4Uscy+jIjFOCureUewdiaFc4uD+JYH5KGgN2X+Sypb4N9gHA6OS/YR2L74jJuX43xGTVjA25KDxQ5MmECrx2539FvZ2el+nAZBesLDSKK7pgnCeIOuPjuPckmt8HDeSFlAirP8CoXvVLrgw129zHpHZoSBVP44j6QjS4wlc9eDQClqyqMcjOju+eAD1638N4GYNNR2W0J4UO21tkLv2DfDs9fNJvcWyT1gAR1R1SFXCpeHVRqN7FnC8y6vUDRIMMXVu8HA1uZIykh6NUG7IuMVMEUhTttXWvrnj08qFlv9QbsRYCAhzaetQRHWy8JBezXUIIWaiDlqRAKZO4LbhHemLLJiMTRD798kDXXDRPgi91gew9E7EioRWgaxIgd8Z1JkurbF1yyb9qJrW+GhhcR5FIxg33blBNXbQ2+euuSjvyqwD6806h3ItanC5bgyR8Ge0x5iMYLVGZesF9MQ0UrV4b8PhQb6ZtGBA/fy8ep6ZXhAvtmK8vRU4U9qwu4p2pweJvjNMCdXvT0xdFSw9mP4eRwE+d6b+QFu3RlvR3/5T25LOZ+7EcMBJt+bzYhuMkKk+91oPXhmAy5ZrwF+LiFdiCu981zkfmmcPUdJ6msNNhvsJY0jQ5EDlw7rNekjNl7CXI+qnK/t/B6SNYko8AlwDulTozrTz8rQ3q2VYZTgpK2Wgzmejf4d1PVQ7YC+yYNm0c5pNrFLcBpJDth5sMnRRhSYk8IVX/JSWB9/AZjXPaSW9N02hRXamUKGjzmafDytgTWwkcR4hFg6zv5FZ1YU+yKTqkBLRHWx4ygICuDUO1xHvIHJrhKYk0nw55AaMpNmeJybTFFfdAD6NKJ39Fjfw2p8ePPyGV7/wQxzHj3SMnNkybX75/cPJZy+4QMrYGEZxemvYEgD1fFl2/siy28ugAzo4/WiPvi8NDiScAlvPGiV8Y1CsWdfWjIKXAonhusEthgdb0CT04AGuxb7Weq7kzGgB5mcrUCaayNfiK1V3uRDXC82fzmsvKbq82v70G/uTT96lpyec9uoX6lbq/rg5yDWJ0mc6ItPf4YVLMirhaKhyBcq+bOR263VUk2v4cXojRJLUmO+PDcWPV5oeM0vJMg+sxWxZHD1URNMhOrdXlTEXhXPnhXbHhZCHZdIHpTTjrXtRczPgYJX3l1Ep60q+uOEArmpK/itRmod2LL/ooarNvTV2nrnqiszuHe8d506yNs5csBSGtcArFq1fAAcO7xfQalOMntSuFC6myd+Go3yhxaJgCYwunSoSUIiPbC+EbSfQtxtjlc373NZicuFP7p07jXc0JMZsStKTwk0HQ7R+Lk8GJA1x1FxNLhHjTJ7xhhen1Dr/NIToR5+q4/iIMe2CsUnuDKnGwHFjAHWHfcEDXUABfdsxR4BFg34uT0Sbjtj2oxOHRyXWZ0U5J0VThk71y8LsLJ8QLAoXAKhVqniW9PXmHRe/oVFrbivR4jL9lbGgq7bslCayCF056sBe+AwxtrUoem8AxP1sjpNYNDHXLSw0yy/xmO0ML0auueQ0G0lGkcMMVXIbLpD2CvW6xyoM7gYIZaJW/tcM81X3TSdY+5I+YP7wJo0SG80hEIlfU8cA+ZRsBqseCKxgce9AgAZhhru+0wKsKLFOqys5ZgyiruJd/T6sa7PNjk51yb1vv0mG8483wXwfr0qSfxsY7hgCUzPBXJVrUs7w8HWNeUPuM7wEM3egBP103x49ng+KSdHk4BGt72kduVjy+m8CAxyFVN2OJjbGROUKdwDgDuajdBofB+wtCHcwV2b+dO2BobhziU/FZ5dkzhEd4xwfqnIjTDPZm1dVWSkUOGD7CWXEghRoTVlsqJbI6wMkdkKBwd1z7grsVXqtR6y9V93rhbOXEc9HJDKT4adON2yJEYQ8MohlU0kFQsR6aTdj2DZ9mtTARhUni0jPTtYoE9gRXhEm4AIDNfMYAXWAsiB0cncmgykSVpFZ7OTnrgBFLrycSzIux6ifXiUb2UE1BNzxvDJhsFpDyz6/FfBMO84Px6svAF8yqNkGOvdpa5urrK2f0y7QzxotfryUQ9Q9yTd4JVYLWea0/k9ayhDdAz+3Lti2RNXrCnU+VACxk8fTqvpy71+LRvBMyWXjcHvM5guwmfUtxlx9s9TJRnW/XdN8kmtgDrM3mzei0+4KJPNAMOAzSH4QV766xRBGA/0vCavMH+wO60zDu+nLyPVbkvVxTHxs3eFe85whbdymVz7fetoVZ7J0ff8DFXpks60ZOoIw3ne9Ir6Hs4FKDXC3T20EHiar0fH9ZqpX4lpZHaesbZW3d70LWLC73gZk90djdpkiM4LN3sg3dYG1ffeXw9aaZwCRyHOh686tktHayvHlgETMvngfdTDW0up6CbHuEF8Yf3YbfXdykjW2ixzEf3hIMUBWnqB7ZjGVEs75YL7MmvspQDmtYHhLpEWInnWSU506iP4cpz7d6HXfsIdqVlROXYl/Xh726xXKQxjIlLLY67SYyJnnflVqbBIWXKdVZ7o9ufMAGsdb/4oqcwGQKio55hinbqWK43qw7Wae95+x0EUimk4pxpHYiTeyh1KvBGVizPUg/GJvzkrbGYK4zFLzus119PrFHDW2MZ0/H16klaOBuadhzDz9XIiRxNa8n2fgmwPpEgh8BbgFWqRyh1AUxqmWRrxWHSChhEyn7LImAmSwyHm0HgO2rmu4epQ/o0D+0uJ6ia9C08uOstEDvruxbEIxBk2s0GYJzf/0Fys/dhl0KJr7BGtZzDgX+BNf/JbjZlIGrxqIcJTr0nW6x6CqPWk9Az5nUA6y20k6efQGLGKqQfRANykcw6dXkH0CW1DE1DTg8aIdUqTjPewyF7zmrVi2+xQ2HsZZEph14cVj8HK0PxyUgZalOYWoDVJkx50Dg0rraOzNCzXcQA9XJ2ybE0nSF5XPjqQ7uGnNYAN4Nn9066HqsF7YrbG7boHLCX6r3OpWkn06tnJd1HxwUiKZXlm8hZM9LFPLXsA5SVWGHfo3xd88sGB6OKBU37zn4+UQ7IdLs6ZayFiiHYo9iNKi7C4HUzeK4chNhJmgaPYILNv8keQTMovy5r8Vyo3PBeF+rGHfFDM7j5B49dgAP2Lo4dDf3gWkZT4q6jgz13+KjweMF+rfWUu/zNP8wzLGAv+/SCW+i7vio0X/BYSVl5TS7pbCCs3kl7XfN0tM7+yU7LyaQS4MnZYHL4eLv5cGBbgLl2g9mXWKrLr42dwJDUNZLgFrS2BrgCcQTwiay13yAAmJMOsK0YH5LTTEaaXxYtzxEac1oL/kl5rVcv2A9lyqO1XSfZqHrfr+qOw5EObhInazvdR5ejx8p2hH7eerzyi0dY4MSWS6vNdffkmGz+Lx+9LVdE/ECdS/eLr82VfMFj8zC3QETiom9/+z/IKrjMB4sAAA==&quot;, 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&quot;, 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&quot;, &quot;title&quot;: null, &quot;type&quot;: &quot;GeoJSON&quot;, &quot;viz&quot;: &quot;@v3f25a1: prop(\\u0027txn_amt_class\\u0027)\\ncolor: opacity(ramp(buckets(prop(\\u0027txn_amt_class\\u0027), [\\u0027Top 5%\\u0027]), [#009B9E]), 0.9)\\nfilter: 1\\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;: []}], &quot;show_info&quot;: null, &quot;size&quot;: null, &quot;theme&quot;: null, &quot;title&quot;: null, &quot;token&quot;: &quot;&quot;, &quot;viewport&quot;: null}];\n",
