Final Assignment - Analysis and Report.ipynb

{
    "nbformat_minor": 1, 
    "cells": [
        {
            "source": "<h1>Analysis and Reporting Data<br>Assignment By: Derek Evans<br>Submitted On: Jan. 20th, 2019</h1>\n\n<hr>\n", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 1, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Requirement not upgraded as not directly required: foursquare in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages\nRequirement not upgraded as not directly required: six in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from foursquare)\nRequirement not upgraded as not directly required: requests>=2.1 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from foursquare)\nRequirement not upgraded as not directly required: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests>=2.1->foursquare)\nRequirement not upgraded as not directly required: idna<2.7,>=2.5 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests>=2.1->foursquare)\nRequirement not upgraded as not directly required: urllib3<1.23,>=1.21.1 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests>=2.1->foursquare)\nRequirement not upgraded as not directly required: certifi>=2017.4.17 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests>=2.1->foursquare)\nFetching package metadata .............\nSolving package specifications: .\n\n# All requested packages already installed.\n# packages in environment at /opt/conda/envs/DSX-Python35:\n#\ngeopy                     1.18.1                     py_0    conda-forge\nRequirement not upgraded as not directly required: folium in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages\nRequirement not upgraded as not directly required: branca>=0.3.0 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium)\nRequirement not upgraded as not directly required: six in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium)\nRequirement not upgraded as not directly required: jinja2 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium)\nRequirement not upgraded as not directly required: requests in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium)\nRequirement not upgraded as not directly required: numpy in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from folium)\nRequirement not upgraded as not directly required: MarkupSafe>=0.23 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from jinja2->folium)\nRequirement not upgraded as not directly required: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium)\nRequirement not upgraded as not directly required: idna<2.7,>=2.5 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium)\nRequirement not upgraded as not directly required: urllib3<1.23,>=1.21.1 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium)\nRequirement not upgraded as not directly required: certifi>=2017.4.17 in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from requests->folium)\n"
                }
            ], 
            "source": "!pip install foursquare\n!conda install -c conda-forge geopy --yes\n!pip install folium\nimport folium\nimport foursquare\nimport types\nfrom ibm_botocore.client import Config\nimport ibm_boto3\nfrom geopy.geocoders import Nominatim\nimport requests\nimport pandas as pd\nimport numpy as np\nfrom IPython.display import Image\nfrom IPython.core.display import HTML\nfrom pandas.io.json import json_normalize\nfrom datetime import datetime, timezone\nimport pytz\nimport time\nimport ibm_db\nimport ibm_db_dbi\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport scipy\nfrom sklearn import preprocessing"
        }, 
        {
            "execution_count": 2, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# DATE & TIME VARIABLES\nIT = pytz.timezone('America/Creston')\ndate_now = datetime.now(IT)\ndate_date = date_now.strftime(\"%x\")\ndate_time = date_now.strftime(\"%X\")\ndate_weekday = date_now.strftime(\"%A\")\ndate_year = date_now.strftime(\"%Y\")\ndate_month = date_now.strftime(\"%B\")\ndate_ampm = date_now.strftime(\"%p\")\ndate_zone = date_now.strftime(\"%Z\")"
        }, 
        {
            "execution_count": 3, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# The code was removed by Watson Studio for sharing."
        }, 
        {
            "execution_count": 4, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "<---- ibm_db API Connected to DataSciDB Database on IBM DB2 Cloud ---->\n"
                }
            ], 
            "source": "# The code was removed by Watson Studio for sharing."
        }, 
        {
            "execution_count": 5, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "<---- ibm_db_dbi API Connected to DataSciDB Database on IBM DB2 Cloud ---->\n"
                }
            ], 
            "source": "# The code was removed by Watson Studio for sharing."
        }, 
        {
            "execution_count": 6, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "<---- SELECT * FROM CITY_DATA SQL STATEMENT RESULTS ASSIGNED TO PANDAS DATAFRAME Trending_Data_DF ---->\n"
                }
            ], 
            "source": "# The code was removed by Watson Studio for sharing."
        }, 
        {
            "execution_count": 7, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "<---- SELECT * FROM TRENDING_DATA SQL STATEMENT RESULTS ASSIGNED TO PANDAS DATAFRAME Trending_Data_DF ---->\n"
                }
            ], 
            "source": "# The code was removed by Watson Studio for sharing."
        }, 
        {
            "execution_count": 8, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "<---- SELECT * FROM CITY_DATA SQL STATEMENT RESULTS ASSIGNED TO PANDAS DATAFRAME Updated_Trend_Data_DF ---->\n"
                }
            ], 
            "source": "# The code was removed by Watson Studio for sharing."
        }, 
        {
            "execution_count": 9, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "<---- SELECT * FROM ORIGINAL_CITY_DATA SQL STATEMENT RESULTS ASSIGNED TO PANDAS DATAFRAME Original_City_Data_DF ---->\n"
                }
            ], 
            "source": "# The code was removed by Watson Studio for sharing."
        }, 
        {
            "execution_count": 10, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 10, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "True"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "ibm_db.close(conn)"
        }, 
        {
            "source": "<hr>\n\n<h1 align=\"center\">Original City Data from Web Scraper Notebook</h1>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 11, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "SELECT QUERY TIMESTAMP - DATE: 01/21/19 - TIME: 13:55:47\n"
                }, 
                {
                    "execution_count": 11, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CITY</th>\n      <th>STATE</th>\n      <th>CITY_STATE1</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>STATE_ID</th>\n      <th>CITY_STATE2</th>\n      <th>WIKI_LINK</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Grand Rapids</td>\n      <td>Michigan</td>\n      <td>Grand Rapids, Michigan</td>\n      <td>42.961111111111</td>\n      <td>-85.655555555556</td>\n      <td>MI</td>\n      <td>Grand Rapids, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Grand_Rapids,_Mi...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Battle Creek</td>\n      <td>Michigan</td>\n      <td>Battle Creek, Michigan</td>\n      <td>42.312222222222</td>\n      <td>-85.204166666667</td>\n      <td>MI</td>\n      <td>Battle Creek, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Battle_Creek,_Mi...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Lansing</td>\n      <td>Michigan</td>\n      <td>Lansing, Michigan</td>\n      <td>42.733611111111</td>\n      <td>-84.546666666667</td>\n      <td>MI</td>\n      <td>Lansing, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Lansing,_Michigan</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>East Lansing</td>\n      <td>Michigan</td>\n      <td>East Lansing, Michigan</td>\n      <td>42.734722222222</td>\n      <td>-84.480555555556</td>\n      <td>MI</td>\n      <td>East Lansing, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/East_Lansing,_Mi...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Kalamazoo</td>\n      <td>Michigan</td>\n      <td>Kalamazoo, Michigan</td>\n      <td>42.29</td>\n      <td>-85.585833333333</td>\n      <td>MI</td>\n      <td>Kalamazoo, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Kalamazoo,_Michigan</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Detroit</td>\n      <td>Michigan</td>\n      <td>Detroit, Michigan</td>\n      <td>42.331388888889</td>\n      <td>-83.045833333333</td>\n      <td>MI</td>\n      <td>Detroit, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Detroit,_Michigan</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Sterling Heights</td>\n      <td>Michigan</td>\n      <td>Sterling Heights, Michigan</td>\n      <td>42.580277777778</td>\n      <td>-83.030277777778</td>\n      <td>MI</td>\n      <td>Sterling Heights, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Sterling_Heights...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Dearborn</td>\n      <td>Michigan</td>\n      <td>Dearborn, Michigan</td>\n      <td>42.314444444444</td>\n      <td>-83.213333333333</td>\n      <td>MI</td>\n      <td>Dearborn, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Dearborn,_Michigan</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Livonia</td>\n      <td>Michigan</td>\n      <td>Livonia, Michigan</td>\n      <td>42.397222222222</td>\n      <td>-83.373611111111</td>\n      <td>MI</td>\n      <td>Livonia, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Livonia,_Michigan</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Troy</td>\n      <td>Michigan</td>\n      <td>Troy, Michigan</td>\n      <td>42.580277777778</td>\n      <td>-83.143055555556</td>\n      <td>MI</td>\n      <td>Troy, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Troy,_Michigan</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Warren</td>\n      <td>Michigan</td>\n      <td>Warren, Michigan</td>\n      <td>42.491944444444</td>\n      <td>-83.023888888889</td>\n      <td>MI</td>\n      <td>Warren, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Warren,_Michigan</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Flint</td>\n      <td>Michigan</td>\n      <td>Flint, Michigan</td>\n      <td>43.018888888889</td>\n      <td>-83.693333333333</td>\n      <td>MI</td>\n      <td>Flint, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Flint,_Michigan</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Grand Haven</td>\n      <td>Michigan</td>\n      <td>Grand Haven, Michigan</td>\n      <td>43.063055555556</td>\n      <td>-86.228333333333</td>\n      <td>MI</td>\n      <td>Grand Haven, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Grand_Haven,_Mic...</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Holland</td>\n      <td>Michigan</td>\n      <td>Holland, Michigan</td>\n      <td>42.783333333333</td>\n      <td>-86.1</td>\n      <td>MI</td>\n      <td>Holland, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Holland,_Michigan</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Muskegon</td>\n      <td>Michigan</td>\n      <td>Muskegon, Michigan</td>\n      <td>43.234166666667</td>\n      <td>-86.248333333333</td>\n      <td>MI</td>\n      <td>Muskegon, MI United States</td>\n      <td>https://en.wikipedia.org/wiki/Muskegon,_Michigan</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Indianapolis</td>\n      <td>Indiana</td>\n      <td>Indianapolis, Indiana</td>\n      <td>39.791</td>\n      <td>-86.148</td>\n      <td>IN</td>\n      <td>Indianapolis, IN United States</td>\n      <td>https://en.wikipedia.org/wiki/Indianapolis</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Fort Wayne</td>\n      <td>Indiana</td>\n      <td>Fort Wayne, Indiana</td>\n      <td>41.08045</td>\n      <td>-85.13915</td>\n      <td>IN</td>\n      <td>Fort Wayne, IN United States</td>\n      <td>https://en.wikipedia.org/wiki/Fort_Wayne,_Indiana</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Fargo</td>\n      <td>North Dakota</td>\n      <td>Fargo, North Dakota</td>\n      <td>46.877222222222</td>\n      <td>-96.789444444444</td>\n      <td>ND</td>\n      <td>Fargo, ND United States</td>\n      <td>https://en.wikipedia.org/wiki/Fargo,_North_Dakota</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Minot</td>\n      <td>North Dakota</td>\n      <td>Minot, North Dakota</td>\n      <td>46.813343</td>\n      <td>-100.779004</td>\n      <td>ND</td>\n      <td>Minot, ND United States</td>\n      <td>https://en.wikipedia.org/wiki/Bismarck,_North_...</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Williston</td>\n      <td>North Dakota</td>\n      <td>Williston, North Dakota</td>\n      <td>48.156388888889</td>\n      <td>-103.62805555556</td>\n      <td>ND</td>\n      <td>Williston, ND United States</td>\n      <td>https://en.wikipedia.org/wiki/Williston,_North...</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Mino</td>\n      <td>inot</td>\n      <td>Minot</td>\n      <td>48.233055555556</td>\n      <td>-101.29222222222</td>\n      <td>ND</td>\n      <td>Mino, ND United States</td>\n      <td>https://en.wikipedia.org/wiki/Minot,_North_Dakota</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Saint Paul</td>\n      <td>Minnesota</td>\n      <td>Saint Paul, Minnesota</td>\n      <td>44.944166666667</td>\n      <td>-93.093611111111</td>\n      <td>MN</td>\n      <td>Saint Paul, MN United States</td>\n      <td>https://en.wikipedia.org/wiki/Saint_Paul,_Minn...</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Minneapolis</td>\n      <td>Minnesota</td>\n      <td>Minneapolis, Minnesota</td>\n      <td>44.983333333333</td>\n      <td>-93.266666666667</td>\n      <td>MN</td>\n      <td>Minneapolis, MN United States</td>\n      <td>https://en.wikipedia.org/wiki/Minneapolis</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>St. Cloud</td>\n      <td>Minnesota</td>\n      <td>St. Cloud, Minnesota</td>\n      <td>45.55</td>\n      <td>-94.166666666667</td>\n      <td>MN</td>\n      <td>St. Cloud, MN United States</td>\n      <td>https://en.wikipedia.org/wiki/St._Cloud,_Minne...</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Green Bay</td>\n      <td>Wisconsin</td>\n      <td>Green Bay, Wisconsin</td>\n      <td>44.513333333333</td>\n      <td>-88.015833333333</td>\n      <td>WI</td>\n      <td>, WI United States</td>\n      <td>https://en.wikipedia.org/wiki/Green_Bay,_Wisco...</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Rapid City</td>\n      <td>South Dakota</td>\n      <td>Rapid City, South Dakota</td>\n      <td>44.076</td>\n      <td>-103.228</td>\n      <td>SD</td>\n      <td>Rapid City, SD United States</td>\n      <td>https://en.wikipedia.org/wiki/Rapid_City,_Sout...</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Sioux Falls</td>\n      <td>South Dakota</td>\n      <td>Sioux Falls, South Dakota</td>\n      <td>43.536388888889</td>\n      <td>-96.731666666667</td>\n      <td>SD</td>\n      <td>Sioux Falls, SD United States</td>\n      <td>https://en.wikipedia.org/wiki/Sioux_Falls,_Sou...</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Pierre</td>\n      <td>South Dakota</td>\n      <td>Pierre, South Dakota</td>\n      <td>44.367966</td>\n      <td>-100.336378</td>\n      <td>SD</td>\n      <td>Pierre, SD United States</td>\n      <td>https://en.wikipedia.org/wiki/Pierre,_South_Da...</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Aberdee</td>\n      <td>berdeen</td>\n      <td>Aberdeen</td>\n      <td>45.464722222222</td>\n      <td>-98.486388888889</td>\n      <td>SD</td>\n      <td>Aberdee, SD United States</td>\n      <td>https://en.wikipedia.org/wiki/Aberdeen,_South_...</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Gillette</td>\n      <td>Wyoming</td>\n      <td>Gillette, Wyoming</td>\n      <td>44.282777777778</td>\n      <td>-105.50527777778</td>\n      <td>WY</td>\n      <td>Gillette, WY United States</td>\n      <td>https://en.wikipedia.org/wiki/Gillette,_Wyoming</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>Springfield</td>\n      <td>Massachusetts</td>\n      <td>Springfield, Massachusetts</td>\n      <td>42.112411</td>\n      <td>-72.547455</td>\n      <td>MA</td>\n      <td>Springfield, MA United States</td>\n      <td>https://en.wikipedia.org/wiki/Springfield,_Mas...</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>Lowell</td>\n      <td>Massachusetts</td>\n      <td>Lowell, Massachusetts</td>\n      <td>42.639444444444</td>\n      <td>-71.314722222222</td>\n      <td>MA</td>\n      <td>Lowell, MA United States</td>\n      <td>https://en.wikipedia.org/wiki/Lowell,_Massachu...</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>Cambridge</td>\n      <td>Massachusetts</td>\n      <td>Cambridge, Massachusetts</td>\n      <td>42.373611111111</td>\n      <td>-71.110555555556</td>\n      <td>MA</td>\n      <td>Cambridge, MA United States</td>\n      <td>https://en.wikipedia.org/wiki/Cambridge,_Massa...</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>Brockton</td>\n      <td>Massachusetts</td>\n      <td>Brockton, Massachusetts</td>\n      <td>42.083333333333</td>\n      <td>-71.018888888889</td>\n      <td>MA</td>\n      <td>Brockton, MA United States</td>\n      <td>https://en.wikipedia.org/wiki/Brockton,_Massac...</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>New Bedford</td>\n      <td>Massachusetts</td>\n      <td>New Bedford, Massachusetts</td>\n      <td>41.636111111111</td>\n      <td>-70.934722222222</td>\n      <td>MA</td>\n      <td>New Bedford, MA United States</td>\n      <td>https://en.wikipedia.org/wiki/New_Bedford,_Mas...</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>Quincy</td>\n      <td>Massachusetts</td>\n      <td>Quincy, Massachusetts</td>\n      <td>42.25</td>\n      <td>-71.0</td>\n      <td>MA</td>\n      <td>Quincy, MA United States</td>\n      <td>https://en.wikipedia.org/wiki/Quincy,_Massachu...</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>Lynn</td>\n      <td>Massachusetts</td>\n      <td>Lynn, Massachusetts</td>\n      <td>42.466666666667</td>\n      <td>-70.95</td>\n      <td>MA</td>\n      <td>Lynn, MA United States</td>\n      <td>https://en.wikipedia.org/wiki/Lynn,_Massachusetts</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>Fall Rive</td>\n      <td>all River</td>\n      <td>Fall River</td>\n      <td>41.701388888889</td>\n      <td>-71.155555555556</td>\n      <td>MA</td>\n      <td>Fall Rive, MA United States</td>\n      <td>https://en.wikipedia.org/wiki/Fall_River,_Mass...</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>Boston</td>\n      <td>Massachusetts</td>\n      <td>Boston, Massachusetts</td>\n      <td>42.358055555556</td>\n      <td>-71.063611111111</td>\n      <td>MA</td>\n      <td>Boston, MA United States</td>\n      <td>https://en.wikipedia.org/wiki/Boston,_Massachu...</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>San Jose</td>\n      <td>California</td>\n      <td>San Jose, California</td>\n      <td>37.333333333333</td>\n      <td>-121.9</td>\n      <td>CA</td>\n      <td>San Jose, CA United States</td>\n      <td>https://en.wikipedia.org/wiki/San_Jose,_Califo...</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>San Francisco</td>\n      <td>California</td>\n      <td>San Francisco, California</td>\n      <td>37.783333333333</td>\n      <td>-122.41666666667</td>\n      <td>CA</td>\n      <td>San Francisco, CA United States</td>\n      <td>https://en.wikipedia.org/wiki/San_Francisco,_C...</td>\n    </tr>\n    <tr>\n      <th>107</th>\n      <td>Fresno</td>\n      <td>California</td>\n      <td>Fresno, California</td>\n      <td>36.75</td>\n      <td>-119.76666666667</td>\n      <td>CA</td>\n      <td>Fresno, CA United States</td>\n      <td>https://en.wikipedia.org/wiki/Fresno,_California</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>Sacramento</td>\n      <td>California</td>\n      <td>Sacramento, California</td>\n      <td>38.555555555556</td>\n      <td>-121.46888888889</td>\n      <td>CA</td>\n      <td>Sacramento, CA United States</td>\n      <td>https://en.wikipedia.org/wiki/Sacramento,_Cali...</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>Long Beach</td>\n      <td>California</td>\n      <td>Long Beach, California</td>\n      <td>33.768333333333</td>\n      <td>-118.19555555556</td>\n      <td>CA</td>\n      <td>Long Beach, CA United States</td>\n      <td>https://en.wikipedia.org/wiki/Long_Beach,_Cali...</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>Oakland</td>\n      <td>California</td>\n      <td>Oakland, California</td>\n      <td>37.804444444444</td>\n      <td>-122.27083333333</td>\n      <td>CA</td>\n      <td>Oakland, CA United States</td>\n      <td>https://en.wikipedia.org/wiki/Oakland,_California</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>Bakersfield</td>\n      <td>California</td>\n      <td>Bakersfield, California</td>\n      <td>35.366666666667</td>\n      <td>-119.01666666667</td>\n      <td>CA</td>\n      <td>Bakersfield, CA United States</td>\n      <td>https://en.wikipedia.org/wiki/Bakersfield,_Cal...</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>Anaheim</td>\n      <td>California</td>\n      <td>Anaheim, California</td>\n      <td>33.836111111111</td>\n      <td>-117.88972222222</td>\n      <td>CA</td>\n      <td>Anaheim, CA United States</td>\n      <td>https://en.wikipedia.org/wiki/Anaheim,_California</td>\n    </tr>\n    <tr>\n      <th>113</th>\n      <td>New Orleans</td>\n      <td>Louisiana</td>\n      <td>New Orleans, Louisiana</td>\n      <td>29.95</td>\n      <td>-90.08</td>\n      <td>LA</td>\n      <td>New Orleans, LA United States</td>\n      <td>https://en.wikipedia.org/wiki/New_Orleans,_Lou...</td>\n    </tr>\n    <tr>\n      <th>114</th>\n      <td>Baton Roug</td>\n      <td>aton Rouge</td>\n      <td>Baton Rouge</td>\n      <td>30.4475</td>\n      <td>-91.178611111111</td>\n      <td>LA</td>\n      <td>Baton Roug, LA United States</td>\n      <td>https://en.wikipedia.org/wiki/Baton_Rouge,_Lou...</td>\n    </tr>\n    <tr>\n      <th>115</th>\n      <td>Shreveport</td>\n      <td>Louisiana</td>\n      <td>Shreveport, Louisiana</td>\n      <td>32.514722222222</td>\n      <td>-93.747222222222</td>\n      <td>LA</td>\n      <td>Shreveport, LA United States</td>\n      <td>https://en.wikipedia.org/wiki/Shreveport,_Loui...</td>\n    </tr>\n    <tr>\n      <th>116</th>\n      <td>Lafayette</td>\n      <td>Louisiana</td>\n      <td>Lafayette, Louisiana</td>\n      <td>30.216666666667</td>\n      <td>-92.033333333333</td>\n      <td>LA</td>\n      <td>Lafayette, LA United States</td>\n      <td>https://en.wikipedia.org/wiki/Lafayette,_Louis...</td>\n    </tr>\n    <tr>\n      <th>117</th>\n      <td>Lake Charles</td>\n      <td>Louisiana</td>\n      <td>Lake Charles, Louisiana</td>\n      <td>30.214722222222</td>\n      <td>-93.208611111111</td>\n      <td>LA</td>\n      <td>Lake Charles, LA United States</td>\n      <td>https://en.wikipedia.org/wiki/Lake_Charles,_Lo...</td>\n    </tr>\n    <tr>\n      <th>118</th>\n      <td>New Orleans</td>\n      <td>Louisiana</td>\n      <td>New Orleans, Louisiana</td>\n      <td>29.95</td>\n      <td>-90.08</td>\n      <td>LA</td>\n      <td>New Orleans, LA United States</td>\n      <td>https://en.wikipedia.org/wiki/New_Orleans,_Lou...</td>\n    </tr>\n    <tr>\n      <th>119</th>\n      <td>Philadelphia</td>\n      <td>Pennsylvania</td>\n      <td>Philadelphia, Pennsylvania</td>\n      <td>39.952777777778</td>\n      <td>-75.163611111111</td>\n      <td>PA</td>\n      <td>Philadelphia, PA United States</td>\n      <td>https://en.wikipedia.org/wiki/Philadelphia,_Pe...</td>\n    </tr>\n    <tr>\n      <th>120</th>\n      <td>Pittsburgh</td>\n      <td>Pennsylvania</td>\n      <td>Pittsburgh, Pennsylvania</td>\n      <td>40.439722222222</td>\n      <td>-79.976388888889</td>\n      <td>PA</td>\n      <td>Pittsburgh, PA United States</td>\n      <td>https://en.wikipedia.org/wiki/Pittsburgh,_Penn...</td>\n    </tr>\n    <tr>\n      <th>121</th>\n      <td>Allentown</td>\n      <td>Pennsylvania</td>\n      <td>Allentown, Pennsylvania</td>\n      <td>40.601666666667</td>\n      <td>-75.477222222222</td>\n      <td>PA</td>\n      <td>Allentown, PA United States</td>\n      <td>https://en.wikipedia.org/wiki/Allentown,_Penns...</td>\n    </tr>\n    <tr>\n      <th>122</th>\n      <td>Erie</td>\n      <td>Pennsylvania</td>\n      <td>Erie, Pennsylvania</td>\n      <td>42.129444444444</td>\n      <td>-80.085</td>\n      <td>PA</td>\n      <td>Erie, PA United States</td>\n      <td>https://en.wikipedia.org/wiki/Erie,_Pennsylvania</td>\n    </tr>\n    <tr>\n      <th>123</th>\n      <td>Reading</td>\n      <td>Pennsylvania</td>\n      <td>Reading, Pennsylvania</td>\n      <td>40.341666666667</td>\n      <td>-75.926388888889</td>\n      <td>PA</td>\n      <td>Reading, PA United States</td>\n      <td>https://en.wikipedia.org/wiki/Reading,_Pennsyl...</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>Foxborough</td>\n      <td>Massachusetts</td>\n      <td>Foxborough, Massachusetts</td>\n      <td>42.065278</td>\n      <td>-71.248333</td>\n      <td>MA</td>\n      <td>Foxborough, MA United States</td>\n      <td>https://en.wikipedia.org/wiki/Foxborough,_Mass...</td>\n    </tr>\n    <tr>\n      <th>125</th>\n      <td>San Diego</td>\n      <td>California</td>\n      <td>San Diego, California</td>\n      <td>32.715</td>\n      <td>-117.1625</td>\n      <td>CA</td>\n      <td>San Diego, CA United States</td>\n      <td>https://en.wikipedia.org/wiki/San_Diego</td>\n    </tr>\n  </tbody>\n</table>\n<p>126 rows \u00d7 8 columns</p>\n</div>", 
