FinalProject_Group044.ipynb

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   "source": [
    "# COGS 108 - TEAM 44: FINAL PROJECT"
   ]
  },
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   "source": [
    "# Overview\n",
    "Today we live in a society where businesses' success, especially franchises, are greatly impacted by the number of stores they own. Of course, when we look into Starbucks, we can tell that their goal is to open as many stores as they can. However, opening too many stores that bring little profit can cause businesses downfall. For Starbucks, too, to thrive, their choice of location must be taken in great consideration. Moreover, it makes sense that businesses would want to open their stores where their customers are. Looking to improve Starbucks franchise's growth and expansion of their units, we used Starbucks dataset and Census API to try to establish a profile of Starbucks customer base and predict the most promising locations (that not yet had a Starbucks) to open a new store. With our data analysis, we can hopefully give further insight to the Starbucks chain to help them to expand their business."
   ]
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   "cell_type": "markdown",
   "metadata": {
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    "id": "O17HYcG2ndYi"
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   "source": [
    "# Group Member IDs and Names \n",
    "\n",
    "XXXXXXXXX Baichuan Tang\n",
    "\n",
    "XXXXXXXXX Catherine Wang\n",
    "\n",
    "XXXXXXXXX Jian Fan\n",
    "\n",
    "XXXXXXXXX Kim Pham\n",
    "\n",
    "XXXXXXXXX Ryan Eveloff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "7sWKJfwHndYm"
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   "source": [
    "# Research Question\n",
    "\n",
    "Our project sets out to predict *the best location(s) to open the next Starbucks*.\n",
    "And to answer that question, we will be looking at Starbucks store data and how that maps to Census demographics to develop a \"profile\" (list of criteria) of a city where Starbucks is likely to expand its business. Using this \"profile\", we will produce a list of potential cities meeting those criteria.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "PdiDIlUXndYq"
   },
   "source": [
    "# Background and Prior Work"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "Before diving into the real nitty-gritty code work, we did some research to best assist us. First, to stress the importance of our project, we looked at how Starbucks has been employing technology to its favor: in a recent <a href=\"https://www.geekwire.com/2019/microsoft-teams-starbucks-predictive-drive-thru-ordering-bean-cup-blockchain/\">collaboration</a> with Microsoft, they use machine learning to gather customer preferences, connecting coffeemakers in stores to the internet to blockchain services for tracing coffee. They are talking about the increased hiring of software engineers at companies outside the industry that are embracing high tech solutions. This is important for our project because we are using data science to figure out how the relationships and concepts we learn from our data can help a company and franchise like Starbucks. If we are able to find factors that affect and optimize a certain location for Starbucks to open, it can be really useful for companies that can benefit from data scientists and software engineers.\n",
    "Fortunately a <a href=\"https://pdfs.semanticscholar.org/00b4/46b1a605c29326690d8c038c30030b7342cd.pdf\">study</a> by Chungyu Institute of Technology provides us some pointers. It suggested that \"Starbucks Coffee should also pay more attention to male, young customers who are under 35 years old, and consumers who have a high school diploma or equivalent and associate\n",
    "degree for increasing market share.\" Therefore we will be analyzing factors such as gender, age, and education level to see if there's a correlation (if any) between certain demographics, the locations established by Starbucks, and its success in those locations.\n",
    "\n",
    "Additionally, we found a similar <a href=\"https://towardsdatascience.com/analyzing-and-predicting-starbucks-location-strategy-3c5026d31c21?fbclid=IwAR2F6BPuAQfFJU9OXS5WQaSlv2zuB6wPl89WXqU5ORcaPfq-2rtMH-2zOfA\">project</a> in R done by Jordan Bean. He is someone that is naturally curious about how Starbucks think about location selection, and how their store locations can reveal their customer base and how they view locations as more attractive than others. His project is similar to our project in many ways because we also want to look into the locations of Starbucks. However, we want to focus on generating an idea of where Starbucks locations can be and are most successful, not necessarily what Starbucks themselves plan when choosing a location to open their stores. Jordan Bean usefully uses zip codes to differentiate locations of Starbucks just like us too. \n",
    "\n",
    "References (include links):\n",
    "- 1) Starbucks' collaboration with Microsoft: https://www.geekwire.com/2019/microsoft-teams-starbucks-predictive-drive-thru-ordering-bean-cup-blockchain/\n",
    "- 2) Jordan Bean's article: https://towardsdatascience.com/analyzing-and-predicting-starbucks-location-strategy-3c5026d31c21?fbclid=IwAR2F6BPuAQfFJU9OXS5WQaSlv2zuB6wPl89WXqU5ORcaPfq-2rtMH-2zOfA\n",
    "- 3) Study: https://pdfs.semanticscholar.org/00b4/46b1a605c29326690d8c038c30030b7342cd.pdf"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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    "id": "i4BSBoh_ndYw"
   },
   "source": [
    "# Hypothesis\n"
   ]
  },
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    "id": "Z-_qO9ZOndYx"
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   "source": [
    "Our project primarily focuses on determining the relationship between Starbucks locations and associated demographic factors such as gender, age, income, and education level. We predict that there will be a positive correlation between the factors stated above and the existing Starbucks locations."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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    "id": "qSpPepvXndYy"
   },
   "source": [
    "# Dataset(s)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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    "id": "3kyTLgXwndYz"
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   "source": [
    "## a) Starbucks store data\n",
    "- Dataset Name: storedata.csv\n",
    "- Link to the dataset: https://www.kaggle.com/starbucks/store-locations\n",
    "- Number of observations: 25600\n",
    "\n",
    "*We used a Starbucks locations worldwide dataset that has a record for every Starbucks or subsidiary store location currently in operation as of February 2017. Scraped from the Starbucks store locator webpage by Github user chrismeller, this dataset contains columns from store number to location based columns such as address, postal code, longitude, and latitude.*\n",
    "\n",
    "## b) Census data\n",
    "- Dataset Name: Census API\n",
    "- Link to the dataset: https://api.census.gov/data/2016/acs/acs5/variables.html\n",
    "- Number of observations: 33120\n",
    "\n",
    "*We used a census API key to access the information about population in each state and later put it in a comprehensive table. The dataset we used from the United States Census Bureau was _Detailed Table_ of the American Community Survey 5-Year Data (2013-2017). It contains detailed social, economic, housing, and demographic information. The data are presented as population counts. We narrow down this table to contain the information that we need for our analysis, which includes gender count for age ranges 15-17, 18-19, 20, 21, 22-24, 25-29, 30-34, 35-39, median age, median household income, count of male/female workers, total population, median rent, median home value, and count of people with high school diploma, Bachelor's Degree, and Master's Degree. More information can be found <a href=https://api.census.gov/data/2016/acs/acs5/variables.html>here</a>*\n",
    "\n",
    "*We plan on combining these datasets to visualize a Starbucks profile, by looking at the census information of areas that have Starbucks stores. We can then visualize information such as gender in each age range of all Starbucks customers, Starbucks customer education level, and what the bigger picture of the datasets can reveal for us—how the Starbucks profile differ from the rest of the population.*"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fMSy9FfendY0"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Analysis and Results, we must install geopandas, lippysal, mapclassify, and dill. The following code box is commented out because we only needed to install them once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install geopandas\n",
    "#!pip install libpysal\n",
    "#!pip install mapclassify\n",
    "#!pip install dill\n",
    "#!pip install descartes\n",
    "#!pip install census\n",
    "#!pip install us\n",
    "#!pip install censusdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "# Imports\n",
    "\n",
    "# Display plots directly in the notebook instead of in a new window\n",
    "%matplotlib inline\n",
    "\n",
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from census import Census\n",
    "from us import states\n",
    "import censusdata\n",
    "from scipy import stats \n",
    "\n",
    "# Erase warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Configure libraries\n",
    "# The seaborn library makes plots look nicer\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "\n",
    "#For geo infomation processing\n",
    "import geopandas as gpd\n",
    "import libpysal\n",
    "import mapclassify\n",
    "from shapely.geometry import Point\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Don't display too many rows/cols of DataFrames\n",
    "pd.options.display.max_rows = 7\n",
    "pd.options.display.max_columns = 8\n",
    "\n",
    "# Round decimals when displaying DataFrames\n",
    "pd.set_option('precision', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hHCtV5M9ndY6"
   },
   "source": [
    "# Data Cleaning\n",
    "Data files:\n",
    "    - Starbucks store_data.csv\n",
    "    - Census "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a) Starbucks: Load Data\n",
    "Import data into a DataFrame structure called `df_starbucks`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Store Number</th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Ownership Type</th>\n",
       "      <th>...</th>\n",
       "      <th>Phone Number</th>\n",
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       "      <th>Latitude</th>\n",
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       "      <th>0</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>47370-257954</td>\n",
       "      <td>Meritxell, 96</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>...</td>\n",
       "      <td>376818720</td>\n",
       "      <td>GMT+1:00 Europe/Andorra</td>\n",
       "      <td>1.53</td>\n",
       "      <td>42.51</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>22331-212325</td>\n",
       "      <td>Ajman Drive Thru</td>\n",
       "      <td>Licensed</td>\n",
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       "      <td>25.42</td>\n",
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       "      <th>2</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>47089-256771</td>\n",
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       "      <td>Licensed</td>\n",
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       "      <td>Rosebank Mall</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>...</td>\n",
       "      <td>27873500159</td>\n",
       "      <td>GMT+000000 Africa/Johannesburg</td>\n",
       "      <td>28.04</td>\n",
       "      <td>-26.15</td>\n",
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       "      <th>25598</th>\n",
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       "      <td>Menlyn Maine</td>\n",
       "      <td>Licensed</td>\n",
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       "      <td>GMT+000000 Africa/Johannesburg</td>\n",
       "      <td>28.28</td>\n",
       "      <td>-25.79</td>\n",
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       "    <tr>\n",
       "      <th>25599</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>47609-253286</td>\n",
       "      <td>Mall of Africa</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>...</td>\n",
       "      <td>27873500215</td>\n",
       "      <td>GMT+000000 Africa/Johannesburg</td>\n",
       "      <td>28.11</td>\n",
       "      <td>-26.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25600 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "           Brand  Store Number        Store Name Ownership Type  ...  \\\n",
       "0      Starbucks  47370-257954     Meritxell, 96       Licensed  ...   \n",
       "1      Starbucks  22331-212325  Ajman Drive Thru       Licensed  ...   \n",
       "2      Starbucks  47089-256771         Dana Mall       Licensed  ...   \n",
       "...          ...           ...               ...            ...  ...   \n",
       "25597  Starbucks  47608-253804     Rosebank Mall       Licensed  ...   \n",
       "25598  Starbucks  47640-253809      Menlyn Maine       Licensed  ...   \n",
       "25599  Starbucks  47609-253286    Mall of Africa       Licensed  ...   \n",
       "\n",
       "      Phone Number                        Timezone Longitude Latitude  \n",
       "0        376818720         GMT+1:00 Europe/Andorra      1.53    42.51  \n",
       "1              NaN            GMT+04:00 Asia/Dubai     55.47    25.42  \n",
       "2              NaN            GMT+04:00 Asia/Dubai     55.47    25.39  \n",
       "...            ...                             ...       ...      ...  \n",
       "25597  27873500159  GMT+000000 Africa/Johannesburg     28.04   -26.15  \n",
       "25598          NaN  GMT+000000 Africa/Johannesburg     28.28   -25.79  \n",
       "25599  27873500215  GMT+000000 Africa/Johannesburg     28.11   -26.02  \n",
       "\n",
       "[25600 rows x 13 columns]"
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   "source": [
    "df_starbucks=pd.read_csv('store_data.csv')\n",
    "df_starbucks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to create a map to visualize, therefore we need a Longitude and Latitude component to create a geometry column that has points of Longitude and Latitude for every existing Starbucks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <td>Meritxell, 96</td>\n",
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       "      <td>Starbucks</td>\n",
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       "      <td>Starbucks</td>\n",
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       "      <td>Dana Mall</td>\n",
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       "      <td>Rosebank Mall</td>\n",
       "      <td>Licensed</td>\n",
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       "      <td>GMT+000000 Africa/Johannesburg</td>\n",
       "      <td>28.04</td>\n",
       "      <td>-26.15</td>\n",
       "      <td>POINT (28.04 -26.15)</td>\n",
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       "      <th>25598</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>47640-253809</td>\n",
       "      <td>Menlyn Maine</td>\n",
       "      <td>Licensed</td>\n",
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       "      <td>GMT+000000 Africa/Johannesburg</td>\n",
       "      <td>28.28</td>\n",
       "      <td>-25.79</td>\n",
       "      <td>POINT (28.28 -25.79)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25599</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>47609-253286</td>\n",
       "      <td>Mall of Africa</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>...</td>\n",
       "      <td>GMT+000000 Africa/Johannesburg</td>\n",
       "      <td>28.11</td>\n",
       "      <td>-26.02</td>\n",
       "      <td>POINT (28.11 -26.02)</td>\n",
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       "           Brand  Store Number        Store Name Ownership Type  ...  \\\n",
       "0      Starbucks  47370-257954     Meritxell, 96       Licensed  ...   \n",
       "1      Starbucks  22331-212325  Ajman Drive Thru       Licensed  ...   \n",
       "2      Starbucks  47089-256771         Dana Mall       Licensed  ...   \n",
       "...          ...           ...               ...            ...  ...   \n",
       "25597  Starbucks  47608-253804     Rosebank Mall       Licensed  ...   \n",
       "25598  Starbucks  47640-253809      Menlyn Maine       Licensed  ...   \n",
       "25599  Starbucks  47609-253286    Mall of Africa       Licensed  ...   \n",
       "\n",
       "                             Timezone Longitude Latitude              geometry  \n",