       "const is_static = 'False' === 'true';\n",
       "\n",
       "init({\n",
       "  is_static,\n",
       "  maps\n",
       "});\n",
       "</script>\n",
       "</html>\n",
       "\">\n",
       "\n",
       "</iframe>"
      ],
      "text/plain": [
       "<cartoframes.viz.layout.Layout at 0x7f5ea01b6f98>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Layout([Map(Layer(ch_ret[ch_ret[f'{target_var}_class'] != 'Rest'], \n",
    "                  geom_col='geom',\n",
    "                  style=color_category_style(f'{target_var}_class', \n",
    "                                             cat=['Top 5%'], \n",
    "                                             palette=['#009B9E']),\n",
    "                  legends=color_category_legend('Performers'))),\n",
    "        Map(Layer(la_ret[la_ret[f'{target_var}_class'] != 'Rest'], \n",
    "                  geom_col='geom',\n",
    "                  style=color_category_style(f'{target_var}_class', \n",
    "                                             cat=['Top 5%'],\n",
    "                                             palette=['#009B9E']),\n",
    "                  legends=color_category_legend('Performers')))], \n",
    "       map_height=420\n",
    "      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0h09MnLUHsJ8"
   },
   "source": [
    "<a id='section22'></a>\n",
    "#### 2.2 Characterization of top performers\n",
    "\n",
    "In this section, we'll identify what social segments characterize top performers in Chicago and LA and compare the two cities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gXHw4cUOHsJ8"
   },
   "source": [
    "##### Characterization based on social segments\n",
    "\n",
    "<b>Main insights</b>:\n",
    " - Both cities share 4 out of 7 of their most important social segments (although not with the same order). These segments are related to food and drinks, and lgbtq culture.\n",
    " - Regarding the social segments which are different from one city to the other, while in Chicago Asian food and culture, whiskey business, and sweet treats are on the top 7 segments, in LA it is film lovers, fitness fashion, and heartfelt sharing. This shows two very different cities in terms of social behavior patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "gJL2Xd3AHsJ8"
   },
   "outputs": [],
   "source": [
    "social_segment_columns = ch_ret.columns[17:-22]\n",
    "\n",
    "top_ch_ret = ch_ret[ch_ret[f'{target_var}_class'] == 'Top 5%']\n",
    "top_ch_ret = top_ch_ret[social_segment_columns].describe().transpose()\n",
    "top_ch_ret.sort_values(['50%'], ascending=False, inplace=True)\n",
    "\n",
    "top_la_ret = la_ret[la_ret[f'{target_var}_class'] == 'Top 5%']\n",
    "top_la_ret = top_la_ret[social_segment_columns].describe().transpose()\n",
    "top_la_ret.sort_values(['50%'], ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the following function to make sure social segments have same color in both cities for an easier comparison.\n",
    "\n",
    "Note we'll use CARTO's bold palette colors. [Here](https://carto.com/carto-colors/) you can explore all our palettes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "ucswIkkkHsJ-"
