Pandas_Exercise_Ans.ipynb

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        "<a href=\"https://colab.research.google.com/github/Vaibhavyadav350/AI-Learnings/blob/main/Pandas_Exercise_Ans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      "source": [
        "# Getting and Knowing your Data"
      ]
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      "cell_type": "markdown",
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        "id": "sWQpn-8ssXtZ"
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      "source": [
        "### Step 1. Import the necessary libraries"
      ]
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        "id": "LY3Ec_VtsXtb"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlMjIsdxsXtc"
      },
      "source": [
        "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LnXG7IqTsXtd"
      },
      "source": [
        "### Step 3. Assign it to a variable."
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "6GIvo9gisXte"
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      "outputs": [],
      "source": [
        "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n",
        "df = pd.read_table(url)"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### Step 4. See the first 5 entries"
      ]
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              "   order_id  quantity                              item_name  \\\n",
              "0         1         1           Chips and Fresh Tomato Salsa   \n",
              "1         1         1                                   Izze   \n",
              "2         1         1                       Nantucket Nectar   \n",
              "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
              "4         2         2                           Chicken Bowl   \n",
              "\n",
              "                                  choice_description item_price  \n",
              "0                                                NaN     $2.39   \n",
              "1                                       [Clementine]     $3.39   \n",
              "2                                            [Apple]     $3.39   \n",
              "3                                                NaN     $2.39   \n",
              "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   "
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              "      <td>[Clementine]</td>\n",
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              "      <td>Chicken Bowl</td>\n",
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        "df.head()"
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        ""
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      "source": [
        "### Step 5. What is the number of observations in the dataset?\n",
        "### How many rows and columns?\n",
        "#### Print the column names"
      ]
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            "text/plain": [
              "(4622, 5)"
            ]
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          "metadata": {},
          "execution_count": 7
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      ],
      "source": [
        "# Solution 1\n",
        "df.shape"
      ]
    },
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          "data": {
            "text/plain": [
              "Index(['order_id', 'quantity', 'item_name', 'choice_description',\n",
              "       'item_price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Solution 2\n",
        "\n",
        "df.columns"
      ]
    },
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        "id": "P1ayUf_Fnb9x"
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      "source": [
        ""
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        "id": "P0KaVKbMsXtm"
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      "source": [
        "### Step 6. Slicing between rows"
      ]
    },
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              "    order_id  quantity            item_name  \\\n",
              "5          3         1         Chicken Bowl   \n",
              "6          3         1        Side of Chips   \n",
              "7          4         1        Steak Burrito   \n",
              "8          4         1     Steak Soft Tacos   \n",
              "9          5         1        Steak Burrito   \n",
              "10         5         1  Chips and Guacamole   \n",
              "\n",
              "                                   choice_description item_price  \n",
              "5   [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
              "6                                                 NaN     $1.69   \n",
              "7   [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
              "8   [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
              "9   [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   \n",
              "10                                                NaN     $4.45   "
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              "      <th>8</th>\n",
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              "      <td>Steak Soft Tacos</td>\n",
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              "      <th>9</th>\n",
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              "      <th>10</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>Chips and Guacamole</td>\n",
              "      <td>NaN</td>\n",
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      "source": [
        "df[5:11]"
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    {
      "cell_type": "markdown",
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        "id": "xfNnqllosXto"
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      "source": [
        "### Step 7. Getting Subset of DataFrame/ Series\n",
        "\n",
        "#### Hint : Learn about .iloc operator"
      ]
    },
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              "                                  item_name item_price\n",
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              "3     Chips and Tomatillo-Green Chili Salsa     $2.39 \n",