       "0             GMT+1:00 Europe/Andorra      1.53    42.51    POINT (1.53 42.51)  \n",
       "1                GMT+04:00 Asia/Dubai     55.47    25.42   POINT (55.47 25.42)  \n",
       "2                GMT+04:00 Asia/Dubai     55.47    25.39   POINT (55.47 25.39)  \n",
       "...                               ...       ...      ...                   ...  \n",
       "25597  GMT+000000 Africa/Johannesburg     28.04   -26.15  POINT (28.04 -26.15)  \n",
       "25598  GMT+000000 Africa/Johannesburg     28.28   -25.79  POINT (28.28 -25.79)  \n",
       "25599  GMT+000000 Africa/Johannesburg     28.11   -26.02  POINT (28.11 -26.02)  \n",
       "\n",
       "[25600 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geometry=pd.Series()\n",
    "for i in df_starbucks.index:\n",
    "    geometry.loc[i]=Point((df_starbucks.loc[i,'Longitude'],df_starbucks.loc[i,'Latitude']))\n",
    "df_starbucks['geometry']=geometry\n",
    "df_starbucks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get count of starbucks in each country\n",
    "top10country=df_starbucks.groupby('Country').apply(lambda x:len(x)).sort_values(ascending=False)[:10]\n",
    "#draw horizontal bar chart\n",
    "y_pos = np.arange(len(top10country))\n",
    "plt.barh(y_pos, top10country.values, align='center', alpha=0.5)\n",
    "plt.yticks(y_pos, top10country.index)\n",
    "#plt.xlabel('Usage')\n",
    "plt.title('Number of Stores in Each Country')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing that there are exceptionally huge amount of Starbucks in the US, we will focus our analysis on to just those Starbucks. Store data in `df_starbucks_US`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 13608 Starbucks in the US\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Store Number</th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Ownership Type</th>\n",
       "      <th>...</th>\n",
       "      <th>Timezone</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11964</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>3513-125945</td>\n",
       "      <td>Safeway-Anchorage #1809</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>...</td>\n",
       "      <td>GMT-09:00 America/Anchorage</td>\n",
       "      <td>-149.78</td>\n",
       "      <td>61.21</td>\n",
       "      <td>POINT (-149.78 61.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11965</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>74352-84449</td>\n",
       "      <td>Safeway-Anchorage #2628</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>...</td>\n",
       "      <td>GMT-09:00 America/Anchorage</td>\n",
       "      <td>-149.84</td>\n",
       "      <td>61.14</td>\n",
       "      <td>POINT (-149.84 61.14)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11966</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>12449-152385</td>\n",
       "      <td>Safeway - Anchorage #1813</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>...</td>\n",
       "      <td>GMT-09:00 America/Anchorage</td>\n",
       "      <td>-149.85</td>\n",
       "      <td>61.11</td>\n",
       "      <td>POINT (-149.85 61.11)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25569</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>22425-219024</td>\n",
       "      <td>Laramie - Grand &amp; 30th</td>\n",
       "      <td>Company Owned</td>\n",
       "      <td>...</td>\n",
       "      <td>GMT-07:00 America/Denver</td>\n",
       "      <td>-105.56</td>\n",
       "      <td>41.31</td>\n",
       "      <td>POINT (-105.56 41.31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25570</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>10849-103163</td>\n",
       "      <td>I-80 &amp; Dewar Dr-Rock Springs</td>\n",
       "      <td>Company Owned</td>\n",
       "      <td>...</td>\n",
       "      <td>GMT-07:00 America/Denver</td>\n",
       "      <td>-109.25</td>\n",
       "      <td>41.58</td>\n",
       "      <td>POINT (-109.25 41.58)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25571</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>10769-102454</td>\n",
       "      <td>Coffeen &amp; Brundage Lane-Sheridan</td>\n",
       "      <td>Company Owned</td>\n",
       "      <td>...</td>\n",
       "      <td>GMT-07:00 America/Denver</td>\n",
       "      <td>-106.94</td>\n",
       "      <td>44.77</td>\n",
       "      <td>POINT (-106.94 44.77)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13608 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Brand  Store Number                        Store Name  \\\n",
       "11964  Starbucks   3513-125945           Safeway-Anchorage #1809   \n",
       "11965  Starbucks   74352-84449           Safeway-Anchorage #2628   \n",
       "11966  Starbucks  12449-152385         Safeway - Anchorage #1813   \n",
       "...          ...           ...                               ...   \n",
       "25569  Starbucks  22425-219024            Laramie - Grand & 30th   \n",
       "25570  Starbucks  10849-103163      I-80 & Dewar Dr-Rock Springs   \n",
       "25571  Starbucks  10769-102454  Coffeen & Brundage Lane-Sheridan   \n",
       "\n",
       "      Ownership Type  ...                     Timezone Longitude Latitude  \\\n",
       "11964       Licensed  ...  GMT-09:00 America/Anchorage   -149.78    61.21   \n",
       "11965       Licensed  ...  GMT-09:00 America/Anchorage   -149.84    61.14   \n",
       "11966       Licensed  ...  GMT-09:00 America/Anchorage   -149.85    61.11   \n",
       "...              ...  ...                          ...       ...      ...   \n",
       "25569  Company Owned  ...     GMT-07:00 America/Denver   -105.56    41.31   \n",
       "25570  Company Owned  ...     GMT-07:00 America/Denver   -109.25    41.58   \n",
       "25571  Company Owned  ...     GMT-07:00 America/Denver   -106.94    44.77   \n",
       "\n",
       "                    geometry  \n",
       "11964  POINT (-149.78 61.21)  \n",
       "11965  POINT (-149.84 61.14)  \n",
       "11966  POINT (-149.85 61.11)  \n",
       "...                      ...  \n",
       "25569  POINT (-105.56 41.31)  \n",
       "25570  POINT (-109.25 41.58)  \n",
       "25571  POINT (-106.94 44.77)  \n",
       "\n",
       "[13608 rows x 14 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_starbucks_US = df_starbucks[df_starbucks['Country']=='US']\n",
    "print(\"There are %s Starbucks in the US\"%len(df_starbucks_US))\n",
    "df_starbucks_US"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Narrow down even more to have the table only contain information we need. In other words, drop columns \"Brand\" (obviously they're all Starbucks), \"Store Number\", \"Ownership Type\", \"Country\", \"Phone Number\", and \"Timezone\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>zip</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Safeway-Anchorage #1809</td>\n",
       "      <td>5600 Debarr Rd Ste 9</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "      <td>995042300</td>\n",
       "      <td>POINT (-149.78 61.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Safeway-Anchorage #2628</td>\n",
       "      <td>1725 Abbott Rd</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "      <td>995073444</td>\n",
       "      <td>POINT (-149.84 61.14)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Safeway - Anchorage #1813</td>\n",
       "      <td>1501 Huffman Rd</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "      <td>995153596</td>\n",
       "      <td>POINT (-149.85 61.11)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13605</th>\n",
       "      <td>Laramie - Grand &amp; 30th</td>\n",
       "      <td>3021 Grand Ave</td>\n",
       "      <td>Laramie</td>\n",
       "      <td>WY</td>\n",
       "      <td>82070</td>\n",
       "      <td>POINT (-105.56 41.31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13606</th>\n",
       "      <td>I-80 &amp; Dewar Dr-Rock Springs</td>\n",
       "      <td>118 Westland Way</td>\n",
       "      <td>Rock Springs</td>\n",
       "      <td>WY</td>\n",
       "      <td>829015751</td>\n",
       "      <td>POINT (-109.25 41.58)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13607</th>\n",
       "      <td>Coffeen &amp; Brundage Lane-Sheridan</td>\n",
       "      <td>2208 Coffeen Ave</td>\n",
       "      <td>Sheridian</td>\n",
       "      <td>WY</td>\n",
       "      <td>828016213</td>\n",
       "      <td>POINT (-106.94 44.77)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13608 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Store Name        Street Address          City  \\\n",
       "0               Safeway-Anchorage #1809  5600 Debarr Rd Ste 9     Anchorage   \n",
       "1               Safeway-Anchorage #2628        1725 Abbott Rd     Anchorage   \n",
       "2             Safeway - Anchorage #1813       1501 Huffman Rd     Anchorage   \n",
       "...                                 ...                   ...           ...   \n",
       "13605            Laramie - Grand & 30th        3021 Grand Ave       Laramie   \n",
       "13606      I-80 & Dewar Dr-Rock Springs      118 Westland Way  Rock Springs   \n",
       "13607  Coffeen & Brundage Lane-Sheridan      2208 Coffeen Ave     Sheridian   \n",
       "\n",
       "      State        zip               geometry  \n",
       "0        AK  995042300  POINT (-149.78 61.21)  \n",
       "1        AK  995073444  POINT (-149.84 61.14)  \n",
       "2        AK  995153596  POINT (-149.85 61.11)  \n",
       "...     ...        ...                    ...  \n",
       "13605    WY      82070  POINT (-105.56 41.31)  \n",
       "13606    WY  829015751  POINT (-109.25 41.58)  \n",
       "13607    WY  828016213  POINT (-106.94 44.77)  \n",
       "\n",
       "[13608 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_starbucks_US = df_starbucks_US.drop(columns=['Brand','Store Number','Ownership Type','Country','Phone Number','Timezone','Longitude','Latitude'])\n",
    "#reseting index to start from 0\n",
    "df_starbucks_US.reset_index(drop=True,inplace=True)\n",
    "#Rename some columns to have simpler name\n",
    "df_starbucks_US=df_starbucks_US.rename({'State/Province':'State','Postcode':'zip'}, axis='columns')\n",
    "df_starbucks_US"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b) Starbucks: Find Missing Data\n",
    "Drop rows with missing data. We will try to minimize the number of rows dropped to maximize our prediction accuracy. Therefore we will only drop rows with missing data in the following columns \"Store Number\", \"Ownership Type\", \"City\", \"State\", and \"Zip\" because they are of importance to our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>zip</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Safeway-Anchorage #1809</td>\n",
       "      <td>5600 Debarr Rd Ste 9</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "      <td>995042300</td>\n",
       "      <td>POINT (-149.78 61.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Safeway-Anchorage #2628</td>\n",
       "      <td>1725 Abbott Rd</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "      <td>995073444</td>\n",
       "      <td>POINT (-149.84 61.14)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Safeway - Anchorage #1813</td>\n",
       "      <td>1501 Huffman Rd</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "      <td>995153596</td>\n",
       "      <td>POINT (-149.85 61.11)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13605</th>\n",
       "      <td>Laramie - Grand &amp; 30th</td>\n",
       "      <td>3021 Grand Ave</td>\n",
       "      <td>Laramie</td>\n",
       "      <td>WY</td>\n",
       "      <td>82070</td>\n",
       "      <td>POINT (-105.56 41.31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13606</th>\n",
       "      <td>I-80 &amp; Dewar Dr-Rock Springs</td>\n",
       "      <td>118 Westland Way</td>\n",
       "      <td>Rock Springs</td>\n",
       "      <td>WY</td>\n",
       "      <td>829015751</td>\n",
       "      <td>POINT (-109.25 41.58)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13607</th>\n",
       "      <td>Coffeen &amp; Brundage Lane-Sheridan</td>\n",
       "      <td>2208 Coffeen Ave</td>\n",
       "      <td>Sheridian</td>\n",
       "      <td>WY</td>\n",
       "      <td>828016213</td>\n",
       "      <td>POINT (-106.94 44.77)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13607 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Store Name        Street Address          City  \\\n",
       "0               Safeway-Anchorage #1809  5600 Debarr Rd Ste 9     Anchorage   \n",
       "1               Safeway-Anchorage #2628        1725 Abbott Rd     Anchorage   \n",
       "2             Safeway - Anchorage #1813       1501 Huffman Rd     Anchorage   \n",
       "...                                 ...                   ...           ...   \n",
       "13605            Laramie - Grand & 30th        3021 Grand Ave       Laramie   \n",
       "13606      I-80 & Dewar Dr-Rock Springs      118 Westland Way  Rock Springs   \n",
       "13607  Coffeen & Brundage Lane-Sheridan      2208 Coffeen Ave     Sheridian   \n",
       "\n",
       "      State        zip               geometry  \n",
       "0        AK  995042300  POINT (-149.78 61.21)  \n",
       "1        AK  995073444  POINT (-149.84 61.14)  \n",
       "2        AK  995153596  POINT (-149.85 61.11)  \n",
       "...     ...        ...                    ...  \n",
       "13605    WY      82070  POINT (-105.56 41.31)  \n",
       "13606    WY  829015751  POINT (-109.25 41.58)  \n",
       "13607    WY  828016213  POINT (-106.94 44.77)  \n",
       "\n",
       "[13607 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_starbucks_US = df_starbucks_US.dropna(subset=['City','State','zip'])\n",
    "df_starbucks_US"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## c) Starbucks: Standardize Zip Codes\n",
    "We can see that some zipcodes have 9 digits, some have 5, some have less than 4. We will standardize them to have 5 digits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardize_zip (zip):\n",
    "    if len(zip) < 5:\n",
    "        #prepend 0's to shorter zipcodes\n",
    "        newzip = zip.zfill(5)\n",
    "    else:\n",
    "        newzip = str(zip)\n",
    "        #cut longer zipcodes to 5-digits\n",
    "        newzip = zip[:5]\n",
    "    return newzip        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INPUT \t\t-\t OUTPUT\n",
      "828016213  \t-\t 82801     \n",
      "92843      \t-\t 92843     \n",
      "06608      \t-\t 06608     \n",
      "066081234  \t-\t 06608     \n",
      "501        \t-\t 00501     \n",
      "1035       \t-\t 01035     \n"
     ]
    }
   ],
   "source": [
    "# Test function:\n",
    "print('INPUT', '\\t\\t-\\t', 'OUTPUT')\n",
    "for inp in ['828016213', '92843', '06608', '066081234', '501','1035']:\n",
    "    print('{:10s} \\t-\\t {:10s}'.format(inp, standardize_zip(inp)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>zip</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Safeway-Anchorage #1809</td>\n",
       "      <td>5600 Debarr Rd Ste 9</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "      <td>99504</td>\n",
       "      <td>POINT (-149.78 61.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Safeway-Anchorage #2628</td>\n",
       "      <td>1725 Abbott Rd</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "      <td>99507</td>\n",
       "      <td>POINT (-149.84 61.14)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Safeway - Anchorage #1813</td>\n",
       "      <td>1501 Huffman Rd</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "      <td>99515</td>\n",
       "      <td>POINT (-149.85 61.11)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13605</th>\n",
       "      <td>Laramie - Grand &amp; 30th</td>\n",
       "      <td>3021 Grand Ave</td>\n",
       "      <td>Laramie</td>\n",
       "      <td>WY</td>\n",
       "      <td>82070</td>\n",
       "      <td>POINT (-105.56 41.31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13606</th>\n",
       "      <td>I-80 &amp; Dewar Dr-Rock Springs</td>\n",
       "      <td>118 Westland Way</td>\n",
       "      <td>Rock Springs</td>\n",
       "      <td>WY</td>\n",
       "      <td>82901</td>\n",
       "      <td>POINT (-109.25 41.58)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13607</th>\n",
       "      <td>Coffeen &amp; Brundage Lane-Sheridan</td>\n",
       "      <td>2208 Coffeen Ave</td>\n",
       "      <td>Sheridian</td>\n",
       "      <td>WY</td>\n",
       "      <td>82801</td>\n",
       "      <td>POINT (-106.94 44.77)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13607 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Store Name        Street Address          City  \\\n",
       "0               Safeway-Anchorage #1809  5600 Debarr Rd Ste 9     Anchorage   \n",
