Guided_project_5.ipynb

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        "# Chapter 5\n",
        "##Pandas\n",
        "The sales department wants to compare the performances of this and last year. Firstly, they delivered your team the performance review from last year. The data is in a comma-separated format, a .csv file called “employee_revenue_lastyear.csv”. It is in tabular form and includes information about 11 employees. \n"
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        "###Importing libraries\n",
        "First, you need to import the required libraries. \n",
        "\n",
        "📌 Import the Pandas and NumPy libraries."
      ]
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      "source": [
        "#Import Pandas and NumPy\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
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        "###Get the data from last year\n",
        "Next, you need to get the data from the .csv file that the sales team provided. \n",
        "\n",
        "📌 Use the .read_csv() function of the Pandas library to import the data from \"employee_revenue_lastyear.csv\" and assign it to the variable \"last_year\"."
      ]
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      "source": [
        "#Read the data from the \"employee_revenue_lastyear.csv\" file using the .read_csv() method\n",
        "last_year = pd.read_csv(\"employee_revenue_lastyear.csv\")"
      ]
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        "###Check the imported data\n",
        "It is important to check if everything is included. But you don't need to see the whole DataFrame, it will be enough to see the first and last rows.\n",
        "\n",
        "📌 Use the .head() function to get the first n-rows of the DataFrame:\n",
        "  1. Without specifying any number to get the first 5 rows by default.\n",
        "  2. Give a number n as an argument to print the first n-rows, for example 8."
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              "     Name  Number of Calls  Average Deal Size  Revenue\n",
              "0    Omer              400                8.0   2000.0\n",
              "1  Arnold              150               10.0   1000.0\n",
              "2   Aidan              170               15.0   2000.0\n",
              "3     Sue              220                5.0   1000.0\n",
              "4     Ben              140               22.0      NaN"
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        "#Use the .head() function without specifying any number\n",
        "last_year.head()"
      ]
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              "     Name  Number of Calls  Average Deal Size  Revenue\n",
              "0    Omer              400                8.0   2000.0\n",
              "1  Arnold              150               10.0   1000.0\n",
              "2   Aidan              170               15.0   2000.0\n",
              "3     Sue              220                5.0   1000.0\n",
              "4     Ben              140               22.0      NaN\n",
              "5    Rose              350                8.0   1800.0\n",
              "6    Lucy               50               12.0    500.0\n",
              "7   Ellen              560                NaN   3300.0"
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              "      <th>6</th>\n",
              "      <td>Lucy</td>\n",
              "      <td>50</td>\n",
              "      <td>12.0</td>\n",
              "      <td>500.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ellen</td>\n",
              "      <td>560</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3300.0</td>\n",
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      "source": [
        "#Use the .head() function with a number n as an argument\n",
        "last_year.head(8)"
      ]
    },
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        "📌 Use the .tail() function to get the last n-rows of the DataFrame:\n",
        "  1. Without specifying any number to get the last 5 rows by default.\n",
        "  2. Give a number n as an argument to print the last n-rows, for example 6."
      ]
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              "      Name  Number of Calls  Average Deal Size  Revenue\n",
              "6     Lucy               50               12.0    500.0\n",
              "7    Ellen              560                NaN   3300.0\n",
              "8    Karen               20               25.0    500.0\n",
              "9   Jamaal               30               30.0    800.0\n",
              "10    Omer              400                8.0   2000.0"
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              "      <th>Revenue</th>\n",
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              "      <th>6</th>\n",
              "      <td>Lucy</td>\n",
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              "      <td>Ellen</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>3300.0</td>\n",
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              "      <th>8</th>\n",
              "      <td>Karen</td>\n",
              "      <td>20</td>\n",
              "      <td>25.0</td>\n",
              "      <td>500.0</td>\n",
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              "      <th>9</th>\n",
              "      <td>Jamaal</td>\n",
              "      <td>30</td>\n",
              "      <td>30.0</td>\n",
              "      <td>800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Omer</td>\n",
              "      <td>400</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3cb43b4d-67ec-499a-8725-b49095bf4163')\"\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",
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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",
              "      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-3cb43b4d-67ec-499a-8725-b49095bf4163 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-3cb43b4d-67ec-499a-8725-b49095bf4163');\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": 5
        }
      ],
      "source": [
        "#Use the .tail() function without specifying any number\n",
        "last_year.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "39ed1b7f",
      "metadata": {
        "id": "39ed1b7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "488842ef-3c49-4672-f10c-65f7c7955d5e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Number of Calls  Average Deal Size  Revenue\n",
              "5     Rose              350                8.0   1800.0\n",
              "6     Lucy               50               12.0    500.0\n",
