yelp_data_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "yelp_data_analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "50eYGS5Rjkhk",
        "TnM_nlcDjqEj",
        "HpnPCxxHjt_Z",
        "IntGbc7OhgJr",
        "0PUxfQOoldJS",
        "QvkftEgdj2hx",
        "OHgAPqSnn3bx",
        "ts30qbAAnhAx",
        "zmCXnjw5nm0m"
      ],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "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": "3.6.10"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/stxupengyu/NCF-MF-for-Recommendation/blob/main/yelp_data_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50eYGS5Rjkhk"
      },
      "source": [
        "#Link Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9leA14iGH1vT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff887d33-8efb-4f35-c9c1-9441d872a291"
      },
      "source": [
        "#link google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dE5LVJLH1_7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3c8282a-b13b-4bfd-a165-f27c6db0deeb"
      },
      "source": [
        "#enter the folder\n",
        "!ls\n",
        "import os\n",
        "os.chdir('/content/drive/My Drive/Project/Deep-Rec-Sys')\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset_Agreement.pdf\t\t     yelp_academic_dataset_tip.json\n",
            "kaggle.json\t\t\t     yelp_academic_dataset_user.json\n",
            "yelp_academic_dataset_business.json  yelp_data_analysis.ipynb\n",
            "yelp_academic_dataset_checkin.json   yelp_recomendation.ipynb\n",
            "yelp_academic_dataset_review.json\n",
            "Dataset_Agreement.pdf\t\t     yelp_academic_dataset_tip.json\n",
            "kaggle.json\t\t\t     yelp_academic_dataset_user.json\n",
            "yelp_academic_dataset_business.json  yelp_data_analysis.ipynb\n",
            "yelp_academic_dataset_checkin.json   yelp_recomendation.ipynb\n",
            "yelp_academic_dataset_review.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TnM_nlcDjqEj"
      },
      "source": [
        "#Download Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHgiIN-OP2k1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f85ca4c4-a544-4f20-8990-235d6be165e8"
      },
      "source": [
        "#下载并上传kaggle.json文件\n",
        "!pip install kaggle\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = \"/content/drive/My Drive/Project/Deep-Rec-Sys\"\n",
        "#download kaggle dataset(10GB)\n",
        "!kaggle datasets download -d yelp-dataset/yelp-dataset\n",
        "!ls\n",
        "!unzip yelp-dataset.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.9)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.8.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2020.6.20)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (0.0.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.0.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.41.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "yelp-dataset.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "Dataset_Agreement.pdf\t\t     yelp_academic_dataset_tip.json\n",
            "kaggle.json\t\t\t     yelp_academic_dataset_user.json\n",
            "yelp_academic_dataset_business.json  yelp_data_analysis.ipynb\n",
            "yelp_academic_dataset_checkin.json   yelp-dataset.zip\n",
            "yelp_academic_dataset_review.json    yelp_recomendation.ipynb\n",
            "Archive:  yelp-dataset.zip\n",
            "replace Dataset_Agreement.pdf? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HpnPCxxHjt_Z"
      },
      "source": [
        "#Read Bussiness Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVypinrlbOzE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "8e1638fe-9edd-4246-ce10-bbf4d9530d5d"
      },
      "source": [
        "#read the dataset:yelp,review&business\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "# We only use the first 1,000,000 data in this assignment\n",
        "users = []\n",
        "with open('yelp_academic_dataset_business.json') as fl:\n",
        "    for i, line in enumerate(fl):\n",
        "        users.append(json.loads(line))\n",
        "        if i+1 >= 1000000:\n",
        "            break\n",
        "subset_business = pd.DataFrame(users)\n",
        "print('The shape of the data:',subset_business.shape)\n",
        "display(subset_business.tail(2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The shape of the data: (209393, 14)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>name</th>\n",
              "      <th>address</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>postal_code</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>stars</th>\n",
              "      <th>review_count</th>\n",
              "      <th>is_open</th>\n",
              "      <th>attributes</th>\n",
              "      <th>categories</th>\n",
              "      <th>hours</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>209391</th>\n",
              "      <td>RSSIsgO00OuWQTRoITacpA</td>\n",
              "      <td>Indian Trail Dog Training</td>\n",
              "      <td></td>\n",
              "      <td>Indian Trail</td>\n",
              "      <td>NC</td>\n",
              "      <td>28079</td>\n",
              "      <td>35.101965</td>\n",
