exploratory_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)\n",
    "\n",
    "This notebook demonstrates key elements of exploratory data anlysis (EDA) using Python. EDA is the non-standard and often ad hoc process of learning about a data set, identifying basic trends, and performing data cleaning and wrangling tasks.\n",
    "\n",
    "We will use data from the [Yelp Dataset Challenge](https://github.com/vc1492a/Yelp-Challenge-Dataset) as well as [U.S. Census data](https://www.census.gov/data/tables/2016/demo/popest/total-cities-and-towns.html).\n",
    "\n",
    "We will use common Python libraries such as Pandas, Matplotlib, and Numpy. Before getting started, make sure to install the requirements with:\n",
    "\n",
    "```\n",
    "pip install -r requirements.txt\n",
    "```\n",
    "\n",
    "Let's start by importing these packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.plotting import scatter_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started\n",
    "Now, we can read in the Yelp data as a Pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp = pd.read_csv('data/yelp.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the basic features of the yelp data by asking for its `.info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 57795 entries, 0 to 57794\n",
      "Data columns (total 10 columns):\n",
      "business_id           57795 non-null object\n",
      "city                  57794 non-null object\n",
      "name                  57795 non-null object\n",
      "review_count          57795 non-null int64\n",
      "stars                 57795 non-null float64\n",
      "state                 57795 non-null object\n",
      "price_range           32757 non-null float64\n",
      "number_of_checkins    57795 non-null int64\n",
      "category              16080 non-null object\n",
      "restaurant            57065 non-null float64\n",
      "dtypes: float64(3), int64(2), object(5)\n",
      "memory usage: 4.4+ MB\n"
     ]
    }
   ],
   "source": [
    "yelp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also take a look at the first 5 rows by calling `.head()` on the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>business_id</th>\n",
       "      <th>city</th>\n",
       "      <th>name</th>\n",
       "      <th>review_count</th>\n",
       "      <th>stars</th>\n",
       "      <th>state</th>\n",
       "      <th>price_range</th>\n",
       "      <th>number_of_checkins</th>\n",
       "      <th>category</th>\n",
       "      <th>restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5UmKMjUEUNdYWqANhGckJw</td>\n",
       "      <td>Dravosburg</td>\n",
       "      <td>Mr Hoagie</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>PA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UsFtqoBl7naz8AVUBZMjQQ</td>\n",
       "      <td>Dravosburg</td>\n",
       "      <td>Clancy's Pub</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>PA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Nightlife</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3eu6MEFlq2Dg7bQh8QbdOg</td>\n",
       "      <td>Dravosburg</td>\n",
       "      <td>Joe Cislo's Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>PA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cE27W9VPgO88Qxe4ol6y_g</td>\n",
       "      <td>Bethel Park</td>\n",
       "      <td>Cool Springs Golf Center</td>\n",
       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>PA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HZdLhv6COCleJMo7nPl-RA</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Verizon</td>\n",
       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>PA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id         city                      name  \\\n",
       "0  5UmKMjUEUNdYWqANhGckJw   Dravosburg                 Mr Hoagie   \n",
       "1  UsFtqoBl7naz8AVUBZMjQQ   Dravosburg              Clancy's Pub   \n",
       "2  3eu6MEFlq2Dg7bQh8QbdOg   Dravosburg          Joe Cislo's Auto   \n",
       "3  cE27W9VPgO88Qxe4ol6y_g  Bethel Park  Cool Springs Golf Center   \n",
       "4  HZdLhv6COCleJMo7nPl-RA   Pittsburgh                   Verizon   \n",
       "\n",
       "   review_count  stars state  price_range  number_of_checkins   category  \\\n",
       "0             4    4.5    PA          1.0                   0  Fast Food   \n",
       "1             4    3.5    PA          1.0                   0  Nightlife   \n",
       "2             3    5.0    PA          NaN                   0        NaN   \n",
       "3             5    2.5    PA          NaN                   9        NaN   \n",
       "4             5    2.5    PA          2.0                   0        NaN   \n",
       "\n",
       "   restaurant  \n",
       "0         1.0  \n",
       "1         0.0  \n",
       "2         0.0  \n",
       "3         0.0  \n",
       "4         0.0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic exploration\n",
    "Let's start by determining how many restaurants are in the dataset. Since we know that the field `restaurant` is binary `(1=restaurant, 0=not restaurant)` we can simply sum that field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11366.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.restaurant.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing data: let's look only at restaurants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a slicer on the dataframe to redefine the dataframe to only contain restaurants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp = yelp[yelp['restaurant'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also confirm that the new dataframe has the expected number of rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11366, 10)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregations: where are the restaurants located?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AZ    5015\n",
       "NV    3208\n",
       "NC    1319\n",
       "PA     944\n",
       "WI     619\n",
       "IL     188\n",
       "SC      73\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.state.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas gives us an easy way to plot our restaurant counts by state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c66af98>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yelp.state.value_counts().plot(kind='bar', figsize=(8,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What categories of restaurants are there?\n",
    "We can easily determine the types of restaurants in the dataset as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mexican                   1645\n",
       "Pizza                     1202\n",
       "Chinese                    956\n",
       "Sandwiches                 695\n",
       "American (Traditional)     649\n",
       "Fast Food                  599\n",
       "American (New)             583\n",
       "Italian                    468\n",
       "Food                       426\n",
       "Burgers                    339\n",
       "Barbeque                   292\n",
       "Thai                       260\n",
       "Breakfast & Brunch         241\n",
       "Seafood                    228\n",
       "Japanese                   211\n",
       "Vietnamese                 143\n",
       "Indian                     135\n",
       "Sushi Bars                 107\n",
       "Mediterranean              104\n",
       "Asian Fusion                92\n",
       "Diners                      89\n",
       "Greek                       75\n",
       "Korean                      63\n",
       "Vegetarian                  51\n",
       "Middle Eastern              46\n",
       "Nightlife                   28\n",
       "Pizzza                       3\n",
       "Pakistani                    3\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.category.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### But wait...what is 'Pizzza'?!\n",
