Synthetic_agent_data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOBIbc+J0xRbqXZ14jTtZHU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/antonytonito/tonito254/blob/master/Synthetic_agent_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4AgpWS6QXVc4"
      },
      "outputs": [],
      "source": [
        "from numpy import float64\n",
        "from pandas._libs.tslibs import dtypes\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "import gspread\n",
        "from google.auth import default\n",
        "creds, _ = default()\n",
        "\n",
        "gc = gspread.authorize(creds)\n",
        "\n",
        "wb = gc.open_by_key('1_8qAlosRLZ4anev_doV1FNGR6UyBXIAPDJZaclwjZUY')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "worksheetdata = wb.worksheet('data')\n",
        "\n",
        "# get_all_values gives a list of rows.\n",
        "rowsdata = worksheetdata.get_all_values()\n",
        "#print(rowsMorning)\n",
        "\n",
        "# Convert to a DataFrame and render.\n",
        "import pandas as pd\n",
        "df = pd.DataFrame.from_records(rowsdata[1:],columns=rowsdata[0])"
      ],
      "metadata": {
        "id": "iWj8CsHOYX28"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.copy()"
      ],
      "metadata": {
        "id": "Yy6khjtvelp9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.set_index(\"Day\",inplace=True)"
      ],
      "metadata": {
        "id": "TY0F7BWBeog3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "KHBzUMN7Yz3Q",
        "outputId": "335f88ae-806f-436d-9497-f5d39738a4a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Agent 0  Agent 1  Agent 2  Agent 3  Agent 4  Agent 5  Agent 6  Agent 7  \\\n",
              "Day                                                                           \n",
              "0.0      1.0      2.0      1.0      0.0      1.0      1.0      0.0      0.0   \n",
              "1.0      9.0     10.0      8.0      2.0     15.0      7.0      0.0      2.0   \n",
              "2.0      9.0      7.0     11.0      3.0     10.0      7.0      0.0      1.0   \n",
              "3.0     11.0     14.0     13.0      0.0      8.0      9.0      0.0      0.0   \n",
              "4.0     11.0     13.0      7.0      1.0      7.0      8.0      1.0      0.0   \n",
              "\n",
              "     Agent 8  Agent 9  ...  Agent 1191  Agent 1192  Agent 1193  Agent 1194  \\\n",
              "Day                    ...                                                   \n",
              "0.0      0.0      2.0  ...         2.0         2.0         0.0         2.0   \n",
              "1.0      2.0     11.0  ...         9.0         8.0         0.0        14.0   \n",
              "2.0      3.0     11.0  ...         9.0        10.0         0.0         9.0   \n",
              "3.0      3.0     14.0  ...        14.0        11.0         1.0         7.0   \n",
              "4.0      4.0     13.0  ...        14.0         8.0         0.0         7.0   \n",
              "\n",
              "     Agent 1195  Agent 1196  Agent 1197  Agent 1198  Agent 1199   Total  \n",
              "Day                                                                      \n",
              "0.0         0.0         0.0         0.0         1.0         0.0   706.0  \n",
              "1.0         0.0         1.0         0.0         7.0         1.0  4988.0  \n",
              "2.0         0.0         2.0         1.0        12.0         0.0  5055.0  \n",
              "3.0         2.0         3.0         1.0        10.0         2.0  5171.0  \n",
              "4.0         1.0         2.0         1.0        15.0         1.0  5153.0  \n",
              "\n",
              "[5 rows x 1201 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e62700a9-4089-4d4f-a209-e5bd7988d04f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Agent 0</th>\n",
              "      <th>Agent 1</th>\n",
              "      <th>Agent 2</th>\n",
              "      <th>Agent 3</th>\n",
              "      <th>Agent 4</th>\n",
              "      <th>Agent 5</th>\n",
              "      <th>Agent 6</th>\n",
              "      <th>Agent 7</th>\n",
              "      <th>Agent 8</th>\n",
              "      <th>Agent 9</th>\n",
              "      <th>...</th>\n",
              "      <th>Agent 1191</th>\n",
              "      <th>Agent 1192</th>\n",
              "      <th>Agent 1193</th>\n",
              "      <th>Agent 1194</th>\n",
              "      <th>Agent 1195</th>\n",
              "      <th>Agent 1196</th>\n",
              "      <th>Agent 1197</th>\n",
              "      <th>Agent 1198</th>\n",