              "      <td>-80.599385</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>None</td>\n",
              "      <td>Pet Services, Pet Training, Pets</td>\n",
              "      <td>{'Monday': '0:0-0:0', 'Tuesday': '7:0-19:0', '...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209392</th>\n",
              "      <td>tOcYmewXFhQeZh3V42ymwg</td>\n",
              "      <td>Kudlow Ye</td>\n",
              "      <td>21 St Clair Avenue E, Suite 401</td>\n",
              "      <td>Toronto</td>\n",
              "      <td>ON</td>\n",
              "      <td>M4T 1L9</td>\n",
              "      <td>43.688214</td>\n",
              "      <td>-79.392756</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>None</td>\n",
              "      <td>Tax Services, Professional Services, Accountan...</td>\n",
              "      <td>{'Monday': '9:0-18:0', 'Tuesday': '9:0-18:0', ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   business_id  ...                                              hours\n",
              "209391  RSSIsgO00OuWQTRoITacpA  ...  {'Monday': '0:0-0:0', 'Tuesday': '7:0-19:0', '...\n",
              "209392  tOcYmewXFhQeZh3V42ymwg  ...  {'Monday': '9:0-18:0', 'Tuesday': '9:0-18:0', ...\n",
              "\n",
              "[2 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IntGbc7OhgJr"
      },
      "source": [
        "# Ratings Distribution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FetXSRJzg1RT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "524569f8-7581-4213-a862-b423eff19028"
      },
      "source": [
        "#Get the distribution of the ratings\n",
        "x=subset_business['stars'].value_counts()\n",
        "x=x.sort_index()\n",
        "#plot\n",
        "plt.figure(figsize=(8,4))\n",
        "ax= sns.barplot(x.index, x.values, alpha=0.8)\n",
        "plt.title(\"Star Rating Distribution\")\n",
        "plt.ylabel('# of businesses', fontsize=12)\n",
        "plt.xlabel('Star Ratings ', fontsize=12)\n",
        "\n",
        "#adding the text labels\n",
        "rects = ax.patches\n",
        "labels = x.values\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha='center', va='bottom')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0PUxfQOoldJS"
      },
      "source": [
        "# Categories Distribution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvgcPLUYlcex",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "6fc6e959-17a9-40b4-bbe4-6a852829af20"
      },
      "source": [
        "business_cats = ''.join(subset_business['categories'].astype('str'))\n",
        "\n",
        "cats=pd.DataFrame(business_cats.split(','),columns=['categories'])\n",
        "\n",
        "#prep for chart\n",
        "x=cats.categories.value_counts()\n",
        "\n",
        "x=x.sort_values(ascending=False)\n",
        "x=x.iloc[0:20]\n",
        "\n",
        "#chart\n",
        "plt.figure(figsize=(16,4))\n",
        "ax = sns.barplot(x.index, x.values, alpha=0.8)#,color=color[5])\n",
        "plt.title(\"What are the top categories?\",fontsize=25)\n",
        "locs, labels = plt.xticks()\n",
        "plt.setp(labels, rotation=80)\n",
        "plt.ylabel('# businesses', fontsize=12)\n",
        "plt.xlabel('Category', fontsize=12)\n",
        "\n",
        "#adding the text labels\n",
        "# rects = ax.patches\n",
        "# labels = x.values\n",
        "# for rect, label in zip(rects, labels):\n",
        "#     height = rect.get_height()\n",
        "#     ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha='center', va='bottom')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QvkftEgdj2hx"
      },
      "source": [
        "#Read Review Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhLHf4_MdgKX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "791f4c8b-dee1-4f21-cca4-6074c526793e"
      },
      "source": [
        "# We only use the first 1,000,000 data in this assignment\n",
        "users = []\n",
        "with open('yelp_academic_dataset_review.json') as fl:\n",
        "    for i, line in enumerate(fl):\n",
        "        users.append(json.loads(line))\n",
        "        if i+1 >= 1000000:\n",
        "            break\n",
        "subset_review = pd.DataFrame(users)\n",
        "print('The shape of the data:',subset_review.shape)\n",
        "display(subset_review.tail(2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The shape of the data: (1000000, 9)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>business_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>useful</th>\n",
              "      <th>funny</th>\n",
              "      <th>cool</th>\n",
              "      <th>text</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>999998</th>\n",
              "      <td>7yqTN-PM-bnfwuryhIb7rA</td>\n",
              "      <td>WJrx5OjI8Ux2RNFDa3HKyA</td>\n",
              "      <td>IcHVYlgNGR-Zz8fnceiJ6g</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Best Long Island Iced Teas anywhere. I love co...</td>\n",
              "      <td>2014-05-17 17:27:17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999999</th>\n",
              "      <td>pKYvRnfg9wDlXu7vhyUu4g</td>\n",
              "      <td>A_lR_ibGJtp8WwBzZrX6vQ</td>\n",
              "      <td>rtwojGcYuhbLbQ9D1Z2gSg</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>I love the mirrors on all the pillars. It give...</td>\n",