              "4                              Chicken Bowl    $16.98 \n",
              "...                                     ...        ...\n",
              "4617                          Steak Burrito    $11.75 \n",
              "4618                          Steak Burrito    $11.75 \n",
              "4619                     Chicken Salad Bowl    $11.25 \n",
              "4620                     Chicken Salad Bowl     $8.75 \n",
              "4621                     Chicken Salad Bowl     $8.75 \n",
              "\n",
              "[4622 rows x 2 columns]"
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nantucket Nectar</td>\n",
              "      <td>$3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
              "      <td>$2.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chicken Bowl</td>\n",
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              "    <tr>\n",
              "      <th>4617</th>\n",
              "      <td>Steak Burrito</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4618</th>\n",
              "      <td>Steak Burrito</td>\n",
              "      <td>$11.75</td>\n",
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              "    <tr>\n",
              "      <th>4619</th>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>$11.25</td>\n",
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              "    <tr>\n",
              "      <th>4620</th>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>$8.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4621</th>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>$8.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>4622 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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      ],
      "source": [
        "df[['item_name','item_price']]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " # Get th ecolumn having index 2(item name) and 4(item price)\n",
        "df.iloc[:,[2,4]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eLUPJ5GlReK",
        "outputId": "5e65a172-21da-458a-e3bc-da48fabea3e8"
      },
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "                                  item_name item_price\n",
              "0              Chips and Fresh Tomato Salsa     $2.39 \n",
              "1                                      Izze     $3.39 \n",
              "2                          Nantucket Nectar     $3.39 \n",
              "3     Chips and Tomatillo-Green Chili Salsa     $2.39 \n",
              "4                              Chicken Bowl    $16.98 \n",
              "...                                     ...        ...\n",
              "4617                          Steak Burrito    $11.75 \n",
              "4618                          Steak Burrito    $11.75 \n",
              "4619                     Chicken Salad Bowl    $11.25 \n",
              "4620                     Chicken Salad Bowl     $8.75 \n",
              "4621                     Chicken Salad Bowl     $8.75 \n",
              "\n",
              "[4622 rows x 2 columns]"
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              "      <th>2</th>\n",
              "      <td>Nantucket Nectar</td>\n",
              "      <td>$3.39</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
              "      <td>$2.39</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>4617</th>\n",
              "      <td>Steak Burrito</td>\n",
              "      <td>$11.75</td>\n",
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              "    <tr>\n",
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              "      <td>Steak Burrito</td>\n",
              "      <td>$11.75</td>\n",
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              "    <tr>\n",
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              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>$11.25</td>\n",
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              "    <tr>\n",
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              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>$8.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4621</th>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>$8.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4622 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e9d2f4d-e845-4331-80ac-914d135a8e02')\"\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e9d2f4d-e845-4331-80ac-914d135a8e02 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-0e9d2f4d-e845-4331-80ac-914d135a8e02');\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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              "          element.appendChild(docLink);\n",
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              "  "
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    {
      "cell_type": "code",
      "source": [
        "df.iloc[:,2:4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFUmZnn0mSEm",
        "outputId": "2531ade4-2067-4acf-88c6-a0b84f9cdb79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  item_name  \\\n",
              "0              Chips and Fresh Tomato Salsa   \n",
              "1                                      Izze   \n",
              "2                          Nantucket Nectar   \n",
              "3     Chips and Tomatillo-Green Chili Salsa   \n",
              "4                              Chicken Bowl   \n",
              "...                                     ...   \n",
              "4617                          Steak Burrito   \n",
              "4618                          Steak Burrito   \n",
              "4619                     Chicken Salad Bowl   \n",
              "4620                     Chicken Salad Bowl   \n",
              "4621                     Chicken Salad Bowl   \n",
              "\n",
              "                                     choice_description  \n",
              "0                                                   NaN  \n",
              "1                                          [Clementine]  \n",
              "2                                               [Apple]  \n",
              "3                                                   NaN  \n",
