EDA and Feature Engineering.ipynb

{"cells":[{"cell_type":"markdown","id":"4e0c25f4","metadata":{"id":"4e0c25f4"},"source":["## Zomato Dataset Exploratory Data Analysis"]},{"cell_type":"code","execution_count":null,"id":"c7807f09","metadata":{"id":"c7807f09"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","%matplotlib inline"]},{"cell_type":"code","execution_count":null,"id":"237756b9","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":855},"id":"237756b9","executionInfo":{"status":"ok","timestamp":1675740977783,"user_tz":-330,"elapsed":1923,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}},"outputId":"eba6330a-07a7-4611-ce89-28ac8dc15514"},"outputs":[{"output_type":"execute_result","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  ...          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]"],"text/html":["\n","  <div id=\"df-64a99f0c-65bf-43ad-b805-9d0ae692a509\">\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>Restaurant ID</th>\n","      <th>Restaurant Name</th>\n","      <th>Country Code</th>\n","      <th>City</th>\n","      <th>Address</th>\n","      <th>Locality</th>\n","      <th>Locality Verbose</th>\n","      <th>Longitude</th>\n","      <th>Latitude</th>\n","      <th>Cuisines</th>\n","      <th>...</th>\n","      <th>Currency</th>\n","      <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","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <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","      <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>4.8</td>\n","      <td>Dark Green</td>\n","      <td>Excellent</td>\n","      <td>229</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 21 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64a99f0c-65bf-43ad-b805-9d0ae692a509')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <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-64a99f0c-65bf-43ad-b805-9d0ae692a509 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pYPayWFX42wX","executionInfo":{"status":"ok","timestamp":1675741011952,"user_tz":-330,"elapsed":9044,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}},"outputId":"08ccadab-51d2-4fbb-fbbb-802e5a1b6523"},"id":"pYPayWFX42wX","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","execution_count":null,"id":"5ad99cf7","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5ad99cf7","executionInfo":{"status":"ok","timestamp":1675741043630,"user_tz":-330,"elapsed":717,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}},"outputId":"0ad77013-b996-4b56-ee28-3c152a506009"},"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":8}],"source":["df.columns"]},{"cell_type":"code","execution_count":null,"id":"607b3304","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"607b3304","executionInfo":{"status":"ok","timestamp":1675741048484,"user_tz":-330,"elapsed":21,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}},"outputId":"2f45e142-18e9-4309-db47-530e994835c4"},"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"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":null,"id":"3f22b356","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":399},"id":"3f22b356","executionInfo":{"status":"ok","timestamp":1675741060619,"user_tz":-330,"elapsed":791,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}},"outputId":"6f01f7bb-3809-45af-8d53-104c7e37e8a4"},"outputs":[{"output_type":"execute_result","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  "],"text/html":["\n","  <div id=\"df-a34ec6ef-e927-4ff6-81bd-672c1d1b436c\">\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>Restaurant ID</th>\n","      <th>Country Code</th>\n","      <th>Longitude</th>\n","      <th>Latitude</th>\n","      <th>Average Cost for two</th>\n","      <th>Price range</th>\n","      <th>Aggregate rating</th>\n","      <th>Votes</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <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","    </tr>\n","    <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","    </tr>\n","    <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","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a34ec6ef-e927-4ff6-81bd-672c1d1b436c')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <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 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Finding Relationship between features"]},{"cell_type":"code","execution_count":null,"id":"d1b650a9","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d1b650a9","executionInfo":{"status":"ok","timestamp":1675741073798,"user_tz":-330,"elapsed":1190,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}},"outputId":"d72fd34b-7e97-40fd-cf6e-42b24f09badb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(9551, 21)"]},"metadata":{},"execution_count":11}],"source":["df.shape"]},{"cell_type":"code","execution_count":null,"id":"11cfdebd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"11cfdebd","executionInfo":{"status":"ok","timestamp":1675741079212,"user_tz":-330,"elapsed":564,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}},"outputId":"075ff19b-7eff-48f5-c418-7f9ee3600a9d"},"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":12}],"source":["df.isnull().sum()"]},{"cell_type":"code","execution_count":null,"id":"b51762fa","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b51762fa","executionInfo":{"status":"ok","timestamp":1675741082474,"user_tz":-330,"elapsed":25,"user":{"displayName":"Majid 