Data_Analysis.ipynb

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      "name": "Data Analysis.ipynb",
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "%matplotlib inline\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zomato=pd.read_csv('/content/zomato.csv',encoding='latin-1')\n",
        "zomato.head()"
      ],
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          "height": 855
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            "text/plain": [
              "   Restaurant ID         Restaurant Name  Country Code              City  \\\n",
              "0        6317637        Le Petit Souffle           162       Makati City   \n",
              "1        6304287        Izakaya Kikufuji           162       Makati City   \n",
              "2        6300002  Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
              "3        6318506                    Ooma           162  Mandaluyong City   \n",
              "4        6314302             Sambo Kojin           162  Mandaluyong City   \n",
              "\n",
              "                                             Address  \\\n",
              "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
              "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
              "2  Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
              "3  Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
              "4  Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
              "\n",
              "                                     Locality  \\\n",
              "0   Century City Mall, Poblacion, Makati City   \n",
              "1  Little Tokyo, Legaspi Village, Makati City   \n",
              "2  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
              "3      SM Megamall, Ortigas, Mandaluyong City   \n",
              "4      SM Megamall, Ortigas, Mandaluyong City   \n",
              "\n",
              "                                    Locality Verbose   Longitude   Latitude  \\\n",
              "0  Century City Mall, Poblacion, Makati City, Mak...  121.027535  14.565443   \n",
              "1  Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101  14.553708   \n",
              "2  Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831  14.581404   \n",
              "3  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475  14.585318   \n",
              "4  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508  14.584450   \n",
              "\n",
              "                           Cuisines  ...          Currency Has Table booking  \\\n",
              "0        French, Japanese, Desserts  ...  Botswana Pula(P)               Yes   \n",
              "1                          Japanese  ...  Botswana Pula(P)               Yes   \n",
              "2  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)               Yes   \n",
              "3                   Japanese, Sushi  ...  Botswana Pula(P)                No   \n",
              "4                  Japanese, Korean  ...  Botswana Pula(P)               Yes   \n",
              "\n",
              "  Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
              "0                  No                No                   No           3   \n",
              "1                  No                No                   No           3   \n",
              "2                  No                No                   No           4   \n",
              "3                  No                No                   No           4   \n",
              "4                  No                No                   No           4   \n",
              "\n",
              "   Aggregate rating  Rating color Rating text Votes  \n",
              "0               4.8    Dark Green   Excellent   314  \n",
              "1               4.5    Dark Green   Excellent   591  \n",
              "2               4.4         Green   Very Good   270  \n",
              "3               4.9    Dark Green   Excellent   365  \n",
              "4               4.8    Dark Green   Excellent   229  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <th>Restaurant ID</th>\n",
              "      <th>Restaurant Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>City</th>\n",
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              "      <th>Latitude</th>\n",
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              "      <th>...</th>\n",
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              "      <th>Has Table booking</th>\n",
              "      <th>Has Online delivery</th>\n",
              "      <th>Is delivering now</th>\n",
              "      <th>Switch to order menu</th>\n",
              "      <th>Price range</th>\n",
              "      <th>Aggregate rating</th>\n",
              "      <th>Rating color</th>\n",
              "      <th>Rating text</th>\n",
              "      <th>Votes</th>\n",
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              "      <td>6317637</td>\n",
              "      <td>Le Petit Souffle</td>\n",
              "      <td>162</td>\n",
              "      <td>Makati City</td>\n",
              "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
              "      <td>Century City Mall, Poblacion, Makati City</td>\n",
              "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
              "      <td>121.027535</td>\n",
              "      <td>14.565443</td>\n",
              "      <td>French, Japanese, Desserts</td>\n",
              "      <td>...</td>\n",
              "      <td>Botswana Pula(P)</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>3</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6304287</td>\n",
              "      <td>Izakaya Kikufuji</td>\n",
              "      <td>162</td>\n",
              "      <td>Makati City</td>\n",
              "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
              "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
              "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
              "      <td>121.014101</td>\n",
              "      <td>14.553708</td>\n",
              "      <td>Japanese</td>\n",
              "      <td>...</td>\n",
              "      <td>Botswana Pula(P)</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>3</td>\n",
              "      <td>4.5</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6300002</td>\n",
              "      <td>Heat - Edsa Shangri-La</td>\n",
              "      <td>162</td>\n",
              "      <td>Mandaluyong City</td>\n",
              "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
              "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
              "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
              "      <td>121.056831</td>\n",
              "      <td>14.581404</td>\n",