              "7    Ellen              560                NaN   3300.0\n",
              "8    Karen               20               25.0    500.0\n",
              "9   Jamaal               30               30.0    800.0\n",
              "10    Omer              400                8.0   2000.0"
            ],
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              "\n",
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              "      <th>Name</th>\n",
              "      <th>Number of Calls</th>\n",
              "      <th>Average Deal Size</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rose</td>\n",
              "      <td>350</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Lucy</td>\n",
              "      <td>50</td>\n",
              "      <td>12.0</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ellen</td>\n",
              "      <td>560</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Karen</td>\n",
              "      <td>20</td>\n",
              "      <td>25.0</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Jamaal</td>\n",
              "      <td>30</td>\n",
              "      <td>30.0</td>\n",
              "      <td>800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Omer</td>\n",
              "      <td>400</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33564136-1f56-4ac8-b0d7-8ba8c93c37ba')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-33564136-1f56-4ac8-b0d7-8ba8c93c37ba 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-33564136-1f56-4ac8-b0d7-8ba8c93c37ba');\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": 6
        }
      ],
      "source": [
        "#Use the .tail() function with a number n as an argument\n",
        "last_year.tail(6)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eb4a1671",
      "metadata": {
        "id": "eb4a1671"
      },
      "source": [
        "Another way to quickly get the number of rows and columns is using the shape attribute.\n",
        "\n",
        "📌 Use the *shape* attribute to display the number of rows and columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9faef542",
      "metadata": {
        "id": "9faef542",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe3c7aa9-b9dc-492b-b5e8-71aafc032e80"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "#Use the shape attribute to display the number of rows and columns \n",
        "last_year.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b4f6debd",
      "metadata": {
        "id": "b4f6debd"
      },
      "source": [
        "If you want to get more details about the DataFrame, you can use the .info() function.\n",
        "\n",
        "📌 Use the .info() function to get more details about the DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "91b7885a",
      "metadata": {
        "id": "91b7885a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3374bb8a-987e-4b12-eaab-04f9c8b43a92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11 entries, 0 to 10\n",
            "Data columns (total 4 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Name               11 non-null     object \n",
            " 1   Number of Calls    11 non-null     int64  \n",
            " 2   Average Deal Size  10 non-null     float64\n",
            " 3   Revenue            10 non-null     float64\n",
            "dtypes: float64(2), int64(1), object(1)\n",
            "memory usage: 480.0+ bytes\n"
          ]
        }
      ],
      "source": [
        "#Use the .info() function to get more details\n",
        "last_year.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c8018dfd",
      "metadata": {
        "id": "c8018dfd"
      },
      "source": [
        "###Add information to the DataFrame\n",
        "Now you should have enough insights about the DataFrame. :)\n",
        "\n",
        "You know that the data in the DataFrame is from 2021. This information should be included, so you decide to add the column \"Year\".\n",
        "\n",
        "📌 Add the column \"Year\" to the DataFrame and pass the value \"2021\" to its rows. Then display the DataFrame to see the result."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "84ca959b",
      "metadata": {
        "id": "84ca959b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "2a0d6275-b835-4940-c71d-d8ad5cc43d73"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Number of Calls  Average Deal Size  Revenue  Year\n",
              "0     Omer              400                8.0   2000.0  2021\n",
              "1   Arnold              150               10.0   1000.0  2021\n",
              "2    Aidan              170               15.0   2000.0  2021\n",
              "3      Sue              220                5.0   1000.0  2021\n",
              "4      Ben              140               22.0      NaN  2021\n",
              "5     Rose              350                8.0   1800.0  2021\n",
              "6     Lucy               50               12.0    500.0  2021\n",
              "7    Ellen              560                NaN   3300.0  2021\n",
              "8    Karen               20               25.0    500.0  2021\n",
              "9   Jamaal               30               30.0    800.0  2021\n",
              "10    Omer              400                8.0   2000.0  2021"
            ],
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              "\n",
              "  <div id=\"df-5817d3c0-9561-4339-9802-a7843be00e53\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Arnold</td>\n",
              "      <td>150</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>2021</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aidan</td>\n",
              "      <td>170</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sue</td>\n",
              "      <td>220</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ben</td>\n",
              "      <td>140</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rose</td>\n",
              "      <td>350</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1800.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Lucy</td>\n",
              "      <td>50</td>\n",
              "      <td>12.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ellen</td>\n",
              "      <td>560</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3300.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Karen</td>\n",
              "      <td>20</td>\n",
              "      <td>25.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Jamaal</td>\n",
              "      <td>30</td>\n",
              "      <td>30.0</td>\n",
              "      <td>800.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Omer</td>\n",