       "1               Safeway-Anchorage #2628        1725 Abbott Rd     Anchorage   \n",
       "2             Safeway - Anchorage #1813       1501 Huffman Rd     Anchorage   \n",
       "...                                 ...                   ...           ...   \n",
       "13605            Laramie - Grand & 30th        3021 Grand Ave       Laramie   \n",
       "13606      I-80 & Dewar Dr-Rock Springs      118 Westland Way  Rock Springs   \n",
       "13607  Coffeen & Brundage Lane-Sheridan      2208 Coffeen Ave     Sheridian   \n",
       "\n",
       "      State    zip               geometry  \n",
       "0        AK  99504  POINT (-149.78 61.21)  \n",
       "1        AK  99507  POINT (-149.84 61.14)  \n",
       "2        AK  99515  POINT (-149.85 61.11)  \n",
       "...     ...    ...                    ...  \n",
       "13605    WY  82070  POINT (-105.56 41.31)  \n",
       "13606    WY  82901  POINT (-109.25 41.58)  \n",
       "13607    WY  82801  POINT (-106.94 44.77)  \n",
       "\n",
       "[13607 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_starbucks_US['zip'] = df_starbucks_US['zip'].apply(standardize_zip)\n",
    "df_starbucks_US"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## d) Census: Import and Putting Data Into Comprehensive Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain a census API key to access the information about population in each state\n",
    "c = Census(\"d05cc8c102603c7b50d7705c8f45251177e6346b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are totally 50 raw features selected\n"
     ]
    }
   ],
   "source": [
    "# Creating a dict that matches ids to real values. More information can be found here:\n",
    "# https://api.census.gov/data/2016/acs/acs1/variables.html\n",
    "info_dict = dict(male_15_17 = 'B01001_006E',\n",
    "male_18_19 = 'B01001_007E',\n",
    "male_20 = 'B01001_008E',\n",
    "male_21 = 'B01001_009E',\n",
    "male_22_24 = 'B01001_010E',\n",
    "male_25_29 = 'B01001_011E',\n",
    "male_30_34 = 'B01001_012E',\n",
    "male_35_39 = 'B01001_013E',\n",
    "male_40_44 = 'B01001_014E',\n",
    "male_45_49 = 'B01001_015E',\n",
    "male_50_54 = 'B01001_016E',\n",
    "male_55_59 = 'B01001_017E',\n",
    "male_60_61 = 'B01001_018E',\n",
    "male_62_64 = 'B01001_019E',\n",
    "male_65_66 = 'B01001_020E',\n",
    "male_67_69 = 'B01001_021E',\n",
    "male_70_74 = 'B01001_022E',\n",
    "male_75_79 = 'B01001_023E',\n",
    "male_80_84 = 'B01001_024E',\n",
    "male_85_plus = 'B01001_025E',\n",
    "female_15_17 = 'B01001_030E',\n",
    "female_18_19 = 'B01001_031E',\n",
    "female_20 = 'B01001_032E',\n",
    "female_21 = 'B01001_033E',\n",
    "female_22_24 = 'B01001_034E',\n",
    "female_25_29 = 'B01001_035E',\n",
    "female_30_34 = 'B01001_036E',\n",
    "female_35_39 = 'B01001_037E',\n",
    "female_40_44 = 'B01001_038E',\n",
    "female_45_49 = 'B01001_039E',\n",
    "female_50_54 = 'B01001_040E',\n",
    "female_55_59 = 'B01001_041E',\n",
    "female_60_61 = 'B01001_042E',\n",
    "female_62_64 = 'B01001_043E',\n",
    "female_65_66 = 'B01001_044E',\n",
    "female_67_69 = 'B01001_045E',\n",
    "female_70_74 = 'B01001_046E',\n",
    "female_75_79 = 'B01001_047E',\n",
    "female_80_84 = 'B01001_048E',\n",
    "female_85_plus = 'B01001_049E',\n",
    "median_age = 'B01002_001E', \n",
    "median_hh_income = 'B19013_001E',\n",
    "male_workers = 'B23022_003E',\n",
    "female_workers = 'B23022_027E',\n",
    "total_population = 'B01001_001E', \n",
    "median_rent = 'B25031_001E',\n",
    "median_home_value = 'B25077_001E',\n",
    "high_school_diploma = 'B15003_017E',\n",
    "bachelors_degree = 'B15003_022E',\n",
    "masters_degree = 'B15003_023E')\n",
    "print(\"There are totally %s raw features selected\"%len(info_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put data into a DataFrame structure called `df_census`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B01001_006E</th>\n",
       "      <th>B01001_007E</th>\n",
       "      <th>B01001_008E</th>\n",
       "      <th>B01001_009E</th>\n",
       "      <th>...</th>\n",
       "      <th>B15003_017E</th>\n",
       "      <th>B15003_022E</th>\n",
       "      <th>B15003_023E</th>\n",
       "      <th>zip code tabulation area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>84306</td>\n",
       "    </tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>33120 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       B01001_006E  B01001_007E  B01001_008E  B01001_009E  ...  B15003_017E  \\\n",
       "0              0.0          0.0          0.0          0.0  ...         24.0   \n",
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       "33119         51.0         23.0         11.0          6.0  ...        428.0   \n",
       "\n",
       "       B15003_022E  B15003_023E  zip code tabulation area  \n",
       "0             35.0         18.0                     84306  \n",
       "1             71.0         58.0                     84775  \n",
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       "...            ...          ...                       ...  \n",
       "33117        388.0        302.0                     14840  \n",
       "33118        950.0        822.0                     14843  \n",
       "33119        254.0        163.0                     14847  \n",
       "\n",
       "[33120 rows x 51 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_census=pd.DataFrame(c.acs5.zipcode(list(info_dict.values()), Census.ALL))\n",
    "#because the dataframe's columns' order was not by what we provide('info_dict.values()'), but the order of alphabet\n",
    "#So we need to re-order it first\n",
    "cols=list(info_dict.values())\n",
    "cols.append('zip code tabulation area')\n",
    "df_census =df_census[cols]\n",
    "df_census"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace census data variables with the dictionary of more meaningful titles we created above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       male_15_17  male_18_19  male_20  male_21  ...  high_school_diploma  \\\n",
       "0             0.0         0.0      0.0      0.0  ...                 24.0   \n",
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic=dict(info_dict)\n",
    "#reverse dict\n",
    "dic={v:k for k,v in dic.items()}\n",
    "df_census.rename(dic,axis='columns',inplace=True)\n",
    "df_census.rename(columns={'zip code tabulation area':'zip'}, inplace=True)\n",
    "df_census"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "<p>26696 rows × 51 columns</p>\n",
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      ],
      "text/plain": [
       "       male_15_17  male_18_19  male_20  male_21  ...  high_school_diploma  \\\n",
       "5           217.0        67.0     18.0     59.0  ...                433.0   \n",
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       "33119        51.0        23.0     11.0      6.0  ...                428.0   \n",
       "\n",
       "       bachelors_degree  masters_degree    zip  \n",
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       "6                  94.0            17.0  84513  \n",
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       "\n",
       "[26696 rows x 51 columns]"
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean null values\n",
    "df_census = df_census.dropna()\n",
    "#drop negative values because our table contains only people count and age therefore negative values indicate invalid input \n",
    "df_census['total_population']=df_census['total_population'].apply(float)\n",
    "df_census=df_census[(df_census.loc[:,'male_15_17':'masters_degree'] >= 0).all(1)]\n",
    "df_census"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA VISUALIZATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a) Visualizing Starbucks \"Profile\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame `df_profile` contains Census information of areas that have Starbucks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <td>940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13606</th>\n",
       "      <td>82801</td>\n",
       "      <td>544.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>...</td>\n",
       "      <td>233000.0</td>\n",
       "      <td>3427.0</td>\n",
       "      <td>3229.0</td>\n",
       "      <td>1665.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13607 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         zip  male_15_17  male_18_19  male_20  ...  median_home_value  \\\n",
       "0      99504      1068.0       551.0    595.0  ...           256400.0   \n",
       "1      99507       630.0       432.0    134.0  ...           292400.0   \n",
       "2      99515       591.0       293.0    130.0  ...           315600.0   \n",
       "...      ...         ...         ...      ...  ...                ...   \n",
       "13604  82070       216.0       644.0    404.0  ...           222600.0   \n",
       "13605  82901       637.0       475.0    138.0  ...           201000.0   \n",
       "13606  82801       544.0       345.0    245.0  ...           233000.0   \n",
       "\n",
       "       high_school_diploma  bachelors_degree  masters_degree  \n",
       "0                   5745.0            5119.0          2131.0  \n",
       "1                   4383.0            5900.0          2237.0  \n",
       "2                   3263.0            3265.0          1235.0  \n",
       "...                    ...               ...             ...  \n",
       "13604               1675.0            3151.0          1633.0  \n",
       "13605               5071.0            2788.0           940.0  \n",
       "13606               3427.0            3229.0          1665.0  \n",
       "\n",
       "[13607 rows x 51 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_profile = pd.merge(df_starbucks_US['zip'], df_census, how='left', on='zip')\n",
    "df_profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the gender in each age range of all Starbucks customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clumping age columns together so that age range is 5 years each\n",
    "df_gender_age= pd.DataFrame(columns=['male_15_19','male_20_24','male_25_29','male_30_34','male_35_39','male_40_44','male_45_49','male_50_54','male_55_59','male_60_64','male_65_69','male_70_74','male_75_79','male_80_84','male_85_plus','female_15_19','female_20_24','female_25_29','female_30_34','female_35_39','female_40_44','female_45_49', 'female_50_54','female_55_59','female_60_64','female_65_69','female_70_74','female_75_79','female_80_84','female_85_plus'])\n",
    "df_gender_age['male_15_19'] = df_profile['male_15_17'] + df_profile['male_18_19']\n",
    "df_gender_age['male_20_24'] = df_profile['male_20'] + df_profile['male_21'] + df_profile['male_22_24'] \n",
    "df_gender_age.loc[:,'male_25_29':'male_55_59'] = df_profile.loc[:,'male_25_29':'male_55_59']\n",
    "df_gender_age['male_60_64'] = df_profile['male_60_61'] + df_profile['male_62_64']\n",
    "df_gender_age['male_65_69'] = df_profile['male_65_66'] + df_profile['male_67_69'] \n",
    "df_gender_age.loc[:,'male_70_74':'male_85_plus'] = df_profile.loc[:,'male_70_74':'male_85_plus']\n",
    "\n",
    "df_gender_age['female_15_19'] = df_profile['female_15_17'] + df_profile['female_18_19']\n",
    "df_gender_age['female_20_24'] = df_profile['female_20'] + df_profile['female_21'] + df_profile['female_22_24'] \n",
    "df_gender_age.loc[:,'female_25_29':'female_55_59'] = df_profile.loc[:,'female_25_29':'female_55_59']\n",
    "df_gender_age['female_60_64'] = df_profile['female_60_61'] + df_profile['female_62_64']\n",
    "df_gender_age['female_65_69'] = df_profile['female_65_66'] + df_profile['female_67_69'] \n",
    "df_gender_age.loc[:,'female_70_74':'female_85_plus'] = df_profile.loc[:,'female_70_74':'female_85_plus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male_15_19</th>\n",
       "      <th>male_20_24</th>\n",
       "      <th>male_25_29</th>\n",
       "      <th>male_30_34</th>\n",
       "      <th>...</th>\n",
       "      <th>female_70_74</th>\n",
       "      <th>female_75_79</th>\n",
       "      <th>female_80_84</th>\n",
       "      <th>female_85_plus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1619.0</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>2226.0</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>...</td>\n",
       "      <td>568.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1062.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>...</td>\n",
       "      <td>500.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>884.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>...</td>\n",
       "      <td>393.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>238.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13604</th>\n",
       "      <td>860.0</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>907.0</td>\n",
       "      <td>...</td>\n",
       "      <td>333.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13605</th>\n",
       "      <td>1112.0</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>...</td>\n",
       "      <td>244.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13606</th>\n",
       "      <td>889.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>...</td>\n",
       "      <td>669.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>433.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13607 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       male_15_19  male_20_24  male_25_29  male_30_34  ...  female_70_74  \\\n",
       "0          1619.0      2534.0      2226.0      1531.0  ...         568.0   \n",
       "1          1062.0      1422.0      2023.0      1550.0  ...         500.0   \n",
       "2           884.0       710.0      1011.0      1037.0  ...         393.0   \n",
       "...           ...         ...         ...         ...  ...           ...   \n",
       "13604       860.0      2093.0      1002.0       907.0  ...         333.0   \n",
       "13605      1112.0      1075.0      1275.0      1262.0  ...         244.0   \n",
       "13606       889.0       736.0       720.0       770.0  ...         669.0   \n",
       "\n",
       "       female_75_79  female_80_84  female_85_plus  \n",
       "0             292.0         288.0           218.0  \n",
       "1             272.0         162.0           256.0  \n",
       "2             257.0         275.0           238.0  \n",
       "...             ...           ...             ...  \n",
       "13604         163.0         178.0           164.0  \n",
       "13605         230.0         230.0           106.0  \n",
       "13606         314.0         344.0           433.0  \n",
       "\n",
       "[13607 rows x 30 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gender_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Concatenate age ranges for each gender\n",
    "s_male = df_gender_age.loc[:, 'male_15_19':'male_85_plus'].sum()\n",
    "s_female = df_gender_age.loc[:, 'female_15_19':'female_85_plus'].sum()\n",
    "\n",
    "# Clean up indices\n",
    "headers = [s.replace('male_','').replace('_','-') for s in s_male.index]\n",
    "headers[-1] = '85+'\n",
    "s_male.index, s_female.index = headers, headers\n",
    "\n",
    "# Combine the two genders into one series\n",
    "s_both = pd.concat([s_male, s_female], axis=1)\n",
    "s_both.columns = ['male','female']\n",
    "\n",
    "# Make the yticks for the plot\n",
    "max_tick = np.max(s_male.values) if np.max(s_male.values) > np.max(s_female.values) else np.max(s_female.values)\n",
    "min_tick = np.min(s_male.values) if np.min(s_male.values) < np.min(s_female.values) else np.min(s_female.values)\n",
    "diff_tick = max_tick - min_tick\n",
    "plot_ticks = [min_tick,min_tick + (diff_tick*.25),min_tick + (diff_tick*.5),min_tick + (diff_tick*.75),max_tick]\n",
    "plot_labels = ['0th','25th','50th','75th','100th']\n",
    "\n",
    "# Make the xticks for the plot\n",
    "N = len(s_both.index)\n",
    "ind = np.arange(N)\n",
    "width = 0.35\n",
    "\n",
    "# Plot the lines\n",
    "male_fig = plt.plot(ind, s_both['male'], width, label='Men',color='slateblue')\n",
    "female_fig = plt.plot(ind, s_both['female'], width, label='Women',color='deeppink')\n",
    "\n",
    "# Make it look pretty\n",
    "plt.ylabel('Population Percentile',fontsize=14)\n",
    "plt.xlabel('Age Range',fontsize=14)\n",
    "plt.title('Figure 1: Starbucks Gender Breakdown by Age Group')\n",
    "plt.ticklabel_format(style='plain')\n",
    "plt.xticks(ind, s_both.index, rotation=70,fontsize=14)\n",
    "plt.yticks(plot_ticks,labels=plot_labels,fontsize=14)\n",
