Data_Analysis_With_Pandas.ipynb

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        "## Pandas \n",
        "The Pandas library is built on NumPy and provides easy-to-use data\n",
        "structures and data analysis tools for the Python programming language.\n",
        "\n",
        "is also Python library used for working with data sets. It has functions for analyzing, cleaning, exploring, and manipulating data. The name \"Pandas\" has a reference to both \"Panel Data\", and \"Python Data Analysis\" and was created by Wes McKinney in 2008."
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        "## Core components of pandas: Series and DataFrames\n",
        "\n",
        "The primary two components of pandas are : the Series and DataFrame.\n",
        "\n",
        "A Series is essentially a column, \n",
        "\n",
        "A DataFrame is a multi-dimensional table made up of a collection of Series."
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        "import pandas as pd\n",
        "#creating a dictionary{key:values}\n",
        "data = {\n",
        "    'apples': [3, 2, 0, 1], \n",
        "    'oranges': [0, 3, 7, 2],\n",
        "    \"Mangoes\":[6,7,10,3]\n",
        "}"
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        "#pass the data variable to the  pandas DataFrame constructor\n",
        "df=pd.DataFrame(data)\n",
        "#viewing the data\n",
        "df.head()"
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              "   apples  oranges  Mangoes\n",
              "0       3        0        6\n",
              "1       2        3        7\n",
              "2       0        7       10\n",
              "3       1        2        3"
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        "# create index column by yourself\n",
        "purchases = pd.DataFrame(data, index=['James', 'Robert', 'Lily', 'Mary'])\n",
        "\n",
        "purchases"
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              "        apples  oranges  Mangoes\n",
              "James        3        0        6\n",
              "Robert       2        3        7\n",
              "Lily         0        7       10\n",
              "Mary         1        2        3"
            ]
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      "source": [
        "#we can use loc in pandas to acces individual person details\n",
        "purchases.loc['Robert']"
      ],
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              "apples     2\n",
              "oranges    3\n",
              "Mangoes    7\n",
              "Name: Robert, dtype: int64"
            ]
          },
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          "execution_count": 4
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      "source": [
        "# we can also set index to be apples for example\n",
        "df = purchases.set_index('apples')\n",
        "\n",
        "df"
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d66b3e42-f8f0-4e94-bdca-ba76ee4322f3 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-d66b3e42-f8f0-4e94-bdca-ba76ee4322f3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        oranges  Mangoes\n",
              "apples                  \n",
              "3             0        6\n",
              "2             3        7\n",
              "0             7       10\n",
              "1             2        3"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop values from a row(axis=0)\n",
        "df.drop([3,1],axis=0,inplace=True)\n",
        "\n",
        "df\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "LDsvUiFEEAui",
        "outputId": "d3fd904d-634d-47c3-932f-9db42249bd3a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a36ead6e-e370-4d06-be91-2cbad2aa9946\">\n",
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              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>oranges</th>\n",
              "      <th>Mangoes</th>\n",
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              "    <tr>\n",
              "      <th>apples</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <td>7</td>\n",
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              "      <td>10</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        oranges  Mangoes\n",
              "apples                  \n",
              "2             3        7\n",
              "0             7       10"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping columns axis=1\n",
        "df.drop('oranges', axis=1,inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Tj0TUzVuH6bU",
        "outputId": "64473f88-cada-4ac2-8acc-01b8c1e2bad1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <tr>\n",
              "      <th>apples</th>\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-eb69829b-79ca-4d74-9080-9dc94a496a81 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-eb69829b-79ca-4d74-9080-9dc94a496a81');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ],
            "text/plain": [
              "        Mangoes\n",
              "apples         \n",
              "2             7\n",
              "0            10"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.rank()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "T_OXpk8FH6Uo",
        "outputId": "35620d7f-0480-4d4e-8785-b0a39b34030f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4424f207-554d-4032-b945-229c42118cfb\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