              "      <td>400</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5817d3c0-9561-4339-9802-a7843be00e53')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "                                                     [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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#Add the column \"Year\" and assign the value \"2021\" to its rows\n",
        "last_year[\"Year\"] = 2021\n",
        "\n",
        "#Display the DataFrame\n",
        "last_year"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5fd0ab2f",
      "metadata": {
        "id": "5fd0ab2f"
      },
      "source": [
        "### Get the data from this year\n",
        "\n",
        "You also need to add the data from this year to be able to compare both years.\n",
        "You already prepared this data in chapter 4.\n",
        "\n",
        "📌 Copy and paste the NumPy arrays \"names\", \"call_numbers\", \"average_deal_sizes\", and \"revenues\" from chapter 4."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ac4f458",
      "metadata": {
        "id": "1ac4f458"
      },
      "outputs": [],
      "source": [
        "#Copy and paste the NumPy arrays \"names\", \"call_numbers\", \"average_deal_sizes\", and \"revenues\"\n",
        "names = ['Ben', 'Omer', 'Karen', 'Celine', 'Sue', 'Bora', 'Rose', 'Ellen', 'Bob', 'Taylor', 'Jude']\n",
        "call_numbers = [300, 10, 500, 70, 100, 100, 600, 800, 200, 450, 80]\n",
        "average_deal_sizes = [8, 6, 24, 32, 5, 25, 25, 40, 15, 10, 12]\n",
        "revenues = [2400, 60, 12000, 2275, 500, 770, 4000, 6000, 800, 1200, 500]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fdd7b85b",
      "metadata": {
        "id": "fdd7b85b"
      },
      "source": [
        "Now create a DataFrame using these arrays. \n",
        "\n",
        "📌 Create a dictionary with the arrays. Specify the column names in the keys.\n",
        "\n",
        "📌 Convert the dictionary to a Pandas DataFrame \"current_year\" and use the .head() function to check it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cba9c5c5",
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        {
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            "text/plain": [
              "     name  calls  avg_deal_size  revenue\n",
              "0     Ben    300              8     2400\n",
              "1    Omer     10              6       60\n",
              "2   Karen    500             24    12000\n",
              "3  Celine     70             32     2275\n",
              "4     Sue    100              5      500"
            ],
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              "\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>Celine</td>\n",
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              "      <td>2275</td>\n",
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              "    <tr>\n",
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              "      <td>100</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ],
      "source": [
        "#Create a dictionary with the column names as keys and the arrays as values\n",
        "dictionary = {\"name\": names,\n",
        "              \"calls\": call_numbers,\n",
        "              \"avg_deal_size\": average_deal_sizes,\n",
        "              \"revenue\": revenues}\n",
        "#Convert the dictionary to a Pandas Dataframe\n",
        "current_year = pd.DataFrame(dictionary)\n",
        "#Use the .head() function to check it\n",
        "current_year.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "036b6b7b",
      "metadata": {
        "id": "036b6b7b"
      },
      "source": [
        "Similar to what we did with last year's data, add the year information.\n",
        "\n",
        "📌 Add the column \"Year\" to the DataFrame and pass the value \"2022\" to its rows. Then use the .head() function to see the result."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "     name  calls  avg_deal_size  revenue  Year\n",
              "0     Ben    300              8     2400  2022\n",
              "1    Omer     10              6       60  2022\n",
              "2   Karen    500             24    12000  2022\n",
              "3  Celine     70             32     2275  2022\n",
              "4     Sue    100              5      500  2022"
            ],
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              "\n",
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              "      <th>0</th>\n",
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              "      <td>2400</td>\n",
              "      <td>2022</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Omer</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>60</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Karen</td>\n",
              "      <td>500</td>\n",
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              "      <td>12000</td>\n",
              "      <td>2022</td>\n",
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              "    <tr>\n",
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              "      <td>Celine</td>\n",
              "      <td>70</td>\n",
              "      <td>32</td>\n",
              "      <td>2275</td>\n",
              "      <td>2022</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sue</td>\n",
              "      <td>100</td>\n",
              "      <td>5</td>\n",
              "      <td>500</td>\n",
              "      <td>2022</td>\n",
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              "\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",
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              "          document.querySelector('#df-7505ccd7-1444-48f2-8da2-49e8e47ccc31 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-7505ccd7-1444-48f2-8da2-49e8e47ccc31');\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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              "  </div>\n",
              "  "
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          "metadata": {},
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        }
      ],
      "source": [
        "#Add the column \"Year\" and assign the value \"2022\" to its rows\n",
        "current_year[\"Year\"] = 2022\n",
        "#Use the .head() function to see the result\n",
        "current_year.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "30a98a5b",
      "metadata": {
        "id": "30a98a5b"
      },
      "source": [
        "###Compare the two DataFrames\n",
        "\n",
        "Now that you printed the DataFrame \"current_year\", print \"last_year\" as well to compare them.\n",
        "\n",
        "📌 Use the .head() function to print the DataFrame \"last_year\"."
