4_Pandas.ipynb

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        "<a href=\"https://colab.research.google.com/github/kopiladevkota/Python/blob/main/4_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxxC48HAwQnE"
      },
      "source": [
        "---\n",
        "\n",
        "<center><h1>📍 📍 Introduction to Pandas 📍 📍</h1><center>\n",
        "\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtxuqH_BwQnJ"
      },
      "source": [
        " - **Pandas:** Open source library for data manipulation and analysis.\n",
        "\n",
        " ---\n",
        "\n",
        " - We can read and write differnt formats of file like CSV, JSON, EXCEL, HTML, etc.\n",
        " - We can summarize the data.\n",
        " - We can filter and modify the data based on multiple conditions.\n",
        " - We can merge multiple files.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hf0QFR7KwQnK"
      },
      "source": [
        "# import the library\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1WFsKW3wQnP"
      },
      "source": [
        "***If you got an error while running the above cell, import it by using the following command.***\n",
        "\n",
        "If you are using anaconda with python3: ***`!pip install pandas`***\n",
        "\n",
        "If you are using jupyter with python3: ***`!pip3 install pandas`***\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvenQ8Q-wQnQ",
        "outputId": "db925ec3-fd12-42cc-8b72-132e8fe2fcfb"
      },
      "source": [
        "# check the version of the pandas library\n",
        "pd.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0.25.3'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHyou6dzwQnd"
      },
      "source": [
        "# read the file\n",
        "data = pd.read_csv('big_mart_sales.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYH_INipwQni",
        "outputId": "759c2d52-1b30-4d77-a1f2-ed70e21376cf"
      },
      "source": [
        "# view the data\n",
        "data.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.30</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>17.50</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>Meat</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
              "0           FDA15         9.30          Low Fat         0.016047        Dairy   \n",
              "1           DRC01         5.92          Regular         0.019278  Soft Drinks   \n",
              "2           FDN15        17.50          Low Fat         0.016760         Meat   \n",
              "\n",
              "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
              "0  249.8092            OUT049                       1999      Medium   \n",
              "1   48.2692            OUT018                       2009      Medium   \n",
              "2  141.6180            OUT049                       1999      Medium   \n",
              "\n",
              "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
              "0               Tier 1  Supermarket Type1          3735.1380  \n",
              "1               Tier 3  Supermarket Type2           443.4228  \n",
              "2               Tier 1  Supermarket Type1          2097.2700  "
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 4
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    {
      "cell_type": "code",
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        "outputId": "9b1f863a-2aa5-4dac-e274-6fcb3ebf8b1d"
      },
      "source": [
        "# shape of the data\n",
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8523, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCQSMk9gwQnu",
        "outputId": "92a72531-82c0-4185-9288-acdee3029e7e"
      },
      "source": [
        "# check the column names\n",
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility',\n",
              "       'Item_Type', 'Item_MRP', 'Outlet_Identifier',\n",
              "       'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type',\n",
              "       'Outlet_Type', 'Item_Outlet_Sales'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
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    {
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      "metadata": {
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        "outputId": "2d6d1ead-b39c-4a6f-b1eb-fbe49498fdc6"
      },
      "source": [
        "# check the data types in the columns\n",
        "\n",
        "data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier               object\n",
              "Item_Weight                  float64\n",
              "Item_Fat_Content              object\n",
              "Item_Visibility              float64\n",
              "Item_Type                     object\n",
              "Item_MRP                     float64\n",
              "Outlet_Identifier             object\n",
              "Outlet_Establishment_Year      int64\n",
              "Outlet_Size                   object\n",
              "Outlet_Location_Type          object\n",
              "Outlet_Type                   object\n",
              "Item_Outlet_Sales            float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFU-6K0EwQn6",
        "outputId": "64fed01d-01bf-48ea-f99b-17e8aaf27625"
      },
      "source": [
        "# check the null values in the data\n",
        "data.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier                 0\n",
              "Item_Weight                  1463\n",
              "Item_Fat_Content                0\n",
              "Item_Visibility                 0\n",
              "Item_Type                       0\n",
              "Item_MRP                        0\n",
              "Outlet_Identifier               0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Size                  2410\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Type                     0\n",
              "Item_Outlet_Sales               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hryCAlMLwQn-",
        "outputId": "c1fb5ac3-13f7-47e3-cc70-f6d675e80e2f"
      },
      "source": [
        "# summary of the data\n",
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7060.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.857645</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>140.992782</td>\n",
              "      <td>1997.831867</td>\n",
              "      <td>2181.288914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.643456</td>\n",
              "      <td>0.051598</td>\n",
              "      <td>62.275067</td>\n",
              "      <td>8.371760</td>\n",
              "      <td>1706.499616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.555000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.290000</td>\n",
              "      <td>1985.000000</td>\n",
              "      <td>33.290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.773750</td>\n",
              "      <td>0.026989</td>\n",
              "      <td>93.826500</td>\n",
              "      <td>1987.000000</td>\n",
              "      <td>834.247400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.053931</td>\n",
              "      <td>143.012800</td>\n",
              "      <td>1999.000000</td>\n",
              "      <td>1794.331000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.850000</td>\n",
              "      <td>0.094585</td>\n",
              "      <td>185.643700</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>3101.296400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21.350000</td>\n",
              "      <td>0.328391</td>\n",
              "      <td>266.888400</td>\n",
              "      <td>2009.000000</td>\n",
              "      <td>13086.964800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
              "count  7060.000000      8523.000000  8523.000000                8523.000000   \n",
              "mean     12.857645         0.066132   140.992782                1997.831867   \n",
              "std       4.643456         0.051598    62.275067                   8.371760   \n",
              "min       4.555000         0.000000    31.290000                1985.000000   \n",
              "25%       8.773750         0.026989    93.826500                1987.000000   \n",
              "50%      12.600000         0.053931   143.012800                1999.000000   \n",
              "75%      16.850000         0.094585   185.643700                2004.000000   \n",
              "max      21.350000         0.328391   266.888400                2009.000000   \n",
              "\n",
              "       Item_Outlet_Sales  \n",
              "count        8523.000000  \n",
              "mean         2181.288914  \n",
              "std          1706.499616  \n",
              "min            33.290000  \n",
              "25%           834.247400  \n",
              "50%          1794.331000  \n",
              "75%          3101.296400  \n",
              "max         13086.964800  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bODnaFkSwQoC"
      },
      "source": [
        "---\n",
        "\n",
        "***Learn more about Pandas here:*** https://pandas.pydata.org/docs/"
      ]
    }
  ]
}