              "      <th>Agent 1199</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Day</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>706.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4988.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5055.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>11.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>...</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5171.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>...</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5153.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1201 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e62700a9-4089-4d4f-a209-e5bd7988d04f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e62700a9-4089-4d4f-a209-e5bd7988d04f 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-e62700a9-4089-4d4f-a209-e5bd7988d04f');\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": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.iloc[:,:4].info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1dvR6g6XnGl",
        "outputId": "6cdd8451-656e-42a0-89cb-171326d6b396"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Float64Index: 84 entries, 0.0 to 83.0\n",
            "Data columns (total 4 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   Agent 0  84 non-null     float64\n",
            " 1   Agent 1  84 non-null     float64\n",
            " 2   Agent 2  84 non-null     float64\n",
            " 3   Agent 3  84 non-null     float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 3.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KyYyIZ2wu6RX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "2ESbcaM0gnua",
        "outputId": "f7caca94-bf3d-4768-9b63-599d0e12659e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Agent 0    Agent 1    Agent 2    Agent 3    Agent 4    Agent 5  \\\n",
              "count  84.000000  84.000000  84.000000  84.000000  84.000000  84.000000   \n",
              "mean    6.940476   6.702381   7.000000   0.535714   7.297619   7.357143   \n",
              "std     3.816108   3.867628   4.032996   0.870486   4.236331   4.303663   \n",
              "min     1.000000   1.000000   1.000000   0.000000   1.000000   1.000000   \n",
              "25%     2.000000   2.000000   2.000000   0.000000   2.000000   2.000000   \n",
              "50%     7.000000   7.000000   7.500000   0.000000   8.000000   7.000000   \n",
              "75%    10.000000   9.000000   9.000000   1.000000  10.000000  11.000000   \n",
              "max    14.000000  15.000000  15.000000   3.000000  15.000000  15.000000   \n",
              "\n",
              "         Agent 6    Agent 7    Agent 8    Agent 9  ...  Agent 1191  \\\n",
              "count  84.000000  84.000000  84.000000  84.000000  ...   84.000000   \n",
              "mean    0.500000   0.476190   2.595238   7.440476  ...    7.178571   \n",
              "std     0.828702   0.842775   3.066017   4.380546  ...    4.045407   \n",
              "min     0.000000   0.000000   0.000000   1.000000  ...    1.000000   \n",
              "25%     0.000000   0.000000   0.000000   2.000000  ...    2.000000   \n",
              "50%     0.000000   0.000000   2.000000   8.000000  ...    8.000000   \n",
              "75%     1.000000   1.000000   4.000000  11.000000  ...   10.000000   \n",
              "max     3.000000   3.000000  11.000000  15.000000  ...   15.000000   \n",
              "\n",
              "       Agent 1192  Agent 1193  Agent 1194  Agent 1195  Agent 1196  Agent 1197  \\\n",
              "count   84.000000   84.000000   84.000000   84.000000   84.000000   84.000000   \n",
              "mean     7.464286    2.690476    6.988095    0.309524    2.083333    1.714286   \n",
              "std      4.246746    3.215086    4.221272    0.559177    1.716036    1.923673   \n",
              "min      1.000000    0.000000    1.000000    0.000000    0.000000    0.000000   \n",
              "25%      2.000000    0.000000    2.000000    0.000000    0.000000    0.000000   \n",
              "50%      8.000000    2.000000    7.000000    0.000000    2.000000    1.000000   \n",
              "75%     11.000000    4.000000    9.000000    1.000000    3.000000    3.000000   \n",
              "max     15.000000   10.000000   15.000000    2.000000    6.000000    8.000000   \n",
              "\n",
              "       Agent 1198  Agent 1199         Total  \n",
              "count   84.000000   84.000000     84.000000  \n",
              "mean     7.285714    0.595238   8544.357143  \n",
              "std      4.249937    0.879746   4531.295726  \n",
              "min      1.000000    0.000000   1394.000000  \n",
              "25%      2.000000    0.000000   1497.750000  \n",
              "50%      7.000000    0.000000  11231.500000  \n",
              "75%     10.250000    1.000000  11672.000000  \n",
              "max     15.000000    3.000000  12095.000000  \n",
              "\n",
              "[8 rows x 1201 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-77407508-65fe-4ee9-8bef-e7dc7d8aa400\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Agent 0</th>\n",
              "      <th>Agent 1</th>\n",
              "      <th>Agent 2</th>\n",
              "      <th>Agent 3</th>\n",
              "      <th>Agent 4</th>\n",
              "      <th>Agent 5</th>\n",