                        "text/plain": "                 CITY          STATE                 CITY_STATE1  \\\n0        Grand Rapids       Michigan      Grand Rapids, Michigan   \n1        Battle Creek       Michigan      Battle Creek, Michigan   \n2             Lansing       Michigan           Lansing, Michigan   \n3        East Lansing       Michigan      East Lansing, Michigan   \n4           Kalamazoo       Michigan         Kalamazoo, Michigan   \n5             Detroit       Michigan           Detroit, Michigan   \n6    Sterling Heights       Michigan  Sterling Heights, Michigan   \n7            Dearborn       Michigan          Dearborn, Michigan   \n8             Livonia       Michigan           Livonia, Michigan   \n9                Troy       Michigan              Troy, Michigan   \n10             Warren       Michigan            Warren, Michigan   \n11              Flint       Michigan             Flint, Michigan   \n12        Grand Haven       Michigan       Grand Haven, Michigan   \n13            Holland       Michigan           Holland, Michigan   \n14           Muskegon       Michigan          Muskegon, Michigan   \n15       Indianapolis        Indiana       Indianapolis, Indiana   \n16         Fort Wayne        Indiana         Fort Wayne, Indiana   \n17              Fargo   North Dakota         Fargo, North Dakota   \n18              Minot   North Dakota         Minot, North Dakota   \n19          Williston   North Dakota     Williston, North Dakota   \n20               Mino           inot                       Minot   \n21         Saint Paul      Minnesota       Saint Paul, Minnesota   \n22        Minneapolis      Minnesota      Minneapolis, Minnesota   \n23          St. Cloud      Minnesota        St. Cloud, Minnesota   \n24          Green Bay      Wisconsin        Green Bay, Wisconsin   \n25         Rapid City   South Dakota    Rapid City, South Dakota   \n26        Sioux Falls   South Dakota   Sioux Falls, South Dakota   \n27             Pierre   South Dakota        Pierre, South Dakota   \n28            Aberdee        berdeen                    Aberdeen   \n29           Gillette        Wyoming           Gillette, Wyoming   \n..                ...            ...                         ...   \n96        Springfield  Massachusetts  Springfield, Massachusetts   \n97             Lowell  Massachusetts       Lowell, Massachusetts   \n98          Cambridge  Massachusetts    Cambridge, Massachusetts   \n99           Brockton  Massachusetts     Brockton, Massachusetts   \n100       New Bedford  Massachusetts  New Bedford, Massachusetts   \n101            Quincy  Massachusetts       Quincy, Massachusetts   \n102              Lynn  Massachusetts         Lynn, Massachusetts   \n103         Fall Rive      all River                  Fall River   \n104            Boston  Massachusetts       Boston, Massachusetts   \n105          San Jose     California        San Jose, California   \n106     San Francisco     California   San Francisco, California   \n107            Fresno     California          Fresno, California   \n108        Sacramento     California      Sacramento, California   \n109        Long Beach     California      Long Beach, California   \n110           Oakland     California         Oakland, California   \n111       Bakersfield     California     Bakersfield, California   \n112           Anaheim     California         Anaheim, California   \n113       New Orleans      Louisiana      New Orleans, Louisiana   \n114        Baton Roug     aton Rouge                 Baton Rouge   \n115        Shreveport      Louisiana       Shreveport, Louisiana   \n116         Lafayette      Louisiana        Lafayette, Louisiana   \n117      Lake Charles      Louisiana     Lake Charles, Louisiana   \n118       New Orleans      Louisiana      New Orleans, Louisiana   \n119      Philadelphia   Pennsylvania  Philadelphia, Pennsylvania   \n120        Pittsburgh   Pennsylvania    Pittsburgh, Pennsylvania   \n121         Allentown   Pennsylvania     Allentown, Pennsylvania   \n122              Erie   Pennsylvania          Erie, Pennsylvania   \n123           Reading   Pennsylvania       Reading, Pennsylvania   \n124        Foxborough  Massachusetts   Foxborough, Massachusetts   \n125         San Diego     California       San Diego, California   \n\n            LATITUDE         LONGITUDE STATE_ID  \\\n0    42.961111111111  -85.655555555556       MI   \n1    42.312222222222  -85.204166666667       MI   \n2    42.733611111111  -84.546666666667       MI   \n3    42.734722222222  -84.480555555556       MI   \n4              42.29  -85.585833333333       MI   \n5    42.331388888889  -83.045833333333       MI   \n6    42.580277777778  -83.030277777778       MI   \n7    42.314444444444  -83.213333333333       MI   \n8    42.397222222222  -83.373611111111       MI   \n9    42.580277777778  -83.143055555556       MI   \n10   42.491944444444  -83.023888888889       MI   \n11   43.018888888889  -83.693333333333       MI   \n12   43.063055555556  -86.228333333333       MI   \n13   42.783333333333             -86.1       MI   \n14   43.234166666667  -86.248333333333       MI   \n15            39.791           -86.148       IN   \n16          41.08045         -85.13915       IN   \n17   46.877222222222  -96.789444444444       ND   \n18         46.813343       -100.779004       ND   \n19   48.156388888889  -103.62805555556       ND   \n20   48.233055555556  -101.29222222222       ND   \n21   44.944166666667  -93.093611111111       MN   \n22   44.983333333333  -93.266666666667       MN   \n23             45.55  -94.166666666667       MN   \n24   44.513333333333  -88.015833333333       WI   \n25            44.076          -103.228       SD   \n26   43.536388888889  -96.731666666667       SD   \n27         44.367966       -100.336378       SD   \n28   45.464722222222  -98.486388888889       SD   \n29   44.282777777778  -105.50527777778       WY   \n..               ...               ...      ...   \n96         42.112411        -72.547455       MA   \n97   42.639444444444  -71.314722222222       MA   \n98   42.373611111111  -71.110555555556       MA   \n99   42.083333333333  -71.018888888889       MA   \n100  41.636111111111  -70.934722222222       MA   \n101            42.25             -71.0       MA   \n102  42.466666666667            -70.95       MA   \n103  41.701388888889  -71.155555555556       MA   \n104  42.358055555556  -71.063611111111       MA   \n105  37.333333333333            -121.9       CA   \n106  37.783333333333  -122.41666666667       CA   \n107            36.75  -119.76666666667       CA   \n108  38.555555555556  -121.46888888889       CA   \n109  33.768333333333  -118.19555555556       CA   \n110  37.804444444444  -122.27083333333       CA   \n111  35.366666666667  -119.01666666667       CA   \n112  33.836111111111  -117.88972222222       CA   \n113            29.95            -90.08       LA   \n114          30.4475  -91.178611111111       LA   \n115  32.514722222222  -93.747222222222       LA   \n116  30.216666666667  -92.033333333333       LA   \n117  30.214722222222  -93.208611111111       LA   \n118            29.95            -90.08       LA   \n119  39.952777777778  -75.163611111111       PA   \n120  40.439722222222  -79.976388888889       PA   \n121  40.601666666667  -75.477222222222       PA   \n122  42.129444444444           -80.085       PA   \n123  40.341666666667  -75.926388888889       PA   \n124        42.065278        -71.248333       MA   \n125           32.715         -117.1625       CA   \n\n                            CITY_STATE2  \\\n0        Grand Rapids, MI United States   \n1        Battle Creek, MI United States   \n2             Lansing, MI United States   \n3        East Lansing, MI United States   \n4           Kalamazoo, MI United States   \n5             Detroit, MI United States   \n6    Sterling Heights, MI United States   \n7            Dearborn, MI United States   \n8             Livonia, MI United States   \n9                Troy, MI United States   \n10             Warren, MI United States   \n11              Flint, MI United States   \n12        Grand Haven, MI United States   \n13            Holland, MI United States   \n14           Muskegon, MI United States   \n15       Indianapolis, IN United States   \n16         Fort Wayne, IN United States   \n17              Fargo, ND United States   \n18              Minot, ND United States   \n19          Williston, ND United States   \n20               Mino, ND United States   \n21         Saint Paul, MN United States   \n22        Minneapolis, MN United States   \n23          St. Cloud, MN United States   \n24                   , WI United States   \n25         Rapid City, SD United States   \n26        Sioux Falls, SD United States   \n27             Pierre, SD United States   \n28            Aberdee, SD United States   \n29           Gillette, WY United States   \n..                                  ...   \n96        Springfield, MA United States   \n97             Lowell, MA United States   \n98          Cambridge, MA United States   \n99           Brockton, MA United States   \n100       New Bedford, MA United States   \n101            Quincy, MA United States   \n102              Lynn, MA United States   \n103         Fall Rive, MA United States   \n104            Boston, MA United States   \n105          San Jose, CA United States   \n106     San Francisco, CA United States   \n107            Fresno, CA United States   \n108        Sacramento, CA United States   \n109        Long Beach, CA United States   \n110           Oakland, CA United States   \n111       Bakersfield, CA United States   \n112           Anaheim, CA United States   \n113       New Orleans, LA United States   \n114        Baton Roug, LA United States   \n115        Shreveport, LA United States   \n116         Lafayette, LA United States   \n117      Lake Charles, LA United States   \n118       New Orleans, LA United States   \n119      Philadelphia, PA United States   \n120        Pittsburgh, PA United States   \n121         Allentown, PA United States   \n122              Erie, PA United States   \n123           Reading, PA United States   \n124        Foxborough, MA United States   \n125         San Diego, CA United States   \n\n                                             WIKI_LINK  \n0    https://en.wikipedia.org/wiki/Grand_Rapids,_Mi...  \n1    https://en.wikipedia.org/wiki/Battle_Creek,_Mi...  \n2      https://en.wikipedia.org/wiki/Lansing,_Michigan  \n3    https://en.wikipedia.org/wiki/East_Lansing,_Mi...  \n4    https://en.wikipedia.org/wiki/Kalamazoo,_Michigan  \n5      https://en.wikipedia.org/wiki/Detroit,_Michigan  \n6    https://en.wikipedia.org/wiki/Sterling_Heights...  \n7     https://en.wikipedia.org/wiki/Dearborn,_Michigan  \n8      https://en.wikipedia.org/wiki/Livonia,_Michigan  \n9         https://en.wikipedia.org/wiki/Troy,_Michigan  \n10      https://en.wikipedia.org/wiki/Warren,_Michigan  \n11       https://en.wikipedia.org/wiki/Flint,_Michigan  \n12   https://en.wikipedia.org/wiki/Grand_Haven,_Mic...  \n13     https://en.wikipedia.org/wiki/Holland,_Michigan  \n14    https://en.wikipedia.org/wiki/Muskegon,_Michigan  \n15          https://en.wikipedia.org/wiki/Indianapolis  \n16   https://en.wikipedia.org/wiki/Fort_Wayne,_Indiana  \n17   https://en.wikipedia.org/wiki/Fargo,_North_Dakota  \n18   https://en.wikipedia.org/wiki/Bismarck,_North_...  \n19   https://en.wikipedia.org/wiki/Williston,_North...  \n20   https://en.wikipedia.org/wiki/Minot,_North_Dakota  \n21   https://en.wikipedia.org/wiki/Saint_Paul,_Minn...  \n22           https://en.wikipedia.org/wiki/Minneapolis  \n23   https://en.wikipedia.org/wiki/St._Cloud,_Minne...  \n24   https://en.wikipedia.org/wiki/Green_Bay,_Wisco...  \n25   https://en.wikipedia.org/wiki/Rapid_City,_Sout...  \n26   https://en.wikipedia.org/wiki/Sioux_Falls,_Sou...  \n27   https://en.wikipedia.org/wiki/Pierre,_South_Da...  \n28   https://en.wikipedia.org/wiki/Aberdeen,_South_...  \n29     https://en.wikipedia.org/wiki/Gillette,_Wyoming  \n..                                                 ...  \n96   https://en.wikipedia.org/wiki/Springfield,_Mas...  \n97   https://en.wikipedia.org/wiki/Lowell,_Massachu...  \n98   https://en.wikipedia.org/wiki/Cambridge,_Massa...  \n99   https://en.wikipedia.org/wiki/Brockton,_Massac...  \n100  https://en.wikipedia.org/wiki/New_Bedford,_Mas...  \n101  https://en.wikipedia.org/wiki/Quincy,_Massachu...  \n102  https://en.wikipedia.org/wiki/Lynn,_Massachusetts  \n103  https://en.wikipedia.org/wiki/Fall_River,_Mass...  \n104  https://en.wikipedia.org/wiki/Boston,_Massachu...  \n105  https://en.wikipedia.org/wiki/San_Jose,_Califo...  \n106  https://en.wikipedia.org/wiki/San_Francisco,_C...  \n107   https://en.wikipedia.org/wiki/Fresno,_California  \n108  https://en.wikipedia.org/wiki/Sacramento,_Cali...  \n109  https://en.wikipedia.org/wiki/Long_Beach,_Cali...  \n110  https://en.wikipedia.org/wiki/Oakland,_California  \n111  https://en.wikipedia.org/wiki/Bakersfield,_Cal...  \n112  https://en.wikipedia.org/wiki/Anaheim,_California  \n113  https://en.wikipedia.org/wiki/New_Orleans,_Lou...  \n114  https://en.wikipedia.org/wiki/Baton_Rouge,_Lou...  \n115  https://en.wikipedia.org/wiki/Shreveport,_Loui...  \n116  https://en.wikipedia.org/wiki/Lafayette,_Louis...  \n117  https://en.wikipedia.org/wiki/Lake_Charles,_Lo...  \n118  https://en.wikipedia.org/wiki/New_Orleans,_Lou...  \n119  https://en.wikipedia.org/wiki/Philadelphia,_Pe...  \n120  https://en.wikipedia.org/wiki/Pittsburgh,_Penn...  \n121  https://en.wikipedia.org/wiki/Allentown,_Penns...  \n122   https://en.wikipedia.org/wiki/Erie,_Pennsylvania  \n123  https://en.wikipedia.org/wiki/Reading,_Pennsyl...  \n124  https://en.wikipedia.org/wiki/Foxborough,_Mass...  \n125            https://en.wikipedia.org/wiki/San_Diego  \n\n[126 rows x 8 columns]"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "print(query_results_timestamp4)\nOriginal_City_Data_DF[0:len(Original_City_Data_DF)]"
        }, 
        {
            "source": "<hr>\n\n<h1 align=\"center\">Adjusted City Data Modified from Original City Data Generated by the Web Scraper Notebook</h1>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 12, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "SELECT QUERY TIMESTAMP - DATE: 01/21/19 - TIME: 13:55:47\n"
                }, 
                {
                    "execution_count": 12, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CITY_ID</th>\n      <th>CITY</th>\n      <th>STATE</th>\n      <th>CITY_STATE1</th>\n      <th>LATITUDE</th>\n      <th>LONGITUDE</th>\n      <th>LAT_LONG</th>\n      <th>CITY_STATE2</th>\n      <th>WIKI_LINK</th>\n      <th>STATE_ID</th>\n      <th>CITY_STATE3</th>\n      <th>CITY_STATE4</th>\n      <th>COUNTRY</th>\n      <th>NE_LAT</th>\n      <th>NE_LONG</th>\n      <th>SW_LAT</th>\n      <th>SW_LONG</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CID00001</td>\n      <td>Detroit</td>\n      <td>Michigan</td>\n      <td>Detroit, Michigan</td>\n      <td>42.331389</td>\n      <td>-83.045833</td>\n      <td>42.33138889, -83.04583333</td>\n      <td>Detroit,_Michigan</td>\n      <td>https://en.wikipedia.org/wiki/Detroit_Michigan</td>\n      <td>MI</td>\n      <td>Detroit, MI</td>\n      <td>Detroit, MI, United States</td>\n      <td>United States</td>\n      <td>42.461460</td>\n      <td>-82.890644</td>\n      <td>42.239702</td>\n      <td>-83.310184</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CID00002</td>\n      <td>Indianapolis</td>\n      <td>Indiana</td>\n      <td>Indianapolis, Indiana</td>\n      <td>39.791000</td>\n      <td>-86.148000</td>\n      <td>39.791, -86.148</td>\n      <td>Indianapolis</td>\n      <td>https://en.wikipedia.org/wiki/Indianapolis</td>\n      <td>IN</td>\n      <td>Indianapolis, IN</td>\n      <td>Indianapolis, IN, United States</td>\n      <td>United States</td>\n      <td>39.990799</td>\n      <td>-85.714302</td>\n      <td>39.529467</td>\n      <td>-86.553383</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CID00003</td>\n      <td>Milwaukee</td>\n      <td>Wisconsin</td>\n      <td>Milwaukee, Wisconsin</td>\n      <td>43.050000</td>\n      <td>-87.950000</td>\n      <td>43.05, -87.95</td>\n      <td>Milwaukee</td>\n      <td>https://en.wikipedia.org/wiki/Milwaukee</td>\n      <td>WI</td>\n      <td>Milwaukee, WI</td>\n      <td>Milwaukee, WI, United States</td>\n      <td>United States</td>\n      <td>43.257206</td>\n      <td>-87.553139</td>\n      <td>42.818573</td>\n      <td>-88.392220</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CID00004</td>\n      <td>Madison</td>\n      <td>Wisconsin</td>\n      <td>Madison, Wisconsin</td>\n      <td>43.066667</td>\n      <td>-89.400000</td>\n      <td>43.06666667, -89.4</td>\n      <td>Madison,_Wisconsin</td>\n      <td>https://en.wikipedia.org/wiki/Madison_Wisconsin</td>\n      <td>WI</td>\n      <td>Madison, WI</td>\n      <td>Madison, WI, United States</td>\n      <td>United States</td>\n      <td>43.182399</td>\n      <td>-89.197826</td>\n      <td>42.963206</td>\n      <td>-89.617367</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CID00005</td>\n      <td>Saint Paul</td>\n      <td>Minnesota</td>\n      <td>Saint Paul, Minnesota</td>\n      <td>44.944167</td>\n      <td>-93.093611</td>\n      <td>44.94416667, -93.09361111</td>\n      <td>Saint_Paul,_Minnesota</td>\n      <td>https://en.wikipedia.org/wiki/Saint_Paul_Minne...</td>\n      <td>MN</td>\n      <td>Saint Paul, MN</td>\n      <td>Saint Paul, MN, United States</td>\n      <td>United States</td>\n      <td>45.037384</td>\n      <td>-92.900219</td>\n      <td>44.824952</td>\n      <td>-93.319759</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CID00006</td>\n      <td>Minneapolis</td>\n      <td>Minnesota</td>\n      <td>Minneapolis, Minnesota</td>\n      <td>44.983333</td>\n      <td>-93.266667</td>\n      <td>44.98333333, -93.26666667</td>\n      <td>Minneapolis</td>\n      <td>https://en.wikipedia.org/wiki/Minneapolis</td>\n      <td>MN</td>\n      <td>Minneapolis, MN</td>\n      <td>Minneapolis, MN, United States</td>\n      <td>United States</td>\n      <td>45.069156</td>\n      <td>-93.057461</td>\n      <td>44.856842</td>\n      <td>-93.477001</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CID00007</td>\n      <td>Green Bay</td>\n      <td>Wisconsin</td>\n      <td>Green Bay, Wisconsin</td>\n      <td>44.513333</td>\n      <td>-88.015833</td>\n      <td>44.51333333, -88.01583333</td>\n      <td>Green_Bay,_Wisconsin</td>\n      <td>https://en.wikipedia.org/wiki/Green_Bay_Wisconsin</td>\n      <td>WI</td>\n      <td>Green Bay, WI</td>\n      <td>Green Bay, WI, United States</td>\n      <td>United States</td>\n      <td>44.615645</td>\n      <td>-87.782307</td>\n      <td>44.401656</td>\n      <td>-88.201847</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CID00008</td>\n      <td>Boston</td>\n      <td>Massachusetts</td>\n      <td>Boston, Massachusetts</td>\n      <td>42.358056</td>\n      <td>-71.063611</td>\n      <td>42.35805556, -71.06361111</td>\n      <td>Boston,_Massachusetts</td>\n      <td>https://en.wikipedia.org/wiki/Boston_Massachus...</td>\n      <td>MA</td>\n      <td>Boston, MA</td>\n      <td>Boston, MA, United States</td>\n      <td>United States</td>\n      <td>42.440434</td>\n      <td>-70.858898</td>\n      <td>42.218602</td>\n      <td>-71.278439</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CID00009</td>\n      <td>San Jose</td>\n      <td>California</td>\n      <td>San Jose, California</td>\n      <td>37.333333</td>\n      <td>-121.900000</td>\n      <td>37.33333333, -121.9</td>\n      <td>San_Jose,_California</td>\n      <td>https://en.wikipedia.org/wiki/San_Jose_California</td>\n      <td>CA</td>\n      <td>San Jose, CA</td>\n      <td>San Jose, CA, United States</td>\n      <td>United States</td>\n      <td>37.523886</td>\n      <td>-121.413689</td>\n      <td>37.046409</td>\n      <td>-122.252769</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CID00010</td>\n      <td>San Francisco</td>\n      <td>California</td>\n      <td>San Francisco, California</td>\n      <td>37.783333</td>\n      <td>-122.416667</td>\n      <td>37.78333333, -122.4166667</td>\n      <td>San_Francisco,_California</td>\n      <td>https://en.wikipedia.org/wiki/San_Francisco_Ca...</td>\n      <td>CA</td>\n      <td>San Francisco, CA</td>\n      <td>San Francisco, CA, United States</td>\n      <td>United States</td>\n      <td>37.879189</td>\n      <td>-122.225819</td>\n      <td>37.641966</td>\n      <td>-122.645359</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>CID00011</td>\n      <td>Anaheim</td>\n      <td>California</td>\n      <td>Anaheim, California</td>\n      <td>33.836111</td>\n      <td>-117.889722</td>\n      <td>33.83611111, -117.8897222</td>\n      <td>Anaheim,_California</td>\n      <td>https://en.wikipedia.org/wiki/Anaheim_California</td>\n      <td>CA</td>\n      <td>Anaheim, CA</td>\n      <td>Anaheim, CA, United States</td>\n      <td>United States</td>\n      <td>33.944499</td>\n      <td>-117.633190</td>\n      <td>33.695209</td>\n      <td>-118.052731</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>CID00012</td>\n      <td>New Orleans</td>\n      <td>Louisiana</td>\n      <td>New Orleans, Louisiana</td>\n      <td>29.950000</td>\n      <td>-90.080000</td>\n      <td>29.95, -90.08</td>\n      <td>New_Orleans,_Louisiana</td>\n      <td>https://en.wikipedia.org/wiki/New_Orleans_Loui...</td>\n      <td>LA</td>\n      <td>New Orleans, LA</td>\n      <td>New Orleans, LA, United States</td>\n      <td>United States</td>\n      <td>30.260846</td>\n      <td>-89.463387</td>\n      <td>29.741128</td>\n      <td>-90.302467</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CID00013</td>\n      <td>Philadelphia</td>\n      <td>Pennsylvania</td>\n      <td>Philadelphia, Pennsylvania</td>\n      <td>39.952778</td>\n      <td>-75.163611</td>\n      <td>39.95277778, -75.16361111</td>\n      <td>Philadelphia,_Pennsylvania</td>\n      <td>https://en.wikipedia.org/wiki/Philadelphia_Pen...</td>\n      <td>PA</td>\n      <td>Philadelphia, PA</td>\n      <td>Philadelphia, PA, United States</td>\n      <td>United States</td>\n      <td>40.215587</td>\n      <td>-74.699821</td>\n      <td>39.755769</td>\n      <td>-75.538902</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>CID00014</td>\n      <td>Pittsburgh</td>\n      <td>Pennsylvania</td>\n      <td>Pittsburgh, Pennsylvania</td>\n      <td>40.439722</td>\n      <td>-79.976389</td>\n      <td>40.43972222, -79.97638889</td>\n      <td>Pittsburgh,_Pennsylvania</td>\n      <td>https://en.wikipedia.org/wiki/Pittsburgh_Penns...</td>\n      <td>PA</td>\n      <td>Pittsburgh, PA</td>\n      <td>Pittsburgh, PA, United States</td>\n      <td>United States</td>\n      <td>40.540156</td>\n      <td>-79.770870</td>\n      <td>40.311734</td>\n      <td>-80.190411</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CID00015</td>\n      <td>Atlanta</td>\n      <td>Georgia</td>\n      <td>Atlanta, Georgia</td>\n      <td>-33.755000</td>\n      <td>95.610000</td>\n      <td>-33.755, 95.61</td>\n      <td>Atlanta,_Georgia</td>\n      <td>https://en.wikipedia.org/wiki/Atlanta</td>\n      <td>GA</td>\n      <td>Atlanta, GA</td>\n      <td>Atlanta, GA, United States</td>\n      <td>United States</td>\n      <td>33.990626</td>\n      <td>-83.998375</td>\n      <td>33.491589</td>\n      <td>-84.837456</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>CID00016</td>\n      <td>Charlotte</td>\n      <td>North Carolina</td>\n      <td>Charlotte, North Carolina</td>\n      <td>-35.227222</td>\n      <td>99.156944</td>\n      <td>-35.227222, 99.156944</td>\n      <td>Charlotte,_North_Carolina</td>\n      <td>https://en.wikipedia.org/wiki/Charlotte,_North...</td>\n      <td>NC</td>\n      <td>Charlotte, NC</td>\n      <td>Charlotte, NC United States</td>\n      <td>United  States</td>\n      <td>35.428785</td>\n      <td>-80.412712</td>\n      <td>34.938296</td>\n      <td>-81.251793</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "     CITY_ID           CITY           STATE                 CITY_STATE1  \\\n0   CID00001        Detroit        Michigan           Detroit, Michigan   \n1   CID00002   Indianapolis         Indiana       Indianapolis, Indiana   \n2   CID00003      Milwaukee       Wisconsin        Milwaukee, Wisconsin   \n3   CID00004        Madison       Wisconsin          Madison, Wisconsin   \n4   CID00005     Saint Paul       Minnesota       Saint Paul, Minnesota   \n5   CID00006    Minneapolis       Minnesota      Minneapolis, Minnesota   \n6   CID00007      Green Bay       Wisconsin        Green Bay, Wisconsin   \n7   CID00008         Boston   Massachusetts       Boston, Massachusetts   \n8   CID00009       San Jose      California        San Jose, California   \n9   CID00010  San Francisco      California   San Francisco, California   \n10  CID00011        Anaheim      California         Anaheim, California   \n11  CID00012    New Orleans       Louisiana      New Orleans, Louisiana   \n12  CID00013   Philadelphia    Pennsylvania  Philadelphia, Pennsylvania   \n13  CID00014     Pittsburgh    Pennsylvania    Pittsburgh, Pennsylvania   \n14  CID00015        Atlanta         Georgia            Atlanta, Georgia   \n15  CID00016      Charlotte  North Carolina   Charlotte, North Carolina   \n\n     LATITUDE   LONGITUDE                   LAT_LONG  \\\n0   42.331389  -83.045833  42.33138889, -83.04583333   \n1   39.791000  -86.148000            39.791, -86.148   \n2   43.050000  -87.950000              43.05, -87.95   \n3   43.066667  -89.400000         43.06666667, -89.4   \n4   44.944167  -93.093611  44.94416667, -93.09361111   \n5   44.983333  -93.266667  44.98333333, -93.26666667   \n6   44.513333  -88.015833  44.51333333, -88.01583333   \n7   42.358056  -71.063611  42.35805556, -71.06361111   \n8   37.333333 -121.900000        37.33333333, -121.9   \n9   37.783333 -122.416667  37.78333333, -122.4166667   \n10  33.836111 -117.889722  33.83611111, -117.8897222   \n11  29.950000  -90.080000              29.95, -90.08   \n12  39.952778  -75.163611  39.95277778, -75.16361111   \n13  40.439722  -79.976389  40.43972222, -79.97638889   \n14 -33.755000   95.610000             -33.755, 95.61   \n15 -35.227222   99.156944      -35.227222, 99.156944   \n\n                   CITY_STATE2  \\\n0            Detroit,_Michigan   \n1                 Indianapolis   \n2                    Milwaukee   \n3           Madison,_Wisconsin   \n4        Saint_Paul,_Minnesota   \n5                  Minneapolis   \n6         Green_Bay,_Wisconsin   \n7        Boston,_Massachusetts   \n8         San_Jose,_California   \n9    San_Francisco,_California   \n10         Anaheim,_California   \n11      New_Orleans,_Louisiana   \n12  Philadelphia,_Pennsylvania   \n13    Pittsburgh,_Pennsylvania   \n14            Atlanta,_Georgia   \n15   Charlotte,_North_Carolina   \n\n                                            WIKI_LINK STATE_ID  \\\n0      https://en.wikipedia.org/wiki/Detroit_Michigan       MI   \n1          https://en.wikipedia.org/wiki/Indianapolis       IN   \n2             https://en.wikipedia.org/wiki/Milwaukee       WI   \n3     https://en.wikipedia.org/wiki/Madison_Wisconsin       WI   \n4   https://en.wikipedia.org/wiki/Saint_Paul_Minne...       MN   \n5           https://en.wikipedia.org/wiki/Minneapolis       MN   \n6   https://en.wikipedia.org/wiki/Green_Bay_Wisconsin       WI   \n7   https://en.wikipedia.org/wiki/Boston_Massachus...       MA   \n8   https://en.wikipedia.org/wiki/San_Jose_California       CA   \n9   https://en.wikipedia.org/wiki/San_Francisco_Ca...       CA   \n10   https://en.wikipedia.org/wiki/Anaheim_California       CA   \n11  https://en.wikipedia.org/wiki/New_Orleans_Loui...       LA   \n12  https://en.wikipedia.org/wiki/Philadelphia_Pen...       PA   \n13  https://en.wikipedia.org/wiki/Pittsburgh_Penns...       PA   \n14              https://en.wikipedia.org/wiki/Atlanta       GA   \n15  https://en.wikipedia.org/wiki/Charlotte,_North...       NC   \n\n          CITY_STATE3                       CITY_STATE4         COUNTRY  \\\n0         Detroit, MI        Detroit, MI, United States   United States   \n1    Indianapolis, IN   Indianapolis, IN, United States   United States   \n2       Milwaukee, WI      Milwaukee, WI, United States   United States   \n3         Madison, WI        Madison, WI, United States   United States   \n4      Saint Paul, MN     Saint Paul, MN, United States   United States   \n5     Minneapolis, MN    Minneapolis, MN, United States   United States   \n6       Green Bay, WI      Green Bay, WI, United States   United States   \n7          Boston, MA         Boston, MA, United States   United States   \n8        San Jose, CA       San Jose, CA, United States   United States   \n9   San Francisco, CA  San Francisco, CA, United States   United States   \n10        Anaheim, CA        Anaheim, CA, United States   United States   \n11    New Orleans, LA    New Orleans, LA, United States   United States   \n12   Philadelphia, PA   Philadelphia, PA, United States   United States   \n13     Pittsburgh, PA     Pittsburgh, PA, United States   United States   \n14        Atlanta, GA        Atlanta, GA, United States   United States   \n15      Charlotte, NC       Charlotte, NC United States  United  States   \n\n     NE_LAT\\t     NE_LONG     SW_LAT     SW_LONG  \n0   42.461460  -82.890644  42.239702  -83.310184  \n1   39.990799  -85.714302  39.529467  -86.553383  \n2   43.257206  -87.553139  42.818573  -88.392220  \n3   43.182399  -89.197826  42.963206  -89.617367  \n4   45.037384  -92.900219  44.824952  -93.319759  \n5   45.069156  -93.057461  44.856842  -93.477001  \n6   44.615645  -87.782307  44.401656  -88.201847  \n7   42.440434  -70.858898  42.218602  -71.278439  \n8   37.523886 -121.413689  37.046409 -122.252769  \n9   37.879189 -122.225819  37.641966 -122.645359  \n10  33.944499 -117.633190  33.695209 -118.052731  \n11  30.260846  -89.463387  29.741128  -90.302467  \n12  40.215587  -74.699821  39.755769  -75.538902  \n13  40.540156  -79.770870  40.311734  -80.190411  \n14  33.990626  -83.998375  33.491589  -84.837456  \n15  35.428785  -80.412712  34.938296  -81.251793  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "print(query_results_timestamp1)\nCity_Data_DF[0:len(City_Data_DF)]"
        }, 
        {
            "source": "<hr>\n\n<h1 align=\"center\">Trending City Data from Data Collector Notebooks</h1>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 13, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "SELECT QUERY TIMESTAMP - DATE: 01/21/19 - TIME: 13:55:47\n"
                }, 
                {
