highest_amount_of_NH3_and_CO_pollutant.ipynb

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      "source": [
        "# Importing the dependencies\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading in the dataset\n",
        "df = pd.read_csv('/content/Air_Quality.csv')\n",
        "df.head()"
      ],
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              "   id country           state       city                         station  \\\n",
              "0   1   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
              "1   2   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
              "2   3   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
              "3   4   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
              "4   5   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
              "\n",
              "  pollutant_id          last_update  pollutant_min  pollutant_max  \\\n",
              "0        PM2.5  21-10-2021 01:00:00           69.0          109.0   \n",
              "1         PM10  21-10-2021 01:00:00           82.0          138.0   \n",
              "2          NO2  21-10-2021 01:00:00           10.0           42.0   \n",
              "3          NH3  21-10-2021 01:00:00            4.0            5.0   \n",
              "4          SO2  21-10-2021 01:00:00           16.0           42.0   \n",
              "\n",
              "   pollutant_avg  \n",
              "0           86.0  \n",
              "1          105.0  \n",
              "2           19.0  \n",
              "3            4.0  \n",
              "4           27.0  "
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>India</td>\n",
              "      <td>Andhra_Pradesh</td>\n",
              "      <td>Amaravati</td>\n",
              "      <td>Secretariat, Amaravati - APPCB</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>19.0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
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              "      <td>SO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
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      "source": [
        "# checking all the columns\n",
        "df.columns\n"
      ],
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        "id": "svRhIfyCUBU0",
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          "data": {
            "text/plain": [
              "Index(['id', 'country', 'state', 'city', 'station', 'pollutant_id',\n",
              "       'last_update', 'pollutant_min', 'pollutant_max', 'pollutant_avg'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pollutant = ['NH3' ,'NO2']"
      ],
      "metadata": {
        "id": "r95wdQCPZuvf"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Indexing the rows which have the pollutant  'NH3'\n",
        "df_nh3 = df.loc[df['pollutant_id'] == 'NH3']\n",
        "\n",
        "# Indexing the rows which have the pollutant  'NO2'\n",
        "df_no2 = df.loc[df['pollutant_id'] == 'NO2']"
      ],
      "metadata": {
        "id": "w_nrjcUccyFd"
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      "execution_count": 11,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df_nh3.head() "
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            "text/plain": [
              "    id country           state               city  \\\n",
              "3    4   India  Andhra_Pradesh          Amaravati   \n",
              "10  11   India  Andhra_Pradesh  Rajamahendravaram   \n",
              "17  18   India  Andhra_Pradesh           Tirupati   \n",
              "24  25   India  Andhra_Pradesh      Visakhapatnam   \n",
              "30  31   India           Assam           Guwahati   \n",
              "\n",
              "                                           station pollutant_id  \\\n",
              "3                   Secretariat, Amaravati - APPCB          NH3   \n",
              "10  Anand Kala Kshetram, Rajamahendravaram - APPCB          NH3   \n",
              "17                      Tirumala, Tirupati - APPCB          NH3   \n",
              "24          GVM Corporation, Visakhapatnam - APPCB          NH3   \n",
              "30                     Pan Bazaar, Guwahati - APCB          NH3   \n",
              "\n",
              "            last_update  pollutant_min  pollutant_max  pollutant_avg  \n",
              "3   21-10-2021 01:00:00            4.0            5.0            4.0  \n",
              "10  21-10-2021 01:00:00            2.0            6.0            4.0  \n",
              "17  21-10-2021 01:00:00            1.0            3.0            2.0  \n",
              "24  21-10-2021 01:00:00            2.0            9.0            4.0  \n",
              "30  21-10-2021 01:00:00            1.0            4.0            2.0  "
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>India</td>\n",