              "      <td>Seafood, Asian, Filipino, Indian</td>\n",
              "      <td>...</td>\n",
              "      <td>Botswana Pula(P)</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>4</td>\n",
              "      <td>4.4</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6318506</td>\n",
              "      <td>Ooma</td>\n",
              "      <td>162</td>\n",
              "      <td>Mandaluyong City</td>\n",
              "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
              "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
              "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
              "      <td>121.056475</td>\n",
              "      <td>14.585318</td>\n",
              "      <td>Japanese, Sushi</td>\n",
              "      <td>...</td>\n",
              "      <td>Botswana Pula(P)</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>4</td>\n",
              "      <td>4.9</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6314302</td>\n",
              "      <td>Sambo Kojin</td>\n",
              "      <td>162</td>\n",
              "      <td>Mandaluyong City</td>\n",
              "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
              "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
              "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
              "      <td>121.057508</td>\n",
              "      <td>14.584450</td>\n",
              "      <td>Japanese, Korean</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>4</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>229</td>\n",
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              "<p>5 rows × 21 columns</p>\n",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
              "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
              "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
              "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
              "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
              "       'Votes'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
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    {
      "cell_type": "code",
      "source": [
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        "id": "5xZ6i9S3hG-m",
        "outputId": "eb6bcc83-86be-42e7-ec55-a55bc3f008a8"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9551 entries, 0 to 9550\n",
            "Data columns (total 21 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   Restaurant ID         9551 non-null   int64  \n",
            " 1   Restaurant Name       9551 non-null   object \n",
            " 2   Country Code          9551 non-null   int64  \n",
            " 3   City                  9551 non-null   object \n",
            " 4   Address               9551 non-null   object \n",
            " 5   Locality              9551 non-null   object \n",
            " 6   Locality Verbose      9551 non-null   object \n",
            " 7   Longitude             9551 non-null   float64\n",
            " 8   Latitude              9551 non-null   float64\n",
            " 9   Cuisines              9542 non-null   object \n",
            " 10  Average Cost for two  9551 non-null   int64  \n",
            " 11  Currency              9551 non-null   object \n",
            " 12  Has Table booking     9551 non-null   object \n",
            " 13  Has Online delivery   9551 non-null   object \n",
            " 14  Is delivering now     9551 non-null   object \n",
            " 15  Switch to order menu  9551 non-null   object \n",
            " 16  Price range           9551 non-null   int64  \n",
            " 17  Aggregate rating      9551 non-null   float64\n",
            " 18  Rating color          9551 non-null   object \n",
            " 19  Rating text           9551 non-null   object \n",
            " 20  Votes                 9551 non-null   int64  \n",
            "dtypes: float64(3), int64(5), object(13)\n",
            "memory usage: 1.5+ MB\n"
          ]
        }
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              "(9551, 21)"
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        ""
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Restaurant ID           0\n",
              "Restaurant Name         0\n",
              "Country Code            0\n",
              "City                    0\n",
              "Address                 0\n",
              "Locality                0\n",
              "Locality Verbose        0\n",
              "Longitude               0\n",
              "Latitude                0\n",
              "Cuisines                9\n",
              "Average Cost for two    0\n",
              "Currency                0\n",
              "Has Table booking       0\n",
              "Has Online delivery     0\n",
              "Is delivering now       0\n",
              "Switch to order menu    0\n",
              "Price range             0\n",
              "Aggregate rating        0\n",
              "Rating color            0\n",
              "Rating text             0\n",
              "Votes                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
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        "zomato.describe()"
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          "data": {
            "text/plain": [
              "       Restaurant ID  Country Code    Longitude     Latitude  \\\n",
              "count   9.551000e+03   9551.000000  9551.000000  9551.000000   \n",
              "mean    9.051128e+06     18.365616    64.126574    25.854381   \n",
              "std     8.791521e+06     56.750546    41.467058    11.007935   \n",
              "min     5.300000e+01      1.000000  -157.948486   -41.330428   \n",
              "25%     3.019625e+05      1.000000    77.081343    28.478713   \n",
              "50%     6.004089e+06      1.000000    77.191964    28.570469   \n",
              "75%     1.835229e+07      1.000000    77.282006    28.642758   \n",
              "max     1.850065e+07    216.000000   174.832089    55.976980   \n",
              "\n",
              "       Average Cost for two  Price range  Aggregate rating         Votes  \n",
              "count           9551.000000  9551.000000       9551.000000   9551.000000  \n",
              "mean            1199.210763     1.804837          2.666370    156.909748  \n",
              "std            16121.183073     0.905609          1.516378    430.169145  \n",
              "min                0.000000     1.000000          0.000000      0.000000  \n",
              "25%              250.000000     1.000000          2.500000      5.000000  \n",
              "50%              400.000000     2.000000          3.200000     31.000000  \n",
              "75%              700.000000     2.000000          3.700000    131.000000  \n",
