jovian_final_project.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/samyukta-15/Air-Quality-Analysis/blob/main/jovian_final_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e026a1c0",
      "metadata": {
        "id": "e026a1c0"
      },
      "source": [
        "# Air Quality Analysis\n",
        "\n",
        "The dataset for the project has been downloaded from Kaggle. We're trying to analyse the air quality in different cities of India using pandas for data manipulation and seaborn for data visualisation.\n",
        "\n",
        "I express immense gratitude to the entire team of [Data Analysis with Python: Zero to Pandas](zerotopandas.com) for creating a beautifully structured course. The information is short and crisp. The assignments help implement the concepts explained in the course. Lastly, the articles linked for further information are super useful and helpful."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "53e48a92",
      "metadata": {
        "id": "53e48a92"
      },
      "source": [
        "## Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94e38098",
      "metadata": {
        "id": "94e38098"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d8893fee",
      "metadata": {
        "id": "d8893fee"
      },
      "source": [
        "## Downloading the Dataset\n",
        "\n",
        "The dataset has been downloaded from Kaggle as a .csv file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8081e91f",
      "metadata": {
        "id": "8081e91f"
      },
      "outputs": [],
      "source": [
        "air = pd.read_csv('/Users/samyuktavasaikar/Downloads/archive/city_day.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5394ca3f",
      "metadata": {
        "id": "5394ca3f"
      },
      "outputs": [],
      "source": [
        "project_name = \"zerotopandas-course-project\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c499999f",
      "metadata": {
        "id": "c499999f",
        "outputId": "f1d1ab36-6a38-4e9c-ef85-c307e1e7f316"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[jovian] Updating notebook \"samyukta-vasaikar/zerotopandas-course-project\" on https://jovian.ai/\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/samyukta-vasaikar/zerotopandas-course-project\u001b[0m\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "'https://jovian.ai/samyukta-vasaikar/zerotopandas-course-project'"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import jovian\n",
        "\n",
        "jovian.commit(project=project_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4f90fd0e",
      "metadata": {
        "id": "4f90fd0e"
      },
      "source": [
        "## Data Preparation and Cleaning\n",
        "\n",
        "- We've glanced over the dataset by generating a random sample and using .sample()\n",
        "- We explored the number of rows and columns by using .shape\n",
        "- We understood the column names by using .columns\n",
        "- We converted the necessary data into date-time format and numerical format\n",
        "- We wanted to create a simplified AQI value, we needed to choose if we wanted to consider PM 2.5 or PM 10, hence, we checked the number of null and non-null values in each of the two columns, and went ahead with the column with the least number of null values "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4b46b4a",
      "metadata": {
        "id": "e4b46b4a"
      },
      "source": [
        ".sample() is used to generate a random sample of rows from the given dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "31155dec",
      "metadata": {
        "id": "31155dec",
        "outputId": "7640ae59-dc24-441d-dbc1-ab88cf0d5f65"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>City</th>\n",
              "      <th>Date</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>PM10</th>\n",
              "      <th>NO</th>\n",
              "      <th>NO2</th>\n",
              "      <th>NOx</th>\n",
              "      <th>NH3</th>\n",
              "      <th>CO</th>\n",
              "      <th>SO2</th>\n",
              "      <th>O3</th>\n",
              "      <th>Benzene</th>\n",
              "      <th>Toluene</th>\n",
              "      <th>Xylene</th>\n",
              "      <th>AQI</th>\n",
              "      <th>AQI_Bucket</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14565</th>\n",
              "      <td>Guwahati</td>\n",
              "      <td>2020-06-16</td>\n",
              "      <td>16.03</td>\n",
              "      <td>26.82</td>\n",
              "      <td>1.89</td>\n",
              "      <td>9.27</td>\n",
              "      <td>5.92</td>\n",
              "      <td>1.60</td>\n",
              "      <td>0.40</td>\n",
              "      <td>15.63</td>\n",
              "      <td>24.49</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19495</th>\n",
              "      <td>Kolkata</td>\n",
              "      <td>2019-07-17</td>\n",
              "      <td>57.60</td>\n",
              "      <td>104.21</td>\n",
              "      <td>39.95</td>\n",
              "      <td>27.41</td>\n",
              "      <td>60.92</td>\n",
              "      <td>23.78</td>\n",
              "      <td>0.79</td>\n",
              "      <td>4.60</td>\n",
              "      <td>27.24</td>\n",
              "      <td>18.23</td>\n",
              "      <td>12.03</td>\n",
              "      <td>5.34</td>\n",
              "      <td>106.0</td>\n",