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              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4424f207-554d-4032-b945-229c42118cfb 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-4424f207-554d-4032-b945-229c42118cfb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ],
            "text/plain": [
              "        Mangoes\n",
              "apples         \n",
              "2           1.0\n",
              "0           2.0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "puchases=purchases.sort_values(by='oranges')\n",
        "purchases"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "3oQPEsehH6R5",
        "outputId": "6fe3f436-0947-4e74-cf70-9b16c8a3dd85"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8b41cc87-7b04-4848-99d5-9eeeae6c5f0c\">\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>apples</th>\n",
              "      <th>oranges</th>\n",
              "      <th>Mangoes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>James</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Robert</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lily</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mary</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b41cc87-7b04-4848-99d5-9eeeae6c5f0c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8b41cc87-7b04-4848-99d5-9eeeae6c5f0c 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-8b41cc87-7b04-4848-99d5-9eeeae6c5f0c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        apples  oranges  Mangoes\n",
              "James        3        0        6\n",
              "Robert       2        3        7\n",
              "Lily         0        7       10\n",
              "Mary         1        2        3"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#reading data using pandas\n",
        "#link to dataset http://bit.ly/WorldPopulationDataset1\n",
        "#reading excel file\n",
        "\n",
        "world_pop=pd.read_excel(\"http://bit.ly/WorldPopulationDataset1\")\n",
        "world_pop.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "HPRimIhUH6IM",
        "outputId": "cb235e37-8a4b-4a8a-a13d-26cdced965ed"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ad87d92a-5e1d-4058-9c17-3707436559df\">\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>Rank</th>\n",
              "      <th>Country</th>\n",
              "      <th>Population</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>China</td>\n",
              "      <td>1380914176</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>India</td>\n",
              "      <td>1311559168</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>333928672</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>265253184</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>220632960</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>210797840</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>202670592</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Bangladesh</td>\n",
              "      <td>180203952</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Russia</td>\n",
              "      <td>133026408</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Japan</td>\n",
              "      <td>125853032</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad87d92a-5e1d-4058-9c17-3707436559df')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ad87d92a-5e1d-4058-9c17-3707436559df 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-ad87d92a-5e1d-4058-9c17-3707436559df');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Rank                   Country  Population         Year\n",
              "0     1                     China  1380914176  2019 (Est.)\n",
              "1     2                     India  1311559168  2019 (Est.)\n",
              "2     3  United States of America   333928672  2019 (Est.)\n",
              "3     4                 Indonesia   265253184  2019 (Est.)\n",
              "4     5                    Brazil   220632960  2019 (Est.)\n",
              "5     6                  Pakistan   210797840  2019 (Est.)\n",
              "6     7                   Nigeria   202670592  2019 (Est.)\n",
              "7     8                Bangladesh   180203952  2019 (Est.)\n",
              "8     9                    Russia   133026408  2019 (Est.)\n",
              "9    10                     Japan   125853032  2019 (Est.)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#reading csvs(comma separated values) \n",
        "Diabetes_df=pd.read_csv('http://bit.ly/AtaraaDiabetesDataset1')\n",
        "Diabetes_df.sample(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "yzLccdH6H56F",
        "outputId": "bf81c71a-8504-4d88-ddf4-250a55df4b76"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a649ad65-df88-4e26-b327-3e30dd5fa2b9\">\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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>669</th>\n",
              "      <td>9</td>\n",
              "      <td>154</td>\n",
              "      <td>78</td>\n",
              "      <td>30</td>\n",
              "      <td>100</td>\n",
              "      <td>30.9</td>\n",
              "      <td>0.164</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>1</td>\n",
              "      <td>111</td>\n",
              "      <td>86</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.143</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>2</td>\n",
              "      <td>123</td>\n",
              "      <td>48</td>\n",
              "      <td>32</td>\n",
              "      <td>165</td>\n",
              "      <td>42.1</td>\n",