   },
   "outputs": [],
   "source": [
    "def palette(segments, color_dict):\n",
    "    return [color_dict[segment] for segment in segments]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_segments = np.unique(top_ch_ret.head(7).index.tolist() + top_la_ret.head(7).index.tolist())\n",
    "palette_c=['#7F3C8D','#11A579','#3969AC','#F2B701','#E73F74','#80BA5A','#E68310','#008695','#CF1C90','#f97b72', '#4b4b8f', '#A5AA99']\n",
    "color_dict = dict(zip(unique_segments, palette_c))\n",
    "\n",
    "def plot_top_segments(df, ax, color_dict):\n",
    "    sns.barplot(x='50%', y='social_segment', data=df.head(7).reset_index().rename(columns={'index':'social_segment'}), \n",
    "                alpha=0.96, ax=ax, palette=palette(df.head(7).index.tolist(), color_dict))\n",
    "    ax.set_title('Chicago', fontsize=18, fontweight='bold')\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    ax.tick_params(labelsize=15)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(18, 6))\n",
    "plot_top_segments(top_ch_ret, axs[0], color_dict)\n",
    "plot_top_segments(top_la_ret, axs[1], color_dict)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "93S22LzIHsKC"
   },
   "source": [
    "##### Characterization based on indices\n",
    "\n",
    "<b>Main insights</b>:\n",
    "- Both cities share 4 out of the 7 most impactful social indices: Breakfast+brunch, coffe, foodie, and high end affinity.\n",
    "- In Chicago top eating places performers are located where late night, fashion, and entertainment affinity are high, whereas in LA they are located where politically liberal, personal care, and organic+local affinity are high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "YvuCW5knHsKC"
   },
   "outputs": [],
   "source": [
    "social_indices_columns = ch_ret.columns[-22:-3]\n",
    "\n",
    "top_ch_ret_ix = ch_ret[ch_ret[f'{target_var}_class'] == 'Top 5%']\n",
    "top_ch_ret_ix = top_ch_ret_ix[social_indices_columns].describe().transpose()\n",
    "top_ch_ret_ix.sort_values(['50%'], ascending=False, inplace=True)\n",
    "\n",
    "top_la_ret_ix = la_ret[la_ret[f'{target_var}_class'] == 'Top 5%']\n",
    "top_la_ret_ix = top_la_ret_ix[social_indices_columns].describe().transpose()\n",
    "top_la_ret_ix.sort_values(['50%'], ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_segments = np.unique(top_ch_ret_ix.head(7).index.tolist() + top_la_ret_ix.head(7).index.tolist())\n",
    "palette_c=['#7F3C8D','#11A579','#3969AC','#F2B701','#E73F74','#80BA5A','#E68310','#008695','#CF1C90','#f97b72']\n",
    "color_dict = dict(zip(unique_segments, palette_c))\n",
    "\n",
    "def plot_top_indices(df, ax, color_dict):\n",
    "    sns.barplot(x='50%', y='index', data=df.head(7).reset_index(), \n",
    "                alpha=0.96, ax=ax, palette=palette(df.head(7).index.tolist(), color_dict))\n",
    "    ax.set_title('Chicago', fontsize=18, fontweight='bold')\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    ax.tick_params(labelsize=11)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(18, 6))\n",
    "plot_top_indices(top_ch_ret_ix, axs[0], color_dict)\n",
    "plot_top_indices(top_la_ret_ix, axs[1], color_dict)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cbWC9pjlHsKH"
   },
   "source": [
    "<a id='section23'></a>\n",
    "#### 2.3 Correlation analysis\n",
    "\n",
    "In this section we'll carry out a deeper analysis on how geosocial data can help explain retail performance. We'll compare the most important features for Chicago and LA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iIDt1LRuHsKI"
