2-2-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例2-2. Yelp データセット内の店舗に対するレビュー件数の可視化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>categories</th>\n",
       "      <th>city</th>\n",
       "      <th>full_address</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>name</th>\n",
       "      <th>neighborhoods</th>\n",
       "      <th>open</th>\n",
       "      <th>review_count</th>\n",
       "      <th>stars</th>\n",
       "      <th>state</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rncjoVoEFUJGCUoC1JgnUA</td>\n",
       "      <td>[Accountants, Professional Services, Tax Servi...</td>\n",
       "      <td>Peoria</td>\n",
       "      <td>8466 W Peoria Ave\\nSte 6\\nPeoria, AZ 85345</td>\n",
       "      <td>33.581867</td>\n",
       "      <td>-112.241596</td>\n",
       "      <td>Peoria Income Tax Service</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AZ</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0FNFSzCFP_rGUoJx8W7tJg</td>\n",
       "      <td>[Sporting Goods, Bikes, Shopping]</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>2149 W Wood Dr\\nPhoenix, AZ 85029</td>\n",
       "      <td>33.604054</td>\n",
       "      <td>-112.105933</td>\n",
       "      <td>Bike Doctor</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AZ</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3f_lyB6vFK48ukH6ScvLHg</td>\n",
       "      <td>[]</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>1134 N Central Ave\\nPhoenix, AZ 85004</td>\n",
       "      <td>33.460526</td>\n",
       "      <td>-112.073933</td>\n",
       "      <td>Valley Permaculture Alliance</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AZ</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>usAsSV36QmUej8--yvN-dg</td>\n",
       "      <td>[Food, Grocery]</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>845 W Southern Ave\\nPhoenix, AZ 85041</td>\n",
       "      <td>33.392210</td>\n",
       "      <td>-112.085377</td>\n",
       "      <td>Food City</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>AZ</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PzOqRohWw7F7YEPBz6AubA</td>\n",
       "      <td>[Food, Bagels, Delis, Restaurants]</td>\n",
       "      <td>Glendale Az</td>\n",
       "      <td>6520 W Happy Valley Rd\\nSte 101\\nGlendale Az, ...</td>\n",
       "      <td>33.712797</td>\n",
       "      <td>-112.200264</td>\n",
       "      <td>Hot Bagels &amp; Deli</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "      <td>3.5</td>\n",
       "      <td>AZ</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id                                         categories  \\\n",
       "0  rncjoVoEFUJGCUoC1JgnUA  [Accountants, Professional Services, Tax Servi...   \n",
       "1  0FNFSzCFP_rGUoJx8W7tJg                  [Sporting Goods, Bikes, Shopping]   \n",
       "2  3f_lyB6vFK48ukH6ScvLHg                                                 []   \n",
       "3  usAsSV36QmUej8--yvN-dg                                    [Food, Grocery]   \n",
       "4  PzOqRohWw7F7YEPBz6AubA                 [Food, Bagels, Delis, Restaurants]   \n",
       "\n",
       "          city                                       full_address   latitude  \\\n",
       "0       Peoria         8466 W Peoria Ave\\nSte 6\\nPeoria, AZ 85345  33.581867   \n",
       "1      Phoenix                  2149 W Wood Dr\\nPhoenix, AZ 85029  33.604054   \n",
       "2      Phoenix              1134 N Central Ave\\nPhoenix, AZ 85004  33.460526   \n",
       "3      Phoenix              845 W Southern Ave\\nPhoenix, AZ 85041  33.392210   \n",
       "4  Glendale Az  6520 W Happy Valley Rd\\nSte 101\\nGlendale Az, ...  33.712797   \n",
       "\n",
       "    longitude                          name neighborhoods  open  review_count  \\\n",
       "0 -112.241596     Peoria Income Tax Service            []  True             3   \n",
       "1 -112.105933                   Bike Doctor            []  True             5   \n",
       "2 -112.073933  Valley Permaculture Alliance            []  True             4   \n",
       "3 -112.085377                     Food City            []  True             5   \n",
       "4 -112.200264             Hot Bagels & Deli            []  True            14   \n",
       "\n",
       "   stars state      type  \n",
       "0    5.0    AZ  business  \n",
