Yelp Prices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: oauthlib in c:\\users\\zoshihyang\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: requests_oauthlib in c:\\users\\zoshihyang\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: oauthlib>=0.6.2 in c:\\users\\zoshihyang\\anaconda3\\lib\\site-packages (from requests_oauthlib)\n",
      "Requirement already satisfied: requests>=2.0.0 in c:\\users\\zoshihyang\\anaconda3\\lib\\site-packages (from requests_oauthlib)\n"
     ]
    }
   ],
   "source": [
    "import requests as req\n",
    "!pip install oauthlib\n",
    "!pip install requests_oauthlib\n",
    "from oauthlib.oauth2 import MobileApplicationClient\n",
    "from requests_oauthlib import OAuth2Session\n",
    "from oauthlib.oauth2 import BackendApplicationClient\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "url = \"https://api.yelp.com/v3/businesses/search\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "client_id = 'G5xpoUQV8dUq8bL2KQZGnQ'\n",
    "client_secret = 'mHLQsY6gyBiuT6YviJYzFazV7i4WGQR28PLhWzh21AmYbgQJV0lfyfVhkrMIONi5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCESS TOKEN: 7pJig_NPk4rUhim9wWkULRt0-7RFCdJmPJL7W8PmfUlQHOZMyH3UFxInvm7l4u2-KeT7eChQ1D-6GiN-GJnnI4cszIxIXV0GDNwMUmYnkyWkZzpAQ5pMBFDqbr6hWXYx\n"
     ]
    }
   ],
   "source": [
    "client = BackendApplicationClient(client_id=client_id)\n",
    "oauth = OAuth2Session(client=client)\n",
    "token = oauth.fetch_token(token_url='https://api.yelp.com/oauth2/token', client_id=client_id,\n",
    "        client_secret=client_secret)\n",
    "print(\"ACCESS TOKEN: \" + token['access_token'])\n",
    "tok = token['access_token']\n",
    "# url_params = {\n",
    "#               'term': 'deli', \n",
    "#               'location': 'Mission District, San Francisco',\n",
    "#               'limit': 50\n",
    "#              }\n",
    "# url = \"https://api.yelp.com/v3/businesses/search\"\n",
    "# headers = {\n",
    "#     'Authorization': 'Bearer %s' % tok,\n",
    "# # }\n",
    "# busn = req.get(url, headers=headers, params=url_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print(busn.json())\n",
    "# dels = busn.json()\n",
    "\n",
    "# for x in dels['businesses']:\n",
    "#     print(json.dumps(x['name'], sort_keys=True, indent=4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Castro District': {'$$': 313, '$': 102, '$$$$': 11, '$$$': 48}, 'Chinatown': {'$': 102, '$$': 309, '$$$': 74, '$$$$': 12}, 'Ternderloin': {'$$': 297, '$': 132, '$$$': 45, '$$$$': 20}, 'Inner Richmond': {'$$': 326, '$': 112, '$$$': 44, '$$$$': 14}, 'Inner Sunset': {'$$': 233, '$': 147, '$$$': 10, '$$$$': 2}, 'Alamo Square': {'$': 69, '$$': 241, '$$$$': 21, '$$$': 35}, 'Russian Hill': {'$$': 300, '$': 126, '$$$$': 16, '$$$': 49}, 'Mission District': {'$': 109, '$$': 310, '$$$': 58, '$$$$': 18}, 'NorthBeach/Telegraph': {'$$': 219, '$': 113, '$$$$': 14, '$$$': 46}, 'SoMa': {'$$': 295, '$': 124, '$$$': 50, '$$$$': 22}}\n"
     ]
    }
   ],
   "source": [
    "Districts = [\"Castro District\", \"Chinatown\", \"Ternderloin\", \"Inner Richmond\", \"Inner Sunset\", \"Alamo Square\", \"Russian Hill\", \"Mission District\", \"NorthBeach/Telegraph\", \"SoMa\"]\n",
    "\n",
    "types = [\"Chinese\", \"Mexican\", \"French\", \"Japanese\", \"Mediterranean\", \"American\", \"Italian\", \"Korean\", \"Thai\", \"Indian\"]\n",
    "\n",
    "priceByType = {}\n",
    "priceByHood = {}\n",
    "\n",
    "for x in Districts:\n",
    "    districts = {}\n",
    "    for y in types:\n",
    "        \n",
    "        url_params = {\n",
    "            'term': y,\n",
    "            'location': x,\n",
    "            'limit': 50\n",
    "        }\n",
    "        \n",
    "        headers = {\n",
    "            'Authorization': 'Bearer %s' % tok\n",
    "        }\n",
    "        \n",
    "        call = req.get(url, headers=headers, params=url_params)\n",
    "        jcall = call.json()\n",
    "        for price in jcall['businesses']:\n",
    "            try:\n",
    "                money = price['price']\n",
    "                if money not in districts:\n",
    "                    districts[money]=1\n",
    "                else:\n",
    "                    districts[money]+=1\n",
    "            except:\n",
    "                continue\n",
    "    priceByHood[x] = districts\n",
    "\n",