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Ali","userId":"10865913695116108118"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Country Code    Country\n","0             1      India\n","1            14  Australia\n","2            30     Brazil\n","3            37     Canada\n","4            94  Indonesia"],"text/html":["\n","  <div id=\"df-83dee61c-a0b3-4da3-9634-8cff4f44f218\">\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>Country Code</th>\n","      <th>Country</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      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Has Table booking Has Online delivery  \\\n","0  French, Japanese, Desserts  ...                Yes                  No   \n","1                    Japanese  ...                Yes                  No   \n","\n","  Is delivering now Switch to order menu Price range Aggregate rating  \\\n","0                No                   No           3              4.8   \n","1                No                   No           3              4.5   \n","\n","   Rating color  Rating text Votes      Country  \n","0    Dark Green    Excellent   314  Phillipines  \n","1    Dark Green    Excellent   591  Phillipines  \n","\n","[2 rows x 22 columns]"],"text/html":["\n","  <div id=\"df-6b50ae90-e5a7-4402-9b31-91db7d9b82ac\">\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>Restaurant ID</th>\n","      <th>Restaurant Name</th>\n","      <th>Country Code</th>\n","      <th>City</th>\n","      <th>Address</th>\n","      <th>Locality</th>\n","      <th>Locality Verbose</th>\n","      <th>Longitude</th>\n","      <th>Latitude</th>\n","      <th>Cuisines</th>\n","      <th>...</th>\n","      <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","      <th>Country</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <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>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","      <td>Phillipines</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>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      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Ali","userId":"10865913695116108118"}}},"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":24}],"source":["##To check Data Types\n","final_df.dtypes"]},{"cell_type":"code","execution_count":null,"id":"8d8f8c19","metadata":{"id":"8d8f8c19","outputId":"7e0fccbb-a97e-4699-b7fa-b69f5f32f62d","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675744763078,"user_tz":-330,"elapsed":15,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"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":25}],"source":["final_df.columns"]},{"cell_type":"code","execution_count":null,"id":"eff05865","metadata":{"id":"eff05865"},"outputs":[],"source":["country_names=final_df.Country.value_counts().index"]},{"cell_type":"code","execution_count":null,"id":"5555b734","metadata":{"id":"5555b734"},"outputs":[],"source":["country_val=final_df.Country.value_counts().values"]},{"cell_type":"code","execution_count":null,"id":"1988daed","metadata":{"id":"1988daed","outputId":"d2f52d58-63a2-4b74-b276-abbd1af7f044","colab":{"base_uri":"https://localhost:8080/","height":567},"executionInfo":{"status":"ok","timestamp":1675744780419,"user_tz":-330,"elapsed":1213,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["([<matplotlib.patches.Wedge at 0x7fe42db7b7f0>,\n","  <matplotlib.patches.Wedge at 0x7fe42db4deb0>,\n","  <matplotlib.patches.Wedge at 0x7fe42dadd580>],\n"," 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\n"},"metadata":{}}],"source":["## Pie Chart- Top 3 countries that uses zomato\n","plt.pie(country_val[:3],labels=country_names[:3],autopct='%1.2f%%')"]},{"cell_type":"markdown","id":"315213fd","metadata":{"id":"315213fd"},"source":["Observation:Zomato maximum records or transaction are from India\n","After that USA and then United Kingdoms"]},{"cell_type":"code","execution_count":null,"id":"602f31ec","metadata":{"id":"602f31ec","outputId":"51582c3c-41d5-4980-c1d8-c507a759f261","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675744788586,"user_tz":-330,"elapsed":965,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"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":29}],"source":["final_df.columns"]},{"cell_type":"code","execution_count":null,"id":"25f902aa","metadata":{"id":"25f902aa"},"outputs":[],"source":["ratings=final_df.groupby(['Aggregate rating','Rating color','Rating text']).size().reset_index().rename(columns={0:'Rating Count'})"]},{"cell_type":"code","execution_count":null,"id":"bca55a2f","metadata":{"id":"bca55a2f","outputId":"620d3bf0-3ae6-41de-f373-231c43487791","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1675744841325,"user_tz":-330,"elapsed":35,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"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-28cd6f8f-f712-4ba8-ba66-b57cd0e16f55\">\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-28cd6f8f-f712-4ba8-ba66-b57cd0e16f55')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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When Rating are between 4.0 to 3.4--->very good\n","3. when Rating is between 3.5 to 