              "4     [Tomatillo-Red Chili Salsa (Hot), [Black Beans...  \n",
              "...                                                 ...  \n",
              "4617  [Fresh Tomato Salsa, [Rice, Black Beans, Sour ...  \n",
              "4618  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...  \n",
              "4619  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...  \n",
              "4620  [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...  \n",
              "4621  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...  \n",
              "\n",
              "[4622 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9aa19bdc-c976-4c1d-8a13-8cb9535ae294\">\n",
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              "      <th></th>\n",
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              "      <th>2</th>\n",
              "      <td>Nantucket Nectar</td>\n",
              "      <td>[Apple]</td>\n",
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              "      <th>3</th>\n",
              "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chicken Bowl</td>\n",
              "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4617</th>\n",
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              "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...</td>\n",
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              "    <tr>\n",
              "      <th>4618</th>\n",
              "      <td>Steak Burrito</td>\n",
              "      <td>[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...</td>\n",
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              "    <tr>\n",
              "      <th>4619</th>\n",
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              "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4620</th>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4621</th>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>4622 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9aa19bdc-c976-4c1d-8a13-8cb9535ae294')\"\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-9aa19bdc-c976-4c1d-8a13-8cb9535ae294 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-9aa19bdc-c976-4c1d-8a13-8cb9535ae294');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVDtNvIHsXtp"
      },
      "source": [
        "### Step 8. Check for duplicates and drop them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EfbtCENIsXtq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6bf292b-bd58-4d02-ca9c-05585d0d319a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "len(df[df.duplicated()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ejdkf2hcTso2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "712bd157-8c2e-4c0a-8f23-5baff8715667"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      order_id  quantity                              item_name  \\\n",
              "0            1         1           Chips and Fresh Tomato Salsa   \n",
              "1            1         1                                   Izze   \n",
              "2            1         1                       Nantucket Nectar   \n",
              "3            1         1  Chips and Tomatillo-Green Chili Salsa   \n",
              "4            2         2                           Chicken Bowl   \n",
              "...        ...       ...                                    ...   \n",
              "4617      1833         1                          Steak Burrito   \n",
              "4618      1833         1                          Steak Burrito   \n",
              "4619      1834         1                     Chicken Salad Bowl   \n",
              "4620      1834         1                     Chicken Salad Bowl   \n",
              "4621      1834         1                     Chicken Salad Bowl   \n",
              "\n",
              "                                     choice_description item_price  \n",
              "0                                                   NaN     $2.39   \n",
              "1                                          [Clementine]     $3.39   \n",
              "2                                               [Apple]     $3.39   \n",
              "3                                                   NaN     $2.39   \n",
              "4     [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
              "...                                                 ...        ...  \n",
              "4617  [Fresh Tomato Salsa, [Rice, Black Beans, Sour ...    $11.75   \n",
              "4618  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...    $11.75   \n",
              "4619  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...    $11.25   \n",
              "4620  [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...     $8.75   \n",
              "4621  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...     $8.75   \n",
              "\n",
              "[4563 rows x 5 columns]"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>quantity</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Izze</td>\n",
              "      <td>[Clementine]</td>\n",
              "      <td>$3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Nantucket Nectar</td>\n",
              "      <td>[Apple]</td>\n",
              "      <td>$3.39</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$2.39</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <th>4617</th>\n",
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              "      <td>Steak Burrito</td>\n",
              "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...</td>\n",
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              "      <th>4618</th>\n",
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              "      <td>1</td>\n",
              "      <td>Steak Burrito</td>\n",
              "      <td>[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...</td>\n",
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              "    <tr>\n",
              "      <th>4620</th>\n",
              "      <td>1834</td>\n",
              "      <td>1</td>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...</td>\n",