              "max           800000.000000     4.000000          4.900000  10934.000000  "
            ],
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              "      <th>Restaurant ID</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Latitude</th>\n",
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>9.551000e+03</td>\n",
              "      <td>9551.000000</td>\n",
              "      <td>9551.000000</td>\n",
              "      <td>9551.000000</td>\n",
              "      <td>9551.000000</td>\n",
              "      <td>9551.000000</td>\n",
              "      <td>9551.000000</td>\n",
              "      <td>9551.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.051128e+06</td>\n",
              "      <td>18.365616</td>\n",
              "      <td>64.126574</td>\n",
              "      <td>25.854381</td>\n",
              "      <td>1199.210763</td>\n",
              "      <td>1.804837</td>\n",
              "      <td>2.666370</td>\n",
              "      <td>156.909748</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.791521e+06</td>\n",
              "      <td>56.750546</td>\n",
              "      <td>41.467058</td>\n",
              "      <td>11.007935</td>\n",
              "      <td>16121.183073</td>\n",
              "      <td>0.905609</td>\n",
              "      <td>1.516378</td>\n",
              "      <td>430.169145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.300000e+01</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-157.948486</td>\n",
              "      <td>-41.330428</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.019625e+05</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>77.081343</td>\n",
              "      <td>28.478713</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.004089e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>77.191964</td>\n",
              "      <td>28.570469</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.835229e+07</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>77.282006</td>\n",
              "      <td>28.642758</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.700000</td>\n",
              "      <td>131.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.850065e+07</td>\n",
              "      <td>216.000000</td>\n",
              "      <td>174.832089</td>\n",
              "      <td>55.976980</td>\n",
              "      <td>800000.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>10934.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zomato.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3KkslK2DknuD",
        "outputId": "698f20ec-098b-472e-ab5b-082d1f6ba3a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Restaurant ID           0\n",
              "Restaurant Name         0\n",
              "Country Code            0\n",
              "City                    0\n",
              "Address                 0\n",
              "Locality                0\n",
              "Locality Verbose        0\n",
              "Longitude               0\n",
              "Latitude                0\n",
              "Cuisines                9\n",
              "Average Cost for two    0\n",
              "Currency                0\n",
              "Has Table booking       0\n",
              "Has Online delivery     0\n",
              "Is delivering now       0\n",
              "Switch to order menu    0\n",
              "Price range             0\n",
              "Aggregate rating        0\n",
              "Rating color            0\n",
              "Rating text             0\n",
              "Votes                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[features for features in zomato.columns if  zomato[features].isnull().sum()>0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qffgUxcKn1Tm",
        "outputId": "ae0f7638-e322-469f-bb28-e2ad2edfdeb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Cuisines']"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(zomato.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "-a6oKtUsok2j",
        "outputId": "a0ccdba1-be35-4f52-c941-2ecd2f9b2366"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faea7db3590>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_zomato=pd.read_excel('/content/Country-Code.xlsx')\n",
        "df_zomato.head()"
      ],
      "metadata": {
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              "   Country Code    Country\n",
              "0             1      India\n",
              "1            14  Australia\n",
              "2            30     Brazil\n",
              "3            37     Canada\n",
              "4            94  Indonesia"
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            "text/plain": [
              "(15, 2)"
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          "metadata": {},
          "execution_count": 27
        }
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15 entries, 0 to 14\n",
            "Data columns (total 2 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Country Code  15 non-null     int64 \n",
            " 1   Country       15 non-null     object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 368.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zomato.columns"
      ],
      "metadata": {
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        "id": "IqjEViZKrwpX",
        "outputId": "a257fa21-770e-4b49-bfec-8887e4786b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
              "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
              "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
              "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
              "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
              "       'Votes'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df=pd.merge(zomato,df_zomato,on='Country Code',how='left')\n",
        "final_df.head()"
      ],
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          "height": 855
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        "id": "SgX229v5sUEY",
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "   Restaurant ID         Restaurant Name  Country Code              City  \\\n",
              "0        6317637        Le Petit Souffle           162       Makati City   \n",
              "1        6304287        Izakaya Kikufuji           162       Makati City   \n",
              "2        6300002  Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
              "3        6318506                    Ooma           162  Mandaluyong City   \n",
              "4        6314302             Sambo Kojin           162  Mandaluyong City   \n",
              "\n",
              "                                             Address  \\\n",
              "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
              "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