    "print(priceByHood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Chinese': {'$$': 238, '$': 234, '$$$': 9, '$$$$': 6}, 'Mexican': {'$$': 230, '$': 240, '$$$$': 4, '$$$': 16}, 'French': {'$$$': 153, '$$': 200, '$$$$': 43, '$': 25}, 'Japanese': {'$$': 356, '$$$': 49, '$$$$': 54, '$': 36}, 'Mediterranean': {'$': 140, '$$': 202, '$$$': 36}, 'American': {'$$$': 60, '$$': 350, '$': 68, '$$$$': 14}, 'Italian': {'$$$': 79, '$$': 343, '$': 65, '$$$$': 12}, 'Korean': {'$$': 349, '$': 93, '$$$$': 13, '$$$': 24}, 'Thai': {'$$': 360, '$': 108, '$$$': 8}, 'Indian': {'$$': 216, '$': 127, '$$$': 24, '$$$$': 4}}\n"
     ]
    }
   ],
   "source": [
    "for x in types:\n",
    "    types = {}\n",
    "    for y in Districts:\n",
    "        \n",
    "        url_params = {\n",
    "            'term': x,\n",
    "            'location': y,\n",
    "            'limit': 50\n",
    "        }\n",
    "        \n",
    "        headers = {\n",
    "            'Authorization': 'Bearer %s' % tok\n",
    "        }\n",
    "        \n",
    "        call = req.get(url, headers=headers, params=url_params)\n",
    "        jcall = call.json()\n",
    "        for price in jcall['businesses']:\n",
    "            try:\n",
    "                money = price['price']\n",
    "                if money not in types:\n",
    "                    types[money]=1\n",
    "                else:\n",
    "                    types[money]+=1\n",
    "            except:\n",
    "                continue\n",
    "    priceByType[x] = types\n",
    "\n",
    "print(priceByType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Cheap</th>\n",
       "      <th>Not Expensive</th>\n",
       "      <th>Expensive</th>\n",
       "      <th>Super Expensive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alamo Square</td>\n",
       "      <td>69</td>\n",
       "      <td>241</td>\n",
       "      <td>35</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Castro District</td>\n",
       "      <td>102</td>\n",
       "      <td>313</td>\n",
       "      <td>48</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinatown</td>\n",
       "      <td>102</td>\n",
       "      <td>309</td>\n",
       "      <td>74</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inner Richmond</td>\n",
       "      <td>112</td>\n",
       "      <td>326</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inner Sunset</td>\n",
       "      <td>147</td>\n",
       "      <td>233</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mission District</td>\n",
       "      <td>109</td>\n",
       "      <td>310</td>\n",
       "      <td>58</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NorthBeach/Telegraph</td>\n",
       "      <td>113</td>\n",
       "      <td>219</td>\n",
       "      <td>46</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Russian Hill</td>\n",
       "      <td>126</td>\n",
       "      <td>300</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SoMa</td>\n",
       "      <td>124</td>\n",
       "      <td>295</td>\n",
       "      <td>50</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ternderloin</td>\n",
       "      <td>132</td>\n",
       "      <td>297</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  index  Cheap  Not Expensive  Expensive  Super Expensive\n",
       "0          Alamo Square     69            241         35               21\n",
       "1       Castro District    102            313         48               11\n",
       "2             Chinatown    102            309         74               12\n",
       "3        Inner Richmond    112            326         44               14\n",
       "4          Inner Sunset    147            233         10                2\n",
       "5      Mission District    109            310         58               18\n",
       "6  NorthBeach/Telegraph    113            219         46               14\n",
       "7          Russian Hill    126            300         49               16\n",
       "8                  SoMa    124            295         50               22\n",
       "9           Ternderloin    132            297         45               20"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "priceByHoodDF=pd.DataFrame.from_records(priceByHood).reset_index().rename(columns={'index':'Hood'}).set_index('Hood').T\n",
    "priceByHoodDFIndex = priceByHoodDF.index.values\n",
    "priceByHoodDF2=priceByHoodDF.reset_index()\n",
    "priceByHoodDF2.columns = ['index','Cheap','Not Expensive','Expensive','Super Expensive']\n",