    "Looks like we have some bad data. There are 3 restaurants categorized as `Pizzza`, but these should really be included in the `Pizza` category. Let's take care of that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp.loc[yelp.category=='Pizzza', 'category'] = 'Pizza'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot these categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c6803c8>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PiIMjYr2I2BDYAzg7IvYCfgLsVp62D3BK+f3Ucp/y+NkREYO+vpmZ2Wy2NOaBvxN4q6RF5Bj30WX70cBDyva3Agcthdc2MzObFYaymElEnAOcU36/Dtimx3P+CbxkGK9nZmY22zkTm5mZWQu5AjczM2shrwdus9rDfnJZ3+fcsv1jR1ASM7N63AI3MzNrIVfgZmZmLeQK3MzMrIVcgZuZmbWQK3AzM7MWchS6WUMbHvSDaR//3eE7jagkZjabuAVuZmbWQq7AzczMWsgVuJmZWQt5DNxshvUbQwePo5vZZG6Bm5mZtZBb4GbLg0PX6PP47aMph5mNjFvgZmZmLTRwC1zS+sBXgbWBAI6KiE9JejDwLWBD4HfASyPiNkkCPgU8H7gLeGVEXNKs+GY2LFseu+W0j1+5z5UjKomZVdGkBb4EeFtEbA5sCxwgaXPgIOCsiJgHnFXuAzwPmFdu+wNHNHhtMzOzWW3gFnhE3AzcXH6/Q9I1wLrALsAzytOOBc4B3lm2fzUiArhA0pqS1inHMbOWu2bTzaZ9fLNrrxlRScxmh6GMgUvaEHgccCGwdlelfAvZxQ5Zud/QtduNZZuZmZnV1LgCl7Q6cBLw5oj4W/djpbUdNY+3v6QFkhYsXry4afHMzMyWS40qcEn3Iyvvb0TEyWXzHyWtUx5fB7i1bL8JWL9r9/XKtnEi4qiImB8R8+fOndukeGZmZsutgSvwElV+NHBNRPxf10OnAvuU3/cBTunavrfStsDtHv82MzMbTJNELk8BXgFcKemysu1dwOHACZL2A34PvLQ8dho5hWwROY1s3wavbWZmNqs1iUL/KaApHt6hx/MDOGDQ1zMzM7MxzsRmZmbWQq7AzczMWsiLmZjZMuNzrz172scPOPKZIyqJ2bLPFbiZLTc+vvvO0z7+tm99f0QlMVv63IVuZmbWQq7AzczMWshd6GZmXW486PxpH1/v8KeOqCRm03MFbmY2RIceemijx82qcgVuZraMOevsjad9fIdn/nbaxx/2k8umfRzglu0fW6tMtuzxGLiZmVkLuQVuZmaTbHjQD6Z9/HeH7zSikthU3AI3MzNrIbfAzcxs6Pq14MGt+KZcgZuZ2bLp0DX6PH77aMqxjHIFbmZmy6Utj92y73Ou3OfKEZRk6fAYuJmZWQuNvAUuaUfgU8Ac4EsRcfioy2BmZlbFNZtuNu3jm117zYhKMtlIW+CS5gCfA54HbA7sKWnzUZbBzMxseTDqLvRtgEURcV1E/As4HthlxGUwMzNrvVF3oa8L3NB1/0bgiSMug5mZ2Uh87rVn933OAUc+c6BjKyIG2nGgF5N2A3aMiFeX+68AnhgRb+h6zv7A/uXuJsCv+hx2LeBPDYrVdH+XYfkqwzCO4TK4DC7DslmGYRxjFGV4RETM7XuUiBjZDXgS8OOu+wcDBzc85oKZ3N9lWL7KsLz8HS6Dy+AyLN9/R0SMfAz8YmCepI0krQTsAZw64jKYmZm13kjHwCNiiaQ3AD8mp5EdExFXj7IMZmZmy4ORzwOPiNOA04Z4yKNmeH+XYfkqwzCO4TK4DC7DslmGYRxjWSgDMOIgNjMzMxsOp1I1MzNrIVfgZmZmLeTVyFpKkoAtgIcD/wCujog/V9jvP6d7PCJOrlGGhwJP6SrDVeT0iHurHsOWH5JWBd4GbBARr5E0D9gkIr4/w0UbmKQVgNUj4m8z8NpPBjak6zwdEV8ddTls2TVrK3BJW5D52FfpbKvz5ZC0HTAvIr4saS75Jb9++CWd9LobAu8AdgSuBxaTf8M8SX8FjgS+HlMHN7yg/Hwo8GSgkyZoe+DnQN8KXNL2wEHAg4FLgVtLGXYFNpb0beDjVU96ktYFHsH4E9V5FfZ7/HSPR8QlVV6/HOslwI8i4g5J7wEeD/xvnWPUJenlEfF1SW/t9XhE/F+NYz0aeDuT/4+DpXgazJeBhWS+B4CbgBOBkVfgZd2FtRn/v/hDxX2/CbwW+Dc59fWBkj4VER9dGmWdogxfAzYGLivlAAhgZBW4pOdFxA8nbHttRBxZ8ziNLkSavJdl/7nAa3qU4VVVj9GEpGdGxNlTNZ7qNJomamUF3vQNkXQI8AyyAj+NXFzlp1T8cpT955OZ4r4M3A/4OtkarUTSgWXfO4AvAY8DDoqI0/vs+hGykn7DxJaupHWAvYB9gK/02jki9i3PPR3YPCJu7tq35z49PB94Ta8vkaQVgZ2BZwMn9TuQpA8DuwO/ZPyJqm8FDny8/FyFfD8uBwRsBSxgrCKp4r0RcWK5MHsW8FHgCCqm+pW0NvAh4OER8byySM+TIuLoaXZbrfx8QI1yTuVE8nPxRcb+j5VI+gz5P+8pIt5U8VAbR8TukvYs+91VeorqlGWQ/+PEY7wROAT4I9D5jgT5uahi84j4m6S9gB+SF6sLyc9E1TKsDLyYyeeo91c8xPxSjoGjjCV9BPhfsnfsR+Tf/5aI+HrFQ7xX0t0RcXY53jvIC/3KFXjTC5EhvJcApwDnA2dS87vRVY5Hk+eDtSNiC0lbAS+MiP+tsPvTyYbSC3o8FlRoNE1pGNlgRn0jW4ofBl5KfkleDLy4xv5XkuP/l5f7awNn1Nj/MrKiuLRr2xU1/4bOaz+3vIGPAS4Z4f/wmgn3V5i4bUTl+BWwcsNjnAxs2XV/C+DbNY9xafl5GPCy7m0V9/9h+Tx23tcVgStH+H9c2GDffcrtKPJC9o3ldh5wZI3j/By4f+dzTJ64L6pZlsb/R2AR8JAG/4+ryYvyE4Gnl22X1zzGj4Bvkb1lb+vcaux/IrBOw8/EZeXni4CjgTXq/B1kus8LgKcCHyQvyFeqWYZrKLOdBvwbGr2X3f+Hhsc4l1yMq/ucf1XT4za9tbIFDqwaEe9ssP8/IuJeSUskPZDsAl6/xv7/ioiQFACSVuu3Qw+dlsnzga9FxNVVWiuSXjjd4xFRNbPdWZJ+DBxX7u9OXqFWJum35Bf8fOD8GCwpz3XkyfLuAfbt2CQiruzciYirJE2/iO9kN0n6Atlz8OHSgqoT5LlWRJwg6eBShiWSKl3tS1oF2I+8iOse0unboyTpweXX70l6PfAduv6XEfGXfseIiGPLsV4HbBcRS8r9I8n3tqpDyIprfUnfIHukXlljf2jwf+xyA3B7zX26HQn8juzROU/SI4C6Y+DrRcSODcqwFvBLSRcx/v2c9vs/wf3Kz52AEyPi9jodIhHxp3K+OZPsgdgtSs1Vw1XAw4Cba+7X0fS9BPi+pOdH5iAZ1KoRcdGE/9+SOgcYQq/MJG2twJu+IQskrUl2Ny4E/g78osb+J5ST/ZqSXgO8qhyrjoWlG3sj4GBJD2Csi2g6L5nmsaBiatqIeIOkFwFPK5uOiojvVNm3y+ZkF/NTgY9K2oTsiXhRjWPcBVwm6SzGn6iqdtsCXCHpS+QwBuQwwhU19ods9e0IfCwi/lqGFN5eY/87JT2E0hUtaVuqn3i+BlxL9sa8nyz/NRX3XVhes3Nm6S5zAI+seByABwEPBDqV/uplWyURcYakS4BtS3kOjIi6iz40+T92XAecI+kHjP9M9Y0pKEFrf4yIdbu2/YHsOq7j55K27L6wrOnQAffrdqqka8ku9NeVocd/9ttJ0h2MH1JZifwc7SYpIuKBNcrQ9EJk4Peyy4HAuyTdDdxDfjbr/h1/krQxY5/L3ah/UXIK+VleSLMGy31amcilfMBWI/8Jg74hnWNtCDwwImqd8CU9G3hOee0fR8QZNfdfAXgscF2pMB4CrFu3HE2U8cZtyA/lRRFxa839VwSeQI7xbAc8hKzA/6vGMfbptb3TKqx4jFWA1zF2MXIecERE9D1Zlf3nkFH8m1Z9zR7HeDzwGbL7/ipgLtli6ft+Sro0Ih4n6YqI2ErS/cgejW0HLc8gJO1LVhw/IT/XTwMOrfpeSHoK2V15p6SXk4GAn4qI39cow8D/x65jHNJre0T8T8X9F0TE/KqvN8Uxfgk8igw0vZuxc1Tlsdvy/XxCuVvr+1nOL9uSF4a3R8S/S0/hAyLilqrHaUrS03ttj4hzK+7f9L0UsH7UCHqb4jiPJIeYngzcRr6ve9X8bF8VEVs0KcckM92HPxM34Czg+RO2HVVj/9WAOeX3TYAXAverWQYBLwfeV+5vAGxT8xjPBd4KvKtzq7HvS4HfA8eSASXXkyfKOq9/F3Ah2f3eaJxqCO/pSsCW5Im/1ntR9j+FnP7UpAwrkt3gtcpAGScmLzy2IFst19V87QOANbvuPwh4/QB/w8OAXcpn+mE1972ifK63Bi4pZTp3VP/HIX6WDgf+mxxWe3DnVvMYj+h1q7H/ML6flWM4pti/c456b7m/ft1z1LJwo0EsCtmLBPCU8rNzETTIsY6iK1ZnGLdWtsABJD0ImMf4McMqkctIuo4cWzk7ypWcpEsiYtppSV37LyS7jR9EBv0sIMfF96pR/iPILvNnRsRm5e85PSKe0GfXzv6fB9YkW0lfJsdWLojqkfiXA8+OclVfutfOjIita/wNu5At722Af5FBTOdFxFk1jjGPDBybOKWvctevpGeQJ7rfkSed9YF9qn4eyjHOI2cCXATc2VWOSl19kvbutT0qTJeR9GoyQGgr8r1cnbywqxPte1lEPHbCtksj4nFVj1H2eSFjPRnnRsT3aux7SUQ8XtL7gJsi4ug636tyjMbT+cpn+R1MjimoNKVOUq/poFHnM9l1rIdOKEPVqWzD+H5+jBwaPDkGONE3PUeVY2xL9qhsRl5kzwHujIq9pU3fy3KMY4HPRsTFVffp2veyiHhs3c/xFMdq3CszyUxfHQ14JfNqMpL8NrK77x9kZVx1/0vIq/zPA98jozMrR4AzFmX7RuAd5fdakY5dx+iOaqwTIXpF9z7kVKTzaux/5YT7K0zcVuNYmwJvIVsM/6i570+BHcjW2yPILtz31zzGQjKQrXP/0dSMyiaHASbdauz/ma7bF8mxu1qR8E1u5fugrvudYYE6xzic7J16VbmdAXyoxv7nAgcDvyFb8rU/U12f6+3Kd3sn4MKaxzidDAq8pryPxwAfHtV7UcrwwvJ/uJM8Yd9b5/0YxveTnKJ6LznM+Ldy/2819m90jirPX0BWWpeWz+S+wGGjfC/JYYQlwG/LeeZKKs4aIoN8O+/jFV23ysfoOlajXplet7YGsR1Ijg1dEBHbS9qUnDtalSIjbV8v6ZVkJVI5WIccWnkSGWy0X9k2p8b+APeUsddOUMRcqgWxdfyj/PynpIcBfyYzolX1ox5R6LWCAiWdRHaX/pbs/t2b7FKv4/4RcZYyOub3wKGlh+N9NY5xv4j4VedORPy6jCNXFhHnlmjjeRFxpjKrWOX3NCLe2H2/BEkeX2XfIUWn/gj4VgmuBPivsq2O5wOPjZJfoLRcLiWHZ6rYHXgZ8KqIuEXSBtSYO110Is53Ar4YET+QVGWubbeHRLb+D4wcaz1XUq3WlxomegI+QI5BnxkZ37A92R1dVePvZ0Q0zS/Q9BzVKcciSXMi4t/AlyVdSl7oVdH4vSSHGgcSEXuW8+uPyYuygZXz26RemSbaWoH/MyL+KQlJK0fEtcoI6Kru65qMiK9IupIcr6vqzeQH8DuR078eSbYW6vg0OeXnoZI+COwGvKfG/j8slcTHGEuSUPkEExFvV2YG2q5sGiQK/TDy6nyg5AjF3SXg5jfKteJvIruQ61igyVHoC+ocQDmbYH9yvHNjYF3yc7JDzbJ03EnOMKhiGNGp7yQr7deV+2eQCYLqWpOxKPQ16uxYKu2TyKEtgD+Rn/E6mk7ng2xxAtwsaSfg/5HvayVqmOipU4aI+LOkFSStEBE/kfTJqjuX7+eLGUsOVfv7WQK49gI2iogPSFqfnFt+UcVDdM5Raw94jgK4S9JK5EyTj5CR23Xez0bvJWTFqR6ZM2vsfwvZUGmkDE99nGxo3Uq2wK8hhwcG06T5PlM38kO1Jtndeh55Ajytxv5Hky2N7m2HDlCOVRv+HZuSFw5vADZrcJz7UzPIpuy3NpkdaGfgoYP8/eQX+qhyfx6wc81jPIH8Mq1Hjv+eDGxb8xgrk8F8J5fbW6iZHIa8CFqJ8d2FlbssyaGYU8vt+2QX+uEV953xhBClHHuSwyBfIWO+kLHoAAAgAElEQVQKrgd2r7H/a8jUo7/t+jycNcBn6j/Jky3AOsBzah5jZ/LiYwvywnohmTWr6v6NEj2Vfc4sn+vPkq3oTwE/H/H7eQTwOUqCJrKX8eKax2h0jiIrqVXI6YmHAP8HPGpU72U5xiHl+/nrcv/hwM8q7ntC12eiaRf65eRMnU7SqO2Boxu9x6P8QC2NGzku8kJqZAgCbiz/zL27ttUZA38SmfrzD+X+1sDnByj7nPJh2qBzq7n/NmS06ss6txr7DiPKtZNp6qpyf1WGkPVowM9B0yj0C8vPzpdrxTpfUMaPnT+FTORRdd/G0anlNc8Afk1ePFxPzUj2cpx1yvdpkCj0RhdBXftsB+xbfp9LtiBH+VnqzApYWCoeAdfWPMZq5EXAimSWuzdRYaYG8NPy8w5y3LpzqzV+XY4xjDHsxu8F2cDYpO5+Q3w/B86cScmGxxDGr8mFniDrnhUGeT8m3lrZhV4iG6+OiDsixy4fSEYQVx1/vZW8+vm6pCeSY+p1cjZ/khxXORUgIi6X9LTpdxlP43P8/ru8flAxx6+kr5BdfBNzDH+zYhHeDTwhJkS5At+uuD80yH0t6ZMR8WZJ36NHHu6okXGqVxS6pH2iRhQ6Obb2LuD+yjn+ryev2iuJivNau0m6ihxTXBHYt8yOGDQ69Wiy52EhA+Z7Lp7AWBR6UON/ANwdEf/qfASUeQImvbfTUYN1BiS9IyI+oilyu0f15EBNEz0RORe+E1NxbNWYiojYrvwcRn78RmPYTd6LrmO8gBzmWwnYSNJjySDVab/fQ3wvoUHmzChrRUSN+d7T+Kuk1cle429IupWuGS+DaGUFTnYNdYf0/73HtukoIm4HXiDpUOAc6o/33TChrqp70jyQvCrtuwToFLYlFzsYdOnOFWJ8Yog/U3+s8V+S7s/YCWJjqo/hfq38/FjN1+zl42Q3669KOR5Ndlv+R41jHEQGJF5JjiWfRo0xZE3OXnXfQ0ydZGhdMpnPMNweE1aOqkvS4WQF/o2y6U2SnhQRVYPYGl0EFS8iL8YvAYiI/6fMUlhFJ3tdrfiHiSLi9eXXIyX9iMESPTWKqSjfpRsj4u5ygboV8NWI+GuNYjSNs2nyXnQcSvYUnlOOcZmkKrEhQ3kvi16ZMyt9t7u+150G1n0PUT952C5k8PFbyNiENYBKCWmm0tYKXFH6HwAi85rX+VvuSzcaEZ2o57fU2P8G5RJ5UaKdD6R66sv7jkGzHL9Xk11afxxw/8ZRrjTIfR0RC8vP2i3XHoYRhX4v2eKqmxK345NkgM7XyC/3XmT323TR9NcP6coe4CeSPkrGAHSnnKyzHGrTKPRGF0FFk9bS98rPyln8JirnkX+XMqxPtkB/O8ChDiArrgtLmX5Too+rOgmYL+lR5BDLKWTv2vOrHiAivlHObTuQn8ldI6LOeWoYaz7cE5NzsPftlYmI75Xegy0j4r8HeN3uY32sXFD+jexNeF9UzJw5pJ6QjvdFruFxL9ljiHI1xoHX9WhlIhdJJ5NXdEeUTa8Hto+IXUf0+muRQSnPIr8Yp5MZe/q2pjW29vNjyA/TQDl+JZ1JXh1fMGH/nmvOTnGM7ijX86N+FDrKFLCd3NcXRMXc1yXyf8oPX53uY0nHkF+K7ij0OVFjvd8pynM72QL4337vraTLY0KSjV7bJjx+IxnU01PVz0I5Vq9ZEBH1El5cATwjygIoyoVSzqnZld+IpP8mg9+eTc5yeBXwzYj4TIV9ew7HdFTotn0Nucrh38lpYG8nW5+PA46JiA9X/DOQdGFEPFFjaXJXJMekqw6RdZLivJ2cdfMZDZaYp8m66AO/F13HOJrMLXAQOVXyTeQF92sr7v+LiKizLHCvY3w4Jix+1Wtbn2N8LSJe0W9bn2NMSgajkj656jEmamsL/LVk99B7yC/sWWR31bQk/TQituvR3VmrO6RUUpWzrk3QuaL7Q7mtVG51HTbIi0vaNiIuAIiIk6iwZnePY2wwYVNnwYZVJW1Q8QSxc93XncbryBZPZ1zsfDJJTx0/JIdBOjEEe5BBebeQUdm91vLtdqdy/ejjyc/WnvQf35pDRirXWjO7l4iou9hGL4cBl5aLgU4u9IOq7qzMhX4oGeCzImPfq8oZzJq0lhgbjvlPMpFM54JuT6r1VL2Z7O5+ANmj9ojIFblWJaPrK1fgNB9OuKfEluzD2GevVq+SGsbZNHwvOt5IxtvcTX63fkyuUV7VZZJOJZdX7c6QWGcN7WczuZX7vB7bpjNuqle5IKs0RKdc5e/1wMblIrnjAcDPapRh8rHb2AKfaSUY5DVMTrxRucU3pHKsRXbxQUY49m39dl8FDnp129Va7a54guzSf2hE1E1qM5AaFwtVjtXr6rjTCroyIrbss/+GZK/MU8j/xc+AN0fE7+q85qCU6UsniQrJYCQ9JSJ+ppxz/WDGL6BReeEL5cpXkwLpBonzUAamdn+3+i6L2rXvpMVIem3rsd99LdyJvSd1W7/K3Ab70bXgEfClqHjClbQ52VD5RUQcV8aNX1qzF2AR8MQB//9zyCQ0A18YlmN8uEkXuKQv99gcVc61XRXnIxk/DPIAchpZ38Q6ymVt30VG0t/V2Uymjj4qIvompJG0BjmF7zDGXxDfUedz3UurWuBqGJmoXLXqtWRqvyvIbrFaa7oWp5CtvDMZMOJX0hnASzpBKco8w8dHRKWsQaX7+xOlHCIDbt5SoRu8u9IdKBvQxMqsVF7vJIcU6mTEa5or+buUwEVJJ0XEi+u89gRzJG0TJcmFpCcwFjXc9zNSKupdar5m45Z3l+7W/ipkD0fV8c5Pk62JX5QLiqpryk80jEC6/yIDe/5JDot0Wo118pCvJumREXFdOeZG5LSufu4v6XFkMOdK5XeVW63vStOYioj4JWM9SkTE9dTrAYAGcTaRq5fdK2mNyIDfQY+xXf9nTnuMfRvs/k2yZ23gijMiDgMOk3RYlcp6imPcDtyuzO1/S3QFJkqqG5g4TqsqcJpHJh5LZvY5nwwGeQwZgFbXqnXGT6Ywt/uNi4jbaga5vI+cBvZHAOXSg6fTP/PVCuViYYWu3++rSGq2dOaR3WNPJCPB3xQR90y/1ySfJburTyR7E/Ymc5lXKkLX77UXmphgPzLNYydD0x3AfiVwp+9whTLy/Qhg7YjYQtJWZMKJ6boLB83yNklEfHxCeT5GtvqquEfSUcB6kj7d49hVp+wMI5Duv4EtqsZSTOEt5BrS15GfkUeQQXX93MxYTMItjI9PqLUEZ9PhBOWCKr0aKXU+503X0v47cGVpbHR3X9eZwnVpky7w0ujaj8mLmfRtgQ+z4oyIgyWty9j72dleZ6pq48DEiVpVgcfYykjfiglrPZfu5H4277QeS3BF1ZSCE31f0vMjom7Udrd/d3cBK+eM1hnPWKFTeRe3Um0a2BpkF2en8us+uVZq6SjzRL+b/FJ9BNgvGqRTjcFzJccUv9dSujsfGRFblu6uzpe/44QKh/kiGfT0hbL/FZK+yTTjfU27z/pYlcxuV8XOZO/Jc8nPxqCeWH52d1UHUDmQjuzqvKvvs6YRET8qF5ed9d2vjYi+0xuHFEfQ0XRefvf/cBXgJdRMIUrzOJtOZkMY+37V7TVahZyi2v0ZiK7j9vM1cjGS5wLvJ2OP6s74aVxxKqdY7kEm8OrOu1GnAr83IpYoU1h/JkpgYo39J5erjWPgJRBg/04wVulOPiwipm25TRxzHHQMsgTBrUZe1d7DAHMCJe1IfpjOLfs/lfybKrWaJH2cPEF1poHtQZ6oGk25qPja/ya7535Aj5NTnSt05TKezyKnG91CtoJeGRWWTSzluJP8/00co6r7fvQdI+2z/8UR8YQJ46iTlvhcWjQ+in4OGY/w/oj4bI1jbB0Rly+N8tUow+PIpCEXMr7VWKfVh3Ka54aMby3VyWXeiEoU+pCPuTAi6uQ2GPR1diEzCX6u3L+I/DwF8M6IOHFpl6GrLJ0o/isiYivl9NDzI2LbGsfoxLK8g1wtsXZEv6RfAVtVuRCc5hgXktNN3w28ICKul3RVRGwx6DFb1QLvshdwjKRzyFSkD6HaVf7Wkv5Wfhc55vU36kehN54bWFoJjyenYEEGPNXpNvxvMh1qZxrYsdTLotbEfjRo8U7wCrLn4A1ki2V9Moq4rxhusNyZymkz32J8V1/VVvKflMk3OnNmdyMvRkalO6p/CfDHAeI7fiXpAAboruxQLjgxcf86q6p9ATibnNkwUJIiSV8jo8knZikcWQVOw+GEcm7oWIFskVc6X6t5lsN3kA2CjpXIGInVyYuryhW4pPXIGJf7pquSU25vrHiIzpDcX0vP3y1AnaFGGIvo35sBI/rJ4Yj7MfhiQ5BLqb4W+GCpvDdiLKHVQFrZAgeQtCv5x98BPC0iFo3gNTeNXPmsZ6u9ypdzGMdYniiXCfxUv20jKMf1PTbXGbN8JNmj8mRynfrrgb1ieIlapnvtztrfm/Z98vTHOZHsrnwZXd2VEVEpTkTSkWTX/fZkj8puZCT7ftPuOP4Ytec69zjGNeRw2Yyd3NRwXv6E/ZeQn6ePR1fComn2/Y+IWCjp6b0ejz7Jkzq9SV33PxsRbyi/X1Cz9XsG2V3dqaheTn4vnl1x/1eTXeBbktM5VwfeGxFfmG6/CccYOKJfYwHT65JrXpxFg56hYWtlBV7Grzcmr2geTU7f+Uyny2cpvu5REbF/ky9n02NIOjcini7pNnrPZa87TlabpC8Cn4qIq3o8thqZ1e3uiPjGpJ0nP7/X9K3GJ/GZUv7+FSLijhG/7inAG6PBtLqm3ZVd+3V+rg78MCKeWqMMHyJz2n+P8SfKOsGVJ5IBlbV6QJbHi2vllFciYnGNfRZFxKOmeOy3EbFxjWNNGkaqM7QkaaPICPxpty0tkvaZ7vGokPVP0gkR8VJNkbwqZmEilyuBV5cr7OuVC5JUzlo1qIjYv/wcONhlCMfo7FclaG9KapZZ6HPA+yRtCVwFLCa7TOeRqzcdw1g+7alef0+ypbeRMkq14wGMrUc9MpL27rW96ripMiPdIeTqTSHpp+QY9KC57ut6EHB1Ga/sHgKovCgMzbsr/1F+3iXp4WTw0jo19odMugLjgxjrTiNbC/hl+V90XwT0+1+8lUwI9fEej9UNxhtoOEHSVyLileX3fapUEFMc51ByWGqFvKslZCOnynDGhZJeExHjpsApp/jVDfz9s6SXMxarsyf5uajqJCavcfFtaqxzoAxoPIxc/Kn7vej7mRr0/z9BpwdrmMmrgJZW4BHxSUn3V0Zx/yoyYrhyN90wNA2SKSf4c8kxoZ9VbbHF2OIlR3e+6F3H/AoVc5EzObPQHCp+KSLiMuClpYU1nzxJ/4Psbu3bxVf8nBwjXovxJ8w7yDn6o/aErt9XIad4XUL1cdPjyYjUzlz0vcjx9GcNq4B9vHcIxzhKOa3wveRc8NXJ6YpVfV+5itdHyf9dUHMedERUWeiin0MH2WkYF+gdUw0nVNi1O3jzQErO7Jqv/VZyzPkJnZZqGeI5Qpkr4hN9DvEW4LuSXsbYLJX/AFYG6qarfhU5Bv4J8vPwcyqcoyRtSp6j1lBGbXc8kPr5K75MXlx/gnw/9qXmwk1TtJ4rpVru6gnaPCbkSZD0WnKRm4G0tQv9viXqImIjVVyiboiv3zNIps54SBmHeWq5bUu2FM6PiEqLqkzsei4V8BUR8ZhpdkNDyCw0G5SK6PiI2LHi8ydFk6pCBrelQTml8s8zPAa8MrBKDJAEpLT+J7aWRhmANowL9IGGEzQ+U+Kgs2QuBZ4dE4JiS3f66VWHpyQ9k7EL/asj4uwaZVg/Im6Y4rGdI+L7ffbfhbxYeCHjEwvdQX4vf16jLAsj4j+6v4+qGdEv6SNMnWp5u4jol2oZST8H3tP5Pyqj4rePiOdVLcdErWyB03uJuqaJPOqYT8MgmcgoxH+SFee/yCvDzfrtJ+mdZFahB0jqdDV3slUdXeF1G2cWGgYNtvzmKN0J1GkNni5pD8bmjO9G9UQqA1NmsjucHHb4ABkstBaZpGfviPhRjWOtTWbSe3hEPK8E/zwpIvp+rsr+q5CpK7cj39ufSjoiJuRs6HOMQ4BnkBX4aWTO6p9SoSdkWJ+pqS7Qq5ShS+dvrjuc0EmmI3ok1qnYSLjfxMq77LtYNVbpKxVN5Up7gjMk7RgTUglL2pdcw2LaCjwiTgFOUS5nW2st9h7uVuZ6+I2kNwA3kb1LdTxrwsXUlRqbntY3JWvxQrKX6u3AjuQ04LrZG8dpawXea4m6QdfFHsRV5GIJA08TkvRb4E/kFd3RZABSlb/hI2SX87j0gFE/kcr3Ja0WEXeWD+DjycC0pR41DcOZijdMGj/lZgWyAumbwEXj1wt+M2PRtnPITFZLe17+Z8kelTXIk+3zIuKC0gV5HLnca1VfIbsb313u/5ocBqhUgZMV3B1klylkjMPXyCQkVe1GdiNfGhH7louKr/fZBxjqZ6rxBTrwvQGHE97e9fugGSf/NeBjw/RW8qJ2p4j4DdzX+/cyoGd0/BT+LOks6mU4nOhAsrX8JvIid3tykZg6GqVaBohcGOeFZAruhcBuTXvJ2tqF3miJugav2znJPwB4LDmmVSdIpvtYB5ItlfXJqTvnAudFxLRrD0uaF7m2cM/IxYioNH6sTIazNbky0VfIcbqXRkSdL9dyQ+On3CwBfh8V56oqryTXjyEtrFKHuiJ6JV0TEZt1PVY3WUWjZDSSfhkRm/fb1ucYF0XENsp1rLcnLwiuiYZT5OrQgFHsXfuvAGzb6eZtMpww4Ot3EhxNeqiUo+4c6EHLsQM5r39X4NVkr+lOEXFbjWOcS8lw2PWZbJT8pBxjxaiRJ6FU2Mcwtnrg38i/6Wryb5ryYr9Hz9BK5DkmaNjb2NYWePcSdceRXZUfGMHrnkqurXv+hO1PpWZrPHKe86fK2Ni+5LDAeoxd1U3lYDIwpNeUuSCXgKxiSUREGWv6bEQcLalWIKAy//fbmZwfuFa07rIguubGdsaQa+wbynzTIx/vZnzP0z8mPFb36vxOZTR9JxnNttRbDOMSdS1Xq5wdUrcVuaC0XL9ItlL+DjTtQq1kwgX6IFHsnefdK+lz5DriRGbvapIApJYY0WqA/UTEWaXL/BwyeO2ZdYZTilUj4qIJva2VKl6V5aPL7xNn2FzE5Oj2KUXExcBAqZaXZm9jK1vgM0XS94GDI+LKCdu3BD5UJZCha5+Pky3w1ckP90/JILbrhljk6V7/XLJ7dV+y0r8VuLxO0JWky8kIyonLRzbJpz1S040hA5XHkCUdS14IXby0yjrF606XTrZWa0s5//kzwBbkMNFcspuvaq/ONeS60Z2eiA2AX1FaG1FzvqtylbsHVn39pjRF4pOO6JMAZcKxPkZeeJw8k8GEM2XC0NLK5BTF+9YkrxGP8ENyOtyJZbx5N3Lthb6BXxN6kiYG/VbqnZL08oj4ujKyf5KovjBM53gPIqfbdgdo1smnPk6rWuAaP194kjpd2ANae2LlXV73ynKyqeMXwEdi/IIkfZUxlClFRNWlIHcnx6P2i4hbJG1AjtfVsSQijqi5z7JmWGPITwT2kvR7xirU2pVWXcNobZXuwRsi4pJSif0XOTR1OlA15SVkYE5jmrDqk6SnNTnJVdWpoJXJeP5RWtKPJoON6i6T+l/kOPCSEqy6rARnjsQQW50HkBkON5V0E5mRrmrQ2HQXTlUvqjrL0Db+e5RZ5Q4ke1ovI2cf/YKa+QXGHbNNF4eSFpOLaBxHLnYwrl+lzhXygK//m4iYN8VjU2YvmvC8abttok+2pxIhC9lKfDIlEp8MDPl5RAy8NF1dymQRt5JLmA6UNWumDWsMWbma3CSjCgpsQtIlZJTtXyQ9jZzT/kYyzmOziNit5vEeyvgWRuXYAEkfJi8ux636NIKL8+4yLCSHxR4E/Ay4GPhXROxVcf/GMRHKKV+vYfJUtsp56ZcnGiDDoXI52beRvWkfZSygVGTjqXJGuWFQziV/AnBBRDy2NBI+FBGV1n7opVUtcDLy+9lkNp+XkathHRcRV4/o9Reod4aiV1N9GcZO0pJVyGjXy8kP1FbkeOGTptu5M44j6XQyUvamcn9dqkcLd7qOP0NOXVuJEjUdEWtUPQZjkZzdkbN1s2bNtKGMIXcq6omVV0vM6bro2p3MB3AScJKky6oepPQOfZxcYOhWshV9DROSBvWxK7BJNFj1aQgUEXeVmJDPR8RHynBRJUOKiTiFjLU5k8GWI10uTOy6LmPhtwMLIxNKTedccupW5/fuIc5KPTqSpktkFBFRJ/bqnxHxT0lIWjkybe8mNfafpFUVeORUqR8BPyqRnXuSC9b/T9RYNrGBNwPfkbQXYxX2fLICfFGVA0TJ8iTpZODxnS55ZfKKQ2uUZb1O5V38P3LMsarPkskITiT/hr3JvPKVxXCyZs20zgp13avTUe5XroiHVHnNlDldUbk7kOlEO+qcIz5AdgueGZlTfXuqd3d2DGPVp6Yk6UlkNr1OYGetzF1kQN8TGsRErBoR7xxw3+XJ/HL7Xrm/M5mp8bWSToyIj0y1Y0TsO4TX7xXNvxr5uXgI9YKnbywBmt8l58nfBjTqoWtVBQ73TcnYiay8NwQ+TXbhLnVlvPrJ5cTUmcbwg6iRoajLJt3j6RFxlaS+iVy6nFOu8js5hndnrDu9kohYJGlOuTD6sjKDU63kLloGsmY1McSI3WFUXo1JekCnm1HSo6LaKn3HAedK+hPZC3F+Z3/qRaHfExF/lrSCpBUi4ieSPlnzT7gLuEw593emVn16M/k9+E5EXK1MEtVr8aHpNI2J+L6k50fEaTVfd3mzHtnQ+Tvcl+jnB2Tg7UIyL8ZSExH3pXmW9AByDHtfcpipV8786Y7VaeQdqlzMag3q5WmYpG1j4F8lK87TyHR6k1bDagtJx5Ff7E6Sir2A1SNiz6n3Gre/yAQZndSM5wHfrhrxKuk8Mk/3l8h0gDcDr4yIrafdcfwxembNqjtmujyQtCAi5peu1seVAKjL6/w/h1SOy8lAn28Ch1Ud5ytDKuuQqTbvLNseTX4mq65hfSbZBX442Tq5lczH/eQa5e+VYCPadFEIzWMiShT3auRFzD3MsiC4DknXAltGxD3l/srkbJlN68SoNCzDg8mAxL3I3PSfinpz2VchlzN9FLkQ19FRYw76tMduWQV+L2NdGr2W0mzNh7u8qa9jbN72eUCttJMNX/8RwB/J7v+3kFeDn6/YYusc40rGsmZtrZI1Kyqu9bs86aq8DiMDDGtXXgO+7qpkgNWSrm2vowyRRMSJS/P1e5SlE3H9cnLhiW/UCWrUMrA+fGkd9Vr2se5qZNsB8yLiyyUobfUY0TKYywtJ7yWHJ08pm15A5uP4OBmrUSmwsMHrfxT4TzIS/nOdnoCax/gWeRF2PtnI+X1EHDj9XhWP3aYK3MaUqT+dILSVyZPm3f0uYpQruA0lY5iWgaxZy4rO1CNyrHQv8oLoG7GUlxOVdAGwa0TcUu6/CPgfssXwlojYaWm+fnnNXjnIOzNE/gn8Fnh3RJxV4Vgzvj68pO5FLlYhp9QtiYh31DjGIeTY7SYR8WhlPvQTI+IpNY4x1DnDbSVpPrm6GuTKjbWSA5ULy7cBG0TEa5TLi24SfRZUKfveS/aCdDKn3fcQFRuNGr+IyorARRM/44Nq3Rj48kLSU8igtYlZzKpGcH+ebOUcT6YofGU5Vj/fpWQgknRSRLy4z/OnM2NZs5Y1nW5n4N4SmzCq1cDu31V5709OPdohcuGKw0fw+tPO+VWukrcFuT78lOkvtQytDx+TExH9TJmVrY4XkZnYLinH/H9lDLUSLYU5wy22CvC3Tk+GpI1q9mR8mTw/dWb43EQG7/atwCOibvBiL/d0HW+JxmeVa8QV+Mw5muy6HpfFrIYVIuJXJXr4HuCLJQjtPX326/70NJruFRGvL78eKelHjDBr1rJCQ1wNbEB/Lq299clK41ERcZukdcjhkRlVAiQvl/SZPk9dZtaHL2OeHSuQa2HXmV4JOawRkjppaVfrt8MEBzI2Z3h7lTnDNY/Ret09GWRFfD8ybqhyTwawcUTsXi4SiZwiOLxatL/OTBcYP9ul8dCvK/CZc3tMWNy9pjslrUSeHD9EnvyqRFTHFL/XVrprz46I2yPid5LWlLRrRHy3yXFbZpirgQ3iJWQsxa/J6V+nl9iE7RlbVWzGRcQX+jz+e3JKzZNKfMa8iDhT0v3JFLGVE3gMwULG0oAuIYMCa60TAJwg6QvAmpJeQ65f8KUa+w99znBLNerJKP5VPkedi6mNWU5y03sMfIaU7s05wMmMny5TNeL3keTc71XI8Z01yFzcv+6z33S5s2tdDarHSlWjHq+caRriamBDKs/DydbJFRHxq1G+9jCUym5/4MERsXEZrzwyInaY4aLVJunZwHPI79aPI+KMGvt+h5yu9Gay2/w2csXFkWVaXBZ0xdl01t5eDfhFjel4nffhPeRsmdPJ78crI+KcpVLoEXIFPkNKpOtEUSXStYwrfjki9h5+yaqTdMXEL1J3wMZs0B10NTEAq1dAlk1PmfltG+DCGFuIYiSfKY3lhO/EFOxNBrD9Hji0ZjT9h2NCIpZe2yoe6+mUOcMRMar1vJcJkv6bDOR7NjnD41XANyOi35DMxOM8hIwjEDks8adhl3UmuAJvKUk/BbbvzI+coTIcA/yVsaVNDyBbTq+cqTKNWp8ejZGtvby8kHRhRDyx03tRonYvqdPiavDaQ8sJP0U0/aQL3h77PTAi/jZhHP4+dS4i2ky5HOs3I+Jng/ZkqOG6E23gMfAZJGknMtVm9zSR91fc/bfA+ZJOoSvdX0R8eqiFnN4bgfcC3yr3zyAr8VljaY5vzVLnSnoXGejzbOD1jKXRXNoa54RXzsF/PfBISd3Bd3L7MKQAAAz1SURBVA8gF0bp55tkutDucfiOtq0z0MSvgY+VYMwTyMr80prHmC5TWrAcRPS7BT5DJB0JrEoGG30J2I2cH1gpWEZSzxy8EfHeoRXSWmOYXbYzSdIKZMDYfS0u4EujmJIn6SrgsWWqz7XA/p1515Kuiogpp8F1HWMNchWzw4CDuh66Y7a0noepBDTuUW73JwNDj+sX6zNbuAKfIZ3utK6fqwM/jIin9tnvQxHxrhEVc6oyfDIi3izpe/TOWDWypR8tDdpla2MkvRt4PvAncmGgx5epYI8Cjo0KSViG1QWuzBNxWUTcKenlZO6GT8aQkjC1kaTHAccAW1Xp+ZI07TKdEXHysMo2U9yFPnM6S1feVSKH/0Lmou5nR3La0kzqrEn+sRkthQ2jy3aZIOmEiHhpmQLX66JwqV+IRMQHlYuodHLCd8qxAjlcVMWwusCPIOcPb03OMvkS+b17esX9lwslBuJ5ZAt8B3LBpkMr7t5ZPvShwJPJaZ6QvZ4/J2cAtZor8JnzfWUWs48wtjRplXmic5QpFnsmIhhFN11ELCyR8PvHUs5FbH19E/gh7e+y/bsyd/gLaJifoImIuKDHtsrdtRGxc/nZdKndJaX1vws5PfRo5frks0KJf9iT7BG5iAwo3D/GMh72FWU5UUmnA5tHxM3l/jrAV4Zd5pngCnzEuqaqfKDcX51coeZa4BMVDrEpWeH3qsBHFuQSEf+W9AhJK822qS3Lkoi4Hbhd0nuAWyLibknPALaS9NWI+OvMlrCyy4GPkq3fE8hxzrpBSzNuiJHPd0g6mEyX/LQSGzCbZjQcTF6cvi1qrPw1hfU7lXfxR3KIpPU8Bj5iTaeqLEuJUpTLu25Grg7UHQn/fzNWqFmqREnPBzYkl3Y9BXhM2xJ/tD1oqSu/wyrk+3E5ebG9FbAgIp401b4TjvMwMjf8xRFxvqQNgGdEy5ZVXRZI+iw5l/y4sml3YFFEVB0WWWa5Ah8xda0RXeY6Lo6IQ8v9SZnNeuy/LFXgh/TaHhH/M+qyzHZdmareAfwjIj6zLH1WBlE3aGlZIulk4JCIuLLc34JMBlN5LvmE4z2VXB52Vk3THJYS0NYJED4vIr4zk+UZFnehj94c5QIkS8igjP27HqvyfoxsXeR+OhW1pFUj4q5+z7el6h7lYg17Mxa807ou14ZBS8uSTTqVN0BEXCVps+l2mKhcwLyMzHd/PXDScIs4e5SI89YHrU3kCnz0jiOTVfyJjEQ/H6BMVbm9wv7bSVrYfXLoKHmCdyfXBf/GEMvck6QnkauqrQ5sUCJm/yvGVimz0dkXeC3wwYi4XtJGjM0WWOYNI2hpGXOFpC+RK2dBrhHfd1U1SY8m/w97ktPZvkX2lG6/tAq6vFLvdervEw1WAVtWuAt9BiiXoOxMVbmzbHs0sHq/IBdJjyWnkW0JXAUsJsfb5gEPJLscj4yIpb7ajqQLyQQ0p8ZY3upKCS/Mukk6mwxaOmkIQUszTtIq5CpxTyubzgOOiIh/9tnvXvKifr+IWFS2XRcRsyUD29CVpFc3kxe0Ii+m1omI981owYbAFXhLlej1+eSFwD+Aa2LEq09NzFtdtt03xm+jo1y16zByxaXu1Lw+8c8Q5RKWG9T5XkralRw+eAq5FO3xZCa6ptPSZq1e56Tl5Ty1wkwXwAYTEX+PiHMi4riI+O6oK+/iBklPBkLS/ZQrB10zA+Uw+DKZ/GMJmajiq4x139qISXohcBllPXhJj5V0ar/9ynd5D3K66E/I5UQfKukISc9ZmmVejt0paS9JcyStIGkvumbNtJlb4DYwSWuRQXXPIrumTgcOjIg/z2jBZqESF/Ef6lp6s7Ntpss2G0laSC6WcU40XBa1JG56CbB7tHBd9JkmaUPyPPUUckz8Z8CbI+J3M1eq4XAQmw0sck1dZ2JbNtxdkn38RtIbgJvI4EKbGfdExO3SuHxLA7WWSkzAUeVmNZWKepeZLsfS4ArcBlYind9IJg+577PkxUxmxIHk6nZvAj5AdqPvM6Mlmt2ulvQyctroPPJ9+fkMl2lWkfSOiPiIpM/QO7/+m2agWEPlCrylStT624FHML7yHOUat98lp5F9D7h3hK9rhaSvRcQrgCdHxMXA38kpZTaz3gi8G7ibnDr6Y/LCykanE4+zYEZLsRR5DLylJF0OHEnmRf93Z3tELJxyp+GX4cKIeOKoXs8mk/RLMgbhh8AzmJAjv2ULmphZDa7AW2pZCFAqXYTzyOC1++ad11iwwRqS9CZyvvEjyXHvcctXehrZaPWLNPfw0ujMhvfCFXhLSToUuBX4DuMrz5G1uCQdBrwC+C1jXegx4m58AyQdERGvm+lyzHaSFgM3kN3mFzK5R+TcmSjXbDQb3gtX4C0l6foem0fa4pK0iFxn18uJzjBJ+0XE0RO2HR4RB021jw2fpDlAJy3sVsAPyNXUrp7Rgs1Cs+G9cCKXloqIjXrcRt1dehWw5ohf03p7cUlQAdy30t1DZ7A8s1JE/DsifhQR+wDbAouAc8rUPhuh2fBeOAq9xcoShRNTZ45yveA1gWslXcz4bvzWjy210IuBU0su7R2Bv0bEq2a4TLOSpJWBnciW34bAp8mhLhux5f29cBd6S5W1uJ9BVuCnkUsw/nTQ9YYHLMPTe21fHsaW2kLSg7vuPoCc2vcz4H3gKPRRk/RVYAvyO3l8RFw1w0WatWbDe+EKvKUkXQlsDVwaEVtLWhv4ekQ8ewbLtB2wZ0QcMFNlmG1KLESQATqdnx2OQh+x0gPSybPdfXIV+X60fgnLtpgN74W70NvrHxFxr6Qlkh5IRqSvP+pCSHoc8DIyV/P1wEmjLsNs5lWqli0R4biiZcRseC9cgbfXAklrAl8kk7n8HfjFKF64ZIHbs9z+BHyL7M3ZfhSvb70tAzERZjZC7kJfDpTVdh4YEVeM6PXuBc4H9ouIRWXbde6unTnLQkyEmY3Wct/FsLyS9CJJa8B9q+38QdKuI3r5/wRuBn4i6YuSdmBCkgQbud2AHYBbImJfMj5ijZktkpktTa7A2+uQiLi9cyci/gocMooXjojvRsQewKbAT4A3Aw+VdISk54yiDDbJPyLiXmBGYyLMbHRcgbdXr/dupDENEXFnRHwzIl4ArAdcCv+/vXsHsbMIwzj+f5SItyIRtQwqKIkouoJGxcbEQitBEElUEAQFg8QuIF4qOxEFxUK00EJSeCFpZMELXsAoJhYriRAjmEKEBFMpRuJr8X1rsttYnTM75/x/cNjd77DwNLtz3pl5Z9g9zQz6z+o9EQeY0p4ISW24Bt6pJG8BJ4HXxkc7gUuq6pFmobQmTHtPhKQ2rMD79SRwimEH+B6Gk9Dsv55TGTyU5LlxT8TJJLe0ziVpcqzApRmQ5HWGG+G2VtXmJBuAxaq6uXE0SRNiH3hnkrxcVU8l2cfK04UAzyGfY1uq6qYkBwGq6vck57UOJWlyHMD788749cWmKbTW/D1en1gASS7jzB3tkmaQA3hnquq78R/1Y1X14P/+gubF8i1Llyd5gaEv/Jm2kSRNkmvgnUryJcN656nWWbQ2JNnEcJhLgI+r6lDjSJImyAq8X0eBr5Ls5cyNO1TVS+0iqYVxRuaHqtoEHG6dR9J0OID366fxdQ7DPdCaU1V1OsmPSTZW1S+t80iaDqfQO5fkwqr6o3UOtZXkc2AB+IaVMzJ2JUgzygq8U0luA94ELgY2JrkBeLyqnmibTI082zqApOmyAu9Ukv0MO433VtXC+Gypqq5rm0ytJbkUOFH+cUszzaNUO1ZVx1Y9Ot0kiJpJcmuSz5K8n2QhyRKwBPyW5O7W+SRNjlPo/TqW5HagkqwDdgG2Dc2fV4GnGe7+/gS4p6q+HlvK3gU+ahlO0uQ4hd6pcZr0FeAuhr7fRWBXVZ1oGkxTleT7qrpx/P5QVW0+672Dy8srkmaPFXinquo44ElsOvu41D9Xveenc2mGWYF3KsmVDFeKXsFZH8RsG5ovSU4ztI0FuABYbikMcH5VrWuVTdJkWYH360OGNrJ9eGnF3Kqqc1tnkNSGFXinkuyvqi2tc0iS2nAA71SSHcDVDJvX/lp+XlUHmoWSJE2NU+j9uh54GNjKmSn0Gn+WJM04K/BOJTkCXOt1opI0nzyJrV9LwPrWISRJbTiF3q/1wOEk37JyDdw2MkmaAw7g/Xq+dQBJUjuugc+IJHcA26tqZ+sskqTJswLvWJIFYAdwP/Az8F7bRJKkaXEA70ySa4Dt4+s4sIdhJuXOpsEkSVPlFHpnkvwDfAE8WlVHxmdHq+qqtskkSdNkG1l/7gN+BT5N8kaSbQwXV0iS5ogVeKeSXATcyzCVvhV4G/igqhabBpMkTYUD+AxIsoFhI9sDVbWtdR5J0uQ5gEuS1CHXwCVJ6pADuCRJHXIAlySpQw7gkiR1yAFckqQO/QuDkyJtuGY9NAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yelp.category.value_counts().plot(kind='bar', figsize=(8,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine: let's add in population data\n",
    "This will help us enrich our understanding of the Yelp data and contextualize some of the aggregations we've performed.\n",
    "\n",
    "We can start by reading in the census data as a dataframe and taking a look at it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>GEO.id</th>\n",
       "      <th>GEO.id2</th>\n",
       "      <th>GEO.display-label</th>\n",
       "      <th>GC_RANK.target-geo-id</th>\n",
       "      <th>GC_RANK.target-geo-id2</th>\n",
       "      <th>GC_RANK.rank-label</th>\n",
       "      <th>GC_RANK.display-label</th>\n",
       "      <th>GC_RANK.display-label.1</th>\n",
       "      <th>rescensus42010</th>\n",
       "      <th>resbase42010</th>\n",
       "      <th>respop72010</th>\n",
       "      <th>respop72011</th>\n",
       "      <th>respop72012</th>\n",
       "      <th>respop72013</th>\n",
       "      <th>respop72014</th>\n",
       "      <th>respop72015</th>\n",
       "      <th>respop72016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0100000US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>1620000US3651000</td>\n",
       "      <td>3651000</td>\n",
       "      <td>1</td>\n",
       "      <td>United States - New York city, New York</td>\n",
       "      <td>New York city, New York</td>\n",
       "      <td>8175133.0</td>\n",
       "      <td>8174962</td>\n",
       "      <td>8192026</td>\n",
       "      <td>8284098</td>\n",
       "      <td>8361179</td>\n",
       "      <td>8422460</td>\n",
       "      <td>8471990</td>\n",
       "      <td>8516502</td>\n",