              "2  Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
              "3  Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
              "4  Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
              "\n",
              "                                     Locality  \\\n",
              "0   Century City Mall, Poblacion, Makati City   \n",
              "1  Little Tokyo, Legaspi Village, Makati City   \n",
              "2  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
              "3      SM Megamall, Ortigas, Mandaluyong City   \n",
              "4      SM Megamall, Ortigas, Mandaluyong City   \n",
              "\n",
              "                                    Locality Verbose   Longitude   Latitude  \\\n",
              "0  Century City Mall, Poblacion, Makati City, Mak...  121.027535  14.565443   \n",
              "1  Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101  14.553708   \n",
              "2  Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831  14.581404   \n",
              "3  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475  14.585318   \n",
              "4  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508  14.584450   \n",
              "\n",
              "                           Cuisines  ...  Has Table booking  \\\n",
              "0        French, Japanese, Desserts  ...                Yes   \n",
              "1                          Japanese  ...                Yes   \n",
              "2  Seafood, Asian, Filipino, Indian  ...                Yes   \n",
              "3                   Japanese, Sushi  ...                 No   \n",
              "4                  Japanese, Korean  ...                Yes   \n",
              "\n",
              "  Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
              "0                  No                No                   No           3   \n",
              "1                  No                No                   No           3   \n",
              "2                  No                No                   No           4   \n",
              "3                  No                No                   No           4   \n",
              "4                  No                No                   No           4   \n",
              "\n",
              "  Aggregate rating  Rating color  Rating text Votes      Country  \n",
              "0              4.8    Dark Green    Excellent   314  Phillipines  \n",
              "1              4.5    Dark Green    Excellent   591  Phillipines  \n",
              "2              4.4         Green    Very Good   270  Phillipines  \n",
              "3              4.9    Dark Green    Excellent   365  Phillipines  \n",
              "4              4.8    Dark Green    Excellent   229  Phillipines  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
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              "      <th>Restaurant ID</th>\n",
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              "      <th>Latitude</th>\n",
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              "      <th>...</th>\n",
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              "      <th>0</th>\n",
              "      <td>6317637</td>\n",
              "      <td>Le Petit Souffle</td>\n",
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              "      <td>Makati City</td>\n",
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              "      <td>4.8</td>\n",
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              "      <td>Phillipines</td>\n",
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              "      <td>6304287</td>\n",
              "      <td>Izakaya Kikufuji</td>\n",
              "      <td>162</td>\n",
              "      <td>Makati City</td>\n",
              "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
              "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
              "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
              "      <td>121.014101</td>\n",
              "      <td>14.553708</td>\n",
              "      <td>Japanese</td>\n",
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              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>3</td>\n",
              "      <td>4.5</td>\n",
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              "      <td>Heat - Edsa Shangri-La</td>\n",
              "      <td>162</td>\n",
              "      <td>Mandaluyong City</td>\n",
              "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
              "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
              "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
              "      <td>121.056831</td>\n",
              "      <td>14.581404</td>\n",
              "      <td>Seafood, Asian, Filipino, Indian</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>4</td>\n",
              "      <td>4.4</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>270</td>\n",
              "      <td>Phillipines</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>6318506</td>\n",
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              "      <td>162</td>\n",
              "      <td>Mandaluyong City</td>\n",
              "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
              "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
              "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
              "      <td>121.056475</td>\n",
              "      <td>14.585318</td>\n",
              "      <td>Japanese, Sushi</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>4</td>\n",
              "      <td>4.9</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>365</td>\n",
              "      <td>Phillipines</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>Sambo Kojin</td>\n",
              "      <td>162</td>\n",
              "      <td>Mandaluyong City</td>\n",
              "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
              "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
              "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
              "      <td>121.057508</td>\n",
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              "      <td>4.8</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>229</td>\n",
              "      <td>Phillipines</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 22 columns</p>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aab30038-847c-4a2a-82be-b0d20dd0a925 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aab30038-847c-4a2a-82be-b0d20dd0a925');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkJHJcx7uBae",
        "outputId": "0c0f8920-9623-4e38-f4fc-7e0b2808fcc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Restaurant ID             int64\n",