      ]
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              "     Name  Number of Calls  Average Deal Size  Revenue  Year\n",
              "0    Omer              400                8.0   2000.0  2021\n",
              "1  Arnold              150               10.0   1000.0  2021\n",
              "2   Aidan              170               15.0   2000.0  2021\n",
              "3     Sue              220                5.0   1000.0  2021\n",
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              "                                                     [key], {});\n",
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              "  "
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "#Use the .head() function to print the DataFrame \"last_year\"\n",
        "last_year.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c98633be",
      "metadata": {
        "id": "c98633be"
      },
      "source": [
        "You notice that the column names of the two DataFrames are different. You need to fix this problem.\n",
        "\n",
        "📌 Assign the column names of \"last_year\" to \"current_year\" by using the *columns* attribute."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1b285acc",
      "metadata": {
        "id": "1b285acc"
      },
      "outputs": [],
      "source": [
        "#Use the columns attribute to assign the column names of \"last_year\" to \"current_year\"\n",
        "current_year.columns = last_year.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1377bf5f",
      "metadata": {
        "id": "1377bf5f"
      },
      "source": [
        "### Concatenate two DataFrames\n",
        "Now that the two DataFrames have the same column names, You can merge - or concatenate - them into a single DataFrame \"all_data\".\n",
        "\n",
        "📌 Use the .concat() function with the argument \"axis\" set to 0. Then display the DataFrame."
      ]
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              "      Name  Number of Calls  Average Deal Size  Revenue  Year\n",
              "0     Omer              400                8.0   2000.0  2021\n",
              "1   Arnold              150               10.0   1000.0  2021\n",
              "2    Aidan              170               15.0   2000.0  2021\n",
              "3      Sue              220                5.0   1000.0  2021\n",
              "4      Ben              140               22.0      NaN  2021\n",
              "5     Rose              350                8.0   1800.0  2021\n",
              "6     Lucy               50               12.0    500.0  2021\n",
              "7    Ellen              560                NaN   3300.0  2021\n",
              "8    Karen               20               25.0    500.0  2021\n",
              "9   Jamaal               30               30.0    800.0  2021\n",
              "10    Omer              400                8.0   2000.0  2021\n",
              "0      Ben              300                8.0   2400.0  2022\n",
              "1     Omer               10                6.0     60.0  2022\n",
              "2    Karen              500               24.0  12000.0  2022\n",
              "3   Celine               70               32.0   2275.0  2022\n",
              "4      Sue              100                5.0    500.0  2022\n",
              "5     Bora              100               25.0    770.0  2022\n",
              "6     Rose              600               25.0   4000.0  2022\n",
              "7    Ellen              800               40.0   6000.0  2022\n",
              "8      Bob              200               15.0    800.0  2022\n",
              "9   Taylor              450               10.0   1200.0  2022\n",
              "10    Jude               80               12.0    500.0  2022"
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b5b75d5e-d9af-462d-b7d9-b70d0d28e8c9');\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": 16
        }
      ],
      "source": [
        "#Use the .concat() function to concatenate the two DataFrames\n",
        "all_data = pd.concat([last_year, current_year], axis=0)\n",
        "#Display the DataFrame \"all_data\"\n",
        "all_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52fe2da3",
      "metadata": {
        "id": "52fe2da3"
      },
      "source": [
        "###Check the data\n",
        "\n",
        "This worked out well, but you noticed that the indexes are incorrect. You need to reset them.\n",
        "\n",
        "📌 Use the .reset_index() function. Set the arguments \"drop\" and \"inplace\" to \"True\". Then display the DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b9cfaeff",
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          "height": 698
        },
        "outputId": "532f2b16-c4ee-4dc7-9a05-eb6fe11df308"
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          "data": {
            "text/plain": [
              "      Name  Number of Calls  Average Deal Size  Revenue  Year\n",
              "0     Omer              400                8.0   2000.0  2021\n",
              "1   Arnold              150               10.0   1000.0  2021\n",
              "2    Aidan              170               15.0   2000.0  2021\n",
              "3      Sue              220                5.0   1000.0  2021\n",
              "4      Ben              140               22.0      NaN  2021\n",
              "5     Rose              350                8.0   1800.0  2021\n",
              "6     Lucy               50               12.0    500.0  2021\n",
              "7    Ellen              560                NaN   3300.0  2021\n",
              "8    Karen               20               25.0    500.0  2021\n",
              "9   Jamaal               30               30.0    800.0  2021\n",
              "10    Omer              400                8.0   2000.0  2021\n",
              "11     Ben              300                8.0   2400.0  2022\n",
              "12    Omer               10                6.0     60.0  2022\n",
              "13   Karen              500               24.0  12000.0  2022\n",
              "14  Celine               70               32.0   2275.0  2022\n",
              "15     Sue              100                5.0    500.0  2022\n",
              "16    Bora              100               25.0    770.0  2022\n",
              "17    Rose              600               25.0   4000.0  2022\n",
              "18   Ellen              800               40.0   6000.0  2022\n",
              "19     Bob              200               15.0    800.0  2022\n",
              "20  Taylor              450               10.0   1200.0  2022\n",
              "21    Jude               80               12.0    500.0  2022"
            ],
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Lucy</td>\n",
              "      <td>50</td>\n",