              "      <td>Moderate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9295</th>\n",
              "      <td>Chennai</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>122.43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.07</td>\n",
              "      <td>13.22</td>\n",
              "      <td>18.62</td>\n",
              "      <td>28.40</td>\n",
              "      <td>0.86</td>\n",
              "      <td>18.33</td>\n",
              "      <td>56.82</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>212.0</td>\n",
              "      <td>Poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5627</th>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>2018-08-26</td>\n",
              "      <td>18.42</td>\n",
              "      <td>60.03</td>\n",
              "      <td>21.34</td>\n",
              "      <td>18.03</td>\n",
              "      <td>31.57</td>\n",
              "      <td>23.73</td>\n",
              "      <td>0.93</td>\n",
              "      <td>7.07</td>\n",
              "      <td>13.19</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.83</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70.0</td>\n",
              "      <td>Satisfactory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15536</th>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>2017-08-16</td>\n",
              "      <td>20.12</td>\n",
              "      <td>56.17</td>\n",
              "      <td>7.78</td>\n",
              "      <td>12.73</td>\n",
              "      <td>6.88</td>\n",
              "      <td>4.92</td>\n",
              "      <td>0.18</td>\n",
              "      <td>4.97</td>\n",
              "      <td>32.41</td>\n",
              "      <td>0.41</td>\n",
              "      <td>2.13</td>\n",
              "      <td>0.84</td>\n",
              "      <td>63.0</td>\n",
              "      <td>Satisfactory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11477</th>\n",
              "      <td>Delhi</td>\n",
              "      <td>2018-06-02</td>\n",
              "      <td>52.60</td>\n",
              "      <td>154.66</td>\n",
              "      <td>12.65</td>\n",
              "      <td>29.28</td>\n",
              "      <td>35.13</td>\n",
              "      <td>41.24</td>\n",
              "      <td>0.98</td>\n",
              "      <td>9.08</td>\n",
              "      <td>60.12</td>\n",
              "      <td>1.51</td>\n",
              "      <td>3.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>183.0</td>\n",
              "      <td>Moderate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18175</th>\n",
              "      <td>Jorapokhar</td>\n",
              "      <td>2018-08-07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>109.67</td>\n",
              "      <td>7.09</td>\n",
              "      <td>7.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.66</td>\n",
              "      <td>0.34</td>\n",
              "      <td>21.87</td>\n",
              "      <td>40.13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>113.0</td>\n",
              "      <td>Moderate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22127</th>\n",
              "      <td>Mumbai</td>\n",
              "      <td>2015-09-30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>96.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2015-05-16</td>\n",
              "      <td>129.58</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.23</td>\n",
              "      <td>28.79</td>\n",
              "      <td>45.40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.23</td>\n",
              "      <td>17.66</td>\n",
              "      <td>34.42</td>\n",
              "      <td>20.53</td>\n",
              "      <td>85.73</td>\n",
              "      <td>11.56</td>\n",
              "      <td>577.0</td>\n",
              "      <td>Severe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>2016-01-02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             City        Date   PM2.5    PM10     NO    NO2    NOx    NH3  \\\n",
              "14565    Guwahati  2020-06-16   16.03   26.82   1.89   9.27   5.92   1.60   \n",
              "19495     Kolkata  2019-07-17   57.60  104.21  39.95  27.41  60.92  23.78   \n",
              "9295      Chennai  2019-01-01  122.43     NaN   6.07  13.22  18.62  28.40   \n",
              "5627    Bengaluru  2018-08-26   18.42   60.03  21.34  18.03  31.57  23.73   \n",
              "15536   Hyderabad  2017-08-16   20.12   56.17   7.78  12.73   6.88   4.92   \n",
              "11477       Delhi  2018-06-02   52.60  154.66  12.65  29.28  35.13  41.24   \n",
              "18175  Jorapokhar  2018-08-07     NaN  109.67   7.09   7.14    NaN   6.66   \n",
              "22127      Mumbai  2015-09-30     NaN     NaN    NaN    NaN  96.75    NaN   \n",
              "135     Ahmedabad  2015-05-16  129.58     NaN  21.23  28.79  45.40    NaN   \n",
              "366     Ahmedabad  2016-01-02     NaN     NaN    NaN    NaN    NaN    NaN   \n",
              "\n",
              "          CO    SO2     O3  Benzene  Toluene  Xylene    AQI    AQI_Bucket  \n",
              "14565   0.40  15.63  24.49     0.00      NaN     NaN   34.0          Good  \n",
              "19495   0.79   4.60  27.24    18.23    12.03    5.34  106.0      Moderate  \n",
              "9295    0.86  18.33  56.82     0.16     0.44     NaN  212.0          Poor  \n",
              "5627    0.93   7.07  13.19     0.29     1.83     NaN   70.0  Satisfactory  \n",