              "Restaurant Name          object\n",
              "Country Code              int64\n",
              "City                     object\n",
              "Address                  object\n",
              "Locality                 object\n",
              "Locality Verbose         object\n",
              "Longitude               float64\n",
              "Latitude                float64\n",
              "Cuisines                 object\n",
              "Average Cost for two      int64\n",
              "Currency                 object\n",
              "Has Table booking        object\n",
              "Has Online delivery      object\n",
              "Is delivering now        object\n",
              "Switch to order menu     object\n",
              "Price range               int64\n",
              "Aggregate rating        float64\n",
              "Rating color             object\n",
              "Rating text              object\n",
              "Votes                     int64\n",
              "Country                  object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Iz4aGSsuU8Q",
        "outputId": "43af9701-7d52-489c-f605-4e5f3b2543b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
              "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
              "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
              "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
              "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
              "       'Votes', 'Country'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hello=final_df.Country.value_counts().index\n",
        "hello"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZAn9SoTuYtI",
        "outputId": "597c50f2-e173-4a77-9023-1f6e4f7c07b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['India', 'United States', 'United Kingdom', 'Brazil', 'UAE',\n",
              "       'South Africa', 'New Zealand', 'Turkey', 'Australia', 'Phillipines',\n",
              "       'Indonesia', 'Singapore', 'Qatar', 'Sri Lanka', 'Canada'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_value=final_df.Country.value_counts().values\n",
        "country_value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ke7QVpe_vx4p",
        "outputId": "b5e4f54f-c7a7-4b85-e43d-ae98506f86db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8652,  434,   80,   60,   60,   60,   40,   34,   24,   22,   21,\n",
              "         20,   20,   20,    4])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(country_value[:3],labels=hello[:3],autopct='%1.2f%%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "t5jkXTBmwB5X",
        "outputId": "d5cbe510-bd5a-4196-b797-999004c3b6d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7faea6559610>,\n",
              "  <matplotlib.patches.Wedge at 0x7faea6559f10>,\n",
              "  <matplotlib.patches.Wedge at 0x7faea67d6090>],\n",
              " [Text(-1.0829742700952103, 0.19278674827836725, 'India'),\n",
              "  Text(1.077281715838356, -0.22240527134123297, 'United States'),\n",
              "  Text(1.0995865153823035, -0.03015783794312073, 'United Kingdom')],\n",
              " [Text(-0.590713238233751, 0.10515640815183668, '94.39%'),\n",
              "  Text(0.5876082086391032, -0.12131196618612707, '4.73%'),\n",
              "  Text(0.5997744629358018, -0.01644972978715676, '0.87%')])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wRKc2JXpxpL7",
        "outputId": "dcf4d27c-dba1-4d8b-d3e0-b8e0d8a85b28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
              "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
              "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
              "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
              "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
              "       'Votes', 'Country'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings=final_df.groupby(['Aggregate rating','Rating color','Rating text']).size().reset_index().rename(columns={0:'Rating Count'})\n",
        "ratings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZvBJ0xpfxvXF",
        "outputId": "3596cecf-5178-4eb2-bb06-68b80d7b7f05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Aggregate rating Rating color Rating text  Rating Count\n",
              "0                0.0        White   Not rated          2148\n",
              "1                1.8          Red        Poor             1\n",
              "2                1.9          Red        Poor             2\n",
              "3                2.0          Red        Poor             7\n",
              "4                2.1          Red        Poor            15\n",
              "5                2.2          Red        Poor            27\n",
              "6                2.3          Red        Poor            47\n",
              "7                2.4          Red        Poor            87\n",
              "8                2.5       Orange     Average           110\n",
              "9                2.6       Orange     Average           191\n",
              "10               2.7       Orange     Average           250\n",
              "11               2.8       Orange     Average           315\n",
              "12               2.9       Orange     Average           381\n",
              "13               3.0       Orange     Average           468\n",
              "14               3.1       Orange     Average           519\n",
              "15               3.2       Orange     Average           522\n",
              "16               3.3       Orange     Average           483\n",
              "17               3.4       Orange     Average           498\n",
              "18               3.5       Yellow        Good           480\n",
              "19               3.6       Yellow        Good           458\n",
              "20               3.7       Yellow        Good           427\n",
              "21               3.8       Yellow        Good           400\n",
              "22               3.9       Yellow        Good           335\n",
              "23               4.0        Green   Very Good           266\n",
              "24               4.1        Green   Very Good           274\n",
              "25               4.2        Green   Very Good           221\n",
              "26               4.3        Green   Very Good           174\n",
              "27               4.4        Green   Very Good           144\n",
              "28               4.5   Dark Green   Excellent            95\n",
              "29               4.6   Dark Green   Excellent            78\n",
              "30               4.7   Dark Green   Excellent            42\n",
              "31               4.8   Dark Green   Excellent            25\n",