              "      <td>12.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ellen</td>\n",
              "      <td>560</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3300.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Karen</td>\n",
              "      <td>20</td>\n",
              "      <td>25.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Jamaal</td>\n",
              "      <td>30</td>\n",
              "      <td>30.0</td>\n",
              "      <td>800.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Omer</td>\n",
              "      <td>400</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Ben</td>\n",
              "      <td>300</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2400.0</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Omer</td>\n",
              "      <td>10</td>\n",
              "      <td>6.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Karen</td>\n",
              "      <td>500</td>\n",
              "      <td>24.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Celine</td>\n",
              "      <td>70</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2275.0</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Sue</td>\n",
              "      <td>100</td>\n",
              "      <td>5.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Bora</td>\n",
              "      <td>100</td>\n",
              "      <td>25.0</td>\n",
              "      <td>770.0</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Rose</td>\n",
              "      <td>600</td>\n",
              "      <td>25.0</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Ellen</td>\n",
              "      <td>800</td>\n",
              "      <td>40.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bob</td>\n",
              "      <td>200</td>\n",
              "      <td>15.0</td>\n",
              "      <td>800.0</td>\n",
              "      <td>2022</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Taylor</td>\n",
              "      <td>450</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2022</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Jude</td>\n",
              "      <td>80</td>\n",
              "      <td>12.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>2022</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#Use the .reset_index() function to reset the indexes\n",
        "all_data.reset_index(drop = True, inplace = True)\n",
        "#Display the DataFrame \"all_data\"\n",
        "all_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "45dfd1c5",
      "metadata": {
        "id": "45dfd1c5"
      },
      "source": [
        "Next, you need to check the entries for missing values.\n",
        "\n",
        "📌 Use the isna.() and .any() function to see if there are missing values in the DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "66ab8074",
      "metadata": {
        "id": "66ab8074",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad3f405f-cb38-4472-96ec-6786c9dd4bd3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name                 False\n",
              "Number of Calls      False\n",
              "Average Deal Size     True\n",
              "Revenue               True\n",
              "Year                 False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "#Check the DataFrame for missing values\n",
        "all_data.isna().any()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9596dfbb",
      "metadata": {
        "id": "9596dfbb"
      },
      "source": [
        "From the output, you see that there are missing values in the columns \"Average deal size\" and \"Revenue\". You decide to fix this problem by filling the missing values using with the mean of the respective column.\n",
        "\n",
        "📌 Use the .fillna() function to fill the missing values. Use the .mean() function to set the argument \"value\" to the mean of \"all_data\". Again, set \"inplace\" to \"True\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9784b237",
      "metadata": {
        "id": "9784b237",
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          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "outputId": "5add0bf2-f980-41e7-df82-10c380fb0d9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3438: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Number of Calls  Average Deal Size       Revenue  Year\n",
              "0     Omer              400           8.000000   2000.000000  2021\n",
              "1   Arnold              150          10.000000   1000.000000  2021\n",
              "2    Aidan              170          15.000000   2000.000000  2021\n",
              "3      Sue              220           5.000000   1000.000000  2021\n",
              "4      Ben              140          22.000000   2162.142857  2021\n",
              "5     Rose              350           8.000000   1800.000000  2021\n",
              "6     Lucy               50          12.000000    500.000000  2021\n",
              "7    Ellen              560          16.428571   3300.000000  2021\n",
              "8    Karen               20          25.000000    500.000000  2021\n",
              "9   Jamaal               30          30.000000    800.000000  2021\n",
              "10    Omer              400           8.000000   2000.000000  2021\n",
              "11     Ben              300           8.000000   2400.000000  2022\n",
              "12    Omer               10           6.000000     60.000000  2022\n",
              "13   Karen              500          24.000000  12000.000000  2022\n",
              "14  Celine               70          32.000000   2275.000000  2022\n",
              "15     Sue              100           5.000000    500.000000  2022\n",
              "16    Bora              100          25.000000    770.000000  2022\n",
              "17    Rose              600          25.000000   4000.000000  2022\n",
              "18   Ellen              800          40.000000   6000.000000  2022\n",
              "19     Bob              200          15.000000    800.000000  2022\n",
              "20  Taylor              450          10.000000   1200.000000  2022\n",
              "21    Jude               80          12.000000    500.000000  2022"
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              "      <td>1000.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ben</td>\n",
              "      <td>140</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>2162.142857</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rose</td>\n",
              "      <td>350</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1800.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Lucy</td>\n",
              "      <td>50</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ellen</td>\n",
              "      <td>560</td>\n",