              "      <th>Agent 6</th>\n",
              "      <th>Agent 7</th>\n",
              "      <th>Agent 8</th>\n",
              "      <th>Agent 9</th>\n",
              "      <th>...</th>\n",
              "      <th>Agent 1191</th>\n",
              "      <th>Agent 1192</th>\n",
              "      <th>Agent 1193</th>\n",
              "      <th>Agent 1194</th>\n",
              "      <th>Agent 1195</th>\n",
              "      <th>Agent 1196</th>\n",
              "      <th>Agent 1197</th>\n",
              "      <th>Agent 1198</th>\n",
              "      <th>Agent 1199</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>84.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.940476</td>\n",
              "      <td>6.702381</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>7.297619</td>\n",
              "      <td>7.357143</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.476190</td>\n",
              "      <td>2.595238</td>\n",
              "      <td>7.440476</td>\n",
              "      <td>...</td>\n",
              "      <td>7.178571</td>\n",
              "      <td>7.464286</td>\n",
              "      <td>2.690476</td>\n",
              "      <td>6.988095</td>\n",
              "      <td>0.309524</td>\n",
              "      <td>2.083333</td>\n",
              "      <td>1.714286</td>\n",
              "      <td>7.285714</td>\n",
              "      <td>0.595238</td>\n",
              "      <td>8544.357143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.816108</td>\n",
              "      <td>3.867628</td>\n",
              "      <td>4.032996</td>\n",
              "      <td>0.870486</td>\n",
              "      <td>4.236331</td>\n",
              "      <td>4.303663</td>\n",
              "      <td>0.828702</td>\n",
              "      <td>0.842775</td>\n",
              "      <td>3.066017</td>\n",
              "      <td>4.380546</td>\n",
              "      <td>...</td>\n",
              "      <td>4.045407</td>\n",
              "      <td>4.246746</td>\n",
              "      <td>3.215086</td>\n",
              "      <td>4.221272</td>\n",
              "      <td>0.559177</td>\n",
              "      <td>1.716036</td>\n",
              "      <td>1.923673</td>\n",
              "      <td>4.249937</td>\n",
              "      <td>0.879746</td>\n",
              "      <td>4531.295726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1394.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1497.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11231.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11672.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>12095.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 1201 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77407508-65fe-4ee9-8bef-e7dc7d8aa400')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-77407508-65fe-4ee9-8bef-e7dc7d8aa400 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-77407508-65fe-4ee9-8bef-e7dc7d8aa400');\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": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.describe().round(0).to_csv(\"Statistics.csv\")"
      ],
      "metadata": {
        "id": "iGCUkjGuXeg3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oiuvEK6_8r6p",
        "outputId": "9898def2-884b-467a-a5c3-cf5748bf4537"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Total tasks                                                       356537.0\n",
              "Rating                   BBCAACDBABCCBDCAABCBDDDABABDDDCBBCDADDCBDBBCDD...\n",
              "Remuneration_per_task                                               355900\n",
              "Bonus                                                           13003920.0\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download(\"remuneration.csv\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "yWIWatH1dp4f",
        "outputId": "8975b47c-5e2e-44b2-ad04-43cd2a5ed058"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_87fd71e5-e23d-4d60-9c74-bd4c7160b68c\", \"remuneration.csv\", 34603)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df1.columns:\n",
        "  df1[col]=df1[col].astype(float64)"
      ],
      "metadata": {
        "id": "cIsFpjPsZcSf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1['Total']=df1.sum(axis=1)"
      ],
      "metadata": {
        "id": "QoYNDb2UZ8S6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NdoU1W6xvEn1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_five_agents=df1.iloc[:,:5]"
      ],
      "metadata": {
        "id": "7mRUNOx0cXSz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_five_agentsdf=first_five_agents.sum().to_frame()"
      ],
      "metadata": {
        "id": "_Igf0ic0eZsB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_five_agentsdf."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "x_nrpe6ph5uO",
        "outputId": "aafdb625-3bd6-43c3-eca4-043313945602"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0\n",