              "32               4.9   Dark Green   Excellent            61"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d2fee75a-2bda-47b4-af57-8ee50af1fc1e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Aggregate rating</th>\n",
              "      <th>Rating color</th>\n",
              "      <th>Rating text</th>\n",
              "      <th>Rating Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>White</td>\n",
              "      <td>Not rated</td>\n",
              "      <td>2148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.8</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.9</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.1</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.2</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.3</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.4</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.5</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.6</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2.7</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2.8</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2.9</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3.0</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>3.1</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>3.2</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>3.3</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>3.4</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>3.5</td>\n",
              "      <td>Yellow</td>\n",
              "      <td>Good</td>\n",
              "      <td>480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>3.6</td>\n",
              "      <td>Yellow</td>\n",
              "      <td>Good</td>\n",
              "      <td>458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>3.7</td>\n",
              "      <td>Yellow</td>\n",
              "      <td>Good</td>\n",
              "      <td>427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>3.8</td>\n",
              "      <td>Yellow</td>\n",
              "      <td>Good</td>\n",
              "      <td>400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>3.9</td>\n",
              "      <td>Yellow</td>\n",
              "      <td>Good</td>\n",
              "      <td>335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>4.0</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4.1</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>4.2</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>4.3</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>4.4</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>4.5</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>4.6</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>4.7</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>4.8</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>4.9</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d2fee75a-2bda-47b4-af57-8ee50af1fc1e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Aggregate rating Rating color Rating text  Rating Count\n",
              "0               0.0        White   Not rated          2148\n",
              "1               1.8          Red        Poor             1\n",
              "2               1.9          Red        Poor             2\n",
              "3               2.0          Red        Poor             7\n",
              "4               2.1          Red        Poor            15"
            ],
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              "      <th>Rating text</th>\n",
              "      <th>Rating Count</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>White</td>\n",
              "      <td>Not rated</td>\n",
              "      <td>2148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.8</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.9</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.1</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>15</td>\n",
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              "\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0c1033cb-82fb-4625-ae7e-c756e4e3c81d');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib\n",
        "matplotlib.rcParams['figure.figsize'] = (12, 6)\n",
        "sns.barplot(x=\"Aggregate rating\",y=\"Rating Count\",data=ratings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "hAHtS5CzuYzj",
        "outputId": "22f19359-b10f-47f3-8c6e-c24d3f6921c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faea6507d90>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=\"Aggregate rating\",y=\"Rating Count\",hue='Rating color',data=ratings,palette=['blue','red','orange','yellow','green','green'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "G1Vu7hHex-d1",
        "outputId": "8cc921d0-3c08-43e3-e05f-966e6d498d62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faea629a7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"Rating color\",data=ratings,palette=['blue','red','orange','yellow','green','green'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "XMBPXN5uyGem",
        "outputId": "7624011f-104d-41a7-8c89-1ab43159eca2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faea5dfdd50>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LzvUaDnUyIrw",
        "outputId": "1346341d-1ef2-429c-f924-b297c9e50c25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Aggregate rating Rating color Rating text  Rating Count\n",
              "0                0.0        White   Not rated          2148\n",
              "1                1.8          Red        Poor             1\n",
              "2                1.9          Red        Poor             2\n",
              "3                2.0          Red        Poor             7\n",
              "4                2.1          Red        Poor            15\n",
              "5                2.2          Red        Poor            27\n",
              "6                2.3          Red        Poor            47\n",
              "7                2.4          Red        Poor            87\n",
              "8                2.5       Orange     Average           110\n",
              "9                2.6       Orange     Average           191\n",
              "10               2.7       Orange     Average           250\n",
              "11               2.8       Orange     Average           315\n",
              "12               2.9       Orange     Average           381\n",
              "13               3.0       Orange     Average           468\n",
              "14               3.1       Orange     Average           519\n",
              "15               3.2       Orange     Average           522\n",
              "16               3.3       Orange     Average           483\n",
              "17               3.4       Orange     Average           498\n",
              "18               3.5       Yellow        Good           480\n",