              "15536   0.18   4.97  32.41     0.41     2.13    0.84   63.0  Satisfactory  \n",
              "11477   0.98   9.08  60.12     1.51     3.67    0.00  183.0      Moderate  \n",
              "18175   0.34  21.87  40.13      NaN      NaN     NaN  113.0      Moderate  \n",
              "22127   0.00    NaN    NaN     0.00     0.00    0.00    NaN           NaN  \n",
              "135    21.23  17.66  34.42    20.53    85.73   11.56  577.0        Severe  \n",
              "366      NaN    NaN    NaN      NaN      NaN     NaN    NaN           NaN  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "air.sample(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8b89c82c",
      "metadata": {
        "id": "8b89c82c"
      },
      "source": [
        ".shape is used to understand the total number of rows and columns in the given dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68e3cfef",
      "metadata": {
        "id": "68e3cfef",
        "outputId": "947a89b6-f8dd-4980-983d-0a629524a561"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(29531, 16)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "air.shape\n",
        "\n",
        "# the dataset has 29531 rows and 16 columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e77346db",
      "metadata": {
        "id": "e77346db"
      },
      "source": [
        ".columns is used to have a look at all the column names in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3100d5d9",
      "metadata": {
        "id": "3100d5d9",
        "outputId": "b1f63fa4-6ead-40b1-c127-4f3ce37198df"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['City', 'Date', 'PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
              "       'O3', 'Benzene', 'Toluene', 'Xylene', 'AQI', 'AQI_Bucket'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "air.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3528a71c",
      "metadata": {
        "id": "3528a71c"
      },
      "source": [
        "pd.to_datetime is used to convert the given data into date-time format"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be0a9c1b",
      "metadata": {
        "id": "be0a9c1b"
      },
      "outputs": [],
      "source": [
        "air['Date'] = pd.to_datetime(air.Date)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4de03962",
      "metadata": {
        "id": "4de03962"
      },
      "source": [
        "pd.to_numeric is used to convert given data into numerical format to implement calculations easily"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c4978ffc",
      "metadata": {
        "id": "c4978ffc"
      },
      "outputs": [],
      "source": [
        "air['PM2.5'] = pd.to_numeric(air['PM2.5'], errors='coerce')\n",
        "air['PM10'] = pd.to_numeric(air.PM10, errors='coerce')\n",
        "air['NO'] = pd.to_numeric(air.NO, errors='coerce')\n",
        "air['NO2'] = pd.to_numeric(air.NO2, errors='coerce')\n",
        "air['NOx'] = pd.to_numeric(air.NOx, errors='coerce')\n",
        "air['NH3'] = pd.to_numeric(air.NH3, errors='coerce')\n",
        "air['CO'] = pd.to_numeric(air.CO, errors='coerce')\n",
        "air['SO2'] = pd.to_numeric(air.SO2, errors='coerce')\n",
        "air['O3'] = pd.to_numeric(air.O3, errors='coerce')\n",
        "air['Benzene'] = pd.to_numeric(air.Benzene, errors='coerce')\n",
        "air['Toluene'] = pd.to_numeric(air.Toluene, errors='coerce')\n",
        "air['Xylene'] = pd.to_numeric(air.Xylene, errors='coerce')\n",
        "air['AQI'] = pd.to_numeric(air.AQI, errors='coerce')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "233bedc4",
      "metadata": {
        "id": "233bedc4"
      },
      "source": [
        "isnull().sum() is used to calculate the total number of null values\n",
        "\n",
        "notnull().sum() is used to calculate the total number of non null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "da8fd535",
      "metadata": {
        "id": "da8fd535",
        "outputId": "8ba77510-b2c4-40c2-a45b-9baea5fdcdee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "No. of null values in the PM2.5 column = 4598\n",
            "No. of null values in the PM10 column = 11140\n",
            "\n",
            "No. of non null values in the PM2.5 column = 24933\n",
            "No. of non null values in the PM10 column = 18391\n",
            "\n"
          ]
        }
      ],
      "source": [
        "null_PM2 = air['PM2.5'].isnull().sum()\n",
        "\n",
        "null_PM10 = air['PM10'].isnull().sum()\n",
        "\n",
        "not_null_PM2 = air['PM2.5'].notnull().sum()\n",
        "\n",
        "not_null_PM10 = air['PM10'].notnull().sum()\n",
        "\n",
        "print(f'No. of null values in the PM2.5 column = {null_PM2}\\nNo. of null values in the PM10 column = {null_PM10}\\n')\n",
        "\n",
        "print(f'No. of non null values in the PM2.5 column = {not_null_PM2}\\nNo. of non null values in the PM10 column = {not_null_PM10}\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fec44b9c",
      "metadata": {
        "id": "fec44b9c"
      },
      "outputs": [],
      "source": [
        "# create a new column \n",
        "\n",
        "air['new_aqi'] = air['PM2.5'] + air['CO'] + air['O3']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d8c11161",
      "metadata": {
        "id": "d8c11161",