              "      <td>2013-09-16 01:42:01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     review_id  ...                 date\n",
              "999998  7yqTN-PM-bnfwuryhIb7rA  ...  2014-05-17 17:27:17\n",
              "999999  pKYvRnfg9wDlXu7vhyUu4g  ...  2013-09-16 01:42:01\n",
              "\n",
              "[2 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQysTapQU3J9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "467c9652-8bf9-4f74-8867-8e9b4f5d7dcc"
      },
      "source": [
        "# pull out needed columns from subset_review table\n",
        "df_review = subset_review[['user_id','business_id','stars', 'useful','funny','cool','text']]\n",
        "display(df_review.tail(2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "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>user_id</th>\n",
              "      <th>business_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>useful</th>\n",
              "      <th>funny</th>\n",
              "      <th>cool</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>999998</th>\n",
              "      <td>WJrx5OjI8Ux2RNFDa3HKyA</td>\n",
              "      <td>IcHVYlgNGR-Zz8fnceiJ6g</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Best Long Island Iced Teas anywhere. I love co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999999</th>\n",
              "      <td>A_lR_ibGJtp8WwBzZrX6vQ</td>\n",
              "      <td>rtwojGcYuhbLbQ9D1Z2gSg</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>I love the mirrors on all the pillars. It give...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       user_id  ...                                               text\n",
              "999998  WJrx5OjI8Ux2RNFDa3HKyA  ...  Best Long Island Iced Teas anywhere. I love co...\n",
              "999999  A_lR_ibGJtp8WwBzZrX6vQ  ...  I love the mirrors on all the pillars. It give...\n",
              "\n",
              "[2 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHgAPqSnn3bx"
      },
      "source": [
        "#Combine Two Data in Toronto"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMUlDPqRCrys",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "outputId": "6632b399-6667-4d67-b148-46c81f664030"
      },
      "source": [
        "# Businesses in Toronto and currently open business\n",
        "city = subset_business[(subset_business['city'] == 'Toronto')] #& (subset_business['is_open'] == 1)]\n",
        "toronto = city[['business_id','name','address', 'categories', 'attributes','stars']]\n",
        "rest = toronto.reset_index()\n",
        "display(rest.tail(2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "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>index</th>\n",
              "      <th>business_id</th>\n",
              "      <th>name</th>\n",
              "      <th>address</th>\n",
              "      <th>categories</th>\n",
              "      <th>attributes</th>\n",
              "      <th>stars</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20364</th>\n",
              "      <td>209377</td>\n",
              "      <td>n2BKYb8aw4AhRKLkdUjQRg</td>\n",
              "      <td>Steak &amp; Cheese &amp; Quick Pita Restaurant</td>\n",
              "      <td>199 College St</td>\n",
              "      <td>Sandwiches, Restaurants</td>\n",
              "      <td>{'OutdoorSeating': 'False', 'Alcohol': 'u'none...</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20365</th>\n",
              "      <td>209392</td>\n",
              "      <td>tOcYmewXFhQeZh3V42ymwg</td>\n",
              "      <td>Kudlow Ye</td>\n",
              "      <td>21 St Clair Avenue E, Suite 401</td>\n",
              "      <td>Tax Services, Professional Services, Accountan...</td>\n",
              "      <td>None</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        index  ... stars\n",
              "20364  209377  ...   2.5\n",
              "20365  209392  ...   5.0\n",
              "\n",
              "[2 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tm-wvEdL-V8H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "7cb8acf9-3e1b-4eb4-ffcf-55970fab01c7"
      },
      "source": [
        "# pull out names and addresses of the restaurants from rest table\n",
        "restaurant = rest[['business_id', 'name', 'address','categories']]\n",
        "display(restaurant.tail(2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "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>name</th>\n",
              "      <th>address</th>\n",
              "      <th>categories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20364</th>\n",
              "      <td>n2BKYb8aw4AhRKLkdUjQRg</td>\n",
              "      <td>Steak &amp; Cheese &amp; Quick Pita Restaurant</td>\n",
              "      <td>199 College St</td>\n",
              "      <td>Sandwiches, Restaurants</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20365</th>\n",
              "      <td>tOcYmewXFhQeZh3V42ymwg</td>\n",
              "      <td>Kudlow Ye</td>\n",
              "      <td>21 St Clair Avenue E, Suite 401</td>\n",
              "      <td>Tax Services, Professional Services, Accountan...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  business_id  ...                                         categories\n",