    "plt.legend((male_fig[0], female_fig[0]), s_both.columns, handles=[male_fig[0],female_fig[0]],loc='upper right',fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//n0GDhxI48aNWbhwIbVq1QJg1qxZREdHP1e7CsLa2prMzEwSExP1ElfOsJCNjY3SW3raZ92uXTvatWvH/fv32b9/P4sXL2bAgAEcPHiQSpUqYWdnx9KlS5873pzjW4+Kj4/Ps1dpY2NDRkYGycnJejvDxMTE59qh56VSpUp5HkPKGZq3trbOc7gtPj4+14+Ggr5vYWzbR12+fJnbt2/neUwIsrfrhAkTmDBhAufOnePHH38kMjKS6tWrK9Pkn0VGRgbp6el6CePR7aNSqXJ9Dwvy4yPnfzm/79DzfA4gEzFKTIMGDbCwsMg1a+vxoQZLS0vu3LmjPM/KyjLoALe3tzexsbG4ubnh7u6Ou7s7Tk5OzJkzhwsXLuT7ulGjRnH06FHmzJnz3Akrx+uvv87x48fZuHFjnkODOfHqdDpSU1OVeN3d3Tl06BArVqzA2NiYZs2aceHCBb1fhIcOHco11JcjNjaWpKQkevfurSSsrKwsDh48WKxn8nt7ewPkmhn4888/Y2dnp0x3BvQ+69u3b+sdK1i4cKFyDK1SpUoEBATQt29f7t+/T0pKCt7e3iQkJFCxYkW9bbht2zZ+/PHHAsX766+/6m3XmJgY/v77b72ZpTl8fHwA9CbcJCUlcerUKYPf01BeXl5cuHCB69ev68XWv39/oqOj8fb25siRI3o//hISEjh06FCesRuqsLbto5YuXYqJiQmdO3fOVZeQkECbNm3Ys2cPkL2/GDNmDM7OzsrM15we8rN4dNbtnTt3OHXqlPI5Wlpa8uDBA5KTk5Vljh8/rvf6J723hYUFDRo0yPV9P3DgAPfv33+uzwGkp1ViKlWqRO/evVm6dCmmpqY0aNCAf//735w5c0bv2Iqvry+RkZGsXr2aunXrsn79eu7evas3/JSXQYMG0b17d4YPH07Xrl3RaDRERERw8+bNfI9P7dmzhz179tC5c2ecnZ31djoVKlSgfv36AJw9exZTU1ODz7Nq0aIFFhYWrFy5kokTJ+a5TIMGDejQoQOfffYZQ4YMoU6dOvz5558sXryYfv36oVar6dy5M8uXL2fgwIGMHDmSBw8eMG/evHx/zbu4uGBhYUFERARZWVk8ePCAtWvXEh0drQxDPLqti0r9+vXp0KEDM2fOJDU1FVdXV3799Ve2b9/O5MmTUavVWFtb4+HhwfLly3FycsLIyCjXSaHNmjVj0aJFTJw4kYCAAJKSkliyZAne3t7Y2trStm1b3N3d6d+/P0OGDMHJyYndu3fz3XffERoaanC8AwcOpHv37gQFBfHRRx9x//595s+fT7Vq1fLcwbq4uPD2228r0++dnZ1ZunTpE2fOPquuXbuyYsUKBg4cyJAhQzAyMlIO+r/66qvUqVOHH374gT59+jBo0CB0Oh2LFy/G1NTUoKtH5Od5tu2DBw+U/yWtVsvdu3fZvn07O3fuZMyYMXrH53LY2tpSq1Ytpk6dSmpqKk5OTuzbt4/r16/Tvn17IPt4X3p6Or/88guvvPKKwW1Rq9VMnz6d9PR0LCwsCA8Px8bGRpkk1Lp1a2bMmMGECRP48MMPiY6OZu3atXrryPle7tmzR7niz6OGDh3KoEGDGDFiBF26dOHmzZvMnTsXLy+vfA8BGEqSVgkaMmQIWq2WlStXkpKSQps2bejRowc//PCDsszAgQOJi4tj3rx5GBsb8/bbbzNgwADWrFnzxHW7ubmxcuVK5s+fz7BhwzAzM6Nx48bMmjVLbzjyUTm9vq1bt7J161a9unr16imz/YYMGUK1atVYvXq1Qe00MTGhbdu2/PTTT3kODeb48ssvWbBgAcuWLePu3btUq1aNTz/9lL59+wJgZmbGypUrCQsLY+zYsVhZWTFy5Ehmz56d5/oqVarEwoULmTVrFsHBwVSuXJkmTZqwYMEChg0bRlRUVLGdEJnTthUrVpCYmIiLiwuzZ8/m7bffVpaZMWMGISEhjBo1Cnt7e/r378/BgweVeh8fH+bOncuyZcv46aefMDMzw9fXV5kRZ2RkxLfffsuXX37J7NmzSUlJoVatWsyYMcPgGYrwv+/O3LlzGT58OObm5vj5+fHZZ5/lOm6VY9q0adja2rJw4UIyMjLo1q0bjo6OPHjw4Bm3WN6srKxYs2YNM2fOZOzYsZiamirbwNjYGCcnJ7777jtmz57NmDFjMDIywsfHh3nz5lG1atVnft/n2bZXr17l/fffV9ZjZ2dH/fr1+eabb2jdunW+r5s7dy6zZs3iyy+/JDExkdq1azNnzhxl9mJAQABbt25lxIgRDB8+3ODEZWxszMSJE5kxYwZxcXE0bdqUr776Shm2q1OnDlOnTmXx4sUEBQXh4eHBV199pTdTtnnz5rRq1YqwsDDee+89+vTpo/ce7dq1Y9GiRSxatIhBgwZhY2PDm2++yciRI584ccoQKl1JX3GxnNJoNPz888+0atVKb/LFp59+SmxsrF7iEkIIkU16WiXE1NSUiIgINm3aRL9+/TA3N+fQoUP8/PPPTJ06taTDE0KIUkl6WiXo0qVLfPnllxw/fpy0tDRq167NRx99xL/+9a+SDk0IIUolSVpCCCHKDJnyLoQQosyQY1qFKOdchuedHSOEEOWJVqsF/ndO45NI0hKFJueL99xJW5cFGf89sdHEClRlY0Cg0NpfRkn7y3f7i4skrUKU82UtrnN/SpucSywZeufVfKVchh//+4vr7Utg+dLzra+YFFr7yyhpf/lu//MoyNVTJGmJ0sesCrT47n+PhRDivyRpidLHxBJe6vH05YQQ5U7ZOFgghBBCID0tURrpsiDzv7dCMLYoMxMxhBBFT/YGovRJvQKbrLL/UnPfO0kIUX5J0hJCCFFmSNISQghRZkjSEkIIUWbIRAxRopIzNJxNStQrM0uPx+u/j0/ei+fhg4pKXUNrG6xMTIsxQiFEaSJJS5Sos0mJfHDod72yall3+OO/j4eeOMJ19SWlbl1zP16t4lCMEQohShMZHhRCCFFmSNISQghRZsjwoCh14lU29DKfoDwWQogckrREqfNQZcoBY4+SDkMIUQpJ0hKFxs7ODjMzs5IOQwjxApOkJQqNmZkZGcZGRMXfMWj5JrZ533bESKelqu4uALdUdmhVclM9IUQ2SVqiUOU1hT0/UW+8k2d5Vd1d/kgdAkAri3Cuq2SKuxAiW7HOHrxy5QoDBw6kadOm+Pr6MnPmTB4+fAjAoUOH6Nq1K15eXnTo0IFNmzbpvbZDhw64urrq/Z07d06pX716Nb6+vnh5eTFu3DjS0tKUOo1Gw6RJk2jatCktW7bk66+/1lv39evX6dOnD56ennTs2JHffzdspyuEEKJ4FVtPS6PRMHDgQOrWrcv69eu5e/cu48ePB6B79+4MGDCAQYMG0bFjR6KiopgwYQJ2dna0a9cOjUbD1atXWbduHTVq1FDWWblyZQB2797N/PnzmTVrFg4ODowbN46ZM2cyZcoUAGbNmsXJkyeJjIzk1q1bjB49GmdnZwICAtDpdAwaNIg6derw/fffs3fvXoYNG8ZPP/2k915CCCFKXrElrdOnT3PlyhU2bdqEhYUFderUYfjw4cycORMrKysaNGjAwIEDAahVqxZHjx5l27ZttGvXjtjYWFQqFe7u7piYmORa98qVK+nZsyf+/v4AhISE8PHHHzNmzBhUKhUbN25kyZIluLm54ebmRr9+/VizZg0BAQEcPnyYS5cu8d1332FpaUndunU5ePAg33//PSNHjiyuzSOEEMIAxZa0XFxcWLZsGRYWFkqZSqVCo9HQsWNHfH199ZZXqVTK0GFMTAzVq1fPM2FptVr++usvgoODlTJPT0+0Wi3nzp1DrVaj0Wjw9vZW6r29vYmIiCAzM5OoqCgaNmyIpaWlXv2xY8eeqZ1arZaYmJhnem1Z5+zsXCzvk56ezo0bN4rlvZ5Fef38c0j7y3f7n4VWq8XIyLAJV8V2TMvW1pYWLVooz7OyslizZg3e3t7Url0bNzc3pS4+Pp7t27fTtGlTAC5evIiRkRH9+vWjZcuW9OzZk6ioKACSk5N5+PAhDg7/O1hvbGyMjY0Nt27dIi4uDmtra72p2FWqVCEjI4OEhATi4uL0XgvZU7dv3bpVJNtBCCHEsyux2YMzZszg3LlzfP/993rlaWlpDBkyBAcHB7p37w5k/3JJTk5m1KhRODo6snHjRnr37s1PP/2kZGdTU/0rf5uamqLRaMjMzMyzDrKPs6Wnp+fqwZmampKRkfFM7TIyMqJOnTrP9NqyLj09vVjex9zcvFRu45xf2KUxtuIg7S/f7X8ep06dMnjZYk9aOp2OadOmsW7dOhYsWEC9evWUuvv37zNgwACuXbvG2rVrMTc3B2DOnDk8fPhQGcILCQnhxIkTbN26lR49egDZCehRGo2GChUqKEOQj9dB9s7PzMyMlJSUPF8rhBCidCnWpJWVlcWECRPYtm0b8+bN47XXXlPqEhIS6Nu3L/Hx8axatYqaNWsqdSYmJnq9IZVKhYuLC3fu3MHGxgYzMzPi4+N5+eWXAcjMzCQxMREHBwfUajXJycloNBqlhxUXF4epqSnW1tY4OjoSHR2tF2d8fDz29vZFuSnEE8SpKvNWxZnKYyGEyFGs52nNnDmTbdu2sXDhQl5//XWlPGc6/L179/juu+9wcXHRe123bt1YtmyZ8jwrK4vz58/j4uKCWq3G3d2d48ePK/WnTp3CyMiIBg0a0KBBA0xMTDh58qRSf/z4cRo1aoSxsTEeHh5ER0frndd1/PhxPD09i2ITCANoVCb8n5EL/2fkgkaVe/KNEKL8Krae1qlTp1i5ciWffvopbm5uxMXFKXU//PADZ86c4ZtvvsHc3FypMzExwcbGhjZt2vDtt9/i6upKjRo1WLFiBUlJSXTt2hWAHj16MHHiRFxdXXFyciI0NJSuXbsqMxU7d+5MaGgoM2fOJC4ujuXLlxMWFgaAj48Pzs7OjB07lqFDh/Lbb78RFRXFtGnTimvTCCGEMFCxJa1du3YB2cen5syZo1fXqFEjMjMz+eijj/TKGzduzLp16wgODiYrK4vPP/+chIQEPDw8WLFiBZUqVQIgICCA69evExISgkajoX379owdO1ZZz7hx4wgJCaF3795YWFgwePBgOnXqBGRPnIiIiGDChAl06dKFmjVrEh4eTvXq1Ytwa4gnMdVl8HLWVQD+VteQ3pYQQqHS6XS6kg7iRZEzA6a8Di2mp6cTlXq/QNcezOtahdWy7uhfe1D9v1MS1jX349UqpfNahOV99pi0v3y3/3kUZN8pdy4WQghRZkjSEkIIUWZI0hKF5vGTuIUQorDJ/bTEEyVnaDiblGjQsvnd1FEIIQqLJC3xRIVxU0chhCgsMjwohBCizJCkJYQQosx4puFBjUZDdHQ0tWvXVk7wFaKw3FLZ0coiXHkshBA5DOppXbp0ie7du3PixAlSUlLo0qUL7733Hm3atHnmmyUKkR+tyojrageuqx3Qqgy7MZwQonwwKGmFhYXh4ODASy+9xObNm0lISOD333+nT58+zJw5s6hjFEIIIQADk9aJEyf47LPPsLW15ddff8Xf3x9HR0c6d+7MhQsXijpGUc6Y6TS0zoyidWYUZjrN018ghCg3DEpaFStWJDk5mYSEBE6cOIGfnx8Aly9fxsbGpkgDFOVPFV0iq9KnsSp9GlV0hp0jJoQoHwyaiNG+fXtGjhyJmZkZDg4O+Pr68vPPPzNt2jTl9iBCCCFEUTMoaU2aNIk1a9Zw/fp1evbsiampKRqNhuDgYD788MOijlEIIYQADExaxsbGue511blz56KIRwghhMhXvkkrMDAQlUpl0EpWrVpVaAEJIYQQ+ck3aXl7eyuPExMT2bRpE/7+/ri5uWFiYsLZs2fZtWuXDA8KIYQoNvkmrREjRiiP+/Tpw4QJE+jRo4feMs2aNeP7778vuuiEEEKIRxh8nlbz5s1zlXt5eREdHV3oQQkhhBB5MShpNWzYkKVLl/LgwQOl7P79+8yfPx9PT88iC06UTzdUVXCzXImb5UpuqOQeXUKI/zFo9mBYWBj9+/enRYsW1KxZE51Ox5UrV3B2dmbZsmVFHaMoZ3QqNamYl3QYQohSyKCkVadOHXbs2EyQOLIAACAASURBVMHBgweJiYkBoF69erRo0QJjY7mPpBBCiOKRb8bJysrSX9DYGF9fX3x9fXMtp1bLbblE4amoe8Brmdl3D/jFuAlpqgolHJEQorTIN2k1bNjQ4PO0zp07V2gBCVFZl8yCB18B0MoiXJKWEEKRb9KSE4aFEEKUNvkmLR8fn1xlMTExxMTEoNVqqV27NvXr1y/S4IQQQohHGTSLIikpiTFjxrBv3z6sra3RarWkpqbSpEkTIiIiqFSpUlHHKQQAtSwsSzoEIUQJMnjKe1xcHDt27KB27doAXLx4kbFjxzJjxgymT59epEEKkcPC2JjkDA1nkwy7z1ZDaxusTEyLOCohRHExKGn99ttvrFy5UklYAHXr1mXy5MkEBQUVWXBC5OVsUiIfHPrdoGXXNffj1SoORRyREKK4GDRXvUKFvGdvqVQqtFptoQYkhBBC5MegpNWuXTumTJnCpUuXlLLY2FjCwsJo27ZtkQUnyi8NRmgwKukwhBCljEHDg5999hmDBw+mY8eOWFpmHwhPTU3Fz8+PSZMmFWmAovy5rnbAtdK6kg5DCFEKGZS0rKysWL16NdHR0cTGxmJmZoaLi4veMS4hhBCiqBXowoG2trZYWFgoz69evQpAjRo1CjcqIYQQIg8GJa0//viDMWPGkJCQoFeu0+lQqVRyGSdRqCx1aXTNyJ4duNnEjxRVxRKOSAhRWhh8nlbz5s3p16+fnEgsipy1LoWQh5EA/GLsLUlLCKEwKGndvHmTb775RoYBhRBClCiDpry/+uqrHD16tKhjEUIIIZ4o357WggULlMf29vZ8/vnnHDhwgJo1a+a6f9bw4cOLLkIhhBDiv/JNWseOHdN77unpSXx8PPHx8Xrlht5zSwghhHhe+Sat1atXF2ccQgghxFMZdEwLIDo6mokTJxIYGMjt27dZs2YN+/fvL8rYhBBCCD0GJa0DBw7QvXt3tFotUVFRaDQaEhISGDRoENu2bSvqGEU5k4Wa26rK3FZVJsvw31VCiHLAoCnv8+bNY9y4cbz//vvs3LkTgGHDhmFvb09ERARvvfVWkQYpypeb6iq8arm0pMMQQpRCBv2MjY2NpUWLFrnKW7ZsyfXr1ws9KCGEECIvBiWt6tWrc+rUqVzle/fulROOhRBCFBuDhgdHjBjB6NGj+euvv9BqtWzevJmrV6+ya9cuZs+eXdQxinKmki6VPprtACw3DeC+yuIprxBClBcG9bRee+011q5dS2JiIvXq1WPfvn1kZWXx3Xff0bFjx6KOUZQzVrpURmi+Z4Tme6x0qSUdjhCiFDH41iT169dn1qxZyvOEhAQqV65cJEEJIYQQeTGopxUfH8+nn37KuXPn0Gg09O7dm5YtW+Lv78+FCxeKOkYhhBACMDBphYaGcuvWLaysrPj3v//NmTNnWLNmDa1btyYsLKyoYxTimdWysCzpEIQQhcig4cGDBw+yceNGqlWrxp49e2jbti3e3t44ODjIOVqiVLMwNiY5Q8PZpESDlm9obYOViWkRRyWEeFYG9bSMjY3R6XSkpaVx5MgR/Pz8gOxhQwuLgs/s0mg0vPnmmxw8eFApW7JkCa6urnp/06ZNU+qjo6N5//338fDwoEuXLpw+fVpvnT///DPt27fHw8OD4OBg7t69q9TpdDrmzZtH8+bNadq0KV988QVarVapT0xMZNiwYTRu3Jh27drxww8/FLhNovQ6m5TIB4d+N+jP0OQmhCgZBiWt5s2bM2HCBAYPHoyZmRlt27bl4MGDjB8/Hn9//wK94cOHD/nkk09yHQu7ePEigYGB/PHHH8pfzi1P0tLS6NevHx4eHmzZsgVvb28GDBhASkoKAKdPn2bs2LEEBwezYcMGUlJSGD16tLLuFStWsGXLFhYsWEB4eDg//fQT3377rVI/duxYEhMTWbduHYMGDWLy5MmcOHGiQO0SQghR9AxKWlOnTsXd3R1zc3OWLVuGhYUFFy5coE2bNowfP97gN7t48SLvvfceV65cyVUXExNDw4YNsbe3V/4sLbOPR/z888+YmJgwduxY6tSpw/jx46lUqRI7duwAYM2aNbz++ut06dJFmeX4xx9/8M8//wCwcuVKhg0bho+PD82aNWPUqFF89913AFy5coXffvuNsLAwXF1d6datG2+//TZr1641uF2icGViTLS6BtHqGmQaPsFVCFEOGLRHsLS0ZOLEiXplvXv3LvCbHTt2jJYtWzJ06FA8PT2Vcp1Ox6VLl6hdu3aer4uKiqJx48bKzSdVKhWNGzfm5MmTvPvuu0RFRdGnTx9leScnJ6pVq8bJkyepUKECN2/epEmTJkq9t7c3t27d4ubNm0RFRWFvb0+tWrX06iMiIgrcPlE4bqtt6Wgxp6TDEEKUQgYlrUeH2vLy6PlbT9K9e/c8y69du0Z6ejobN27kk08+oUKFCnTt2pU+ffqgVquJi4vLldDs7OyIjo4G4M6dOzg4OOSqv337NnFxcQB69VWqVAHg1q1bxMXF5fnaW7duGdSmx2m1WmJiYp7ptaWNs7NzSYdQItLT07lx48Yzv/5F+fyflbS/fLf/WWi1WoyMjAxa1qCk9fjKMjMzuXr1KufOnXumHtfjcj5kR0dHlixZwtmzZ5VJGP369SM9PR1TU/0ZXaampmg0GgAePHiQb/2DBw+U54/WQfaEkPzWnZGRgU6nkzszCyFEKWJQ0poxY0ae5ZGRkZw9e/a5g2jTpg2HDx9WrrDh6urKvXv3+O677+jXrx9mZmZKgsqh0WioUKECwBPrH01QJiYmymMAc3PzJ772WRKWkZERderUKfDrSq3U+8X+lja6+4x4uAmA+WbvkqiqVKzvb25u/kyfYc6Prxfq8y8AaX/5bv/zyOuC7Pl5rjvstW/fnl9++eV5VqF4/JJQderU4c6dO0B2DyxnmC9HfHw89vb2Sn18fHye9Y6OjsrzHDnryqnP77WiZFjo0umdsZPeGTux0KWXdDhCiFLEoKSVlZWV6y85OZmVK1cWyvUHV65cmesk5bNnzyrHsTw8PDh58iQ6nQ7Inrhx8uRJZTKHh4cHx48fV1578+ZNbty4gaenJ46Ojjg7O+vVHz9+HAcHB5ycnPD09OT27dtcu3ZNr97Dw+O52yWEEKJwGTQ82LBhwzyHyszMzJg6depzB9G6dWvmzp3LnDlz6NatG6dPn+brr78mNDQUgDfeeIM5c+YQFhZGjx492LhxIykpKXTq1AmADz74gMDAQBo3boyHhwfTpk3D19eXl156SamfO3cuTk5OGBkZMXfuXHr16gVAjRo1aNWqFWPGjGHSpEmcOXOGbdu2sWrVqudulxBCiMJlUNJ6fAeuUqkwMTGhbt26yrlUz8PFxYUlS5bw5ZdfsmrVKqpUqcKoUaOU3pelpSVLly7l888/Z9OmTbi6urJs2TLlvb28vAgLC+Orr74iMTGRFi1a6F0TsW/fviQkJDBs2DDUajVdunShb9++Sv2sWbOYMGEC7733HlWqVGHq1Kl4eXk9d7uEEEIULoOSlo+PD5B9oDEmJgatVkvt2rWfK2GdP39e73nz5s3ZvHlzvsu/8sorT7y80r/+9S/+9a9/5VlnZGTE2LFjGTt2bJ71dnZ2LFmyxICohRBClCSDklZSUhJjxoxh3759WFtbo9VqSU1NpUmTJkRERFCpUvHO7hJCCFE+GTQRIywsjLi4OHbs2MGRI0c4duwY27ZtIz09Pd/p8EIIIURhM6in9dtvv7Fy5Uq9q1LUrVuXyZMnExQUVGTBifLpIaYcNmqoPBZCiBwGJa2ck3gfp1Kp9G7xIURhiFfb8EHFkJIOQwhRChk0PNiuXTumTJnCpUuXlLLY2FjCwsJo27ZtkQUnhBBCPMqgntZnn33G4MGD6dixozJjMDU1FT8/PyZNmlSkAQohhBA5DEpaVlZWrF69mujoaGJjYzEzM8PFxSXfW4kI8Txss5KZ/DASgClmH5OgtirhiIQQpUWB7rBXv3596tevX1SxCAGAOQ94J/M/AMw2+wCQpCWEyJZv0qpfv77BVzk/d+5coQUkhBBC5CffpBUZGan3XKfTERwcTGhoqHLldCGEEKI45Zu0mjdvnqtMrVbj7e1NjRo1ijQoIYQQIi/PdT8tIYQQojhJ0hJCCFFmSNISQghRZuR7TOv777/PVZaVlcWOHTuwtbXVK+/WrVvhRybKrQeYscPYR3kshBA58k1aERERucrs7OxYv369XplKpZKkJQrVXbU1g8xHlXQYQohSKN+ktXfv3uKMQ4hSoZbF89+JWwhRdAp0RQwhXnQWxsYkZ2g4m5Ro0PINrW2wMpHbpwhRXCRpiVKnSlYicx+EA/BJhSHEq22K9f3PJiXywaHfDVp2XXM/Xq3iUMQRCSFySNISpY4ZGlprTyuPhRAiR75T3jdt2kRqampxxiKEEEI8Ub5Ja+rUqSQmZo/rN2jQgISEhGILSoiy4tGJG87Ozjg7O5dgNEK8+PIdHqxZsyaDBw+mXr166HQ6QkNDMTPL+5yZWbNmFVmAQpRmeU7cSL2f7/IycUOI55Nv0vrmm2/49ttvuX8/+x9QrVZjZGRUbIEJUVbIxA0hik++ScvR0ZHx48cDcO3aNUJDQ7GykpvxCSGEKDkGzR5cvXo1aWlprF27ltjYWLRaLS4uLgQEBOS6pJMQQghRVAxKWtHR0fTr1w8TExPc3NzQarXs3buXRYsWsWbNGurWrVvUcYpyJI0KrDPxVx4LIUQOg5LWtGnTaN26NWFhYRgbZ78kMzOTSZMmMW3atFx3ORbiedxTWzG+woCSDkMIUQoZdGuS06dPExQUpCQsAGNjY4KCgjh16lSRBSeEEEI8yqCk5eDgwJUrV3KVX758GUtLucCoEEKI4mHQ8OD777/PxIkTGTp0KK+88goAUVFRhIeH07179yINUJQ/DlkJfJP+BQD9zMdwRy2TfYQQ2QxKWn379iU9PZ25c+eSlJQEgL29PX379uWjjz4qyvhEOWRCJu5Zl5THQgiRw6CkpVKpGDp0KEOHDuXu3buYmZnJsKAQQohiV+CrvNvZ2RVFHKKYFOReUU1sqxRxNEIIUTBya5JypiCXHIp6450ijkYIIQrGoNmDQgghRGlgUNIKDw/nxo0bRR2LEEII8UQGJa0VK1ag1WqLOhYhhBDiiQxKWu+88w6LFi0iJiaG9PR0srKy9P6EKEz3VRVZZNqZRaadua+qWNLhCCFKEYMmYvzyyy/cvn2bf//733nWnzt3rlCDEuVbssqSL816lHQYQohSyKCkNXv27KKOQwghhHgqg5KWj48PALdv3+bSpUt4enqSkpJClSpyHo8QQojiY9AxrdTUVIYNG4afnx99+vQhLi6OSZMm0b17d+7evVvUMYpypmrWXQ6kDOZAymCqZsn3SwjxPwYlrS+++IJ79+7x66+/YmZmBsDYsWMBmDp1atFFJ8olI7RU18VRXReHETJrVQjxPwYlrb179zJu3DiqVaumlNWqVYuQkBD+85//FFlwQgghxKMMSloPHjzAxMQkV7lGo0Gn0xV6UEIIIUReDEpa/v7+zJkzh+TkZKXs8uXLhIWF0aZNm6KKTQghhNBjUNKaNGkSJiYmNGvWjPT0dN555x06duyIjY0NEyZMKOoYhRBCCMDAKe+WlpYsXLiQq1evEhMTQ2ZmJrVr16ZOnTpFHZ8QQgihMPgq71lZWcTGxhIbG8vNmze5c+dOUcYlhBBC5GJQT+uff/6hT58+3Lt3j5deegmdTsfly5epVasW4eHhVK9evajjFOVIssqSaWaBymMhhMhhUNIaM2YMXl5ehIaGYmFhAUBycjLjx49n0qRJREZGFmmQony5r6rIN6ZvlXQYQohSyKDhwbNnzzJ48GAlYQFYWVkxcuRITpw4UWTBCSGEEI8yKGl5enpy6NChXOXHjx+nQYMGhRLIli1bcHV1zfPvxo0bTJo0KVf5ihUrlNcfPnyYt956Cw8PDwIDA/nnn3/01r969Wp8fX3x8vJi3LhxpKWlKXUajYZJkybRtGlTWrZsyddff10obRJCCFG48h0eXLBggfK4evXqTJs2jcOHD+Pu7o5arebvv//m559/plevXoUSSKdOnWjdurXyPCsri+DgYKpXr46zszMXL15k9OjRvP3228oylpbZxztu3rxJcHAwgwYNom3btixatIhBgwaxbds21Go1u3fvZv78+cyaNQsHBwfGjRvHzJkzmTJlCgCzZs3i5MmTREZGcuvWLUaPHo2zszMBAQGF0jZRMNWy4tiZ+ikAb1jM4bravoQjEkKUFvkmrWPHjuk9b9y4Mffu3WP//v1KmaenJ3/99VehBFKhQgUqVKigPF+zZg03btxQjpfFxsbi5uaGvX3uHdjGjRupX78+QUFBAEyfPp2WLVty+PBhWrRowcqVK+nZsyf+/v4AhISE8PHHHzNmzBhUKhUbN25kyZIluLm54ebmRr9+/VizZo0krRKjw5IHymMhhMiRb9JavXp1ccahJyUlhfDwcIYNG4a1tTVxcXEkJiZSu3btPJePioqiadOmynNzc3MaNWrEyZMnadasGX/99RfBwcFKvaenJ1qtlnPnzqFWq9FoNHh7eyv13t7eREREkJmZibGxQXNVFFqtlpiYmAK2uHg4OzuXdAgCSE9P58aNGyUdRpEprd//4lLe2/8stFotRkZGBi1r8B553759xMbGotFo9MpVKhUDBgwoWIRPsWHDBkxNTXn33XcBuHjxIsbGxixYsID9+/dTuXJlPvroI7p06QJAXFwcDg4Oeuuws7Pj9u3bJCcn8/DhQ716Y2NjbGxsuHXrFiYmJlhbWytXrweoUqUKGRkZJCQk5FqvEEKIkmNQ0ho3bhzbtm2jTp06ekN4UPhJS6fTsWHDBnr27KlcpDc2NhaA+vXrExgYyJ9//snkyZMxNzenY8eOpKenY2pqqrceU1NTNBoNDx48UJ7nVZ+ZmZlnHZArQRvCyMiodF8pJPV+SUdQ7pmbm5fu78gzyulhvIhtM0R5b//zOHXqlMHLGpS0du3axfz583nttdeeOShDnTlzhitXrvDOO+8oZT169CAgIAAbGxsgO3n9888/rFu3jo4dO2JmZpYrwWg0GmxsbJQeVF71FSpUQKVS5VkH2TsXIYQQpYdBU94dHR1xdHQs6lgA2L9/Px4eHnrvp1KplISVw8XFhdu3byvxxcXF6dXHx8djb2+vJK74+HilLjMzk8TERBwcHHB0dCQ5OVkvccXFxWFqaoq1tXVRNFEIIcQzMqinFRoaSmhoKD179sTJyQm1Wj/XPToJ4nk9PqkCYObMmVy6dImlS5cqZefOncPFxQUADw8PvdmO6enpnD17luDgYNRqNe7u7hw/fpwWLVoA2V1RIyMjGjRogFqtxsTERJm0AdnnnzVq1KjAkzCEEEIULYP2ymfOnOHcuXOMHTs2V51KpeLcuXOFFtCFCxfo2LGjXlm7du3o3bs3q1atok2bNhw4cICtW7cqJxd37dqVb7/9lsWLF9O+fXsiIiJwdnamefPmQPbw4sSJE3F1dcXJyYnQ0FC6du2qXOGjc+fOhIaGMnPmTOLi4li+fDlhYWGF1iZRMPdUVoyqMEh5LIQQOQxKWosXL2bkyJF88MEHuSZiFLb4+PhcQ4E+Pj7MmTOHiIgIZs+eTY0aNZg7dy5NmjQBsk9+XrhwITNmzGDJkiV4eHgQERGh9AgDAgK4fv06ISEhaDQa2rdvr5eAx40bR0hICL1798bCwoLBgwfTqVOnIm2nyF+aqgKbTdqUdBhCiFLIoKRlamqKv7+/3rUHi8rp06fzLO/UqdMTE4mfnx9+fn751vfv35/+/fvnWWdubs4XX3zBF198UbBghRBCFCuDJmJ88sknfPHFF8p5WllZWXp/QhQqnQ4TXSYmukzQyRUxhBD/Y1BPa8GCBdy9e5fff/89z/rCPKYlRDVdHH+kDgGglUU411VygrcQIptBSWv27NlFHYcQQgjxVAYlLR8fn6KOQwghhHgqg5KWn58fKpUq3/p9+/YVVjxCCCFEvgxKWiNGjNB7npmZydWrV/nhhx9y1QkhhBBFxaCk9a9//SvPci8vL5YtW0bXrl0LNSghhBAiLwZNec+Pi4sLZ86cKaxYhBBCiCcyqKd16NChXGWpqalERkZSr169Qg9KCCGEyItBSevjjz/OVWZiYoK7uztTp04t9KBE+XZXZU0/89HK4xdJLQvLkg5BiDLNoKQVHR1d1HEIoXigMuNX4yYlHUaRsDA2JjlDw9mkRIOWb2htg5WJ6dMXFKKcyDdpFeTyTI/fqkQIkb+zSYl8cCjvq8s8bl1zP16tIlcEESJHvkmrYcOGTzw3K4dKpeLs2bOFGpQo34x0Wux0SUD28KBWZVTCEQkhSot8k9aqVavyfdHt27eZP38+169fl1t4iEJXVXdXrj0ohMhTvkkrr0s3ZWVlsWrVKsLDw7G3tycyMlK50aIQQghR1Ay+n/zJkycJDQ3l8uXLDBw4kL59+2JiYlKUsQlR7slsQyH0PTVpJSYmMnv2bLZs2UKbNm1YtGgR1apVK47YhCj3CjrbEGTGoXixPTFpbdq0iTlz5mBhYUF4eDj+/v7FFZcQ4r8KMtsQZMaheLHlm7S6d+9OVFQUzs7OfPzxx9y7d4/vv/8+z2W7detWZAEKIYQQOfJNWnfu3MHJyQmdTsfy5cvzXYFKpZKkJYQQoljkm7T27t1bnHEIIYQQT2Xw7EEhikucyoauFcOUx0IIkUOSlih1NCpTThi5lnQYQohSSC4aKIQQosyQnpYodUx0mbhk3QAgVu1Mhkq+pkKIbLI3EKWOgy6BnWmjALn2oBBCnwwPCiGEKDMkaQkhhCgzJGkJIYQoMyRpCSGEKDMkaQkhhCgzJGkJIYQoMyRpCSGEKDPkPC1R6txW2dLOYr7yWAghckjSKuMKclfbJrZVijiawpGpMuaSyrmkwxBClEKStMq4gtzVNuqNd4o4GlEa1LKwLOkQhCgykrREqWOq0+ClvQDASaN6aFSmJRxR2WJhbFygHrhXZTvMjIyKOCohCockLVHq2OsSWZ8eCsi1B59VQXvgD7O0Bie5htY2WJnIDwlRMiRpCSEKlOTWNffj1SryQ0KUDJnyLoQQosyQpCWEEKLMkKQlhBCizJCkJYQQosyQpCWEEKLMkKQlhBCizJAp76LUuamqgpfltwAkY1HC0QghShNJWqLUyVKpSaRSSYchhCiFZHhQCCFEmSE9LVHqmOse4Jd5CoDfjT1JV1Uo4YiEEKWFJC1R6tjqkln8YC6Qc+1BSVqlSX5XkXd2ltvJiKInSUsIUSBPvYp86n29p3KBXVGYJGkJIQpMLrArSookrf/SaDSEhYWxc+dOTE1N+eijjwgKCir2OF7EOxGL8k1uSikKkySt/5o1axYnT54kMjKSW7duMXr0aJydnQkICCjWOOROxOJFIzelFIVJkhaQlpbGxo0bWbJkCW5ubri5udGvXz/WrFlT7ElLiBdRUd6UEiTRlSeStIDo6Gg0Gg3e3t5Kmbe3NxEREWRmZmJsLJtJiOJUkCQHBU90TWyrkKbNLNTen8yeLB4qnU6nK+kgStquXbuYPHkyR44cUcpiYmLo1KkTBw4cwMHBsIPIx48ff6441Orsc721Bn4kRmo16HQv3PIqsjDKTAFAa2yJ7pFz4MtKG0pq+dIY04vSZpXB0RSP4th1q1QFa7VOpyMrK+uZ3+/RjkN+pAsBpKenY2qqPyU357lGoynw+oyec5jCuCBfFJXqBVxeDaY2AOS5JctEG0pw+dIY04vQ5lKmoAmlOKhUqmfa/2m1WoOXlaQFmJmZ5UpOOc/Nzc0NXo8hvxKEEEI8O7n2IODo6EhycrJe4oqLi8PU1BRra+sSjEwIIcSjJGkBDRo0wMTEhJMnTyplx48fp1GjRjIJQwghShFJWmQPAXbu3JnQ0FBOnz7Nr7/+yvLly+nVq1dJhyaEEOIRMnvwv9LT0wkJCWH37t1YWFjQp08f+vTpU9JhCSGEeIQkLSGEEGWGDA8KIYQoMyRpCSGEKDMkaQkhhCgzJGkJIYQoMyRpCSGEKDMkaQkhhCgzJGkJIYQoMyRpCSFEMXv89Fg5XdZwkrREsXme++y8aMr7Tqs8t1+r1aJSqdBqtWi1Wh4+fFgqbzNSWskVMUSxyMrKQq1Wk5aWxt69e4mPj8fX1xdbW1tsbGyU+vJAq9ViZGREZmYm6enpAFSqVKmEoyo+5bn9Op0OlUpFSkoKw4cPR6PREBcXR1BQED4+PtSoUaOkQyz1JGmJYpOamkqXLl2wsbEhLi4OW1tbatWqxcCBA6lXr165SFyP7rSCgoLQarVcvXqVXr160aJFCzw8PEo6xCJV3tsPkJGRwQcffICjoyOdOnXi0qVLbNu2jcaNG9O5c2eaNWtW0iGWakYhISEhJR2EKB8iIyOJi4tjxYoV9O3bFysrKy5cuMD69evx8PDA0dGRrKysF3qoRKVSkZmZSVBQEJUrVyY4OJi6deuyb98+Tpw4gYmJCfXq1SvpMItMeW8/wL1799i5cycTJ06kWbNm+Pj4ULNmTf7880+ioqKws7OTHtcTvNg/a0WpkpGRgZ2dHWZmZgC88cYb9OvXD1dXV0aNGkV0dDRqtfqFP76RkZFBVlYWvXr1wsfHhw8//JBPP/0UJycnli9fzvbt20s6xCJV3tuv0+m4cOECZ86cUcr8/PwICgpCrVazadMmvTqhT3paokjkDAM96p9//mHdunW8/fbbyjEMBwcHatasydWrV/nll19o0qTJC323aJ1Ox8OHDwkPD+ell15ShsOqVq1KjRo1uHPnDvv378fe3p5atWqVcLSFr7y1P6+RAwsLC27dukV0dDQvv/wylStXBsDZ2RlbW1t+++03Hj58yKuvvvrCjzw8C0laotBptVrU2CA7qAAAHk1JREFUajWZmZkkJSWhVqsxNjamfv36nDhxgsOHD+Pt7Y2FhQUAVapUwdLSkhMnTmBhYUHDhg3zTHpl0eM7HZVKhampKQ8fPuSXX37BxcUFJycnAGxtbXFwcCA6Opp//vkHHx8fTE1Ny/R2KM/tf3Ty0a5duzh48CBWVlZUrFgRW1tbtm7dSmZmJrVr11b+F2rUqIGZmRnz588nICDghf4B96wkaYlCpdPpUKvVpKSkMGjQINatW8eePXs4f/48rVu3pkqVKhw6dIi///4