              "Agent 0  583.0\n",
              "Agent 1  563.0\n",
              "Agent 2  588.0\n",
              "Agent 3   45.0\n",
              "Agent 4  613.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ee929f29-9424-49be-858d-7bd7a7b06396\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Agent 0</th>\n",
              "      <td>583.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1</th>\n",
              "      <td>563.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 2</th>\n",
              "      <td>588.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 3</th>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 4</th>\n",
              "      <td>613.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee929f29-9424-49be-858d-7bd7a7b06396')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ee929f29-9424-49be-858d-7bd7a7b06396 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-ee929f29-9424-49be-858d-7bd7a7b06396');\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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first_five_agentsdf.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yA5cKSPJlqeS",
        "outputId": "026815e6-39aa-4834-9437-b69bcd972820"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                0\n",
              "count    5.000000\n",
              "mean   478.400000\n",
              "std    242.931678\n",
              "min     45.000000\n",
              "25%    563.000000\n",
              "50%    583.000000\n",
              "75%    588.000000\n",
              "max    613.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e347384-a1c3-46ff-89cc-603e18b25e5f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>478.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>242.931678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>563.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>583.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>588.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>613.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e347384-a1c3-46ff-89cc-603e18b25e5f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e347384-a1c3-46ff-89cc-603e18b25e5f 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-0e347384-a1c3-46ff-89cc-603e18b25e5f');\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": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"Day\")[\"Agent 8\"].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "xqUHXAEbbMIm",
        "outputId": "d17b7296-398b-472b-d996-aa81888c8796"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      count  mean  std  min  25%  50%  75%  max\n",
              "Day                                            \n",
              "0.0     1.0   0.0  NaN  0.0  0.0  0.0  0.0  0.0\n",
              "1.0     1.0   2.0  NaN  2.0  2.0  2.0  2.0  2.0\n",
              "2.0     1.0   3.0  NaN  3.0  3.0  3.0  3.0  3.0\n",
              "3.0     1.0   3.0  NaN  3.0  3.0  3.0  3.0  3.0\n",
              "4.0     1.0   4.0  NaN  4.0  4.0  4.0  4.0  4.0\n",
              "...     ...   ...  ...  ...  ...  ...  ...  ...\n",
              "79.0    1.0   2.0  NaN  2.0  2.0  2.0  2.0  2.0\n",
              "80.0    1.0   3.0  NaN  3.0  3.0  3.0  3.0  3.0\n",
              "81.0    1.0   3.0  NaN  3.0  3.0  3.0  3.0  3.0\n",
              "82.0    1.0   2.0  NaN  2.0  2.0  2.0  2.0  2.0\n",
              "83.0    1.0   0.0  NaN  0.0  0.0  0.0  0.0  0.0\n",
              "\n",
              "[84 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-73d20279-e575-4f52-b9ab-aa9050e98a49\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Day</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>84 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73d20279-e575-4f52-b9ab-aa9050e98a49')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-73d20279-e575-4f52-b9ab-aa9050e98a49 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-73d20279-e575-4f52-b9ab-aa9050e98a49');\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": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 describe "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "id": "YIB1v1jX6U_u",
        "outputId": "7f5fe896-c21b-47d4-82eb-02d53b90301d"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-81e1b4b79f3c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    df2 describe\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H8uOqW1W6YJF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wPH1xkO5-Lo2",
        "outputId": "ff84b234-d7a4-471a-cb16-abc058c6340e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Total tasks Rating  Remuneration_per_task    Bonus\n",
              "Agent 1           563.0      B                    350  19705.0\n",
              "Agent 2           588.0      B                    350  20580.0\n",
              "Agent 3            45.0      C                    275   1237.5\n",
              "Agent 4           613.0      A                    400  24520.0\n",