              "      <td>$8.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4621</th>\n",
              "      <td>1834</td>\n",
              "      <td>1</td>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
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              "<p>4563 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b211388-e49b-41c7-a474-bd66e29747e6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      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-2b211388-e49b-41c7-a474-bd66e29747e6 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-2b211388-e49b-41c7-a474-bd66e29747e6');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df = df.drop_duplicates()\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HdijV0mPTHJc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bebc4a3-e830-483f-cc32-0b10e1a20a09"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4563"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "4622-59"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dAgWqULDlbe0"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lTuMNCJVsXtq"
      },
      "source": [
        "### Step 9. Which was the most-ordered item? "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UlFolXdosXtr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e6baeab-2786-4c45-d180-fcf4c136bedb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Chicken Bowl    717\n",
              "Name: item_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "df['item_name'].value_counts().head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-Rhh0lRsXtr"
      },
      "source": [
        "### Step 10. For the most-ordered item, how many items were ordered?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1jfVA4a2sXts"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AgCv1YACsXts"
      },
      "source": [
        "### Step 11. What was the most ordered item in the choice_description column?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        },
        "id": "QD8HI0Cpjttm",
        "outputId": "6cd3b777-d091-4a5d-8e98-387ca800ec4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-b666bf274d0a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EriwWBypsXtt"
      },
      "outputs": [],
      "source": [
        "df['choice_description'].value_counts().head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xFeM3vSgsXtt"
      },
      "source": [
        "### Step 12. How many items were orderd in total?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6iPi9cAlsXtt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2539d03e-61c3-46bd-861f-ab91262f584d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4913"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df['quantity'].sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_vztTQrzQIu4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db0d85b6-ffb4-4515-ba34-55bef064ee21"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     4296\n",
              "2      224\n",
              "3       28\n",
              "4       10\n",
              "5        1\n",
              "15       1\n",
              "7        1\n",
              "8        1\n",
              "10       1\n",
              "Name: quantity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df['quantity'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UPEHHz_Enzrt"
      },
      "source": [
        "### Step 12a. Get the details of highest ordered item\n",
        "#### Hint : Use Conditions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZPT4DHPHn8Mv"
      },
      "outputs": [],
      "source": [
        "h_quantity = df['quantity'].max()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y6wOX3cwQIu4",
        "colab": {
          "base_uri": "https://localhost:8080/",
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            "text/plain": [
              "      order_id  quantity                     item_name choice_description  \\\n",
              "3598      1443        15  Chips and Fresh Tomato Salsa                NaN   \n",
              "\n",
              "     item_price  \n",
              "3598    $44.25   "
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df[df['quantity']==h_quantity]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elTnhM_AsXtt"
      },
      "source": [
        "### Step 13. Turn the item price into a float"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ELoW59msXtu"
      },
      "source": [
        "#### Step 13.a. Check the item price type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AQpGKg3vsXtu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd477172-b3bf-42b8-9858-0c963b0148e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df['item_price'].dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N40OfrWWsXtv"
      },
      "source": [
        "#### Step 13.b. Change the type of item price to float"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0TZ2WjIgpbZK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "474ac409-c8fe-4ba3-b25b-1de8bdb12b5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ],
      "source": [
        "df['item_price'] = df['item_price'].str.replace('$','').astype('float')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itmp4TNbsXtw"
      },
      "source": [
        "#### Step 13.c. Check the item price type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gy36tpuYsXtw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "929cca85-74f9-4432-ae6d-ea83b2286b64"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "df['item_price'].dtype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "au-6mJsSQIu5"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQVYYGtSsXtw"
      },
      "source": [
        "### Step 14. How much was the revenue for the period in the dataset?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fVxmpY2RQIu6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c845703a-1695-46ff-cf63-a16689d82628"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34177.25"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "df['item_price'].sum()\n",
        "# This isnt the final revenue ( only for quantity = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R9-qBQKJsXtx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a204fba-c97f-42dc-ead1-682260f0f9b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38914.11"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "# Revenue = Item_Price * Quantity\n",