bNzc35Z+1Zs2aHD58mD///JMuXbqU2R3Vo3J2WqmpqWzcuJG9e/ei0+kwNzenXr16/Oc//+HKlSvUqFEDOzs7AOzt7alUqRJLliyhXbt22Nvbl3Arnl15b79KpVImH/39998cOnSIAwcOcPLkSTp16kTNmjVZunQpKpWKatWqKaMPtWvX5scff8Td3f2F6G0WNklaolCpVCoyMjIICgrC2tqa3r17Y21tzYEDB1i3bh29evWiQoUKHD58mDNnztCgQQOsrKyA7APUd+7coUOHDi/ELEKVSkVaWhpdunTh1q1b/P3335w+fZqffvqJNm3a4OXlxZYtW7h58yaOjo7KDrpGjRps27aNmjVrUr9+/RJuxbMr7+2H3JOPrK2tiY6OZuXKlfTt25d69eqxYsUKEhMTqVy5Mg4ODly/fp0tW7bw2muvyYSMPJT9PYModZKSkkhKSqJXr174+/vTt29fFixYgIWFBR9//DHNmzenR48exMXFMXDgQLZv387OnTtZvnw59vb2GBkZlXQTCs3WrVuxsbFhxYoVbN26lVGjRlG7dm26du1KxYoVGT9+PDExMSxdupT//Oc/AFy5coXU1FQsLS1LOPrnV97bn9fko/79+1O3bl369u2Ll5cXkydP5vLly4wYMYLu3bvTp08fXF1dad68eQlHXzoZl3QA4sWi0+mUYaH79+8DkJmZibOzM0uXLiU4OJgBAwawefNm3N3dWb58ObNnz8bBwQF3d3cmT56srOdFGCI0MjLC1NRUScQeHh5Uq1YNExMTunXrxpYtWwgJCWHFihV88sknODg4kJKSQv369fH39y/h6J9feWp/Xt9ZR0dHIiMj/7+9Ow+rqlofOP5lBgGZBAGJGUQUnB4VByyna4kIijkwagIKDkVaZl1zKLyViYU5lpIiTqiZVt5KfVS0rjcLvWaKXURTRCQOgkxyOOzfH/7YcQQtyytuWJ/n8Un2WXu739056+Ws/e61KCgokO/ddenShfj4eNauXcu8efNYvnw5S5Ys4fz58+Tk5GBjY0NoaChAq3h28YFJgvAXaDSaJrdPnz5dGjlypPyzWq2WJEmSiouLpbCwMCk+Pl6qq6uTt1VUVPzuMR939fE09OWXX0qdO3eWcnJytLaXlJRIr732mjRs2DDp6tWrkiRJ0tmzZ6VPPvlE+vrrr+V2SroWrTn+2tpaSZLuvM9VKpVUXV0tb4+Li5NmzpwpFRYWau1z7NgxKTo6Wtq1a1eTx1RK7I+auKcl/GkNq6O2bdvG+fPn+eWXX/Dy8iIgIIA9e/Zw5MgRgoOD5WpCU1NT3NzcOHToEJ06dcLOzg5DQ0N5+ET6/0IOpamvmFSr1RQVFVFTU4OJiQkeHh7k5uayY8cO+vfvj4WFhVyM4OLiwn//+18KCwvp1asXdnZ2+Pj44O7uDijrt+zWHL/0F4qPTpw4wYkTJxgzZkyj47aEkYb/hcf/HSE8tuoTVnBwMHv37mXXrl0sWrSIhIQEVCoVc+fOpaioiOnTpwOgr39nNNrFxYX8/HyuX78OoHUPS4kfVEmS0NPTo7y8nKioKCZPnkxsbCzTp0+ntLSU2NhYnJycWLhwIVeuXJFj9PDwkBO4RqNpdFwldNgg4q8vPpo2bRpmZmbMmjWLgQMH8v333zNmzBg8PDwYOXIkeXl5pKSkkJ+fL+/bs2dPrKysmoxfaJoy3hXCY+uTTz7BxcWFzMxMNm7cyJ49e8jLyyM5ORlJkkhMTOTq1auEh4dTVlZGeXk5+vr6WFpayt+ulK5+aqKkpCTs7Ox48803mTp1KmVlZYwZM4bKykpGjx6NoaEh8+bNIy8vT963a9euWFpacvv27WaM4K9p7fGDKD56lETSEv6SW7duoVKpqK2txcTEBAcHBzIzM6murubjjz/GxsaGt956C7VaTWhoKJGRkYwfPx4HBwcCAwOb+/QfmurqaoqLiwkODqZnz54MHz6c9PR0OnfuzPPPP4+5uTkxMTFYW1szbtw4Pv74YzIyMli2bBkODg6Kr5RrzfFLv1N81K5dO6ZOncrgwYNZtGgRvXv3ZunSpWzYsKFR8ZHw+8Qs78IfJjWojqpfXuLgwYNs2LCB119/nY4dO8rbb926RUxMDJaWlmzYsAGAvXv3otFokCRJHsNXyn2L31NRUcH06dMZOnQokZGR1NbWysOhL7zwAqdOnWLTpk088cQTrFy5kqNHj2JsbIyLiwtvvPEGoOyKydYU/73eszNmzODy5cvs27cPQL4GKpWK+Ph4bGxsWLNmDTo6OqhUKoyNjWnTps19jyk0JpKW8IfUJyO1Wo1araa6uhpra2vKysqIiIjA2dmZlJQUjIyM5LY3b95k+PDhjB8/nhdffLHRMZX6Qb3Xeb/xxhscPXqU7du3Y21tTU1NDYaGhgBMmTKFX3/9lU8//RSAyspKDA0N5Y5dSdeiNcffsPho165dGBoaYm5uzogRI1CpVMTExMjfsOC3xPXdd9/x7rvvMn/+fLp06SJ/RkA5yfpxIZKW8LukBmsgTZ06FbVazbVr1xg3bhzh4eFoNBpCQ0MZOHAgycnJ6Ovryx/KDRs2cPz4cVavXo2+vr4iOqb7aTg1UVpaGjo6OpiZmRETEwNAREQEVVVV7N69G0DuuAsKCpg6dSqzZs1i6NChiu20Wnv8gFx8ZG1tjY6ODpcvX6ZHjx7Mnj2b69evk5KSgoODAytXrpT3uXHjBmPGjGHhwoUMHTq0Gc9e+ZTdgwiPRP2N9mnTpmFlZcWsWbOYM2cOX331FQsWLCA3N5e0tDSOHDnCSy+9xI0bN+QOycTEBLVajY6OjuITFtypaKuqqiIkJIRjx45x9uxZVq1aRXR0NN988w2LFy8GYOzYsVrfNMzMzNBoNJSVlQHKrZhs7fGDKD5qbsrvRYRHorKykrq6OmJjYxkwYAChoaG8/fbbGBoa8uGHH3L9+nW2bdtGdnY2r732GitWrODLL78kIyMDDw8PDAwMmjuEh+bw4cM4ODiwZcsWVq1axaFDhzA0NGT16tWcPn2aN998Ex0dHUJCQvjpp5+4fv06arUaoEVch9Yevyg+al4iaQlNqqur0/rZwMCAgoICvvvuO3lb586dmTFjBu3atWPr1q2o1Wr27NmDq6srx48fJy0tDT8/PxYsWAC0nOqokpISrly5Qm1tLXBnfaTU1FTs7e3Zu3cvV69e5cMPP8TNzY05c+YwYcIEJk6cSIcOHQgODm7ms39wd/9/U6lUrSb+hrHXP0vl5eWFqakpubm58nZzc3PWrVtHSUkJK1asoFOnTmRmZvLCCy8QExNDQkICH374IdD4syU8oEc084agIPVT0ty6dUtKT0+XVCqVJEmSlJqaKk2dOlU6ffq0Vvvz589LkydPlubNmydJkiTV1NRINTU1UklJidxGqVPSNDU10blz56Tx48dLBw8e1IqroqJCmjlzphQZGSkVFxdLkiRJ33zzjXTgwAFFTk0kSb+9F27fvi1PQ3Tu3DlpwoQJLT7++thramqkiooKOabS0lJp5MiRUmJiotZ0TZJ0Z3qq3r17S8uWLWvymEqJ/XEmpnEStNTfIC8vL2fIkCFYWFgQFBQE3Lk/9dVXX1FSUoKrqyuWlpbAnUUcnZ2deeedd+jduzdOTk7o6elhbGwMKH9qppqaGlQqFaWlpZibm2Nubs7Ro0c5deqU/HAs3Pk2GhgYSFpaGnl5efLSEu7u7oqbmgjQenwhKCgINzc3vLy8MDY25siRI5w9exZ/f/8WGb/UYGqm2NhYdu/ezbp16ygvL8fX15fg4GCWL1/OxYsXGTRokFx81KZNG/T09Pj+++955pln0NHRabQIpvDXiKQlyBp+UIODg+nduzdvv/02cKezsbe3p127dmzatImysjIcHBzkxfscHBz4+uuv6dixI15eXlrHVeIHteG1iIyMZN++faxYsYIbN25ga2vLuHHjWL9+PdnZ2fTs2VNeE8zQ0BA7Ozs+//xzRowY0WjlXaVci7q6OvmXl9DQULy9vUlKSgLuxBgQEMC6des4e/Ys3bt3b3Hx1xcfxcbGYmVlxZQpU+jWrRuZmZlkZ2fj5OTExIkTef/99zl37hw9evSQF3Gsn609JCRELukXHp7H/1ce4ZHR0dGhpqaG2NhY2rdvT0pKivyaWq2mtraWQYMGkZyczDfffMNHH33EkSNHAMjPz6e0tLRF3GiHO9dCo9GQlJSEvb09ycnJvPfee1y7do2lS5dy9OhRduzYQV5eHgsWLODUqVPyvpIkYWxsrOgS//pnkUaNGkXnzp1Zs2aN1uu2trZkZGSQk5PTIuMHqKqqEsVHjyHxnJag9ZzMjRs3ePXVV3F2dub555+XVx3ev38/Fy5coFu3bowfPx49PT1WrFjB2bNn5Q7e09OzUeemZLW1tcTFxRERESE/W3Pp0iV27tzJsWPHiI2N5amnniIuLg4jIyMcHBzo1q0bGzdupGvXrvzjH/9o5gj+mk2bNvHBBx+wc+dOnJ2dAbhw4QLXrl2jvLycp556irq6OqZNm4ahoWGLi7+qqopnnnmGiIgI4uLi5O25ubmsWbOGsrIy5syZg62tLStXruTMmTMAuLm5ybFLCnsGTQlE0hLQaDRUV1dTUlKClZUVRUVFREZGkpiYSJ8+fYiKiiIkJISKigrKysr48ccfSUlJwcfHh0uXLnH69GmsrKzkjl0p9y1+jyRJjBo1iv79+/PKK6/I2+t/w/7222+ZOXMmPXr0YNeuXRw+fBhdXV2cnJy0KiaV2mnV1NQQGRmJtbU1a9as4fjx48ydOxcHBwdyc3Px9vYmMjKSv/3tb2RmZnLo0CHFxn+v92xqaipnz55l+vTp+Pv7y9tzcnJ4++23sbe3Z8mSJXJJf0VFhXyPr6V8Dh43Imm1cj/99BNbt27lyJEj3L59Gz8/P1JTUzl+/DhJSUn06dOHIUOGEB4eDtz5prFu3ToAFi9e3GjMXqkf1LvPW61WY2BgQGZmJjt27CAhIYHBgwfLr1+9epW1a9eiUql45513aNOmDTo6OlRXV8sFKEq6Fnefa/2DwcXFxQQFBTFw4ECqq6vp168fI0eORKVSsX37dr7//nveeOMN+T6mEuNvOMvHxo0buXLlCl26dKFv374YGBgwe/ZsunTpQkxMDC4uLvJ+2dnZxMTEkJaWRs+ePbWOqZRkrUSP/ztK+J/Ky8vjq6++IjAwkNdff53o6GiqqqoYNmwYCQkJHD9+HBMTE7m9q6srPj4+HDt2jOrq6kbHU0In1ZT6mR4+/vhj4LeHYHv16kWHDh3Yt28fJ0+elNs7OTkxadIkTp48yYkTJ+QOSqkVk3fHb2hoSE1NDTY2NixZsoQzZ86Ql5fHkCFDMDMzw9nZmaioKEpLS8nOzpaPo8T46xNWWFgY//nPf7h58yYHDx5k0aJFGBoa8uKLL3LkyBHS09P5+eef5f26d++Op6cnhYWFjY4pEtb/jqgebMUkSWL16tXo6enJi9F16tSJ9u3bA2BlZUXPnj0JCQnR2u/mzZtcunSJoKAgeZqeluDGjRvEx8djZGQk/+ZsaWmJnZ0dhw4dIi8vj7Zt2/LEE08AYG1tTVZWFnZ2dlpDR6DMTuvu+OunWrKwsOCXX37BwcGBkSNHyu3NzMzIysqibdu29OrVS+tYSot/zZo1VFZWsmrVKkaNGoWzszOHDh2iqqqK0aNH4+joyM6dO7l27RoGBga4urqSn5/Ptm3bePLJJ/Hw8GjuEFoNUY/ZitWXNXfr1o3Ro0ezc+dOKioqsLe35/PPP8fU1BRfX18APv30U0xMTHB0dGTp0qX4+fkpeg2ku2k0GnnW8WXLllFaWsqcOXMA6NGjB7NmzWLt2rVs3LiR/Px8QkNDyc/Pp6CgoEVch/vFb21tTVJSEtbW1gBcu3YNR0dHCgsLuXz5MsOGDWvOU/9TGg7fSZJEQUEB7dq1k79hd+/enU6dOrF//34SEhIYNmwYbdu2Zfv27SxZsoQlS5ag0Wjw8vJSZPxKJpJWK6anp0dISAhxcXE4OjpSWVnJ+vXrKSwsxNraGh8fH7Zu3cpnn31G+/bt2b17N126dMHLy0vx1VF3d1p6enpUVlZiaWnJhAkTWL9+PRqNhrlz5wJ3hglNTU354osvWLFiBR988AG6urp4e3sTGhranKH8KQ8av4WFBQAZGRlkZGRgY2PDzZs3cXV1ZezYsc0Wx59VV1dHdXU1N2/epE2bNnTt2pXNmzejUqmwtLREV1cXd3d3vvvuO/k69enTB09PT4qLizl16hTW1tYtrvhICUTSauUCAwNJTk4mNTUVPT09/Pz88PDwICwsDF9fX6Kiopg5cybjxo0jNjYWfX19eXhMyR/U+k6rtLQUSZLo0KEDBQUFeHl5ER4ejru7u9xh1//X19cXDw8PoqOjOXPmDKampgQEBMjHU9K1eND464cK/f39kSSJoqIinJycePbZZ+XjKSX+hsVHNTU1dOvWjWHDhjFp0iTgt/uyJiYmlJWVUVVVhb6+PgYGBlhYWGBjY4O3t7d8PCXF3hKIpCUQFhbGoEGDUKvVtG/fnosXL/LFF19QUlKCi4sLtra2GBgY4ObmJu+jpBvtd2vYaanVavz9/eVn05588kmsra15+umnARolLl1dXezs7BgyZIh8PKV1Wn8lfj8/P/z8/LSOp7T464uPhg4dSt++fbG2tsbPzw8jIyOte7QajQYdHR3q6uowMDCgtLSU+fPnM2PGDK2kpaTYWwKRtAQA+X6FWq3GycmJqKgoNm/ejEajoaKiQp7hup4ShwTrNey0+vXrR9u2bTEzM8PGxkbrt+3hw4cDdzpsXV1dXnrpJQwMDBoNiSqt0/or8QNaCzjWt1UKSZI4cOAArq6uuLm5YWBggLOzszwFU0NlZWXo6+tjampKWVkZEyZMoEOHDloJS3j0RNIStBQXF8tLgpeUlLB582ZsbW3lYTClu7vT0tfXx8PDQ55DsSE9PT25437llVe4desWixcvVnTCfhjxN0xYSnOv4qMrV66wd+9eTE1N8fT0ZMKECRgZGaGvr09RURExMTE4Ojry0UcfycdRUrJuSUTSErRYWFjQtWtXtm/fjqurK3369MHX15crV67g5eWl+AlA79VpOTg4yJ2Wt7c348aNA37ruKurq7We01Kq1h7//YqPbGxs6NixIzt27EClUjFgwABSUlIYNWoUPj4+rF+/HhAJq7mJGTGEJlVUVKCrq4uJiQmlpaXo6em1iNJugKysLOLi4pg3bx4qlYoDBw5odVqHDx8mMjKSl19+Wd7n7mo7JX/bau3xA+zatUur+EiSJMLCwujUqRO5ubkkJiYyc+ZM3n//fZ555hneeustQCSsx4FIWkKrdL9O6+LFiyQmJrJy5Ur69u3b3Kf6P9Ha44c7KzDfXXzk5OSEm5sb8+fP5+9//zu1tbX069cPEAnrcaHssR5B+JPuVzHp7u6Oo6Mjt2/fbu7T/J9p7fHD/YuPSktLqaqq4sknnwREwnqciKQltFr367QqKysbVUy2NK09/nr3Kj5qOAmuSFiPD5G0hFbvXp1Wnz59mvvUHonWHn9LLz5qacQ9LaHVq6qqYv369XKnZWNjg6+vL4GBga2i02rt8ddrycVHLYlIWoLw/1p7p9Xa4xeUQSQtQRAEQTHE3UVBEARBMUTSEgRBEBRDJC1BEARBMUTSEgRBEBRDJC1BEARBMUTSEoS/KCoqiuXLlzf3aTwUL7/8MocPHwbuHdfly5fp2LEjV69elbft37+fsLAw/P396dWrF9OmTeOnn36SXz9x4gQdO3aU//j6+tKvXz9efvllfv31V7ndzz//TGRkJKKoWbgXkbQEQQDg5MmT/PLLLzz11FMPtN/hw4d57bXXiI6O5rPPPiM9PR0rKyuioqK4du2aVtujR49y7NgxDh48yHvvvcfFixeJiYmhsrISAC8vLxwdHfnkk08eVlhCCyOSliAIAKxatYqJEyc+8H67du0iNDSUkJAQnJ2d8fHxITk5GQsLCz777DOttjY2Ntja2uLg4EDv3r1Zt24dRUVFbNu2TW4TERHBmjVrxLctoUkiaQnCQ7R7924mTpzIBx98QEBAAAMGDCA5OZm6ujq5TXp6OkOGDKF79+5ER0eTm5urtf+IESPw9/dnzJgxnDhxQn5t8ODBbN++XR6GmzJlCvn5+cyYMYOuXbsSGhqqdayTJ08yduxY/P39CQoKYs+ePfc878uXL/Pvf//7gb9lAejo6HD69GnKy8vlbbq6umzatEleTPJerK2tGTp0KF9//bW8zd/fn8rKSo4fP/7A5yK0fCJpCcJDdubMGXJzc8nIyOCFF15g8+bNZGVlAZCZmcny5ctJSkpiz5492Nvbk5iYiCRJ7N69m8WLFxMfH8+nn35K//79iY+P1xpiS01NJSkpiYyMDH788UdGjx5NYGAgmZmZ6Orq8t577wFQVFREfHw8wcHB7Nu3j+nTp/Pmm29y6NChJs85KyuLzp07Y2Fh8cDxhoeHc/78eQIDA5k1axZbtmwhPz8fJycnLC0tf3d/T09PrWSro6NDQEAAR48efeBzEVo+kbQE4SGrra1l8eLFeHh4MHbsWHx8fDhz5gwA27ZtIyoqipEjR+Li4sL8+fMZPHgw5eXlpKenExERQWhoKG5ubsyePRsfHx/S09PlY4eEhDBgwAD8/Pzo3bs33t7ejB8/Hm9vb0aNGsXFixcByMjIoE+fPsTExODi4sKIESOYNGkSGzdubPKcf/zxR9zd3f9UvAEBAWzdupXAwECysrJYtGgRQ4YMYfbs2X9oTS5zc3MqKiq0tnl6esrXTBAaah3TNwvCI2RlZYW5ubn8s5mZGbW1tQDk5uYybdo0+TVzc3Pmzp0rv5aQkKB1rG7dusmJCOCJJ56Q/25kZISjo6PWzzU1NQBcvHiRrKwsunfvLr9eW1srr6F1t+LiYry8vLS26evrN3lfqX6o08DAQN7m7+9PamoqNTU1/PDDD3z++efs3LkTGxsbXn311Sb/zXrl5eWNJua1tLREpVLddz+hdRJJSxAesoadeb36zr+p1+oZGxs32qbRaLQWY7x7mZB7LU5YW1tLUFAQiYmJf6i9jo5OowRlbm6udZ+qXllZmfx6RUUFKSkpTJ48GScnJwwNDQkICCAgIAAzMzN5WPR+cnJyGiVMjUYjFl4UmiTeFYLwCLm4uGg9v1RZWUn//v25cOEC7u7unD59Wqv96dOncXNze+B/x83NjUuXLuHi4iL/OXbsGDt37myyfbt27SgpKdHa1rFjR7Kzsxu1zc7OxtXVlTZt2mBsbMzevXv54osvGrUzNzfHysrqvud58+ZNDhw4wNNPP91oe7t27X4vTKEVEklLEB6h6Oho0tPT+fLLL7l06RILFizA0tIST09PJk+ezJYtW9izZw95eXksW7aMc+fO/W4FXlPCw8M5d+4cy5Yt49KlS/zzn/9k6dKltG/fvsn2nTt3JicnR2vb2LFjuXz5MgsXLuTnn38mLy+PzMxMVq1axZQpUwDQ09MjISGB1NRUVq5cSW5uLrm5uezYsYMNGzbI7eoVFxdTVFREQUEB3377Lc899xwODg48++yzWu1ycnLo0qXLA8cttHxieFAQHqFRo0ZRWFhIcnIyt27dokePHqxevRpdXV2GDx9OUVERqampFBUV0alTJ9LS0hoNnf0RHTp0YO3atbz77rukpaVha2vLzJkzCQ8Pb7L9wIEDWbJkCbdu3ZLvx7Vv354tW7aQkpJCREQEt2/fxsXFhVdeeYUxY8bI+z733HNYWVmxdetWPvroI+rq6vDx8eGdd95pVEI/cOBA4M4wqb29PYMGDSIhIQEjIyO5jSRJ/PDDD/c8V6F1E4tACoIAwKRJkwgKCmr0redR+9e//sWCBQvYv3+/uK8lNCLeEYIgADB16lStmSmay7Zt24iLixMJS2iSeFcIggBA3759cXV15eDBg812DhcuXKCwsJCwsLBmOwfh8SaGBwVBEATFEN+0BEEQBMUQSUsQBEFQDJG0BEEQBMUQSUsQBEFQDJG0BEEQBMX4P+sz3DEZAd8eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Frequent Income is ~$59917/year\n",
      "(Marked by dashed line)\n"
     ]
    }
   ],
   "source": [
    "# Plot histogram\n",
    "xt = [0,50000,100000,150000,200000]\n",
    "\n",
    "plt.title('Normalized Median Household Income')\n",
    "\n",
    "# Beta-fit for line of best fit\n",
    "x_min, x_max = min(xt), max(xt)  \n",
    "lnspc = np.linspace(x_min, x_max, len(df_profile['median_hh_income']))\n",
    "ab,bb,cb,db = stats.beta.fit(df_profile['median_hh_income'])  \n",
    "pdf_beta = stats.beta.pdf(lnspc, ab, bb,cb, db)  \n",
    "\n",
    "# axes\n",
    "y_headers = [0,75000,150000,225000]\n",
    "plt.axes(facecolor='white',yticklabels=y_headers)\n",
    "plt.grid(color='gainsboro', linestyle='-', linewidth=2)\n",
    "\n",
    "# x-ticks\n",
    "plt.xticks(xt,labels=['$' + str(i) for i in xt],rotation=45,fontsize=14)\n",
    "plt.xlabel('Income (USD)',fontsize=14)\n",
    "# y-ticks\n",
    "plt.ylabel('Number of Households',fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# figure\n",
    "plt.title('Figure 2: Median Household Income Distribution',fontsize=16)\n",
    "y, x, _ = plt.hist(df_profile['median_hh_income'],bins=25,normed=True,color='lightseagreen')\n",
    "plt.plot(lnspc, pdf_beta, label=\"Beta\",color='red')\n",
    "\n",
    "# peak\n",
    "y = y.tolist()\n",
    "xpos = y.index(max(y))\n",
    "plt.axvline(x=(x[xpos]+5000),color='orange',ls='dashed')\n",
    "\n",
    "\n",
    "plt.show()\n",
    "print(\"Most Frequent Income is ~$%.0f/year\\n(Marked by dashed line)\" %x[xpos])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Concatenate across zips\n",
    "s_hs = pd.Series([df_profile['high_school_diploma'].sum()])\n",
    "s_bs = pd.Series([df_profile['bachelors_degree'].sum()])\n",
    "s_ms = pd.Series([df_profile['masters_degree'].sum()])\n",
    "s_total = s_hs + s_bs + s_ms\n",
    "\n",
    "# Clean up indices\n",
    "hs_trunc = np.around(s_hs.values[0]/s_total*100,3)\n",
    "bs_trunc = np.around(s_bs.values[0]/s_total*100,3)\n",
    "ms_trunc = np.around(s_ms.values[0]/s_total*100,3)\n",
    "headers = [\"High School Diploma: %.2f%%\" %hs_trunc,\"Bachelor's: %.2f%%\" %bs_trunc,\"Master's: %.2f%%\" %ms_trunc]\n",
    "\n",
    "# Hide ticks to make it more of an image, make it pretty\n",
    "plt.yticks([s_hs.values[0]*.5,s_hs[0]+s_bs[0]*.5,s_hs[0]+s_bs[0]+s_ms[0]*.5],labels=headers)\n",
    "plt.bar([0],s_hs,color='darkcyan')\n",
    "plt.bar([0],s_bs,color='lightseagreen',bottom=s_hs)\n",
    "plt.bar([0],s_ms,color='darkturquoise',bottom=s_bs+s_hs)\n",
    "plt.title('Figure 3: Starbucks Customer Education Level')\n",
    "plt.box(False)\n",
    "plt.xticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These visualizations give insight to Starbucks customer profile: (slightly) more male customers within 25-29 age range, under 100000 household income, and with a Bachelor's degree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b) How Starbucks Profile Differ From The Rest of Population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataframe `df_final` contains census data  + additional columns \"starbucks_count\" and \"has starbucks\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male_15_17</th>\n",
       "      <th>male_18_19</th>\n",
       "      <th>male_20</th>\n",
       "      <th>male_21</th>\n",
       "      <th>...</th>\n",
       "      <th>masters_degree</th>\n",
       "      <th>zip</th>\n",
       "      <th>starbucks_count</th>\n",
       "      <th>has_starbucks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>217.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>236.0</td>\n",
       "      <td>84511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>84513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>178.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>498.0</td>\n",
       "      <td>84532</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26693</th>\n",
       "      <td>33.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>302.0</td>\n",
       "      <td>14840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26694</th>\n",
       "      <td>360.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>...</td>\n",
       "      <td>822.0</td>\n",
       "      <td>14843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26695</th>\n",
       "      <td>51.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>163.0</td>\n",
       "      <td>14847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26696 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       male_15_17  male_18_19  male_20  male_21  ...  masters_degree    zip  \\\n",
       "0           217.0        67.0     18.0     59.0  ...           236.0  84511   \n",
       "1            43.0         8.0      0.0      0.0  ...            17.0  84513   \n",
       "2           178.0        31.0     28.0     82.0  ...           498.0  84532   \n",
       "...           ...         ...      ...      ...  ...             ...    ...   \n",
       "26693        33.0        42.0      9.0     13.0  ...           302.0  14840   \n",
       "26694       360.0       162.0    117.0     68.0  ...           822.0  14843   \n",
       "26695        51.0        23.0     11.0      6.0  ...           163.0  14847   \n",
       "\n",
       "       starbucks_count  has_starbucks  \n",
       "0                  0.0              0  \n",
       "1                  0.0              0  \n",
       "2                  1.0              1  \n",
       "...                ...            ...  \n",
       "26693              0.0              0  \n",
       "26694              0.0              0  \n",
       "26695              0.0              0  \n",
       "\n",
       "[26696 rows x 53 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get number of starbucks in each zipcode\n",
    "num_starbucks_in_zipcode = df_starbucks_US.pivot_table(index=['zip'], aggfunc = 'size')\n",
    "num_starbucks_in_zipcode = pd.DataFrame({'zip':num_starbucks_in_zipcode.index, 'starbucks_count':num_starbucks_in_zipcode.values})\n",
    "#merge to get df_final\n",
    "df_final = df_census.merge(num_starbucks_in_zipcode, how='left', on='zip')\n",
    "df_final = df_final.fillna(0)\n",