       "      <td>8537673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0100000US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>1620000US0644000</td>\n",
       "      <td>644000</td>\n",
       "      <td>2</td>\n",
       "      <td>United States - Los Angeles city, California</td>\n",
       "      <td>Los Angeles city, California</td>\n",
       "      <td>3792621.0</td>\n",
       "      <td>3792584</td>\n",
       "      <td>3796292</td>\n",
       "      <td>3825393</td>\n",
       "      <td>3858137</td>\n",
       "      <td>3890436</td>\n",
       "      <td>3920173</td>\n",
       "      <td>3949149</td>\n",
       "      <td>3976322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0100000US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>1620000US1714000</td>\n",
       "      <td>1714000</td>\n",
       "      <td>3</td>\n",
       "      <td>United States - Chicago city, Illinois</td>\n",
       "      <td>Chicago city, Illinois</td>\n",
       "      <td>2695598.0</td>\n",
       "      <td>2695620</td>\n",
       "      <td>2697736</td>\n",
       "      <td>2705404</td>\n",
       "      <td>2714120</td>\n",
       "      <td>2718887</td>\n",
       "      <td>2718530</td>\n",
       "      <td>2713596</td>\n",
       "      <td>2704958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0100000US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>1620000US4835000</td>\n",
       "      <td>4835000</td>\n",
       "      <td>4</td>\n",
       "      <td>United States - Houston city, Texas</td>\n",
       "      <td>Houston city, Texas</td>\n",
       "      <td>2099451.0</td>\n",
       "      <td>2100277</td>\n",
       "      <td>2105625</td>\n",
       "      <td>2132157</td>\n",
       "      <td>2166458</td>\n",
       "      <td>2204406</td>\n",
       "      <td>2243999</td>\n",
       "      <td>2284816</td>\n",
       "      <td>2303482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0100000US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>1620000US0455000</td>\n",
       "      <td>455000</td>\n",
       "      <td>5</td>\n",
       "      <td>United States - Phoenix city, Arizona</td>\n",
       "      <td>Phoenix city, Arizona</td>\n",
       "      <td>1445632.0</td>\n",
       "      <td>1447624</td>\n",
       "      <td>1450629</td>\n",
       "      <td>1469353</td>\n",
       "      <td>1499007</td>\n",
       "      <td>1525562</td>\n",
       "      <td>1554179</td>\n",
       "      <td>1582904</td>\n",
       "      <td>1615017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      GEO.id  GEO.id2 GEO.display-label GC_RANK.target-geo-id  \\\n",
       "0  0100000US      NaN     United States      1620000US3651000   \n",
       "1  0100000US      NaN     United States      1620000US0644000   \n",
       "2  0100000US      NaN     United States      1620000US1714000   \n",
       "3  0100000US      NaN     United States      1620000US4835000   \n",
       "4  0100000US      NaN     United States      1620000US0455000   \n",
       "\n",
       "   GC_RANK.target-geo-id2  GC_RANK.rank-label  \\\n",
       "0                 3651000                   1   \n",
       "1                  644000                   2   \n",
       "2                 1714000                   3   \n",
       "3                 4835000                   4   \n",
       "4                  455000                   5   \n",
       "\n",
       "                          GC_RANK.display-label       GC_RANK.display-label.1  \\\n",
       "0       United States - New York city, New York       New York city, New York   \n",
       "1  United States - Los Angeles city, California  Los Angeles city, California   \n",
       "2        United States - Chicago city, Illinois        Chicago city, Illinois   \n",
       "3           United States - Houston city, Texas           Houston city, Texas   \n",
       "4         United States - Phoenix city, Arizona         Phoenix city, Arizona   \n",
       "\n",
       "   rescensus42010  resbase42010  respop72010  respop72011  respop72012  \\\n",
       "0       8175133.0       8174962      8192026      8284098      8361179   \n",
       "1       3792621.0       3792584      3796292      3825393      3858137   \n",
       "2       2695598.0       2695620      2697736      2705404      2714120   \n",
       "3       2099451.0       2100277      2105625      2132157      2166458   \n",
       "4       1445632.0       1447624      1450629      1469353      1499007   \n",
       "\n",
       "   respop72013  respop72014  respop72015  respop72016  \n",
       "0      8422460      8471990      8516502      8537673  \n",
       "1      3890436      3920173      3949149      3976322  \n",
       "2      2718887      2718530      2713596      2704958  \n",
       "3      2204406      2243999      2284816      2303482  \n",
       "4      1525562      1554179      1582904      1615017  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop = pd.read_csv('data/2016PopulationData.csv')\n",
    "pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Uh Oh! Labels for city and state don't match\n",
    "Of course, that would be too easy! We need to extract the city and state from each row from the census data in order to match it with the Yelp data. We also need to replace the Yelp state abbreviation's with their respective state names for our matching to work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     Dravosburg\n",
      "15      Carnegie\n",
      "20      Carnegie\n",
      "22      Carnegie\n",
      "24    Pittsburgh\n",
      "Name: city, dtype: object\n",
      "0         New York city, New York\n",
      "1    Los Angeles city, California\n",
      "2          Chicago city, Illinois\n",
      "3             Houston city, Texas\n",
      "4           Phoenix city, Arizona\n",
      "Name: GC_RANK.display-label.1, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(yelp.city.head())\n",
    "print(pop['GC_RANK.display-label.1'].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Create 'city' and 'state' variable for the census data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop['city'] = pop['GC_RANK.display-label.1'].apply(lambda x: ' '.join(x.split(',')[0].split()[:-1]).strip())\n",
    "pop['state'] = pop['GC_RANK.display-label.1'].apply(lambda x: x.split(',')[1].strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we're at it, let's rename the strange column name `respop72016` to something more intuitive. How about `population`? Works for me!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop['population'] = pop['respop72016']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Expand 'state' abbreviation in yelp data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PA', 'NC', 'SC', 'WI', 'IL', 'AZ', 'NV'], dtype=object)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.state.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a dictionary (yes, it's actually called this!) to translate State abbreviations to their full state names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_abbrev_dictionary = {'PA': 'Pennsylvania', 'NC': 'North Carolina', 'SC': 'South Carolina', 'WI': 'Wisconsin', \n",
    "               'IL': 'Illinois', 'AZ': 'Arizona', 'NV': 'Nevada'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use this dictionary to replace the abbreviations with the full state names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Pennsylvania', 'North Carolina', 'South Carolina', 'Wisconsin',\n",
       "       'Illinois', 'Arizona', 'Nevada'], dtype=object)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.state.replace(state_abbrev_dictionary, inplace=True)\n",
    "yelp.state.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's merge the datasets, bringing population information into the Yelp dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp = yelp.merge(pop[['city', 'state', 'population']], on=['city', 'state'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 11366 entries, 0 to 11365\n",