              "      <td>Andhra_Pradesh</td>\n",
              "      <td>Amaravati</td>\n",
              "      <td>Secretariat, Amaravati - APPCB</td>\n",
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              "      <td>Rajamahendravaram</td>\n",
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              "      <td>Tirumala, Tirupati - APPCB</td>\n",
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              "      <th>24</th>\n",
              "      <td>25</td>\n",
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              "      <th>30</th>\n",
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              "      <td>Assam</td>\n",
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              "      <td>NH3</td>\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",
              "          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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting the 10 rows with highest 'NH3' pollutant_avg\n",
        "nh3_max = df_nh3.nlargest(10 ,['pollutant_avg'])\n",
        "nh3_max"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "ipe7j8jNfOuf",
        "outputId": "0e37ca24-d0dc-4686-9c92-b31674e3a210"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id country          state         city  \\\n",
              "1099  1100   India    Maharashtra       Mumbai   \n",
              "1648  1649   India  Uttar_Pradesh       Meerut   \n",
              "1259  1260   India         Punjab       Khanna   \n",
              "1743  1744   India  Uttar_Pradesh    Vrindavan   \n",
              "1509  1510   India  Uttar_Pradesh    Firozabad   \n",
              "1488  1489   India  Uttar_Pradesh         Agra   \n",
              "174    175   India          Delhi        Delhi   \n",
              "1312  1313   India      Rajasthan       Jaipur   \n",
              "469    470   India        Haryana  Bahadurgarh   \n",
              "561    562   India        Haryana        Hisar   \n",
              "\n",
              "                                  station pollutant_id          last_update  \\\n",
              "1099           Mulund West, Mumbai - MPCB          NH3  21-10-2021 01:00:00   \n",
              "1648  Pallavpuram Phase 2, Meerut - UPPCB          NH3  21-10-2021 01:00:00   \n",
              "1259           Kalal Majra, Khanna - PPCB          NH3  21-10-2021 01:00:00   \n",
              "1743     Omex Eternity, Vrindavan - UPPCB          NH3  21-10-2021 01:00:00   \n",
              "1509        Nagla Bhau, Firozabad - UPPCB          NH3  21-10-2021 01:00:00   \n",
              "1488           Shastripuram, Agra - UPPCB          NH3  21-10-2021 01:00:00   \n",
              "174                     DTU, Delhi - CPCB          NH3  21-10-2021 01:00:00   \n",
              "1312         Adarsh Nagar, Jaipur - RSPCB          NH3  21-10-2021 01:00:00   \n",
              "469       Arya Nagar, Bahadurgarh - HSPCB          NH3  21-10-2021 01:00:00   \n",
              "561        Urban Estate-II, Hisar - HSPCB          NH3  21-10-2021 01:00:00   \n",
              "\n",
              "      pollutant_min  pollutant_max  pollutant_avg  \n",
              "1099           17.0           32.0           28.0  \n",
              "1648           17.0           42.0           25.0  \n",
              "1259            9.0           39.0           24.0  \n",
              "1743            3.0           31.0           23.0  \n",
              "1509           10.0           48.0           22.0  \n",
              "1488           15.0           26.0           21.0  \n",
              "174            10.0           35.0           19.0  \n",
              "1312            6.0           60.0           18.0  \n",
              "469            17.0           19.0           17.0  \n",
              "561            15.0           19.0           17.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47e67864-8b45-461d-aa91-d0a4ab5ff3ea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>country</th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th>station</th>\n",
              "      <th>pollutant_id</th>\n",
              "      <th>last_update</th>\n",
              "      <th>pollutant_min</th>\n",
              "      <th>pollutant_max</th>\n",
              "      <th>pollutant_avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1099</th>\n",
              "      <td>1100</td>\n",
              "      <td>India</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>Mulund West, Mumbai - MPCB</td>\n",
              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>17.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1648</th>\n",
              "      <td>1649</td>\n",
              "      <td>India</td>\n",
              "      <td>Uttar_Pradesh</td>\n",
              "      <td>Meerut</td>\n",