                    "execution_count": 13, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CATEGORIES1</th>\n      <th>CATEGORIES2</th>\n      <th>CATEGORIES3</th>\n      <th>ID</th>\n      <th>LOCATION.ADDRESS</th>\n      <th>LOCATION.CC</th>\n      <th>LOCATION.CITY</th>\n      <th>LOCATION.COUNTRY</th>\n      <th>LOCATION.CROSSSTREET</th>\n      <th>LOCATION.FORMATTEDADDRESS</th>\n      <th>...</th>\n      <th>LOCATION.POSTALCODE</th>\n      <th>LOCATION.STATE</th>\n      <th>NAME</th>\n      <th>VENUEPAGE.ID</th>\n      <th>DATE</th>\n      <th>TIME</th>\n      <th>WEEKDAY</th>\n      <th>YEAR</th>\n      <th>MONTH</th>\n      <th>AM_PM</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Basketball Stadiums</td>\n      <td>Basketball</td>\n      <td>Basketball Stadiu</td>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>125 S Pennsylvania St</td>\n      <td>US</td>\n      <td>Indianapolis</td>\n      <td>United States</td>\n      <td>at E Georgia St</td>\n      <td>125 S Pennsylvania St (at E Georgia St), India...</td>\n      <td>...</td>\n      <td>46204</td>\n      <td>IN</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>42217987</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Convention Centers</td>\n      <td>Convention Center</td>\n      <td>Convention Center</td>\n      <td>blank</td>\n      <td>US</td>\n      <td>San Jose</td>\n      <td>blank</td>\n      <td>San Jose, CA 95113, United States</td>\n      <td>37.329998</td>\n      <td>-121.889275</td>\n      <td>...</td>\n      <td>blank</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n      <td>blank</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Movie Theaters</td>\n      <td>Movie Theater</td>\n      <td>Movie Theater</td>\n      <td>19.0</td>\n      <td>19 movies</td>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>135 4th St Ste 3000</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Sports Bars</td>\n      <td>Sports Bar</td>\n      <td>Sports Bar</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>365 11th St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Caf\u00e9s</td>\n      <td>Caf\u00e9</td>\n      <td>Caf\u00e9</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>1160 Howard St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Basketball Stadiums</td>\n      <td>Basketball</td>\n      <td>Basketball Stadiu</td>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>125 S Pennsylvania St</td>\n      <td>US</td>\n      <td>Indianapolis</td>\n      <td>United States</td>\n      <td>at E Georgia St</td>\n      <td>125 S Pennsylvania St (at E Georgia St), India...</td>\n      <td>...</td>\n      <td>46204</td>\n      <td>IN</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>42217987</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Convention Centers</td>\n      <td>Convention Center</td>\n      <td>Convention Center</td>\n      <td>blank</td>\n      <td>US</td>\n      <td>San Jose</td>\n      <td>blank</td>\n      <td>San Jose, CA 95113, United States</td>\n      <td>37.329998</td>\n      <td>-121.889275</td>\n      <td>...</td>\n      <td>blank</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n      <td>blank</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Indie Movie Theaters</td>\n      <td>Indie Movies</td>\n      <td>Indie Movie Theater</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>429 Castro St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>94114</td>\n      <td>CA</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Movie Theaters</td>\n      <td>Movie Theater</td>\n      <td>Movie Theater</td>\n      <td>19.0</td>\n      <td>19 movies</td>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>135 4th St Ste 3000</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Sports Bars</td>\n      <td>Sports Bar</td>\n      <td>Sports Bar</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>365 11th St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Caf\u00e9s</td>\n      <td>Caf\u00e9</td>\n      <td>Caf\u00e9</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>1160 Howard St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Basketball Stadiums</td>\n      <td>Basketball</td>\n      <td>Basketball Stadiu</td>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>125 S Pennsylvania St</td>\n      <td>US</td>\n      <td>Indianapolis</td>\n      <td>United States</td>\n      <td>at E Georgia St</td>\n      <td>125 S Pennsylvania St (at E Georgia St), India...</td>\n      <td>...</td>\n      <td>46204</td>\n      <td>IN</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>42217987</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Convention Centers</td>\n      <td>Convention Center</td>\n      <td>Convention Center</td>\n      <td>blank</td>\n      <td>US</td>\n      <td>San Jose</td>\n      <td>blank</td>\n      <td>San Jose, CA 95113, United States</td>\n      <td>37.329998</td>\n      <td>-121.889275</td>\n      <td>...</td>\n      <td>blank</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n      <td>blank</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Indie Movie Theaters</td>\n      <td>Indie Movies</td>\n      <td>Indie Movie Theater</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>429 Castro St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>94114</td>\n      <td>CA</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Movie Theaters</td>\n      <td>Movie Theater</td>\n      <td>Movie Theater</td>\n      <td>19.0</td>\n      <td>19 movies</td>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>135 4th St Ste 3000</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Sports Bars</td>\n      <td>Sports Bar</td>\n      <td>Sports Bar</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>365 11th St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Caf\u00e9s</td>\n      <td>Caf\u00e9</td>\n      <td>Caf\u00e9</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>1160 Howard St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Souvlaki Shops</td>\n      <td>Souvlaki</td>\n      <td>Souvlaki Shop</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>5748e1f0498e1c0c6d214098</td>\n      <td>531 Divisadero St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77457655200334</td>\n      <td>-122.4378085167028</td>\n      <td>94117</td>\n      <td>CA</td>\n      <td>Souvla</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Basketball Stadiums</td>\n      <td>Basketball</td>\n      <td>Basketball Stadiu</td>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>125 S Pennsylvania St</td>\n      <td>US</td>\n      <td>Indianapolis</td>\n      <td>United States</td>\n      <td>at E Georgia St</td>\n      <td>125 S Pennsylvania St (at E Georgia St), India...</td>\n      <td>...</td>\n      <td>46204</td>\n      <td>IN</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>42217987</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Convention Centers</td>\n      <td>Convention Center</td>\n      <td>Convention Center</td>\n      <td>5c40f328345cbe002c99e458</td>\n      <td>US</td>\n      <td>San Jose</td>\n      <td>United States</td>\n      <td>San Jose, CA 95113, United States</td>\n      <td>37.329998</td>\n      <td>-121.889275</td>\n      <td>...</td>\n      <td>Further Confusion 2019</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n      <td>blank</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Indie Movie Theaters</td>\n      <td>Indie Movies</td>\n      <td>Indie Movie Theater</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>429 Castro St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>94114</td>\n      <td>CA</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Movie Theaters</td>\n      <td>Movie Theater</td>\n      <td>Movie Theater</td>\n      <td>19.0</td>\n      <td>19 movies</td>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>135 4th St Ste 3000</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Sports Bars</td>\n      <td>Sports Bar</td>\n      <td>Sports Bar</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>365 11th St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Caf\u00e9s</td>\n      <td>Caf\u00e9</td>\n      <td>Caf\u00e9</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>1160 Howard St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Souvlaki Shops</td>\n      <td>Souvlaki</td>\n      <td>Souvlaki Shop</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>5748e1f0498e1c0c6d214098</td>\n      <td>531 Divisadero St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77457655200334</td>\n      <td>-122.4378085167028</td>\n      <td>94117</td>\n      <td>CA</td>\n      <td>Souvla</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Basketball Stadiums</td>\n      <td>Basketball</td>\n      <td>Basketball Stadiu</td>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>125 S Pennsylvania St</td>\n      <td>US</td>\n      <td>Indianapolis</td>\n      <td>United States</td>\n      <td>at E Georgia St</td>\n      <td>125 S Pennsylvania St (at E Georgia St), India...</td>\n      <td>...</td>\n      <td>46204</td>\n      <td>IN</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>42217987</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Convention Centers</td>\n      <td>Convention Center</td>\n      <td>Convention Center</td>\n      <td>5c40f328345cbe002c99e458</td>\n      <td>US</td>\n      <td>San Jose</td>\n      <td>United States</td>\n      <td>San Jose, CA 95113, United States</td>\n      <td>37.329998</td>\n      <td>-121.889275</td>\n      <td>...</td>\n      <td>Further Confusion 2019</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n      <td>blank</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Indie Movie Theaters</td>\n      <td>Indie Movies</td>\n      <td>Indie Movie Theater</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>429 Castro St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>94114</td>\n      <td>CA</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Movie Theaters</td>\n      <td>Movie Theater</td>\n      <td>Movie Theater</td>\n      <td>19.0</td>\n      <td>19 movies</td>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>135 4th St Ste 3000</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Sports Bars</td>\n      <td>Sports Bar</td>\n      <td>Sports Bar</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>365 11th St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>94103</td>\n      <td>CA</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>228</th>\n      <td>Grocery Stores</td>\n      <td>Grocery Store</td>\n      <td>Grocery Store</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>49c3dfb3f964a52082561fe3</td>\n      <td>1737 Post St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.78529223246809</td>\n      <td>-122.43104474803255</td>\n      <td>94115</td>\n      <td>CA</td>\n      <td>Nijiya Market</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:20:59</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>229</th>\n      <td>Nightclubs</td>\n      <td>Nightclub'</td>\n      <td>Nightclu</td>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>830 Hennepin Ave</td>\n      <td>US</td>\n      <td>Minneapolis</td>\n      <td>United States</td>\n      <td>9th</td>\n      <td>830 Hennepin Ave (9th), Minneapolis, MN 55403,...</td>\n      <td>...</td>\n      <td>55403</td>\n      <td>MN</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>230</th>\n      <td>Concert Halls</td>\n      <td>Concert Hall'</td>\n      <td>Concert Hall</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>1111 California St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>94108</td>\n      <td>CA</td>\n      <td>The Masonic</td>\n      <td>39508081</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>231</th>\n      <td>Indie Movie Theaters</td>\n      <td>Indie Movies'</td>\n      <td>Indie Movie Theater</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>429 Castro St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>94114</td>\n      <td>CA</td>\n      <td>Castro Theatre</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>232</th>\n      <td>Movie Theaters</td>\n      <td>Movie Theater'</td>\n      <td>Movie Theater</td>\n      <td>16.0</td>\n      <td>16 movies</td>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>1000 Van Ness Ave</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>94109</td>\n      <td>CA</td>\n      <td>AMC Van Ness 14</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>Performing Arts Venues</td>\n      <td>Performing Arts'</td>\n      <td>Performing Arts Venue</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4b65eb87f964a52065082be3</td>\n      <td>609 Sutter St Fl 2</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.788772390242656</td>\n      <td>-122.4102445497535</td>\n      <td>94102</td>\n      <td>CA</td>\n      <td>Marines' Memorial Theatre</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>Nightclubs'</td>\n      <td>Nightclub</td>\n      <td>Nightclu</td>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>830 Hennepin Ave</td>\n      <td>US</td>\n      <td>Minneapolis</td>\n      <td>United States</td>\n      <td>9th</td>\n      <td>830 Hennepin Ave (9th), Minneapolis, MN 55403,...</td>\n      <td>...</td>\n      <td>55403</td>\n      <td>MN</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>Concert Halls'</td>\n      <td>Concert Hall</td>\n      <td>Concert Hall</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>1111 California St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>94108</td>\n      <td>CA</td>\n      <td>The Masonic</td>\n      <td>39508081</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>Indie Movie Theaters'</td>\n      <td>Indie Movies</td>\n      <td>Indie Movie Theater</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>429 Castro St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>94114</td>\n      <td>CA</td>\n      <td>Castro Theatre</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>Movie Theaters'</td>\n      <td>Movie Theater</td>\n      <td>Movie Theater</td>\n      <td>16.0</td>\n      <td>16 movies</td>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>1000 Van Ness Ave</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>94109</td>\n      <td>CA</td>\n      <td>AMC Van Ness 14</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>238</th>\n      <td>Music Venues'</td>\n      <td>Music Venue</td>\n      <td>Music Venue</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>1805 Geary Blvd</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>94115</td>\n      <td>CA</td>\n      <td>The Fillmore</td>\n      <td>39508222</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>239</th>\n      <td>Nightclubs</td>\n      <td>Nightclub</td>\n      <td>Nightclu</td>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>830 Hennepin Ave</td>\n      <td>US</td>\n      <td>Minneapolis</td>\n      <td>United States</td>\n      <td>9th</td>\n      <td>830 Hennepin Ave (9th), Minneapolis, MN 55403,...</td>\n      <td>...</td>\n      <td>55403</td>\n      <td>MN</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>240</th>\n      <td>Concert Halls</td>\n      <td>Concert Hall</td>\n      <td>Concert Hall</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>1111 California St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>94108</td>\n      <td>CA</td>\n      <td>The Masonic</td>\n      <td>39508081</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>241</th>\n      <td>Indie Movie Theaters</td>\n      <td>Indie Movies</td>\n      <td>Indie Movie Theater</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>429 Castro St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>94114</td>\n      <td>CA</td>\n      <td>Castro Theatre</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>242</th>\n      <td>Movie Theaters</td>\n      <td>Movie Theater</td>\n      <td>Movie Theater</td>\n      <td>16.0</td>\n      <td>16 movies</td>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>1000 Van Ness Ave</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>94109</td>\n      <td>CA</td>\n      <td>AMC Van Ness 14</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>243</th>\n      <td>Music Venues</td>\n      <td>Music Venue</td>\n      <td>Music Venue</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>1805 Geary Blvd</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>94115</td>\n      <td>CA</td>\n      <td>The Fillmore</td>\n      <td>39508222</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>244</th>\n      <td>Nightclubs</td>\n      <td>Nightclub</td>\n      <td>Nightclu</td>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>830 Hennepin Ave</td>\n      <td>US</td>\n      <td>Minneapolis</td>\n      <td>United States</td>\n      <td>9th</td>\n      <td>830 Hennepin Ave (9th), Minneapolis, MN 55403,...</td>\n      <td>...</td>\n      <td>55403</td>\n      <td>MN</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>245</th>\n      <td>Concert Halls</td>\n      <td>Concert Hall</td>\n      <td>Concert Hall</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>1111 California St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>94108</td>\n      <td>CA</td>\n      <td>The Masonic</td>\n      <td>39508081</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>246</th>\n      <td>Indie Movie Theaters</td>\n      <td>Indie Movies</td>\n      <td>Indie Movie Theater</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>429 Castro St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>94114</td>\n      <td>CA</td>\n      <td>Castro Theatre</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>247</th>\n      <td>Movie Theaters</td>\n      <td>Movie Theater</td>\n      <td>Movie Theater</td>\n      <td>16.0</td>\n      <td>16 movies</td>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>1000 Van Ness Ave</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>94109</td>\n      <td>CA</td>\n      <td>AMC Van Ness 14</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>248</th>\n      <td>Music Venues</td>\n      <td>Music Venue</td>\n      <td>Music Venue</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>1805 Geary Blvd</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>94115</td>\n      <td>CA</td>\n      <td>The Fillmore</td>\n      <td>39508222</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>249</th>\n      <td>Nightclubs</td>\n      <td>Nightclub'</td>\n      <td>Nightclu</td>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>830 Hennepin Ave</td>\n      <td>US</td>\n      <td>Minneapolis</td>\n      <td>United States</td>\n      <td>9th</td>\n      <td>830 Hennepin Ave (9th), Minneapolis, MN 55403,...</td>\n      <td>...</td>\n      <td>55403</td>\n      <td>MN</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>250</th>\n      <td>Concert Halls</td>\n      <td>Concert Hall'</td>\n      <td>Concert Hall</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>1111 California St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>94108</td>\n      <td>CA</td>\n      <td>The Masonic</td>\n      <td>39508081</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>251</th>\n      <td>Indie Movie Theaters</td>\n      <td>Indie Movies'</td>\n      <td>Indie Movie Theater</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>429 Castro St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>94114</td>\n      <td>CA</td>\n      <td>Castro Theatre</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>252</th>\n      <td>Movie Theaters</td>\n      <td>Movie Theater'</td>\n      <td>Movie Theater</td>\n      <td>16.0</td>\n      <td>16 movies</td>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>1000 Van Ness Ave</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>94109</td>\n      <td>CA</td>\n      <td>AMC Van Ness 14</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>253</th>\n      <td>Music Venues</td>\n      <td>Music Venue'</td>\n      <td>Music Venue</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>1805 Geary Blvd</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>94115</td>\n      <td>CA</td>\n      <td>The Fillmore</td>\n      <td>39508222</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>254</th>\n      <td>Hotels</td>\n      <td>Hotel</td>\n      <td>Hotel</td>\n      <td>58029b16d67cb63bf30ad301</td>\n      <td>401 Hennepin Ave</td>\n      <td>US</td>\n      <td>Minneapolis</td>\n      <td>United States</td>\n      <td>401 Hennepin Ave, Minneapolis, MN 55401, Unite...</td>\n      <td>44.980133682004194</td>\n      <td>...</td>\n      <td>MN</td>\n      <td>AC Hotel Downtown Minneapolis</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n      <td>Sunday</td>\n      <td>2019</td>\n      <td>January</td>\n      <td>PM</td>\n      <td>blank</td>\n      <td>blank</td>\n    </tr>\n    <tr>\n      <th>255</th>\n      <td>Concert Halls</td>\n      <td>Concert Hall</td>\n      <td>Concert Hall'</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>1111 California St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>94108</td>\n      <td>CA</td>\n      <td>The Masonic</td>\n      <td>39508081</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n      <td>Sunday</td>\n      <td>2019</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>Indie Movie Theaters</td>\n      <td>Indie Movies</td>\n      <td>Indie Movie Theater'</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>429 Castro St</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>94114</td>\n      <td>CA</td>\n      <td>Castro Theatre</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n      <td>Sunday</td>\n    </tr>\n    <tr>\n      <th>257</th>\n      <td>Movie Theaters</td>\n      <td>Movie Theater</td>\n      <td>Movie Theater'</td>\n      <td>16.0</td>\n      <td>16 movies</td>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>1000 Van Ness Ave</td>\n      <td>US</td>\n      <td>San Francisco</td>\n      <td>United States</td>\n      <td>...</td>\n      <td>nan</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>94109</td>\n      <td>CA</td>\n      <td>AMC Van Ness 14</td>\n      <td>nan</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n      <td>Sunday</td>\n    </tr>\n  </tbody>\n</table>\n<p>258 rows \u00d7 22 columns</p>\n</div>", 
                        "text/plain": "                CATEGORIES1        CATEGORIES2            CATEGORIES3  \\\n0       Basketball Stadiums         Basketball      Basketball Stadiu   \n1        Convention Centers  Convention Center      Convention Center   \n2            Movie Theaters      Movie Theater          Movie Theater   \n3               Sports Bars         Sports Bar             Sports Bar   \n4                     Caf\u00e9s               Caf\u00e9                   Caf\u00e9   \n5       Basketball Stadiums         Basketball      Basketball Stadiu   \n6        Convention Centers  Convention Center      Convention Center   \n7      Indie Movie Theaters       Indie Movies    Indie Movie Theater   \n8            Movie Theaters      Movie Theater          Movie Theater   \n9               Sports Bars         Sports Bar             Sports Bar   \n10                    Caf\u00e9s               Caf\u00e9                   Caf\u00e9   \n11      Basketball Stadiums         Basketball      Basketball Stadiu   \n12       Convention Centers  Convention Center      Convention Center   \n13     Indie Movie Theaters       Indie Movies    Indie Movie Theater   \n14           Movie Theaters      Movie Theater          Movie Theater   \n15              Sports Bars         Sports Bar             Sports Bar   \n16                    Caf\u00e9s               Caf\u00e9                   Caf\u00e9   \n17           Souvlaki Shops           Souvlaki          Souvlaki Shop   \n18      Basketball Stadiums         Basketball      Basketball Stadiu   \n19       Convention Centers  Convention Center      Convention Center   \n20     Indie Movie Theaters       Indie Movies    Indie Movie Theater   \n21           Movie Theaters      Movie Theater          Movie Theater   \n22              Sports Bars         Sports Bar             Sports Bar   \n23                    Caf\u00e9s               Caf\u00e9                   Caf\u00e9   \n24           Souvlaki Shops           Souvlaki          Souvlaki Shop   \n25      Basketball Stadiums         Basketball      Basketball Stadiu   \n26       Convention Centers  Convention Center      Convention Center   \n27     Indie Movie Theaters       Indie Movies    Indie Movie Theater   \n28           Movie Theaters      Movie Theater          Movie Theater   \n29              Sports Bars         Sports Bar             Sports Bar   \n..                      ...                ...                    ...   \n228          Grocery Stores      Grocery Store          Grocery Store   \n229              Nightclubs         Nightclub'               Nightclu   \n230           Concert Halls      Concert Hall'           Concert Hall   \n231    Indie Movie Theaters      Indie Movies'    Indie Movie Theater   \n232          Movie Theaters     Movie Theater'          Movie Theater   \n233  Performing Arts Venues   Performing Arts'  Performing Arts Venue   \n234             Nightclubs'          Nightclub               Nightclu   \n235          Concert Halls'       Concert Hall           Concert Hall   \n236   Indie Movie Theaters'       Indie Movies    Indie Movie Theater   \n237         Movie Theaters'      Movie Theater          Movie Theater   \n238           Music Venues'        Music Venue            Music Venue   \n239              Nightclubs          Nightclub               Nightclu   \n240           Concert Halls       Concert Hall           Concert Hall   \n241    Indie Movie Theaters       Indie Movies    Indie Movie Theater   \n242          Movie Theaters      Movie Theater          Movie Theater   \n243            Music Venues        Music Venue            Music Venue   \n244              Nightclubs          Nightclub               Nightclu   \n245           Concert Halls       Concert Hall           Concert Hall   \n246    Indie Movie Theaters       Indie Movies    Indie Movie Theater   \n247          Movie Theaters      Movie Theater          Movie Theater   \n248            Music Venues        Music Venue            Music Venue   \n249              Nightclubs         Nightclub'               Nightclu   \n250           Concert Halls      Concert Hall'           Concert Hall   \n251    Indie Movie Theaters      Indie Movies'    Indie Movie Theater   \n252          Movie Theaters     Movie Theater'          Movie Theater   \n253            Music Venues       Music Venue'            Music Venue   \n254                  Hotels              Hotel                  Hotel   \n255           Concert Halls       Concert Hall          Concert Hall'   \n256    Indie Movie Theaters       Indie Movies   Indie Movie Theater'   \n257          Movie Theaters      Movie Theater         Movie Theater'   \n\n                           ID       LOCATION.ADDRESS  \\\n0    4ad4bff8f964a52002ea20e3  125 S Pennsylvania St   \n1                       blank                     US   \n2                        19.0              19 movies   \n3                         nan                    nan   \n4                         nan                    nan   \n5    4ad4bff8f964a52002ea20e3  125 S Pennsylvania St   \n6                       blank                     US   \n7                         nan                    nan   \n8                        19.0              19 movies   \n9                         nan                    nan   \n10                        nan                    nan   \n11   4ad4bff8f964a52002ea20e3  125 S Pennsylvania St   \n12                      blank                     US   \n13                        nan                    nan   \n14                       19.0              19 movies   \n15                        nan                    nan   \n16                        nan                    nan   \n17                        nan                    nan   \n18   4ad4bff8f964a52002ea20e3  125 S Pennsylvania St   \n19   5c40f328345cbe002c99e458                     US   \n20                        nan                    nan   \n21                       19.0              19 movies   \n22                        nan                    nan   \n23                        nan                    nan   \n24                        nan                    nan   \n25   4ad4bff8f964a52002ea20e3  125 S Pennsylvania St   \n26   5c40f328345cbe002c99e458                     US   \n27                        nan                    nan   \n28                       19.0              19 movies   \n29                        nan                    nan   \n..                        ...                    ...   \n228                       nan                    nan   \n229  4c60c443de6920a111ec9664       830 Hennepin Ave   \n230                       nan                    nan   \n231                       nan                    nan   \n232                      16.0              16 movies   \n233                       nan                    nan   \n234  4c60c443de6920a111ec9664       830 Hennepin Ave   \n235                       nan                    nan   \n236                       nan                    nan   \n237                      16.0              16 movies   \n238                       nan                    nan   \n239  4c60c443de6920a111ec9664       830 Hennepin Ave   \n240                       nan                    nan   \n241                       nan                    nan   \n242                      16.0              16 movies   \n243                       nan                    nan   \n244  4c60c443de6920a111ec9664       830 Hennepin Ave   \n245                       nan                    nan   \n246                       nan                    nan   \n247                      16.0              16 movies   \n248                       nan                    nan   \n249  4c60c443de6920a111ec9664       830 Hennepin Ave   \n250                       nan                    nan   \n251                       nan                    nan   \n252                      16.0              16 movies   \n253                       nan                    nan   \n254  58029b16d67cb63bf30ad301       401 Hennepin Ave   \n255                       nan                    nan   \n256                       nan                    nan   \n257                      16.0              16 movies   \n\n                  LOCATION.CC        LOCATION.CITY  \\\n0                          US         Indianapolis   \n1                    San Jose                blank   \n2    4a5824e5f964a5206db71fe3  135 4th St Ste 3000   \n3    57587782498e1367ff5c38ec          365 11th St   \n4    55554f0a498e7ffc6883325c       1160 Howard St   \n5                          US         Indianapolis   \n6                    San Jose                blank   \n7    4089ae00f964a520bff21ee3        429 Castro St   \n8    4a5824e5f964a5206db71fe3  135 4th St Ste 3000   \n9    57587782498e1367ff5c38ec          365 11th St   \n10   55554f0a498e7ffc6883325c       1160 Howard St   \n11                         US         Indianapolis   \n12                   San Jose                blank   \n13   4089ae00f964a520bff21ee3        429 Castro St   \n14   4a5824e5f964a5206db71fe3  135 4th St Ste 3000   \n15   57587782498e1367ff5c38ec          365 11th St   \n16   55554f0a498e7ffc6883325c       1160 Howard St   \n17   5748e1f0498e1c0c6d214098    531 Divisadero St   \n18                         US         Indianapolis   \n19                   San Jose        United States   \n20   4089ae00f964a520bff21ee3        429 Castro St   \n21   4a5824e5f964a5206db71fe3  135 4th St Ste 3000   \n22   57587782498e1367ff5c38ec          365 11th St   \n23   55554f0a498e7ffc6883325c       1160 Howard St   \n24   5748e1f0498e1c0c6d214098    531 Divisadero St   \n25                         US         Indianapolis   \n26                   San Jose        United States   \n27   4089ae00f964a520bff21ee3        429 Castro St   \n28   4a5824e5f964a5206db71fe3  135 4th St Ste 3000   \n29   57587782498e1367ff5c38ec          365 11th St   \n..                        ...                  ...   \n228  49c3dfb3f964a52082561fe3         1737 Post St   \n229                        US          Minneapolis   \n230  4ae269baf964a520248e21e3   1111 California St   \n231  4089ae00f964a520bff21ee3        429 Castro St   \n232  4390a026f964a5204d2b1fe3    1000 Van Ness Ave   \n233  4b65eb87f964a52065082be3   609 Sutter St Fl 2   \n234                        US          Minneapolis   \n235  4ae269baf964a520248e21e3   1111 California St   \n236  4089ae00f964a520bff21ee3        429 Castro St   \n237  4390a026f964a5204d2b1fe3    1000 Van Ness Ave   \n238  40c3b000f964a520e7001fe3      1805 Geary Blvd   \n239                        US          Minneapolis   \n240  4ae269baf964a520248e21e3   1111 California St   \n241  4089ae00f964a520bff21ee3        429 Castro St   \n242  4390a026f964a5204d2b1fe3    1000 Van Ness Ave   \n243  40c3b000f964a520e7001fe3      1805 Geary Blvd   \n244                        US          Minneapolis   \n245  4ae269baf964a520248e21e3   1111 California St   \n246  4089ae00f964a520bff21ee3        429 Castro St   \n247  4390a026f964a5204d2b1fe3    1000 Van Ness Ave   \n248  40c3b000f964a520e7001fe3      1805 Geary Blvd   \n249                        US          Minneapolis   \n250  4ae269baf964a520248e21e3   1111 California St   \n251  4089ae00f964a520bff21ee3        429 Castro St   \n252  4390a026f964a5204d2b1fe3    1000 Van Ness Ave   \n253  40c3b000f964a520e7001fe3      1805 Geary Blvd   \n254                        US          Minneapolis   \n255  4ae269baf964a520248e21e3   1111 California St   \n256  4089ae00f964a520bff21ee3        429 Castro St   \n257  4390a026f964a5204d2b1fe3    1000 Van Ness Ave   \n\n                      LOCATION.COUNTRY  \\\n0                        United States   \n1    San Jose, CA 95113, United States   \n2                                   US   \n3                                   US   \n4                                   US   \n5                        United States   \n6    San Jose, CA 95113, United States   \n7                                   US   \n8                                   US   \n9                                   US   \n10                                  US   \n11                       United States   \n12   San Jose, CA 95113, United States   \n13                                  US   \n14                                  US   \n15                                  US   \n16                                  US   \n17                                  US   \n18                       United States   \n19   San Jose, CA 95113, United States   \n20                                  US   \n21                                  US   \n22                                  US   \n23                                  US   \n24                                  US   \n25                       United States   \n26   San Jose, CA 95113, United States   \n27                                  US   \n28                                  US   \n29                                  US   \n..                                 ...   \n228                                 US   \n229                      United States   \n230                                 US   \n231                                 US   \n232                                 US   \n233                                 US   \n234                      United States   \n235                                 US   \n236                                 US   \n237                                 US   \n238                                 US   \n239                      United States   \n240                                 US   \n241                                 US   \n242                                 US   \n243                                 US   \n244                      United States   \n245                                 US   \n246                                 US   \n247                                 US   \n248                                 US   \n249                      United States   \n250                                 US   \n251                                 US   \n252                                 US   \n253                                 US   \n254                      United States   \n255                                 US   \n256                                 US   \n257                                 US   \n\n                                  LOCATION.CROSSSTREET  \\\n0                                      at E Georgia St   \n1                                            37.329998   \n2                                        San Francisco   \n3                                        San Francisco   \n4                                        San Francisco   \n5                                      at E Georgia St   \n6                                            37.329998   \n7                                        San Francisco   \n8                                        San Francisco   \n9                                        San Francisco   \n10                                       San Francisco   \n11                                     at E Georgia St   \n12                                           37.329998   \n13                                       San Francisco   \n14                                       San Francisco   \n15                                       San Francisco   \n16                                       San Francisco   \n17                                       San Francisco   \n18                                     at E Georgia St   \n19                                           37.329998   \n20                                       San Francisco   \n21                                       San Francisco   \n22                                       San Francisco   \n23                                       San Francisco   \n24                                       San Francisco   \n25                                     at E Georgia St   \n26                                           37.329998   \n27                                       San Francisco   \n28                                       San Francisco   \n29                                       San Francisco   \n..                                                 ...   \n228                                      San Francisco   \n229                                                9th   \n230                                      San Francisco   \n231                                      San Francisco   \n232                                      San Francisco   \n233                                      San Francisco   \n234                                                9th   \n235                                      San Francisco   \n236                                      San Francisco   \n237                                      San Francisco   \n238                                      San Francisco   \n239                                                9th   \n240                                      San Francisco   \n241                                      San Francisco   \n242                                      San Francisco   \n243                                      San Francisco   \n244                                                9th   \n245                                      San Francisco   \n246                                      San Francisco   \n247                                      San Francisco   \n248                                      San Francisco   \n249                                                9th   \n250                                      San Francisco   \n251                                      San Francisco   \n252                                      San Francisco   \n253                                      San Francisco   \n254  401 Hennepin Ave, Minneapolis, MN 55401, Unite...   \n255                                      San Francisco   \n256                                      San Francisco   \n257                                      San Francisco   \n\n                             LOCATION.FORMATTEDADDRESS   ...     \\\n0    125 S Pennsylvania St (at E Georgia St), India...   ...      \n1                                          -121.889275   ...      \n2                                        United States   ...      \n3                                        United States   ...      \n4                                        United States   ...      \n5    125 S Pennsylvania St (at E Georgia St), India...   ...      \n6                                          -121.889275   ...      \n7                                        United States   ...      \n8                                        United States   ...      \n9                                        United States   ...      \n10                                       United States   ...      \n11   125 S Pennsylvania St (at E Georgia St), India...   ...      \n12                                         -121.889275   ...      \n13                                       United States   ...      \n14                                       United States   ...      \n15                                       United States   ...      \n16                                       United States   ...      \n17                                       United States   ...      \n18   125 S Pennsylvania St (at E Georgia St), India...   ...      \n19                                         -121.889275   ...      \n20                                       United States   ...      \n21                                       United States   ...      \n22                                       United States   ...      \n23                                       United States   ...      \n24                                       United States   ...      \n25   125 S Pennsylvania St (at E Georgia St), India...   ...      \n26                                         -121.889275   ...      \n27                                       United States   ...      \n28                                       United States   ...      \n29                                       United States   ...      \n..                                                 ...   ...      \n228                                      United States   ...      \n229  830 Hennepin Ave (9th), Minneapolis, MN 55403,...   ...      \n230                                      United States   ...      \n231                                      United States   ...      \n232                                      United States   ...      \n233                                      United States   ...      \n234  830 Hennepin Ave (9th), Minneapolis, MN 55403,...   ...      \n235                                      United States   ...      \n236                                      United States   ...      \n237                                      United States   ...      \n238                                      United States   ...      \n239  830 Hennepin Ave (9th), Minneapolis, MN 55403,...   ...      \n240                                      United States   ...      \n241                                      United States   ...      \n242                                      United States   ...      \n243                                      United States   ...      \n244  830 Hennepin Ave (9th), Minneapolis, MN 55403,...   ...      \n245                                      United States   ...      \n246                                      United States   ...      \n247                                      United States   ...      \n248                                      United States   ...      \n249  830 Hennepin Ave (9th), Minneapolis, MN 55403,...   ...      \n250                                      United States   ...      \n251                                      United States   ...      \n252                                      United States   ...      \n253                                      United States   ...      \n254                                 44.980133682004194   ...      \n255                                      United States   ...      \n256                                      United States   ...      \n257                                      United States   ...      \n\n        LOCATION.POSTALCODE                 LOCATION.STATE  \\\n0                     46204                             IN   \n1                     blank                       01/20/19   \n2                       nan              37.78457546024026   \n3         37.77124505563008            -122.41299663242239   \n4         37.77701166643817            -122.41089851840502   \n5                     46204                             IN   \n6                     blank                       01/20/19   \n7                       nan             37.761991717624404   \n8                       nan              37.78457546024026   \n9         37.77124505563008            -122.41299663242239   \n10        37.77701166643817            -122.41089851840502   \n11                    46204                             IN   \n12                    blank                       01/20/19   \n13                      nan             37.761991717624404   \n14                      nan              37.78457546024026   \n15        37.77124505563008            -122.41299663242239   \n16        37.77701166643817            -122.41089851840502   \n17        37.77457655200334             -122.4378085167028   \n18                    46204                             IN   \n19   Further Confusion 2019                       01/20/19   \n20                      nan             37.761991717624404   \n21                      nan              37.78457546024026   \n22        37.77124505563008            -122.41299663242239   \n23        37.77701166643817            -122.41089851840502   \n24        37.77457655200334             -122.4378085167028   \n25                    46204                             IN   \n26   Further Confusion 2019                       01/20/19   \n27                      nan             37.761991717624404   \n28                      nan              37.78457546024026   \n29        37.77124505563008            -122.41299663242239   \n..                      ...                            ...   \n228       37.78529223246809            -122.43104474803255   \n229                   55403                             MN   \n230       37.79118818627268            -122.41302191054984   \n231                     nan             37.761991717624404   \n232                     nan              37.78515072462566   \n233      37.788772390242656             -122.4102445497535   \n234                   55403                             MN   \n235       37.79118818627268            -122.41302191054984   \n236                     nan             37.761991717624404   \n237                     nan              37.78515072462566   \n238                     nan             37.784079069766605   \n239                   55403                             MN   \n240       37.79118818627268            -122.41302191054984   \n241                     nan             37.761991717624404   \n242                     nan              37.78515072462566   \n243                     nan             37.784079069766605   \n244                   55403                             MN   \n245       37.79118818627268            -122.41302191054984   \n246                     nan             37.761991717624404   \n247                     nan              37.78515072462566   \n248                     nan             37.784079069766605   \n249                   55403                             MN   \n250       37.79118818627268            -122.41302191054984   \n251                     nan             37.761991717624404   \n252                     nan              37.78515072462566   \n253                     nan             37.784079069766605   \n254                      MN  AC Hotel Downtown Minneapolis   \n255       37.79118818627268            -122.41302191054984   \n256                     nan             37.761991717624404   \n257                     nan              37.78515072462566   \n\n                        NAME VENUEPAGE.ID                       DATE  \\\n0    Bankers Life Fieldhouse     42217987                   01/20/19   \n1                   16:54:07       Sunday                       2019   \n2        -122.40323635482677        94103                         CA   \n3                      94103           CA                  BuzzWorks   \n4                      94103           CA              Vive La Tarte   \n5    Bankers Life Fieldhouse     42217987                   01/20/19   \n6                   17:16:47       Sunday                       2019   \n7        -122.43496924638747        94114                         CA   \n8        -122.40323635482677        94103                         CA   \n9                      94103           CA                  BuzzWorks   \n10                     94103           CA              Vive La Tarte   \n11   Bankers Life Fieldhouse     42217987                   01/20/19   \n12                  17:19:51       Sunday                       2019   \n13       -122.43496924638747        94114                         CA   \n14       -122.40323635482677        94103                         CA   \n15                     94103           CA                  BuzzWorks   \n16                     94103           CA              Vive La Tarte   \n17                     94117           CA                     Souvla   \n18   Bankers Life Fieldhouse     42217987                   01/20/19   \n19                  17:25:18       Sunday                       2019   \n20       -122.43496924638747        94114                         CA   \n21       -122.40323635482677        94103                         CA   \n22                     94103           CA                  BuzzWorks   \n23                     94103           CA              Vive La Tarte   \n24                     94117           CA                     Souvla   \n25   Bankers Life Fieldhouse     42217987                   01/20/19   \n26                  17:32:20       Sunday                       2019   \n27       -122.43496924638747        94114                         CA   \n28       -122.40323635482677        94103                         CA   \n29                     94103           CA                  BuzzWorks   \n..                       ...          ...                        ...   \n228                    94115           CA              Nijiya Market   \n229              Hard Monday     01/20/19                   22:25:58   \n230                    94108           CA                The Masonic   \n231      -122.43496924638747        94114                         CA   \n232      -122.42012248044318        94109                         CA   \n233                    94102           CA  Marines' Memorial Theatre   \n234              Hard Monday     01/20/19                   22:31:03   \n235                    94108           CA                The Masonic   \n236      -122.43496924638747        94114                         CA   \n237      -122.42012248044318        94109                         CA   \n238      -122.43300234064807        94115                         CA   \n239              Hard Monday     01/20/19                   22:36:02   \n240                    94108           CA                The Masonic   \n241      -122.43496924638747        94114                         CA   \n242      -122.42012248044318        94109                         CA   \n243      -122.43300234064807        94115                         CA   \n244              Hard Monday     01/20/19                   22:41:00   \n245                    94108           CA                The Masonic   \n246      -122.43496924638747        94114                         CA   \n247      -122.42012248044318        94109                         CA   \n248      -122.43300234064807        94115                         CA   \n249              Hard Monday     01/20/19                   22:46:01   \n250                    94108           CA                The Masonic   \n251      -122.43496924638747        94114                         CA   \n252      -122.42012248044318        94109                         CA   \n253      -122.43300234064807        94115                         CA   \n254                 01/20/19     22:51:12                     Sunday   \n255                    94108           CA                The Masonic   \n256      -122.43496924638747        94114                         CA   \n257      -122.42012248044318        94109                         CA   \n\n                TIME   WEEKDAY      YEAR     MONTH    AM_PM  \n0           16:54:07    Sunday      2019   January       PM  \n1            January        PM     blank     blank    blank  \n2     AMC Metreon 16  01/20/19  16:54:07    Sunday     2019  \n3           01/20/19  16:54:07    Sunday      2019  January  \n4           01/20/19  16:54:07    Sunday      2019  January  \n5           17:16:47    Sunday      2019   January       PM  \n6            January        PM     blank     blank    blank  \n7     Castro Theatre  01/20/19  17:16:47    Sunday     2019  \n8     AMC Metreon 16  01/20/19  17:16:47    Sunday     2019  \n9           01/20/19  17:16:47    Sunday      2019  January  \n10          01/20/19  17:16:47    Sunday      2019  January  \n11          17:19:51    Sunday      2019   January       PM  \n12           January        PM     blank     blank    blank  \n13    Castro Theatre  01/20/19  17:19:51    Sunday     2019  \n14    AMC Metreon 16  01/20/19  17:19:51    Sunday     2019  \n15          01/20/19  17:19:51    Sunday      2019  January  \n16          01/20/19  17:19:51    Sunday      2019  January  \n17          01/20/19  17:19:51    Sunday      2019  January  \n18          17:25:18    Sunday      2019   January       PM  \n19           January        PM     blank     blank    blank  \n20    Castro Theatre  01/20/19  17:25:18    Sunday     2019  \n21    AMC Metreon 16  01/20/19  17:25:18    Sunday     2019  \n22          01/20/19  17:25:18    Sunday      2019  January  \n23          01/20/19  17:25:18    Sunday      2019  January  \n24          01/20/19  17:25:18    Sunday      2019  January  \n25          17:32:20    Sunday      2019   January       PM  \n26           January        PM     blank     blank    blank  \n27    Castro Theatre  01/20/19  17:32:20    Sunday     2019  \n28    AMC Metreon 16  01/20/19  17:32:20    Sunday     2019  \n29          01/20/19  17:32:20    Sunday      2019  January  \n..               ...       ...       ...       ...      ...  \n228              nan  01/20/19  22:20:59    Sunday     2019  \n229           Sunday      2019   January        PM    blank  \n230         39508081  01/20/19  22:25:58    Sunday     2019  \n231   Castro Theatre       nan  01/20/19  22:25:58   Sunday  \n232  AMC Van Ness 14       nan  01/20/19  22:25:58   Sunday  \n233              nan  01/20/19  22:25:58    Sunday     2019  \n234           Sunday      2019   January        PM    blank  \n235         39508081  01/20/19  22:31:03    Sunday     2019  \n236   Castro Theatre       nan  01/20/19  22:31:03   Sunday  \n237  AMC Van Ness 14       nan  01/20/19  22:31:03   Sunday  \n238     The Fillmore  39508222  01/20/19  22:31:03   Sunday  \n239           Sunday      2019   January        PM    blank  \n240         39508081  01/20/19  22:36:02    Sunday     2019  \n241   Castro Theatre       nan  01/20/19  22:36:02   Sunday  \n242  AMC Van Ness 14       nan  01/20/19  22:36:02   Sunday  \n243     The Fillmore  39508222  01/20/19  22:36:02   Sunday  \n244           Sunday      2019   January        PM    blank  \n245         39508081  01/20/19  22:41:00    Sunday     2019  \n246   Castro Theatre       nan  01/20/19  22:41:00   Sunday  \n247  AMC Van Ness 14       nan  01/20/19  22:41:00   Sunday  \n248     The Fillmore  39508222  01/20/19  22:41:00   Sunday  \n249           Sunday      2019   January        PM    blank  \n250         39508081  01/20/19  22:46:01    Sunday     2019  \n251   Castro Theatre       nan  01/20/19  22:46:01   Sunday  \n252  AMC Van Ness 14       nan  01/20/19  22:46:01   Sunday  \n253     The Fillmore  39508222  01/20/19  22:46:01   Sunday  \n254             2019   January        PM     blank    blank  \n255         39508081  01/20/19  22:51:12    Sunday     2019  \n256   Castro Theatre       nan  01/20/19  22:51:12   Sunday  \n257  AMC Van Ness 14       nan  01/20/19  22:51:12   Sunday  \n\n[258 rows x 22 columns]"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "print(query_results_timestamp2)\nTrending_Data_DF[0:len(Trending_Data_DF)]"