       "1    5.0    AZ  business  \n",
       "2    5.0    AZ  business  \n",
       "3    3.5    AZ  business  \n",
       "4    3.5    AZ  business  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "# 店舗についてのデータを読み込む\n",
    "with open('data/yelp/yelp_academic_dataset_business.json') as biz_file:\n",
    "    biz_df = pd.DataFrame([json.loads(x) for x in biz_file.readlines()])\n",
    "\n",
    "biz_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# レビュー件数のヒストグラムを描画\n",
    "sns.set_style('whitegrid')\n",
    "fig, ax = plt.subplots()\n",
    "biz_df['review_count'].hist(ax=ax, bins=100)\n",
    "ax.set_yscale('log')\n",
    "ax.tick_params(labelsize=14)\n",
    "ax.set_xlabel('Review Count', fontsize=14)\n",
    "ax.set_ylabel('Occurrence', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例 2-3. 固定幅によるカウントの離散化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([37, 12, 72,  9, 75,  5, 79, 64, 16,  1, 76, 71,  6, 25, 50, 20, 18,\n",
       "       84, 11, 28])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.seed(seed=1)\n",
    "\n",
    "# 0から99までの整数を一様分布からランダムに20個生成する\n",
    "small_counts = np.random.randint(0, 100, 20)\n",
    "\n",
    "small_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 7, 0, 7, 0, 7, 6, 1, 0, 7, 7, 0, 2, 5, 2, 1, 8, 1, 2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 除算により 0-9 までの階級を割り当てる\n",
    "np.floor_divide(small_counts, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 3., 4., 1., 0., 2., 2., 3., 3., 4., 4., 1., 1., 3., 2., 2., 4.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 複数の桁にまたがるカウントデータの配列\n",
    "large_counts = [296, 8286, 64011, 80, 3, 725, 867, 2215, 7689, 11495, 91897, \n",
    "                44, 28, 7971, 926, 122, 22222]\n",
    "# 対数変換により指数幅の階級を割り当てる\n",
    "np.floor(np.log10(large_counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例 2-4. Yelp ビジネスレビュー件数の十分位数を計算する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1     3.0\n",
       "0.2     3.0\n",
       "0.3     4.0\n",
       "0.4     5.0\n",
       "0.5     6.0\n",
       "0.6     8.0\n",
       "0.7    12.0\n",
       "0.8    23.0\n",
       "0.9    50.0\n",
       "Name: review_count, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deciles = biz_df['review_count'].quantile([.1, .2, .3, .4, .5, .6, .7, .8, .9])\n",
    "deciles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ヒストグラムに十分位数を上書きする\n",
    "sns.set_style('whitegrid')\n",
    "fig, ax = plt.subplots()\n",
    "biz_df['review_count'].hist(ax=ax, bins=100)\n",
    "for pos in deciles:\n",
    "    handle = plt.axvline(pos, color='r')\n",
    "ax.legend([handle], ['deciles'], fontsize=14)\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "ax.tick_params(labelsize=14)\n",
    "ax.set_xlabel('Review Count', fontsize=14)\n",
    "ax.set_ylabel('Occurrence', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例 2-5. 分位数によるカウントの離散化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0, 0, 1, 1, 2, 2, 3, 3, 0, 0, 2, 1, 0, 3])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 例2-3の large_count を引き続き使う\n",
    "import pandas as pd\n",
    "\n",
    "# 四分位数に変換\n",
    "pd.qcut(large_counts, 4, labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25     122.0\n",
       "0.50     926.0\n",
       "0.75    8286.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分位数の計算\n",
    "large_counts_series = pd.Series(large_counts)\n",
    "large_counts_series.quantile([0.25, 0.5, 0.75])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 例 2-6. 対数変換の前後でレビュー件数のヒストグラムを比較する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 例2-2で読み込んだ Yelp データセットの\n",
    "# データフレーム biz_df を使用して、レビュー件数を対数変換する。\n",
    "# レビュー件数 0 を対数変換してマイナス無限大になるのを防ぐために\n",
    "# 対数変換の前に生データに 1 を加算していることに注意。\n",
    "biz_df['log_review_count'] = np.log10(biz_df['review_count'] + 1)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2,1)\n",
    "biz_df['review_count'].hist(ax=ax1, bins=100)\n",
    "ax1.tick_params(labelsize=14)\n",
    "ax1.set_xlabel('review_count', fontsize=14)\n",
    "ax1.set_ylabel('Occurrence', fontsize=14)\n",
    "\n",
    "biz_df['log_review_count'].hist(ax=ax2, bins=100)\n",
    "ax2.tick_params(labelsize=14)\n",
    "ax2.set_xlabel('log10(review_count))', fontsize=14)\n",
    "ax2.set_ylabel('Occurrence', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}