    "priceByHoodDF2\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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///nPu1UmERGRbKN7cZaxDz74gCVLlhTNL1y4kAMPPBAIfdDmz58PwBNPPLHN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\nVSqVSqVSqTSM6qBVKpVKpVKpNIz/B/nzQcMGIG6vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27eeaf55e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the positions and width for the bars\n",
    "\n",
    "pos = list(range(len(priceByHoodDF2['Cheap'])))\n",
    "width = 0.20\n",
    "\n",
    "# Plotting the bars\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "\n",
    "# fig=plt.figure(1,[15,6])\n",
    "# ax = fig.add_subplot(111)\n",
    "\n",
    "# Create a bar with pre_score data,\n",
    "# in position pos,\n",
    "plt.bar(pos,\n",
    "        #using df['pre_score'] data,\n",
    "        priceByHoodDF2['Cheap'],\n",
    "        # of width\n",
    "        width,\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#EE3224',\n",
    "        # with label the first value in first_name\n",
    "        label=priceByHoodDF2['index'][0])\n",
    "\n",
    "# Create a bar with mid_score data,\n",
    "# in position pos + some width buffer,\n",
    "plt.bar([p + width for p in pos],\n",
    "        #using df['mid_score'] data,\n",
    "        priceByHoodDF2['Not Expensive'],\n",
    "        # of width\n",
    "        width,\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#F78F1E',\n",
    "        # with label the second value in first_name\n",
    "        label=priceByHoodDF2['index'][1])\n",
    "\n",
    "# Create a bar with post_score data,\n",
    "# in position pos + some width buffer,\n",
    "plt.bar([p + width*2 for p in pos],\n",
    "        #using df['post_score'] data,\n",
    "        priceByHoodDF2['Expensive'],\n",
    "        # of width\n",
    "        width,\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#FFC222',\n",
    "        # with label the third value in first_name\n",
    "        label=priceByHoodDF2['index'][2])\n",
    "\n",
    "plt.bar([p + width*3 for p in pos],\n",
    "        #using df['post_score'] data,\n",
    "        priceByHoodDF2['Super Expensive'],\n",
    "        # of width\n",
    "        width,\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#cdaf95',\n",
    "        # with label the third value in first_name\n",
    "        label=priceByHoodDF2['index'][3])\n",
    "\n",
    "\n",
    "\n",
    "# Set the y axis label\n",
    "ax.set_ylabel('Price Popularity')\n",
    "\n",
    "# Set the chart's title\n",
    "ax.set_title('Expensiveness vs Neighbourhoods')\n",
    "\n",
    "# Set the position of the x ticks\n",
    "ax.set_xticks([p + 1.5 * width for p in pos])\n",
    "\n",
    "# Set the labels for the x ticks\n",
    "ax.set_xticklabels(priceByHoodDF2['index'])\n",
    "\n",
    "for label in ax.get_xticklabels():\n",
    "    label.set_rotation(45) \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Setting the x-axis and y-axis limits\n",
    "plt.xlim(min(pos)-width, max(pos)+width*4)\n",
    "plt.ylim([0, max(priceByHoodDF2['Cheap'] + priceByHoodDF2['Not Expensive'] + priceByHoodDF2['Expensive'] + priceByHoodDF2['Super Expensive'])] )\n",
    "\n",
    "# Adding the legend and showing the plot\n",
    "plt.legend(['Cheap', 'Not Expensive', 'Expensive','Super Expensive'], loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Cheap</th>\n",
       "      <th>Not Expensive</th>\n",
       "      <th>Expensive</th>\n",
       "      <th>Super Expensive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>American</td>\n",
       "      <td>68.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>234.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>French</td>\n",
       "      <td>25.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indian</td>\n",
       "      <td>127.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Italian</td>\n",
       "      <td>65.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Japanese</td>\n",
       "      <td>36.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Korean</td>\n",
       "      <td>93.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>140.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mexican</td>\n",
       "      <td>240.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Thai</td>\n",
       "      <td>108.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Type  Cheap  Not Expensive  Expensive  Super Expensive\n",