              "19               3.6       Yellow        Good           458\n",
              "20               3.7       Yellow        Good           427\n",
              "21               3.8       Yellow        Good           400\n",
              "22               3.9       Yellow        Good           335\n",
              "23               4.0        Green   Very Good           266\n",
              "24               4.1        Green   Very Good           274\n",
              "25               4.2        Green   Very Good           221\n",
              "26               4.3        Green   Very Good           174\n",
              "27               4.4        Green   Very Good           144\n",
              "28               4.5   Dark Green   Excellent            95\n",
              "29               4.6   Dark Green   Excellent            78\n",
              "30               4.7   Dark Green   Excellent            42\n",
              "31               4.8   Dark Green   Excellent            25\n",
              "32               4.9   Dark Green   Excellent            61"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b7e6775-d2ea-4a47-9b16-65287aacfc4e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Aggregate rating</th>\n",
              "      <th>Rating color</th>\n",
              "      <th>Rating text</th>\n",
              "      <th>Rating Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>White</td>\n",
              "      <td>Not rated</td>\n",
              "      <td>2148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.8</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.9</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.1</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.2</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.3</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.4</td>\n",
              "      <td>Red</td>\n",
              "      <td>Poor</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.5</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.6</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2.7</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>250</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2.8</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>315</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2.9</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>381</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3.0</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>468</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>3.1</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>3.2</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>522</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>3.3</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>3.4</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Average</td>\n",
              "      <td>498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>3.5</td>\n",
              "      <td>Yellow</td>\n",
              "      <td>Good</td>\n",
              "      <td>480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>3.6</td>\n",
              "      <td>Yellow</td>\n",
              "      <td>Good</td>\n",
              "      <td>458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>3.7</td>\n",
              "      <td>Yellow</td>\n",
              "      <td>Good</td>\n",
              "      <td>427</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>3.8</td>\n",
              "      <td>Yellow</td>\n",
              "      <td>Good</td>\n",
              "      <td>400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>3.9</td>\n",
              "      <td>Yellow</td>\n",
              "      <td>Good</td>\n",
              "      <td>335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>4.0</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4.1</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>4.2</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>4.3</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>4.4</td>\n",
              "      <td>Green</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>4.5</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>4.6</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>4.7</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>4.8</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>4.9</td>\n",
              "      <td>Dark Green</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>61</td>\n",
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        " \n",
        "final_df[final_df['Rating color']=='White'].groupby('Country').size().reset_index()"
      ],
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Country     0\n",
              "0          Brazil     5\n",
              "1           India  2139\n",
              "2  United Kingdom     1\n",
              "3   United States     3"
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    {
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      "metadata": {
        "id": "byslfkYayWkU"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "final_df.groupby(['Aggregate rating','Country']).size().reset_index().head(5)"
      ],
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          "height": 206
        },
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              "   Aggregate rating         Country     0\n",
              "0               0.0          Brazil     5\n",
              "1               0.0           India  2139\n",
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              "3               0.0   United States     3\n",
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        "final_df.columns"
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
              "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
              "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
              "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
              "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
              "       'Votes', 'Country'],\n",
              "      dtype='object')"
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          "metadata": {},
          "execution_count": 63
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              "           Country                Currency     0\n",
              "0        Australia               Dollar($)    24\n",
              "1           Brazil      Brazilian Real(R$)    60\n",
              "2           Canada               Dollar($)     4\n",
              "3            India      Indian Rupees(Rs.)  8652\n",
              "4        Indonesia  Indonesian Rupiah(IDR)    21\n",
              "5      New Zealand           NewZealand($)    40\n",
              "6      Phillipines        Botswana Pula(P)    22\n",
              "7            Qatar         Qatari Rial(QR)    20\n",
              "8        Singapore               Dollar($)    20\n",