              "      <td>0.520</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>8</td>\n",
              "      <td>167</td>\n",
              "      <td>106</td>\n",
              "      <td>46</td>\n",
              "      <td>231</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.165</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a649ad65-df88-4e26-b327-3e30dd5fa2b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a649ad65-df88-4e26-b327-3e30dd5fa2b9 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-a649ad65-df88-4e26-b327-3e30dd5fa2b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Pregnancies  Glucose  ...  Age  Outcome\n",
              "669            9      154  ...   45        0\n",
              "249            1      111  ...   23        0\n",
              "405            2      123  ...   26        0\n",
              "662            8      167  ...   43        1\n",
              "\n",
              "[4 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##incase of reading json \n",
        "#use pd.read_json(\"\")"
      ],
      "metadata": {
        "id": "ROSBhaKJMlZE"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Selecting, Boolean Indexing & Setting"
      ],
      "metadata": {
        "id": "YelCtyniNmHu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selecing by position\n"
      ],
      "metadata": {
        "id": "7yUG7aXXN8vz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#selecting single value by row and column\n",
        "world_pop.iloc[1,2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNuo2NtkNZHw",
        "outputId": "e793c8bc-9da0-42d0-e1b8-7294a8518eaa"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1311559168"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selecting by  label"
      ],
      "metadata": {
        "id": "4Sdi3-wwOBW1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('The Country for index 5 is' +  world_pop.loc[5,['Country']])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-lIC88VyOSUq",
        "outputId": "05932417-b543-461c-9418-76c9a36f7311"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Country    The Country for index 5 isPakistan\n",
            "Name: 5, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setting\n"
      ],
      "metadata": {
        "id": "HfBEQTuYPugM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "world_pop['Rank']='Rankings'\n",
        "world_pop"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "lmWXVY_xOjgv",
        "outputId": "70793d2a-1dc7-4521-df64-ab42594d0e12"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c72f485a-b4b8-4c64-816d-97a3e1e35527\">\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>Rank</th>\n",
              "      <th>Country</th>\n",
              "      <th>Population</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>China</td>\n",
              "      <td>1380914176</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>India</td>\n",
              "      <td>1311559168</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>333928672</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>265253184</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>220632960</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>223</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>Cook Islands</td>\n",
              "      <td>8799</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>Saint Helena</td>\n",
              "      <td>7852</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>Saint Barthélemy</td>\n",
              "      <td>7140</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>Saint Pierre and Miquelon</td>\n",
              "      <td>5409</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>Montserrat</td>\n",
              "      <td>5342</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>228 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c72f485a-b4b8-4c64-816d-97a3e1e35527')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c72f485a-b4b8-4c64-816d-97a3e1e35527 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-c72f485a-b4b8-4c64-816d-97a3e1e35527');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Rank                    Country  Population         Year\n",
              "0    Rankings                      China  1380914176  2019 (Est.)\n",
              "1    Rankings                      India  1311559168  2019 (Est.)\n",
              "2    Rankings   United States of America   333928672  2019 (Est.)\n",
              "3    Rankings                  Indonesia   265253184  2019 (Est.)\n",
              "4    Rankings                     Brazil   220632960  2019 (Est.)\n",
              "..        ...                        ...         ...          ...\n",
              "223  Rankings               Cook Islands        8799  2019 (Est.)\n",
              "224  Rankings               Saint Helena        7852  2019 (Est.)\n",
              "225  Rankings           Saint Barthélemy        7140  2019 (Est.)\n",
              "226  Rankings  Saint Pierre and Miquelon        5409  2019 (Est.)\n",
              "227  Rankings                 Montserrat        5342  2019 (Est.)\n",
              "\n",
              "[228 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Boolen Indexing"
      ],
      "metadata": {
        "id": "yGv-BlSjQOJ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "world_pop[world_pop['Population']>1200000000]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "OIP0WzqEPfSr",