        "outputId": "98359b44-9e23-4eda-e6d2-1a45c2b37f69"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[jovian] Updating notebook \"samyukta-vasaikar/zerotopandas-course-project\" on https://jovian.ai/\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/samyukta-vasaikar/zerotopandas-course-project\u001b[0m\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "'https://jovian.ai/samyukta-vasaikar/zerotopandas-course-project'"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "jovian.commit()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "70ede3c8",
      "metadata": {
        "id": "70ede3c8"
      },
      "source": [
        "## Exploratory Analysis and Visualization\n",
        "\n",
        "- We've computed the mean, sum, min, max by using .describe()\n",
        "- We've visualised the data using data visualisation libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0d4f8f7e",
      "metadata": {
        "id": "0d4f8f7e"
      },
      "source": [
        ".info() will display the number of non-null values in the rows and their datatypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4493b53d",
      "metadata": {
        "id": "4493b53d",
        "outputId": "2faae225-2364-41f5-ebf0-1545afc59560"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 29531 entries, 0 to 29530\n",
            "Data columns (total 17 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   City        29531 non-null  object        \n",
            " 1   Date        29531 non-null  datetime64[ns]\n",
            " 2   PM2.5       24933 non-null  float64       \n",
            " 3   PM10        18391 non-null  float64       \n",
            " 4   NO          25949 non-null  float64       \n",
            " 5   NO2         25946 non-null  float64       \n",
            " 6   NOx         25346 non-null  float64       \n",
            " 7   NH3         19203 non-null  float64       \n",
            " 8   CO          27472 non-null  float64       \n",
            " 9   SO2         25677 non-null  float64       \n",
            " 10  O3          25509 non-null  float64       \n",
            " 11  Benzene     23908 non-null  float64       \n",
            " 12  Toluene     21490 non-null  float64       \n",
            " 13  Xylene      11422 non-null  float64       \n",
            " 14  AQI         24850 non-null  float64       \n",
            " 15  AQI_Bucket  24850 non-null  object        \n",
            " 16  new_aqi     23819 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(14), object(2)\n",
            "memory usage: 3.8+ MB\n"
          ]
        }
      ],
      "source": [
        "air.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5dda8209",
      "metadata": {
        "id": "5dda8209"
      },
      "source": [
        ".describe() implements statistical functions like mean, standard deviation, minimum and maximum value on the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ba8ead72",
      "metadata": {
        "id": "ba8ead72",
        "outputId": "1c7c8f0a-9ca8-4599-ece8-d4a9af0f0cb7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>PM10</th>\n",
              "      <th>NO</th>\n",
              "      <th>NO2</th>\n",
              "      <th>NOx</th>\n",
              "      <th>NH3</th>\n",
              "      <th>CO</th>\n",
              "      <th>SO2</th>\n",
              "      <th>O3</th>\n",
              "      <th>Benzene</th>\n",
              "      <th>Toluene</th>\n",
              "      <th>Xylene</th>\n",
              "      <th>AQI</th>\n",
              "      <th>new_aqi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>24933.000000</td>\n",
              "      <td>18391.000000</td>\n",
              "      <td>25949.000000</td>\n",
              "      <td>25946.000000</td>\n",
              "      <td>25346.000000</td>\n",
              "      <td>19203.000000</td>\n",
              "      <td>27472.000000</td>\n",
              "      <td>25677.000000</td>\n",
              "      <td>25509.000000</td>\n",
              "      <td>23908.000000</td>\n",
              "      <td>21490.000000</td>\n",
              "      <td>11422.000000</td>\n",
              "      <td>24850.000000</td>\n",
              "      <td>23819.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>67.450578</td>\n",
              "      <td>118.127103</td>\n",
              "      <td>17.574730</td>\n",
              "      <td>28.560659</td>\n",
              "      <td>32.309123</td>\n",
              "      <td>23.483476</td>\n",
              "      <td>2.248598</td>\n",
              "      <td>14.531977</td>\n",
              "      <td>34.491430</td>\n",
              "      <td>3.280840</td>\n",
              "      <td>8.700972</td>\n",
              "      <td>3.070128</td>\n",
              "      <td>166.463581</td>\n",
              "      <td>104.954355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>64.661449</td>\n",
              "      <td>90.605110</td>\n",
              "      <td>22.785846</td>\n",
              "      <td>24.474746</td>\n",
              "      <td>31.646011</td>\n",
              "      <td>25.684275</td>\n",
              "      <td>6.962884</td>\n",
              "      <td>18.133775</td>\n",
              "      <td>21.694928</td>\n",
              "      <td>15.811136</td>\n",
              "      <td>19.969164</td>\n",
              "      <td>6.323247</td>\n",
              "      <td>140.696585</td>\n",
              "      <td>72.637912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>28.820000</td>\n",
              "      <td>56.255000</td>\n",
              "      <td>5.630000</td>\n",
              "      <td>11.750000</td>\n",