              "9     South Africa                 Rand(R)    60\n",
              "10       Sri Lanka   Sri Lankan Rupee(LKR)    20\n",
              "11          Turkey        Turkish Lira(TL)    34\n",
              "12             UAE      Emirati Diram(AED)    60\n",
              "13  United Kingdom              Pounds(£)    80\n",
              "14   United States               Dollar($)   434"
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        "outputId": "b9fdc96b-f676-401d-c1b4-e9dc1ee69404"
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        {
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          "data": {
            "text/plain": [
              "India    2423\n",
              "UAE        28\n",
              "Name: Country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 65
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              "   Has Online delivery         Country     0\n",
              "0                   No       Australia    24\n",
              "1                   No          Brazil    60\n",
              "2                   No          Canada     4\n",
              "3                   No           India  6229\n",
              "4                   No       Indonesia    21\n",
              "5                   No     New Zealand    40\n",
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              "7                   No           Qatar    20\n",
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              "9                   No    South Africa    60\n",
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              "11                  No          Turkey    34\n",
              "12                  No             UAE    32\n",
              "13                  No  United Kingdom    80\n",
              "14                  No   United States   434\n",
              "15                 Yes           India  2423\n",
              "16                 Yes             UAE    28"
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08043993-af1d-4eae-989a-4a6ef496b1c9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-08043993-af1d-4eae-989a-4a6ef496b1c9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQNICkADymip",
        "outputId": "fc56d921-8e0d-4643-f441-826d242502de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
              "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
              "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
              "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
              "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
              "       'Votes', 'Country'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.City.value_counts().index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0QlSap2Iyowd",
        "outputId": "9edd55a5-a5e1-4b48-c393-9bc87e87d804"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['New Delhi', 'Gurgaon', 'Noida', 'Faridabad', 'Ghaziabad',\n",
              "       'Bhubaneshwar', 'Amritsar', 'Ahmedabad', 'Lucknow', 'Guwahati',\n",
              "       ...\n",
              "       'Ojo Caliente', 'Montville', 'Monroe', 'Miller', 'Middleton Beach',\n",
              "       'Panchkula', 'Mc Millan', 'Mayfield', 'Macedon', 'Vineland Station'],\n",
              "      dtype='object', length=141)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "city_values=final_df.City.value_counts().values\n",
        "city_labels=final_df.City.value_counts().index"
      ],
      "metadata": {
        "id": "iPp4axgDysQt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "city_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AuyOdImCyv4H",
        "outputId": "088af853-fe25-42f9-deab-bb3130894811"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5473, 1118, 1080,  251,   25,   21,   21,   21,   21,   21,   20,\n",
              "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
              "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
              "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
              "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
              "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
              "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
              "         18,   18,   16,   14,   11,    6,    4,    4,    3,    3,    2,\n",
              "          2,    2,    2,    2,    2,    2,    2,    1,    1,    1,    1,\n",
              "          1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
              "          1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
              "          1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
              "          1,    1,    1,    1,    1,    1,    1,    1,    1])"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "city_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hokZE-5KyzVf",
        "outputId": "41b7021a-2449-45c6-86fe-d3852c8170b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['New Delhi', 'Gurgaon', 'Noida', 'Faridabad', 'Ghaziabad',\n",
              "       'Bhubaneshwar', 'Amritsar', 'Ahmedabad', 'Lucknow', 'Guwahati',\n",
              "       ...\n",
              "       'Ojo Caliente', 'Montville', 'Monroe', 'Miller', 'Middleton Beach',\n",
              "       'Panchkula', 'Mc Millan', 'Mayfield', 'Macedon', 'Vineland Station'],\n",
              "      dtype='object', length=141)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(city_values[:5],labels=city_labels[:5],autopct='%1.2f%%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "LwLpq75by1R2",
        "outputId": "46dd518a-6c0e-4afd-952b-14a8c6bea102"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7faea5d2dc90>,\n",
              "  <matplotlib.patches.Wedge at 0x7faea5d383d0>,\n",
              "  <matplotlib.patches.Wedge at 0x7faea5d38c50>,\n",
              "  <matplotlib.patches.Wedge at 0x7faea5d45590>,\n",
              "  <matplotlib.patches.Wedge at 0x7faea5d45f90>],\n",
              " [Text(-0.6145352824185932, 0.9123301960708633, 'New Delhi'),\n",
              "  Text(0.0623675251198054, -1.0982305276263407, 'Gurgaon'),\n",
              "  Text(0.8789045225625368, -0.6614581167535246, 'Noida'),\n",
              "  Text(1.0922218418223437, -0.13058119407559224, 'Faridabad'),\n",
              "  Text(1.099946280005612, -0.010871113182029924, 'Ghaziabad')],\n",
              " [Text(-0.3352010631374145, 0.497634652402289, '68.87%'),\n",
              "  Text(0.0340186500653484, -0.5990348332507311, '14.07%'),\n",
              "  Text(0.47940246685229276, -0.36079533641101336, '13.59%'),\n",
              "  Text(0.5957573682667329, -0.07122610585941394, '3.16%'),\n",
              "  Text(0.5999706981848791, -0.005929698099289049, '0.31%')])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "GJk0I9cUy6Gs"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}