        }, 
        {
            "source": "<h2 align=\"center\">Restructured Data from Data Retrieval Notebook</h2>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 14, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "SELECT QUERY TIMESTAMP - DATE: 01/21/19 - TIME: 13:55:47\n"
                }, 
                {
                    "execution_count": 14, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>VENUE_ID</th>\n      <th>CITY</th>\n      <th>LAT</th>\n      <th>LONG</th>\n      <th>VENUE_NAME</th>\n      <th>TREND_DATE</th>\n      <th>TREND_TIME</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>San Francisco</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>San Francisco</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>San Francisco</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>San Francisco</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>San Francisco</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>San Francisco</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>5748e1f0498e1c0c6d214098</td>\n      <td>San Francisco</td>\n      <td>37.77457655200334</td>\n      <td>-122.4378085167028</td>\n      <td>Souvla</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>5c40f328345cbe002c99e458</td>\n      <td>San Jose</td>\n      <td>37.329998</td>\n      <td>-121.889275</td>\n      <td>Further Confusion 2019</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>San Francisco</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>San Francisco</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>5748e1f0498e1c0c6d214098</td>\n      <td>San Francisco</td>\n      <td>37.77457655200334</td>\n      <td>-122.4378085167028</td>\n      <td>Souvla</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>5c40f328345cbe002c99e458</td>\n      <td>San Jose</td>\n      <td>37.329998</td>\n      <td>-121.889275</td>\n      <td>Further Confusion 2019</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>San Francisco</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>5748e1f0498e1c0c6d214098</td>\n      <td>San Francisco</td>\n      <td>37.77457655200334</td>\n      <td>-122.4378085167028</td>\n      <td>Souvla</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>18:11:20</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>225</th>\n      <td>49c3dfb3f964a52082561fe3</td>\n      <td>San Francisco</td>\n      <td>37.78529223246809</td>\n      <td>-122.43104474803255</td>\n      <td>Nijiya Market</td>\n      <td>01/20/19</td>\n      <td>22:20:59</td>\n    </tr>\n    <tr>\n      <th>226</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n    </tr>\n    <tr>\n      <th>227</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n    </tr>\n    <tr>\n      <th>228</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n    </tr>\n    <tr>\n      <th>229</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n    </tr>\n    <tr>\n      <th>230</th>\n      <td>4b65eb87f964a52065082be3</td>\n      <td>San Francisco</td>\n      <td>37.788772390242656</td>\n      <td>-122.4102445497535</td>\n      <td>Marines' Memorial Theatre</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n    </tr>\n    <tr>\n      <th>231</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n    </tr>\n    <tr>\n      <th>232</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>San Francisco</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>The Fillmore</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n    </tr>\n    <tr>\n      <th>238</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n    </tr>\n    <tr>\n      <th>239</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n    </tr>\n    <tr>\n      <th>240</th>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>San Francisco</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>The Fillmore</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n    </tr>\n    <tr>\n      <th>241</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n    </tr>\n    <tr>\n      <th>242</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n    </tr>\n    <tr>\n      <th>243</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n    </tr>\n    <tr>\n      <th>244</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n    </tr>\n    <tr>\n      <th>245</th>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>San Francisco</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>The Fillmore</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n    </tr>\n    <tr>\n      <th>246</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n    </tr>\n    <tr>\n      <th>247</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n    </tr>\n    <tr>\n      <th>248</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n    </tr>\n    <tr>\n      <th>249</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n    </tr>\n    <tr>\n      <th>250</th>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>San Francisco</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>The Fillmore</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n    </tr>\n    <tr>\n      <th>251</th>\n      <td>58029b16d67cb63bf30ad301</td>\n      <td>Minneapolis</td>\n      <td>44.980133682004194</td>\n      <td>-93.27134549617762</td>\n      <td>AC Hotel Downtown Minneapolis</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n    </tr>\n    <tr>\n      <th>252</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n    </tr>\n    <tr>\n      <th>253</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n    </tr>\n    <tr>\n      <th>254</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n    </tr>\n  </tbody>\n</table>\n<p>255 rows \u00d7 7 columns</p>\n</div>", 
                        "text/plain": "                     VENUE_ID           CITY                 LAT  \\\n0    4ad4bff8f964a52002ea20e3   Indianapolis   39.76409464732229   \n1    4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n2    57587782498e1367ff5c38ec  San Francisco   37.77124505563008   \n3    55554f0a498e7ffc6883325c  San Francisco   37.77701166643817   \n4    4ad4bff8f964a52002ea20e3   Indianapolis   39.76409464732229   \n5    4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n6    4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n7    57587782498e1367ff5c38ec  San Francisco   37.77124505563008   \n8    55554f0a498e7ffc6883325c  San Francisco   37.77701166643817   \n9    4ad4bff8f964a52002ea20e3   Indianapolis   39.76409464732229   \n10   4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n11   4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n12   57587782498e1367ff5c38ec  San Francisco   37.77124505563008   \n13   55554f0a498e7ffc6883325c  San Francisco   37.77701166643817   \n14   5748e1f0498e1c0c6d214098  San Francisco   37.77457655200334   \n15   4ad4bff8f964a52002ea20e3   Indianapolis   39.76409464732229   \n16   5c40f328345cbe002c99e458       San Jose           37.329998   \n17   4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n18   4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n19   57587782498e1367ff5c38ec  San Francisco   37.77124505563008   \n20   55554f0a498e7ffc6883325c  San Francisco   37.77701166643817   \n21   5748e1f0498e1c0c6d214098  San Francisco   37.77457655200334   \n22   4ad4bff8f964a52002ea20e3   Indianapolis   39.76409464732229   \n23   5c40f328345cbe002c99e458       San Jose           37.329998   \n24   4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n25   4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n26   57587782498e1367ff5c38ec  San Francisco   37.77124505563008   \n27   4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n28   5748e1f0498e1c0c6d214098  San Francisco   37.77457655200334   \n29   4ad4bff8f964a52002ea20e3   Indianapolis   39.76409464732229   \n..                        ...            ...                 ...   \n225  49c3dfb3f964a52082561fe3  San Francisco   37.78529223246809   \n226  4c60c443de6920a111ec9664    Minneapolis   44.97664765105814   \n227  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n228  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n229  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n230  4b65eb87f964a52065082be3  San Francisco  37.788772390242656   \n231  4c60c443de6920a111ec9664    Minneapolis   44.97664765105814   \n232  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n233  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n234  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n235  40c3b000f964a520e7001fe3  San Francisco  37.784079069766605   \n236  4c60c443de6920a111ec9664    Minneapolis   44.97664765105814   \n237  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n238  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n239  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n240  40c3b000f964a520e7001fe3  San Francisco  37.784079069766605   \n241  4c60c443de6920a111ec9664    Minneapolis   44.97664765105814   \n242  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n243  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n244  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n245  40c3b000f964a520e7001fe3  San Francisco  37.784079069766605   \n246  4c60c443de6920a111ec9664    Minneapolis   44.97664765105814   \n247  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n248  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n249  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n250  40c3b000f964a520e7001fe3  San Francisco  37.784079069766605   \n251  58029b16d67cb63bf30ad301    Minneapolis  44.980133682004194   \n252  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n253  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n254  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n\n                    LONG                     VENUE_NAME TREND_DATE TREND_TIME  \n0     -86.15550016846848        Bankers Life Fieldhouse   01/20/19   16:54:07  \n1    -122.40323635482677                 AMC Metreon 16   01/20/19   16:54:07  \n2    -122.41299663242239                      BuzzWorks   01/20/19   16:54:07  \n3    -122.41089851840502                  Vive La Tarte   01/20/19   16:54:07  \n4     -86.15550016846848        Bankers Life Fieldhouse   01/20/19   17:16:47  \n5    -122.43496924638747                 Castro Theatre   01/20/19   17:16:47  \n6    -122.40323635482677                 AMC Metreon 16   01/20/19   17:16:47  \n7    -122.41299663242239                      BuzzWorks   01/20/19   17:16:47  \n8    -122.41089851840502                  Vive La Tarte   01/20/19   17:16:47  \n9     -86.15550016846848        Bankers Life Fieldhouse   01/20/19   17:19:51  \n10   -122.43496924638747                 Castro Theatre   01/20/19   17:19:51  \n11   -122.40323635482677                 AMC Metreon 16   01/20/19   17:19:51  \n12   -122.41299663242239                      BuzzWorks   01/20/19   17:19:51  \n13   -122.41089851840502                  Vive La Tarte   01/20/19   17:19:51  \n14    -122.4378085167028                         Souvla   01/20/19   17:19:51  \n15    -86.15550016846848        Bankers Life Fieldhouse   01/20/19   17:25:18  \n16           -121.889275         Further Confusion 2019   01/20/19   17:25:18  \n17   -122.43496924638747                 Castro Theatre   01/20/19   17:25:18  \n18   -122.40323635482677                 AMC Metreon 16   01/20/19   17:25:18  \n19   -122.41299663242239                      BuzzWorks   01/20/19   17:25:18  \n20   -122.41089851840502                  Vive La Tarte   01/20/19   17:25:18  \n21    -122.4378085167028                         Souvla   01/20/19   17:25:18  \n22    -86.15550016846848        Bankers Life Fieldhouse   01/20/19   17:32:20  \n23           -121.889275         Further Confusion 2019   01/20/19   17:32:20  \n24   -122.43496924638747                 Castro Theatre   01/20/19   17:32:20  \n25   -122.40323635482677                 AMC Metreon 16   01/20/19   17:32:20  \n26   -122.41299663242239                      BuzzWorks   01/20/19   17:32:20  \n27   -122.42012248044318                AMC Van Ness 14   01/20/19   17:32:20  \n28    -122.4378085167028                         Souvla   01/20/19   17:32:20  \n29    -86.15550016846848        Bankers Life Fieldhouse   01/20/19   18:11:20  \n..                   ...                            ...        ...        ...  \n225  -122.43104474803255                  Nijiya Market   01/20/19   22:20:59  \n226   -93.27673707725614                    Hard Monday   01/20/19   22:25:58  \n227  -122.41302191054984                    The Masonic   01/20/19   22:25:58  \n228  -122.43496924638747                 Castro Theatre   01/20/19   22:25:58  \n229  -122.42012248044318                AMC Van Ness 14   01/20/19   22:25:58  \n230   -122.4102445497535      Marines' Memorial Theatre   01/20/19   22:25:58  \n231   -93.27673707725614                    Hard Monday   01/20/19   22:31:03  \n232  -122.41302191054984                    The Masonic   01/20/19   22:31:03  \n233  -122.43496924638747                 Castro Theatre   01/20/19   22:31:03  \n234  -122.42012248044318                AMC Van Ness 14   01/20/19   22:31:03  \n235  -122.43300234064807                   The Fillmore   01/20/19   22:31:03  \n236   -93.27673707725614                    Hard Monday   01/20/19   22:36:02  \n237  -122.41302191054984                    The Masonic   01/20/19   22:36:02  \n238  -122.43496924638747                 Castro Theatre   01/20/19   22:36:02  \n239  -122.42012248044318                AMC Van Ness 14   01/20/19   22:36:02  \n240  -122.43300234064807                   The Fillmore   01/20/19   22:36:02  \n241   -93.27673707725614                    Hard Monday   01/20/19   22:41:00  \n242  -122.41302191054984                    The Masonic   01/20/19   22:41:00  \n243  -122.43496924638747                 Castro Theatre   01/20/19   22:41:00  \n244  -122.42012248044318                AMC Van Ness 14   01/20/19   22:41:00  \n245  -122.43300234064807                   The Fillmore   01/20/19   22:41:00  \n246   -93.27673707725614                    Hard Monday   01/20/19   22:46:01  \n247  -122.41302191054984                    The Masonic   01/20/19   22:46:01  \n248  -122.43496924638747                 Castro Theatre   01/20/19   22:46:01  \n249  -122.42012248044318                AMC Van Ness 14   01/20/19   22:46:01  \n250  -122.43300234064807                   The Fillmore   01/20/19   22:46:01  \n251   -93.27134549617762  AC Hotel Downtown Minneapolis   01/20/19   22:51:12  \n252  -122.41302191054984                    The Masonic   01/20/19   22:51:12  \n253  -122.43496924638747                 Castro Theatre   01/20/19   22:51:12  \n254  -122.42012248044318                AMC Van Ness 14   01/20/19   22:51:12  \n\n[255 rows x 7 columns]"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "print(query_results_timestamp3)\nUpdated_Trend_Data_DF[0:len(Updated_Trend_Data_DF)]"
        }, 
        {
            "source": "<hr>\n\n<h1 align=\"center\">Trending City Summary</h1>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 15, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "group2 = Updated_Trend_Data_DF.groupby(['CITY'])\ngroup3 = group2['TREND_DATE']\ngroup4 = group2['TREND_TIME']\ngroup5 = group2['VENUE_NAME']\ngroup1 = Updated_Trend_Data_DF.groupby(['VENUE_NAME'])\ngroup6 = Updated_Trend_Data_DF.groupby(['TREND_TIME'])"
        }, 
        {
            "execution_count": 16, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "group_list1 = []\ngroup_list2 = []\nfor name, group in group2:\n    group_list1.append(name)\n    group_list2.append(group)"
        }, 
        {
            "execution_count": 17, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "city_summary_list1 = []\ncity_summary_list2 = []\ncity_summary_str1 = ''\ncity_summary_counter1 = 0\nfor item in group_list1:\n    city_summary_list1.append(str(item))\n    city_summary_list1.append(len(group_list2[city_summary_counter1]))\n    city_summary_list2.append(list(city_summary_list1))\n    city_summary_list1 = []\n    city_summary_counter1 += 1"
        }, 
        {
            "execution_count": 18, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "city_summary_1 = pd.DataFrame(columns=['City', 'Trending_Sum'])"
        }, 
        {
            "execution_count": 19, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "for row in city_summary_list2:\n    city_summary_1 = city_summary_1.append(pd.Series(row, index=['City', 'Trending_Sum']), ignore_index=True)"
        }, 
        {
            "execution_count": 20, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f03143f5198>"
                    }, 
                    "metadata": {}
                }
            ], 
            "source": "plt.rcdefaults()\nfig, ax = plt.subplots()\n\nCities = city_summary_1.City[0:len(city_summary_1)]\ny_pos = np.arange(len(Cities))\nTotals = city_summary_1.Trending_Sum[0:len(city_summary_1)]\n\nax.barh(y_pos, Totals, align='center', color='blue')\nax.set_yticks(y_pos)\nax.set_yticklabels(Cities)\nax.set_xlabel('Cities')\nax.set_title('Trending City Data')\n\nplt.show()"
        }, 
        {
            "execution_count": 21, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "execution_count": 21, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>City</th>\n      <th>Trending_Sum</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Indianapolis</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Minneapolis</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>New Orleans</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>San Francisco</td>\n      <td>225</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>San Jose</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "            City Trending_Sum\n0   Indianapolis            9\n1    Minneapolis            7\n2    New Orleans           12\n3  San Francisco          225\n4       San Jose            2"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "city_summary_1"
        }, 
        {
            "source": "<hr>\n\n<h1 align=\"center\">Trending City Data for Indianapolis, IN</h1>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 22, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "ind_city_df = group_list2[0]"
        }, 
        {
            "execution_count": 23, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "ind_group1 = ind_city_df.groupby(['VENUE_NAME'])\nind_group_list1 = []\nind_group_list2 = []\nfor name, group in ind_group1:\n    ind_group_list1.append(name)\n    ind_group_list2.append(group)"
        }, 
        {
            "execution_count": 24, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "ind_summary_list1 = []\nind_summary_list2 = []\nind_summary_str1 = ''\nind_summary_counter1 = 0\nfor item in ind_group_list1:\n    ind_summary_list1.append(str(item))\n    ind_summary_list1.append(len(ind_group_list2[ind_summary_counter1]))\n    ind_summary_list1.append('Indianapolis')\n    ind_summary_list2.append(list(ind_summary_list1))\n    ind_summary_list1 = []\n    ind_summary_counter1 += 1"
        }, 
        {
            "execution_count": 25, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "ind_summary_1 = pd.DataFrame(columns=['Venue_Name', 'Trending_Sum', 'City'])"
        }, 
        {
            "execution_count": 26, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "for row in ind_summary_list2:\n    ind_summary_1 = ind_summary_1.append(pd.Series(row, index=['Venue_Name', 'Trending_Sum', 'City']), ignore_index=True)"
        }, 
        {
            "execution_count": 27, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f03143f56d8>"
                    }, 
                    "metadata": {}
                }
            ], 
            "source": "plt.rcdefaults()\nfig, ax = plt.subplots()\n\nVenues = ind_summary_1.Venue_Name[0:len(ind_summary_1)]\ny_pos = np.arange(len(Venues))\nTotals = ind_summary_1.Trending_Sum[0:len(ind_summary_1)]\n\nax.barh(y_pos, Totals, align='center', color='blue')\nax.set_yticks(y_pos)\nax.set_yticklabels(Venues)\nax.set_xlabel('Venues')\nax.set_title('Trending Venues in Indianapolis')\n\nplt.show()"
        }, 
        {
            "execution_count": 28, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 28, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Venue_Name</th>\n      <th>Trending_Sum</th>\n      <th>City</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Bankers Life Fieldhouse</td>\n      <td>9</td>\n      <td>Indianapolis</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                Venue_Name Trending_Sum          City\n0  Bankers Life Fieldhouse            9  Indianapolis"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "ind_summary_1"
        }, 
        {
            "execution_count": 29, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 29, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>VENUE_ID</th>\n      <th>CITY</th>\n      <th>LAT</th>\n      <th>LONG</th>\n      <th>VENUE_NAME</th>\n      <th>TREND_DATE</th>\n      <th>TREND_TIME</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>18:11:20</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>18:16:02</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>18:21:02</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>4ad4bff8f964a52002ea20e3</td>\n      <td>Indianapolis</td>\n      <td>39.76409464732229</td>\n      <td>-86.15550016846848</td>\n      <td>Bankers Life Fieldhouse</td>\n      <td>01/20/19</td>\n      <td>18:26:01</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                    VENUE_ID          CITY                LAT  \\\n0   4ad4bff8f964a52002ea20e3  Indianapolis  39.76409464732229   \n4   4ad4bff8f964a52002ea20e3  Indianapolis  39.76409464732229   \n9   4ad4bff8f964a52002ea20e3  Indianapolis  39.76409464732229   \n15  4ad4bff8f964a52002ea20e3  Indianapolis  39.76409464732229   \n22  4ad4bff8f964a52002ea20e3  Indianapolis  39.76409464732229   \n29  4ad4bff8f964a52002ea20e3  Indianapolis  39.76409464732229   \n33  4ad4bff8f964a52002ea20e3  Indianapolis  39.76409464732229   \n37  4ad4bff8f964a52002ea20e3  Indianapolis  39.76409464732229   \n41  4ad4bff8f964a52002ea20e3  Indianapolis  39.76409464732229   \n\n                  LONG               VENUE_NAME TREND_DATE TREND_TIME  \n0   -86.15550016846848  Bankers Life Fieldhouse   01/20/19   16:54:07  \n4   -86.15550016846848  Bankers Life Fieldhouse   01/20/19   17:16:47  \n9   -86.15550016846848  Bankers Life Fieldhouse   01/20/19   17:19:51  \n15  -86.15550016846848  Bankers Life Fieldhouse   01/20/19   17:25:18  \n22  -86.15550016846848  Bankers Life Fieldhouse   01/20/19   17:32:20  \n29  -86.15550016846848  Bankers Life Fieldhouse   01/20/19   18:11:20  \n33  -86.15550016846848  Bankers Life Fieldhouse   01/20/19   18:16:02  \n37  -86.15550016846848  Bankers Life Fieldhouse   01/20/19   18:21:02  \n41  -86.15550016846848  Bankers Life Fieldhouse   01/20/19   18:26:01  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "ind_city_df"
        }, 
        {
            "source": "<hr>\n\n<h1 align=\"center\">Trending City Data for Minneapolis, MN</h1>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 30, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "minn_city_df = group_list2[1]"
        }, 
        {
            "execution_count": 31, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "minn_group1 = minn_city_df.groupby(['VENUE_NAME'])\nminn_group_list1 = []\nminn_group_list2 = []\nfor name, group in minn_group1:\n    minn_group_list1.append(name)\n    minn_group_list2.append(group)"
        }, 
        {
            "execution_count": 32, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "minn_summary_list1 = []\nminn_summary_list2 = []\nminn_summary_str1 = ''\nminn_summary_counter1 = 0\nfor item in minn_group_list1:\n    minn_summary_list1.append(str(item))\n    minn_summary_list1.append(len(minn_group_list2[minn_summary_counter1]))\n    minn_summary_list1.append('Minneapolis')\n    minn_summary_list2.append(list(minn_summary_list1))\n    minn_summary_list1 = []\n    minn_summary_counter1 += 1"
        }, 
        {
            "execution_count": 33, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "minn_summary_1 = pd.DataFrame(columns=['Venue_Name', 'Trending_Sum', 'City'])"
        }, 
        {
            "execution_count": 34, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "for row in minn_summary_list2:\n    minn_summary_1 = minn_summary_1.append(pd.Series(row, index=['Venue_Name', 'Trending_Sum', 'City']), ignore_index=True)"
        }, 
        {
            "execution_count": 35, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f0310090320>"
                    }, 
                    "metadata": {}
                }
            ], 
            "source": "plt.rcdefaults()\nfig, ax = plt.subplots()\n\nVenues = minn_summary_1.Venue_Name[0:len(minn_summary_1)]\ny_pos = np.arange(len(Venues))\nTotals = minn_summary_1.Trending_Sum[0:len(minn_summary_1)]\n\nax.barh(y_pos, Totals, align='center', color='blue')\nax.set_yticks(y_pos)\nax.set_yticklabels(Venues)\nax.set_xlabel('Venues')\nax.set_title('Trending Venues in Minneapolis')\n\nplt.show()\n"
        }, 
        {
            "execution_count": 36, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 36, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Venue_Name</th>\n      <th>Trending_Sum</th>\n      <th>City</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AC Hotel Downtown Minneapolis</td>\n      <td>1</td>\n      <td>Minneapolis</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Hard Monday</td>\n      <td>6</td>\n      <td>Minneapolis</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                      Venue_Name Trending_Sum         City\n0  AC Hotel Downtown Minneapolis            1  Minneapolis\n1                    Hard Monday            6  Minneapolis"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "minn_summary_1"
        }, 
        {
            "execution_count": 37, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 37, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>VENUE_ID</th>\n      <th>CITY</th>\n      <th>LAT</th>\n      <th>LONG</th>\n      <th>VENUE_NAME</th>\n      <th>TREND_DATE</th>\n      <th>TREND_TIME</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>220</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:20:59</td>\n    </tr>\n    <tr>\n      <th>226</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n    </tr>\n    <tr>\n      <th>231</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n    </tr>\n    <tr>\n      <th>241</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n    </tr>\n    <tr>\n      <th>246</th>\n      <td>4c60c443de6920a111ec9664</td>\n      <td>Minneapolis</td>\n      <td>44.97664765105814</td>\n      <td>-93.27673707725614</td>\n      <td>Hard Monday</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n    </tr>\n    <tr>\n      <th>251</th>\n      <td>58029b16d67cb63bf30ad301</td>\n      <td>Minneapolis</td>\n      <td>44.980133682004194</td>\n      <td>-93.27134549617762</td>\n      <td>AC Hotel Downtown Minneapolis</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                     VENUE_ID         CITY                 LAT  \\\n220  4c60c443de6920a111ec9664  Minneapolis   44.97664765105814   \n226  4c60c443de6920a111ec9664  Minneapolis   44.97664765105814   \n231  4c60c443de6920a111ec9664  Minneapolis   44.97664765105814   \n236  4c60c443de6920a111ec9664  Minneapolis   44.97664765105814   \n241  4c60c443de6920a111ec9664  Minneapolis   44.97664765105814   \n246  4c60c443de6920a111ec9664  Minneapolis   44.97664765105814   \n251  58029b16d67cb63bf30ad301  Minneapolis  44.980133682004194   \n\n                   LONG                     VENUE_NAME TREND_DATE TREND_TIME  \n220  -93.27673707725614                    Hard Monday   01/20/19   22:20:59  \n226  -93.27673707725614                    Hard Monday   01/20/19   22:25:58  \n231  -93.27673707725614                    Hard Monday   01/20/19   22:31:03  \n236  -93.27673707725614                    Hard Monday   01/20/19   22:36:02  \n241  -93.27673707725614                    Hard Monday   01/20/19   22:41:00  \n246  -93.27673707725614                    Hard Monday   01/20/19   22:46:01  \n251  -93.27134549617762  AC Hotel Downtown Minneapolis   01/20/19   22:51:12  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "minn_city_df"