              "20364  n2BKYb8aw4AhRKLkdUjQRg  ...                            Sandwiches, Restaurants\n",
              "20365  tOcYmewXFhQeZh3V42ymwg  ...  Tax Services, Professional Services, Accountan...\n",
              "\n",
              "[2 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmZezR9wuD_p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "2dc52cf0-b731-4f90-a9eb-6716343b495f"
      },
      "source": [
        "# combine df_review and restaurant table\n",
        "combined_business_data = pd.merge(df_review, restaurant, on='business_id')\n",
        "display(combined_business_data.tail(2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "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>user_id</th>\n",
              "      <th>business_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>useful</th>\n",
              "      <th>funny</th>\n",
              "      <th>cool</th>\n",
              "      <th>text</th>\n",
              "      <th>name</th>\n",
              "      <th>address</th>\n",
              "      <th>categories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>71008</th>\n",
              "      <td>4yQtGuSUtHhQTcWFrD2Lnw</td>\n",
              "      <td>fFKbj19pBWMQosCn9qsTdw</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>I was looking for hairdressers to go to becaus...</td>\n",
              "      <td>Lebel &amp; Crowe</td>\n",
              "      <td>1491 Dundas Street W</td>\n",
              "      <td>Beauty &amp; Spas, Hair Salons</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71009</th>\n",
              "      <td>eXhwli2CzubJ058MEXtCsg</td>\n",
              "      <td>os9fE0YwboRi9PDZZ7gf2w</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>I'm so pleased to have stumbled across this ut...</td>\n",
              "      <td>EweKnit</td>\n",
              "      <td>585 Markham Street</td>\n",
              "      <td>Shopping, Knitting Supplies</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      user_id  ...                   categories\n",
              "71008  4yQtGuSUtHhQTcWFrD2Lnw  ...   Beauty & Spas, Hair Salons\n",
              "71009  eXhwli2CzubJ058MEXtCsg  ...  Shopping, Knitting Supplies\n",
              "\n",
              "[2 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YmYrlbyE0st",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53299ec7-b1eb-4b75-baf8-3f5a2faf2a3c"
      },
      "source": [
        "print('how many users?：',len(combined_business_data['user_id'].drop_duplicates()))\n",
        "print('how many locations?：',len(combined_business_data['business_id'].drop_duplicates()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "how many users?： 35090\n",
            "how many locations?： 3527\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ts30qbAAnhAx"
      },
      "source": [
        "# Ratings Distribution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAsmzlzUnhA6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "82762042-ddf9-41b5-9f1d-d7cf53633afc"
      },
      "source": [
        "#Get the distribution of the ratings\n",
        "x=combined_business_data['stars'].value_counts()\n",
        "x=x.sort_index()\n",
        "#plot\n",
        "plt.figure(figsize=(8,4))\n",
        "ax= sns.barplot(x.index, x.values, alpha=0.8)\n",
        "plt.title(\"Star Rating Distribution\")\n",
        "plt.ylabel('# of businesses', fontsize=12)\n",
        "plt.xlabel('Star Ratings ', fontsize=12)\n",
        "\n",
        "#adding the text labels\n",
        "rects = ax.patches\n",
        "labels = x.values\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha='center', va='bottom')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zmCXnjw5nm0m"
      },
      "source": [
        "# Categories Distribution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJfIDur4nm0n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "3f9e3fbb-97b0-410e-d4b6-96ea6888d585"
      },
      "source": [
        "business_cats = ''.join(combined_business_data['categories'].astype('str'))\n",
        "\n",
        "cats=pd.DataFrame(business_cats.split(','),columns=['categories'])\n",
        "\n",
        "#prep for chart\n",
        "x=cats.categories.value_counts()\n",
        "\n",
        "x=x.sort_values(ascending=False)\n",
        "x=x.iloc[0:20]\n",
        "\n",
        "#chart\n",
        "plt.figure(figsize=(16,4))\n",
        "ax = sns.barplot(x.index, x.values, alpha=0.8)#,color=color[5])\n",
        "plt.title(\"What are the top categories?\",fontsize=25)\n",
        "locs, labels = plt.xticks()\n",
        "plt.setp(labels, rotation=80)\n",
        "plt.ylabel('# businesses', fontsize=12)\n",
        "plt.xlabel('Category', fontsize=12)\n",
        "\n",
        "#adding the text labels\n",
        "rects = ax.patches\n",
        "labels = x.values\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha='center', va='bottom')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fvsmvmCVqm_0"
      },
      "source": [
        "#Save Data to CSV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zplg2DKMEeTl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "d88e2324-57ed-43ad-f2d2-1548c4f3f7d2"
      },
      "source": [