              "Agent 5           618.0      A                    400  24720.0\n",
              "...                 ...    ...                    ...      ...\n",
              "Agent 1195         26.0      D                    175    455.0\n",
              "Agent 1196        175.0      C                    275   4812.5\n",
              "Agent 1197        144.0      C                    275   3960.0\n",
              "Agent 1198        612.0      A                    400  24480.0\n",
              "Agent 1199         50.0      C                    275   1375.0\n",
              "\n",
              "[1199 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-05e85bef-2b8a-4a1b-ac50-8b03c733e126\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total tasks</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Remuneration_per_task</th>\n",
              "      <th>Bonus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Agent 1</th>\n",
              "      <td>563.0</td>\n",
              "      <td>B</td>\n",
              "      <td>350</td>\n",
              "      <td>19705.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 2</th>\n",
              "      <td>588.0</td>\n",
              "      <td>B</td>\n",
              "      <td>350</td>\n",
              "      <td>20580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 3</th>\n",
              "      <td>45.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>1237.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 4</th>\n",
              "      <td>613.0</td>\n",
              "      <td>A</td>\n",
              "      <td>400</td>\n",
              "      <td>24520.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 5</th>\n",
              "      <td>618.0</td>\n",
              "      <td>A</td>\n",
              "      <td>400</td>\n",
              "      <td>24720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1195</th>\n",
              "      <td>26.0</td>\n",
              "      <td>D</td>\n",
              "      <td>175</td>\n",
              "      <td>455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1196</th>\n",
              "      <td>175.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>4812.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1197</th>\n",
              "      <td>144.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>3960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1198</th>\n",
              "      <td>612.0</td>\n",
              "      <td>A</td>\n",
              "      <td>400</td>\n",
              "      <td>24480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1199</th>\n",
              "      <td>50.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>1375.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1199 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05e85bef-2b8a-4a1b-ac50-8b03c733e126')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-05e85bef-2b8a-4a1b-ac50-8b03c733e126 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-05e85bef-2b8a-4a1b-ac50-8b03c733e126');\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": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df.sum().to_frame().rename(columns={0:'Total tasks'})"
      ],
      "metadata": {
        "id": "vafq32z-lL22"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df2.iloc[1:-1,:]"
      ],
      "metadata": {
        "id": "GiZDXsZfveoO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "9k43_rPqvZyq",
        "outputId": "ba848abb-5c77-4262-ebfc-373aef0b6bef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Total tasks\n",
              "count  1199.000000\n",
              "mean    297.361968\n",
              "std     257.780958\n",
              "min      16.000000\n",
              "25%      41.000000\n",
              "50%     202.000000\n",
              "75%     594.000000\n",
              "max     660.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8311b70f-cc1b-46e8-91d0-170c610328e6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total tasks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1199.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>297.361968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.780958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>41.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>202.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>594.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>660.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8311b70f-cc1b-46e8-91d0-170c610328e6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8311b70f-cc1b-46e8-91d0-170c610328e6 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-8311b70f-cc1b-46e8-91d0-170c610328e6');\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": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Qwu8o0vrC1mo",
        "outputId": "e5e05b35-8a50-45cf-ddd2-5c433ecb5053"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Total tasks  Remuneration_per_task         Bonus\n",