      "Data columns (total 11 columns):\n",
      "business_id           11366 non-null object\n",
      "city                  11366 non-null object\n",
      "name                  11366 non-null object\n",
      "review_count          11366 non-null int64\n",
      "stars                 11366 non-null float64\n",
      "state                 11366 non-null object\n",
      "price_range           10780 non-null float64\n",
      "number_of_checkins    11366 non-null int64\n",
      "category              9733 non-null object\n",
      "restaurant            11366 non-null float64\n",
      "population            10288 non-null float64\n",
      "dtypes: float64(4), int64(2), object(5)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "yelp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What did we just do?  **Entity resolution!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive statistics\n",
    "What more can we learn about this dataset? Let's find out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>review_count</th>\n",
       "      <th>stars</th>\n",
       "      <th>price_range</th>\n",
       "      <th>number_of_checkins</th>\n",
       "      <th>restaurant</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11366.000000</td>\n",
       "      <td>11366.000000</td>\n",
       "      <td>10780.000000</td>\n",
       "      <td>11366.000000</td>\n",
       "      <td>11366.0</td>\n",
       "      <td>1.028800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>62.447387</td>\n",
       "      <td>3.441448</td>\n",
       "      <td>1.538404</td>\n",
       "      <td>164.311103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.365891e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>163.232207</td>\n",
       "      <td>0.802070</td>\n",
       "      <td>0.637678</td>\n",
       "      <td>489.513931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.801052e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.453400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.474770e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.329120e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.420510e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5642.000000</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16641.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.615017e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       review_count         stars   price_range  number_of_checkins  \\\n",
       "count  11366.000000  11366.000000  10780.000000        11366.000000   \n",
       "mean      62.447387      3.441448      1.538404          164.311103   \n",
       "std      163.232207      0.802070      0.637678          489.513931   \n",
       "min        3.000000      1.000000      1.000000            0.000000   \n",
       "25%        8.000000      3.000000      1.000000           10.000000   \n",
       "50%       21.000000      3.500000      1.000000           41.000000   \n",
       "75%       62.000000      4.000000      2.000000          142.000000   \n",
       "max     5642.000000     33.500000      4.000000        16641.000000   \n",
       "\n",
       "       restaurant    population  \n",
       "count     11366.0  1.028800e+04  \n",
       "mean          1.0  6.365891e+05  \n",
       "std           0.0  4.801052e+05  \n",
       "min           1.0  5.453400e+04  \n",
       "25%           1.0  2.474770e+05  \n",
       "50%           1.0  6.329120e+05  \n",
       "75%           1.0  8.420510e+05  \n",
       "max           1.0  1.615017e+06  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 33.5 reviews huh? Sounds like bad data.\n",
    "Let's find those restaurants and see what's going on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>business_id</th>\n",
       "      <th>city</th>\n",
       "      <th>name</th>\n",
       "      <th>review_count</th>\n",
       "      <th>stars</th>\n",
       "      <th>state</th>\n",
       "      <th>price_range</th>\n",
       "      <th>number_of_checkins</th>\n",
       "      <th>category</th>\n",
       "      <th>restaurant</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>XmdTkxKyJd7TuDLoiic1kg</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Lidia's</td>\n",
       "      <td>277</td>\n",
       "      <td>33.5</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>3.0</td>\n",
       "      <td>261</td>\n",
       "      <td>Italian</td>\n",
       "      <td>1.0</td>\n",
       "      <td>303625.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                business_id        city     name  review_count  stars  \\\n",
       "105  XmdTkxKyJd7TuDLoiic1kg  Pittsburgh  Lidia's           277   33.5   \n",
       "\n",
       "            state  price_range  number_of_checkins category  restaurant  \\\n",
       "105  Pennsylvania          3.0                 261  Italian         1.0   \n",
       "\n",
       "     population  \n",
       "105    303625.0  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp[yelp.stars > 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like it is just one offending restaurant. 33.5 stars is probably a typo for 3.5 stars. Let's fix that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp.loc[yelp.stars==33.5, 'stars'] = 3.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's do some further visual analysis of the data\n",
    "Histograms are a great way to characterize datasets. Let's take a look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp.drop('restaurant', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11cd74c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a6fdc88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11c709f98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a7172e8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a7425f8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a742630>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yelp.hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing our data\n",
    "Taking the `log()` of data can help normalize a distribution (that does not contain zero). Similarly, we can take the square root of data to normalize distributions as well.\n",
    "\n",
    "This will help us visually understand trends when there are major outliers in play. In this case, some cities have much, much higher populations than others. Similarly, the top restaurants are major outliers and have far more check ins than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp['population'] = np.log(yelp['population'])\n",
    "yelp['number_of_checkins'] = np.sqrt(yelp['number_of_checkins'])\n",
    "yelp['review_count'] = np.sqrt(yelp['review_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11a6ddda0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bb43fd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11bb6f6a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bb96cf8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11bbc93c8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bbc9400>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yelp.hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use box plots to show the distributions of our data's features. Box plots show the median, the inter-quartile range (between the first and third quartiles -- the box) and min/max. They also show us outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = list(yelp.select_dtypes(include=[np.number]).columns)\n",
    "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(10,10))\n",
    "for i, ax in enumerate(axes.reshape(-1)):\n",
    "    yelp[cols[i]].plot.box(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using correlation to identify trends\n",
    "We can create a correlation matrix plot to demonstrate relationships between the various features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = scatter_matrix(yelp, alpha=0.5, diagonal='kde', figsize=(10,10))\n",