        }, 
        {
            "source": "<hr>\n\n<h1 align=\"center\">Trending City Data for New Orleans, LA</h1>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 38, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "no_city_df = group_list2[2]"
        }, 
        {
            "execution_count": 39, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "no_group1 = no_city_df.groupby(['VENUE_NAME'])\nno_group_list1 = []\nno_group_list2 = []\nfor name, group in no_group1:\n    no_group_list1.append(name)\n    no_group_list2.append(group)"
        }, 
        {
            "execution_count": 40, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "no_summary_list1 = []\nno_summary_list2 = []\nno_summary_str1 = ''\nno_summary_counter1 = 0\nfor item in no_group_list1:\n    no_summary_list1.append(str(item))\n    no_summary_list1.append(len(no_group_list2[no_summary_counter1]))\n    no_summary_list1.append('New Orleans')\n    no_summary_list2.append(list(no_summary_list1))\n    no_summary_list1 = []\n    no_summary_counter1 += 1"
        }, 
        {
            "execution_count": 41, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "no_summary_1 = pd.DataFrame(columns=['Venue_Name', 'Trending_Sum', 'City'])"
        }, 
        {
            "execution_count": 42, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "for row in no_summary_list2:\n    no_summary_1 = no_summary_1.append(pd.Series(row, index=['Venue_Name', 'Trending_Sum', 'City']), ignore_index=True)"
        }, 
        {
            "execution_count": 43, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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effVVDRw4UJ07d9Zzzz2njIwMjRs3Ll+Xi/Krfv36euqppzRp0iRZlqVHHnlEe/bs0dSpU1WiRIk7vn/oZs8//7xGjhwpm82m2NhYubm5OcwvXbq0/vd//1d//etfdeLECfsX5v7888/avXu3MjMz9fbbbxdo/O7u7vbLUuXLl1fNmjW1efNmJSUl5Vo2ISFBkZGRat68ufr27auQkBCdPXtW+/bt04YNG+xfS9GyZUs99NBDql+/vvz9/XXgwAElJiaqevXqBT4z+nvw9/fXyy+/rDFjxuSaN3z4cK1YsUIRERGKjY3VAw88oOvXr+vw4cNau3atXnvtNdWuXVvSjegIDQ3VunXr1L59e/v3s7Vs2VKxsbE6evSoBg8efMfxuLu7a8mSJWrdurUaNWqkl19+2f6Ft0uXLtXs2bPVpk0bTZgwodD7nN/nrl27dho0aJCGDh2qxx9/XIcOHVJcXJzKly+v48ePF3i7R48eVceOHdWtWzdVq1ZNXl5e2rZtm1JTU2/5Jc/4YyPUgCKqQoUK2rlzp8aPH6/4+HgdPXpUPj4+qlSpktq0aVPgS3qSNGDAALm4uGjKlClasWKFKlWqpLi4OK1cuVJpaWl3bezz58/X8OHDlZiYqClTpqhBgwb68MMPFRkZWaAzIH369NGYMWOUlZV1y0tU/fr1U6VKlfTmm28qJiZGly5dUtmyZVWvXr1Cn2WbMWOGXF1dNX78eF2+fFnR0dFaunSpw89sSTeidNeuXRo/frxGjBihn3/+WaVLl1a1atX05JNP2pd79NFH9fHHH2vmzJk6f/68goKC1L59e40ZM6ZA4fp7GjJkiGbMmJErPkqVKqVt27Zp0qRJSkhIUHp6ury8vBQSEqJWrVqpXLlyDsu3bNlS77zzjsP70GrWrKng4GAdO3YsXx8kkKQHH3xQX331laZMmaJ3331XY8aMkZubm2rXrq3ExETFxMT8pjNa+X3uBgwYoF9++UWzZ89WQkKCwsLCNHHiRKWlpWnmzJkF3q6Pj48efPBBzZ49W+np6crKytL999+vcePGaejQoYXeHxRdNqsg1xoA4HeyceNGtWjRQh999JE6duzo7OEAgBEINQD33OrVq7V7927Vq1dP7u7u2r17t15//XWVLVtWu3fvNu4niwDAWbj0CeCe8/Hx0apVq/Tmm2/qwoULKlOmjJ544gn71yEAAG7gjBoAAIChzHiXKgAAAHIh1AAAAAxFqAEAABiKDxMUMdnZ2Tp27Ji8vb3z/QPWAADAuSzL0vnz5xUcHFyg70ck1IqYY8eO5fqRYQAAUDQcOXKkQL84QqgVMTnfNn/kyJG78vuOAADg93fu3DlVqFChwL8aQ6gVMTmXO318fAg1AACKmIK+bYkPEwAAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxV39gBQSL6+zh4BAAB/LJbl7BHkwhk1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItR+pU+fPurQoYOzhwEAAFCwUOvTp49sNptsNptcXV1VqVIlDRs2TBcvXizQOvIbQkeOHFFMTIyCg4Pl5uamkJAQxcbGKiMjw2G55s2ba+7cufb7999/v32cLi4uCg4OVkxMjM6cOXPHbU6dOtVhXXfD3Llz1bx587u6TgAA8MdX4DNqbdq00fHjx3Xw4EFNmDBBM2bM0LBhw+76wA4ePKjw8HDt27dPCxcu1P79+zVz5kxt2LBBTZo00S+//HLbx8fFxen48eM6fPiw5s+fr9TUVA0aNOiWy2dlZSk7O1u+vr4qVarU3d4dAACAAitwqLm7uyswMFAVKlRQjx491LNnTy1btkzSjdiJiYlRaGioPD09Va1aNU2dOtX+2FdffVXz5s3T8uXL7We8UlJS8tzOgAED5ObmpnXr1ikyMlIVK1ZU27ZttX79eh09elSjRo267Ti9vb0VGBiocuXKKSoqSr169dKuXbvs8+fOnatSpUpp5cqVqlmzptzd3ZWenp7rjJ9lWXrjjTdUqVIleXp66sEHH9SHH35on5+SkiKbzaYNGzYoPDxcXl5eatq0qX744Ydbji0lJUWNGjVSiRIlVKpUKTVr1kzp6em33R8AAPDnU/y3rsDT01PXrl2TJGVnZ6t8+fJatGiR/P39tXXrVvXr109BQUHq0qWLhg0bpu+++07nzp3TnDlzJEmlS5fOtc5ffvlFn3zyiSZOnChPT0+HeYGBgerZs6eSkpI0Y8YM2Wy2O47x6NGjWrlypRo3buww/dKlS4qPj9e7774rPz8/BQQE5Hrs6NGjtWTJEiUmJiosLEypqal65plnVKZMGUVGRtqXGzVqlN566y2VKVNG/fv31/PPP68tW7bkWt/169fVoUMH9e3bVwsXLtTVq1e1ffv2W+7HlStXdOXKFfv9c+fO3XF/AQDAH4RVAL1797aefPJJ+/0vvvjC8vPzs7p06XLLx/ztb3+zOnXqdMt15OXzzz+3JFlLly7Nc/7bb79tSbJOnjyZ5/yQkBDLzc3NKlGihOXh4WFJsho3bmydOXPGvsycOXMsSVZaWtot9/HChQuWh4eHtXXrVodlYmJirO7du1uWZVmbNm2yJFnr16+3z1+1apUlycrMzMw1toyMDEuSlZKScttjkGPcuHGWpFy3s5JlcePGjRs3btzu3u13dPbsWUuSdfbs2QI9rsCXPleuXKmSJUvKw8NDTZo00SOPPKJp06bZ58+cOVPh4eEqU6aMSpYsqXfeeUeHDx/+rT3pwLIsSbrt2bSXX35ZaWlp2rNnjzZs2CBJevzxx5WVlWVfxs3NTXXq1LnlOr799ltdvnxZ0dHRKlmypP323nvv6cCBAw7L3ryeoKAgSdKpU6dyrbN06dLq06ePWrdurfbt22vq1Kk6fvz4Lcfwyiuv6OzZs/bbkSNHbrksAAD4Yynwpc+oqCglJibK1dVVwcHBcnV1tc9btGiRXnrpJb311ltq0qSJvL29NWXKFH3xxRcF2kaVKlVks9n07bff5vkJ0e+//1733Xef/P39b7kOf39/ValSRZIUFhamv//972rSpIk2bdqkli1bSrpx2fZ2sZednS1JWrVqlcqVK+cwz93d3eH+zcchZ505j/+1OXPmaNCgQVq7dq2SkpI0evRoJScn66GHHsq1rLu7e65tAQCAP4cCh1qJEiXsAfRrn332mZo2baq//e1v9mm/PvPk5ubmcFYrL35+foqOjtaMGTP00ksvObxP7cSJE5o/f7569eqVr/en5XBxcZEkZWZm5vsxOR8yOHz4sMP70e6GevXqqV69enrllVfUpEkTLViwIM9QAwAAf1539Qtvq1Spoi+//FKffPKJ9u3bpzFjxmjHjh0Oy9x///3as2ePfvjhB50+fdr+QYRfmz59uq5cuaLWrVsrNTVVR44c0dq1axUdHa1y5cpp4sSJtx3L+fPndeLECR0/flzbt2/Xyy+/LH9/fzVt2jTf++Pt7a1hw4bppZde0rx583TgwAHt3r1bCQkJmjdvXr7Xc7NDhw7plVde0bZt25Senq5169Zp3759qlGjRqHWBwAA/rjuaqj1799fHTt2VNeuXdW4cWNlZGQ4nF2TpL59+6patWr297Hl9clI6cblyi+//FKVK1dW165dVblyZfXr109RUVHatm1bnp8WvdnYsWMVFBSk4OBgtWvXTiVKlFBycrL8/PwKtE/jx4/X2LFjFR8frxo1aqh169b6+OOPFRoaWqD15PDy8tL333+vTp06qWrVqurXr58GDhyoF154oVDrAwAAf1w2K+ed+SgSzp07J19fX52V5OPswQAA8EfyOyaR/fX77Fn5+OT/FZzf+gQAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIq698GdAAANeElEQVRQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMFRxZw8AhXT2rOTj4+xRAACA3xFn1AAAAAxFqAEAABiKUAMAADAUoQYAAGAoQg0AAMBQhBoAAIChCDUAAABDEWoAAACGItQAAAAMRagBAAAYilADAAAwFKEGAABgKEINAADAUIQaAACAoQg1AAAAQxV39gBQMJZlSZLOnTvn5JEAAID8ynndznkdzy9CrYjJyMiQJFWoUMHJIwEAAAV1/vx5+fr65nt5Qq2IKV26tCTp8OHDBXqiceP/ZipUqKAjR47Ix8fH2cMpMjhuhcexKzyOXeFx7Arn9z5ulmXp/PnzCg4OLtDjCLUiplixG28r9PX15V/AQvLx8eHYFQLHrfA4doXHsSs8jl3h/J7HrTAnWPgwAQAAgKEINQAAAEO5vPrqq686exAoGBcXFzVv3lzFi3PluqA4doXDcSs8jl3hcewKj2NXOCYeN5tV0M+JAgAA4J7g0icAAIChCDUAAABDEWoAAACGItQAAAAMRagVITNmzFBoaKg8PDzUoEEDffbZZ84ekvHi4+PVsGFDeXt7KyAgQB06dNAPP/zg7GEVSfHx8bLZbBo8eLCzh1IkHD16VM8884z8/Pzk5eWlunXraufOnc4eltGuX7+u0aNHKzQ0VJ6enqpUqZLi4uKUnZ3t7KEZJzU1Ve3bt1dwcLBsNpuWLVvmMN+yLL366qsKDg6Wp6enmjdvrm+++cZJozXL7Y7dtWvXNHz4cNWuXVslSpRQcHCwevXqpWPHjjltvIRaEZGUlKTBgwdr1KhR2r17tx5++GG1bdtWhw8fdvbQjPbpp59qwIAB+vzzz5WcnKzr16+rVatWunjxorOHVqTs2LFDs2bNUp06dZw9lCLhzJkzatasmVxdXbVmzRp9++23euutt1SqVClnD81or7/+umbOnKnp06fru+++0xtvvKEpU6Zo2rRpzh6acS5evKgHH3xQ06dPz3P+G2+8obffflvTp0/Xjh07FBgYqOjoaJ0/f/4ej9Q8tzt2ly5d0q5duzRmzBjt2rVLS5Ys0b59+/TEE084YaT/n4UioVGjRlb//v0dplWvXt0aMWKEk0ZUNJ06dcqSZH366afOHkqRcf78eSssLMxKTk62IiMjrdjYWGcPyXjDhw+3IiIinD2MIufxxx+3nn/+eYdpHTt2tJ555hknjahokGQtXbrUfj87O9sKDAy0Jk+ebJ92+fJly9fX15o5c6YzhmisXx+7vGzfvt2SZKWnp9+jUTnijFoRcPXqVe3cuVOtWrVymN6qVStt3brVSaMqms6ePSvp/37cHnc2YMAAPf7442rZsqWzh1JkrFixQuHh4ercubMCAgJUr149vfPOO84elvEiIiK0YcMG7du3T5L01VdfafPmzXrsscecPLKi5dChQzpx4oTDa4a7u7siIyN5zSiEs2fPymazOe2MuDlfvYtbOn36tLKyslS2bFmH6WXLltWJEyecNKqix7IsDRkyRBEREapVq5azh1Mk/Pvf/9auXbu0Y8cOZw+lSDl48KASExM1ZMgQjRw5Utu3b9egQYPk7u6uXr16OXt4xho+fLjOnj2r6tWry8XFRVlZWZo4caK6d+/u7KEVKTmvC3m9ZqSnpztjSEXW5cuXNWLECPXo0cNpP3BPqBUhNpvN4b5lWbmm4dYGDhyoPXv2aPPmzc4eSpFw5MgRxcbGat26dfLw8HD2cIqU7OxshYeHa9KkSZKkevXq6ZtvvlFiYiKhdhtJSUn64IMPtGDBAj3wwANKS0vT4MGDFRwcrN69ezt7eEUOrxm/zbVr19StWzdlZ2drxowZThsHoVYE+Pv7y8XFJdfZs1OnTuX6Pybk7cUXX9SKFSuUmpqq8uXLO3s4RcLOnTt16tQpNWjQwD4tKytLqampmj59uq5cuSIXFxcnjtBcQUFBqlmzpsO0GjVq6KOPPnLSiIqGl19+WSNGjFC3bt0kSbVr11Z6erri4+MJtQIIDAyUdOPMWlBQkH06rxn5d+3aNXXp0kWHDh3Sxo0bnXY2TeJTn0WCm5ubGjRooOTkZIfpycnJatq0qZNGVTRYlqWBAwdqyZIl2rhxo0JDQ509pCKjRYsW2rt3r9LS0uy38PBw9ezZU2lpaUTabTRr1izX18Ds27dPISEhThpR0XDp0iUVK+b4suTi4sLXcxRQaGioAgMDHV4zrl69qk8//ZTXjHzIibQff/xR69evl5+fn1PHwxm1ImLIkCF69tlnFR4eriZNmmjWrFk6fPiw+vfv7+yhGW3AgAFasGCBli9fLm9vb/tZSV9fX3l6ejp5dGbz9vbO9V6+EiVKyM/Pj/f43cFLL72kpk2batKkSerSpYu2b9+uWbNmadasWc4emtHat2+viRMnqmLFinrggQe0e/duvf3223r++eedPTTjXLhwQfv377ffP3TokNLS0lS6dGlVrFhRgwcP1qRJkxQWFqawsDBNmjRJXl5e6tGjhxNHbYbbHbvg4GA9/fTT2rVrl1auXKmsrCz760bp0qXl5uZ27wfslM+aolASEhKskJAQy83Nzapfvz5fMZEPkvK8zZkzx9lDK5L4eo78+/jjj61atWpZ7u7uVvXq1a1Zs2Y5e0jGO3funBUbG2tVrFjR8vDwsCpVqmSNGjXKunLlirOHZpxNmzbl+d+23r17W5Z14ys6xo0bZwUGBlru7u7WI488Yu3du9e5gzbE7Y7doUOHbvm6sWnTJqeM12ZZlnUvwxAAAAD5w3vUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAgn9q3b6+WLVvmOW/btm2y2WzatWvXPR4VgD8yQg0A8ikmJkYbN25Uenp6rnmzZ89W3bp1Vb9+fSeMDMAfFaEGAPnUrl07BQQEaO7cuQ7TL126pKSkJMXExEiSvv76a7Vt21YlS5ZUYGCgevfurYyMDPvyEREReumllzR06FDdd999CgoK0vjx4+3z9+/fL5vNpq+//to+7fTp07LZbNq8ebN92p22k5SUpFq1asnDw0N+fn6Kjo5WZmbm3T4sAH5HhBoA5FPx4sXVq1cvzZ07Vzf/TPLixYt19epV9ezZU0ePHlVkZKTCw8O1c+dOrV69Wj/99JO6d+/usK7Zs2frvvvu0/bt2zVp0iSNHTtWmzZtyvdY7rSdn376ST179lS/fv30/fffKyUlRU8++eTdORAA7hl+lB0ACuD7779XjRo1tHHjRkVFRUmSIiMjVa5cOS1YsEAjR47UV199pVWrVtkf85///EehoaE6cOCAKlWqpIiICLm6ujqEWf369fXYY49pwoQJ2r9/v8LCwrR3717VqlVL0o0zamXKlNFnn32miIiIO27n9OnTaty4sX766SeVK1fuHh0dAHdbcWcPAACKkurVq6tp06aaPXu2oqKidODAAX322Wdat26dJGnnzp3atGmTSpYsmeuxOaEmSXXq1HGYFxQUpFOnTuV7HHfaTlRUlJo3b66aNWuqdevWatWqlZ5++mmVKlWqILsLwMkINQAooJiYGA0cOFAJCQmaM2eOQkJC1KJFC0lSdna2OnTooEmTJuV6XHBwsP2fXV1dHebZbDZlZ2dLkooVu/GulJsveFy7ds1h+Tttp3jx4tqwYYO2bt2qdevWaerUqRo9erS++OILhYSEFHLPAdxrvEcNAAqoS5cucnFx0YIFCzRv3jw999xzstlskm5cwvzmm28UGhqqKlWqONy8vLzytf6AgABJ0vHjx+3T0tLSHJbJz3aKFSumiIgIxcXFaffu3bLZbFq+fPndOAQA7hFCDQAKqGTJkuratatGjhypY8eOqU+fPvZ5L774ok6ePKkePXpox44dOnDggD755BOHZfKz/vDwcMXHx+u7775TSkqKxo4d67DMnbazdetWxcfH68svv9Thw4f10UcfKSMjQzVq1LgLRwDAvUKoAUAhxMTE6MyZM2rZsqUqVqxon16+fHlt2bJFV69eVXR0tGrXrq3BgwerdOnSBVr/3LlzlZmZqQYNGmjIkCGaMGGCw/w7bcfX11cpKSlq27atqlatqnHjxmnq1KmKjo7+7TsP4J7hU58AAACG4owaAACAoQg1AAAAQxFqAAAAhiLUAAAADEWoAQAAGIpQAwAAMBShBgAAYChCDQAAwFCEGgAAgKEINQAAAEMRagAAAIYi1AAAAAz1/wDmOnYHpIci4wAAAABJRU5ErkJggg==\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f031004fac8>"
                    }, 
                    "metadata": {}
                }
            ], 
            "source": "plt.rcdefaults()\nfig, ax = plt.subplots()\n\nVenues = no_summary_1.Venue_Name[0:len(no_summary_1)]\ny_pos = np.arange(len(Venues))\nTotals = no_summary_1.Trending_Sum[0:len(no_summary_1)]\n\nax.barh(y_pos, Totals, align='center', color='red')\nax.set_yticks(y_pos)\nax.set_yticklabels(Venues)\nax.set_xlabel('Venues')\nax.set_title('Trending Venues in New Orleans')\n\nplt.show()"
        }, 
        {
            "execution_count": 44, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 44, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Venue_Name</th>\n      <th>Trending_Sum</th>\n      <th>City</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Pat O'Brien's</td>\n      <td>12</td>\n      <td>New Orleans</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "      Venue_Name Trending_Sum         City\n0  Pat O'Brien's           12  New Orleans"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "no_summary_1"
        }, 
        {
            "execution_count": 45, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 45, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>VENUE_ID</th>\n      <th>CITY</th>\n      <th>LAT</th>\n      <th>LONG</th>\n      <th>VENUE_NAME</th>\n      <th>TREND_DATE</th>\n      <th>TREND_TIME</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>46</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>18:26:01</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>18:31:05</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>18:36:00</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>18:41:02</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>18:46:02</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>19:06:00</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>19:11:00</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>19:15:59</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>19:21:12</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>19:25:58</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>19:31:08</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>4aed03f5f964a520bccc21e3</td>\n      <td>New Orleans</td>\n      <td>29.95824</td>\n      <td>-90.065279</td>\n      <td>Pat O'Brien's</td>\n      <td>01/20/19</td>\n      <td>19:35:58</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                    VENUE_ID         CITY       LAT        LONG  \\\n46  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n51  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n55  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n59  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n63  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n72  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n75  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n78  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n81  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n84  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n87  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n90  4aed03f5f964a520bccc21e3  New Orleans  29.95824  -90.065279   \n\n       VENUE_NAME TREND_DATE TREND_TIME  \n46  Pat O'Brien's   01/20/19   18:26:01  \n51  Pat O'Brien's   01/20/19   18:31:05  \n55  Pat O'Brien's   01/20/19   18:36:00  \n59  Pat O'Brien's   01/20/19   18:41:02  \n63  Pat O'Brien's   01/20/19   18:46:02  \n72  Pat O'Brien's   01/20/19   19:06:00  \n75  Pat O'Brien's   01/20/19   19:11:00  \n78  Pat O'Brien's   01/20/19   19:15:59  \n81  Pat O'Brien's   01/20/19   19:21:12  \n84  Pat O'Brien's   01/20/19   19:25:58  \n87  Pat O'Brien's   01/20/19   19:31:08  \n90  Pat O'Brien's   01/20/19   19:35:58  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "no_city_df"
        }, 
        {
            "source": "<hr>\n\n<h1 align=\"center\">Trending City Data forSan Francisco, CA</h1>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 46, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [], 
            "source": "sf_city_df = group_list2[3]"
        }, 
        {
            "execution_count": 47, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "sf_group1 = sf_city_df.groupby(['VENUE_NAME'])\nsf_group_list1 = []\nsf_group_list2 = []\nfor name, group in sf_group1:\n    sf_group_list1.append(name)\n    sf_group_list2.append(group)"
        }, 
        {
            "execution_count": 48, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "sf_summary_list1 = []\nsf_summary_list2 = []\nsf_summary_str1 = ''\nsf_summary_counter1 = 0\nfor item in sf_group_list1:\n    sf_summary_list1.append(str(item))\n    sf_summary_list1.append(len(sf_group_list2[sf_summary_counter1]))\n    sf_summary_list1.append('San Francisco')\n    sf_summary_list2.append(list(sf_summary_list1))\n    sf_summary_list1 = []\n    sf_summary_counter1 += 1"
        }, 
        {
            "execution_count": 49, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "sf_summary_1 = pd.DataFrame(columns=['Venue_Name', 'Trending_Sum', 'City'])"
        }, 
        {
            "execution_count": 50, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "for row in sf_summary_list2:\n    sf_summary_1 = sf_summary_1.append(pd.Series(row, index=['Venue_Name', 'Trending_Sum', 'City']), ignore_index=True)"
        }, 
        {
            "execution_count": 51, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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tWhpPhMrJyanSJzpVheJEo3i2oVh5T9Z6lh49emD9+vXYsGFDiQ3p2jIzM4O/vz/u37+P4OBg/PLLL/D09ET37t0RHx+PzMxMjVm3v+vcuTM2bdpU4rHKn332GQwMDODl5VWpmOjlwaSCiIiqzKJFi9C2bVu0a9cO48ePR6NGjfD48WP8+uuvSEhIwGeffQZra2ut+6tVqxbCw8Mxbtw4vP322xg+fDju3r2LefPmlfmI0crw8PBA3759sWjRIoiiKD396eOPP4aJiUmJx5Y+S0hICGbPng1BEDBx4kQYGhpqXLewsMDKlSsxZswY/Pnnn+jTpw8sLS1x+/ZtnDlzBo8fP5ae6KMtmUyGgQMHYu3atahXrx5cXV2Rmppa6kvh1q5diw4dOsDb2xsjR45EgwYNoFKp8Msvv+DgwYP49ttvAQBdunRBmzZt4OHhASsrK1y9ehXr169HkyZNKjzjpItbt27Bzc0NgwYNQqdOnVCvXj3k5OTgwIED+OSTT9CiRQv4+voCeJrUbtq0CUFBQQgKCkJWVhYWL14MpVKJrKysFxZzeTw8PFCnTh1MmjQJCxcuhEKhwO7du5GSklLpPnv16oXevXtj+vTp+O2339CtWzfo6+vj5MmTqFOnTpkJ7ODBg6X3bdja2uKPP/7A4sWLYWNjIy1zW7JkifRUstmzZ6Np06a4c+cOEhIS8OGHH8Le3h6zZs3C559/jq5duyI8PBx16tTBnj17sG3bNsyePbtK/77SvxOTCiIiqjIODg44deoUFi5ciIiICPzxxx8wMzODk5MTunfvXuFlPcD/r/letmwZ9u7dCycnJyxYsADx8fFIS0ursti3bduGGTNmYP369Vi2bBneeOMN7N69Gx06dKjQS+uCg4MRFhaGoqKiMpcYvfvuu3BycsLy5csxYsQIPHr0CLa2tmjRokWlZy/WrVuHWrVqYeHChcjLy0PXrl3xxRdfoEmTJhr1PDw8cPr0aSxcuBAzZ87E7du3YWFhgcaNG2ssxenUqRO+/vprbNiwAQ8ePECdOnXg5+eHsLCwCiVZurKxscH777+P77//HjNmzEBWVhb09PTg5OSEmTNnYvr06VLi5ufnh3Xr1uGjjz7Crl274ODggNDQUBgYGGDChAkvLObymJiYICEhAe+99x5CQkIgk8ng6+uL+Ph4NG7cuFJ9CoKAPXv2YPny5YiJicGGDRtgYmKC1157DeHh4WW269ChA3bs2IGtW7fi/v37sLa2RqdOnfD+++9L+1ycnZ2lx9iGh4fj/v37sLW1ld6zAjz9u3/06FHMnj0bkydPxsOHD+Hs7Iy1a9eWeOs41UyCWJF5XSIiolfI999/j86dO2PPnj3SS8uIiKgkJhVEREQAvvnmG5w5cwYtWrSATCbDmTNnsGTJEtja2uLMmTOoVatWdYdIRPSvxeVPREREeLpJNSEhAcuXL0dubi6sra3Ru3dvREREMKEgIioHZyqIiIiIiEgnfKM2ERERERHphEkFERERERHphEkFERERERHphBu1iei5UKvVuHHjBkxNTaW3xxIREdG/myiKePDgAerWrVuhd9IwqSCi5+LGjRtwcHCo7jCIiIioEjIzM1GvXj2t6zOpIKLnovgtq5mZmTAzM6vmaIiIiEgbOTk5cHBwkH6Oa4tJBRE9F8VLnszMzJhUEBERvWQqunSZG7WJiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnBtUdABHVbEpldUdQkihWdwREREQ1C2cqiIiIiIhIJ0wqiIiIiIhIJ0wqiIiIiIhIJ0wq6JWSlJQEQRBw//796g6FiIiIqMZgUkE1gp+fH7p06VLqtaNHj0IQBJw+fRqenp64efMmlM9x9/C1a9cgCALS0tKqpD9vb28IglDm4ejoqFP/M2fORJs2baokViIiIno18elPVCOMGDEC/fr1w2+//YYGDRpoXIuMjIS7uzs8PDwAAHZ2dtURYqXFxcWhoKAAAJCZmYlWrVohMTERzZo1AwDo6+tXql9RFFFUVFRlcRIREdGrizMVVCP06tULNjY2iI6O1ih/9OgRYmNjMWLECACay59UKhXkcjn27dun0SYuLg4mJibIzc0FAPzxxx8YNGgQzM3NYWlpCX9/f1y7dq3SsV69ehX+/v6wtbWFQqHAm2++icTExDLrW1hYwM7ODnZ2drC2tgYAWFpaliiLjIyEh4cHFAoF6tSpg2HDhuHOnTtSP/v27YMgCEhMTESLFi1gaGiIZcuWYcmSJTh+/Lg087Fz504AwL179xASEgIrKysolUp07doV58+fr/R9ExERUc3FpIJqBAMDAwwbNgzR0dEQ//YSgl27dqGgoACBgYEl2iiVSvTs2RPbtm3TKN++fTv8/f2hUCjw6NEjdOzYEQqFAocOHUJqaioUCgW6d+8uzR5UVG5uLnx9fZGYmIgzZ87Ax8cHfn5+uH79eqX6K/bkyRNERETg7Nmz2LNnD9LT0/Huu++WqDdjxgysWLEC6enpCA4Oxrhx4+Dh4YGbN2/i5s2b6NOnD4qKitC9e3eoVCp89913OHHiBJo0aYLOnTsjJydHpziJiIioBhKJaoj09HQRgPj9999LZV5eXuLgwYOl8x9++EEEIGZnZ4uiKIpxcXGiQqEQHz58KIqiKKpUKtHIyEhMSEgQRVEUP/30U7Fx48aiWq2W+sjPzxflcrm4f//+UuPIyMgQAYhnzpzROnZXV1dxzZo15darSN+HDh0S9fT0xPz8fFEURfHbb78VAYj79u3TqDdjxgyxdevWGmUJCQmipaWlWFBQIJWp1WqxXr16YkxMTKnj5eXliSqVSjoyMzNFACKgEp++bu7fcxAREVHpVCqVCEBUqVQVaseZCqoxmjRpAk9PT0RGRgJ4uswoJSUFISEhZbbp2bMnDAwMsHfvXgDAnj17YGpqim7dugEATp06hStXrsDU1BQKhQIKhQIWFhbIy8vD1atXKxXnw4cPMX36dLi6uqJ27dpQKBS4ePGizjMVJ06cQK9evVC/fn2Ympqie/fuUKvV+P333zXqtWzZsty+Tp06hezsbJibm0v3bWpqihs3bpR53xEREVAqldLh4OCg0/0QERHRy4MbtalGGTFiBMaNG4e1a9ciKioKDRo0QOfOncusb2hoiAEDBmD79u0ICAjA9u3bMWjQIBgYPP2roVar8cYbb5RYIgVA2stQUdOmTcP+/fuxfPlyODs7Qy6XY8CAAZVeTgUAKpUKPj4+8Pf3x/bt22FtbY1ffvkFvXv3LtGviYlJuf2p1Wo4Ojpi//79Ja6Zm5uX2mbWrFmYPHmydJ6Tk8PEgoiI6BXBpIJqlIEDB2LixInYvn07YmJiMHLkSAiC8Mw2gYGB6NatG86fP48ffvgBCxculK55eHggNjYWNjY2MDMzq5IYU1JSEBwcjL59+wJ4usdCl43fAHDu3DlkZ2dj6dKlUrKTkpKiVVtDQ8MST4Hy8PDAokWLIJfLYW9vr1U/MpkMMpmsYoETERFRjcDlT1SjKBQKDBo0CLNnz8aNGzcQHBxcbpsOHTrA1tYWgYGBcHR01HhnQ2BgIKysrODv74+UlBRkZGQgOTkZEydOLLGs6J8uXbqEtLQ0jaOgoADOzs6Ii4tDWloafvrpJwwZMgRqtVqn+3Z0dISBgQFWr16NjIwMxMXFYfHixVq3vXz5Mn7++WfcuXMHBQUF8PX1hbu7O/z9/ZGYmIiMjAwcPnwYs2bNwtmzZ3WKlYiIiGoeJhVU44wYMQLZ2dno0qUL6tevX259QRAwePBg/PTTTyWeEmVsbIxDhw6hfv366NevH5o2bYqQkBA8fvy43JmLgIAAtGjRQuO4ceMGVq5cCXNzc3h6esLPzw8+Pj7SOzQqy97eHps3b8Znn32Gpk2bYtWqVVi+fLlWbQcNGgRvb2+0b98e1tbWiIuLg76+Pr777ju0atUKQ4cORZMmTTBkyBDcuHGj0su+iIiIqOYSRPFvz98kIqoiOTk5f725XAWgapaOVRX+q0dERFS64p/fKpWqQku/OVNBREREREQ6YVJBREREREQ6YVJBREREREQ64SNliei5UqmAKnoaLxEREf1LcaaCiIiIiIh0wqSCiIiIiIh0wqSCiIiIiIh0wqSCiIiIiIh0wo3aRPRcKZXVHUHl8AV5RERE2uNMBRERERER6YRJBRERERER6YRJBRERERER6YRJBRERERER6YRJBdFz1qZNG8ycObO6wyAiIiJ6bphU0CtFEIRnHsHBwdUdotYuXrwIQRBw8eLFKu3TyMioyvojIiKiVwMfKUuvlJs3b0pfx8bGYu7cubh06ZJUJpfLS21XWFiIWrVqPff4SlNUVARBEKCnx98BEBER0b8TP6XQK8XOzk46lEolBEEoUVY8AxAXF4f27dtDJpNh9+7duHXrFgYOHAh7e3sYGxvj9ddfx549ezT6f/DgAYYMGQITExPY29tjzZo1JWLIy8vD5MmTUbduXSgUCnh6euLw4cPS9Q0bNsDOzg5ffPEFmjRpAplMhlu3bml1f4mJiXjjjTcgk8lQt25dhIWFoaioSLquVqvx4YcfwtHREcbGxmjRogW++uqrMvu7evUqfH19Ubt2bZiYmMDNzQ2JiYlaxUJERESvDs5UEJVhxowZWL58Odzc3CCXy/H48WN4enpi9uzZMDU1xVdffYVBgwbh5MmTcHd3BwBMnDgRR48exddffw0rKytMmzYN58+fh7e3t9RvYGAg7ty5g927d8PW1haxsbHo2rUrLly4AEdHRwDA/fv38dFHHyE6Ohq1a9eGubl5ufFeu3YNvXr1wpgxY7Bt2zacP38eI0eOhImJibSnY9q0afjuu++wefNmODk54eDBgxg4cCCSk5PRpk2bEn2OGjUKRkZGSE1NhVwux/nz58uczcnPz0d+fr50npOTo+23moiIiF52ItErKioqSlQqlSXK09PTRQDihg0byu2jU6dO4pw5c0RRFMW7d++K+vr64pdffildv3XrlmhoaCjOmDFDFEVRPH/+vKivry/evn1bo5927dqJ8+fPF0VRFNevXy8CEC9evPjMsYvjTE9PF0VRFCdPniy6ublp1FmxYoVoYWEhiqIoZmdni7Vq1RJPnz6tUScwMFAcPnx4qWM0atRIXLx48TPjKDZv3jwRQCmHSnz6fuqX6yAiInoVqVQqEYCoUqkq1I4zFURlaNmypcb5kydPsGjRIuzatQt//PEHCgoKkJ+fD3t7ewDA5cuXUVRUhLZt20ptbGxs4OTkJJ2fOnUKarVampEolp+fj4YNG0rnCoUCjRs3rlC86enp8PT01Chr164d7t27h6ysLFy6dAmFhYVo3769Rp2CggKNmP9u0qRJmDhxIuLj49GlSxcMGDAAzZo1K7XurFmzMHnyZOk8JycHDg4OFboHIiIiejkxqSAqg4mJicb5okWLsHbtWqxatQqurq4wMTHBmDFjUFBQAAAQRbHcPtVqNQwNDZGWllbimqmpaZlja0MURQiCUKIMePrUK7VaDeDpvgsrKyuNemU98Sk0NBQ9e/ZEQkIC9u/fjw8//BCffPIJ3n333RJ1ZTIZZDJZheMmIiKilx+TCiItpaSkYMCAARg8eDCApzMXly9fhqWlJQDAxcUF+vr6OHbsGHr37g0AyMrKwq+//ir14eHhgfz8fGRnZ+PNN9+s0vhcXV1LbKI+cuQILC0tYW1tjebNm8PAwACZmZml7p8oS4MGDRAaGorQ0FC899572Lx5c6lJBREREb26mFQQacnZ2Rn79u3D8ePHYWpqiiVLliA7O1u6bmFhgaFDh+K9996DmZkZLCwsMHPmTBgaGkp1mjdvjv79+2Pw4MFYsWIF3NzccPv2bRw4cACtWrVC165dKx3f+PHjsXbtWkyePBmjRo3CuXPn8MEHH2D69OlSfBMnTsS4ceOQn5+Ptm3bQqVSISUlBdbW1hgyZEiJPseNGwd/f384Ozvj7t27SE4KQgbuAAAgAElEQVRORvPmzSsdIxEREdVMTCqItLRgwQJkZmaic+fOMDU1RWhoKHr06KFR5+OPP8a7774LX19fKJVKzJw5E/fu3dOos23bNsyfPx8TJ07EjRs3YG1tjbZt26Jfv34Viqd4OZOBwdO/xo6OjoiPj8eMGTOwdu1aWFpaYuzYsZg2bZrUZunSpahTpw4WLlyIjIwMmJub44033sD7779f6hiFhYUYNWoUbty4AaVSCV9fX6xcubJCcRIREVHNJ4jaLAQnon+dpKQkdOzYEQ8ePIBCoajucErIycmBUqkEoAJgVt3hVBj/ZSQioldR8c9vlUoFMzPtf35zpoLoJVNYWIhr165hxYoVaN269b8yoSAiIqJXC9+oTfSSOXXqFF5//XXcv38fkZGR1R0OEREREWcqiF42bdq0waNHj6o7DCIiIiIJkwoieq5UKqACSzKJiIjoJcTlT0REREREpBMmFUREREREpBMmFUREREREpBMmFUREREREpBNu1Cai50qprO4IiKiq8KWQRFQWzlQQEREREZFOmFQQEREREZFOmFQQEREREZFOmFTQK+3atWsQBAFpaWnVHUq1Cw8Ph7u7e3WHQURERC8hJhVUYwmC8MwjODj4uY0dHR0NQRDQtGnTEtc+//xzCIIAR0fH5zZ+ZUydOhUHDx6s7jCIiIjoJcSnP1GNdfPmTenr2NhYzJ07F5cuXZLK5HI5srOzn9v4JiYmyMrKwtGjR9G2bVupPDIyEvXr139u41aWQqGAQqGo7jCIiIjoJcSZCqqx7OzspEOpVEIQhBJlxX799Vd07NgRxsbGeP3113H06FGNvo4cOQIvLy/I5XI4ODhgwoQJePjw4TPHNzAwwJAhQxAZGSmV/f7770hKSsKQIUM06l69ehX+/v6wtbWFQqHAm2++icTERI0669atQ6NGjWBkZARbW1sMGDBAupafn48JEybAxsYGRkZGeOutt3DixAnpelJSEgRBwMGDB9GyZUsYGxvD09NTI8kqbflTZGQkmjVrBplMhjp16mDcuHHPvGciIiJ6NTGpIAIwZ84cTJ06FWlpaXBxccHgwYPx5MkTAMDPP/8MHx8f9OvXD2fPnkVsbCxSU1O1+oA9YsQIxMbG4tGjRwCeLovq3r07bG1tNerl5ubC19cXiYmJOHPmDHx8fODn54fr168DAE6ePIkJEyZgwYIFuHTpEvbt2wcvLy+p/fTp07Fnzx7ExMTg9OnTcHZ2ho+PD+7du1fiPlesWIGTJ0/CwMAAISEhZca+fv16jB07Fu+++y5+/vln7N27F87OzmXWz8/PR05OjsZBRERErwiR6BUQFRUlKpXKEuUZGRkiAHHz5s1S2fnz50UAYnp6uiiKojh06FDx3Xff1WiXkpIi6unpiY8fPy53PHd3dzEmJkZUq9Viw4YNxa+++kpcuXKl2KBBg2fG7OrqKq5Zs0YURVHcs2ePaGZmJubk5JSol5ubK9aqVUvctm2bVFZQUCDWrVtXXLp0qSiKovjDDz+IAMTExESpTkJCgghAuod58+aJr7/+unS9bt264pw5c54Z49/NmzdPBFDKoRKfvjKLBw8eL/tBRDWfSqUSAYgqlapC7ThTQQTAzc1N+rpOnToAgKysLADAqVOnEB0dLe05UCgU8PHxgVqtRkZGRrl9h4SEICoqCsnJydKMxD89fPgQ06dPh6urK2rXrg2FQoGLFy9KMxVdu3ZFgwYN4OTkhKFDh2Lbtm3S7MfVq1dRWFiIdu3aSf3VqlULrVq1Qnp6utb3+XdZWVm4ceMGOnfuXO79FZs1axZUKpV0ZGZmat2WiIiIXm7cqE2Epx/CiwmCAABQq9XSf0eNGoUJEyaUaKfNhuvAwEBMnz4d4eHhGDZsGAwMSv61mzZtGvbv34/ly5fD2dkZcrkcAwYMQEFBAQDA1NQUp0+fRlJSEr777jvMnTsX4eHhOHHiBERR1Ii7mCiKJcqedZ9/J5fLy72vf5LJZJDJZBVuR0RERC8/zlQQlcPDwwPnz5+Hs7NzicPQ0LDc9hYWFujduzeSk5PL3MOQkpKC4OBg9O3bF82bN4ednR2uXbumUcfAwABdunTB0qVLcfbsWVy7dg3ff/+9FEdqaqpUt7CwECdPniz1kbbaMDU1haOjIx8xS0RERFrhTAVROWbMmIE2bdpg7NixGDlyJExMTJCeno4DBw5gzZo1WvURHR2NdevWwdLSstTrzs7OiIuLg5+fHwRBQFhYmMYMQnx8PH799Vd4eXnB3Nwc33zzDdRqNRo3bgwTExOMGTMG06ZNg4WFBerXr4+lS5fi0aNHGDFiRKXvOzw8HKNHj4aNjQ169OiBBw8e4PDhwxg/fnyl+yQiIqKaiUkFUTnc3NyQnJyMOXPmoH379hBFEQ0bNsSgQYO07kMulz9zSdHKlSsREhICT09PWFlZYcaMGRpPT6pduzbi4uIQHh6OvLw8NGrUCDt27ECzZs0AAIsXL4ZarcbQoUPx4MEDtGzZEvv374e5uXml7zsoKAh5eXlYuXIlpk6dCisrK43H2BIREREVE8TiBdlERFUoJyfnr3eBqACYVXc4RFQF+ImBqOYr/vmtUqlgZqb9z2/uqSAiIiIiIp0wqSAiIiIiIp0wqSAiIiIiIp1wozYRPVcqFVCBJZlERET0EuJMBRERERER6YRJBRERERER6YRJBRERERER6YRJBRERERER6YQbtYnouVIqqzuCVw9fUEZERC8aZyqIiIiIiEgnTCqIiIiIiEgnTCqIiIiIiEgnTCrolXft2jUIgoC0tLQXOu7777+Pli1bSufvvPMOBgwYIJ2/9dZbmDp16guNiYiIiKgymFRQjSYIwjOP4ODg5zZ2dHR0qWNu3rwZADBz5kzs37//uY1PRERE9KLw6U9Uo928eVP6OjY2FnPnzsWlS5ekMrlcjuzs7Oc2vpmZmcZ4AKD863FICoUCCoXiuY1dWQUFBTA0NKzuMIiIiOglwpkKqtHs7OykQ6lUQhCEEmXFfv31V3Ts2BHGxsZ4/fXXcfToUY2+jhw5Ai8vL8jlcjg4OGDChAl4+PDhM8f/53h2dnaQy+UASi5/Kk+9evUQERGBwMBAKBQKODo6Ij4+Hrdu3YKfnx9MTEzg5uaG06dPa7TbtWsXXF1dYWhoCEdHR6xcubJEv4sWLcKwYcNgZmaGMWPGAAAyMzMxcOBA1K5dG5aWlujTpw+uX7+udbxERET06mBSQfSXOXPmYOrUqUhLS4OLiwsGDx6MJ0+eAAB+/vln+Pj4oF+/fjh79ixiY2ORmpqKcePGvdAYV6xYAW9vb5w5cwY+Pj545513EBwcjODgYJw5cwaOjo4ICgqS6v/4448ICAhAYGAgzp07h7lz52L27NnYunWrRr9Lly5FixYtcObMGcyePRu5ubnw9vZG7dq1kZKSgpSUFBgZGaFHjx7S94SIiIioGJc/Ef1l6tSp6NmzJwBg/vz5aNasGa5cuYImTZpg2bJlGDJkCCZNmgQAaNSoEVavXo0OHTpg/fr1MDIyKrVPlUqlscRJoVDgzz//rHSMfn5+GDlyJAAgLCwMmzZtQtu2bdG/f38AwPTp09G+fXvcuXMHVlZWWLFiBXx8fDBnzhwAgIuLC86dO4dly5bhnXfekfrt2rUr3nvvPel806ZNkMvl2LRpk1QWExMDpVKJQ4cOoVOnTiViy8/PR35+vnSek5NT6fskIiKilwtnKoj+4ubmJn1dp04dAEBWVhYA4NSpU4iOjpb2QSgUCvj4+ECtViMjI6PMPk1NTZGWliYdR44cqbIYbW1tAQDNmzcvUVYcd3p6Otq1a6fRR7t27XDp0iWo1Wqp7J/LsE6dOoWLFy9q3K+lpSUKCgpw9erVUmOLiIiAUqmUDgcHBx3ulIiIiF4mnKkg+kutWrWkrwVBAADpg7darcaoUaMwYcKEEu3q169fZp96enpwdnZ+rjE+K25RFKWyYqIoatQFABMTE406arUarVu3RkxMTIkYrK2tS41t1qxZmDx5snSek5PDxIKIiOgVwaSCSAseHh44f/58lSYIL4KrqytSU1M1yo4cOYImTZqUSDb+zsPDA19++SVsbW1hamqq1VgymQwymUyneImIiOjlxOVPRFqYMWMGjh49irFjxyItLQ2XL1/G3r17MX78+OoO7ZmmTJmC/fv3Y9GiRfjll18QFRWF9evXl/tSvaFDh0KpVKJPnz5ITU1FRkYGkpKSMH78eI3H9BIREREBTCqItOLm5obk5GRcvnwZ7du3R4sWLRAWFibtvfi3atWqFXbu3ImtW7fitddew/z587Fo0SKNTdqlUSgUOHToEOzt7dG3b180bdoU//3vf1FYWPivfLcGERERVS9BLF5gTURUhXJycv56D4gKgFl1h/NK4b/qRERUWcU/v1UqFczMtP/5zZkKIiIiIiLSCZMKIiIiIiLSCZMKIiIiIiLSCR8pS0TPlUoFVGBJJhEREb2EOFNBREREREQ6YVJBREREREQ6YVJBREREREQ6YVJBREREREQ64UZtInqulMoXNxZf+kZERFQ9OFNBREREREQ6YVJBREREREQ6YVJBREREREQ6YVJBREREREQ6YVJBRAgPD4e7u3t1h0FEREQvKSYVRNUsKysLo0aNQv369SGTyWBnZwcfHx8cPXq0ukMjIiIi0gofKUtUzfr374/CwkLExMTAyckJt27dwsGDB3Hv3r3qDo2IiIhIK5ypIKpG9+/fR2pqKpYsWYKOHTuiQYMGaNWqFWbNmoWePXsCAK5fvw5/f38oFAqYmZlh4MCBuHXrltRHcHAw+vTpo9HvpEmT4O3tDQDYuHEj7O3toVarNer07t0bQUFBpcZ14sQJdO3aFVZWVlAqlejQoQNOnz5dhXdORERENQmTCqJqpFAooFAo8OWXXyI/P7/EdVEU0adPH9y7dw/Jyck4cOAArl69ikGDBmk9xttvv407d+7ghx9+kMqys7Oxf/9+BAYGltrmwYMHCAoKQkpKCo4dO4ZGjRrB19cXDx48KHOc/Px85OTkaBxERET0amBSQVSNDAwMEB0djZiYGNSuXRvt2rXD7NmzcfbsWQBAYmIizp49i+3bt+ONN95A69atsWXLFiQnJ+PEiRNajWFhYYHu3btj+/btUtmuXbtgYWGBzp07l9qmU6dOeOedd9C0aVM0bdoUGzduxKNHj5CcnFzmOBEREVAqldLh4OBQge8EERERvcyYVBBVs/79++PGjRvYu3cvfHx8kJSUBA8PD0RHRyM9PR0ODg4aH9BdXV1Ru3ZtpKenaz1GYGAg9uzZI82GbNu2DQEBAdDX1y+1flZWFkaPHg0XFxcpScjNzcX169fLHGPWrFlQqVTSkZmZqXV8RERE9HJjUkH0L2BkZISuXbti7ty5OHLkCIKDgzFv3jyIoghBEErU/3u5np4eRFHUuF5YWKhx7ufnB7VajYSEBGRmZiIlJQXvvPNOmfEEBwfj1KlTWLVqFY4cOYK0tDRYWlqioKCgzDYymQxmZmYaBxEREb0amFQQ/Qu5urri4cOHcHV1xfXr1zV+63/hwgWoVCo0bdoUAGBtbY2bN29qtE9LS9M4l8vl6NevH7Zt24YdO3bAxcUFb7zxRpnjp6SkYMKECfD19UWzZs0gk8lw586dKrxDIiIiqkmYVBBVo7t376JTp07YunUrzp49i4yMDOzatQtLly6Fv78/unTpAjc3NwQGBuL06dP48ccfMWzYMHTo0AEtW7YE8HT/w8mTJ/HZZ5/h8uXLmDdvHs6dO1dirMDAQCQkJCAyMvKZsxQA4OzsjC1btiA9PR3Hjx9HYGAg5HL5c/keEBER0cuPSQVRNVIoFGjdujVWrlwJLy8vvPbaawgLC8PIkSPxySefQBAEfPnllzA3N4eXlxe6dOkCJycnxMbGSn34+PggLCwM06dPx5tvvokHDx5g2LBhJcbq1KkTLCwscOnSJQwZMuSZcUVGRiI7OxstWrTA0KFDMWHCBNjY2FT5/RMREVHNIIj/XIxNRFQFcnJyoFQqAagAvJj9FfzXjIiISDfFP79VKlWF9kdypoKIiIiIiHTCpIKIiIiIiHTCpIKIiIiIiHRiUN0BEFHNplIBfGUFERFRzcaZCiIiIiIi0gmTCiIiIiIi0gmTCiIiIiIi0gmTCiIiIiIi0gk3ahPRc6VUVncERERElcOXqmqPMxVERERERKQTJhVERERERKQTJhVERERERKQTJhX0yomOjkbt2rXLPA8PD4e7u3t1hPZcCIKAL7/8srrDICIiohqMSQXVGMHBwRAEAYsXL9Yo//LLLyEIgnQ+aNAg/PLLL2WeT506FQcPHnyusSYlJUEQBJibmyMvL0/j2o8//ghBEDRirk7/TLqIiIiI/olJBdUoRkZGWLJkCbKzs8usI5fLYWNjU+a5QqGApaXlc42zmKmpKb744guNssjISNSvX1/nvgsKCnTug4iIiEgbTCqoRunSpQvs7OwQERFRZp2KLH86dOgQatWqhT///FOjjylTpsDLywsAcPfuXQwePBj16tWDsbExmjdvjh07dmgVb1BQECIjI6Xzx48fY+fOnQgKCtKop80Y3t7eGDduHCZPngwrKyt07dq11DEXLFgAW1tbpKWlAXiafEyfPh329vYwMTFB69atkZSUBODpjMrw4cOhUqmk2ZPw8HCt7o2IiIheHUwqqEbR19fHokWLsGbNGvz+++869+fl5QUnJyds2bJFKnvy5Am2bt2K4cOHAwDy8vLwxhtvID4+HufOncO7776LoUOH4vjx4+X2P3ToUKSkpOD69esAgD179sDR0REeHh4a9bQdIyYmBgYGBjh8+DA2btyocU0URUycOBGffvopUlNTpcRp+PDhOHz4MHbu3ImzZ8/i7bffRvfu3XH58mV4enpi1apVMDMzw82bN3Hz5k1MnTq11HvJz89HTk6OxkFERESvBiYVVOP07dsX7u7umDdvXpX0N2LECERFRUnnCQkJePToEQYOHAgAsLe3x9SpU+Hu7g4nJyeMHz8ePj4+2LVrV7l929jYoEePHoiOjgbwdOlTSEhIiXrajuHs7IylS5eicePGaNKkiVT+5MkTDBs2DN999x0OHz6MRo0aAQCuXr2KHTt2YNeuXWjfvj0aNmyIqVOn4q233kJUVBQMDQ2hVCohCALs7OxgZ2cHhUJR6r1ERERAqVRKh4ODQ7n3T0RERDUDkwqqkZYsWYKYmBhcuHBB576Cg4Nx5coVHDt2DMDTD/4DBw6EiYkJAKCoqAgffvgh3NzcYGlpCYVCge+++06afShPSEgIoqOj8euvv+Lo0aMIDAwsUUfbMVq2bFnqGO+99x6OHj2KlJQU1KtXTyo/ffo0RFGEi4sLFAqFdCQnJ+Pq1ataxV9s1qxZUKlU0pGZmVmh9kRERPTyMqjuAIieBy8vL/j4+GD27NkIDg7WqS8bGxv4+fkhKioKTk5O+Oabb6Q9BwCwYsUKrFy5EqtWrULz5s1hYmKCSZMmab1R2tfXF6NGjcKIESPg5+dX6iZxbccoTnT+qWvXrtixYwf279+vkbSo1Wro6+vj1KlT0NfX12hT1oxEWWQyGWQyWYXaEBERUc3ApIJqrMWLF8Pd3R0uLi469/Xf//4XAQEBqFevHho2bIh27dpJ11JSUuDv74933nkHwNMP6pcvX0bTpk216ltfXx9Dhw7F0qVL8e2335ZaR9cxevfuDT8/PwwZMgT6+voICAgAALRo0QJFRUXIyspC+/btS21raGiIoqIircYhIiKiVxOXP1GN1bx5cwQGBmLNmjU69+Xj4wOlUokPPvhA2qBdzNnZGQcOHMCRI0eQnp6OUaNGlXhaVHkWLlyI27dvw8fHp9TrVTFG3759sWXLFgwfPhy7d+8GALi4uCAwMBDDhg1DXFwcMjIycOLECSxZsgTffPMNAMDR0RG5ubk4ePAg7ty5g0ePHlVoXCIiIqr5mFRQjbZw4UKIoqhzP3p6eggODkZRURGGDRumcS0sLAweHh7w8fGBt7c37Ozs0KdPnwr1b2hoCCsrqzJfeFcVYwDAgAEDEBMTg6FDhyIuLg4AEBUVhWHDhmHKlClo3LgxevfujePHj0sbrT09PTF69GgMGjQI1tbWWLp0aYXHJSIioppNEKviExfRS2zjxo1YuHBhuY+gHTlyJG7duoW9e/e+oMhebjk5OVAqlQBUAMyqOxwiIqIKexU/JRf//FapVDAz0/7nN/dU0CstMzMT33zzDZo1a1ZmHZVKhRMnTmDbtm346quvXmB0RERERC8HJhX0SvPw8IC9vb30nojS+Pv748cff8SoUaPKfEs1ERER0auMSQW90m7fvl1unb8/PpaIiIiISmJSQUTPlUoFVGBJJhEREb2E+PQnIiIiIiLSCZMKIiIiIiLSCZMKIiIiIiLSCZMKIiIiIiLSCTdqE9FzpVRWdwQlvYovMyIiInqeOFNBREREREQ6YVJBREREREQ6YVJBREREREQ6YVJBrzRvb29MmjRJOnd0dMSqVau0rl9R165dgyAISEtLA/D0bd2CIOD+/fuV7pOIiIioujGpoBolODgYgiBg9OjRJa6FhoZCEAQEBwdLZXFxcVi4cOELjPDFKf5eFB+Wlpbo3r07zp49W92hERERUQ3DpIJqHAcHB+zcuROPHz+WyvLy8rBjxw7Ur19fo66FhQVMTU1fdIhVqqCgoMxr3bt3x82bN3Hz5k0cPHgQBgYG6NWrl07jFRYW6tSeiIiIah4mFVTjeHh4oH79+oiLi5PK4uLi4ODggBYtWmjULW85U1RUFJRKJQ4cOFDq9X379kGpVOKzzz7TaNO0aVMYGRmhSZMmWLduXYXiP3LkCLy8vCCXy+Hg4IAJEybg4cOH0nVHR0d88MEHCA4OhlKpxMiRI8vsSyaTwc7ODnZ2dnB3d8eMGTOQmZmJ27dvS3VmzJgBFxcXGBsbw8nJCWFhYRqJQ3h4ONzd3REZGQknJyfIZDKIfCYrERER/Q2TCqqRhg8fjqioKOk8MjISISEhFepj+fLlmDp1Kvbv34+uXbuWuL5z504MHDgQn332GYYNGwYA+N///oc5c+bgww8/RHp6OhYtWoSwsDDExMRoNebPP/8MHx8f9OvXD2fPnkVsbCxSU1Mxbtw4jXrLli3Da6+9hlOnTiEsLEyrvnNzc7Ft2zY4OzvD0tJSKjc1NUV0dDQuXLiAjz/+GP/73/+wcuVKjbZXrlzB559/jj179kj7QYiIiIiK8eV3VCMNHToUs2bNkjZGHz58GDt37kRSUpJW7WfNmoWYmBgkJSWhefPmJa6vW7cOs2fPxldffYWOHTtK5QsXLsSKFSvQr18/AMB//vMfXLhwARs3bkRQUFC54y5btgxDhgyRZk8aNWqE1atXo0OHDli/fj2MjIwAAJ06dcLUqVPL7S8+Ph4KhQIA8PDhQ9SpUwfx8fHQ0/v/3ye8//770teOjo6YMmUKYmNjMX36dKm8oKAAW7ZsgbW1dZlj5efnIz8/XzrPyckpNz4iIiKqGZhUUI1kZWWFnj17IiYmBqIoomfPnrCystKq7YoVK/Dw4UOcPHkSTk5OJa7v2bMHt27dQmpqKlq1aiWV3759G5mZmRgxYoTGkqQnT55AqeVrpU+dOoUrV65g27ZtUpkoilCr1cjIyEDTpk0BAC1bttSqv44dO2L9+vUAgHv37mHdunXo0aMHfvzxRzRo0AAAsHv3bqxatQpXrlxBbm4unjx5AjMzM41+GjRo8MyEAgAiIiIwf/58reIiIiKimoXLn6jGCgkJQXR0NGJiYiq09Kl9+/YoKirC559/Xup1d3d3WFtbIyoqSmNvgVqtBvB0CVRaWpp0nDt3DseOHdNqbLVajVGjRmm0/+mnn3D58mU0bNhQqmdiYqJVfyYmJnB2doazszNatWqFTz/9FA8fPsT//vc/AMCxY8cQEBCAHj16ID4+HmfOnMGcOXNKbP7WZrxZs2ZBpVJJR2ZmplYxEhER0cuPMxVUY3Xv3l36cOzj46N1u1atWmH8+PHw8fGBvr4+pk2bpnG9YcOGWLFiBby9vaGvr49PPvkEAGBrawt7e3v8+uuvCAwMrFTMHh4eOH/+PJydnSvVvjyCIEBPT096Mtbhw4fRoEEDzJkzR6rz22+/VapvmUwGmUxWJXESERHRy4VJBdVY+vr6SE9Pl76uiLZt2+Lbb79F9+7dYWBggPfee0/juouLC3744Qd4e3vDwMBAemFeeHg4JkyYADMzM/To0QP5+fk4efIksrOzMXny5HLHnTFjBtq0aYOxY8di5MiRMDExQXp6Og4cOIA1a9ZU6B6Ap/sc/vzzTwBAdnY2PvnkE+Tm5sLPzw8A4OzsjOvXr2Pnzp148803kZCQgC+++KLC4xAREdGrjUkF1Wj/3BtQEe3atUNCQgJ8fX2hr6+PCRMmaFxv3Lgxvv/+e2nGYsWKFfjvf/8LY2NjLFu2DNOnT4eJiQmaN2+u9Vu43dzckJycjDlz5qB9+/YQRRENGzbEoEGDKnUP+/btQ506dQA8fcpTkyZNsGvXLnh7ewMA/P398d5772HcuHHIz89Hz549ERYWhvDw8EqNR0RERK8mQeQD54noOcjJyflrg7oKQOWTu+eB/+oRERGVrvjnt0qlqtAvZ7lRm4iIiIiIdMKkgoj+j707j6s5+/8A/rptt24rIWlSUiKRCtnT2Nd8MRqSss3YGhNjG4wY+4yZobGN0TLGDGasE8JosTRjiUg1SkqGfG25WVKq8/vDt8/PdW+JxAyv5+NxHvqccz7nc87n3nTf93zO50NERERUKQwqiIiIiIioUrhQm4iqlFIJVGK9PBEREf0LcKaCiIiIiIgqhUEFERERERFVCoMKIiIiIiKqFAYVRERERERUKVyoTURVytT0dfeA6N+JD2kkoi8/ytMAACAASURBVH8TzlQQEREREVGlMKggIiIiIqJKYVBBRERERESVwqCCiIiIiIgqhUHFv1h4eDjMzMxedzfeKAEBAejXr1+F62dlZUEmkyExMfGFjxkbGwuZTIY7d+68cBtERERErxODiioQEBAAmUyGMWPGqJWNGzcOMpkMAQEBlT6Oj48P0tLSKt3O85DJZMjKyiqzvGPHjpDJZFi8eLFaWc+ePSGTyRAcHFx1Hayk5cuXIzw8/KW0VRpwlJde5blgEEpERERVhUFFFbG2tsamTZuQn58v5T18+BA///wz6tatW+n2Hz16BAMDA9SqVavSbb1s1tbWCAsLU8m7evUqoqOjYWlp+Zp6Vb7i4mKUlJTA1NT0pX3wtra2Rk5OjpQmT56Mxo0bq+R98sknL+VYL1NhYeHr7gIRERH9yzCoqCJubm6oW7cutm3bJuVt27YN1tbWcHV1VakbFRWFdu3awczMDObm5ujduzcyMjKk8tJvvLds2YKOHTtCX18fP/74o9o3z8HBwWjWrBk2bNgAW1tbmJqa4v3338fdu3elOkIILF26FHZ2djAwMICLiwt+/fVXqTw3Nxe+vr6oWbMmDAwM4ODgoBYgPEvv3r1x69YtHD16VMoLDw9H165d1YKgwsJCTJ06FVZWVjA0NISHhwdiY2NV9jMzM0NkZCQcHR2hUCgwcOBA3L9/HxEREbC1tUW1atUQGBiI4uJilXEMGzYM1apVg0KhQI8ePZCenq6xXScnJ8jlcly6dEnt8qdnvTbl0dbWRu3ataVkZGQEHR0dtbxSCQkJaN68ORQKBdq0aYPz58+rtPfbb7/B3d0d+vr6sLOzw9y5c1FUVCSVf/XVV2jSpAkMDQ1hbW2NcePG4d69ewAeX2I1fPhwKJVKtVkSW1tbzJ8/HwEBATA1NcXo0aMBAFeuXIGPjw+qVasGc3NzeHt7lztLRURERG8vBhVVaPjw4SofyENDQzFixAi1evfv38ekSZNw4sQJHDx4EFpaWvjPf/6DkpISlXrTpk3DRx99hNTUVHTr1k3jMTMyMrBjxw5ERkYiMjIScXFxKpcizZo1C2FhYVi9ejWSk5MRFBSEoUOHIi4uDgAwe/ZspKSkYO/evUhNTcXq1atRo0aN5xq3np4efH19VcYeHh6ucezDhw/H0aNHsWnTJpw9exbvvfceunfvrhIAPHjwACtWrMCmTZsQFRWF2NhY9O/fH3v27MGePXuwYcMGfPfddyrBUUBAAE6ePIldu3bhjz/+gBACPXv2xKNHj1TaXbRoEb7//nskJydrnPWp6GvzMsycORPLli3DyZMnoaOjo3K+9u3bh6FDh+Kjjz5CSkoK1q5di/DwcCxYsECqo6WlhRUrVuDcuXOIiIhAdHQ0pk6dCgBo06YNvvnmG5iYmGicJfniiy/g7OyMhIQEzJ49Gw8ePICXlxeMjIxw6NAhHDlyBEZGRujevXuZMxkFBQXIy8tTSURERPSWEPTS+fv7C29vb3Hjxg0hl8tFZmamyMrKEvr6+uLGjRvC29tb+Pv7l7n/9evXBQCRlJQkhBAiMzNTABDffPONSr2wsDBhamoqbc+ZM0coFAqRl5cn5U2ZMkV4eHgIIYS4d++e0NfXF/Hx8SrtjBw5UgwePFgIIUSfPn3E8OHDX3jsnp6eYuLEieLMmTPC2NhY3Lt3T8TFxYlatWqJwsJC4eLiIubMmSOEEOLChQtCJpOJK1euqLTRqVMnMWPGDGmMAMSFCxek8g8//FAoFApx9+5dKa9bt27iww8/FEIIkZaWJgCIo0ePSuU3b94UBgYGYsuWLSrtJiYmqhy79LUrS1mvzenTp595bubMmSNcXFzU8mNiYgQA8fvvv0t5u3fvFgBEfn6+EEKI9u3bi4ULF6rst2HDBmFpaVnm8bZs2SLMzc2l7affL6VsbGxEv379VPLWr18vHB0dRUlJiZRXUFAgDAwMxL59+8ocHwANSSkePxuYiYnpeRIR0eugVCoFAKFUKp9rP53XEMe8NWrUqIFevXohIiICQgj06tVL47f+GRkZmD17Nv7880/cvHlT+hY8Ozsbzs7OUr3mzZs/85i2trYwNjaWti0tLXH9+nUAQEpKCh4+fIguXbqo7FNYWChdkjV27FgMGDAAp06dQteuXdGvXz+0adPmucfetGlTODg44Ndff0VMTAz8/Pygq6urUufUqVMQQqBBgwYq+QUFBTA3N5e2FQoF6tevL21bWFjA1tZW5dIhCwsLaZypqanQ0dGBh4eHVG5ubg5HR0ekpqZKeXp6emjatGm546joa/MyPNmX0rUn169fR926dZGQkIATJ06ozEwUFxfj4cOHePDgARQKBWJiYrBw4UKkpKQgLy8PRUVFePjwIe7fvw9DQ8Nyj/30eyshIQEXLlxQeS8Bj9cFlXX514wZMzBp0iRpOy8vD9bW1hUbPBEREf2rMaioYiNGjMCECRMAACtXrtRYp0+fPrC2tsa6detQp04dlJSUwNnZWe0yk2d9MASg9sFdJpNJH4RL/929ezesrKxU6snlcgBAjx49cOnSJezevRu///47OnXqhPHjx+PLL7+swGhVjRgxAitXrkRKSgqOHz+uVl5SUgJtbW0kJCRAW1tbpezJgEHTmMobpxBCY3+EEJDJZNK2gYGByrYmFX1tXoYnx1Tarydfu7lz56J///5q++nr6+PSpUvo2bMnxowZg88//xzVq1fHkSNHMHLkSJVLvsry9HurpKQE7u7u2Lhxo1rdmjVramxDLpdL7yMiIiJ6uzCoqGJPXoOuaR3ErVu3kJqairVr16J9+/YAgCNHjlRJX0oXJGdnZ8PT07PMejVr1kRAQAACAgLQvn17TJky5YWCiiFDhuCTTz6Bi4sLnJyc1MpdXV1RXFyM69evS2N/GZycnFBUVIRjx45Jsyy3bt1CWloaGjVqVOF2XuVr8yxubm44f/487O3tNZafPHkSRUVFWLZsGbS0Hi+V2rJli0odPT09lcXszzre5s2bUatWLZiYmFSu80RERPTGY1BRxbS1taVLbp7+Nh6AdGed7777DpaWlsjOzsb06dOrpC/Gxsb45JNPEBQUhJKSErRr1w55eXmIj4+HkZER/P398dlnn8Hd3R2NGzdGQUEBIiMjn+uD+JOqVauGnJwctVmFUg0aNICvry+GDRuGZcuWwdXVFTdv3kR0dDSaNGmCnj17vtBxHRwc4O3tjdGjR2Pt2rUwNjbG9OnTYWVlBW9v7+fq/6t6bZ7ls88+Q+/evWFtbY333nsPWlpaOHv2LJKSkjB//nzUr18fRUVFCAkJQZ8+fXD06FGsWbNGpQ1bW1vcu3cPBw8ehIuLCxQKBRQKhcbj+fr64osvvoC3tzfmzZuHd955B9nZ2di2bRumTJmCd95551UMm4iIiP4lePenV8DExKTMb3u1tLSwadMmJCQkwNnZGUFBQfjiiy+qrC+ff/45PvvsMyxatAiNGjVCt27d8Ntvv6FevXoAHn+bPWPGDDRt2hQdOnSAtrY2Nm3a9MLHMzMzK/eyrbCwMAwbNgyTJ0+Go6Mj+vbti2PHjlX6WvywsDC4u7ujd+/eaN26NYQQ2LNnT5kBjiav+rUpT7du3RAZGYkDBw6gRYsWaNWqFb766ivY2NgAAJo1a4avvvoKS5YsgbOzMzZu3IhFixaptNGmTRuMGTMGPj4+qFmzJpYuXVrm8RQKBQ4dOoS6deuif//+aNSoEUaMGIH8/HzOXBAREZEamSjrAnQiokrIy8uDqakpACUABiJEz4t/nYnodSj9+61UKp/ri0TOVBARERERUaUwqCAiIiIiokphUEFERERERJXCuz8RUZVSKgGu7SYiInqzcaaCiIiIiIgqhUEFERERERFVCoMKIiIiIiKqFAYVRERERERUKVyoTURVytS0/HI+4IuIiOjfjzMVRERERERUKQwqiIiIiIioUhhUEBERERFRpTCooH+12NhYyGQy3Llz53V35aV4+PAhZDIZoqKiXvmxa9eujTVr1rzy4xIREdG/H4MKeuUCAgIgk8kgk8mgq6sLCwsLdOnSBaGhoSgpKXnd3au0J8dXViqLvr4+cnJy8O67777CHhMRERFVDoMKei26d++OnJwcZGVlYe/evfDy8sLEiRPRu3dvFBUVve7uVcry5cuRk5MjJQAICwtTy3taYWEhgMczBnp6eq+sv0RERESVxaCCXgu5XI7atWvDysoKbm5u+PTTT7Fz507s3bsX4eHhAICsrCzIZDIkJiZK+925cwcymQyxsbEq7R09ehQuLi7Q19eHh4cHkpKSVMq3bt2Kxo0bQy6Xw9bWFsuWLVMpLygowNSpU2FtbQ25XA4HBwesX79eKk9JSUHPnj1hZGQECwsL+Pn54ebNmxrHZmpqitq1a0sJAMzMzNTyWrVqhUmTJiEwMBDm5ubo06ePxsufgoKC4ODgAAMDA9SvXx/z5s1TCbymT5+OVq1aITQ0FHXr1oWZmRn8/Pxw//59qY5SqYSPjw8UCgWsrKywcuVKtGrVCtOnTy/zNbp9+zZGjBiBGjVqwNTUFF26dEFycnKZ9YmIiOjtxaCC/jHeffdduLi4YNu2bc+975QpU/Dll1/ixIkTqFWrFvr27YtHjx4BABISEjBo0CC8//77SEpKQnBwMGbPni0FLwAwbNgwbNq0CStWrEBqairWrFkDIyMjAEBOTg48PT3RrFkznDx5ElFRUfjvf/+LQYMGVXrM69atg6mpKf744w+sWLFCYx0zMzP88MMPSE1NxbJly7BixQqsWrVKpU5KSgr279+PvXv3Yvv27YiKisJXX30llU+YMAEJCQnYu3cvoqKiEBUVhZSUlDL7VVxcjO7du0OpVGL//v04ceIEGjZsiE6dOiEvL0/jPgUFBcjLy1NJRERE9JYQRK+Yv7+/8Pb21ljm4+MjGjVqJIQQIjMzUwAQp0+flspzc3MFABETEyOEECImJkYAEJs2bZLq3Lp1SxgYGIjNmzcLIYQYMmSI6NKli8pxpkyZIpycnIQQQpw/f14AEAcOHNDYp9mzZ4uuXbuq5F2+fFkAEOfPn3/meAGI7du3q+V7eHiIVq1aqeTl5+cLAGLv3r1ltjdv3jzRtm1baXvatGnCxMRE3L9/X8oLDAwUnp6eQgghbt68KbS1tcVvv/0mld+4cUPI5XIxbdo0Kc/CwkKsXr1aCCHE7t27hbm5uSgsLJTKS0pKxDvvvCMiIiI09mvOnDkCgIakFI8fcac5ERER0T+HUqkUAIRSqXyu/ThTQf8oQohyFzKXpXXr1tLP1atXh6OjI1JTUwEAqampaNu2rUr9tm3bIj09HcXFxUhMTIS2tjY8PT01tp2QkICYmBgYGRlJqWHDhgCAjIyM5+7rk5o3b/7MOj///DPatGkDCwsLGBkZYcGCBcjOzlapY29vD4VCIW1bWlri+vXrAIALFy6guLgYLVu2lMpr1KgBOzu7Mo+ZkJCA3NxcVKtWTRqzsbExrl69WuaYZ8yYAaVSKaXLly8/c2xERET0ZtB53R0gelJqairq1asHANDSehzzCiGk8tJLmiqiNDjRFKg82aaBgUG57ZSUlKBPnz5YsmSJWpmlpWWF+6OJoaFhueVxcXEYNmwYFixYgE6dOsHExAQRERH4/vvvVerp6uqqbMtkMulOWqVjLe8cPK2kpAS2trbYt2+fWlm1atU07iOXyyGXy8sdDxEREb2ZOFNB/xjR0dFISkrCgAEDAAA1a9YEAJW7JT25aPtJf/75p/Rzbm4u0tLSpNkEJycnHDlyRKV+fHw8GjRoAG1tbTRp0gQlJSWIi4vT2LabmxuSk5Nha2sLe3t7lfSsoKCyjhw5ggYNGmDq1Klwd3eHg4MDsrKynqsNBwcHaGtr4/jx41LerVu3kJmZWeY+bm5u+Pvvv2FgYKA2ZnNz8xcdDhEREb2hGFTQa1FQUIBr167hypUrOHXqFBYuXAhvb2/07t0bw4YNA/B4BqFVq1ZYvHgxUlJScOjQIcyaNUtje/PmzcPBgwdx7tw5BAQEoEaNGujXrx8AYPLkyTh48CA+//xzpKWlISIiAt9++y0++eQTAICtrS38/f0xYsQI7NixA5mZmYiNjcWWLVsAAOPHj8ft27cxePBgHD9+HBcvXsT+/fsxYsQIFBcXV+l5sre3x4ULF7B161ZkZGRg2bJl2L1793O1YW5ujsGDByMoKAiHDh3CuXPnMGLECOjp6ZV5qVnPnj3RrFkzeHt74/fff0dmZiaOHj2KGTNm4OzZsy9jaERERPQGYVBBr0VUVBQsLS1ha2uL7t27IyYmBitWrMDOnTuhra0t1QsNDcWjR4/QvHlzTJw4EfPnz9fY3uLFizFx4kS4u7sjJycHu3btkp714Obmhi1btmDTpk1wdnbGZ599hnnz5iEgIEDaf/Xq1Rg4cCDGjRuHhg0bYvTo0dItWevUqYOjR4+iuLgY3bp1g7OzMyZOnAhTU1PpEq2qMmjQIIwdOxYffPABXF1dcfr0acyYMeO52wkJCUGzZs3QvXt3dO3aFV27dkW9evWgr6+vsb62tjb279+Pli1bws/PDw0bNsSQIUNw9epVaQaJiIiIqJRMlHdhNRG9kfLy8lCnTh2sXbsWvr6+VXYMU1NTAEoAJmXW4/9ARERE/xylf7+VSiVMTMr++/00LtQmegucOHECmZmZcHd3x+3btxEcHAx9fX307t37dXeNiIiI3gAMKojeAiUlJVi0aBHS09Mhl8vRokULHDp06H8zCURERESVw8ufiKhK8PInIiKif58XvfyJC7WJqEopleU9T/t1946IiIheBgYVRERERERUKQwqiIiIiIioUhhUEBERERFRpTCoICIiIiKiSuEtZYmoSvGuta8eF8ATEdGrxpkKIiIiIiKqFAYVRERERERUKQwqiIiIiIioUhhUEBERERFRpTCooOfyzjvv4Ntvv33d3SAiIiKif5DnDiquXbuGwMBA2NnZQS6Xw9raGn369MHBgwdfasc6duyIjz/++KW2+bxiYmLg5eWF6tWrQ6FQwMHBAf7+/igqKqrS4wYHB0Mmk6ml33//vUqPWxGnT5/GiBEjXtvxhRBYs2YNWrZsCSMjI5iZmaFFixZYsWIF8vPzX9pxvv/+e9SoUeOltafJgQMH0Lp1axgbG8PS0hIzZsxAcXHxM/eLiIiAo6Mj9PX1Ua9ePSxcuPCZ+9y9exe2traYOnWqSv7FixdhZGSE8PBwAMDvv/8uvd+0tLRgamoKNzc3TJ8+HdeuXXuhcRIREdGb77mCiqysLLi7uyM6OhpLly5FUlISoqKi4OXlhfHjx1dVHyvl0aNHL7RfcnIyevTogRYtWuDQoUNISkpCSEgIdHV1UVJS8pJ7qa5x48bIyclRSR06dNBYt7CwsMr7U6pmzZpQKBSv7HhPEkJg8ODBmDx5Mvr374/Y2FgkJiZi5syZ2Lp160sPbCviRc/9qVOn0KtXL/Tu3RunT5/Gzz//jG3btmHmzJnl7nfhwgUMHz4cAwcORGpqKjZt2gQbG5tnHs/Y2BhhYWH4+uuvER8fDwAoKSmBv78/vLy8EBAQoFI/IyMDV69exYkTJzBlyhRERUXB2dkZKSkpLzReIiIiesOJ59CjRw9hZWUl7t27p1aWm5sr/Xznzh0xevRoUbNmTWFsbCy8vLxEYmKiVD5nzhzh4uIifvjhB2FjYyNMTEyEj4+PyMvLE0II4e/vLwCopMzMTBEWFiZMTU1Vjrt9+3bx5DBK216/fr2oV6+ekMlkIjw8XFSvXl08fPhQZd/+/fsLPz8/jWP9+uuvha2tbbnn4+bNm+L9998XVlZWwsDAQDg7O4uffvpJpY6np6cIDAwUU6ZMEdWqVRMWFhZizpw55bZbOoay+Pr6igEDBojPP/9cWFpaivr16wshhAgPDxdubm7C0NBQWFhYCF9fX3H9+nVpvwMHDggAIjo6Wri6ugqFQiHatm0r0tLSVNrfvn27cHNzE3K5XNSoUUMMHDhQKrOyshIhISFCCCFKSkrErFmzhLW1tdDT0xN16tQRH3/8sVQ3Pz9fTJ48WVhZWQm5XC7s7e1FWFiYVB4dHS3c3d2Fnp6esLS0FJ9++qkoKioqc9wbN24UAERkZKRaWUlJibhz5460vW7dOuHo6Cjkcrlo2LChWLNmjVSWnp4uAIjt27eLDh06CAMDA+Hi4iL+/PNPlfP0ZPr888+l8S9YsED4+fkJY2NjMWLECCGEENnZ2eK9994Tpqamonr16sLb21tcunSpzLFMmTJFtGrVSiXvl19+EQqFQuPvV6mMjAyhpaUlMjIyyqxTnsDAQOHg4CDu378vvvjiC1G9enVx9epVqbx07Hfv3lXZ7969e8LBwUF4enpW+FhKpfJ/508pHj85gelVJSIiohdV+vdbqVQ+134Vnqm4ffs2oqKiMH78eBgaGqqVm5mZlQYp6NWrF65du4Y9e/YgISEBbm5u6NSpE27fvi3Vz8jIwI4dOxAZGYnIyEjExcVh8eLFAIDly5ejdevWGD16tPQtvbW1dYUDpQsXLmDLli3YunUrEhMTMWjQIBQXF2PXrl1SnZs3byIyMhLDhw/X2Ebt2rWRk5ODQ4cOlXmchw8fwt3dHZGRkTh37hw++OAD+Pn54dixYyr1IiIiYGhoiGPHjmHp0qWYN28eDhw4UOHxaLJv3z5kZGTgwIED2LlzJ4DHszILFizA2bNnsX37dqSnp2PkyJFq+86cORPLly/H8ePHIYTAqFGjpLJdu3Zh4MCB6Nu3L06fPo0DBw7A3d1dYx82b96MkJAQrFu3Dunp6di2bRucnZ2lcl9fX/zyyy9YuXIlUlJSsGrVKmmW4/Lly+jZsyfatGmDM2fO4Ntvv8WaNWuwaNGiMse8ceNGODk5oVevXmplMpkMpv97ytrq1asRHByMRYsWITU1FfPnz8f06dOxceNGtfMwffp0JCYmws7ODkOGDEFxcTE6dOiAZcuWoXr16tL7LygoSNpv6dKlcHV1xenTp/Hpp5/i3r176NixI8zMzHD48GEcPnwY+vr66NGjR5mXyhUUFEBfX18lz8DAAA8ePMDp06fLPAfW1tZwdXXF+PHjUVBQUGa9spT+jg0dOhSzZ8/GypUrYWlp+cz9DA0N8cEHH+DQoUO4deuWxjoFBQXIy8tTSURERPSWqGj0cezYMQFAbNu2rdx6Bw8eFCYmJmqzAvXr1xdr164VQjz+Jl6hUEgzE0I8/ubWw8ND2vb09BQTJ05UaaOiMxW6uroq39ALIcTYsWNFjx49pO1vvvlG2NnZiZKSEo3jKCoqEgEBAQKAqF27tujXr58ICQl5ZtTWs2dPMXnyZJVxtGvXTqVOixYtxLRp08psY86cOUJLS0sYGhpKqUWLFlK5r6+vqFOnjigsLCy3L/Hx8QKAePDggRDi/7+Fjo2Nlers3LlTyGQyUVBQIPXN39+/zDafnKlYsmSJaNSokXj06JFaveTkZAFAxMTEaGxn6tSpwsnJSeX8L1++XO31fZKDg4Po379/meWl6tSpI7Zs2aKSN2fOHNG+fXshxP/PVISHh0vlZ86cEQBEenq6EOLxTIe5ubla21ZWViozN0IIsXbtWtG4cWOVvIcPHwq5XC4OHjyosY+7d+8WWlpaYvPmzaKoqEhcvnxZtG7dWgBQ6/uThg4dKlq1aiVGjRol3n33XZXfoW7duqnMFJUlMjJSABB9+vRRKytrpkIIIX777TcBQCQkJGhsd86cOWozPOBMBWcqiIjoX6XKZyqEEAAefyNcnoSEBNy7dw/m5uYwMjKSUmZmJjIyMqR6tra2MDY2lrYtLS1x/fr1inanXDY2NqhZs6ZK3ujRo7F//35cuXIFABAWFoaAgIAyx6OtrY2wsDD8/fffWLp0KerUqYMFCxZIax0AoLi4GAsWLEDTpk2l8e7fvx/Z2dkqbTVt2lRluyJjdXR0RGJiopS2bt2q1qaurq5KXkJCAvr27Yu6devC2NgYnTt3BvB4VqCs/lhaWkIIgZs3bwIAEhMT0alTp3L7VsrHxwd5eXmws7PDBx98gB07dkgLjRMTE6Grq4v27dtr3Dc1NRVt2rRROf9t27aFUqnE1atXNe4jhHjm+y8nJwdXr16Fv7+/yvtv8eLFKu8/QP08AKjQe7B58+Yq2wkJCfjrr79Ujmdubo7CwkK1Y5bq2bMnFi9ejFGjRkEul8PR0RF9+vQB8Pi9p8nZs2fx448/IiIiAmvXroWlpSW8vLxw48YNCCGQkpKCdu3aPbP/oaGhUCgUOHPmDO7evfvM+qWe9X/AjBkzoFQqpfT0+46IiIjeXBUOKhwcHCCTyZCamlpuvZKSElhaWqp8IE5MTMT58+cxZcoUqd7TH4hlMtkzF0BraWlJH2xKaVqIrenyLFdXV7i4uOCHH37AqVOnkJSUpLY4VRMrKyv4+flJl/A8fPgQa9asAQAsW7YMX3/9NaZOnYro6GgkJiaiW7duaot3X2Ssenp6sLe3l9LTl389Pca7d++iW7duMDMzw8aNG3Hy5En88ssvANQXEz/Zn9IPiKX9eZ5F2DY2NkhPT0dISAjkcjnGjBmDjh07oqioCAYGBuXuqylAeNaH1gYNGlTo/Qc8DhqffP+dO3cOR44cUalb3nkoz9PnvqSkBB4eHmrv+bS0NAwaNKjMdqZMmQKlUons7GzcunULvXv3BgDUq1dPY/2zZ89CoVCgQYMG0NLSQnh4OKytrdGuXTusXbsWBQUFGi8Ne9LGjRuxd+9exMfHQ6FQYPLkyc8cb6nU1FTIZLIyF4bL5XKYmJioJCIiIno7VDioqF69Orp164aVK1fi/v37auV37twBALi5ueHatWvQ0dFR+VBsb2//XLfo1NPTU7u9Zs2aNXH37l2V4ycmJla4zVGjRiEsLAyhoaHo3Lnz9uTsqgAAIABJREFUc63TAIBq1arB0tJSOv7hw4fh7e2NoUOHwsXFBXZ2dkhPT3+uNl+WlJQU3Lp1C0uWLEH79u3h6Oj4QjM/TZo0ea67KBkYGMDb2xshISE4ePAgjhw5gpSUFDRp0gSPHj3C4cOHNe7n5OSEo0ePqgSJ8fHxMDMzK/Ma/yFDhiAlJQW7d+9WKxNCQKlUok6dOrCwsMDFixfV3n9lfVjXRNP7ryxubm5IS0uDhYWF2jFL13mURSaToU6dOtDX18fPP/8MW1tbuLi4aKxrZWWFBw8eICEhAQCgo6ODTZs2oW7duhg7dixmzZqltk7jSTk5OQgMDMTChQvh4uKC8PBwhIaGVmh9z/3797Fu3TrpFstERERET3quW8quWrUKxcXFaNmyJbZu3Yr09HSkpqZixYoVaN26NQCgc+fOaN26Nfr164d9+/YhKysL8fHxmDVrFk6ePFnhY9na2uLYsWPIysrCzZs3pW+DFQoFPv30U1y4cAE//fSTdH/9ivD19cWVK1ewbt26Zz5rYe3atRg7diz279+PjIwMJCcnY9q0aUhOTpYuU7G3t8eBAwcQHx+P1NRUfPjhh6/tXv42NjbQ1dXFihUrcPHiRezYsaNCzy942pw5c/Djjz9i3rx5+Ouvv5CUlISlS5dqrBsaGoqwsDAkJyfj4sWL+PHHH6FQKFC3bl3Y29tj6NChCAgIwM6dO5GZmYmYmBhp9mTChAm4ePEiPv74Y/z111/Yvn075s6dW+4350OGDMHAgQMxaNAgLF68GAkJCcjKysJvv/2Gd999F4cPH4ZMJkNwcDDmz5+PkJAQpKWl4ezZswgNDcU333xT4fNga2sLpVKJ2NhY3Lx5s9xnYPj5+cHU1BT9+vXDkSNHkJmZidjYWAQGBkqXyj2tpKQEy5Ytw7lz55CcnIy5c+fiyy+/xIoVK6ClpfnX0tPTEx4eHnjvvfewa9cuZGRkYM+ePcjKyoKhoSE2btxYbj9Hjx6NJk2aYOLEiQAADw8PTJ48GaNGjVK7DOr69eu4du0a0tPT8fPPP0uXpq1cufJZp46IiIjeRs+7eOPq1ati/PjxwsbGRujp6QkrKyvRt29flQW5eXl5IjAwUNSpU0fo6uoKa2tr4evrK7Kzs4UQmm+Z+vXXXwsbGxtp+/z586JVq1bCwMBAAI9vKSvE44XZ9vb2Ql9fX/Tu3Vt89913agu1y7sdq5+fn8bbyz7t1KlTYujQoaJevXpCLpcLc3Nz0aFDB7Fr1y6pzq1bt4S3t7cwMjIStWrVErNmzRLDhg0T3t7eUh1NC869vb3LXQxd0VvKPm3Dhg3CxsZGyOVy0bZtW7Fz504BQCQlJQkhNC/CPXHihAAgLl++LOX98ssvomnTpkJPT0/UrFlTDBo0SCp7cqH2r7/+Klq2bClMTEyEkZGRaN26tYiOjpbqPnjwQEycOFHUrl1b6OnpCQcHBxERESGVP3lL2dq1a4sZM2aUe0tZIYQoLi4WK1euFM2bNxcKhUKYmpqK5s2bi5CQEJGfny/V++GHH4SLi4vQ09MT1atXF56enmLHjh1CiP9fqF16XoQQ4saNGwKAOHz4sBDi8S1qR48eLczNzQWgekvZ0vE/6cqVK8LPz0/UqFFDyOVyUb9+ffHhhx+qLKR+ehyenp7CxMRE6Ovri9atW4t9+/aVO3YhHi+e+vjjj4Wtra2Qy+WicePGYsWKFeLy5cuiTp06YsCAARpvPrB+/XphaGiodivahw8fikaNGonRo0cLIVRvpyuTyYSxsbFwcXER06ZNE9euXXtm/57uK7hQmwu1iYjoX+VFF2rLhHhqkcIbrkuXLmjUqBFWrFjxurtC9EbLy8v73+VfSgBcX/EqvV3/qxMR0ctU+vdbqVQ+1/pInSrs0z/K7du3sX//fkRHR+Pbb7993d0hIiIiInpjvDVBhZubG3Jzc7FkyRI4Ojq+7u4QEREREb0x3pqgIisr63V3gYiIiIjojfTWBBVE9HoolQAfWUFERPRme65byhIRERERET2NQQUREREREVUKgwoiIiIiIqoUrqkgoiplavq6e0BERPRm+Sc+j4gzFUREREREVCkMKoiIiIiIqFIYVBARERERUaUwqCAiIiIiokphUEH0mgUHB6NZs2avuxtEREREL4xBBf2jXbt2DYGBgbCzs4NcLoe1tTX69OmDgwcPvpT2Y2NjIZPJcOfOnZfS3pPCw8Mhk8nKTbGxsS/9uGUJCAhAv379XtnxiIiI6O3BW8rSP1ZWVhbatm0LMzMzLF26FE2bNsWjR4+wb98+jB8/Hn/99dcr60thYSH09PSeax8fHx90795d2u7fvz+cnZ0xb948Ka969eqvNLCoiEePHkFXV/d1d4OIiIj+RThTQf9Y48aNg0wmw/HjxzFw4EA0aNAAjRs3xqRJk/Dnn39K9b766is0adIEhoaGsLa2xrhx43Dv3j2p/NKlS+jTpw+qVasGQ0NDNG7cGHv27EFWVha8vLwAANWqVYNMJkNAQAAAoGPHjpgwYQImTZqEGjVqoEuXLgCA7OxseHt7w8jICCYmJhg0aBD++9//auy/gYEBateuLSU9PT0oFAq1vFIbNmyAra0tTE1N8f777+Pu3btSmRACS5cuhZ2dHQwMDODi4oJff/1VKi8uLsbIkSNRr149GBgYwNHREcuXL5fKg4ODERERgZ07d6rMkmRlZUEmk2HLli3o2LEj9PX18eOPPwIA4uPj0aFDBxgYGMDa2hofffQR7t+//6IvJxEREb3BGFTQP9Lt27cRFRWF8ePHw9DQUK3czMxM+llLSwsrVqzAuXPnEBERgejoaEydOlUqHz9+PAoKCnDo0CEkJSVhyZIlMDIygrW1NbZu3QoAOH/+PHJyclQ+iEdEREBHRwdHjx7F2rVrIYRAv379cPv2bcTFxeHAgQPIyMiAj49PpcebkZGBHTt2IDIyEpGRkYiLi8PixYul8lmzZiEsLAyrV69GcnIygoKCMHToUMTFxQEASkpK8M4772DLli1ISUnBZ599hk8//RRbtmwBAHzyyScYNGgQunfvjpycHOTk5KBNmzZS+9OmTcNHH32E1NRUdOvWDUlJSejWrRv69++Ps2fPYvPmzThy5AgmTJhQ5hgKCgqQl5enkoiIiOgtIYj+gY4dOyYAiG3btj33vlu2bBHm5ubSdpMmTURwcLDGujExMQKAyM3NVcn39PQUzZo1U8nbv3+/0NbWFtnZ2VJecnKyACCOHz/+zH55enqKiRMnquXPmTNHKBQKkZeXJ+VNmTJFeHh4CCGEuHfvntDX1xfx8fEq+40cOVIMHjy4zOONGzdODBgwQNr29/cX3t7eKnUyMzMFAPHNN9+o5Pv5+YkPPvhAJe/w4cNCS0tL5OfnazzenDlzBAANSSkeP/uTiYmJiYmJ6WWkqqRUKgUAoVQqn2s/rqmgfyQhBABAJpM9s25MTAwWLlyIlJQU5OXloaioCA8fPsT9+/dhaGiIjz76CGPHjsX+/fvRuXNnDBgwAE2bNn1mu82bN1fZTk1NhbW1NaytraU8JycnmJmZITU1FS1atHjOUf4/W1tbGBsbS9uWlpa4fv06ACAlJQUPHz6ULsEqVVhYCFdXV2l7zZo1+P7773Hp0iXk5+ejsLCwwneVenqsCQkJuHDhAjZu3CjlCSFQUlKCzMxMNGrUSK2NGTNmYNKkSdJ2Xl6eyrkiIiKiNxcvf6J/JAcHB8hkMqSmppZb79KlS+jZsyecnZ2xdetWJCQkYOXKlQAeLzgGgFGjRuHixYvw8/NDUlISmjdvjpCQkGf24enLroQQGoOcsvKfx9MLo2UyGUpKSgBA+nf37t1ITEyUUkpKirSuYsuWLQgKCsKIESOwf/9+JCYmYvjw4SgsLKzQ8Z8ea0lJCT788EOV4505cwbp6emoX7++xjbkcjlMTExUEhEREb0dOFNB/0jVq1dHt27dsHLlSnz00UdqH3rv3LkDMzMznDx5EkVFRVi2bBm0tB7HyKXrCJ5kbW2NMWPGYMyYMZgxYwbWrVuHwMBAaaF0cXHxM/vk5OSE7OxsXL58WfoGPiUlBUqlUuM39y+Lk5MT5HI5srOz4enpqbHO4cOH0aZNG4wbN07Ky8jIUKmjp6dXoXECgJubG5KTk2Fvb//iHSciIqK3Bmcq6B9r1apVKC4uRsuWLbF161akp6cjNTUVK1asQOvWrQEA9evXR1FREUJCQnDx4kVs2LABa9asUWnn448/xr59+5CZmYlTp04hOjpaCgJsbGwgk8kQGRmJGzduqNw16mmdO3dG06ZN4evri1OnTuH48eMYNmwYPD091S4fepmMjY3xySefICgoCBEREcjIyMDp06excuVKREREAADs7e1x8uRJ7Nu3D2lpaZg9ezZOnDih0o6trS3Onj2L8+fP4+bNm9JMjibTpk3DH3/8gfHjxyMxMRHp6enYtWsXAgMDq2ycRERE9O/FoIL+serVq4dTp07By8sLkydPhrOzM7p06YKDBw9i9erVAIBmzZrhq6++wpIlS+Ds7IyNGzdi0aJFKu0UFxdj/PjxaNSoEbp37w5HR0esWrUKAGBlZYW5c+di+vTpsLCwKPfuRjKZDDt27EC1atXQoUMHdO7cGXZ2dti8eXPVnYT/+fzzz/HZZ59h0aJFaNSoEbp164bffvsN9erVAwCMGTMG/fv3h4+PDzw8PHDr1i2VWQsAGD16NBwdHdG8eXPUrFkTR48eLfN4TZs2RVxcHNLT09G+fXu4urpi9uzZsLS0rNJxEhER0b+TTJSuiCUieony8vJgamoKQAmA6yuIiIhelqr89F7691upVD7X+kjOVBARERERUaUwqCAiIiIiokphUEFERERERJXCW8oSUZVSKgE+soKIiOjNxpkKIiIiIiKqFAYVRERERERUKQwqiIiIiIioUhhUEBERERFRpXChNhFVKVPT192DF8PHghIREVUcZyqIiIiIiKhSGFQQEREREVGlMKggIiIiIqJKYVBB9JYKCAhAv379Xnc3iIiI6A3AoILofwICAiCTyaRkbm6O7t274+zZs1V+7KioKMhkMly7dk0lv3bt2rC2tlbJ+/vvvyGTybB///4q7xcRERFRRTCoIHpC9+7dkZOTg5ycHBw8eBA6Ojro3bt3lR+3Xbt20NHRQWxsrJSXmpqKhw8fIi8vDxcuXJDyY2JioKuri7Zt277QsYqLi1FSUlLZLhMRERFJGFQQPUEul6N27dqoXbs2mjVrhmnTpuHy5cu4ceMGYmNjIZPJcOfOHal+YmIiZDIZsrKyAAAdO3ZUme0oTVlZWQgPD9dYFhwcDCMjI7Ro0UIlqIiNjUW7du3Qrl07tfyWLVvC0NAQAJCbm4thw4ahWrVqUCgU6NGjB9LT06X64eHhMDMzQ2RkJJycnCCXy3Hp0iW1sSckJKBWrVpYsGABAODMmTPw8vKCsbExTExM4O7ujpMnT77Es01ERERvCgYVRGW4d+8eNm7cCHt7e5ibm1don23btkkzHTk5Oejfvz8cHR1hYWEBHx8flbKff/4ZOjo60oyDl5cXYmJipLZiYmLQsWNHeHp6quV7eXlJ2wEBATh58iR27dqFP/74A0II9OzZE48ePZLqPHjwAIsWLcL333+P5ORk1KpVS6XfsbGx6NSpE+bOnYuZM2cCAHx9ffHOO+/gxIkTSEhIwPTp06Grq1vm2AsKCpCXl6eSiIiI6O3Ah98RPSEyMhJGRkYAgPv378PS0hKRkZHQ0qpY/F29enXp56+//hrR0dE4duwYDAwMAED6NyMjAxMmTMDChQvRpUsXAI9nORYuXIicnBxYWloiLi4OU6ZMQUlJCZYvXw4AuHz5MjIzM6WgIj09Hbt27cLRo0fRpk0bAMDGjRthbW2NHTt24L333gMAPHr0CKtWrYKLi4tan3fu3Ak/Pz+sXbsWgwcPlvKzs7MxZcoUNGzYEADg4OBQ7tgXLVqEuXPnVug8ERER0ZuFMxVET/Dy8kJiYiISExNx7NgxdO3aFT169NB4uVB59u7di+nTp2Pz5s1o0KCBSplSqUTv3r3Ro0cPTJkyRcpv27Yt9PT0EBsbi5SUFOTn58PNzQ3u7u7Iy8tDeno6YmJiIJfLpQAiNTUVOjo68PDwkNoxNzeHo6MjUlNTpTw9PT00bdpUrZ/Hjh3DgAEDEBERoRJQAMCkSZMwatQodO7cGYsXL0ZGRka5Y54xYwaUSqWULl++XPETRkRERP9qDCqInmBoaAh7e3vY29ujZcuWWL9+Pe7fv49169ZJsxVCCKn+k5cYlUpJScH777+PxYsXo2vXriplxcXF8PHxgYmJCdatW6dSplAo0LJlS8TExCAmJgbt2rWDtrY2dHR00KZNGym/devW0NfXV+vLk4QQkMlk0raBgYHKdqn69eujYcOGCA0NRWFhoUpZcHAwkpOT0atXL0RHR8PJyQnbt28v89zJ5XKYmJioJCIiIno7MKggKodMJoOWlhby8/NRs2ZNAEBOTo5UnpiYqFL/1q1b6NOnD/r374+goCC19oKCgpCUlITt27dLgcGTvLy8EBsbi9jYWHTs2FHK9/T0lPKfXE/h5OSEoqIiHDt2TKUPaWlpaNSo0TPHV6NGDURHRyMjIwM+Pj5qQVKDBg0QFBSE/fv3o3///ggLC3tmm0RERPT2YVBB9ISCggJcu3YN165dQ2pqKgIDA3Hv3j306dMH9vb2sLa2RnBwMNLS0rB7924sW7ZMZf/+/fvDwMAAwcHBUjvXrl1DcXExwsLCsGrVKqxZswZaWlpS2b1796T9vby8kJ6ejqioKHh6ekr5np6eiIyMRFZWlkpQ4eDgAG9vb4wePRpHjhzBmTNnMHToUFhZWcHb27tCY65Vqxaio6Px119/YfDgwSgqKkJ+fj4mTJiA2NhYXLp0CUePHsWJEycqFKgQERHR24dBBdEToqKiYGlpCUtLS3h4eODEiRP45Zdf0LFjR+jq6uLnn3/GX3/9BRcXFyxZsgTz589X2f/QoUNITk6Gra2t1I6lpSUuX76MuLg4FBcXo2/fviplX375pbR/69atIZfLAQDu7u5SfosWLVBcXAwDAwOV9RMAEBYWBnd3d/Tu3RutW7eGEAJ79uwp905NT6tduzaio6ORlJQEX19faGlp4datWxg2bBgaNGiAQYMGoUePHlyITURERBrJRFkXZRMRVUJeXh5MTU0BKAH8+9ZX8H9GIiJ6G5X+/VYqlc+1PpIzFUREREREVCkMKoiIiIiIqFIYVBARERERUaXwidpEVKWUSoCPrCAiInqzcaaCiIiIiIgqhUEFERERERFVCoMKIiIiIiKqFAYVRERERERUKVyoTURVytT0dfeAiF4WPhSSiMrCmQoiIiIiIqoUBhVERERERFQpDCqIiIiIiKhSGFQQEREREVGlMKggIiIiIqJKYVBBVSY+Ph7a2tro3r27WllWVhZkMhl0dHRw5coVlbKcnBzo6OhAJpMhKytLpWzr1q3o2LEjTE1NYWRkhKZNm2LevHm4ffu22jH++9//QldXFz/++KPG/n344Ydo2rTpiw+wAtq1aweZTIZff/1VJf/LL7+Evb19lR67LA8ePIC/vz+cnZ2ho6ODgQMHlls/Li4O2traaN68+SvqIREREf3bMKigKhMaGorAwEAcOXIE2dnZGuvUqVMHP/zwg0peREQErKys1OrOnDkTPj4+aNGiBfbu3Ytz585h2bJlOHPmDDZs2KBW38LCAr169UJYWJhaWX5+PjZt2oSRI0e+4OgqTl9fHzNnzkRRUVGVH6siiouLoVAo8PHHH8PLy6vcurm5uRg+fDg6der0inpHRERE/0YMKqhK3L9/H1u2bMHYsWPRu3dvhIeHa6zn7++v9qE/PDwc/v7+KnnHjx/HwoULsWzZMnzxxRdo06YNbG1t0aVLF2zdulWtfqmRI0ciJiZGbcbj119/xcOHDzF06FAAwLFjx9C5c2eYm5vD1NQUXl5eSExMlOoXFRVBJpMhNDQUffv2hUKhQIMGDbB79+5nngtfX1/cuHEDoaGh5dbbuXMnXF1doa+vj/r16+Pzzz+XAhEhBGbPno26detCLpfDysoKQUFB0r4hISGwt7eHXC6HhYUFfHx8yjyOsbExVq9ejVGjRsHCwqLcPo0ePRr+/v5o2bLlM8dJREREby8GFVQlNm/eDEdHRzg6OmLo0KEICwuD0PDUpL59+yI3NxdHjhwBABw5cgS3b99Gnz59VOpt3LgRRkZGGDdunMbjmZmZaczv2bMnateurRbUhIaGol+/fjA3NwcA3L17F8OHD0d8fDz++OMP2NraomfPnrh//77KfsHBwfD19cXZs2fRpUsXDBkyBHfu3Cn3XJiZmWHGjBmYO3cuHjx4oLHOnj174O/vj6CgIKSkpGD16tX4/vvvsWTJEgCPz2dISAjWrVuH9PR0bNu2Dc7OzgCAP//8E5MmTcKCBQuQlpaGqKgotGvXrtw+VcS6detw5coVzJo1q0L1CwoKkJeXp5KIiIjo7cCggqrE+vXrpVmA7t274969ezh48KBaPV1dXQwdOlT6Fj80NBRDhw6Frq6uSr309HTY2dmp5T+LtrY2hg0bhvDwcCmoyczMRFxcnMqlT507d4avry8cHR3h5OSEdevWQalU4vDhwyrtjRw5Ej4+PrC3t8eiRYuQl5eHkydPPrMfgYGB0NLSwvLlyzWWz58/HzNnzsSwYcNgZ2eHrl27Yu7cuVi7di0AIDs7G3Xq1EGnTp1Qt25deHh4SP3Pzs6GsbExevXqBRsbG7i6uiIwMPC5ztPTzp8/j1mzZmHDhg3Q1tau0D6LFi2CqamplKytrSvVByIiIvr3YFBBL9358+dx/PhxvP/++wAAHR0d+Pj4lHn5z8iRI/HLL7/g2rVr+OWXXzBixAi1OkIIyGSyF+rPyJEjcenSJURHRwN4HLi888476Ny5s1Tnv//9Lz744AM0aNAApqamMDMzQ35+vtpakCcXdpuYmEChUOD69evP7IO+vj7mzp2LJUuWaFxUnpCQgM8++wxGRkZSGjt2LP7++28UFBTAx8cHeXl5sLOzwwcffIAdO3aguLgYwOOgzdLSEnZ2dhg2bBh++ukn5Ofnv9C5Ah5f6jV48GAsWLDguRaTz5gxA0qlUkqXL19+4T4QERHRvwuDCnrp1q9fj6KiIlhZWUFHRwc6OjpYvXo1tm3bhtzcXLX6zs7OaNiwIQYPHoxGjRpJl/U8qUGDBsjIyMCjR4+euz8ODg5o3749wsLCUFJSgoiICAwfPhxaWv//9vfz88OZM2ewfPlyxMfHIzExEaampigsLFRp6+mZEplMhpKSkgr1w9/fH1ZWVli4cKFamRAC8+fPR2JiopSSkpKQlpYGPT092NjYID09HSEhIZDL5RgzZgw6duyIoqIimJiYIDExERs3boSFhQVmzZqFZs2avfDlR3fu3MHp06cxZswY6fVbuHAhEhISoKOjg7i4OI37yeVymJiYqCQiIiJ6OzCooJeqqKgIP/zwA5YtW6byAfnMmTOwsbHBxo0bNe43YsQIxMbGapylAIAhQ4bg3r17WLVqlcbyZ61rGDlyJLZt24atW7fi77//xvDhw1XKDx8+jI8//hg9evRA48aNoa2t/cw2n5e2tjYWLlyIlStXqn2L7+rqirS0NNjb26ul0hkaAwMDeHt7IyQkBAcPHsSRI0eQkpIC4HGw06VLF3zxxRc4c+YMLly4gNjY2BfqZ7Vq1ZCUlKTy+o0ePRqNGzdGYmIiby1LREREanRedwfozRIZGYnc3FyMHDkSpqamKmUDBw7E+vXrMWHCBLX9Ro8ejffee6/MBdceHh6YOnUqJk+ejCtXruA///kP6tSpgwsXLmDNmjVo164dJk6cWGa/3nvvPXz00Uf48MMP0alTJ9ja2qqU29vb44cffoCrqytyc3MxdepU6OvrP/8JeAZvb2+4ubnhu+++U7lt7pw5c+Dt7Q0rKysMHDgQMpkMZ86cQWpqKubOnYvQ0FDIZDK0bNkSBgYG+PHHH6FQKFC3bl3s3LkT2dnZ6NChA8zMzLBr1y7IZDI0aNCgzH6kpKSgsLAQubm5KCwsRGJiImQyGVxcXKCtra02W1SzZk3o6+trnEUiIiIi4kwFvVTr169H586d1QIKABgwYAASExNx6tQptTIdHR3UqFEDOjplx7lLlizBTz/9hGPHjqFbt25o3LgxJk2ahKZNm5Z5S9lSCoUC77//PnJzczXOhoSHh+PGjRto1qwZAgICEBQUJN0Z6mVbvHgxHj58qJLXs2dP7Nq1C1FRUXB3d0erVq3w9ddfw8bGBgBgamqKNWvWoE2bNnBxcUFcXBwiIyNhZmaGatWq4ddff4WXlxcaNWqE9evXY9OmTWjYsGGZfejatStcXV2xZ88e/P7773B1dYW7u3uVjJeIiIjefDKh6T6fRESVlJeX97/gUgmA6yuI3gT8xED05iv9+61UKp9rfSRnKoiIiIiIqFIYVBARERERUaUwqCAiIiIiokrh3Z+IqEoplQAfWUFERPRm40wFERERERFVCoMKIiIiIiKqFAYVRERERERUKVxTQURVSsNzEOkNxOcXEBG93ThTQURERERElcKggoiIiIiIKoVBBRERERERVQqDCiIiIiIiqhQGFUREREREVCkMKui1iI+Ph7a2Nrp3765WlpWVBZlMBh0dHVy5ckWlLCcnBzo6OpDJZMjK+r/27j4oivOOA/j34OBAT95EhEMkGAExCBFIKBQCVhAwKI61kihRA6bJVAhUnaQ1MVDrSJKaTnVMGjPyIqkOjLWmmiapjvLiKTGKokhAUImAQoiKCMo7T/+w7uQCRswCp5fvZ2Zn4Hme3ee3v4O9+93u7X2j07d7926EhobC0tISarUaXl5eWLduHa5fv37LhkQwAAAUvElEQVTPOBQKBRQKBb788kud9s7OTowdOxYKhQIFBQWD3q9ly5Zh3rx5gx6vL8nJyfD19YVKpcKTTz454BghBDZu3Ag3NzeoVCo4OTlhw4YNIxwpERERPQpYVJBeZGZmIikpCVqtFrW1tQOO0Wg0yMnJ0Wnbvn07HB0d+4194403EBsbi6eeegqff/45zp49i/feew+nT5/Gxx9//KOxODk5ISsrS6dtz549UKvVD7hXg9fd3T1s2x4MIQTi4+MRGxt7zzHJycnYtm0bNm7ciMrKSuzbtw9PP/30CEZJREREjwxBNMLa2trEmDFjRGVlpYiNjRV/+tOfdPpramoEAPHmm28KV1dXnT53d3exdu1aAUDU1NQIIYQ4duyYACD+9re/DThfc3PzPWO5O4+FhYW4ffu21B4eHi7Nk5+fL7XX19eLhQsXCisrK2FjYyPmzp0rxZGamioA6Cz5+fnS/uTl5YmQkBChUqlEZmamEEKII0eOiODgYGFmZiYmTJggkpKSRFtbmzTf9evXxQsvvCCsrKyEubm5iIyMFFVVVVJ/VlaWsLS0FF988YWYMmWKGD16tIiIiBBXrly59wPwPampqcLb27tf+9dffy2USqWorKwc1HYG0tLS8v88tIg732LAxZAXIiIyDHefv1taWh5oPZ6poBGXl5cHd3d3uLu7Iy4uDllZWRADfHPW3Llz0dzcDK1WCwDQarW4fv065syZozNux44dUKvV+N3vfjfgfFZWVj8aj6+vL1xcXLB7924AQF1dHYqKivDCCy/ojLt9+zZmzJgBtVqNoqIiaLVaqNVqREZGoqurC6tXr8bChQsRGRmJhoYGNDQ0IDAwUFr/9ddfx6uvvoqKigpERESgrKwMERERmD9/Ps6cOYO8vDxotVokJiZK6yxbtgwnTpzA3r17UVxcDCEEZs+erXOm4/bt29i4cSM+/vhjFBUVoba2FqtXr/7Rfb6fffv2YdKkSfj000/h4uKCxx57DMuXL//RS8k6Oztx8+ZNnYWIiIh+HlhU0IjLyMhAXFwcACAyMhJtbW04ePBgv3EmJiaIi4tDZmYmgDuXTMXFxcHExERnXHV1NSZNmtSv/UG8+OKL0jxZWVmYPXs2xo0bpzMmNzcXRkZG2LZtG6ZNmwYPDw9kZWWhtrYWBQUFUKvVMDc3h0qlgr29Pezt7WFqaiqtn5KSgvnz58PFxQUajQZ/+ctfsGjRIqSkpMDV1RWBgYHYvHkzcnJy0NHRgerqauzduxfbtm1DcHAwvL29sWPHDly+fBmffPKJtN3u7m58+OGH8PPzg4+PDxITEwfM54O4ePEiLl26hF27diEnJwfZ2dkoKSnBggUL7rlOeno6LC0tpcXJyUlWDERERPToYFFBI+rcuXP46quv8NxzzwEAlEolYmNjpRf0P5SQkIBdu3ahsbERu3btQnx8fL8xQggoFApZccXFxaG4uBgXL15Ednb2gPOUlJTg/PnzGDNmDNRqNdRqNWxsbNDR0YELFy7cdw4/P79+28vOzpa2pVarERERgb6+PtTU1KCiogJKpRL+/v7SOmPHjoW7uzsqKiqktlGjRuHxxx+XfndwcEBTU9NPSYOkr68PnZ2dyMnJQXBwMEJDQ5GRkYH8/HycO3duwHX++Mc/oqWlRVrq6upkxUBERESPDqW+A6Cfl4yMDPT09Oh82FoIARMTEzQ3N8Pa2lpnvKenJ6ZMmYLnn38eHh4e8PT0RGlpqc4YNzc3aLVadHd3/+SzFWPHjkV0dDQSEhLQ0dGBqKgotLa26ozp6+uDr68vduzY0W/9H57VGMjo0aP7be/ll1/Gq6++2m/sxIkTUVVVNeB2flhE/XCfFQrFgJeTPQgHBwcolUq4ublJbR4eHgCA2tpauLu791tHpVJBpVLJmpeIiIgeTTxTQSOmp6cHOTk5eO+991BaWiotp0+fhrOz84Av1gEgPj4eBQUFA549AIBFixahra0NH3zwwYD9N27cGFR8d+dZsmQJjI2N+/X7+PiguroadnZ2mDx5ss5iaWkJADA1NUVvb++g5vPx8UF5eXm/bU2ePBmmpqaYOnUqenp6cOzYMWmda9euoaqqSnqBP1x++ctfoqenR+cMzN0ix9nZeVjnJiIiokcPiwoaMZ9++imam5uRkJAAT09PnWXBggXIyMgYcL2XXnoJ3333HZYvXz5gv7+/P1577TWsWrUKr732GoqLi3Hp0iUcPHgQv/nNb7B9+/ZBxRcZGYnvvvsO69atG7B/8eLFsLW1RUxMDA4fPoyamhoUFhYiOTkZ9fX1AIDHHnsMZ86cwblz53D16tUfvXXs66+/juLiYqxYsQKlpaXSZyiSkpIAAK6uroiJicFLL70ErVaL06dPIy4uDo6OjoiJiRnUPt3L+fPnUVpaisbGRrS3t0sFXldXFwAgLCwMPj4+iI+Px6lTp1BSUoKXX34Z4eHhOmcviIiIiAAWFTSCMjIyEBYWJr2r