    "df_final['has_starbucks'] = np.where(df_final['starbucks_count'] > 0, 1, 0)\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "ax=sns.boxplot(x='has_starbucks',y='median_age',hue='has_starbucks',data=df_final,palette='Set3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.boxplot(x='has_starbucks',y='median_hh_income',hue='has_starbucks',data=df_final,palette='Set3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.boxplot(x='has_starbucks',y='median_rent',hue='has_starbucks',data=df_final,palette='Set3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uTJMmTQr9uXPnjhJz9uxZBg4ciLe3N23btmXhwoXk5OToHCc+Pp5Ro0bh4+NDq1at+PTTT0lPT9eJuX37NiEhIbRq1YoWLVowbtw4bt26pROTkZHB1KlT8fPzw9vbm+HDhxeazi03N5fFixfj7++Pl5cX/fr14/fff9f7PTE1O3fu5MqVK4Xu+RNCVByPnpkx1TM1evf89u7dS8uWLVm/fj23bt3C39+f8PBwGjduTFRUFGPGjMHa2lqvY8bGxjJy5Ehyc3N1tmdkZJCUlERISEihuSCrV68OQEJCAkOGDMHb25vFixfz559/smjRItLT0wkPDwfyJ1EePHgwNWvWZO7cuSQnJzN//nxu3rzJypUrgfyCNWzYMDIzM5kyZQq5ublEREQQHBzM999/j5VV/ls1duxYzp49y/jx47G1tWXZsmUMGjSI3bt3Y29vD8DMmTPZvn07oaGh1KlTh3Xr1jFkyBB27txJvXr19H3LTUJqaqrya/L48eP07NlTrvsJUQHdu3dPp52WlmaSn1W9i99ff/3F0KFDsbS0xNXVFWdnZ06dOkXjxo3x9/enV69efPvtt4VmBi9Kbm4uW7duJSIiosiCefHiRbRaLR07dsTT07PIY6xatQp7e3s+//xz1Go1/v7+2NjYMGPGDEaOHImrqyubNm3i3r177Nixgxo1agDg6urKiBEjOHPmDF5eXuzevZsLFy6wZ88e5bmaNm1Kjx492L9/P926dSMmJoaoqChWr15N+/btgfxTAh07dmTLli2MGDGCq1evsnXrVj755BPefvttANq2bUtgYCBr1qxh6tSp+r7lJkFmeBGicihqMVtTnLJS79OeNjY2OoWqfv36XLp0SWm/9NJLJCUlPdWxTp48yYIFC3jnnXcIDQ0ttP/8+fNUqVKFBg0aPPYY0dHRvPrqq6jVamVb165d0Wg0HD58WInx9fVVCh/kFyRbW1uioqKUmEaNGukU2YL2wzG2trY6i/U6OTnh6+vLoUOHADh27BgajYbAwEAlRq1WExAQoMSYo0dnin+0LYSoGB6+zaGotqnQu+fXtGlTDh06pPTsGjZsyKlTp5T9f/3111OPDvL09OTAgQM4Ozuzbdu2QvsvXryIo6Mj48aNIzo6Go1GQ0BAAJMmTaJmzZrcv3+fGzdu4OHhofM4Jycn7OzsiIuLA/JPq77++us6MSqVirp16+rEPHocyC/uD8e4u7sXmuuufv36REZGKjEODg44OTnpxLi7u3P9+nWysrKwsbF5qvfnYRqNhosXL+r9uIri4YFRBe3K/HqEMCeV9bOq0WgeOzep3j2//v3789NPP9GvXz/S09Pp3r07f/zxB2FhYaxevZr169fz4osvPtWxXFxccHZ2fuz+CxcucPv2bZ599llWrFhBWFgYJ06cYNCgQWRlZZGWlgaAnZ1docfa2toqA1rS0tIMEpOenl6qGMi/jmmOLC0ti20LISqGgvEUj2ubCr17fl27dmX69OmsW7eOqlWr0qZNG4YPH87q1auB/BlhwsLCDJLc5MmT0Wq1eHl5AfnX1zw9PenXrx8//PAD/v7+QNH3oWi1Wp0vWEPEaLXax+b6pJiC7SW9Z0alUtGkSZMSPbYiKLjB/eF2ZX49QpiqBw8eFGpX1s/q6dOnH7uvRD+/33zzTfbs2aN0J0NCQjh48CDbt29n7969jx2coq+XXnpJKXwFWrRogb29PRcuXFB6WI/esgCQmZmpjL60s7MrMiYjI0M5xtPGFNVze9qYgv3m6NVXX9Vpd+jQwUiZCCGK07x582LbpkLv4jdkyBC2bdtWqFDUqVOHpk2b6gw8KY3MzEy+//57Lly4oLNdq9WSk5NDjRo1sLW1xdXVlYSEBJ2Y5ORk0tPTlWt4Hh4ehWI0Gg1Xr14tNgYgMTFRJyYpKalQ7y4hIUGJadiwISkpKaSmphaKqVu3rsHen8omOjpap10wGEkIUbFkZ2cX2zYVehe/xMREJk2ahJ+fHx9++CEHDhwodEO5IVSpUoW5c+eybNkyne0//fQTWVlZyn1/fn5+/Pzzzzr/g/bt24dKpdKJOX78OCkpKUrM4cOHyczMpE2bNkD+6M/Lly8TGxurxFy5coXY2FidmHv37nHkyBEl5s6dO8TExCgxBf/dt2+fEpOdnU1UVJSyzxz9+uuvxbaFEBXDmTNnim2bCr2v+R08eJAzZ86wZ88e9u3bx759+6hevTqBgYH06NGDVq1aGSQxlUrFu+++y5w5c5gxYwYdOnTg0qVLLF26lI4dOyrPExwczO7duxkxYgSDBw8mPj6ehQsX0qdPH2VFin79+vH1118zZMgQxowZQ0pKCvPnz6d9+/a8/PLLAHTr1o0VK1YQHBxMSEgIWq2WiIgInn32WYKCgoD861QtW7Zk3LhxhIaG4ujoyNKlS7G3t1fu6XNzc6NXr17MmDGDzMxM3N3dWbduHampqQQHBxvkvamMnJycuHHjhk5bCFHxFDUy2xTpXfwAvLy88PLyIiwsjJiYGPbs2cP+/fv517/+xTPPPEP37t0ZP358qZMbOnQodnZ2bNiwge+++w4HBwf69u3L+++/r8R4enqydu1a5s2bxwcffECNGjUYOnSoToyTkxMbNmxg1qxZhIaGYmtrS9euXXVyVKvVrFu3jpkzZzJ58mTUajV+fn5MnDhRmd0FYNmyZcyZM4d58+aRl5dHixYtWLx4sc4MCNOmTaN69eqsWrWKzMxMXnjhBdatW4e7u3up35PK6uHp6IpqCyEqBktLS52CZ6ojsy20xQ1h1MOFCxdYvHgxv/zyCxYWFjKxtQEVjFiqzBeeN2zYoDPas1WrVjLDixAVUHh4uM4Cti4uLpV2ZqrivjtL1PMr8Mcff7B371727t1LUlIS1apVo1evXrz22mulOawwQeZyEV2Iyu7u3bs6bVM9S6N38Tt//jyRkZFKwbOysqJdu3aMHTuWDh06UKVKlbLIU1Ryj65qIatcCFExyXp+j9GrVy8sLCzw8fEhODiYrl27muwMAEIIYW6aNm3KuXPndNqmSO/iFxISwmuvvUatWrWeKj4nJ4fTp0/z3HPPKTedC/Pj6Oiocx3B0dHRiNkIIR7n4VHZRbVNhd7DeIYPH/7UhQ/y13EbNGiQzi8JYX4evY7waFsIUTE8/CO1qLapKJcxrAYaUCoqMXO5jiCEqBxM8wYOUeH4+PgU2xZCVAzmsgKLab4qUeH07Nmz2LYQomJ4dDGBR9umQoqfKDcFpzrllKcQFVdmZqZO+/79+0bKpGxJ8RPlIjIyUqf4RUZGGjkjIURRHl21/dGVdUyFFD9RLk6cOKHMF5iXl8eJEyeMnJEQwpxJ8RPlwtfXV1n8WKVS4evra+SMhBDmTIqfKBdBQUE6pz0LlokSQlQsnp6exbZNhRQ/US4cHByUNfycnJx0loASQlQcaWlpxbZNRamK319//cWZM2dIS0sjOzu7yEUPHRwc2LBhA82aNSvNU4lKLjU1lVu3bgFw69YtUlNTjZyREKIof//9d7FtU1Gi4nfy5El69+5NQEAAffv25dy5c/z6668EBASwZ88enVhra2tatmwp83qauZ07dyoz/Wi1Wnbu3GnkjIQQRXF2dtZpu7i4GCmTsqV38fv9998ZOnQoGRkZDB48WNnu4OCAlZUVoaGhREVFGTRJUfnFxMQU2xZCVAyPrrX54MEDI2VStvQufp999hl169Zl586djBgxQvk1/+KLL/LDDz/g6enJypUrDZ6oEEKIsifX/B7j1KlT9O7dGxsbm0IzddjZ2dGnTx8uX75ssASFaZC5PYUQFUmJrvmp1erH7nvw4EGRA1+EeZO5PYUQFYnexc/Ly4tdu3YVuS8zM5PvvvuOF198sdSJCSGEEGVF7+L3wQcf8McffzBgwAB27NiBhYUFv//+Oxs2bKBnz55cvXqVUaNGlUWuohJ7dC5PmdtTCGFMehc/b29vVq5cyc2bN5k7dy5arZZFixYxa9YssrKyWLRoEa+88kpZ5CoqsWPHjhXbFkKI8mRVkgf5+fnx448/8t///pekpCTy8vJwc3OjWbNmWFmV6JDCxGk0mmLbQghRnkpcqSwsLGjWrJnM3CKeyqODoGRQlBDCmPQuftnZ2axevZro6Ghu3bpV5JeYhYUFBw4cMEiCQgghhKHpXfxmzpzJ1q1bqVWrFm5ublhaytzYQgghKhe9i9+PP/5Ijx49WLBgQVnkI4QQQpQ5vbttGo1GFiIVeitYyPZxbSGEKE96F7/AwEB+/PHHsshFmLBGjRrptJ999lkjZSKEECU47TlhwgRGjBhB37596dSpE87OzoXm+AR44403DJKgMA0JCQk67fj4eOMkIoQQlKD4nTx5kj/++IP79+9z+vTpImMsLCyk+AkdWVlZxbaFEKI86V385s2bR7Vq1fj444/x8PCQazdCCCEqHb2LX2JiIh9//DH9+vUri3yEiVKr1TqLZBa3MogQQpQ1vQe8eHh4mOzihqLsPLo69KNtIYQoTyVa1eGrr74iKipKpqgSQghRKeld/L777jvUajWjRo3i5ZdfJiAggI4dO+r86dSpk96JnD9/nhdeeIGbN2/qbD98+DD/+Mc/8PLyokOHDqxdu7bQY8+ePcvAgQPx9vambdu2LFy4kJycHJ2Y+Ph4Ro0ahY+PD61ateLTTz8lPT1dJ+b27duEhITQqlUrWrRowbhx47h165ZOTEZGBlOnTsXPzw9vb2+GDx9eaORibm4uixcvxt/fHy8vL/r168fvv/+u93sihBCibOh9zS8jI4MGDRrQoEEDgyURGxvLyJEjyc3N1dn+22+/MWrUKIKCgvjwww85efIk8+bNQ6vVMmzYMCB/CP2QIUPw9vZm8eLF/PnnnyxatIj09HTCw8MBSE1NZfDgwdSsWZO5c+eSnJzM/PnzuXnzJitXrgTyC9awYcPIzMxkypQp5ObmEhERQXBwMN9//72yWsXYsWM5e/Ys48ePx9bWlmXLljFo0CB2796Nvb09kD8F3Pbt2wkNDaVOnTqsW7eOIUOGsHPnTurVq2ew900IIUTJ6F38Nm7caLAnz83NZevWrURERGBtbV1o/5IlS3j++eeZP38+AO3btyc3N5cVK1YwcOBA1Go1q1atwt7ens8//xy1Wo2/vz82NjbMmDGDkSNH4urqyqZNm7h37x47duygRo0aALi6ujJixAjOnDmDl5cXu3fv5sKFC+zZswdPT08AmjZtSo8ePdi/fz/dunUjJiaGqKgoVq9eTfv27QHw8fGhY8eObNmyhREjRnD16lW2bt3KJ598wttvvw1A27ZtCQwMZM2aNUydOtVg758QQoiSKfGs1CkpKezZs4fVq1ezfv169u/fX+g04pOcPHmSBQsW8M477xAaGqqz78GDB8TExNClSxed7YGBgdy7d4/ffvsNgOjoaF599VWd0YNdu3ZFo9Fw+PBhJcbX11cpfJBfkGxtbYmKilJiGjVqpBQ+QGk/HGNra4ufn58S4+TkhK+vL4cOHQLyF2nVaDQEBgYqMWq1moCAACVGCCGEcZVoPb/Nmzczf/58srKy0Gq1yvYqVaowfvx4+vfv/1TH8fT05MCBAzg7O7Nt2zadfUlJSeTk5ODh4aGz3d3dHYC4uDi8vLy4ceNGoRgnJyfs7OyIi4sD8k+rvv766zoxKpWKunXr6sQ8ehyA+vXr68S4u7sXurexfv36REZGKjEODg44OTkVyvv69etkZWVhY2Pz5DdHCCFEmdG7+B04cIBp06bx/PPPExwcTMOGDdFqtcTGxrJu3TpmzJhBnTp1ePXVV594LBcXl8fuK7idws7OTme7ra0tAOnp6Y+NKYgr6ImmpaU9Vcyj808WxBRMzZWenv7E4xQXA/nXTEtS/DQaDRcvXtT7cRWZqb0eIUxVZf2sajSax07EonfxW716Nc8//zzffPONzqnGpk2b0qVLF9566y3WrFnzVMWvOAU9yqLmDQWwtLQsNkar1eqsNWiImId7uUXlU1zMk16PEEKI8qN38btw4QLjxo0rcoYOa2trevbsyWeffVbqxApGTj56HbGgbW9vr/SwirrWmJmZqRzDzs6uyJiMjAzc3NyeGFPwPHZ2dly9egfPKg4AACAASURBVPWJMRkZGUXGFOwvCZVKRZMmTUr02IrK1F6PEKaqsn5WHzf/NJRgwItareb+/fuP3Z+RkWGQ+T7r16+PSqUiMTFRZ3tB28PDA1tbW1xdXQutGJCcnEx6erpyDc/Dw6NQjEaj4erVq8XGFDzfwzFJSUmFencJCQlKTMOGDUlJSSE1NbVQTN26dWVaLyGEqAD0Ln6+vr5s2rSJv//+u9C+v/76i82bN9OiRYtSJ1alShV8fHzYv3+/TrHZt28f9vb2NGvWDAA/Pz9+/vlnnemy9u3bh0qlomXLlkrM8ePHSUlJUWIOHz5MZmYmbdq0AfJHf16+fJnY2Fgl5sqVK8TGxurE3Lt3jyNHjigxd+7cISYmRokp+O++ffuUmOzsbKKiopR9QgghjEvv054fffQRb731FkFBQbzxxhvKze6xsbH88MMPaDQaPvzwQ4Mk9+677zJ06FDGjh1Lr169OHXqFF9++SUhISFUrVoVgODgYHbv3s2IESMYPHgw8fHxLFy4kD59+lCnTh0A+vXrx9dff82QIUMYM2YMKSkpzJ8/n/bt2/Pyyy8D0K1bN1asWEFwcDAhISFotVoiIiJ49tlnCQoKAvILf8uWLRk3bhyhoaE4OjqydOlS7O3tlXv63Nzc6NWrFzNmzCAzMxN3d3fWrVtHamoqwcHBBnlfhBBClI7exa9x48Z89dVXzJgxg02bNunsa9asGZMnT6Zp06YGSa5169YsXbqUJUuWMGbMGFxdXRk/fjzvvPOOEuPp6cnatWuZN28eH3zwATVq1GDo0KG8//77SoyTkxMbNmxg1qxZhIaGYmtrS9euXRk/frwSo1arWbduHTNnzmTy5Mmo1Wr8/PyYOHGiMrsLwLJly5gzZw7z5s0jLy+PFi1asHjxYhwcHJSYadOmUb16dVatWkVmZiYvvPAC69atU27TEEIIYVwW2uKGMD5BcnIy165dQ6vV4ubmVuytC6LkCi7aNm/e3MiZlNyYMWMKbVu+fLkRMhFCFMeUPqvFfXeW6Cb3As7Ozjg7O5fmEEIIIUS5K1Hx27BhAz/++CO3bt0qtHoC5N/LduDAgVInJ4QQQpQFvYvfF198wWeffYadnR0NGzakSpUqZZGXEEIIUWb0Ln7ffvstPj4+rFy5UpmySwghhKhM9L7PLzk5mddee00KnxBCiEpL7+L37LPPkpSUVBa5CCGEEOVC7+I3btw4tmzZwk8//VTsRM9CCCFERfXEa34dO3YstC0nJ4f33nsPGxsbatSoUWilAhntKYQQoiJ7YvErmCLsSduEEEKIyuKJxW/jxo2leoKcnBxOnz7Nc889pywxJIQQQhiT3tf89JWamsqgQYM4d+5cWT+VEEII8VTKvPhB8SugCyGEEOWtXIqfEEIIUZFI8RNCCGF2pPgJIYQwO1L8hBBCmB0pfkIIIcyOFD8hhBBmp1QruQshnk5GRgb37t0jNzeXvLw8Y6cjnsDS0hIbGxtcXFwKTd8oTIMUPyHKUF5eHtevXyctLQ1LS0usra1RqVTGTks8QU5ODunp6Tx48AA3NzcpgCaozIufg4MDGzZsoGnTpmX9VEJUOKmpqaSlpeHi4oKzszOWlnKlobK4c+cOf/31F7dv36ZmzZrGTkcYWImK36FDh/j3v//N7du30Wg0hfZbWFjw1VdfAWBtbU3Lli1Ll6UQlVR6ejpqtVpOn1VCTk5OpKSkkJWVZexU9KLRaEhJSTHoMZOTk/V+jKOjY4U+y6F38du0aRMzZswAwNnZGbVabfCkhDAVeXl5WFlZSeGrpFQqVaW6RqvRaJg2bRq3b9826HHDw8P1foyLiwvh4eEVtgDqXfw2bNjAc889x+rVq3FxcSmLnIQQQogypXfxu3HjBpMmTZLCJ4QQFYxKpSI8PLxUpz2L6uVNmzZN7+OY3GnP+vXrG7xLLYQQwjBUKhXOzs4lfnzHjh356aeflHbPnj1LdbyKSu+hZyNGjGDjxo1cvny5LPIRwuw0adKEbdu2GTuNYl2/fp3du3eX6hjHjx+nSZMmXL161UBZFbZ06VI6dOhQZsc3B/7+/jrtLl26GCmTsqV3z+/kyZPY2trSs2dPPDw8cHJyKnQx/+HRnkKIym/ChAm4ubnRvXt3Y6ciylHnzp2NnUKZ0bv4/ec//wGgVq1a3L9/n2vXrhk8KSGEEMbXrl07Y6dQZvQ+7Xnw4MGn+iOEeHpxcXEMHTqUl156ibZt27Jy5UplX15eHitXriQwMJBmzZrx8ssvExwcTGJiohITFRVF79698fLyonXr1kycOJHU1NSnfv74+HiGDRtGixYt8Pb2ZtiwYVy8eBGAgQMH8uuvv7J9+3bllOL169cZO3YsrVu35oUXXqB9+/bMnz9fuS1g27ZtdOjQgZkzZ+Lj48PIkSOV5/r555/p0qULL774IoMHD9Z5HQMHDmTixIk6uU2cOJGBAwcq7Tt37jBhwgRatWpFixYtGD58OPHx8UW+rg0bNtCsWTP2798PwO+//06/fv3w9vbG19eX999/n+vXrz/1+yRMR5lMN3Hnzp2yOKwQJuvrr7+mZ8+e7N69m379+rFw4UKOHj0K5H+Br1mzhgkTJrBv3z6WL19OQkICc+bMAfI/b++99x7/+Mc/2LNnD8uWLePEiRPMmzfvqZ9/3LhxuLq68v333/Pdd99haWnJe++9B+RfR/P29iYoKIh//etfALz77rukpqaydu1a9u7dy7Bhw1izZo3OD99r167x119/sX37dkJCQpTtX375JZ988gnff/89VatWpV+/fty/f/+p8szNzWXo0KFcunSJ5cuX8+2336JSqXjnnXfIzc3Vid28eTMLFixgyZIldOnSBY1Gw8iRI/H19eWHH35g/fr1XL9+nUmTJj31+yRMR4lmeNmxYwf79+8nMzNT5wZQjUZDRkYGV65c4dy5cwZLUghT169fP9544w0ARo8ezdq1azl37hytW7emfv36zJkzR+l1ubm50a1bN2UAyl9//UV2djZ16tTBzc0NNzc3VqxYUeTsS4+TmJhImzZtcHNzw9ramlmzZhEbG0teXh6Ojo5YW1tjY2ODk5MTWVlZ9OzZk8DAQNzc3AAYPHgwa9as4eLFi3Tq1Ek57ujRo6lXrx6QP+AF4JNPPlFOp82bNw9/f3927drFm2+++cQ8jx49yoULF4iMjKRhw4YATJ8+nS+//FJneP+3337L3LlzWb58ufJc6enp3L17l2eeeQY3Nzfq1avH4sWLSzR7iaj89C5+q1evZuHChVhbW2NnZ8fdu3epVasWKSkp3L9/HxsbG51TFEKIJ2vQoIFOu3r16jx48ACADh06cObMGT777DPi4uKIi4vjypUruLq6AtC0aVN69OjBqFGjqFmzJn5+fgQEBBAYGPjUzz927FhmzZrF5s2badmyJe3ateP1118vci5SGxsbBgwYwN69e1m3bh0JCQlcvHiRv//+u9BsKI++LoAWLVrovM4GDRpw6dKlp8rz0qVLVK9eXSl8ADVr1tQ5Vfr3338zZcoUrKysqFu3rrLdwcGB4OBgpk+fzpIlS3jllVfw9/enR48eT/XcwrTofdpz27ZtPPfccxw5coStW7ei1WrZsGEDMTExhIeH8+DBA7y8vMoiVyFMVlE3A2u1WgBWrVrFwIEDuXv3Lq1bt2bq1Km88847OrERERFERkYSHBzM3bt3+fjjj3n33Xef+vn79+/PoUOHmDx5Mvb29ixZsoRevXoVeU9vZmYmffv25YsvvqB69er06tWLzZs3U6tWrUKxNjY2T3ytGo1GZ5rEgtdd4OHTmdbW1k+cKs7CwoLVq1fz3HPPERYWplOQQ0NDOXjwIB999BFarZbp06fTt29f5YeGMB96F79r167Rs2dP7OzsqFevHg4ODsTExKBSqejXrx/dunWT2xyEMKAVK1bw3nvvMWXKFN566y2aN29OfHy8UiROnz7NrFmzaNiwIUOGDGHVqlXMmjWLX3755alO6d2+fZtp06aRk5ND7969mT9/Pj/88ANJSUn8+uuvheIPHz7Mf//7XzZu3MgHH3xAt27dsLOzIzk5uVDhKsrDl0Tu3LlDfHw8zz77LABqtZq0tDSd+ISEBOXvnp6epKam6mxLTU3llVde4eTJkwBK73fGjBmcO3dO+T6KjY3l008/xdnZmbfffpslS5awZs0a/vvf/yqDe4T50Lv4WVlZYWtrq7Td3d11/uG0atXqsSOvhBD6q127NtHR0Vy5coXY2FgWLVrE/v37yc7OBsDOzo7Nmzczf/58EhISuHTpEnv27KFBgwbUqFHjicd3dHTkl19+YfLkyZw/f56kpCS++eYbrK2tadasGQC2trZcu3aNmzdvKj28H374gWvXrhETE8Po0aPJyclRcipOeHi4cu1u7Nix1K5dm27dugHg7e3NkSNHOHjwIElJSSxZskTnlGibNm148cUXGT9+PGfOnOHKlStMnDgRBwcHXnzxRZ3nady4McHBwSxevJj4+HgcHR3ZtWsX4eHh/Pnnn8TFxbF9+3YcHBx0TqMK86B38fP09OTUqVNK28PDQ+eX3L17957qAyCEeDrz5s0jKyuLf/zjHwwYMIBLly4xdepUkpOTuXr1Ko0aNWLp0qUcO3aMN954g7fffhuVSsXq1aufav1AKysrJXbIkCF0796dI0eOsGrVKurXrw9A3759uXTpEq+99hrPP/88YWFhbNiwgaCgIMLCwvD19aVHjx6cOXPmic83evRowsLC6NOnD2q1mjVr1iinPYcMGUJgYCAff/yxctp1yJAhymMtLCxYvnw5derUYdiwYfTt2xcLCwudYzz6XHXq1CEsLAxHR0fWrFnDtWvX6NOnD7169eLq1ausW7cOOzu7p/y/IUyFhfZpzlM8ZMuWLUydOpUePXowbdo0/vOf//Dhhx/y3nvv0bBhQ2bNmoW7uzubNm0ySIK5ubm8/PLLhc7JV6tWTSnChw8fZtGiRVy5cgVnZ2cGDBhQ6JrI2bNnmTdvHufOncPW1pbevXvz/vvvY21trcTEx8czZ84c5TRu165d+fjjj3U+GLdv32b27NkcPnyY3Nxc/P39CQsL01nsMiMjgwULFigjYn18fPi///u/Ii/+P43Tp08D0Lx58xI9viIYM2ZMoW3Lly83Qiblq+D0nLu7u5EzESVhjv//kpOTlcmtp02bVqnn9Szuu1Pv0Z5vv/02N2/eZNOmTVhZWdGlSxe6d+/OsmXLgPxTMKGhoaVM+X/i4uJ48OABc+fO1SkeBb9of/vtN0aNGkVQUBAffvghJ0+eZN68eWi1WoYNGwbk/wMeMmQI3t7eLF68mD///JNFixaRnp6u/E9OTU1l8ODB1KxZk7lz55KcnMz8+fO5efOmcsNxbm4uw4YNIzMzkylTppCbm0tERATBwcF8//33WFnlv51jx47l7NmzjB8/HltbW5YtW8agQYPYvXs39vb2BntvhBBClEyJ7vMbO3Ys77//vvJlHxERQd++fUlNTcXb29ugvxQuXLiApaUlgYGBVK1atdD+JUuW8PzzzzN//nwA2rdvT25uLitWrGDgwIGo1WpWrVqFvb09n3/+OWq1Gn9/f2xsbJgxYwYjR47E1dWVTZs2ce/ePXbs2KFcJ3F1dWXEiBGcOXMGLy8vdu/ezYULF9izZw+enp7A/4aZ79+/n27duhETE0NUVBSrV6+mffv2APj4+NCxY0e2bNnCiBEjDPbeCPEkq1ev5vPPPy82JjQ0lP79+5dTRkJUDCUqfoBS+Ar4+vqWOpminD9/nvr16xdZ+B48eEBMTAwfffSRzvbAwEDWrFnDb7/9xiuvvEJ0dDSvvvqqzjWBrl27MnXqVA4fPsw//vEPoqOj8fX11Rkg0LZtW2xtbYmKisLLy4vo6GgaNWqkFD5AaUdFRdGtWzeio6OxtbXFz89PiXFycsLX15dDhw5J8RPlqk+fPk+cld/JyamcshGi4nhi8Rs0aBDvvvsurVu3VtpPYshVHS5evIharWbYsGH89ttvWFlZERQUxPjx47l58yY5OTl4eHjoPKbg/HxcXBxeXl7cuHGjUIyTkxN2dnbExcUB+cOgX3/9dZ0YlUpF3bp1dWIePQ7kr3H4cIy7u3uhe5nq169PZGRkid8HjUZjcsOxTe31FCUrKwt7e3uysrKM8vxVqlRRboYvjrHyq+g0Gg1paWlm9f7cu3dP+XtsbGylXr9Vo9E8dkHdJxa/q1evkpmZqdMuTxcuXCA9PZ0333yTUaNGce7cOZYuXUpcXBzjxo0DKDRSq+BWjPT0dOWeoaJGc9na2pKeng5AWlraU8U0atSoyJiCC+Pp6elPPI4QQgjjemLxe3SFhvJesWHRokU4ODjQpEkTIP/0qrOzMx9//DHR0dEAj53xwdLSUrnptqgYrVarMxTcEDHFDZ59mmHnj6NSqZT3wFSY2uspSsGPoqJmOhEVn0qlwtHR0exGexZo2LChSYz2LEqJr/mVl5YtWxbaFhAQoNN+tEdV0La3t1d6YUX1ujIzM5XRl3Z2dkXGZGRkKJP3FhdT8Dx2dnZF9o4fjhFCCGFcTyx+YWFheh/UwsKCWbNmlSihhyUnJ3Pw4EFeeeUVZWZ4+N/1CWdnZ1Qqlc56YIDS9vDwwNbWFldXV53pkAqOnZ6erlzD8/DwKBSj0Wi4evWqMkGwh4dHkRPwJiYmKvOZenh4cPToUbRarU4vMSEhocjrhUIIIcrfE4vf9u3bC22zsLBQTu85OzuTl5fH3bt3AahatSoODg4GSc7CwoLw8HAGDRqkU4T37NmDSqWiTZs2+Pj4sH//fgYPHqwUm3379mFvb69MzeTn58fPP//M+PHjlRGf+/btQ6VSKT1LPz8/1q5dS0pKCo6OjkD+zfOZmZm0adMGyB/9uXv3bmJjY5XpkAqmnCqYRLht27asWLGCI0eOKCM+79y5Q0xMjM6CnkIIIYznicXvwoULOu1Lly7Rv39/Bg8ezMCBA5VCl56ezqZNm1ixYoWyyGZpOTk50b9/fzZu3IidnR0+Pj6cPHmSFStW0L9/f9zd3Xn33XcZOnQoY8eOpVevXpw6dYovv/ySkJAQ5faI4OBgdu/ezYgRIxg8eDDx8fEsXLiQPn36UKdOHSB/PbWvv/6aIUOGMGbMGFJSUpg/fz7t27fn5ZdfBqBbt26sWLGC4OBgQkJC0Gq1RERE8OyzzxIUFATkX5Ns2bIl48aNIzQ0FEdHR5YuXYq9vT1vv/22Qd4XIZ4kOzu70OKu5c3KyqrIKceEqAj0nt6sT58+eHp6Mnv27CL3f/rpp5w5c4YdO3YYJMGcnBzWr1/P999/z7Vr13B1daVPnz4EBwcrA0h+/PFHlixZQlxcHK6urvTv37/Q9GYxMTHMmzeP8+fPU6NGDd54441C05tdunSJWbNmcerUKWxtbenUqRPjx4/XuVZ348YNZs6cSXR0NGq1Gj8/PyZOnMgzzzyjxKSmpjJnzhwOHDhAXl4eLVq0YOLEiSWePFemN6u8jDE9VnZ2NpM/+YQMI48utrWzY8b06ZW6AMr0ZjK9meLixYvKitNFady4scEKH+Sv3zV8+HCGDx/+2JjOnTvTuXPnYo/j4+PDt99+W2xM48aNWb9+fbExtWvXVqZyexwHBwdmz5792B8IQpSl3NxcMtLTafzPHqge+nFXnjQ5OVz61y5yc3NLXPx27drFF198QVJSEm5ubowcObLY7x4h9KF38atduzZRUVH069ev0D6NRkNkZGSJJ3AWQhiOytoaVZXK2euKjIwkNDSUQYMG0a5dOw4cOMCECROwsbGha9euxk5PmAC9i9/AgQOZPn06H3zwAW+++Sb169fnwYMHxMfH89VXX/Hbb7+xYMGCsshVCGEmFi5cSFBQEJMmTQKgXbt2pKam8tlnn0nxEwahd/Hr378/d+7cYfXq1fz444/Kdq1WS7Vq1QgPD6d79+4GTVIIYT6SkpJITExUZnAqEBgYSGRkJElJSTq3PglREiW6yf39999nwIABHDt2jOvXr2NhYUG9evVo06aNzirvQgihr9jYWIBi5+w11eKn0WhISUkxag4Ft609+ndjcHR0fOzcnKVV4hleatSoQWBgIHfu3KF69eqVekSXEKLieNx8vA/P2WuqUlJSlJGWFcGiRYuM+vxlOdq0RJNNJiQk8P7779OiRQvat2/PyZMnOXr0KG+++SYxMTGGzlEIYUYeNx9vwfbSzJErRAG9e37x8fH06dMHCwsL2rVrp1z3U6lUxMbG8s4777Bhw4ZKfU+aEMJ4CubbfbSHl5GRobPf1I0Z0x4Hh8LrmJYHjSYPAJWq/H9opKbeZ/nyQ2X+PHoXv4ULF2JjY8P27duxsLBg//79QP4E1Hv27OHtt99m2bJlrFmzxuDJCiFMX8G1vsTERJ2VPwpuODeXOXIdHKri5CRjKMqK3mX92LFjvP322zg7Oxc6LeHq6kq/fv04d+6cwRIUQpgXd3d36taty969e3W279+/nwYNGihTEgpRGnr3/LKzs6levfpj91tbW/PgwYNSJSUqJkOPRHt43TB9lOUIMFOiycmptM89ZswYwsLCcHBwICAggIMHDxIZGWn0ARjCdOhd/J577jkOHjxI//79C+3Lzc3lhx9+MItFSs2NRqNh2rRp3L5922DHLOmoNhcXF8LDw6UAPoaVlRW2dnZc+tcuo+Zha2eHlVXJBpT37t2b7Oxs1q5dy3fffUe9evWYO3cu3bp1M3CWwlzp/S9z5MiRjB49mtDQUDp27AjAtWvX+Omnn/jyyy/5448/WLx4scETFUI8HbVazYzp0yv9qg59+/alb9++BsxIiP/Ru/i9+uqrzJw5k1mzZrFrV/4vy08++QStVkuVKlWYMGGCsvirMB0qlYrw8PASn/Ysqpc3bdq0Eh1LTns+mVqtlntvhShGic5J9O7dmy5dunDkyBESExPJy8vDzc0NPz8/ZSFYYXpUKlWJbzi1srLS6Ymo1epKvVSKEKJyK1Hx27FjB9HR0dy6dYu8vDxl+9atW4H8m1O/+uorw2QoTEJ4eLhO708GLgghjEnv4rdo0SJWrlyJtbU1zs7OMtuC0FtJB0EIIYSh6P0ttH37dtq2bcvSpUupWtU4sw+Iyq0izV0ohDBPehe/9PR0AgMDpfAJIUQZSkm5b+wUjKK8Xrfexa9du3YcO3aMN998syzyEUIIs6XRaJS/f/552c9vWdE9/H4Ymt7F75NPPmHo0KGEhITQqVOnIqc5A/D19TVIgkIIIYSh6V38rl+/TlpaGrt372bPnj2F9mu1WiwsLDh//rxBEhRCCHPx8P2ro0e3x9HR/C4vpaTcV3q9ZXk/r97Fb9q0ady7d49hw4bRoEEDGbknRAWUnZ1d6Wd4MXeOjrKqQ1nSu3JdvnyZ9957j+HDh5dFPqIMGHpC6pK4e/dukX83FlOeJSY7O5vw8E9ISzPuiuf29nZMmza91AXw/Pnz/POf/+Snn36iVq1aBspOmDu9i1+tWrXk3r5KJiUlpULdXlARbnCfNm2ayc4wk5ubS1paOpMmdaVqVeOcmbl/P5dZs/aSm5tbquIXGxvLyJEjjd6LFaZH709GcHAwS5cuxd/fn0aNGpVFTkIIA6ha1YqqVSvnacfc3Fy2bt1KREQE1tbWxk7HKFJTjXerg7FXci8Pehe/CxcuYGlpyeuvv069evVwcXEpdPpIpjeruDyCOmBdrZpRnlv7/6fCszDSmYOczEziIg8a5bmFfk6ePMmCBQsYNmwYrq6uTJ482dgplbvly+VWh7Kkd/H7+eefsbS0pFatWuTk5HDjxo2yyEuUEetq1VDby0V0UbF5enpy4MABnJ2d2bZtm7HTESZI7+J38KD8chZClC0XFxdjp2AUjo6OJV7qy1Du3r2rXJcfO3YsNWrUMFouZblKkNynYGZyMjKNnYLRmPNrF5VDaZYNKws1atSoUPkYkhQ/M/DwFEFxe6XnDmU7bZIQouKTexaEEEKYHen5mYGHR+PW9W+NtZFW5DD6aM/797kadRQo22mThBAVnxQ/M1Pw5S9M3/37xrsx3JjPLcTTkOInhImxsrLC3t6OWbP2GjUPe3s7mftXVFjyL9MMyPDpwspyCLWxqdVqpk2bbvQpwQw1sXXv3r3p3bu3ATIS4n+k+JkBGT5tftRqtayoIEQxZLSnEEIIsyPFrwzt2rWL7t2789JLLxEUFMSOHTuMnZIQQgjktGeZiYyMJDQ0lEGDBtGuXTsOHDjAhAkTsLGxoWvXrsZOTwhhokq7fqeh1t6s6GtmSvErIwsXLiQoKIhJkyYB0K5dO1JTU/nss88qbfErzYfKkIvZVvQP1cMsLCxkNplKTKPRVKollTQaDdOmTeP27dsGOV5p1t50cXEhPDy8wn5WpfiVgaSkJBITExk3bpzO9sDAQCIjI0lKSqJevXpGyq5kDPmhKu1ithX9Q/WwKlWqcPfuXTQaTaXIV/xPXl4eOTk5VDXSpBCibEnxKwOxsbEAeHh46Gx3d3cHIC4uTu/ip9FouHjxomESLAGNRkNOTo7Rnv9hOTk5XLx4sVIUk7y8PLKzs0lKSqJmzZqVImcBWq2Wu3fvkpWVhUaj4d69e8ZO6am99dZbZGRklOoYBWcrSvPv1dbWlitXrpQqj9Iq7kenFL8ykJaWBoCdnZ3Odlvb/HX00tPTyz2n0lKpVAwYMKBUHypDfKAg/32sLEXE0tISlUpFamoq6enp2NjYYGmk6d3E08vNzSUrKwsLC4tK82+tgEqlonr16sZOo8KT4lcGtFotkH+9p6jtJfnyU6lUNGnSpPTJCaO4f/8+aWlpZGVlKf8ORMVlbW1NzZo1cXBwkB8rldjp06cfu0+KXxmwt7cHCvfwCnpNBfuF+ahatapcOxKiApGfNGWg4FpfYmKizvaEhASd/UIItGPdcwAADV5JREFUIYxDil8ZcHd3p27duuzdqzux8P79+2nQoAF16tQxUmZCCCFATnuWmTFjxhAWFoaDgwMBAQEcPHiQyMjIUg/zF0IIUXpS/MpI7969yc7OZu3atXz33XfUq1ePuXPn0q1bN2OnJoQQZk+KXxnq27cvffv2NXYaQgghHiHFrxIouD+uuGG7QgghdBU3taAMeBFCCGF2LLRyx60QQggzIz0/IYQQZkeKnxBCCLMjxU8IIYTZkeInhBDC7EjxE0IIYXak+AkhhDA7UvyEEEKYHSl+QgghzI4UPyGEEGZHip8QQgizI8VPCCGE2ZHiJ8zKrl276N69Oy+99BJBQUHs2LHD2CkJoeP8+fO88MIL3Lx509ipmDQpfsJsREZGEhoaip+fH8uXL6dly5ZMmDCBvXv3Gjs1IQCIjY1l5MiR5ObmGjsVkyerOgiz0blzZ5o1a8aiRYuUbR999BEXL14kMjLSiJkJc5ebm8vWrVuJiIjA2tqalJQUoqKiqFWrlrFTM1nS8xNmISkpicTERLp06aKzPTAwkNjYWJKSkoyUmRBw8uRJFixYwDvvvENoaKix0zELUvyEWYiNjQXAw8NDZ7u7uzsAcXFx5Z6TEAU8PT05cOAA7733HiqVytjpmAUrYycgRHlIS0sDwM7OTme7ra0tAOnp6eWekxAFXFxcjJ2C2ZGenzALBZe2LSwsitxuaSkfBSHMiXzihVmwt7cHCvfwMjIydPYLIcyDFD9hFgqu9SUmJupsT0hI0NkvhDAPUvyEWXB3d6du3bqF7unbv38/DRo0oE6dOkbKTAhhDDLgRZiNMWPGEBYWhoODAwEBARw8eJDIyEid+/6EEOZBip8wG7179yY7O5u1a9fy3XffUa9ePebOnUu3bt2MnZoQopzJDC9CCCHMjlzzE0IIYXak+AkhhDA7UvyEEEKYHSl+QgghzI4UPyGEEGZHip8QQgizI8VPiDLUoUMHBg4caOw0SiwvL4+rV68a7Hjbtm2jSZMmHD9+3GDHfJLjx4/TpEkTtm3bVm7PKSo+KX5CiCKlp6fTp08ftm/fbuxUhDA4KX5CiCKlpKRw9uxZY6chRJmQ4ieEEMLsSPETohz8+9//pnv37jRr1ozAwEC2bNmi7NNqtWzZsoV//vOfeHt78+KLL9K1a1dWrVrFw7MPpqamMnHiRAICAmjWrBmdOnUiIiKCBw8e6J2PVqtl2bJlBAYG8uKLL9KmTRs+/vhjbty4AeRfJ+vYsSMAy5Yto0mTJsq1v4SEBCZMmED79u1p1qwZLVu2ZNSoUVy+fFk5fsG1vX379tGhQwe8vLxYunSpsv/vv/9mzJgxeHl50aZNG6ZPn66z1uLjrg0WtT07O5ulS5fSpUsXXnrpJQIDA1m1ahUajeaxr3/37t00bdqUDz74QIn79ddf6d+/Pz4+Pnh7e9O3b18OHjyo93srKgeZ2FqIMnb27FkuXbrEgAEDcHJy4ptvvmHKlCnUrFmTTp06sXjxYlasWEGvXr3o06cPGRkZ7Nixg4iICGrWrEmvXr0A+Oijj/jjjz8YNGgQzzzzDKdOnWLVqlWkpKQwffp0vXJasWIFy5cvp3///kph27BhA+fOnWPXrl14enoSFhbG7Nmz6dy5M507d8bJyYnbt2/Tp08f7OzsGDBgADVq1OD8+fN8++23/Pnnn+zbtw9Ly//9pg4LC2PgwIHY29vTvHlzZT3F8PBwmjZtSmhoKJcuXWLTpk1cunSJDRs2YGFhoddrGTNmDIcOHeK1115j6NCh/P7770RERJCcnExYWFih+P/85z9K8Y6IiEClUhEbG8vIkSNp2rQpY8eOBeDbb79l9OjRfP311/j4+OiVk6j4pPgJUcaysrLYtGkTL7zwAgABAQF07NiR/fv34+/vz9dff0337t2ZM2eO8pg333yT1q1bs2/fPnr16kVycjJHjhxh/PjxDBs2TInRarUkJSX9v/buNaTpLg7g+Lf1bKlZW+rKCwyzYFGEGCJUlmE1utJFKWMl0otMzYlEFyKFzJqVJVgtMu1iZrgueClDw9SQUlgZoRhYROGFcJhplmWz50X4f5o+lb6ILp7PO8//nP85Zy/243cuc9hjKioqYv78+ezdu1cq8/Dw4PLlyzQ3N6PRaFi0aBFGoxGtVsuqVasAyMnJoaOjg9zcXKZMmSK1HTt2LBkZGTQ0NEjzBNDpdFIwgf/+mbBWqyU7O5t//vnyFTRp0iSOHz9OeXk5wcHBQ55HZWUld+/eJT4+nq1btwKwYcMGent7uXTpEtHR0Xb1a2triY2Nxd/fn/T0dORyOQBlZWW8e/eOEydO4OLiAsCyZcsICwujoaFBBL+/kFj2FISfzNvb2y4geHl5SVmUXC7n3r17JCUl2bV5/fo1zs7OvHv3DoBx48bh5OREbm4uJSUlUrnRaOT8+fPDHpO7uzs1NTVcuHABq9UKQFhYGAUFBWg0mm+227JlC/fu3bMLfD09PVK21z+ufoGBgf/7noiICCnwAdJ1kIqKimHNo6KiAplMxsaNG+3Kd+3aRUFBAc7OzlJZY2MjkZGReHl5cerUKcaMGSM9c3d3B2D//v3U1dUBMGHCBEpKSv7oqyrCt4nMTxB+MldX10FlDg4O9Pb2AiCXy6moqKCsrIznz5/z4sUL3rx5AyDt+SkUCpKSkkhISMBgMKBQKAgICECn07F69Wq7L/Kh2LlzJ1FRURw8eBCj0ciMGTMIDg5m3bp1qNXq77bt7e0lLS2N+vp6Xr58SVNTk7Rv1tfX98O5A/j4+Nj9rVQqUSqVNDc3D2sezc3NuLq62gU5ALVaPWgeZ8+eRSaT0dPTQ1tbm12QX7JkCbdv36a4uJji4mLUajVBQUGsWbNGZH1/KZH5CcJP9vUe2ECfP39mx44dGAwGmpqa8PPzY+fOnZSWluLh4WFXd+XKlVRUVHDgwAEWLFjAo0ePSExMZN26dXz8+HFYY5o2bRolJSWYTCZCQ0OxWq2kp6ezbNkynj179s12dXV1LF26FLPZjFKpJCQkhNOnT5OYmDisuf/fvl5fXx+jR4/+7rgHHmKx2WxD3iPUarVcvHiRvr4+9u3bZ/dMLpeTnp5OYWEhsbGxeHp6cv36dfR6PRkZGUN6v/BnEcFPEH4hi8XCjRs3iI6OJjc3lz179hAaGoqXlxcdHR1Sve7ubiwWC6NGjSI0NJTjx49z//59wsPDefLkCVVVVUPu02azUV9fT2trKwsXLiQ5OZnKykrS0tLo7OzkypUr32x7+PBhFAoFN2/e5OjRo0RGRjJv3jy6urqGNe+BGV57eztdXV1SNtYfNAcG9f4l2n6enp5YrVa6u7vtyuvr69m+fTtPnz6VyiIiIvD39yciIoKqqipu3LghPWtpacFisaDVatm2bRtms5ny8nK8vb3Jysoa1tyEP4MIfoLwC/UHuKlTp9qVm81m3r9/z6dPn4Av+1V6vZ6rV69KdRQKBdOnTwf4Ycb0NZvNRnh4OAcPHrQr9/X1Bf4LPP3v/Hops6OjAxcXF+lQCEBXV5f0KzDfu17wtYEBtj/A9F+v6F+ybGhokOp8+vSJ0tJSu3ZBQUH09fUNet/ly5e5desWbm5ug/qOjo7Gw8MDo9FIZ2cn8OX0a0REBK9evZLqubu7M2nSpO9m7sKfS+z5CcIv5Ofnh7OzM0ajkZaWFsaPH09NTQ3FxcWMGTNGymh8fX3x9/cnLS2N1tZWtFotra2t5OTk4OPjw+zZs4fcp0KhYNOmTZw6dYqYmBjmzZtHT08PeXl5ODo6EhISAoBKpUImk3Hnzh08PT3R6XTMnz+fM2fOEBcXR2BgIG1tbVy9elXKyAZmYN9isViIjo4mKCiIhw8fkp+fz9KlS6V5BAQEoFarMZlMfPjwAVdXVwoKCgYdqAkODmbu3LmkpKTQ2NjIzJkzqa2tJT8/n5iYGFQq1aC+nZyc2L17N3FxcaSmppKUlIRer6egoAC9Xs/69etRKpVUV1dTU1ODwWAY8mcr/DlE8BOEX8jNzY2MjAxSU1MxmUwoFAomT57MsWPHePz4MdnZ2VitVtzc3Dh58iQnTpygvLycvLw8lEolOp2OuLg4FArFsPo1GAyoVCquXbvGoUOHGD16NLNmzeLIkSPSSU5HR0fi4+PJysoiOTkZjUZDbGwsNpuN4uJiysvLmThxInPmzGHz5s0sX76c6upqFi9e/MP+09LSyMrK4sCBA6hUKqKiooiJiZGey+VyMjMzSUlJITMzEycnJ1asWIFOp7M72SmTyTCZTJhMJoqKiigsLESj0ZCYmMiGDRu+2f+SJUsIDAzEbDazZs0a/Pz8OHfuHCdPnuTs2bO8ffsWb29vEhIS0Ov1w/pshT/DqM9f/4SEIAiCIIwAYjFbEARBGHHEsqcg/AVsNhvt7e1Dqjtu3DgcHBx+8ogE4fcmgp8g/AX6ry0MhdFoZO3atT95RILwexN7foLwF/jw4QMPHjwYUt2pU6cyceLEnzwiQfi9ieAnCIIgjDjiwIsgCIIw4ojgJwiCIIw4IvgJgiAII44IfoIgCMKII4KfIAiCMOL8CwMiFEigehmzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.boxplot(x='has_starbucks',y='median_home_value',hue='has_starbucks',data=df_final,palette='Set3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These boxplots compare the median values between the population with Starbucks and the population without Starbucks. We can see that the population with Starbucks tend to have lower median age, higher median household income, higher median rent, and higher median home value.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GEOSPATIAL ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a) Does Population Affect Number of Starbucks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_population</th>\n",
       "      <th>state</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>346.90</td>\n",
       "      <td>72</td>\n",
       "      <td>PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>485.08</td>\n",
       "      <td>01</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.86</td>\n",
       "      <td>02</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>717.00</td>\n",
       "      <td>53</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>58.32</td>\n",
       "      <td>56</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>576.32</td>\n",
       "      <td>55</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    total_population state State\n",
       "0             346.90    72    PR\n",
       "1             485.08    01    AL\n",
       "2              73.86    02    AK\n",
       "..               ...   ...   ...\n",
       "49            717.00    53    WA\n",
       "50             58.32    56    WY\n",
       "51            576.32    55    WI\n",
       "\n",
       "[52 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popu_dic={'B01001_001E':'total_population'}\n",
    "df_population=pd.DataFrame(c.acs5.state(list(popu_dic.keys()), Census.ALL))\n",
    "for i in df_population.index:\n",
    "    df_population.loc[i,'State']=states.lookup(df_population['state'][i]).abbr\n",
    "df_population.rename(popu_dic,axis='columns',inplace=True)\n",
    "df_population['total_population']=df_population['total_population'].apply(float)/10000\n",
    "df_population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_population</th>\n",
       "      <th>state</th>\n",
       "      <th>State</th>\n",
       "      <th>AREA</th>\n",
       "      <th>...</th>\n",
       "      <th>STATE_FIPS</th>\n",
       "      <th>SUB_REGION</th>\n",
       "      <th>STATE_ABBR</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>485.08</td>\n",
       "      <td>01</td>\n",
       "      <td>AL</td>\n",
       "      <td>12.90</td>\n",
       "      <td>...</td>\n",
       "      <td>01</td>\n",
       "      <td>E S Cen</td>\n",
       "      <td>AL</td>\n",
       "      <td>POLYGON ((-85.07013702392578 31.98054504394531...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>680.99</td>\n",
       "      <td>04</td>\n",
       "      <td>AZ</td>\n",
       "      <td>28.86</td>\n",
       "      <td>...</td>\n",
       "      <td>04</td>\n",
       "      <td>Mtn</td>\n",
       "      <td>AZ</td>\n",
       "      <td>POLYGON ((-114.5198440551758 33.02766799926758...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>297.79</td>\n",
       "      <td>05</td>\n",
       "      <td>AR</td>\n",
       "      <td>13.52</td>\n",
       "      <td>...</td>\n",
       "      <td>05</td>\n",
       "      <td>W S Cen</td>\n",
       "      <td>AR</td>\n",
       "      <td>POLYGON ((-94.46147918701172 34.1966552734375,...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>717.00</td>\n",
       "      <td>53</td>\n",
       "      <td>WA</td>\n",
       "      <td>20.75</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>WA</td>\n",
       "      <td>(POLYGON ((-122.400749206543 48.22539520263672...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>58.32</td>\n",
       "      <td>56</td>\n",
       "      <td>WY</td>\n",
       "      <td>27.97</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>Mtn</td>\n",
       "      <td>WY</td>\n",
       "      <td>POLYGON ((-104.053108215332 41.69824600219727,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>576.32</td>\n",
       "      <td>55</td>\n",
       "      <td>WI</td>\n",
       "      <td>16.48</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>E N Cen</td>\n",
       "      <td>WI</td>\n",
       "      <td>(POLYGON ((-87.74845123291016 44.9616279602050...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    total_population state State   AREA  ...  STATE_FIPS  SUB_REGION  \\\n",
       "0             485.08    01    AL  12.90  ...          01     E S Cen   \n",
       "1             680.99    04    AZ  28.86  ...          04         Mtn   \n",
       "2             297.79    05    AR  13.52  ...          05     W S Cen   \n",
       "..               ...   ...   ...    ...  ...         ...         ...   \n",
       "45            717.00    53    WA  20.75  ...          53     Pacific   \n",
       "46             58.32    56    WY  27.97  ...          56         Mtn   \n",
       "47            576.32    55    WI  16.48  ...          55     E N Cen   \n",
       "\n",
       "    STATE_ABBR                                           geometry  \n",
       "0           AL  POLYGON ((-85.07013702392578 31.98054504394531...  \n",
       "1           AZ  POLYGON ((-114.5198440551758 33.02766799926758...  \n",
       "2           AR  POLYGON ((-94.46147918701172 34.1966552734375,...  \n",
       "..         ...                                                ...  \n",
       "45          WA  (POLYGON ((-122.400749206543 48.22539520263672...  \n",
       "46          WY  POLYGON ((-104.053108215332 41.69824600219727,...  \n",
       "47          WI  (POLYGON ((-87.74845123291016 44.9616279602050...  \n",
       "\n",
       "[48 rows x 12 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_table = gpd.read_file(libpysal.examples.get_path('us48.shp'))\n",
    "df_population=df_population.merge(geo_table,left_on='State',right_on='STATE_ABBR')\n",
    "df_population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#since there are so few people in Hawaii and Alaska, and we have very little infomation about these two places, we will \n",
    "#be focusing only on the 48 main states.\n",
    "df_starbucks_US = df_starbucks_US[(df_starbucks_US['State']  !='AK') & (df_starbucks_US['State']  !='HI')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (25,12))\n",
    "df_population=gpd.GeoDataFrame(df_population)\n",
    "df_starbucks_US=gpd.GeoDataFrame(df_starbucks_US)\n",
    "base=df_population.plot(ax=ax,column='total_population',cmap='GnBu', legend=True)\n",
    "df_starbucks_US.plot(ax=base,marker='o',color='purple',markersize=3)\n",
    "plt.axis('off')\n",
    "plt.title(\"Starbucks distribution under population density (per ten million)\")\n",
    "plt.show()\n",
    "# scale: ten million"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>starbucks_count</th>\n",
       "      <th>STATE_ABBR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2821</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1042</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>757</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>23</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>13</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>8</td>\n",
       "      <td>VT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    starbucks_count STATE_ABBR\n",
       "0              2821         CA\n",
       "1              1042         TX\n",
       "2               757         WA\n",
       "..              ...        ...\n",
       "46               23         WY\n",
       "47               13         ND\n",
       "48                8         VT\n",
       "\n",
       "[49 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=pd.DataFrame(df_starbucks_US['State'].value_counts())\n",
    "x.columns=['starbucks_count']\n",
    "x['STATE_ABBR']=x.index\n",
    "x= x.reset_index(drop=True)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_population</th>\n",
       "      <th>state</th>\n",
       "      <th>State</th>\n",
       "      <th>AREA</th>\n",
       "      <th>...</th>\n",
       "      <th>STATE_ABBR</th>\n",
       "      <th>geometry</th>\n",
       "      <th>starbucks_count</th>\n",
       "      <th>population_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>485.08</td>\n",
       "      <td>01</td>\n",
       "      <td>AL</td>\n",
       "      <td>12.90</td>\n",
       "      <td>...</td>\n",
       "      <td>AL</td>\n",
       "      <td>POLYGON ((-85.07013702392578 31.98054504394531...</td>\n",
       "      <td>85</td>\n",
       "      <td>37.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>680.99</td>\n",
       "      <td>04</td>\n",
       "      <td>AZ</td>\n",
       "      <td>28.86</td>\n",
       "      <td>...</td>\n",
       "      <td>AZ</td>\n",
       "      <td>POLYGON ((-114.5198440551758 33.02766799926758...</td>\n",
       "      <td>488</td>\n",
       "      <td>23.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>297.79</td>\n",
       "      <td>05</td>\n",
       "      <td>AR</td>\n",
       "      <td>13.52</td>\n",
       "      <td>...</td>\n",
       "      <td>AR</td>\n",
       "      <td>POLYGON ((-94.46147918701172 34.1966552734375,...</td>\n",
       "      <td>55</td>\n",
       "      <td>22.03</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>717.00</td>\n",
       "      <td>53</td>\n",
       "      <td>WA</td>\n",
       "      <td>20.75</td>\n",
       "      <td>...</td>\n",
       "      <td>WA</td>\n",
       "      <td>(POLYGON ((-122.400749206543 48.22539520263672...</td>\n",
       "      <td>757</td>\n",
       "      <td>34.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>58.32</td>\n",
       "      <td>56</td>\n",
       "      <td>WY</td>\n",
       "      <td>27.97</td>\n",
       "      <td>...</td>\n",
       "      <td>WY</td>\n",
       "      <td>POLYGON ((-104.053108215332 41.69824600219727,...</td>\n",
       "      <td>23</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>576.32</td>\n",
       "      <td>55</td>\n",
       "      <td>WI</td>\n",
       "      <td>16.48</td>\n",
       "      <td>...</td>\n",
       "      <td>WI</td>\n",
       "      <td>(POLYGON ((-87.74845123291016 44.9616279602050...</td>\n",
       "      <td>145</td>\n",
       "      <td>34.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    total_population state State   AREA  ...  STATE_ABBR  \\\n",
       "0             485.08    01    AL  12.90  ...          AL   \n",
       "1             680.99    04    AZ  28.86  ...          AZ   \n",
       "2             297.79    05    AR  13.52  ...          AR   \n",
       "..               ...   ...   ...    ...  ...         ...   \n",
       "45            717.00    53    WA  20.75  ...          WA   \n",
       "46             58.32    56    WY  27.97  ...          WY   \n",
       "47            576.32    55    WI  16.48  ...          WI   \n",
       "\n",
       "                                             geometry  starbucks_count  \\\n",
       "0   POLYGON ((-85.07013702392578 31.98054504394531...               85   \n",
       "1   POLYGON ((-114.5198440551758 33.02766799926758...              488   \n",
       "2   POLYGON ((-94.46147918701172 34.1966552734375,...               55   \n",
       "..                                                ...              ...   \n",
       "45  (POLYGON ((-122.400749206543 48.22539520263672...              757   \n",
       "46  POLYGON ((-104.053108215332 41.69824600219727,...               23   \n",
       "47  (POLYGON ((-87.74845123291016 44.9616279602050...              145   \n",
       "\n",
       "   population_density  \n",
       "0               37.61  \n",
       "1               23.60  \n",
       "2               22.03  \n",
       "..                ...  \n",
       "45              34.55  \n",
       "46               2.09  \n",
       "47              34.98  \n",
       "\n",
       "[48 rows x 14 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combine the table with df_population to get the number of starbucks in a state\n",
    "df_population=pd.merge(df_population,x,on=['STATE_ABBR'])\n",
    "df_population['population_density']=df_population['total_population']/df_population['AREA']\n",
    "df_population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        starbucks_count   R-squared:                       0.008\n",
      "Model:                            OLS   Adj. R-squared:                 -0.014\n",