              "      <td>12.820000</td>\n",
              "      <td>8.580000</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>5.670000</td>\n",
              "      <td>18.860000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>56.855000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>48.570000</td>\n",
              "      <td>95.680000</td>\n",
              "      <td>9.890000</td>\n",
              "      <td>21.690000</td>\n",
              "      <td>23.520000</td>\n",
              "      <td>15.850000</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>9.160000</td>\n",
              "      <td>30.840000</td>\n",
              "      <td>1.070000</td>\n",
              "      <td>2.970000</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>88.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>80.590000</td>\n",
              "      <td>149.745000</td>\n",
              "      <td>19.950000</td>\n",
              "      <td>37.620000</td>\n",
              "      <td>40.127500</td>\n",
              "      <td>30.020000</td>\n",
              "      <td>1.450000</td>\n",
              "      <td>15.220000</td>\n",
              "      <td>45.570000</td>\n",
              "      <td>3.080000</td>\n",
              "      <td>9.150000</td>\n",
              "      <td>3.350000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>129.705000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>949.990000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>390.680000</td>\n",
              "      <td>362.210000</td>\n",
              "      <td>467.630000</td>\n",
              "      <td>352.890000</td>\n",
              "      <td>175.810000</td>\n",
              "      <td>193.860000</td>\n",
              "      <td>257.730000</td>\n",
              "      <td>455.030000</td>\n",
              "      <td>454.850000</td>\n",
              "      <td>170.370000</td>\n",
              "      <td>2049.000000</td>\n",
              "      <td>980.820000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              PM2.5          PM10            NO           NO2           NOx  \\\n",
              "count  24933.000000  18391.000000  25949.000000  25946.000000  25346.000000   \n",
              "mean      67.450578    118.127103     17.574730     28.560659     32.309123   \n",
              "std       64.661449     90.605110     22.785846     24.474746     31.646011   \n",
              "min        0.040000      0.010000      0.020000      0.010000      0.000000   \n",
              "25%       28.820000     56.255000      5.630000     11.750000     12.820000   \n",
              "50%       48.570000     95.680000      9.890000     21.690000     23.520000   \n",
              "75%       80.590000    149.745000     19.950000     37.620000     40.127500   \n",
              "max      949.990000   1000.000000    390.680000    362.210000    467.630000   \n",
              "\n",
              "                NH3            CO           SO2            O3       Benzene  \\\n",
              "count  19203.000000  27472.000000  25677.000000  25509.000000  23908.000000   \n",
              "mean      23.483476      2.248598     14.531977     34.491430      3.280840   \n",
              "std       25.684275      6.962884     18.133775     21.694928     15.811136   \n",
              "min        0.010000      0.000000      0.010000      0.010000      0.000000   \n",
              "25%        8.580000      0.510000      5.670000     18.860000      0.120000   \n",
              "50%       15.850000      0.890000      9.160000     30.840000      1.070000   \n",
              "75%       30.020000      1.450000     15.220000     45.570000      3.080000   \n",
              "max      352.890000    175.810000    193.860000    257.730000    455.030000   \n",
              "\n",
              "            Toluene        Xylene           AQI       new_aqi  \n",
              "count  21490.000000  11422.000000  24850.000000  23819.000000  \n",
              "mean       8.700972      3.070128    166.463581    104.954355  \n",
              "std       19.969164      6.323247    140.696585     72.637912  \n",
              "min        0.000000      0.000000     13.000000      1.350000  \n",
              "25%        0.600000      0.140000     81.000000     56.855000  \n",
              "50%        2.970000      0.980000    118.000000     88.100000  \n",
              "75%        9.150000      3.350000    208.000000    129.705000  \n",
              "max      454.850000    170.370000   2049.000000    980.820000  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "air.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "418339b5",
      "metadata": {
        "id": "418339b5"
      },
      "source": [
        "'air_pollutants' dataframe is a subset of the primary 'air' dataframe. Hence, we've chosen columns that contain data about the pollutants i.e column no.2 (PM2.5) to the third last column (Xylene)\n",
        "\n",
        "We've plotted a heatmap that **depicts the corelation between the pollutants**, annotation = True, hence the co-relation values are visible on the heatmap."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a85aaf5d",
      "metadata": {
        "id": "a85aaf5d",
        "outputId": "aeb0e853-10e7-402e-cd0d-17b5075a0d9e"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# heatmap to display corelation between the pollutants\n",
        "\n",
        "air_pollutants = air[air.columns[2:-3]]\n",