        "outputId": "c8474cf7-ed04-4ff4-acd8-24311f88cffd"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-714f49d0-e55b-4d1a-bf40-b8460cd481c3\">\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>Rank</th>\n",
              "      <th>Country</th>\n",
              "      <th>Population</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>China</td>\n",
              "      <td>1380914176</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Rankings</td>\n",
              "      <td>India</td>\n",
              "      <td>1311559168</td>\n",
              "      <td>2019 (Est.)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-714f49d0-e55b-4d1a-bf40-b8460cd481c3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-714f49d0-e55b-4d1a-bf40-b8460cd481c3 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-714f49d0-e55b-4d1a-bf40-b8460cd481c3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       Rank Country  Population         Year\n",
              "0  Rankings   China  1380914176  2019 (Est.)\n",
              "1  Rankings   India  1311559168  2019 (Est.)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Common Pandas Data Analysis functions"
      ],
      "metadata": {
        "id": "hqgYVi1bRCLW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Acquisition"
      ],
      "metadata": {
        "id": "slNaemiwdhKT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#read dataset\n",
        "shopify=pd.read_csv(\"https://bit.ly/ShoprityDS\")\n",
        "#checing the first 5 rows\n",
        "shopify.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "0ZcBethUQcVN",
        "outputId": "32f3ef5b-d28c-44cd-f4e1-f63ef0669f3e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-41dd9879-5045-437f-bf10-8e1f6d0a1666\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\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>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>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>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>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>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.20</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>8.93</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "          const element = document.querySelector('#df-41dd9879-5045-437f-bf10-8e1f6d0a1666');\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Item_Identifier  Item_Weight  ... Outlet_Location_Type  Item_Outlet_Sales\n",
              "0           FDA15         9.30  ...               Tier 1          3735.1380\n",
              "1           DRC01         5.92  ...               Tier 3           443.4228\n",
              "2           FDN15        17.50  ...               Tier 1          2097.2700\n",
              "3           FDX07        19.20  ...               Tier 3           732.3800\n",
              "4           NCD19         8.93  ...               Tier 3           994.7052\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checing the last 5 rows\n",
        "shopify.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "VjighXm6QoH8",
        "outputId": "30577653-06c8-4f33-cfd9-f5fa37aa4296"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c280d98b-9e98-4090-8de8-28af4079f099\">\n",
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              "    }\n",
              "\n",
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              "        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>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8524</th>\n",
              "      <td>FDF22</td>\n",
              "      <td>6.865</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.056783</td>\n",
              "      <td>Snack Foods</td>\n",
              "      <td>214.5218</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>2778.3834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8525</th>\n",
              "      <td>FDS36</td>\n",
              "      <td>8.380</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.046982</td>\n",
              "      <td>Baking Goods</td>\n",
              "      <td>108.1570</td>\n",
              "      <td>OUT045</td>\n",
              "      <td>2002</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tier 2</td>\n",
              "      <td>549.2850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8526</th>\n",
              "      <td>NCJ29</td>\n",
              "      <td>10.600</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.035186</td>\n",
              "      <td>Health and Hygiene</td>\n",
              "      <td>85.1224</td>\n",
              "      <td>OUT035</td>\n",
              "      <td>2004</td>\n",
              "      <td>Small</td>\n",
              "      <td>Tier 2</td>\n",
              "      <td>1193.1136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8527</th>\n",
              "      <td>FDN46</td>\n",
              "      <td>7.210</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.145221</td>\n",
              "      <td>Snack Foods</td>\n",
              "      <td>103.1332</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>1845.5976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8528</th>\n",
              "      <td>DRG01</td>\n",
              "      <td>14.800</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.044878</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>75.4670</td>\n",
              "      <td>OUT046</td>\n",
              "      <td>1997</td>\n",
              "      <td>Small</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>765.6700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