       "0       American   68.0          350.0       60.0             14.0\n",
       "1        Chinese  234.0          238.0        9.0              6.0\n",
       "2         French   25.0          200.0      153.0             43.0\n",
       "3         Indian  127.0          216.0       24.0              4.0\n",
       "4        Italian   65.0          343.0       79.0             12.0\n",
       "5       Japanese   36.0          356.0       49.0             54.0\n",
       "6         Korean   93.0          349.0       24.0             13.0\n",
       "7  Mediterranean  140.0          202.0       36.0              NaN\n",
       "8        Mexican  240.0          230.0       16.0              4.0\n",
       "9           Thai  108.0          360.0        8.0              NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "priceByTypeDF=pd.DataFrame.from_records(priceByType).reset_index().rename(columns={'index':'Type'}).set_index('Type').T.reset_index()\n",
    "priceByTypeDF.columns = ['Type','Cheap','Not Expensive','Expensive','Super Expensive']\n",
    "priceByTypeDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WVhbHHXccX3zxBa+//jqzZ8/miiuuoG3btnzyySdJvc+ECRMYOnQoAO+++y6tW7fmxx9/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\nmvH/y+jK11XENxEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27eeaf69358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the positions and width for the bars\n",
    "\n",
    "pos = list(range(len(priceByTypeDF['Cheap'])))\n",
    "width = 0.20\n",
    "\n",
    "# Plotting the bars\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "\n",
    "# fig=plt.figure(1,[15,6])\n",
    "# ax = fig.add_subplot(111)\n",
    "\n",
    "# Create a bar with pre_score data,\n",
    "# in position pos,\n",
    "plt.bar(pos,\n",
    "        #using df['pre_score'] data,\n",
    "        priceByTypeDF['Cheap'],\n",
    "        # of width\n",
    "        width,\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#EE3224',\n",
    "        # with label the first value in first_name\n",
    "        label=priceByTypeDF['Type'][0])\n",
    "\n",
    "# Create a bar with mid_score data,\n",
    "# in position pos + some width buffer,\n",
    "plt.bar([p + width for p in pos],\n",
    "        #using df['mid_score'] data,\n",
    "        priceByTypeDF['Not Expensive'],\n",
    "        # of width\n",
    "        width,\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#F78F1E',\n",
    "        # with label the second value in first_name\n",
    "        label=priceByTypeDF['Type'][1])\n",
    "\n",
    "# Create a bar with post_score data,\n",
    "# in position pos + some width buffer,\n",
    "plt.bar([p + width*2 for p in pos],\n",
    "        #using df['post_score'] data,\n",
    "        priceByTypeDF['Expensive'],\n",
    "        # of width\n",
    "        width,\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#FFC222',\n",
    "        # with label the third value in first_name\n",
    "        label=priceByTypeDF['Type'][2])\n",
    "\n",
    "plt.bar([p + width*3 for p in pos],\n",
    "        #using df['post_score'] data,\n",
    "        priceByTypeDF['Super Expensive'],\n",
    "        # of width\n",
    "        width,\n",
    "        # with alpha 0.5\n",
    "        alpha=0.5,\n",
    "        # with color\n",
    "        color='#cdaf95',\n",
    "        # with label the third value in first_name\n",
    "        label=priceByTypeDF['Type'][3])\n",
    "\n",
    "\n",
    "\n",
    "# Set the y axis label\n",
    "ax.set_ylabel('Price Popularity')\n",
    "\n",
    "# Set the chart's title\n",
    "ax.set_title('Expensiveness vs Restaurant Types')\n",
    "\n",
    "# Set the position of the x ticks\n",
    "ax.set_xticks([p +1.5 * width for p in pos])\n",
    "\n",
    "# Set the labels for the x ticks\n",
    "ax.set_xticklabels(priceByTypeDF['Type'])\n",
    "\n",
    "for label in ax.get_xticklabels():\n",
    "    label.set_rotation(45) \n",
    "\n",
    "\n",
    "\n",
    "# Setting the x-axis and y-axis limits\n",
    "plt.xlim(min(pos)-width, max(pos)+width*4)\n",
    "plt.ylim([0, max(priceByTypeDF['Cheap'] + priceByTypeDF['Not Expensive'] + priceByTypeDF['Expensive'] + priceByTypeDF['Super Expensive'])] )\n",
    "\n",
    "# Adding the legend and showing the plot\n",
    "plt.legend(['Cheap', 'Not Expensive', 'Expensive','Super Expensive'], loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}