              "      <td>16.428571</td>\n",
              "      <td>3300.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Karen</td>\n",
              "      <td>20</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Jamaal</td>\n",
              "      <td>30</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Omer</td>\n",
              "      <td>400</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Ben</td>\n",
              "      <td>300</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2400.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Omer</td>\n",
              "      <td>10</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Karen</td>\n",
              "      <td>500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>12000.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Celine</td>\n",
              "      <td>70</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>2275.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Sue</td>\n",
              "      <td>100</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Bora</td>\n",
              "      <td>100</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>770.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Rose</td>\n",
              "      <td>600</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>4000.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Ellen</td>\n",
              "      <td>800</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bob</td>\n",
              "      <td>200</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Taylor</td>\n",
              "      <td>450</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1200.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Jude</td>\n",
              "      <td>80</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57836fb3-9a5e-41e8-9349-ce97aea98e6d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\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",
              "      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-57836fb3-9a5e-41e8-9349-ce97aea98e6d 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-57836fb3-9a5e-41e8-9349-ce97aea98e6d');\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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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "#Replace the missing values with the mean of the respective column\n",
        "all_data.fillna(value = np.mean(all_data), inplace = True)\n",
        "#Display the DataFrame\n",
        "all_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "86e42100",
      "metadata": {
        "id": "86e42100"
      },
      "source": [
        "Also, there may be some duplicated rows, you need to drop them.\n",
        "\n",
        "📌 Use the .drop_duplicates() method to remove any duplicated rows.\n",
        "\n",
        "📌 Reset the indexes again using the reset_index() function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5c8b0e48",
      "metadata": {
        "id": "5c8b0e48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "ead67363-65ff-4b24-b22b-ac895fac9e5c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Number of Calls  Average Deal Size       Revenue  Year\n",
              "0     Omer              400           8.000000   2000.000000  2021\n",
              "1   Arnold              150          10.000000   1000.000000  2021\n",
              "2    Aidan              170          15.000000   2000.000000  2021\n",
              "3      Sue              220           5.000000   1000.000000  2021\n",
              "4      Ben              140          22.000000   2162.142857  2021\n",
              "5     Rose              350           8.000000   1800.000000  2021\n",
              "6     Lucy               50          12.000000    500.000000  2021\n",
              "7    Ellen              560          16.428571   3300.000000  2021\n",
              "8    Karen               20          25.000000    500.000000  2021\n",
              "9   Jamaal               30          30.000000    800.000000  2021\n",
              "11     Ben              300           8.000000   2400.000000  2022\n",
              "12    Omer               10           6.000000     60.000000  2022\n",
              "13   Karen              500          24.000000  12000.000000  2022\n",
              "14  Celine               70          32.000000   2275.000000  2022\n",
              "15     Sue              100           5.000000    500.000000  2022\n",
              "16    Bora              100          25.000000    770.000000  2022\n",
              "17    Rose              600          25.000000   4000.000000  2022\n",
              "18   Ellen              800          40.000000   6000.000000  2022\n",
              "19     Bob              200          15.000000    800.000000  2022\n",
              "20  Taylor              450          10.000000   1200.000000  2022\n",
              "21    Jude               80          12.000000    500.000000  2022"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-56cce070-a5ef-4cec-89f0-a1620b1dc453\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Number of Calls</th>\n",
              "      <th>Average Deal Size</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Omer</td>\n",
              "      <td>400</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Arnold</td>\n",
              "      <td>150</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aidan</td>\n",
              "      <td>170</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sue</td>\n",
              "      <td>220</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ben</td>\n",
              "      <td>140</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>2162.142857</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rose</td>\n",
              "      <td>350</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1800.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Lucy</td>\n",
              "      <td>50</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ellen</td>\n",
              "      <td>560</td>\n",
              "      <td>16.428571</td>\n",
              "      <td>3300.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Karen</td>\n",
              "      <td>20</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Jamaal</td>\n",
              "      <td>30</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Ben</td>\n",
              "      <td>300</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2400.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Omer</td>\n",
              "      <td>10</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Karen</td>\n",
              "      <td>500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>12000.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Celine</td>\n",