        "#final necessary data\n",
        "dataset=combined_business_data[['user_id', 'business_id', 'stars','text']]\n",
        "display(dataset.tail(2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "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>user_id</th>\n",
              "      <th>business_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>71008</th>\n",
              "      <td>4yQtGuSUtHhQTcWFrD2Lnw</td>\n",
              "      <td>fFKbj19pBWMQosCn9qsTdw</td>\n",
              "      <td>5.0</td>\n",
              "      <td>I was looking for hairdressers to go to becaus...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71009</th>\n",
              "      <td>eXhwli2CzubJ058MEXtCsg</td>\n",
              "      <td>os9fE0YwboRi9PDZZ7gf2w</td>\n",
              "      <td>5.0</td>\n",
              "      <td>I'm so pleased to have stumbled across this ut...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      user_id  ...                                               text\n",
              "71008  4yQtGuSUtHhQTcWFrD2Lnw  ...  I was looking for hairdressers to go to becaus...\n",
              "71009  eXhwli2CzubJ058MEXtCsg  ...  I'm so pleased to have stumbled across this ut...\n",
              "\n",
              "[2 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UKJjbgQEehd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "a8d7d7bd-df47-45e7-bbca-9e5b01cee441"
      },
      "source": [
        "#convert id to number\n",
        "dictionary_user=dict(zip(list(dataset['user_id'].drop_duplicates().values), range(len(dataset['user_id'].drop_duplicates().values))))\n",
        "dictionary_business=dict(zip(list(dataset['business_id'].drop_duplicates().values), range(len(dataset['business_id'].drop_duplicates().values))))\n",
        "user_set=[]\n",
        "for user in dataset['user_id']:\n",
        "  user_set.append(dictionary_user[user]+1)\n",
        "business_set=[]\n",
        "for business in dataset['business_id']:\n",
        "  business_set.append(dictionary_business[business]+1)\n",
        "dataset['user_id']=user_set\n",
        "dataset['business_id']=business_set\n",
        "display(dataset.tail(2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>user_id</th>\n",
              "      <th>business_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>In the heart of Chinatown, I discovered it enr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Delicious Cavatelli and white chocolate tirami...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Went for a bite after going to the art museum ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Peking duck and pizza are two of my favourite ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Great service, super casual, amazing house mad...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71005</th>\n",
              "      <td>35090</td>\n",
              "      <td>3523</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Quick at fixing my broken cellphone and for a ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71006</th>\n",
              "      <td>17647</td>\n",
              "      <td>3524</td>\n",
              "      <td>5.0</td>\n",
              "      <td>I received top notch service at Origin Wellnes...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71007</th>\n",
              "      <td>6290</td>\n",
              "      <td>3525</td>\n",
              "      <td>2.0</td>\n",
              "      <td>A bunch of my coworkers and I went here for lu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71008</th>\n",
              "      <td>24585</td>\n",
              "      <td>3526</td>\n",
              "      <td>5.0</td>\n",
              "      <td>I was looking for hairdressers to go to becaus...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71009</th>\n",
              "      <td>10713</td>\n",
              "      <td>3527</td>\n",
              "      <td>5.0</td>\n",
              "      <td>I'm so pleased to have stumbled across this ut...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>71010 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       user_id  ...                                               text\n",
              "0            1  ...  In the heart of Chinatown, I discovered it enr...\n",
              "1            2  ...  Delicious Cavatelli and white chocolate tirami...\n",
              "2            3  ...  Went for a bite after going to the art museum ...\n",
              "3            4  ...  Peking duck and pizza are two of my favourite ...\n",
              "4            5  ...  Great service, super casual, amazing house mad...\n",
              "...        ...  ...                                                ...\n",
              "71005    35090  ...  Quick at fixing my broken cellphone and for a ...\n",
              "71006    17647  ...  I received top notch service at Origin Wellnes...\n",
              "71007     6290  ...  A bunch of my coworkers and I went here for lu...\n",
              "71008    24585  ...  I was looking for hairdressers to go to becaus...\n",
              "71009    10713  ...  I'm so pleased to have stumbled across this ut...\n",
              "\n",