        "\n",
        "plt.figure(figsize=(12,12))\n",
        "plt.title('Corelation between the pollutants')\n",
        "\n",
        "dataplot = sns.heatmap(air_pollutants.corr(), cmap=\"YlGnBu\", annot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ca2c984",
      "metadata": {
        "id": "8ca2c984"
      },
      "source": [
        "'city_pollutants' dataframe contains the mean value of each pollutant in each city thereby giving a bird-eye view of the pollutant levels in each city. We've plotted a **stacked bar graph to display the city wise pollutant distribution.**\n",
        "\n",
        "Delhi has the highest pollution levels and Aizawi has the lowest pollution levels in India."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e8a312d4",
      "metadata": {
        "id": "e8a312d4",
        "outputId": "4f520295-00a5-429b-f8d2-afd3ca376d7a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# stacked bar graph to display the city wise pollutant distribution\n",
        "\n",
        "city_pollutants = air_pollutants.groupby(air.City).mean()\n",
        "\n",
        "city_pollutants.plot(figsize=(15,15), kind='barh', stacked=True);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "583f1012",
      "metadata": {
        "id": "583f1012"
      },
      "source": [
        "'air_mumbai' contains all the pollutant levels data for the city of Mumbai. We've created two new columns (Year and Month) to filter out the provided data. pd.DatetimeIndex() is used to access the month and year from the Date column.\n",
        "\n",
        "'air_mum_2019' contains all the pollutant levels data for the city of Mumbai for the year 2019."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "91eef2d1",
      "metadata": {
        "id": "91eef2d1",
        "outputId": "90d34c92-89f9-437e-b4db-46a53f4f8047"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/jz/hgxvtpbn5tnbbbp99t3_glhw0000gn/T/ipykernel_43726/1372945782.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  air_mumbai['Year'] = pd.DatetimeIndex(air_mumbai.Date).year\n",
            "/var/folders/jz/hgxvtpbn5tnbbbp99t3_glhw0000gn/T/ipykernel_43726/1372945782.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  air_mumbai['Month'] = pd.DatetimeIndex(air_mumbai.Date).month\n"
          ]
        }
      ],
      "source": [
        "air_mumbai = air[air.City == 'Mumbai']\n",
        "\n",
        "air_mumbai['Year'] = pd.DatetimeIndex(air_mumbai.Date).year\n",
        "\n",
        "air_mumbai['Month'] = pd.DatetimeIndex(air_mumbai.Date).month\n",
        "\n",
        "air_mum_2019 = air_mumbai[air_mumbai.Year == 2019]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d7fcbf0d",
      "metadata": {
        "id": "d7fcbf0d"
      },
      "source": [
        "We've plotted a **line graph to display AQI index in the year 2019 in Mumbai**. The line graph decreases from January to September and then starts increasing gradually from September to December."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1a42ab56",
      "metadata": {
        "id": "1a42ab56",
        "outputId": "360aa6fd-e325-4fe3-8d0b-950514eb32a2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# line graph to display AQI index in the year 2019 in Mumbai\n",
        "\n",
        "data = air_mum_2019\n",
        "  \n",
        "sns.lineplot(y=\"AQI\", x=\"Month\", data=data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f86f7db",
      "metadata": {
        "id": "3f86f7db"
      },
      "source": [
        "air_mumbai contains all the pollutant levels data for the city of Mumbai. We've created two new columns (Year and Month) to filter out the provided data. pd.DatetimeIndex() is used to access the month and year from the Date column.\n",
        "\n",
        "air_mum_2020 contains all the pollutant levels data for the city of Mumbai for the year 2020."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "288336c5",
      "metadata": {
        "id": "288336c5",
        "outputId": "11a8cb78-a0a1-4ad9-9bc0-9452f539fe79"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/jz/hgxvtpbn5tnbbbp99t3_glhw0000gn/T/ipykernel_43726/702836387.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  air_mumbai['Year'] = pd.DatetimeIndex(air_mumbai.Date).year\n",
            "/var/folders/jz/hgxvtpbn5tnbbbp99t3_glhw0000gn/T/ipykernel_43726/702836387.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  air_mumbai['Month'] = pd.DatetimeIndex(air_mumbai.Date).month\n"
          ]
        }
      ],
      "source": [
        "air_mumbai = air[air.City == 'Mumbai']\n",
        "\n",
        "air_mumbai['Year'] = pd.DatetimeIndex(air_mumbai.Date).year\n",
        "\n",
        "air_mumbai['Month'] = pd.DatetimeIndex(air_mumbai.Date).month\n",
        "\n",
        "air_mum_2020 = air_mumbai[air_mumbai.Year == 2020]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5bde1c09",
      "metadata": {
        "id": "5bde1c09"
      },
      "source": [
        "We've plotted a **line graph to display AQI index in the year 2020 in Mumbai.** The line graph gradually decreases from the month of January 2020 to July 2020."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "64bdd56d",
      "metadata": {
        "id": "64bdd56d",
        "outputId": "9bf00143-02a8-431c-a2ff-0cffe13ec0d0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# line graph to display AQI index in the year 2020 in Mumbai\n",
        "\n",
        "data = air_mum_2020\n",
        "  \n",