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              "  "
            ],
            "text/plain": [
              "     Item_Identifier  Item_Weight  ... Outlet_Location_Type  Item_Outlet_Sales\n",
              "8524           FDF22        6.865  ...               Tier 3          2778.3834\n",
              "8525           FDS36        8.380  ...               Tier 2           549.2850\n",
              "8526           NCJ29       10.600  ...               Tier 2          1193.1136\n",
              "8527           FDN46        7.210  ...               Tier 3          1845.5976\n",
              "8528           DRG01       14.800  ...               Tier 1           765.6700\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checing the records in the dataset\n",
        "\n",
        "print('The no of rows in the dataset  is:\\n ',shopify.shape[0])\n",
        "\n",
        "print('The no of columns in the dataset is:\\n ',shopify.shape[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pfrWPmc-TQ93",
        "outputId": "e1b5eccc-a66c-4ae1-de24-b21c5ad06de9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The no of rows in the dataset  is:\n",
            "  8529\n",
            "The no of columns in the dataset is:\n",
            "  11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking information about our dataset(column names ,missing,values ,datatypes etc)\n",
        "shopify.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SnErlrsqTXgl",
        "outputId": "51d1ff3e-04c8-4916-8ae5-be030c677799"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8529 entries, 0 to 8528\n",
            "Data columns (total 11 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   Item_Identifier            8529 non-null   object \n",
            " 1   Item_Weight                7066 non-null   float64\n",
            " 2   Item_Fat_Content           8529 non-null   object \n",
            " 3   Item_Visibility            8528 non-null   float64\n",
            " 4   Item_Type                  8529 non-null   object \n",
            " 5   Item_MRP                   8527 non-null   float64\n",
            " 6   Outlet_Identifier          8529 non-null   object \n",
            " 7   Outlet_Establishment_Year  8529 non-null   int64  \n",
            " 8   Outlet_Size                6118 non-null   object \n",
            " 9   Outlet_Location_Type       8529 non-null   object \n",
            " 10  Item_Outlet_Sales          8529 non-null   float64\n",
            "dtypes: float64(4), int64(1), object(6)\n",
            "memory usage: 733.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking column names\n",
        "print(\"These are the columns \\n\" ,shopify.columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aOO_DiXJULPb",
        "outputId": "5bb17501-ff9f-4e00-de9c-b91fa54c00f0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "These are the columns \n",
            " 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",
            "       'Item_Outlet_Sales'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking column datatypes\n",
        "\n",
        "print(\"These are the datatypes \\n\" ,shopify.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9PUgRHHNU4Ev",
        "outputId": "4d642414-15fe-421b-9942-d924293d8494"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "These are the datatypes \n",
            " 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",
            "Item_Outlet_Sales            float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Selecting itemtypes with only low fat content from the dataset\n",
        "shopify['Item_Type'][shopify['Item_Fat_Content']=='Low Fat']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejxCLQ8KVmLs",
        "outputId": "8adec2af-421b-40b8-b865-2262cee30ae4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                       Dairy\n",
              "2                        Meat\n",
              "4                   Household\n",
              "8                 Snack Foods\n",
              "11      Fruits and Vegetables\n",
              "                ...          \n",
              "8521             Baking Goods\n",
              "8522                   Others\n",
              "8524              Snack Foods\n",
              "8526       Health and Hygiene\n",
              "8528              Soft Drinks\n",
              "Name: Item_Type, Length: 5092, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Selecting itemtypes with only regular content from the dataset\n",
        "shopify['Item_Type'][shopify['Item_Fat_Content']=='Regular']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vzxqIwolWwrk",
        "outputId": "321a14bb-2b59-4d6d-a24e-167ab3f8984e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1                 Soft Drinks\n",
              "3       Fruits and Vegetables\n",
              "5                Baking Goods\n",
              "6                 Snack Foods\n",
              "7                 Snack Foods\n",
              "                ...          \n",
              "8516              Snack Foods\n",
              "8519                     Meat\n",
              "8520                   Canned\n",
              "8525             Baking Goods\n",
              "8527              Snack Foods\n",
              "Name: Item_Type, Length: 2892, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shopify[shopify['Item_Outlet_Sales']>2000].head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "71U_SWwuX9-I",