              "      <td>70</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>2275.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Sue</td>\n",
              "      <td>100</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Bora</td>\n",
              "      <td>100</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>770.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Rose</td>\n",
              "      <td>600</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>4000.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Ellen</td>\n",
              "      <td>800</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bob</td>\n",
              "      <td>200</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Taylor</td>\n",
              "      <td>450</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1200.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Jude</td>\n",
              "      <td>80</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
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              "</table>\n",
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              "\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-56cce070-a5ef-4cec-89f0-a1620b1dc453 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-56cce070-a5ef-4cec-89f0-a1620b1dc453');\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": 21
        }
      ],
      "source": [
        "#Drop the duplicates\n",
        "all_data.drop_duplicates(inplace=True)\n",
        "#Reset the indexes\n",
        "all_data.reset_index(drop=True)\n",
        "#Display the DataFrame \n",
        "all_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cd7cfeb5",
      "metadata": {
        "id": "cd7cfeb5"
      },
      "source": [
        "### Data analysis\n",
        "#### Statistical analysis\n",
        "The DataFrame is ready, great!\n",
        "\n",
        "Now you can use it to analyse the overall performance of the employees over the last two years. You prepare a summary of the statistics.\n",
        "\n",
        "📌 Use the .describe() method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1b306146",
      "metadata": {
        "id": "1b306146",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "71c72dab-24c7-4930-aa62-eb81d8a561c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Number of Calls  Average Deal Size       Revenue         Year\n",
              "count        21.000000          21.000000     21.000000    21.000000\n",
              "mean        252.380952          16.829932   2169.863946  2021.523810\n",
              "std         223.112186           9.991794   2656.901262     0.511766\n",
              "min          10.000000           5.000000     60.000000  2021.000000\n",
              "25%          80.000000           8.000000    770.000000  2021.000000\n",
              "50%         170.000000          15.000000   1200.000000  2022.000000\n",
              "75%         400.000000          25.000000   2275.000000  2022.000000\n",
              "max         800.000000          40.000000  12000.000000  2022.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4a3a0b9e-cb76-49c9-badb-6050c2792ccb\">\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",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number of Calls</th>\n",
              "      <th>Average Deal Size</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>21.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>252.380952</td>\n",
              "      <td>16.829932</td>\n",
              "      <td>2169.863946</td>\n",
              "      <td>2021.523810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>223.112186</td>\n",
              "      <td>9.991794</td>\n",
              "      <td>2656.901262</td>\n",
              "      <td>0.511766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>770.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>170.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1200.000000</td>\n",
              "      <td>2022.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>2275.000000</td>\n",
              "      <td>2022.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>800.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>12000.000000</td>\n",
              "      <td>2022.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a3a0b9e-cb76-49c9-badb-6050c2792ccb')\"\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",
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              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4a3a0b9e-cb76-49c9-badb-6050c2792ccb 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-4a3a0b9e-cb76-49c9-badb-6050c2792ccb');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "#Use the .descrbe() method to get a summary of the statistics\n",
        "all_data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "24af4eca",
      "metadata": {
        "id": "24af4eca"
      },
      "source": [
        "You can do the same for each year separately. \n",
        "\n",
        "📌 Prepare a summary of the statistics for the data from 2021.\n",
        "\n",
        "📌 Prepare a summary of the statistics for the data from 2022."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0509947e",
      "metadata": {
        "id": "0509947e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "0686dbe3-a0e8-4230-eaad-30d6bf11afc7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Number of Calls  Average Deal Size       Revenue    Year\n",
              "count        11.000000          11.000000     11.000000    11.0\n",
              "mean        291.818182          18.363636   2773.181818  2022.0\n",
              "std         260.223680          11.552253   3542.826748     0.0\n",
              "min          10.000000           5.000000     60.000000  2022.0\n",
              "25%          90.000000           9.000000    635.000000  2022.0\n",
              "50%         200.000000          15.000000   1200.000000  2022.0\n",
              "75%         475.000000          25.000000   3200.000000  2022.0\n",
              "max         800.000000          40.000000  12000.000000  2022.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-af92a7b2-5559-41ad-b69e-5a75f06f90cb\">\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",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number of Calls</th>\n",
              "      <th>Average Deal Size</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>291.818182</td>\n",
              "      <td>18.363636</td>\n",
              "      <td>2773.181818</td>\n",
              "      <td>2022.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>260.223680</td>\n",
              "      <td>11.552253</td>\n",
              "      <td>3542.826748</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>2022.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>635.000000</td>\n",