        "sns.lineplot(y=\"AQI\", x=\"Month\", data=data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ab298f5f",
      "metadata": {
        "id": "ab298f5f"
      },
      "source": [
        "We've plotted a line graph to **display PM2.5 levels in the year 2020 in Mumbai.** The line graph gradually decreases from the month of January to July."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a52df304",
      "metadata": {
        "id": "a52df304",
        "outputId": "09957c56-4a63-4419-b137-493adfe382a3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# line graph to display PM2.5 levels in the year 2020 in Mumbai\n",
        "\n",
        "data = air_mum_2020\n",
        "  \n",
        "sns.lineplot(y=\"PM2.5\", x=\"Month\", data=data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "46c281c1",
      "metadata": {
        "id": "46c281c1"
      },
      "source": [
        "We've plotted a **line graph to display NOx levels in the year 2020 in Mumbai.** The line graph increases from January to February, decreases till April and then gradually increases from April to July."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9a279d90",
      "metadata": {
        "id": "9a279d90",
        "outputId": "69e6ef5a-9815-4666-85ae-f0e4792fdb01"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# line graph to display NOx levels in the year 2020 in Mumbai\n",
        "\n",
        "data = air_mum_2020\n",
        "  \n",
        "sns.lineplot(y=\"NOx\", x=\"Month\", data=data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d664770c",
      "metadata": {
        "id": "d664770c",
        "outputId": "c2fe879d-d501-46a1-98f9-c9105642cbf5"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[jovian] Updating notebook \"samyukta-vasaikar/zerotopandas-course-project\" on https://jovian.ai/\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/samyukta-vasaikar/zerotopandas-course-project\u001b[0m\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "'https://jovian.ai/samyukta-vasaikar/zerotopandas-course-project'"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "jovian.commit()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1bd10458",
      "metadata": {
        "id": "1bd10458"
      },
      "source": [
        "## Asking and Answering Questions"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "574a79bf",
      "metadata": {
        "id": "574a79bf"
      },
      "source": [
        "## Q1. Which city has the highest AQI index?\n",
        "\n",
        "#### As per the bar graph below, Ahmedabad has the highest AQI index"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95508d99",
      "metadata": {
        "id": "95508d99"
      },
      "source": [
        "## Q2. Which city has the lowest AQI index?\n",
        "\n",
        "#### As per the bar graph below, Aizawi has the lowest AQI index"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "57de0adb",
      "metadata": {
        "id": "57de0adb"
      },
      "source": [
        "The bar graph shown below displays the **city-wise AQI index values.**\n",
        "\n",
        "The AQI index values are plotted on the X axis and the city names are plotted on Y axis.\n",
        "\n",
        "We've used seaborn to plot the given bar plot.\n",
        "\n",
        "Ahmedabad has the highest AQI index and Aizawi has the lowest AQI index."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7a3362e6",
      "metadata": {
        "id": "7a3362e6",
        "outputId": "3c59d191-2f6f-41f3-e8e5-55380312bef1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# bar graph of city wise AQI index values\n",
        "\n",
        "plt.figure(figsize=(12,12))\n",
        "sns.barplot(y=air.City, x=air.AQI);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4703a81c",
      "metadata": {
        "id": "4703a81c"
      },
      "source": [
        "## Q3. Which pollutant contributes the most to pollution in India?\n",
        "\n",
        "#### As per the bar graph below, PM10 contributes the most to pollution."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c7b04c3a",
      "metadata": {
        "id": "c7b04c3a"
      },
      "source": [
        "## Q4. Which pollutant contributes the least to pollution in India?\n",
        "\n",
        "#### As per the bar graph below, CO contributes the least to pollution."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3d5737ae",
      "metadata": {
        "id": "3d5737ae"
      },
      "source": [
        "The bar graph shown below shows the **pollutant wise distribution in India.**\n",
        "\n",
        "'pollutants' contains the mean values of the pollutant levels in all cities.\n",
        "\n",
        "We've plotted the mean pollutant values on the x axis and the pollutant names on the y axis.\n",
        "\n",
        "We've used seaborn to plot the given bar plot.\n",
        "\n",
        "PM10 contributes the most to pollution and CO contributes the least to pollution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d698a6ac",
      "metadata": {
        "id": "d698a6ac",
        "outputId": "1b593a9f-1e4b-4caa-b5b1-8e229ded5c21"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# bar graph displaying pollutant wise distribution in India\n",
        "\n",
        "plt.figure(figsize=(9,9))\n",
        "\n",
        "pollutants = air_pollutants.mean()\n",
        "\n",
        "sns.barplot(x=pollutants, y=pollutants.index);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f4b68d48",
      "metadata": {
        "id": "f4b68d48"
      },