        "outputId": "31c5df60-d458-457f-c353-966022fff9a7"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3634e769-1178-4ecf-9dbf-2bd068cf2930\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    .dataframe thead th {\n",
              "        text-align: right;\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>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>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.3</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>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>17.5</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>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>FDP10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.127470</td>\n",
              "      <td>Snack Foods</td>\n",
              "      <td>107.7622</td>\n",
              "      <td>OUT027</td>\n",
              "      <td>1985</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>4022.7636</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3634e769-1178-4ecf-9dbf-2bd068cf2930')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3634e769-1178-4ecf-9dbf-2bd068cf2930 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-3634e769-1178-4ecf-9dbf-2bd068cf2930');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Item_Identifier  Item_Weight  ... Outlet_Location_Type  Item_Outlet_Sales\n",
              "0           FDA15          9.3  ...               Tier 1          3735.1380\n",
              "2           FDN15         17.5  ...               Tier 1          2097.2700\n",
              "8           FDP10          NaN  ...               Tier 3          4022.7636\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Checking For Duplicates\n",
        "shopify.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YZO4qt1gXgbm",
        "outputId": "5ed0444d-82d6-4ddf-be25-eee4d3c8f41a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier                 0\n",
              "Item_Weight                  1463\n",
              "Item_Fat_Content                0\n",
              "Item_Visibility                 1\n",
              "Item_Type                       0\n",
              "Item_MRP                        2\n",
              "Outlet_Identifier               0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Size                  2411\n",
              "Outlet_Location_Type            0\n",
              "Item_Outlet_Sales               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have missing values in our dataset including Item weight column etc."
      ],
      "metadata": {
        "id": "IqqQDt_XaFeP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Luu6W8nMaOov"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pivot Tables\n",
        "    A pivot table is a similar operation that is commonly seen in spreadsheets and other programs that operate on tabular data. The pivot table takes simple column-wise data as input, and groups the entries into a two-dimensional table that provides a multidimensional summarization of the data.\n",
        "\n",
        "    A Pivot Table is used to summarise, sort, reorganise, group, count, total or average data stored in a table. It allows us to transform columns into rows and rows into columns. It allows grouping by any field (column), and using advanced calculations on them.\n",
        "\n",
        "    pandas.pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)[source]¶ Create a spreadsheet-style pivot table as a DataFrame.\n",
        "\n",
        "    The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.\n",
        "\n"
      ],
      "metadata": {
        "id": "uNPP_TegZIjp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Applying multiple aggregate functions to a group\n",
        "\n",
        "pd.pivot_table(shopify, index=['Item_Type', 'Item_Fat_Content'], aggfunc={'Item_Outlet_Sales': sum}).head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "PHBaH0P5X25R",
        "outputId": "3d541d1b-913f-4ec3-9b1a-a7eca0cd1a06"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-86ae0b39-a11e-42da-99c9-defc39b20769\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\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></th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Baking Goods</th>\n",
              "      <th>LF</th>\n",
              "      <td>33415.8362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Fat</th>\n",
              "      <td>536343.1796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Regular</th>\n",
              "      <td>673368.8144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>low fat</th>\n",
              "      <td>10764.6544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reg</th>\n",
              "      <td>14163.5634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Breads</th>\n",
              "      <th>LF</th>\n",
              "      <td>13580.3226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Fat</th>\n",
              "      <td>284773.3128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Regular</th>\n",
              "      <td>244648.2100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>low fat</th>\n",
              "      <td>7327.1290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reg</th>\n",
              "      <td>2908.2144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Breakfast</th>\n",
              "      <th>LF</th>\n",
              "      <td>1635.2048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Fat</th>\n",
              "      <td>97806.6858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Regular</th>\n",