              "      <td>Pallavpuram Phase 2, Meerut - UPPCB</td>\n",
              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>17.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1259</th>\n",
              "      <td>1260</td>\n",
              "      <td>India</td>\n",
              "      <td>Punjab</td>\n",
              "      <td>Khanna</td>\n",
              "      <td>Kalal Majra, Khanna - PPCB</td>\n",
              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>9.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1743</th>\n",
              "      <td>1744</td>\n",
              "      <td>India</td>\n",
              "      <td>Uttar_Pradesh</td>\n",
              "      <td>Vrindavan</td>\n",
              "      <td>Omex Eternity, Vrindavan - UPPCB</td>\n",
              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1509</th>\n",
              "      <td>1510</td>\n",
              "      <td>India</td>\n",
              "      <td>Uttar_Pradesh</td>\n",
              "      <td>Firozabad</td>\n",
              "      <td>Nagla Bhau, Firozabad - UPPCB</td>\n",
              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1488</th>\n",
              "      <td>1489</td>\n",
              "      <td>India</td>\n",
              "      <td>Uttar_Pradesh</td>\n",
              "      <td>Agra</td>\n",
              "      <td>Shastripuram, Agra - UPPCB</td>\n",
              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>15.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>175</td>\n",
              "      <td>India</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>DTU, Delhi - CPCB</td>\n",
              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1312</th>\n",
              "      <td>1313</td>\n",
              "      <td>India</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>Jaipur</td>\n",
              "      <td>Adarsh Nagar, Jaipur - RSPCB</td>\n",
              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>469</th>\n",
              "      <td>470</td>\n",
              "      <td>India</td>\n",
              "      <td>Haryana</td>\n",
              "      <td>Bahadurgarh</td>\n",
              "      <td>Arya Nagar, Bahadurgarh - HSPCB</td>\n",
              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>17.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>561</th>\n",
              "      <td>562</td>\n",
              "      <td>India</td>\n",
              "      <td>Haryana</td>\n",
              "      <td>Hisar</td>\n",
              "      <td>Urban Estate-II, Hisar - HSPCB</td>\n",
              "      <td>NH3</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>15.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47e67864-8b45-461d-aa91-d0a4ab5ff3ea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\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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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# PLotting the 10 cities with highest 'NH3' pollutant_avg\n",
        "plt.figure(figsize=(17,6))\n",
        "plt.xticks(color = 'red' ,size = 12)\n",
        "plt.yticks(color = 'red' ,size = 13)\n",
        "\n",
        "ax = sns.barplot(x = nh3_max['city'] ,y = nh3_max['pollutant_avg'] \n",
        "            ,palette = 'Set1' ,edgecolor='black')\n",
        "\n",
        "plt.xlabel('CITY' ,color = 'PURPLE',size = 15)\n",
        "plt.ylabel('AVG  NH3  POLLUTANT' ,color = 'PURPLE' ,size = 15)\n",
        "\n",
        "plt.grid(b = True ,\n",
        "         linewidth = 0.5,\n",
        "         linestyle = '--',\n",
        "         color = 'gray')\n",
        "\n",
        "for bar in ax.patches:\n",
        "       ax.annotate( s = str(bar.get_height()) , # the text to put\n",
        "                    xy = (bar.get_x() + bar.get_width() / 2, bar.get_height()),\n",
        "                    ha='center',\n",
        "                    va='center',\n",
        "                    size=14,\n",
        "                    xytext=(0, 10),\n",
        "                    textcoords='offset points')\n",
        "       \n",
        "for i,bar in enumerate(ax.patches):\n",
        "       ax.annotate( s = nh3_max['state'].iloc[i], # the text to put\n",
        "                    xy = (bar.get_x() + bar.get_width() / 2, bar.get_height()/2),\n",
        "                    ha='center',\n",
        "                    va='center',\n",
        "                    size=12,\n",
        "                    xytext=(0, 10),\n",
        "                    textcoords='offset points')   \n",
        "plt.title(\"Cities with the highest NH3 pollutant_avg\" ,size=17 ,color = 'pink')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "8214IJ5GfwlT",