              "[71010 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6lTda69Xmbj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "469b246d-fc51-4a85-8eac-74011901fd3b"
      },
      "source": [
        "#store the dataset and then read it \n",
        "dataset.to_csv('rating_text.csv',header=-1,index=0)\n",
        "pd.read_csv('rating_text.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>user_id</th>\n",
              "      <th>business_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>In the heart of Chinatown, I discovered it enr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Delicious Cavatelli and white chocolate tirami...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Went for a bite after going to the art museum ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Peking duck and pizza are two of my favourite ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Great service, super casual, amazing house mad...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71005</th>\n",
              "      <td>35090</td>\n",
              "      <td>3523</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Quick at fixing my broken cellphone and for a ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71006</th>\n",
              "      <td>17647</td>\n",
              "      <td>3524</td>\n",
              "      <td>5.0</td>\n",
              "      <td>I received top notch service at Origin Wellnes...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71007</th>\n",
              "      <td>6290</td>\n",
              "      <td>3525</td>\n",
              "      <td>2.0</td>\n",
              "      <td>A bunch of my coworkers and I went here for lu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71008</th>\n",
              "      <td>24585</td>\n",
              "      <td>3526</td>\n",
              "      <td>5.0</td>\n",
              "      <td>I was looking for hairdressers to go to becaus...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71009</th>\n",
              "      <td>10713</td>\n",
              "      <td>3527</td>\n",
              "      <td>5.0</td>\n",
              "      <td>I'm so pleased to have stumbled across this ut...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>71010 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       user_id  ...                                               text\n",
              "0            1  ...  In the heart of Chinatown, I discovered it enr...\n",
              "1            2  ...  Delicious Cavatelli and white chocolate tirami...\n",
              "2            3  ...  Went for a bite after going to the art museum ...\n",
              "3            4  ...  Peking duck and pizza are two of my favourite ...\n",
              "4            5  ...  Great service, super casual, amazing house mad...\n",
              "...        ...  ...                                                ...\n",
              "71005    35090  ...  Quick at fixing my broken cellphone and for a ...\n",
              "71006    17647  ...  I received top notch service at Origin Wellnes...\n",
              "71007     6290  ...  A bunch of my coworkers and I went here for lu...\n",
              "71008    24585  ...  I was looking for hairdressers to go to becaus...\n",
              "71009    10713  ...  I'm so pleased to have stumbled across this ut...\n",
              "\n",
              "[71010 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YYiiAKOEeLm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "4a2d6274-d39c-43b2-848b-1febd9e78d50"
      },
      "source": [
        "#store the dataset and then read it \n",
        "dataset.iloc[:,:-1].to_csv('rating.csv',header=0,index=0)\n",
        "pd.read_csv('rating.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>1</th>\n",
              "      <th>1.1</th>\n",
              "      <th>4.0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71004</th>\n",
              "      <td>35090</td>\n",
              "      <td>3523</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71005</th>\n",
              "      <td>17647</td>\n",
              "      <td>3524</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71006</th>\n",
              "      <td>6290</td>\n",
              "      <td>3525</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71007</th>\n",
              "      <td>24585</td>\n",
              "      <td>3526</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71008</th>\n",
              "      <td>10713</td>\n",
              "      <td>3527</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>71009 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           1   1.1  4.0\n",
              "0          2     1  4.0\n",
              "1          3     1  4.0\n",
              "2          4     1  5.0\n",
              "3          5     1  5.0\n",
              "4          6     1  5.0\n",
              "...      ...   ...  ...\n",
              "71004  35090  3523  5.0\n",
              "71005  17647  3524  5.0\n",
              "71006   6290  3525  2.0\n",
              "71007  24585  3526  5.0\n",
              "71008  10713  3527  5.0\n",
              "\n",
              "[71009 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtJyFUZXrchK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}