      "source": [
        "## Q5. What trend can be observed using the below graph?\n",
        "\n",
        "#### The AQI index lies between 50 and 250 and it has declined over the months in 2020."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "662ac727",
      "metadata": {
        "id": "662ac727"
      },
      "source": [
        "The scatter graph shown below shows the **AQI index monthwise distribution in the year 2020 in Mumbai.**\n",
        "\n",
        "We've plotted the timeperiod i.e months of 2020 on the x axis and the AQI index on the y axis.\n",
        "\n",
        "We've used seaborn to plot the given scatter plot.\n",
        "\n",
        "The AQI index lies between 50 and 250 and it has declined steadily over the months in 2020."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "509db4c1",
      "metadata": {
        "id": "509db4c1",
        "outputId": "95f5a8ce-93ce-4edc-9c82-741de64204df"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# scatter plot to showcase monthwise AQI index distribution\n",
        "\n",
        "plt.figure(figsize=(12,12))\n",
        "\n",
        "sns.scatterplot(y=\"AQI\", x=\"Date\", data=air_mum_2020)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67af9970",
      "metadata": {
        "id": "67af9970"
      },
      "source": [
        "## Q6. Which pollutant is the majorly responsible for pollution in Mumbai?\n",
        "\n",
        "#### PM 10 is the major pollutant that is responsible for pollution in Mumbai"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b035024f",
      "metadata": {
        "id": "b035024f"
      },
      "source": [
        "The bar graph shown below shows the **pollutant wise distribution in Mumbai from 2018 to 2020**\n",
        "\n",
        "'mumbai_pollutants' represents the mean value of each pollutant given in the dataset.\n",
        "\n",
        "We've plotted the mean of pollutant levels on the x axis and the pollutant names on the y axis.\n",
        "\n",
        "We've used seaborn to plot the given scatter plot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "932d1be6",
      "metadata": {
        "id": "932d1be6",
        "outputId": "951a8518-8b15-4cdc-fdac-2f32a19d6918"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# bar graph representing pollutant wise distribution in mumbai\n",
        "\n",
        "mumbai_pollutants = air_mumbai.iloc[:, 2:14].mean()\n",
        "\n",
        "mumbai_pollutants.plot(figsize=(12,12), kind='barh');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "031aa843",
      "metadata": {
        "id": "031aa843",
        "outputId": "0b255432-429c-46fb-f560-a09f68c14ae4"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[jovian] Updating notebook \"samyukta-vasaikar/zerotopandas-course-project\" on https://jovian.ai/\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/samyukta-vasaikar/zerotopandas-course-project\u001b[0m\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "'https://jovian.ai/samyukta-vasaikar/zerotopandas-course-project'"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "jovian.commit()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "26353c8d",
      "metadata": {
        "id": "26353c8d"
      },
      "source": [
        "## Inferences and Conclusion\n",
        "\n",
        "We've drawn many inferences from the survey. Here's a summary of a few of them:\n",
        "\n",
        "- The dataset includes information about the different levels of pollutants in different cities of India.\n",
        "- The dataset could include more data about PM10 pollutant levels as it is a major contributor to pollution in general. Also, information has been recorded from 2018 to the inital 7 months of 2020.\n",
        "- In 2019, the AQI index was increasing linearly in Mumbai.\n",
        "- However, there has been a major decline in the AQI index of Mumbai due to reduced pollution levels during the pandemic.\n",
        "- NOx levels declined and then started increasing from April 2020 to July 2020 despite the pandemic.\n",
        "- Ahmedabad has the highest AQI index and Aizawi has the lowest AQI index.\n",
        "- The AQI index of Mumbai lies between 50 and 250 in the year 2020.\n",
        "- PM10 is majorly responsible and CO is the least responsible for pollution in India."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "717e3a75",
      "metadata": {
        "id": "717e3a75"
      },
      "source": [
        "## References and Future Work\n",
        "\n",
        "Conduct an in-depth research by analysing the pollutant levels in different cities and understand ways to solve the problem of air pollution efficiently.\n",
        "\n",
        "* Pandas user guide: https://pandas.pydata.org/docs/user_guide/index.html\n",
        "\n",
        "* Matplotlib user guide: https://matplotlib.org/3.3.1/users/index.html\n",
        "\n",
        "* Seaborn user guide & tutorial: https://seaborn.pydata.org/tutorial.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bb54b033",
      "metadata": {
        "id": "bb54b033",
        "outputId": "e6eb65eb-37c0-4782-f5d8-6fe42ae134ae"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "jovian.commit()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "207aee42",
      "metadata": {
        "id": "207aee42"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "language_info": {
      "name": "python"
    },
    "colab": {
      "name": "jovian_final_project.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}