      "Method:                 Least Squares   F-statistic:                    0.3724\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):              0.545\n",
      "Time:                        20:26:57   Log-Likelihood:                -359.42\n",
      "No. Observations:                  48   AIC:                             722.8\n",
      "Df Residuals:                      46   BIC:                             726.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept            248.3082     80.674      3.078      0.004      85.920     410.697\n",
      "population_density     0.4119      0.675      0.610      0.545      -0.947       1.771\n",
      "==============================================================================\n",
      "Omnibus:                       78.665   Durbin-Watson:                   2.064\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1111.973\n",
      "Skew:                           4.319   Prob(JB):                    3.45e-242\n",
      "Kurtosis:                      24.940   Cond. No.                         151.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome_1, predictors_1=patsy.dmatrices('starbucks_count~population_density',df_population)\n",
    "print(sm.OLS(outcome_1, predictors_1).fit().summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        starbucks_count   R-squared:                       0.818\n",
      "Model:                            OLS   Adj. R-squared:                  0.814\n",
      "Method:                 Least Squares   F-statistic:                     206.2\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           1.30e-18\n",
      "Time:                        20:26:58   Log-Likelihood:                -318.77\n",
      "No. Observations:                  48   AIC:                             641.5\n",
      "Df Residuals:                      46   BIC:                             645.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept          -82.6800     37.138     -2.226      0.031    -157.436      -7.924\n",
      "total_population     0.5449      0.038     14.360      0.000       0.469       0.621\n",
      "==============================================================================\n",
      "Omnibus:                       27.395   Durbin-Watson:                   1.799\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               74.171\n",
      "Skew:                           1.444   Prob(JB):                     7.83e-17\n",
      "Kurtosis:                       8.361   Cond. No.                     1.33e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.33e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome_2, predictors_2=patsy.dmatrices('starbucks_count~total_population',df_population)\n",
    "print(sm.OLS(outcome_2, predictors_2).fit().summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to p value, which is high, population density does not significantly predict number of starbucks, but total population can (not advisable to some extent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b) Does Our Profile Affect The Starbucks Count?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        starbucks_count   R-squared:                       0.415\n",
      "Model:                            OLS   Adj. R-squared:                  0.415\n",
      "Method:                 Least Squares   F-statistic:                     6322.\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):               0.00\n",
      "Time:                        20:29:50   Log-Likelihood:                -37594.\n",
      "No. Observations:               26696   AIC:                         7.520e+04\n",
      "Df Residuals:                   26692   BIC:                         7.523e+04\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept           -0.1463      0.018     -8.094      0.000      -0.182      -0.111\n",
      "male_25_29           0.0002   1.67e-05     12.167      0.000       0.000       0.000\n",
      "median_hh_income  1.439e-06    3.1e-07      4.637      0.000    8.31e-07    2.05e-06\n",
      "bachelors_degree     0.0003   4.86e-06     60.841      0.000       0.000       0.000\n",
      "==============================================================================\n",
      "Omnibus:                    28677.587   Durbin-Watson:                   1.748\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          7504528.572\n",
      "Skew:                           4.989   Prob(JB):                         0.00\n",
      "Kurtosis:                      84.530   Cond. No.                     1.84e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.84e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome_3, predictors_3=patsy.dmatrices('starbucks_count~male_25_29 + median_hh_income + bachelors_degree',df_final)\n",
    "print(sm.OLS(outcome_3, predictors_3).fit().summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value for each variable is less than 0.001 we can safely say that there's a positive correlation between the Starbucks and demographic factors of our Starbucks customer profile "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read from csvs\n",
    "\n",
    "#-----df_census contains all information about the population in the US -----\n",
    "#df_census = pd.read_csv('df_census.csv', dtype={'zip':str})\n",
    "\n",
    "#-----df_starbucks contains zipcodes and store information of all starbucks in the US----\n",
    "#df_starbucks_US = pd.read_csv('df_starbucks_US.csv', dtype={'zip':str})\n",
    "\n",
    "#----df_final is basically census but with additional \"count starbucks\" and \"has starbucks\" columns\n",
    "#df_final = pd.read_csv('df_final.csv', dtype={'zip':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating relative numbers\n",
    "# 'age < 18'  are seen as outliers since they are less likely to sit in a Starbucks\n",
    "df_final['male_18_24']=np.sum(df_final.iloc[:,1:5],axis=1)\n",
    "df_final['male_25_29']=np.sum(df_final.iloc[:,5:6],axis=1)\n",
    "df_final['male_30_39']=np.sum(df_final.iloc[:,6:8],axis=1)\n",
    "df_final['male_40_49']=np.sum(df_final.iloc[:,8:10],axis=1)\n",
    "df_final['male_50_plus']=np.sum(df_final.iloc[:,10:20],axis=1)\n",
    "df_final['female_18_24']=np.sum(df_final.iloc[:,21:25],axis=1)\n",
    "df_final['female_25_29']=np.sum(df_final.iloc[:,25:26],axis=1)\n",
    "df_final['female_30_39']=np.sum(df_final.iloc[:,26:28],axis=1)\n",
    "df_final['female_40_49']=np.sum(df_final.iloc[:,28:30],axis=1)\n",
    "df_final['female_50_plus']=np.sum(df_final.iloc[:,30:40],axis=1)\n",
    "\n",
    "#The original format of total_population was str, which cannot be used to calculate\n",
    "df_final['total_population']=df_final['total_population'].apply(float)\n",
    "df_final['percent_18_24']=(df_final['male_18_24']+df_final['female_18_24'])/df_final['total_population']\n",
    "df_final['percent_25_29']=(df_final['male_25_29']+df_final['female_25_29'])/df_final['total_population']\n",
    "df_final['percent_30_39']=(df_final['male_30_39']+df_final['female_30_39'])/df_final['total_population']\n",
    "df_final['percent_40_49']=(df_final['male_40_49']+df_final['female_40_49'])/df_final['total_population']\n",
    "df_final['percent_50_plus']=(df_final['male_50_plus']+df_final['female_50_plus'])/df_final['total_population']\n",
    "\n",
    "#Here we want to see whether there is a significant diference between genders,\n",
    "#so we make three seperate percentages\n",
    "df_final['total_workers']=df_final['male_workers']+df_final['female_workers']\n",
    "df_final['percent_male_workers']=df_final['male_workers']/df_final['total_population']\n",
    "df_final['percent_female_workers']=df_final['female_workers']/df_final['total_population']\n",
    "df_final['percent_workers']=df_final['total_workers']/df_final['total_population']\n",
    "\n",
    "#The following is used to calculate the propotion of high school/bachelor's/master's degree among different age groups\n",
    "df_final['total_18_plus']=(np.sum(df_final.iloc[:,1:20],axis=1)+np.sum(df_final.iloc[:,21:40],axis=1))\n",
    "df_final['total_22_plus']=(np.sum(df_final.iloc[:,4:20],axis=1)+np.sum(df_final.iloc[:,24:40],axis=1))\n",
    "df_final['total_25_plus']=(np.sum(df_final.iloc[:,5:20],axis=1)+np.sum(df_final.iloc[:,25:40],axis=1))\n",
    "df_final['percent_highschool']=df_final['high_school_diploma']/df_final['total_18_plus']\n",
    "df_final['percent_bachelor']=df_final['bachelors_degree']/df_final['total_22_plus']\n",
    "df_final['percent_master']=df_final['masters_degree']/df_final['total_25_plus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final=df_final[['total_population','median_age',\n",
    "                     'median_hh_income','median_rent','median_home_value',\n",
    "                     'percent_18_24','percent_25_29','percent_30_39','percent_40_49',\n",
    "                     'percent_50_plus','total_workers','percent_male_workers',\n",
    "                     'percent_female_workers','percent_workers',\n",
    "                     'percent_highschool','percent_bachelor','percent_master','zip','starbucks_count','has_starbucks']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CLF part analyzes the Starbucks data combined with census data. After we combined the Starbucks and census data, we have a combination of parameters of the zip code and whether or not there is a Starbucks in that zip code. Then use CLF to construct a linear regression model to get the weight of each parameter. Then use the weight of parameters to calculate the value for each zip code to see whether or not there is supposed to be a Starbucks (i.e. is opening a Starbucks at that zip code a good idea). Finally, check the value for the zip codes that currently do not have a Starbucks, and get the top 3 zip codes that is feasible to open a new Starbucks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "clf = linear_model.LinearRegression()\n",
    "nl = df_final.values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the dataframe into a list and split that list into one list of parameters and one list of value (0 or 1, signaling if there is a Starbucks at that zip code), and apply the two lists to the CLF model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlt = []\n",
    "nly = []\n",
    "for i,j in enumerate(nl):\n",
    "    nlt.append(j[:-3])\n",
    "    nly.append(j[-1])\n",
    "clf.fit(nlt, nly)\n",
    "cL = clf.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get the coefficient (weight of parameters) in the list cL, we calculate the result based on the coefficient. We only want to take consideration of those zip codes that currently do not have a Starbucks, so we append the result for those that do not have a Starbucks and -100 for those that have at least one Starbucks as we will not consider them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = []\n",
    "for i in range(len(nl)):\n",
    "    if df_final.loc[i, 'has_starbucks'] == 1:\n",
    "        rs.append(-100)\n",
    "    else:\n",
    "        result = 0\n",
    "        for j in range(len(nl[0])-3):\n",
    "            result += cL[j]*nl[i][j]\n",
    "        rs.append(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the list of results and get the first 3 indexes that have the largest result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['11368', '07030', '11220']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_sort = np.argsort(rs)\n",
    "best_3_id = index_sort[-3:]\n",
    "best_3_zip = []\n",
    "for i in range(len(best_3_id)):\n",
    "    best_3_zip.append(df_final.loc[best_3_id[i], 'zip'])\n",
    "best_3_zip.reverse()\n",
    "best_3_zip\n",
    "# this is the best 3 zipcodes that currently do not have a starbucks\n",
    "# that are feasible spots of a new starbucks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_population</th>\n",
       "      <th>median_age</th>\n",
       "      <th>median_hh_income</th>\n",
       "      <th>median_rent</th>\n",
       "      <th>...</th>\n",
       "      <th>percent_master</th>\n",
       "      <th>zip</th>\n",
       "      <th>starbucks_count</th>\n",
       "      <th>has_starbucks</th>\n",
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       "      <th>25147</th>\n",
       "      <td>54117.0</td>\n",
       "      <td>31.8</td>\n",
       "      <td>127523.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20</td>\n",
       "      <td>07030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25722</th>\n",
       "      <td>100412.0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>42559.0</td>\n",
       "      <td>1304.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>11220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>25792</th>\n",
       "      <td>114647.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>47862.0</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>11368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 20 columns</p>\n",
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      ],
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       "       total_population  median_age  median_hh_income  median_rent  ...  \\\n",
       "25147           54117.0        31.8          127523.0       2100.0  ...   \n",
       "25722          100412.0        33.7           42559.0       1304.0  ...   \n",
       "25792          114647.0        32.0           47862.0       1526.0  ...   \n",
       "\n",
       "       percent_master    zip  starbucks_count  has_starbucks  \n",
       "25147            0.20  07030              0.0              0  \n",
       "25722            0.05  11220              0.0              0  \n",
       "25792            0.03  11368              0.0              0  \n",
       "\n",
       "[3 rows x 20 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pull from df_final to see what demographic factors look like for those three zipcodes\n",
    "df_final[(df_final['zip']=='11368') | (df_final['zip']=='07030') | (df_final['zip']=='11220')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three zip codes we get are the three most feasible places to open a new Starbucks. Based on information of these zip codes, there is a high concentration of males within 25-29 age range with Bachelor's degree and income roughly $60k/year. In other words, these areas have potential customers but no Starbucks. When we view these areas on Google Map, we see many Starbucks around that area but no Starbucks inside. Therefore we think it is a good idea to open a new Starbucks in these areas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src='pic.jpg',width=200.height=200>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<img src='pic.jpg',width=200.height=200>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B20XcMObndZI"
   },
   "source": [
    "# Ethics and Privacy\n",
    "\n",
    "__Personal privacy__: All the kernels or data sets in Kaggle have Apache 2.0 license, meaning they are open source and we acknowledge Kaggle for the opportunity for us to use this data. We also requested U.S. Census Bureau for the use of their datasets.\n",
    "\n",
    "__Eliminate biases__: Our project aims to determine and clarify the traits in the Starbucks that would improve their business. And by no means do we subject any Starbucks to harm or bias. \n",
    "\n",
    "__The potential of misusing the results__: The results of our research are not meant for advertisements to make money or sabotaging the images of established Starbucks that do not possess the characteristics concluded from our project. Our project might positively influence the direction in which Starbucks decide to head for. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FesFl5SnndZL"
   },
   "source": [
    "# Conclusion and Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PF8B6Z_endZN"
   },
   "source": [
    "We successfully developed a profile for Starbucks customers and that is males within the age range of 25-29 years old with a bachelor's degree and a yearly income of approximately $60,000/year (*Figure 1*, *Figure 2*, and *Figure 3*). We also concluded that the profile also has a positive correlation with the number of existing Starbucks. And since there's a positive correlation, we used our Machine Learning model to narrow down three zipcodes: 11368, 07030, and 11220. They are the most promising areas that fit the demographic factors of Starbucks target market, and thus the best areas to open a new Starbucks. A limitation that could have made our analysis better would be having access to every Starbuck store's generated revenue, that way we could also measure Starbucks success and not have to solely rely on only the profile to figure out the potential location of a Starbucks store. Another limitation is not assessing other factors such as the competitive activity of other chains in nearby regions.  "
   ]
  }
 ],
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