3.9----> good\n","4. when Rating is between 3.0 to 3.4----> average\n","5. when Rating is between 2.5 to 2.9----> average\n","6. when Rating is between 2.0 to 2.4----> Poor"]},{"cell_type":"code","execution_count":null,"id":"7cecd96a","metadata":{"id":"7cecd96a","outputId":"5e87444f-4785-43b7-c64f-5bafd9af2a22","colab":{"base_uri":"https://localhost:8080/","height":305},"executionInfo":{"status":"ok","timestamp":1675744856688,"user_tz":-330,"elapsed":976,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"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"],"text/html":["\n","  <div id=\"df-9eb65e44-0f84-42a1-8171-f8e06aa5e274\">\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","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9eb65e44-0f84-42a1-8171-f8e06aa5e274')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <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-9eb65e44-0f84-42a1-8171-f8e06aa5e274 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":32}],"source":["ratings.head()"]},{"cell_type":"code","execution_count":null,"id":"e6567f3b","metadata":{"id":"e6567f3b","outputId":"08d8ad26-2eca-463b-a743-7ca37465e08e","colab":{"base_uri":"https://localhost:8080/","height":209},"executionInfo":{"status":"ok","timestamp":1675744860771,"user_tz":-330,"elapsed":737,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fe42da975e0>"]},"metadata":{},"execution_count":33},{"output_type":"display_data","data":{"text/plain":["<Figure size 864x432 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["import matplotlib\n","matplotlib.rcParams['figure.figsize'] = (12, 6)\n","sns.barplot(x=\"Aggregate rating\",y=\"Rating Count\",data=ratings)"]},{"cell_type":"code","execution_count":null,"id":"98c3d9fa","metadata":{"id":"98c3d9fa","outputId":"390cf67f-4e2c-4cf9-e683-7eda4ad3f3a6","colab":{"base_uri":"https://localhost:8080/","height":209},"executionInfo":{"status":"ok","timestamp":1675744873126,"user_tz":-330,"elapsed":2692,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fe42d96a910>"]},"metadata":{},"execution_count":34},{"output_type":"display_data","data":{"text/plain":["<Figure size 864x432 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["sns.barplot(x=\"Aggregate rating\",y=\"Rating Count\",hue='Rating color',data=ratings,palette=['blue','red','orange','yellow','green','green'])"]},{"cell_type":"markdown","id":"8272299f","metadata":{"id":"8272299f"},"source":["Observation:\n","1. Not Rated count is very high\n","2. Maximum number of rating are between 2.5 to 3.4"]},{"cell_type":"code","execution_count":null,"id":"7b3de528","metadata":{"id":"7b3de528","outputId":"da6edbea-3531-4df2-a792-9cc362250526","colab":{"base_uri":"https://localhost:8080/","height":212},"executionInfo":{"status":"ok","timestamp":1675744884981,"user_tz":-330,"elapsed":509,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fe42d9cae50>"]},"metadata":{},"execution_count":35},{"output_type":"display_data","data":{"text/plain":["<Figure size 864x432 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["## Count plot\n","sns.countplot(x=\"Rating color\",data=ratings,palette=['blue','red','orange','yellow','green','green'])"]},{"cell_type":"code","execution_count":null,"id":"21a25651","metadata":{"id":"21a25651","outputId":"f768c36e-345e-4544-b325-ec2408d7ebc8","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1675744891695,"user_tz":-330,"elapsed":977,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"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-9b1f28cb-a5cc-41b8-895c-f2c6e168ba6d\">\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","  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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","    </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-9b1f28cb-a5cc-41b8-895c-f2c6e168ba6d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <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 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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":36}],"source":["ratings"]},{"cell_type":"code","execution_count":null,"id":"c9bf2725","metadata":{"id":"c9bf2725","outputId":"616b0aa9-ce48-420d-e63d-cb964bdaee31","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1675744896338,"user_tz":-330,"elapsed":34,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"outputs":[{"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"],"text/html":["\n","  <div id=\"df-b34831f6-851b-45cc-b253-29898244cb9d\">\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>Country</th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Brazil</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>India</td>\n","      <td>2139</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>United Kingdom</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>United States</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b34831f6-851b-45cc-b253-29898244cb9d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <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-b34831f6-851b-45cc-b253-29898244cb9d button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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}],"source":["### Find the countries name that has given 0 rating\n","final_df[final_df['Rating