        "revenue = (df['item_price']*df['quantity']).sum()\n",
        "revenue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pbouOfVwsXtx"
      },
      "source": [
        "### Step 15. How many orders were made in the period?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oSqeult0sXtx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e220871d-3f24-42dc-d3f0-4b687fc1b24b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1834"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "df['order_id'].nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oGb6txvsXty"
      },
      "source": [
        "### Step 16. What is the average revenue amount per order?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LjpUhSYNQIu6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26989a69-5dd1-4388-e368-66b58491fcb6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.528185404339267"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "np.mean(df['item_price']*df['quantity'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lQ5nMTG8sXty",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43d6eef3-fecf-4eee-86c8-10d2e0877b2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21.218162486368595"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "revenue/1834"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4= df[df['quantity']==4]"
      ],
      "metadata": {
        "id": "pii87HtYo6BV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Average revenue per order for quantity == 4\n",
        "# Revenue = Item_Price * Quantity\n",
        "# How many unique orders ?\n",
        "# Revenue/Orders"
      ],
      "metadata": {
        "id": "R_9dxrUwpM9a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r_4 = df4['item_price']*df4['quantity']\n",
        "r_4.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0U5oxmBzpbUU",
        "outputId": "edc4e010-01cf-4f2a-f692-485ee4cdd5d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "523.8399999999999"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4['order_id'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xWZQ53MLqcuo",
        "outputId": "41eb6fc7-55e3-41f3-aa17-32603835ef69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "523.8399999/7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ueNK2OwqjHT",
        "outputId": "f5282714-5725-4bed-b633-1a530444d769"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "74.8342857"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmjSD4IfsXtz"
      },
      "source": [
        "### Step 17. How many different items are sold?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QklSjeUTsXtz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d3c1885-b9b4-4bb9-8977-ee43d0bf6c49"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df['item_name'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XfHqQPtMQIu7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f64ee310-ec41-4545-c30c-7681db5d8a74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3386"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "#Task : How many orders have revenue less than 10 dollars\n",
        "    \n",
        "len(df[(df['item_price']*df['quantity'])<10])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use a composition graph ( Pie Chart ) and plot the following:\n",
        "# low = lesser than 5 dollars\n",
        "# high = greater than 10 dollars\n",
        "# medium = in between low and high"
      ],
      "metadata": {
        "id": "yeChWJrtsHFN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Steps for the analysis\n",
        "# Step 1: Create a revenue column by multiplying the values of item and quantity from df\n",
        "# Step 2: Check the length of values having low, high and medium ( Total shld be 4622)\n",
        "# NoTE : Use & operator \n",
        "# Step 3: Plot a pie chart"
      ],
      "metadata": {
        "id": "1qx_4OjstRXF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['revenue'] = df['item_price']*df['quantity']"
      ],
      "metadata": {
        "id": "U5Rb8BLPvS1M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "low = len(df[df['revenue']<5])\n",
        "low"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KYhC9NcIzOnj",
        "outputId": "58e61603-9062-43c9-f20c-fcb23b7b82e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1547"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "high = len(df[df['revenue']>10])\n",
        "high"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wp6dCI7SzU8a",
        "outputId": "760c0a00-b86f-415e-a165-8a67af757669"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1177"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "4563-(low+high)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDEFggdBzelL",
        "outputId": "339948d8-5422-464f-b379-fa475dce8b18"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1839"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fc = df['revenue']>=5\n",
        "sc = df['revenue']<=10\n",
        "med = len(df[(fc)&(sc)])\n",
        "med"
      ],
      "metadata": {
        "colab": {
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          "output_type": "execute_result",
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            "text/plain": [
              "1839"
            ]
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          "metadata": {},
          "execution_count": 30
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      "cell_type": "code",
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        "1547+1177+1839"
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            ]
          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie([low,med,high],labels=['Low','Medium','High'],autopct='%.2f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "4GUDdRHh0L4L",
        "outputId": "52fa5914-3dad-4816-930a-c20c4a88d87f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "8U1cYlF50cAL"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "colab": {
      "name": "Pandas Exercise Ans.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}