              "      <td>2022.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1200.000000</td>\n",
              "      <td>2022.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>475.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>3200.000000</td>\n",
              "      <td>2022.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>800.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>12000.000000</td>\n",
              "      <td>2022.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af92a7b2-5559-41ad-b69e-5a75f06f90cb')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-af92a7b2-5559-41ad-b69e-5a75f06f90cb 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-af92a7b2-5559-41ad-b69e-5a75f06f90cb');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "#Use the .descrbe() method to get a summary of the statistics for 2021\n",
        "all_data[all_data[\"Year\"] == 2021].describe()\n",
        "#Use the .descrbe() method to get a summary of the statistics for 2022\n",
        "all_data[all_data[\"Year\"] == 2022].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "45b770e8",
      "metadata": {
        "id": "45b770e8"
      },
      "source": [
        "#### Ranking of the employees by revenue\n",
        "You also want to rank the employees by the generated revenue. \n",
        "\n",
        "📌 Use the .sort_values() method to sort the values by the column “Revenue”.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5e161229",
      "metadata": {
        "id": "5e161229",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "1521f670-8cc1-42c3-9a24-f464fbd4ac82"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Number of Calls  Average Deal Size       Revenue  Year\n",
              "12    Omer               10           6.000000     60.000000  2022\n",
              "21    Jude               80          12.000000    500.000000  2022\n",
              "6     Lucy               50          12.000000    500.000000  2021\n",
              "15     Sue              100           5.000000    500.000000  2022\n",
              "8    Karen               20          25.000000    500.000000  2021\n",
              "16    Bora              100          25.000000    770.000000  2022\n",
              "19     Bob              200          15.000000    800.000000  2022\n",
              "9   Jamaal               30          30.000000    800.000000  2021\n",
              "1   Arnold              150          10.000000   1000.000000  2021\n",
              "3      Sue              220           5.000000   1000.000000  2021\n",
              "20  Taylor              450          10.000000   1200.000000  2022\n",
              "5     Rose              350           8.000000   1800.000000  2021\n",
              "0     Omer              400           8.000000   2000.000000  2021\n",
              "2    Aidan              170          15.000000   2000.000000  2021\n",
              "4      Ben              140          22.000000   2162.142857  2021\n",
              "14  Celine               70          32.000000   2275.000000  2022\n",
              "11     Ben              300           8.000000   2400.000000  2022\n",
              "7    Ellen              560          16.428571   3300.000000  2021\n",
              "17    Rose              600          25.000000   4000.000000  2022\n",
              "18   Ellen              800          40.000000   6000.000000  2022\n",
              "13   Karen              500          24.000000  12000.000000  2022"
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "#Sort the DataFrame by the column \"Revenue\"\n",
        "all_data.sort_values(by=\"Revenue\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you did with the .describe() function, you can also use conditions to filter information. \n",
        "\n",
        "📌 Sort the revenue values of 2022."
      ],
      "metadata": {
        "id": "BeOqqCKjPqI2"
      },
      "id": "BeOqqCKjPqI2"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dc96cf9a",
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          "height": 373
        },
        "outputId": "652b0add-6fa0-45c9-bf45-51adb7a9bafd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Number of Calls  Average Deal Size  Revenue  Year\n",
              "12    Omer               10                6.0     60.0  2022\n",
              "15     Sue              100                5.0    500.0  2022\n",
              "21    Jude               80               12.0    500.0  2022\n",
              "16    Bora              100               25.0    770.0  2022\n",
              "19     Bob              200               15.0    800.0  2022\n",
              "20  Taylor              450               10.0   1200.0  2022\n",
              "14  Celine               70               32.0   2275.0  2022\n",
              "11     Ben              300                8.0   2400.0  2022\n",
              "17    Rose              600               25.0   4000.0  2022\n",
              "18   Ellen              800               40.0   6000.0  2022\n",
              "13   Karen              500               24.0  12000.0  2022"
            ],
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-819fa666-4fa2-4fc6-9e5f-56e660295e37');\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": 26
        }
      ],
      "source": [
        "#Sort the revenue values od 2022\n",
        "all_data[all_data[\"Year\"] == 2022].sort_values(by=\"Revenue\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ad9997c7",
      "metadata": {
        "id": "ad9997c7"
      },
      "source": [
        "#### How many years of employment?\n",
        "\n",
        "Finally, you can count how many times an employee appears in the DataFrame to determine which employee has worked for the company for two years.\n",
        "\n",
        "📌 Use the value_counts() function for the column \"Name\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "167f8fbc",
      "metadata": {
        "id": "167f8fbc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ef1647a-e709-4ff9-8992-9502ca2d0120"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Omer      2\n",
              "Sue       2\n",
              "Ben       2\n",
              "Rose      2\n",
              "Ellen     2\n",
              "Karen     2\n",
              "Arnold    1\n",
              "Aidan     1\n",
              "Lucy      1\n",
              "Jamaal    1\n",
              "Celine    1\n",
              "Bora      1\n",
              "Bob       1\n",
              "Taylor    1\n",
              "Jude      1\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "#Count how often the names of the employees appear in the DataFrame\n",
        "all_data[\"Name\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "INf8c5DHZ7pY"
      },
      "id": "INf8c5DHZ7pY",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.8"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}