        "outputId": "18ceeb54-6b58-473e-a814-9752f305f8ba"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Cities with the highest NH3 pollutant_avg')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting the 10 cities with highest 'NO2' pollutant_avg\n",
        "no2_max = df_no2.nlargest(10 ,['pollutant_avg']).reset_index()\n",
        "no2_max"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "qq5dLuALfjS4",
        "outputId": "084f5852-d547-4d1f-ba98-93c0d7c16d8c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index    id country           state           city  \\\n",
              "0    454   455   India         Gujarat          Vatva   \n",
              "1    135   136   India           Delhi          Delhi   \n",
              "2   1187  1188   India     Maharashtra           Pune   \n",
              "3    523   524   India         Haryana      Faridabad   \n",
              "4    180   181   India           Delhi          Delhi   \n",
              "5   1529  1530   India   Uttar_Pradesh      Ghaziabad   \n",
              "6    199   200   India           Delhi          Delhi   \n",
              "7    938   939   India  Madhya Pradesh         Indore   \n",
              "8     23    24   India  Andhra_Pradesh  Visakhapatnam   \n",
              "9    290   291   India           Delhi          Delhi   \n",
              "\n",
              "                                        station pollutant_id  \\\n",
              "0                    Phase-4 GIDC, Vatva - GPCB          NO2   \n",
              "1                     Anand Vihar, Delhi - DPCC          NO2   \n",
              "2      Revenue Colony-Shivajinagar, Pune - IITM          NO2   \n",
              "3                Sector- 16A, Faridabad - HSPCB          NO2   \n",
              "4  Dr. Karni Singh Shooting Range, Delhi - DPCC          NO2   \n",
              "5               Sanjay Nagar, Ghaziabad - UPPCB          NO2   \n",
              "6           IHBAS, Dilshad Garden, Delhi - CPCB          NO2   \n",
              "7               Chhoti Gwaltoli, Indore - MPPCB          NO2   \n",
              "8        GVM Corporation, Visakhapatnam - APPCB          NO2   \n",
              "9                   Okhla Phase-2, Delhi - DPCC          NO2   \n",
              "\n",
              "           last_update  pollutant_min  pollutant_max  pollutant_avg  \n",
              "0  21-10-2021 01:00:00           74.0          404.0          195.0  \n",
              "1  21-10-2021 01:00:00           75.0          171.0          128.0  \n",
              "2  21-10-2021 01:00:00           35.0          219.0          118.0  \n",
              "3  21-10-2021 01:00:00           60.0          182.0          114.0  \n",
              "4  21-10-2021 01:00:00           74.0          141.0          106.0  \n",
              "5  21-10-2021 01:00:00           43.0          162.0          105.0  \n",
              "6  21-10-2021 01:00:00           65.0          125.0           99.0  \n",
              "7  21-10-2021 01:00:00           39.0          176.0           96.0  \n",
              "8  21-10-2021 01:00:00           13.0          187.0           91.0  \n",
              "9  21-10-2021 01:00:00           62.0          143.0           90.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-30355e14-00d8-41c0-845e-372d7cebb9c3\">\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>index</th>\n",
              "      <th>id</th>\n",
              "      <th>country</th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th>station</th>\n",
              "      <th>pollutant_id</th>\n",
              "      <th>last_update</th>\n",
              "      <th>pollutant_min</th>\n",
              "      <th>pollutant_max</th>\n",
              "      <th>pollutant_avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>454</td>\n",
              "      <td>455</td>\n",
              "      <td>India</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>Vatva</td>\n",
              "      <td>Phase-4 GIDC, Vatva - GPCB</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>74.0</td>\n",
              "      <td>404.0</td>\n",
              "      <td>195.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>135</td>\n",
              "      <td>136</td>\n",
              "      <td>India</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Anand Vihar, Delhi - DPCC</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>75.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>128.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1187</td>\n",
              "      <td>1188</td>\n",
              "      <td>India</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>Pune</td>\n",
              "      <td>Revenue Colony-Shivajinagar, Pune - IITM</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>35.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>118.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>523</td>\n",