              "count  1199.000000            1199.000000   1199.000000\n",
              "mean    297.361968             296.830692  10845.638032\n",
              "std     257.780958              87.288770  10272.930857\n",
              "min      16.000000             175.000000    280.000000\n",
              "25%      41.000000             175.000000    717.500000\n",
              "50%     202.000000             275.000000   5555.000000\n",
              "75%     594.000000             350.000000  20790.000000\n",
              "max     660.000000             400.000000  26400.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1abfc823-2c25-420e-a5a6-24a9fc167017\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total tasks</th>\n",
              "      <th>Remuneration_per_task</th>\n",
              "      <th>Bonus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1199.000000</td>\n",
              "      <td>1199.000000</td>\n",
              "      <td>1199.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>297.361968</td>\n",
              "      <td>296.830692</td>\n",
              "      <td>10845.638032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.780958</td>\n",
              "      <td>87.288770</td>\n",
              "      <td>10272.930857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>280.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>41.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>717.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>202.000000</td>\n",
              "      <td>275.000000</td>\n",
              "      <td>5555.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>594.000000</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>20790.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>660.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>26400.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1abfc823-2c25-420e-a5a6-24a9fc167017')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1abfc823-2c25-420e-a5a6-24a9fc167017 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-1abfc823-2c25-420e-a5a6-24a9fc167017');\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": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5loH3ZAOvJmO",
        "outputId": "01c4555c-bb5d-42df-e061-8f66eb5df7c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Total tasks Rating  Remuneration_per_task    Bonus\n",
              "Agent 1195         26.0      D                    175    455.0\n",
              "Agent 1196        175.0      C                    275   4812.5\n",
              "Agent 1197        144.0      C                    275   3960.0\n",
              "Agent 1198        612.0      A                    400  24480.0\n",
              "Agent 1199         50.0      C                    275   1375.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a8b7d427-7edf-484f-bbee-f17909d36b81\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total tasks</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Remuneration_per_task</th>\n",
              "      <th>Bonus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Agent 1195</th>\n",
              "      <td>26.0</td>\n",
              "      <td>D</td>\n",
              "      <td>175</td>\n",
              "      <td>455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1196</th>\n",
              "      <td>175.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>4812.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1197</th>\n",
              "      <td>144.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>3960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1198</th>\n",
              "      <td>612.0</td>\n",
              "      <td>A</td>\n",
              "      <td>400</td>\n",
              "      <td>24480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1199</th>\n",
              "      <td>50.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>1375.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8b7d427-7edf-484f-bbee-f17909d36b81')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a8b7d427-7edf-484f-bbee-f17909d36b81 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-a8b7d427-7edf-484f-bbee-f17909d36b81');\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": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "performance = []\n",
        "\n",
        "for i in df2[\"Total tasks\"]:\n",
        "  if i<=41:\n",
        "    performance.append(\"D\")\n",
        "  elif i>41 and i<=202:\n",
        "    performance.append(\"C\")\n",
        "  elif i>202 and i<=594:\n",
        "    performance.append(\"B\")\n",
        "  else:\n",
        "    performance.append(\"A\")\n",
        "df2['Rating']=performance"
      ],
      "metadata": {
        "id": "hsj6joimwT9u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2['Rating'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pnPx73GVx9-F",