/32//vWvUVpaipMnT/brUyqVsLW1hVJ576v13nnnHezcuRPHjh1DREQEnnjiCaxcuRJeXl5YunTpoOJTKBSwtbXV+XD1940aNQpFRUWYOHEi5s+fDw8PD8THx6O9vR0WFhYA7hRA7u7u8PPzw7hx43DkyJF7zufl5YXCwkJUV1cjODgY06dPx9q1a+Hg4CCNycrKgq+vL6KjoxEQEAAhBD777DNZH0oHgOXLl2P69OnYunUrqqqqMH36dEyfPh1XrlwBABgZGWHfvn2wtbXFM888g2effRYeHh7Izc2VNS8REREZJoWQe/E1EdEAbt68+f8CsgWAhb7DoWHGZxIiIsNw9/m7paVFetN0MHimgoiIiIiIZGFRQUREREREsrCoICIiIiIiWfg9FUQ0rFpagAe4JJOIiIgeQTxTQUREREREsrCoICIiIiIiWVhUEBERERGRLCwqiIiIiIhIFhYVREREREQkC4sKIiIiIiKShUUFERERERHJwqKCiIiIiIhkYVFBRERERESysKggIiIiIiJZWFQQEREREZEsLCqIiIiIiEgWFhVERERERCQLiwoiIiIiIpKFRQUREREREcmi1HcARGSYhBAAgJs3b+o5EiIiIhqsu8/bd5/HB4tFBRENi2vXrgEAnJyc9BwJERERPajW1lZYWloOejyLCiIaFjY2NgCA2traBzookTw3b96Ek5MT6urqYGFhoe9wflaYe/1g3vWHudeP4c67EAKtra3QaDQPtB6LCiIaFkZGdz6yZWlpyScbPbCwsGDe9YS51w/mXX+Ye/0Yzrz/lDcD+UFtIiIiIiKShUUFERERERHJYpyWlpam7yCIyDAZGxsjNDQUSiWvtBxJzLv+MPf6wbzrD3OvHw9j3hXiQe8XRURERERE9D28/ImIiIiIiGRhUUFERERERLKwqCAiIiIiIllYVBARERERkSwsKohoyH3wwQdwcXGBmZkZfH19cfjwYX2HZHCKioowZ84caDQaKBQKfPLJJzr9QgikpaVBo9HA3NwcoaGhKC8v11O0hiM9PR1PPfUUxowZAzs7O8ybNw/nzp3TGdPZ2YmkpCTY2tpi9OjRmDt3Lurr6/UUseH4+9//Di8vL+kLvwICAvD5559L/cz7yEhPT4dCoUBKSorUxtwPvbS0NCgUCp3F3t5e6n8Yj/EsKohoSOXl5SElJQVvvPEGTp06heDgYERFRaG2tlbfoRmUW7duwdvbG1u2bBmw/91338Vf//pXbNmyBcePH4e9vT3Cw8PR2to6wpEalsLCQqxYsQJffvklDhw4gJ6eHsyaNQu3bt2SxqSkpGDPnj3Izc2FVqtFW1sboqOj0dvbq8fIH30TJkzA22+/jRMnTuDEiRP41a9+hZiYGOmFFPM+/I4fP46PPvoIXl5eOu3M/fB44okn0NDQIC1lZWVS30N5jBdEREPo6aefFq+88opO25QpU8Qf/vAHPUVk+ACIPXv2SL/39fUJe3t78fbbb0ttHR0dwtLSUnz44Yf6CNFgNTU1CQCisLBQCCHEjRs3hImJicjNzZXGXL58WRgZGYkvvvhCX2EaLGtra7Ft2zbmfQS0trYKV1dXceDAARESEiKSk5OFEPybHy6pqanC29t7wL6H9RjPMxVENGS6urpQUlKCWbNm6bTPmjULR48e1VNUPz81NTVobGzUeRxUKhVCQkL4OAyxlpYWAICNjQ0AoKSkBN3d3Tq512g08PT0ZO6HUG9vL3Jzc3Hr1i0EBAQw7yNgxYoVePbZZxEWFqbTztwPn+rqamg0Gri4uOC5557DxYsXATy8x/iH52v4iOiRd/XqVfT29mL8+PE67ePHj0djY6Oeovr5uZvrgR6HS5cu6SMkgySEwMqVKxEUFARPT08Ad3JvamoKa2trnbH8HxgaZWVlCAgIQEdHB9RqNfbs2YOpU6eitLSUeR9Gubm5OHnyJI4fP96vj3/zw8Pf3x85OTlwc3PDt99+i/Xr1yMwMBDl5eUP7TGeRQURDTmFQqHzuxCiXxsNPz4OwysxMRFnzpyBVqu971jmfmi4u7ujtLQUN27cwO7du7F06VIUFhbeczzzLl9dXR2Sk5Oxf/9+mJmZDXo95l6eqKgo6edp06YhICAAjz/+OLZv345f/OIXAB6+YzwvfyKiIWNrawtjY+N+7041NTX1e0eFhs/dO4TwcRg+SUlJ2Lt3L/Lz8zFhwgSp3d7eHl1dXWhubtYZz9wPDVNTU0yePBl+fn5IT0+Ht7c3Nm3axLwPo5KSEjQ1NcHX1xdKpRJKpRKFhYXYvHkzlEolxo8fz9yPgNGjR2PatGmorq5+aI/xLCqIaMiYmprC19cXBw4c0Gk/cOAAAgMD9RTVz4+Liwvs7e11Hoeuri4UFhbycZBJCIHExET861//wqFDh+Di4qLT7+vrCxMTE53cNzQ04OzZs8z9MBBCoLOzk3kfRjNnzkRZWRlKS0ulxc/PD4sXL5Z+Zu6HX2dnJyoqKuDg4PDQHuON09LS0vQ2OxEZHAsLC6xduxaOjo4wMzPDhg0bkJ+fj6ysLFhZWek7PIPR1taGr7/+Go2Njdi6dSv8/f1hbm6Orq4uWFlZobe3F+np6XB3d0dvby9WrVqFy5cv46OPPoJKpdJ3+I+sFStWYMeOHfjnP/8JjUaDtrY2tLW1wdjYGCYmJjAzM8OVK1ewZcsWeHt7o6WlBa+88grGjBmDd955B0ZGfC/vp1qzZg1MTU0hhEBdXR02b96Mf/zjH3j33XcxdepU5n2YqFQq2NnZ6Sw7d+7EpEmTsGTJEv7ND5PVq1dDpVJBCIGqqiokJiaiqqoKW7dufXiP8Xq66xQRGbD3339fODs7C1NTU+Hj4yPdbpOGTn5+vgDQb1m6dKkQ4s4tB1NTU4W9vb1QqVTimWeeEWVlZfoN2gAMlHMAIisrSxrT3t4uEhMThY2NjTA3NxfR0dGitrZWf0EbiPj4eOm4Mm7cODFz5kyxf/9+qZ95Hznfv6WsEMz9cIiNjRUODg7CxMREaDQaMX/+fFFeXi71P4zHeIUQQuinnCEiIiIiIkPAc1JERERERCQLiwoiIiIiIpKFRQUREREREcnCooKIiIiIiGRhUUFERERERLKwqCAiIiIiIllYVBARERERkSwsKoiIiIiISBYWFURERAZkzpw5CAsLG7CvuLgYCoUCJ0+eHOGoiMjQsaggIiIyIAkJCTh06BAuXbrUry8zMxNPPvkkfHx89BAZERkyFhVEREQGJDo6GnZ2dsjOztZpv337NvLy8pCQkAAAOHv2LKKioqBWq2Fvb4+lS5fi2rVr0vigoCD8/ve/x6pVq2BtbQ0HBwf8+c9/lvrPnz8PhUKBs2fPSm1Xr16FQqGAVquV2u43T15eHjw9PWFmZoaxY8ciPDwc7e3tQ50WIhpmLCqIiIgMiFKpxJIlS5CdnQ0hhNS+a9cudHV1YfHixbh8+TJCQkLg5+eHkpISfPbZZ6ivr8fzzz+vs63MzExYW1vjq6++woYNG/DWW28hPz9/0LHcb576+nosXrwYv/3tb1FZWYmCggLExMQMTSKIaEQpxPePOERERPTIq6yshIeHBw4dOoQZM2YAAEJCQuDo6IidO3dizZo1OH36NP7zn/9I63zzzTdwcXHBhQsXMGnSJAQFBcHExESniPDx8cHs2bOxfv16nD9/Hq6urigrK4OnpyeAO2cqxo0bh8OHDyMoKOi+81y9ehX+/v6or6+Ho6PjCGWHiIaDUt8BEBER0dCaMmUKAgMDkZmZiRkzZuDChQs4fPgw9u/fDwAoKSlBfn4+1Gp1v3XvFhUA4OXlpdPn4OCApqamQcdxv3lmzJiB0NBQTJ06FREREZg1axYWLFgAKyurB9ldInoIsKggIiIyQAkJCUhMTMT777+PrKwsODs7Y+bMmQCAvr4+zJs3Dxs2bOi3nkajkX42MTHR6VMoFOjr6wMAGBnduYL6+xc8dHd364y/3zxKpRIHDx7E0aNHsX//fmzatAlvvvkmjh07Bmdn55+450SkD/xMBRERkQFauHAhjI2NsXPnTmzfvh0vvvgiFAoFgDuXMZWXl8PFxQWTJ0/WWUaNGjWo7dvZ2QEAGhoapLbS0lKdMYOZx8jICEFBQVi3bh1OnToFhUKBf//730ORAiIaQSwqiIiIDJBarUZsbCzWrFmDK1euYNmyZVJfUlISvv32WyxatAjHjx/HhQsX8N///ldnzGC27+fnh/T0dFRUVKCgoABvvfWWzpj7zXP06FGkp6fjxIkTqK2txe7du3Ht2jV4eHgMQQaIaCSxqCAiIjJQCQkJaG5uRlhYGCZOnCi1T5gwAUeOHEFXVxfCw8Mxbdo0pKSkwMbG5oG2n52djfb2dvj6+mLlypVYv369Tv/95rG0tERBQQGioqLg5uaG1NRUbNq0CeHh4fJ3nohGFO/+REREREREsvBMBRERERERycKigoiIiIiIZGFRQUREREREsrCoICIiIiIiWVhUEBERERGRLCwqiIiIiIhIFhYVREREREQkC4sKIiIiIiKShUUFERERERHJwqKCiIiIiIhkYVFBRERERESysKggIiIiIiJZ/gdpDfeEHRedDQAAAABJRU5ErkJggg==\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f031004c0f0>"
                    }, 
                    "metadata": {}
                }
            ], 
            "source": "plt.rcdefaults()\nfig, ax = plt.subplots()\n\nVenues = sf_summary_1.Venue_Name[0:len(sf_summary_1)]\ny_pos = np.arange(len(Venues))\nTotals = sf_summary_1.Trending_Sum[0:len(sf_summary_1)]\n\nax.barh(y_pos, Totals, align='center', color='blue')\nax.set_yticks(y_pos)\nax.set_yticklabels(Venues)\nax.set_xlabel('Venues')\nax.set_title('Trending Venues in San Francisco')\n\nplt.show()"
        }, 
        {
            "execution_count": 52, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 52, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Venue_Name</th>\n      <th>Trending_Sum</th>\n      <th>City</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AMC Metreon 16</td>\n      <td>13</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AMC Van Ness 14</td>\n      <td>26</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BuzzWorks</td>\n      <td>5</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Castro Theatre</td>\n      <td>50</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Century San Francisco Centre 9 &amp; XD</td>\n      <td>10</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Duboce Triangle</td>\n      <td>1</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Marines' Memorial Theatre</td>\n      <td>22</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Mikkeller Bar</td>\n      <td>4</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Nijiya Market</td>\n      <td>46</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Souvla</td>\n      <td>3</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>The Fillmore</td>\n      <td>10</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>The Masonic</td>\n      <td>26</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Trader Joe's</td>\n      <td>5</td>\n      <td>San Francisco</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Vive La Tarte</td>\n      <td>4</td>\n      <td>San Francisco</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                             Venue_Name Trending_Sum           City\n0                        AMC Metreon 16           13  San Francisco\n1                       AMC Van Ness 14           26  San Francisco\n2                             BuzzWorks            5  San Francisco\n3                        Castro Theatre           50  San Francisco\n4   Century San Francisco Centre 9 & XD           10  San Francisco\n5                       Duboce Triangle            1  San Francisco\n6             Marines' Memorial Theatre           22  San Francisco\n7                         Mikkeller Bar            4  San Francisco\n8                         Nijiya Market           46  San Francisco\n9                                Souvla            3  San Francisco\n10                         The Fillmore           10  San Francisco\n11                          The Masonic           26  San Francisco\n12                         Trader Joe's            5  San Francisco\n13                        Vive La Tarte            4  San Francisco"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "sf_summary_1"
        }, 
        {
            "execution_count": 53, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 53, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>VENUE_ID</th>\n      <th>CITY</th>\n      <th>LAT</th>\n      <th>LONG</th>\n      <th>VENUE_NAME</th>\n      <th>TREND_DATE</th>\n      <th>TREND_TIME</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>San Francisco</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>San Francisco</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>16:54:07</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>San Francisco</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>San Francisco</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>17:16:47</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>San Francisco</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>San Francisco</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>5748e1f0498e1c0c6d214098</td>\n      <td>San Francisco</td>\n      <td>37.77457655200334</td>\n      <td>-122.4378085167028</td>\n      <td>Souvla</td>\n      <td>01/20/19</td>\n      <td>17:19:51</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>San Francisco</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>55554f0a498e7ffc6883325c</td>\n      <td>San Francisco</td>\n      <td>37.77701166643817</td>\n      <td>-122.41089851840502</td>\n      <td>Vive La Tarte</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>5748e1f0498e1c0c6d214098</td>\n      <td>San Francisco</td>\n      <td>37.77457655200334</td>\n      <td>-122.4378085167028</td>\n      <td>Souvla</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>57587782498e1367ff5c38ec</td>\n      <td>San Francisco</td>\n      <td>37.77124505563008</td>\n      <td>-122.41299663242239</td>\n      <td>BuzzWorks</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>5748e1f0498e1c0c6d214098</td>\n      <td>San Francisco</td>\n      <td>37.77457655200334</td>\n      <td>-122.4378085167028</td>\n      <td>Souvla</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>18:11:20</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>18:11:20</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>18:11:20</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>18:16:02</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>18:16:02</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>18:16:02</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>18:21:02</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>4a5824e5f964a5206db71fe3</td>\n      <td>San Francisco</td>\n      <td>37.78457546024026</td>\n      <td>-122.40323635482677</td>\n      <td>AMC Metreon 16</td>\n      <td>01/20/19</td>\n      <td>18:21:02</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>218</th>\n      <td>4b65eb87f964a52065082be3</td>\n      <td>San Francisco</td>\n      <td>37.788772390242656</td>\n      <td>-122.4102445497535</td>\n      <td>Marines' Memorial Theatre</td>\n      <td>01/20/19</td>\n      <td>22:15:58</td>\n    </tr>\n    <tr>\n      <th>219</th>\n      <td>49c3dfb3f964a52082561fe3</td>\n      <td>San Francisco</td>\n      <td>37.78529223246809</td>\n      <td>-122.43104474803255</td>\n      <td>Nijiya Market</td>\n      <td>01/20/19</td>\n      <td>22:15:58</td>\n    </tr>\n    <tr>\n      <th>221</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:20:59</td>\n    </tr>\n    <tr>\n      <th>222</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:20:59</td>\n    </tr>\n    <tr>\n      <th>223</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:20:59</td>\n    </tr>\n    <tr>\n      <th>224</th>\n      <td>4b65eb87f964a52065082be3</td>\n      <td>San Francisco</td>\n      <td>37.788772390242656</td>\n      <td>-122.4102445497535</td>\n      <td>Marines' Memorial Theatre</td>\n      <td>01/20/19</td>\n      <td>22:20:59</td>\n    </tr>\n    <tr>\n      <th>225</th>\n      <td>49c3dfb3f964a52082561fe3</td>\n      <td>San Francisco</td>\n      <td>37.78529223246809</td>\n      <td>-122.43104474803255</td>\n      <td>Nijiya Market</td>\n      <td>01/20/19</td>\n      <td>22:20:59</td>\n    </tr>\n    <tr>\n      <th>227</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n    </tr>\n    <tr>\n      <th>228</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n    </tr>\n    <tr>\n      <th>229</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n    </tr>\n    <tr>\n      <th>230</th>\n      <td>4b65eb87f964a52065082be3</td>\n      <td>San Francisco</td>\n      <td>37.788772390242656</td>\n      <td>-122.4102445497535</td>\n      <td>Marines' Memorial Theatre</td>\n      <td>01/20/19</td>\n      <td>22:25:58</td>\n    </tr>\n    <tr>\n      <th>232</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>San Francisco</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>The Fillmore</td>\n      <td>01/20/19</td>\n      <td>22:31:03</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n    </tr>\n    <tr>\n      <th>238</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n    </tr>\n    <tr>\n      <th>239</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n    </tr>\n    <tr>\n      <th>240</th>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>San Francisco</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>The Fillmore</td>\n      <td>01/20/19</td>\n      <td>22:36:02</td>\n    </tr>\n    <tr>\n      <th>242</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n    </tr>\n    <tr>\n      <th>243</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n    </tr>\n    <tr>\n      <th>244</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n    </tr>\n    <tr>\n      <th>245</th>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>San Francisco</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>The Fillmore</td>\n      <td>01/20/19</td>\n      <td>22:41:00</td>\n    </tr>\n    <tr>\n      <th>247</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n    </tr>\n    <tr>\n      <th>248</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n    </tr>\n    <tr>\n      <th>249</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n    </tr>\n    <tr>\n      <th>250</th>\n      <td>40c3b000f964a520e7001fe3</td>\n      <td>San Francisco</td>\n      <td>37.784079069766605</td>\n      <td>-122.43300234064807</td>\n      <td>The Fillmore</td>\n      <td>01/20/19</td>\n      <td>22:46:01</td>\n    </tr>\n    <tr>\n      <th>252</th>\n      <td>4ae269baf964a520248e21e3</td>\n      <td>San Francisco</td>\n      <td>37.79118818627268</td>\n      <td>-122.41302191054984</td>\n      <td>The Masonic</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n    </tr>\n    <tr>\n      <th>253</th>\n      <td>4089ae00f964a520bff21ee3</td>\n      <td>San Francisco</td>\n      <td>37.761991717624404</td>\n      <td>-122.43496924638747</td>\n      <td>Castro Theatre</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n    </tr>\n    <tr>\n      <th>254</th>\n      <td>4390a026f964a5204d2b1fe3</td>\n      <td>San Francisco</td>\n      <td>37.78515072462566</td>\n      <td>-122.42012248044318</td>\n      <td>AMC Van Ness 14</td>\n      <td>01/20/19</td>\n      <td>22:51:12</td>\n    </tr>\n  </tbody>\n</table>\n<p>225 rows \u00d7 7 columns</p>\n</div>", 
                        "text/plain": "                     VENUE_ID           CITY                 LAT  \\\n1    4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n2    57587782498e1367ff5c38ec  San Francisco   37.77124505563008   \n3    55554f0a498e7ffc6883325c  San Francisco   37.77701166643817   \n5    4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n6    4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n7    57587782498e1367ff5c38ec  San Francisco   37.77124505563008   \n8    55554f0a498e7ffc6883325c  San Francisco   37.77701166643817   \n10   4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n11   4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n12   57587782498e1367ff5c38ec  San Francisco   37.77124505563008   \n13   55554f0a498e7ffc6883325c  San Francisco   37.77701166643817   \n14   5748e1f0498e1c0c6d214098  San Francisco   37.77457655200334   \n17   4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n18   4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n19   57587782498e1367ff5c38ec  San Francisco   37.77124505563008   \n20   55554f0a498e7ffc6883325c  San Francisco   37.77701166643817   \n21   5748e1f0498e1c0c6d214098  San Francisco   37.77457655200334   \n24   4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n25   4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n26   57587782498e1367ff5c38ec  San Francisco   37.77124505563008   \n27   4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n28   5748e1f0498e1c0c6d214098  San Francisco   37.77457655200334   \n30   4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n31   4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n32   4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n34   4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n35   4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n36   4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n38   4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n39   4a5824e5f964a5206db71fe3  San Francisco   37.78457546024026   \n..                        ...            ...                 ...   \n218  4b65eb87f964a52065082be3  San Francisco  37.788772390242656   \n219  49c3dfb3f964a52082561fe3  San Francisco   37.78529223246809   \n221  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n222  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n223  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n224  4b65eb87f964a52065082be3  San Francisco  37.788772390242656   \n225  49c3dfb3f964a52082561fe3  San Francisco   37.78529223246809   \n227  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n228  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n229  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n230  4b65eb87f964a52065082be3  San Francisco  37.788772390242656   \n232  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n233  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n234  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n235  40c3b000f964a520e7001fe3  San Francisco  37.784079069766605   \n237  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n238  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n239  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n240  40c3b000f964a520e7001fe3  San Francisco  37.784079069766605   \n242  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n243  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n244  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n245  40c3b000f964a520e7001fe3  San Francisco  37.784079069766605   \n247  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n248  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n249  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n250  40c3b000f964a520e7001fe3  San Francisco  37.784079069766605   \n252  4ae269baf964a520248e21e3  San Francisco   37.79118818627268   \n253  4089ae00f964a520bff21ee3  San Francisco  37.761991717624404   \n254  4390a026f964a5204d2b1fe3  San Francisco   37.78515072462566   \n\n                    LONG                 VENUE_NAME TREND_DATE TREND_TIME  \n1    -122.40323635482677             AMC Metreon 16   01/20/19   16:54:07  \n2    -122.41299663242239                  BuzzWorks   01/20/19   16:54:07  \n3    -122.41089851840502              Vive La Tarte   01/20/19   16:54:07  \n5    -122.43496924638747             Castro Theatre   01/20/19   17:16:47  \n6    -122.40323635482677             AMC Metreon 16   01/20/19   17:16:47  \n7    -122.41299663242239                  BuzzWorks   01/20/19   17:16:47  \n8    -122.41089851840502              Vive La Tarte   01/20/19   17:16:47  \n10   -122.43496924638747             Castro Theatre   01/20/19   17:19:51  \n11   -122.40323635482677             AMC Metreon 16   01/20/19   17:19:51  \n12   -122.41299663242239                  BuzzWorks   01/20/19   17:19:51  \n13   -122.41089851840502              Vive La Tarte   01/20/19   17:19:51  \n14    -122.4378085167028                     Souvla   01/20/19   17:19:51  \n17   -122.43496924638747             Castro Theatre   01/20/19   17:25:18  \n18   -122.40323635482677             AMC Metreon 16   01/20/19   17:25:18  \n19   -122.41299663242239                  BuzzWorks   01/20/19   17:25:18  \n20   -122.41089851840502              Vive La Tarte   01/20/19   17:25:18  \n21    -122.4378085167028                     Souvla   01/20/19   17:25:18  \n24   -122.43496924638747             Castro Theatre   01/20/19   17:32:20  \n25   -122.40323635482677             AMC Metreon 16   01/20/19   17:32:20  \n26   -122.41299663242239                  BuzzWorks   01/20/19   17:32:20  \n27   -122.42012248044318            AMC Van Ness 14   01/20/19   17:32:20  \n28    -122.4378085167028                     Souvla   01/20/19   17:32:20  \n30   -122.43496924638747             Castro Theatre   01/20/19   18:11:20  \n31   -122.40323635482677             AMC Metreon 16   01/20/19   18:11:20  \n32   -122.42012248044318            AMC Van Ness 14   01/20/19   18:11:20  \n34   -122.43496924638747             Castro Theatre   01/20/19   18:16:02  \n35   -122.40323635482677             AMC Metreon 16   01/20/19   18:16:02  \n36   -122.42012248044318            AMC Van Ness 14   01/20/19   18:16:02  \n38   -122.43496924638747             Castro Theatre   01/20/19   18:21:02  \n39   -122.40323635482677             AMC Metreon 16   01/20/19   18:21:02  \n..                   ...                        ...        ...        ...  \n218   -122.4102445497535  Marines' Memorial Theatre   01/20/19   22:15:58  \n219  -122.43104474803255              Nijiya Market   01/20/19   22:15:58  \n221  -122.41302191054984                The Masonic   01/20/19   22:20:59  \n222  -122.43496924638747             Castro Theatre   01/20/19   22:20:59  \n223  -122.42012248044318            AMC Van Ness 14   01/20/19   22:20:59  \n224   -122.4102445497535  Marines' Memorial Theatre   01/20/19   22:20:59  \n225  -122.43104474803255              Nijiya Market   01/20/19   22:20:59  \n227  -122.41302191054984                The Masonic   01/20/19   22:25:58  \n228  -122.43496924638747             Castro Theatre   01/20/19   22:25:58  \n229  -122.42012248044318            AMC Van Ness 14   01/20/19   22:25:58  \n230   -122.4102445497535  Marines' Memorial Theatre   01/20/19   22:25:58  \n232  -122.41302191054984                The Masonic   01/20/19   22:31:03  \n233  -122.43496924638747             Castro Theatre   01/20/19   22:31:03  \n234  -122.42012248044318            AMC Van Ness 14   01/20/19   22:31:03  \n235  -122.43300234064807               The Fillmore   01/20/19   22:31:03  \n237  -122.41302191054984                The Masonic   01/20/19   22:36:02  \n238  -122.43496924638747             Castro Theatre   01/20/19   22:36:02  \n239  -122.42012248044318            AMC Van Ness 14   01/20/19   22:36:02  \n240  -122.43300234064807               The Fillmore   01/20/19   22:36:02  \n242  -122.41302191054984                The Masonic   01/20/19   22:41:00  \n243  -122.43496924638747             Castro Theatre   01/20/19   22:41:00  \n244  -122.42012248044318            AMC Van Ness 14   01/20/19   22:41:00  \n245  -122.43300234064807               The Fillmore   01/20/19   22:41:00  \n247  -122.41302191054984                The Masonic   01/20/19   22:46:01  \n248  -122.43496924638747             Castro Theatre   01/20/19   22:46:01  \n249  -122.42012248044318            AMC Van Ness 14   01/20/19   22:46:01  \n250  -122.43300234064807               The Fillmore   01/20/19   22:46:01  \n252  -122.41302191054984                The Masonic   01/20/19   22:51:12  \n253  -122.43496924638747             Castro Theatre   01/20/19   22:51:12  \n254  -122.42012248044318            AMC Van Ness 14   01/20/19   22:51:12  \n\n[225 rows x 7 columns]"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "sf_city_df"
        }, 
        {
            "source": "<hr>\n\n<h1 align=\"center\">Trending City Data for San Jose, CA</h1>\n\n<hr>", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 54, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [], 
            "source": "sj_city_df = group_list2[4]"
        }, 
        {
            "execution_count": 55, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "sj_group1 = sj_city_df.groupby(['VENUE_NAME'])\nsj_group_list1 = []\nsj_group_list2 = []\nfor name, group in sj_group1:\n    sj_group_list1.append(name)\n    sj_group_list2.append(group)"
        }, 
        {
            "execution_count": 56, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "sj_summary_list1 = []\nsj_summary_list2 = []\nsj_summary_str1 = ''\nsj_summary_counter1 = 0\nfor item in sj_group_list1:\n    sj_summary_list1.append(str(item))\n    sj_summary_list1.append(len(sj_group_list2[sj_summary_counter1]))\n    sj_summary_list1.append('San Jose')\n    sj_summary_list2.append(list(sj_summary_list1))\n    sj_summary_list1 = []\n    sj_summary_counter1 += 1"
        }, 
        {
            "execution_count": 57, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "sj_summary_1 = pd.DataFrame(columns=['Venue_Name', 'Trending_Sum', 'City'])"
        }, 
        {
            "execution_count": 58, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "for row in sj_summary_list2:\n    sj_summary_1 = sj_summary_1.append(pd.Series(row, index=['Venue_Name', 'Trending_Sum', 'City']), ignore_index=True)"
        }, 
        {
            "execution_count": 59, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f0306ffb2b0>"
                    }, 
                    "metadata": {}
                }
            ], 
            "source": "plt.rcdefaults()\nfig, ax = plt.subplots()\n\nVenues = sj_summary_1.Venue_Name[0:len(sj_summary_1)]\ny_pos = np.arange(len(Venues))\nTotals = sj_summary_1.Trending_Sum[0:len(sj_summary_1)]\n\nax.barh(y_pos, Totals, align='center', color='blue')\nax.set_yticks(y_pos)\nax.set_yticklabels(Venues)\nax.set_xlabel('Venues')\nax.set_title('Trending Venues in San Jose')\n\nplt.show()"
        }, 
        {
            "execution_count": 60, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 60, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Venue_Name</th>\n      <th>Trending_Sum</th>\n      <th>City</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Further Confusion 2019</td>\n      <td>2</td>\n      <td>San Jose</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "               Venue_Name Trending_Sum      City\n0  Further Confusion 2019            2  San Jose"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "sj_summary_1"
        }, 
        {
            "execution_count": 61, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 61, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>VENUE_ID</th>\n      <th>CITY</th>\n      <th>LAT</th>\n      <th>LONG</th>\n      <th>VENUE_NAME</th>\n      <th>TREND_DATE</th>\n      <th>TREND_TIME</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>16</th>\n      <td>5c40f328345cbe002c99e458</td>\n      <td>San Jose</td>\n      <td>37.329998</td>\n      <td>-121.889275</td>\n      <td>Further Confusion 2019</td>\n      <td>01/20/19</td>\n      <td>17:25:18</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>5c40f328345cbe002c99e458</td>\n      <td>San Jose</td>\n      <td>37.329998</td>\n      <td>-121.889275</td>\n      <td>Further Confusion 2019</td>\n      <td>01/20/19</td>\n      <td>17:32:20</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                    VENUE_ID      CITY        LAT         LONG  \\\n16  5c40f328345cbe002c99e458  San Jose  37.329998  -121.889275   \n23  5c40f328345cbe002c99e458  San Jose  37.329998  -121.889275   \n\n                VENUE_NAME TREND_DATE TREND_TIME  \n16  Further Confusion 2019   01/20/19   17:25:18  \n23  Further Confusion 2019   01/20/19   17:32:20  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "sj_city_df"
        }
    ], 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.5", 
            "name": "python3", 
            "language": "python"
        }, 
        "language_info": {
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
            "version": "3.5.5", 
            "name": "python", 
            "file_extension": ".py", 
            "pygments_lexer": "ipython3", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }
        }
    }, 
    "nbformat": 4
}