color']=='White'].groupby('Country').size().reset_index()"]},{"cell_type":"code","execution_count":null,"id":"f98cf1e3","metadata":{"id":"f98cf1e3","outputId":"28f14851-2462-4a6a-dbcb-7b69cd813983","colab":{"base_uri":"https://localhost:8080/","height":302},"executionInfo":{"status":"ok","timestamp":1675744904056,"user_tz":-330,"elapsed":808,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Aggregate rating         Country     0\n","0               0.0          Brazil     5\n","1               0.0           India  2139\n","2               0.0  United Kingdom     1\n","3               0.0   United States     3\n","4               1.8           India     1"],"text/html":["\n","  <div id=\"df-305d0e9e-1710-4465-abbc-14c35e7b5aa8\">\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>Country</th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>Brazil</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.0</td>\n","      <td>India</td>\n","      <td>2139</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.0</td>\n","      <td>United Kingdom</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.0</td>\n","      <td>United States</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.8</td>\n","      <td>India</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-305d0e9e-1710-4465-abbc-14c35e7b5aa8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <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-305d0e9e-1710-4465-abbc-14c35e7b5aa8 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":38}],"source":["final_df.groupby(['Aggregate rating','Country']).size().reset_index().head(5)"]},{"cell_type":"markdown","id":"575273b8","metadata":{"id":"575273b8"},"source":["Observations\n","Maximum number of 0 ratings are from Indian customers"]},{"cell_type":"code","execution_count":null,"id":"4865e5ff","metadata":{"id":"4865e5ff","outputId":"541ac859-0ed3-4431-8fb7-8012dc5504e3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675744909334,"user_tz":-330,"elapsed":527,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"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}],"source":["##find out which currency is used by which country?\n","final_df.columns"]},{"cell_type":"code","execution_count":null,"id":"78197a44","metadata":{"id":"78197a44","outputId":"a28f8c63-7b70-4ab6-f2f9-6b07a1192dd4","colab":{"base_uri":"https://localhost:8080/","height":737},"executionInfo":{"status":"ok","timestamp":1675744913741,"user_tz":-330,"elapsed":567,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["           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"],"text/html":["\n","  <div id=\"df-713d06db-5737-4ade-8c9b-042121339911\">\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>Country</th>\n","      <th>Currency</th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Australia</td>\n","      <td>Dollar($)</td>\n","      <td>24</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Brazil</td>\n","      <td>Brazilian Real(R$)</td>\n","      <td>60</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Canada</td>\n","      <td>Dollar($)</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>India</td>\n","      <td>Indian Rupees(Rs.)</td>\n","      <td>8652</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Indonesia</td>\n","      <td>Indonesian Rupiah(IDR)</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>New Zealand</td>\n","      <td>NewZealand($)</td>\n","      <td>40</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Phillipines</td>\n","      <td>Botswana Pula(P)</td>\n","      <td>22</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Qatar</td>\n","      <td>Qatari Rial(QR)</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Singapore</td>\n","      <td>Dollar($)</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>South Africa</td>\n","      <td>Rand(R)</td>\n","      <td>60</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Sri Lanka</td>\n","      <td>Sri Lankan Rupee(LKR)</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Turkey</td>\n","      <td>Turkish Lira(TL)</td>\n","      <td>34</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>UAE</td>\n","      <td>Emirati Diram(AED)</td>\n","      <td>60</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>United Kingdom</td>\n","      <td>Pounds(£)</td>\n","      <td>80</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>United States</td>\n","      <td>Dollar($)</td>\n","      <td>434</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-713d06db-5737-4ade-8c9b-042121339911')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <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","  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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":40}],"source":["final_df[['Country','Currency']].groupby(['Country','Currency']).size().reset_index()"]},{"cell_type":"code","execution_count":null,"id":"3089cc22","metadata":{"id":"3089cc22"},"outputs":[],"source":["## Which Countries do have online deliveries option"]},{"cell_type":"code","execution_count":null,"id":"20f31ff2","metadata":{"id":"20f31ff2","outputId":"43c2e08f-5c1e-4159-ec69-803a376118bc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675744924590,"user_tz":-330,"elapsed":21,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["India    