              "      <td>524</td>\n",
              "      <td>India</td>\n",
              "      <td>Haryana</td>\n",
              "      <td>Faridabad</td>\n",
              "      <td>Sector- 16A, Faridabad - HSPCB</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>60.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>180</td>\n",
              "      <td>181</td>\n",
              "      <td>India</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Dr. Karni Singh Shooting Range, Delhi - DPCC</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>74.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>106.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1529</td>\n",
              "      <td>1530</td>\n",
              "      <td>India</td>\n",
              "      <td>Uttar_Pradesh</td>\n",
              "      <td>Ghaziabad</td>\n",
              "      <td>Sanjay Nagar, Ghaziabad - UPPCB</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>43.0</td>\n",
              "      <td>162.0</td>\n",
              "      <td>105.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>199</td>\n",
              "      <td>200</td>\n",
              "      <td>India</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>IHBAS, Dilshad Garden, Delhi - CPCB</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>65.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>99.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>938</td>\n",
              "      <td>939</td>\n",
              "      <td>India</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>Indore</td>\n",
              "      <td>Chhoti Gwaltoli, Indore - MPPCB</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>39.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>96.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "      <td>India</td>\n",
              "      <td>Andhra_Pradesh</td>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>GVM Corporation, Visakhapatnam - APPCB</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>13.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>91.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>290</td>\n",
              "      <td>291</td>\n",
              "      <td>India</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Okhla Phase-2, Delhi - DPCC</td>\n",
              "      <td>NO2</td>\n",
              "      <td>21-10-2021 01:00:00</td>\n",
              "      <td>62.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30355e14-00d8-41c0-845e-372d7cebb9c3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-30355e14-00d8-41c0-845e-372d7cebb9c3 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-30355e14-00d8-41c0-845e-372d7cebb9c3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "          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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# PLotting the 10 cities with highest 'NO2' pollutant_avg\n",
        "plt.figure(figsize=(17,6))\n",
        "plt.xticks(color = 'red' ,size = 12)\n",
        "plt.yticks(color = 'red' ,size = 13)\n",
        "\n",
        "ax = sns.barplot(x = no2_max['city'] ,y = no2_max['pollutant_avg'] \n",
        "            ,palette = 'Set1' ,edgecolor='black')\n",
        "\n",
        "plt.xlabel('CITY' ,color = 'PURPLE',size = 15)\n",
        "plt.ylabel('AVG  NO2  POLLUTANT' ,color = 'PURPLE' ,size = 15)\n",
        "\n",
        "plt.grid(b = True ,\n",
        "         linewidth = 0.5,\n",
        "         linestyle = '--',\n",
        "         color = 'gray')\n",
        "\n",
        "for bar in ax.patches:\n",
        "       ax.annotate( s = str(bar.get_height()) , # the text to put\n",
        "                    xy = (bar.get_x() + bar.get_width() / 2, bar.get_height()),\n",
        "                    ha='center',\n",
        "                    va='center',\n",
        "                    size=14,\n",
        "                    xytext=(0, 10),\n",
        "                    textcoords='offset points')\n",
        "       \n",
        "for i,bar in enumerate(ax.patches):\n",
        "       ax.annotate( s = no2_max['state'].iloc[i], # the text to put\n",
        "                    xy = (bar.get_x() + bar.get_width() / 2, bar.get_height()/2),\n",
        "                    ha='center',\n",
        "                    va='center',\n",
        "                    size=12,\n",
        "                    xytext=(0, 10),\n",
        "                    textcoords='offset points')   \n",
        "       \n",
        "plt.title(\"Cities with the highest NO2 pollutant_avg\" ,size=17 ,color = 'pink');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "nColV7mXkJJV",
        "outputId": "a1cc318c-5f09-4b04-f312-e83dfa8aaebd"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ezSzQmqPmLOu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}