   },
   "source": [
    "##### Social segments\n",
    "\n",
    "Here we calculate the correlation coefficient between every social segment and the total transaction amount for restaurants. This allows us to identify which segments have a strongest impact and the differences between both cities.\n",
    "\n",
    "This analysis provides very interesting insights. We can see how the social segment _\"sites to see\"_ impacts positively in Chicago, while it barely impacts in LA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "sdxUnCQqHsKI"
   },
   "outputs": [],
   "source": [
    "def calculate_corrcoef(target_var, df, columns):\n",
    "    corr_coefs = []\n",
    "    for ssegment in columns:\n",
    "        corr_aux = df[~df[ssegment].isnull()]\n",
    "        corr_coefs.append(np.corrcoef(corr_aux[target_var], corr_aux[ssegment])[0][1])\n",
    "\n",
    "    corr_df_ch = pd.DataFrame(data={'social_segment':columns, 'corr_coef':corr_coefs})\n",
    "    corr_df_ch['corr_coef_abs'] = np.abs(corr_df_ch['corr_coef'])\n",
    "    corr_df_ch.sort_values('corr_coef_abs', ascending=False, inplace=True)\n",
    "    return corr_df_ch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "3tO7Eh94HsKK"
   },
   "outputs": [],
   "source": [
    "corr_df_ch = calculate_corrcoef(target_var, ch_ret, social_segment_columns)\n",
    "corr_df_la = calculate_corrcoef(target_var, la_ret, social_segment_columns)\n",
    "\n",
    "corr_df_ch['city'] = 'Chicago'\n",
    "corr_df_la['city'] = 'LA'\n",
    "corr_df = pd.concat([corr_df_ch, corr_df_la], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "sns.barplot(x='social_segment', y='corr_coef', hue='city', data=corr_df, alpha=0.99)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('')\n",
    "plt.title(f\"Correlation strength of social segments with target var '{target_var}' - industry '{industry}'\", \n",
    "          fontsize=15, fontweight='light', pad=15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fWG2fLiFHsKb"
   },
   "source": [
    "##### Indices\n",
    "\n",
    "Now, we'll calculate the correlation coefficient between every social index and the metric total transaction amount for restaurants. This allows us to identify which indices have a strongest impact and the differences between both cities.\n",
    "\n",
    "It is interesting to see the different correlation coefficients for older affinity and discount affinity in Chicago and LA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "l2UKdUaoHsKc"
   },
   "outputs": [],
   "source": [
    "corr_df_ch_ix = calculate_corrcoef(target_var, ch_ret, social_indices_columns)\n",
    "corr_df_la_ix = calculate_corrcoef(target_var, la_ret, social_indices_columns)\n",
    "\n",
    "corr_df_ch_ix['city'] = 'Chicago'\n",
    "corr_df_la_ix['city'] = 'LA'\n",
    "corr_df_ix = pd.concat([corr_df_ch_ix, corr_df_la_ix], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "t-R3_TU1HsKj",
    "outputId": "d729c804-7d62-493a-fe69-0bedaa8c9478"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(y='social_segment', x='corr_coef', hue='city', data=corr_df_ix, alpha=0.95)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('')\n",
    "plt.yticks(fontsize=12)\n",
    "plt.title(f\"Correlation strength of indices with target var '{target_var}' - industry '{industry}'\", \n",
    "          fontsize=14, fontweight='light', pad=15)\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "[CARTO] Combining two datasets.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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}