2423\n","UAE        28\n","Name: Country, dtype: int64"]},"metadata":{},"execution_count":42}],"source":["final_df[final_df['Has Online delivery'] ==\"Yes\"].Country.value_counts()"]},{"cell_type":"code","execution_count":null,"id":"c4c0d408","metadata":{"id":"c4c0d408","outputId":"19ae83e4-2068-4ab1-b7bb-21232a05b88d","colab":{"base_uri":"https://localhost:8080/","height":713},"executionInfo":{"status":"ok","timestamp":1675744927232,"user_tz":-330,"elapsed":496,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   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","6                   No     Phillipines    22\n","7                   No           Qatar    20\n","8                   No       Singapore    20\n","9                   No    South Africa    60\n","10                  No       Sri Lanka    20\n","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"],"text/html":["\n","  <div id=\"df-78328030-e3c2-490d-9e1b-7b5d76d79efc\">\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>Has Online delivery</th>\n","      <th>Country</th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>No</td>\n","      <td>Australia</td>\n","      <td>24</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>No</td>\n","      <td>Brazil</td>\n","      <td>60</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>No</td>\n","      <td>Canada</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>No</td>\n","      <td>India</td>\n","      <td>6229</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>No</td>\n","      <td>Indonesia</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>No</td>\n","      <td>New Zealand</td>\n","      <td>40</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>No</td>\n","      <td>Phillipines</td>\n","      <td>22</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>No</td>\n","      <td>Qatar</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>No</td>\n","      <td>Singapore</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>No</td>\n","      <td>South Africa</td>\n","      <td>60</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>No</td>\n","      <td>Sri Lanka</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>No</td>\n","      <td>Turkey</td>\n","      <td>34</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>No</td>\n","      <td>UAE</td>\n","      <td>32</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>No</td>\n","      <td>United Kingdom</td>\n","      <td>80</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>No</td>\n","      <td>United States</td>\n","      <td>434</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Yes</td>\n","      <td>India</td>\n","      <td>2423</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Yes</td>\n","      <td>UAE</td>\n","      <td>28</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78328030-e3c2-490d-9e1b-7b5d76d79efc')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              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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":43}],"source":["final_df[['Has Online delivery','Country']].groupby(['Has Online delivery','Country']).size().reset_index()"]},{"cell_type":"markdown","id":"aaf8d8b5","metadata":{"id":"aaf8d8b5"},"source":["Observations:\n","1. Online Deliveries are available in India and UAE    "]},{"cell_type":"code","execution_count":null,"id":"d87444a8","metadata":{"id":"d87444a8","outputId":"42e6c8b6-2ad4-430d-ce97-a3731f823ac0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675744935888,"user_tz":-330,"elapsed":524,"user":{"displayName":"Majid Ali","userId":"10865913695116108118"}}},"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":44}],"source":["final_df.columns"]},{"cell_type":"code","execution_count":null,"id":"9791411f","metadata":{"id":"9791411f"},"outputs":[],"source":["## Create a pie chart for top 5 cities distribution"]},{"cell_type":"code","execution_count":null,"id":"9d4f069e","metadata":{"id":"9d4f069e","outputId":"0ec07fd3-fea7-4ee6-baf4-276200f7c353"},"outputs":[{"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)"]},"execution_count":72,"metadata":{},"output_type":"execute_result"}],"source":["final_df.City.value_counts().index"]},{"cell_type":"code","execution_count":null,"id":"5cd2d040","metadata":{"id":"5cd2d040"},"outputs":[],"source":["city_values=final_df.City.value_counts().values\n","city_labels=final_df.City.value_counts().index"]},{"cell_type":"code","execution_count":null,"id":"27f4e09c","metadata":{"id":"27f4e09c","outputId":"8bd4ead2-be90-4832-c32b-26380d278f92"},"outputs":[{"data":{"text/plain":["([<matplotlib.patches.Wedge at 0x1efa7165280>,\n","  <matplotlib.patches.Wedge at 0x1efa7165940>,\n","  <matplotlib.patches.Wedge at 0x1efa7165fd0>,\n","  <matplotlib.patches.Wedge at 0x1efa7458760>,\n","  <matplotlib.patches.Wedge at 0x1efa7458e80>],\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%')])"]},"execution_count":78,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 864x432 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.pie(city_values[:5],labels=city_labels[:5],autopct='%1.2f%%')"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.7"},"colab":{"provenance":[{"file_id":"1PwjpKRG6eVHgRf85sZdPGYyhhu3O2YTP","timestamp":1675740404583}]}},"nbformat":4,"nbformat_minor":5}