    "corr = yelp.corr().values\n",
    "for i, j in zip(*plt.np.triu_indices_from(axes, k=1)):\n",
    "    axes[i, j].annotate(\"%.3f\" %corr[i,j], (0.8, 0.8), xycoords='axes fraction', ha='center', va='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use this to _easily_ (ok it may look overwhelming at first, but you will eventually find these plots very useful) identify trends.\n",
    "\n",
    "Note the strong positive correlation between `review_count` and `number_of_checkins`. Not too surprising. But what about the positive correlation between `review_count` and `stars`? Perhaps people are more likely to write good reviews than bad ones!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping data to enhance comparison\n",
    "We can also use a grouping aggregation to compare stars per state. Interestingly, there doesn't seem to be much of a difference between average restaurants in the states in our sample. This also suggests that ratings normal distribution holds across state lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Arizona                AxesSubplot(0.1,0.679412;0.235294x0.220588)\n",
       "Illinois          AxesSubplot(0.382353,0.679412;0.235294x0.220588)\n",
       "Nevada            AxesSubplot(0.664706,0.679412;0.235294x0.220588)\n",
       "North Carolina         AxesSubplot(0.1,0.414706;0.235294x0.220588)\n",
       "Pennsylvania      AxesSubplot(0.382353,0.414706;0.235294x0.220588)\n",
       "South Carolina    AxesSubplot(0.664706,0.414706;0.235294x0.220588)\n",
       "Wisconsin                  AxesSubplot(0.1,0.15;0.235294x0.220588)\n",
       "dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DlyRJUkOrF1shyTuAJwFXVNX9J1+S5iX5idv8tHlJ0u7wtWX5jTLy9S7gCROuQ9sZbo4TTjhhh7dLkrQU868hq1at4g1veAOrVq261e1qY9HwVVWfAr7foBbtQFXxxCc+0XclkqSxWLVqFTfddBMPfvCDuemmm24JYGpn0WnHUSU5DjgemJmZmWEwGIxr1yvWCSecwGAw4LrrrmMwGHDCCSfw+te/3t/tXs5emaz5ftHez16ZjJNOOulWry0nnXQSL37xi/39NpRRRlSSzAJnjnrM19zcXG3atGn3Klvh5oeAq4rBYMDatWtvdZt2T5LNVTW33HXYK+M33y8aD3tluiS5ZeRrvldWr17NzTff7GvLGIzaL57tuIdLwkc/+lHn4yVJY3HzzTezevVqzj///FuCl9oa27Sjxquqbglcr3/96291uyRJu2L+teXmm2/mxS9+8a1uVzuLjnwleT/wGeDeSb6d5HcmX5aga4aqYuPGjbd8L0nS7vC1ZfktOvJVVU9rUYgkSdJK4DFfkiRJDRm+JEmSGhrpUhNL3mnyPeCSse945ToYuHK5i5gyh1fVnZa7CHtlIuyX8bJXppe9Mn4j9ctEwpfGK8mmPeE6O9LewH6RRmOvLB+nHSVJkhoyfEmSJDVk+No7nLLcBUh7EftFGo29skw85kuSJKkhR74kSZIaMnztQZK8KMntlrsOaU9nr0ijsVf2TE477kGSbAHmqmrk664kWVVVfiS9VhR7RRqNvbJnWvSzHTUZSdYAZwB3B1YBHwQOBTYmubKq1iU5GXgYcFvg76vqlf22W4DTgSOA1ye5M/Bc4Cbgwqp6auvHI02KvSKNxl7Zexi+ls8TgMuq6miAJAcBxwLrht6h/FFVfT/JKuDjSR5YVRf0y66qqof0214G3KOqbkgy0/hxSJNmr0ijsVf2Eh7ztXz+DTgiyeuSPLqqrtnBOk9Jch5wPnA/4L5Dy04f+v4C4L1JnkH3LkWaJvaKNBp7ZS9h+FomVfVV4CF0zfKaJK8YXp7kHsBLgcdW1QOBDcD+Q6tsG/r+aOAt/f6+kMQRTU0Ne0Uajb2y9zB8LZMkhwI/qKrTgJPonuBbgQP7VW5P1wjXJDkEeOIC+9kHOKyqNgIvAw4CDphw+VIz9oo0Gntl72GSXT4PAE5K8iPgRuB5wMOBs5Jc1h8YeT5wMfAt4NML7GcVcFo/tx/gTVV19eTLl5qxV6TR2Ct7CS81IUmS1JDTjg0kqST3XO46diTJc5KcO/TzdUl+ZjlrksYlybuSvKbB/dg3ai7JIMnvLncdO5Jktn/tW93//NEkz17uuvYUKzp8JdmS5Ir+2ijzt/1uksFu7HO3myHJvZJ8MMmVSa5JckGSF/enBk9UVR1QVd+c9P1o79b3zg/70PHdPuSs2GNC7BvNS/KoJP/a/+3+fpJPJ3nYGPb7qiSn7eY+bp/kjUn+o+/db/Q/H7y79S2mqp5YVadO+n72Fis6fPVWAS/c3Z2ks9u/zyQ/C3yObj7+AVV1EPBbwBw/PmhyKfubeGDTivWrVXUA3UG9c8AfL3M90rJKcnvgTOCvgTsAdwNeDdywnHUBJLkN8HG6y0s8ge7g+4cDVwG/uAv785jx3WD46s4IeelCF5FL8ogkX+jfxXwhySOGlg2SvDbJp4EfAO8BHg28uX9X8eahXT0uydeSXJ3kLUmyQD2vBv61ql5cVd8BqKp/r6pj5g947EfFLu9r+lSS+w3V9K4kJyf5SJJtwLokByV5d5LvJbkkyR8vFBSHp0j7fb0lyYYkW5N8rg+H8+v+ryTfSnJtks1JHr3ob1tTp6ouBT4K3L9/rr09yXeSXJrkNfNvAOanuJP8RZL/TPJ/k9xytlXfT3/ajxRsTXL2/DvyJPsnOS3JVX0PfSHJIUl+K8nm4Xr6UeJ/3L7OJBcledLQz6v7npi/qORifbWzXhjum6OTnN/3xbeSvGpMv2rt+e4FUFXvr6qbq+qHVXX2/EVMk+zT//29JN2sy7vTHdROkrVJvj28s3QjzI9L8gTgD4Hf7l9bvjS02uE76pkdeBbw08BvVNWFVfWjqrqiqv60qj7S39+J6UbDtia5MMlvDNXynP5+/irJVcCrdvZ4tpehWaER/hYc2/fr1iTfTPLflvS/sBcwfMEmYEB37ZNbSXIHuuugvAm4I/AGYEOSOw6t9kzgOLpRqecA/wL8QT8N8QdD6z2J7iMdHgg8BThygXoeB/z9IjV/FPg54M7AecB7t1t+DPDavqZz6d6FHQT8DPArdE147CL3Me+pdIHwp4Cv9/ud9wXgF+je4b0P+GCS/X9iD5pqSQ4DjqK7aOO76C7IeE/gwcDjgeFp+F8C/h04GHg98PbkVm9EjqF7bt4ZuA0/7stn0z2HD6PrxecCPwT+CbhHkvsM7eOZwLt3UOr7gacN/XwkcGVVndf/vFhf7awXhm2j67EZumslPS/Jry+wrqbLV4Gbk5ya5IlJfmq75c/pv9bR/T0+AHgzi6iqs4A/A07vX1seNLR4oZ7Z3uOAs6rqup3c1TfoBhAOonuun5bkrkPLfwn4JnAI3fN/lx7P0L4W+ltwBd1r5u37x/ZX82+SpoXhq/MK4AVJ7rTd7UcDX6uq91TVTVX1frpTdH91aJ13VdVX+uU37uQ+1lfV1VX1H8BGutCyI3cEvrOzYqvqHVW1tapuAF4FPGi7dxv/WFWfrqr5042fCry832YL8Jd0L1Cj+HBVfb6qbqJ7Mbql7qo6raqu6h/7XwL7Afcecb/a+/3vJFfTBfxPAm+jC2EvqqptVXUF8Fd0z795l1TV3/Uf2nsqcFe6P+Tz3llVX62qH9J9Rt388+1Gut64Zz+isLmqru174HTgGQD9aNUs3dTP9t4HPDnJ7fqfj6ELZMBIfbVgLwyrqkFV/Vs/snBBfx+/suNfoaZJVV0LPAoo4O+A7yX5p3TX1AJ4OvCGqvpmH4JeDjw1uzeFt1DPbG+U15YPVtVl/XP3dOBr3HpK8rKq+uv+b/4Pd/PxLPi3oKo2VNU3qvNJ4Gy6UDg1DF9AVX2Z7o/1idstOhS4ZLvbLqGbx5/3rRHv5vKh73/Awhesu4ruSbhDSVYlWd8PDV8LbOkXDQ81D9d0MLAvt34c2z+GXao7yUv7oeFr+hfhg7arQ9Pt16tqpqoOr6rn0/3h3Bf4Tj81eDXwt3TvyOfd8nyqqh/03x6wo+Xc+vn2HuBjwAeSXJbk9Un27ZedChzTv2t+JnBGH6Bupaq+DlwE/GofwJ5MF8hG7auRejjJLyXZ2E9pXkM3SmdfrBBVdVFVPaeq7g7cn+515I394u1fUy6hu97mIey6sby2ACR5VpIvDvXv/Vn4tQV27/Es+LegHzX8bLoTFq6me1M3VT1k+PqxVwK/x61DyWXA4dut99PApUM/b3+htN29cNr/Af7LTpYfA/wa3RDyQXTv8qG7EN6OariSbtRg+HFs/xiWLN3xXSfQTaH+VFXNANdsV4dWlm/RHVh8cB/KZqrq9lV1v8U2XExV3VhVr66q+wKPoJuSeFa/7LPA/6N7Z3wMXVBbyPzU468BF/aBDEbrq1G9j2469LD+hJm/2cX9aC9XVRfTTcXfv79p+9eUn6abpv8u3XT1/Kjs/MlSw7Mx43htOTJDZ/cPS3I43WjdHwB37P+mf5mFX1tg549nlyTZD/gQ8BfAIX0dH2HKesjw1ev/CJ8O/Pehmz8C3CvJMekOzv1tug8h3dGUxrzv0s1976pXAo9IclKSuwAkuWe6g41n6I7juoHuXczt6I4DWFA/pHsG8NokB/YN9mJgt05Z7uu4CfgesDrdZ4jdfjf3qb1Yf4LI2cBfpjulfZ8kP5tkt6fckqxL8oD+BelaujcUPxpa5d10x5rcWFXn7mgfvQ/QHYf2PPpRr96S+moRBwLfr6rrk/wiXbDTCpDk55O8JMnd+58Powv7n+1XeT/wP5LcI92lWeaP47qJ7nix/dOdsLEv3dnD+w3t/rvAbHb9rPr30L1B+lBf5z5J7pjkD5McBayhC1ff62s/lh+HxoXs7PHsqtvQPe7vATf1B+I/fjf2t0cyfN3an9A9AQGoqqvo3mG/hO6P8gnAk6rqyp3s438B/7U/g+NNSy2gqr5Bd/rvLPCVftriQ3QnBmyle5G5hG7k6kJ+3NQ78wK6d1XfpDs+533AO5Za23Y+BpxF9wfjEuB6Rp+C1fR6Ft0fzwuB/6Q7eWSnUx0juku/r2vppg4/ya1HuN5D90Kx0zcVfUD8DN3o2elDi3alrxbyfOBPkmylO570jN3Yl/YuW+kOJP9curPNP0s3evSSfvk76J6rnwL+L93fzRcAVNU1dM+dt9E9D7cBw2c/frD/96ok57FE/VT84+iOWz6Hrpc+Tzed97mqupDueODP0AW9B7Dwxw/NW/Dx7Kqq2ko3CHIG3d+QY+hGkqeKHy8kaa+X5LZ0Z0g9pKq+ttz1SNLOOPIlaRo8D/iCwUvS3sAr1EraqyXZQncwrtfSkrRXcNpRkiSpIacdJUmSGprItOPBBx9cs7Ozk9j1irRt2zbWrNnhpVm0izZv3nxlVW3/iQbN2SvjZ7+Ml70yveyV8Ru1XyYSvmZnZ9m0adMkdr0iDQYD1q5du9xlTJUk239ywbKwV8bPfhkve2V62SvjN2q/OO0oSZLUkOFLkiSpoZGmHftTubcCNwM3VdXcJIuSJEmaVks55mvdIh+rI0mSpEU47ShJktTQqOGrgLOTbE5y3CQLkiRJmmajTjs+qqouTXJn4JwkF1fVp4ZX6EPZ8cDMzMwMg8FgzKVOv3Xr1u3Sdhs3bhxzJZoke2X32Ssrg70yHrvSL/bKZC3544WSvAq4rqr+YqF15ubmyuuxjM/siRvYsv7o5S5jqiTZvCecOGKvjJ/9Ml72yvSyV8Zv1H5ZdNoxyZokB85/Dzwe+PLulyhJkrTyjDLteAjw4STz67+vqs6aaFWSJElTatHwVVXfBB7UoBZJkqSp56UmJEmSGjJ8SZIkNWT4kiRJasjwJUmS1JDhS5IkqSHDlyRJUkOGL0mSpIYMX5IkSQ0ZviRJkhoyfEmSJDVk+JIkSWrI8CVJktSQ4UuSJKkhw5ckSVJDhi9JkqSGDF+SJEkNGb4kSZIaMnxJkiQ1ZPiSJElqyPAlSZLUkOFLkiSpIcOXJElSQ4YvSZKkhgxfkiRJDRm+JEmSGjJ8SZIkNWT4kiRJamjk8JVkVZLzk5w5yYIkSZKm2VJGvl4IXDSpQiRJklaCkcJXkrsDRwNvm2w5kiRJ023Uka83AicAP5pgLZIkSVNv9WIrJHkScEVVbU6ydifrHQccD8zMzMwwGAzGVuS0+f2Pb2PbjUvbZvbEDUtaf82+8JbHrlnanagJe2V0u9IrsLR+sVf2XPbK6Fr0CtgvY1NVO/0C/hz4NrAFuBz4AXDazrZ56EMfWlrY4S87c0nrb9y4ceL3sdIAm2qR536LL3tl53blebzUfrFXds5e2Tu06JVdvZ+VZNR+WXTasapeXlV3r6pZ4KnAJ6rqGZOJgpIkSdPN63xJkiQ1tOgxX8OqagAMJlKJJEnSCuDIlyRJUkOGL0mSpIYMX5IkSQ0ZviRJkhoyfEmSJDVk+JIkSWrI8CVJktSQ4UuSJKkhw5ckSVJDhi9JkqSGDF+SJEkNGb4kSZIaMnxJkiQ1tHq5C1iJDrzPiTzg1BOXttGpS70PgKOXtpG0h9mlXoEl9Yu9omnQole6+wH7ZfcZvpbB1ovWs2X96E/ewWDA2rVrl3QfsyduWGJV0p5nqb0CS+8Xe0XToEWvgP0yLk47SpIkNWT4kiRJasjwJUmS1JDhS5IkqSHDlyRJUkOGL0mSpIYMX5IkSQ0ZviRJkhoyfEmSJDVk+JIkSWrI8CVJktSQ4UuSJKmhRcNXkv2TfD7Jl5J8JcmrWxQmSZI0jVaPsM4NwGOq6rok+wLnJvloVX12wrVJkiRNnUXDV1UVcF3/4779V02yKEmSpGk10jFfSVYl+SJwBXBOVX1usmVJkiRNp1GmHamqm4FfSDIDfDjJ/avqy8PrJDkOOB6YmZmZYTAYjL3YaTJ74oalbXDW0tZfsy/+H+yh7JWlWXKvwJL6xV7Zc9krSzM5W2nGAAATzUlEQVTpXgH7ZVzSzSouYYPkFcAPquovFlpnbm6uNm3atLu1qTd74ga2rD96ucuYKkk2V9Xcctdhr4yf/TJe9sr0slfGb9R+GeVsxzv1I14kuS1wBHDx7pcoSZK08owy7XhX4NQkq+jC2hlVdeZky5IkSZpOo5zteAHw4Aa1SJIkTT2vcC9JktSQ4UuSJKkhw5ckSVJDhi9JkqSGDF+SJEkNGb4kSZIaMnxJkiQ1ZPiSJElqyPAlSZLUkOFLkiSpIcOXJElSQ4YvSZKkhgxfkiRJDRm+JEmSGjJ8SZIkNWT4kiRJasjwJUmS1JDhS5IkqSHDlyRJUkOGL0mSpIYMX5IkSQ0ZviRJkhoyfEmSJDVk+JIkSWrI8CVJktSQ4UuSJKkhw5ckSVJDi4avJIcl2ZjkwiRfSfLCFoVJkiRNo9UjrHMT8JKqOi/JgcDmJOdU1YUTrk2SJGnqLDryVVXfqarz+u+3AhcBd5t0YZIkSdNoScd8JZkFHgx8bhLFSJIkTbtRph0BSHIA8CHgRVV17Q6WHwccD8zMzMwwGAzGVqTw9zlF7JXJ83c6HeyVyfN3ujxSVYuvlOwLnAl8rKresNj6c3NztWnTpjGUJ4DZEzewZf3Ry13GVEmyuarmlrsOe2X87Jfxsleml70yfqP2yyhnOwZ4O3DRKMFLkiRJCxvlmK9HAs8EHpPki/3XUROuS5IkaSotesxXVZ0LpEEtkiRJU88r3EuSJDVk+JIkSWrI8CVJktSQ4UuSJKkhw5ckSVJDhi9JkqSGDF+SJEkNGb4kSZIaMnxJkiQ1ZPiSJElqyPAlSZLUkOFLkiSpIcOXJElSQ6uXuwD9WJKFl71u4e2qagLVSHuunfUKLNwv9opWol15bbFXJsuRrz1IVe3wa+PGjQsus0G0Eu2sH3bWL9JKZK/seQxfkiRJDRm+JEmSGjJ8SZIkNWT4kiRJasjwJUmS1JDhS5IkqSHDlyRJUkOGL0mSpIYMX5IkSQ0ZviRJkhoyfEmSJDVk+JIkSWpo0fCV5B1Jrkjy5RYFSZIkTbPVI6zzLuDNwLsnW4q2t//++3PDDTfc8vN+++3H9ddfv4wVSZKk3bXoyFdVfQr4foNaNGQ+eB1yyCG8853v5JBDDuGGG25g//33X+7SJEnSbvCYrz3UfPC6/PLLmZ2d5fLLL78lgEmSpL3XKNOOI0lyHHA8MDMzM8NgMBjXrles9evXMxgMuO666xgMBqxfv55jjz3W3+1ezl6ZrPl+0d7PXpkse2X5pKoWXymZBc6sqvuPstO5ubnatGnT7lW2wiW5ZeRrMBiwdu1a7nKXu/Dd736XUf7PtHNJNlfV3HLXYa+M33y/aDzslellr4zfqP3itOMear/99uO73/0ud7nLXdiyZcstwWu//fZb7tIkSdJuGOVSE+8HPgPcO8m3k/zO5MvS9ddff0sAO/bYY28JXp7tKEnS3m3RY76q6mktCtFPmg9aDg1LkjQ9nHaUJElqyPAlSZLU0EhnOy55p8n3gEvGvuOV62DgyuUuYsocXlV3Wu4i7JWJsF/Gy16ZXvbK+I3ULxMJXxqvJJv2hFO9pb2B/SKNxl5ZPk47SpIkNWT4kiRJasjwtXc4ZbkLkPYi9os0GntlmXjMlyRJUkOOfEmSJDVk+JIkSWrI8LUHSfKiJLdb7jqkPZ29Io3GXtkzeczXHiTJFmCuqka+6F2SVVV18+SqkvY89oo0Gntlz7ToB2trMpKsAc4A7g6sAj4IHApsTHJlVa1LcjLwMOC2wN9X1Sv7bbcApwNHAK9PcmfgucBNwIVV9dTWj0eaFHtFGo29svcwfC2fJwCXVdXRAEkOAo4F1g29Q/mjqvp+klXAx5M8sKou6JddVVUP6be9DLhHVd2QZKbx45AmzV6RRmOv7CU85mv5/BtwRJLXJXl0VV2zg3WekuQ84HzgfsB9h5adPvT9BcB7kzyD7l2KNE3sFWk09spewvC1TKrqq8BD6JrlNUleMbw8yT2AlwKPraoHAhuA/YdW2Tb0/dHAW/r9fSGJI5qaGvaKNBp7Ze9h+FomSQ4FflBVpwEn0T3BtwIH9qvcnq4RrklyCPDEBfazD3BYVW0EXgYcBBww4fKlZuwVaTT2yt7DJLt8HgCclORHwI3A84CHA2cluaw/MPJ84GLgW8CnF9jPKuC0fm4/wJuq6urJly81Y69Io7FX9hJeamKCklwHPLCqvrnctcxL8ofAz1TV7y53LZIkrUSGryVI8nLgl6vqiUO3fQ34+g5u+59V9YFlKFOSJO3BPOZraT4FPKI/RZckdwX2BR683W337NeVJEm6FcPX0nyBLmz9Qv/zo4GNwL9vd9s3quqyJJXkngBJjkpyYZKtSS5N8tL5nSb5tSRfTHJtkm8keUJ/+6FJ/inJ95N8PcnvDW3zqiRnJHl3v8+vJJkbWv6y/n62Jvn3JI8d2u60/vvZvsZnJ/mPJFcm+aNJ/fIkSZLha0mq6v8BnwN+ub/pl4F/Ac7d7rYdjXq9HfhvVXUgcH/gEwBJfhF4N3A8MNNvv6Xf5gPAt+muUPxfgT9L8pihfT65X2cG+Cfgzf0+7w38AfCw/v6OHNrnjjwKuDfwWOAVSe6z01+EJEnaZYavpfskPw5aj6YLX/+y3W2f3MF2NwL3TXL7qvrPqjqvv/13gHdU1TlV9aOqurSqLk5yGPBI4GVVdX1VfRF4G/CsoX2eW1Uf6T+D6z3Ag/rbbwb26+9v36raUlXf2MljenVV/bCqvgR8aWg/kiRpzAxfS/cp4FFJ7gDcqaq+Bvwr3bFgd6Ab1drRyNd/AY4CLknyySQP728/DNhRMDoU+H5VbR267RLgbkM/Xz70/Q+A/ZOsrqqvAy8CXgVckeQD/fVfFrL9fryeiyRJE2L4WrrP0F1w7vfor5FSVdcCl/W3XVZV/3f7jarqC1X1a8Cdgf9N9+Gn0F1r5Wd3cD+XAXdIcuDQbT8NXDpKkVX1vqp6FHA4UMDrRtlOkiRNluFriarqh8Am4MV0043zzu1v+4lRryS3SfL0JAdV1Y3AtcCP+sVvB45N8tgk+yS5W5Kfr6pv0Y2o/XmS/ZM8kG6K8rTFakxy7ySPSbIfcD3ww6H7kyRJy8jwtWs+STeCde7Qbf/S37bQJSaeCWxJci3wXODpAFX1ebpPnf8r4Jp+34f32zwNmKUbBfsw8Mqq+j8j1LcfsB64km5K8c7Ay0d7aJIkaZK8yKokSVJDjnxJkiQ1ZPiSJElqyPAlSZLUkOFLkiSpIcOXJElSQ6snsdODDz64ZmdnJ7HrFWnbtm2sWbNmucuYKps3b76yqu603HVIklaeiYSv2dlZNm3aNIldr0iDwYC1a9cudxlTJckly12DJGllctpRkiSpIcOXJElSQyNNOybZAmwFbgZuqqq5SRYlSZI0rZZyzNe6qrpyYpVIkiStAE47SpIkNTTqyFcBZycp4G+r6pTtV0hyHHA8MDMzM8NgMBhflSvEunXrdmm7jRs3jrkSSZI0KamqxVdK7lZVlya5M3AO8IKq+tRC68/NzZWXmhif2RM3sGX90ctdxlRJstljFyVJy2GkacequrT/9wrgw8AvTrIoSZKkabVo+EqyJsmB898Djwe+POnCJEmSptEox3wdAnw4yfz676uqsyZalSRJ0pRaNHxV1TeBBzWoRZIkaep5qQlJkqSGDF+SJEkNGb4kSZIaMnxJkiQ1ZPiSJElqyPAlSZLUkOFLkiSpIcOXJElSQ4YvSZKkhgxfkiRJDRm+JEmSGjJ8SZIkNWT4kiRJasjwJUmS1JDhS5IkqSHDlyRJUkOGL0mSpIYMX5IkSQ0ZviRJkhoyfEmSJDVk+JIkSWrI8CVJktSQ4UuSJKkhw5ckSVJDhi9JkqSGDF+SJEkNGb4kSZIaGjl8JVmV5PwkZ06yIEmSpGm2lJGvFwIXTaoQSZKklWCk8JXk7sDRwNsmW44kSdJ0Wz3iem8ETgAOXGiFJMcBxwMzMzMzDAaD3a9uSv3+x7ex7calbTN74oYlrb9mX3jLY9cs7U4kSdLELRq+kjwJuKKqNidZu9B6VXUKcArA3NxcrV274Kor3razNrBl/dEjrz8YDFjq73P2xA1L3kaSJE3eKNOOjwSenGQL8AHgMUlOm2hVkiRJU2rR8FVVL6+qu1fVLPBU4BNV9YyJVyZJkjSFvM6XJElSQ6MecA9AVQ2AwUQqkSRJWgEc+ZIkSWrI8CVJktSQ4UuSJKkhw5ckSVJDhi9JkqSGDF+SJEkNGb4kSZIaMnxJkiQ1ZPiSJElqyPAlSZLUkOFLkiSpIcOXJElSQ0v6YG2Nx4H3OZEHnHri0jY6dan3AXD00jaSJEkTZ/haBlsvWs+W9aMHo8FgwNq1a5d0H7MnblhiVZIkqQWnHSVJkhoyfEmSJDVk+JIkSWrI8CVJktSQ4UuSJKkhw5ckSVJDhi9JkqSGDF+SJEkNGb4kSZIaMnxJkiQ1ZPiSJElqyPAlSZLUkOFLkiSpoUXDV5L9k3w+yZeSfCXJq1sUJkmSNI1Wj7DODcBjquq6JPsC5yb5aFV9dsK1SZIkTZ1Fw1dVFXBd/+O+/VdNsihJkqRpNcrIF0lWAZuBewJvqarP7WCd44DjgZmZmRkGg8E465w6syduWNoGZy1t/TX74v+BJEl7oHQDWyOunMwAHwZeUFVfXmi9ubm52rRp0xjKE3RBbcv6o5e7jKmSZHNVzS13HZKklWdJZztW1dXARuAJkylHkiRpuo1ytuOd+hEvktwWOAK4eNKFSZIkTaNRjvm6K3Bqf9zXPsAZVXXmZMuSJEmaTqOc7XgB8OAGtUiSJE09r3AvSZLUkOFLkiSpIcOXJElSQ4YvSZKkhgxfkiRJDRm+JEmSGjJ8SZIkNWT4kiRJasjwJUmS1JDhS5IkqSHDlyRJUkOGL0mSpIYMX5IkSQ0ZviRJkhoyfEmSJDVk+JIkSWrI8CVJktSQ4UuSJKkhw5ckSVJDhi9JkqSGDF+SJEkNGb4kSZIaMnxJkiQ1ZPiSJElqyPAlSZLUkOFLkiSpIcOXJElSQ4uGrySHJdmY5MIkX0nywhaFSZIkTaPVI6xzE/CSqjovyYHA5iTnVNWFE65NkiRp6iw68lVV36mq8/rvtwIXAXebdGGSJEnTaJSRr1skmQUeDHxuB8uOA44HZmZmZhgMBmMoT/P8fUqSNB1GDl9JDgA+BLyoqq7dfnlVnQKcAjA3N1dr164dV406awP+PiVJmg4jne2YZF+64PXeqvqHyZYkSZI0vUY52zHA24GLquoNky9JkiRpeo0y8vVI4JnAY5J8sf86asJ1SZIkTaVFj/mqqnOBNKhFkiRp6nmFe0mSpIYMX5IkSQ0ZviRJkhoyfEmSJDVk+JIkSWrI8CVJktSQ4UuSJKkhw5ckSVJDhi9JkqSGDF+SJEkNGb4kSZIaMnxJkiQ1ZPiSJElqaPVyF6AfS7LwstctvF1VTaAaSZI0CY587UGqaodfGzduXHCZwUuSpL2L4UuSJKkhw5ckSVJDhi9JkqSGDF+SJEkNGb4kSZIaMnxJkiQ1ZPiSJElqyPAlSZLUkOFLkiSpIcOXJElSQ4YvSZKkhgxfkiRJDS0avpK8I8kVSb7coiBJkqRpNsrI17uAJ0y4Du3AkUceyT777MO6devYZ599OPLII5e7JEmStJsWDV9V9Sng+w1q0ZAjjzySs88+m+c+97n88z//M8997nM5++yzDWCSJO3lVi93Adqxc845h+c973m89a1vZTAY8Na3vhWAv/mbv1nmyiRJ0u4YW/hKchxwPDAzMzPDYDAY165XpKriqKOOYjAYcN111zEYDDjqqKM4+eST/d1KkrQXG1v4qqpTgFMA5ubmau3atePa9YqUhI985CO3jHytXbuW5z//+STB360kSXsvpx33UEcccQQnn3wyAEcddRTPf/7zOfnkk3n84x+/zJVJkqTdkara+QrJ+4G1wMHAd4FXVtXbd7bN3Nxcbdq0aVw1rlhHHnkk55xzDlVFEo444gg+9rGPLXdZUyHJ5qqaW+46JEkrz6IjX1X1tBaF6CfNB635aUdJkrT38wr3kiRJDRm+JEmSGlr0mK9d2mnyPeCSse945ToYuHK5i5gyh1fVnZa7CEnSyjOR8KXxSrLJg8MlSZoOTjtKkiQ1ZPiSJElqyPC1dzhluQuQJEnj4TFfkiRJDTnyJUmS1JDhaw+S5EVJbrfcdUiSpMlx2nEPkmQLMFdVI1/TK8mqqrp5clVJkqRxWvSzHTUZSdYAZwB3B1YBHwQOBTYmubKq1iU5GXgYcFvg76vqlf22W4DTgSOA1ye5M/Bc4Cbgwqp6auvHI0mSRmP4Wj5PAC6rqqMBkhwEHAusGxr5+qOq+n6SVcDHkzywqi7ol11VVQ/pt70MuEdV3ZBkpvHjkCRJS+AxX8vn34AjkrwuyaOr6podrPOUJOcB5wP3A+47tOz0oe8vAN6b5Bl0o1+SJGkPZfhaJlX1VeAhdCHsNUleMbw8yT2AlwKPraoHAhuA/YdW2Tb0/dHAW/r9fSGJI5qSJO2hDF/LJMmhwA+q6jTgJLrgtBU4sF/l9nQB65okhwBPXGA/+wCHVdVG4GXAQcABEy5fkiTtIkdIls8DgJOS/Ai4EXge8HDgrCSX9Qfcnw9cDHwL+PQC+1kFnNYfMxbgTVV19eTLlyRJu8JLTUiSJDXktKMkSVJDhi9JkqSGDF+SJEkNGb4kSZIaMnxJkiQ1ZPiSJElqyPAlSZLUkOFLkiSpof8faY2nZCwQo0EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yelp.groupby('state').boxplot(column='stars', return_type='axes', figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further explore groupings by looking for top foods by state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Foods\n",
      "Arizona:\n",
      "1. Mexican, 2. Pizza, 3. Food\n",
      "\n",
      "Illinois:\n",
      "1. Mexican, 2. Pizza, 3. Chinese\n",
      "\n",
      "Nevada:\n",
      "1. Mexican, 2. Pizza, 3. Food\n",
      "\n",
      "North Carolina:\n",
      "1. Mexican, 2. Sandwiches, 3. American (Traditional)\n",
      "\n",
      "Pennsylvania:\n",
      "1. Pizza, 2. Food, 3. Sandwiches\n",
      "\n",
      "South Carolina:\n",
      "1. American (New), 2. Mexican, 3. Chinese\n",
      "\n",
      "Wisconsin:\n",
      "1. Food, 2. Pizza, 3. Mexican\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Top Foods')\n",
    "for key, group in yelp[yelp.stars >=4].groupby('state'):\n",
    "    top = list(group.category.value_counts().index)[:3]\n",
    "    print('{}:'.format(key))\n",
    "    print('1. {}, 2. {}, 3. {}\\n'.format(top[0], top[1], top[2]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}