              "      <td>129024.7162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reg</th>\n",
              "      <td>3832.3448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Canned</th>\n",
              "      <th>LF</th>\n",
              "      <td>28011.5376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Fat</th>\n",
              "      <td>700572.7366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Regular</th>\n",
              "      <td>668952.5630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>low fat</th>\n",
              "      <td>24587.3282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reg</th>\n",
              "      <td>22027.3272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dairy</th>\n",
              "      <th>LF</th>\n",
              "      <td>41070.5388</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                               Item_Outlet_Sales\n",
              "Item_Type    Item_Fat_Content                   \n",
              "Baking Goods LF                       33415.8362\n",
              "             Low Fat                 536343.1796\n",
              "             Regular                 673368.8144\n",
              "             low fat                  10764.6544\n",
              "             reg                      14163.5634\n",
              "Breads       LF                       13580.3226\n",
              "             Low Fat                 284773.3128\n",
              "             Regular                 244648.2100\n",
              "             low fat                   7327.1290\n",
              "             reg                       2908.2144\n",
              "Breakfast    LF                        1635.2048\n",
              "             Low Fat                  97806.6858\n",
              "             Regular                 129024.7162\n",
              "             reg                       3832.3448\n",
              "Canned       LF                       28011.5376\n",
              "             Low Fat                 700572.7366\n",
              "             Regular                 668952.5630\n",
              "             low fat                  24587.3282\n",
              "             reg                      22027.3272\n",
              "Dairy        LF                       41070.5388"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for duplicates in our dataset\n",
        "shopify.duplicated().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IK7k8fXMYqr7",
        "outputId": "ce988261-f6bd-456f-9ea4-cf54f80a3beb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "True Means we have duplicates during data cleaning we will drop them"
      ],
      "metadata": {
        "id": "r2RPcZxMZsY8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "aM6xUKqsZoJP"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Cleaning /Data Wrangling"
      ],
      "metadata": {
        "id": "gR7Qr6gsadsf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Removing null values\n",
        "\n",
        "Data Scientists and Analysts regularly face the dilemma of dropping or imputing null values, and is a decision that requires intimate knowledge of your data and its context. Overall, removing null data is only suggested if you have a small amount of missing data."
      ],
      "metadata": {
        "id": "KMW3f9VMevus"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import missingno as msno\n",
        "\n",
        "# Gives a bar chart of the missing values\n",
        "msno.bar(shopify)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        },
        "id": "vwO4sH5Wgy_g",
        "outputId": "b62e1cb3-fc89-4365-dc60-a739430d1f18"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe34b4c7bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# msno.heatmap(shopify, cmap='YlGnBu')"
      ],
      "metadata": {
        "id": "cVQ9VM3ohcPu"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping misssing values\n",
        "# shopify.dropna()"
      ],
      "metadata": {
        "id": "ed1Bvwe8aQdl"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#filling missing values with either mean,mode,median\n",
        "shopify['Item_Weight'].fillna(shopify['Item_Weight'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "S_BV5K7pbPHs"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shopify['Outlet_Size'].fillna(shopify['Outlet_Size'].mode()[0],inplace=True)"
      ],
      "metadata": {
        "id": "b8YuklRxb1VN"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shopify['Item_Visibility'].fillna(shopify['Item_Visibility'].median(),inplace=True)"
      ],
      "metadata": {
        "id": "8pIw_J14cBua"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#using foward filling\n",
        "shopify['Item_MRP'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "ZnCU973BcPEa"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# using backward filling\n",
        "# shopify['Item_MRP '].fillna(method='bfill', inplace=True)"
      ],
      "metadata": {
        "id": "B45C35rFdOaP"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dealing With Duplicates in your Dataset"
      ],
      "metadata": {
        "id": "rqcgAOX5dZLj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#we consider dropping duplicate\n",
        "shopify.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "H5GuvzgNdema"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zkYDOEgBePxY"
      },
      "execution_count": 37,
      "outputs": []
    }
  ]
}