        "outputId": "5d57b9e2-53d2-4c4a-9921-a333d3c82d7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "D    0.280234\n",
              "B    0.251877\n",
              "A    0.247706\n",
              "C    0.220183\n",
              "Name: Rating, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "XgpKJoCkxC6f",
        "outputId": "519d4699-409a-4cfb-9b1c-abb799eb47c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Day  Agent 0  Agent 1  Agent 2  Agent 3  Agent 4  Agent 5  Agent 6  \\\n",
              "0  0.0      1.0      2.0      1.0      0.0      1.0      1.0      0.0   \n",
              "1  1.0      9.0     10.0      8.0      2.0     15.0      7.0      0.0   \n",
              "2  2.0      9.0      7.0     11.0      3.0     10.0      7.0      0.0   \n",
              "3  3.0     11.0     14.0     13.0      0.0      8.0      9.0      0.0   \n",
              "4  4.0     11.0     13.0      7.0      1.0      7.0      8.0      1.0   \n",
              "\n",
              "   Agent 7  Agent 8  ...  Agent 1191  Agent 1192  Agent 1193  Agent 1194  \\\n",
              "0      0.0      0.0  ...         2.0         2.0         0.0         2.0   \n",
              "1      2.0      2.0  ...         9.0         8.0         0.0        14.0   \n",
              "2      1.0      3.0  ...         9.0        10.0         0.0         9.0   \n",
              "3      0.0      3.0  ...        14.0        11.0         1.0         7.0   \n",
              "4      0.0      4.0  ...        14.0         8.0         0.0         7.0   \n",
              "\n",
              "   Agent 1195  Agent 1196  Agent 1197  Agent 1198  Agent 1199   Total  \n",
              "0         0.0         0.0         0.0         1.0         0.0   706.0  \n",
              "1         0.0         1.0         0.0         7.0         1.0  4988.0  \n",
              "2         0.0         2.0         1.0        12.0         0.0  5055.0  \n",
              "3         2.0         3.0         1.0        10.0         2.0  5171.0  \n",
              "4         1.0         2.0         1.0        15.0         1.0  5153.0  \n",
              "\n",
              "[5 rows x 1202 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e235aaf2-cbdc-411f-9991-1eca49d9772a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day</th>\n",
              "      <th>Agent 0</th>\n",
              "      <th>Agent 1</th>\n",
              "      <th>Agent 2</th>\n",
              "      <th>Agent 3</th>\n",
              "      <th>Agent 4</th>\n",
              "      <th>Agent 5</th>\n",
              "      <th>Agent 6</th>\n",
              "      <th>Agent 7</th>\n",
              "      <th>Agent 8</th>\n",
              "      <th>...</th>\n",
              "      <th>Agent 1191</th>\n",
              "      <th>Agent 1192</th>\n",
              "      <th>Agent 1193</th>\n",
              "      <th>Agent 1194</th>\n",
              "      <th>Agent 1195</th>\n",
              "      <th>Agent 1196</th>\n",
              "      <th>Agent 1197</th>\n",
              "      <th>Agent 1198</th>\n",
              "      <th>Agent 1199</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>706.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4988.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5055.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5171.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5153.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1202 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e235aaf2-cbdc-411f-9991-1eca49d9772a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e235aaf2-cbdc-411f-9991-1eca49d9772a 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-e235aaf2-cbdc-411f-9991-1eca49d9772a');\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": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "x5aDxvy9yd9b",
        "outputId": "d4c08f7c-54f7-4458-9d09-f59d2f5996b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Total tasks Rating  Remuneration_per_task    Bonus\n",
              "Agent 1        563.0      B                    350  19705.0\n",
              "Agent 2        588.0      B                    350  20580.0\n",
              "Agent 3         45.0      C                    275   1237.5\n",
              "Agent 4        613.0      A                    400  24520.0\n",
              "Agent 5        618.0      A                    400  24720.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ebfd804-e1fa-4875-b057-316e37b7d5f9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total tasks</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Remuneration_per_task</th>\n",
              "      <th>Bonus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Agent 1</th>\n",
              "      <td>563.0</td>\n",
              "      <td>B</td>\n",
              "      <td>350</td>\n",
              "      <td>19705.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 2</th>\n",
              "      <td>588.0</td>\n",
              "      <td>B</td>\n",
              "      <td>350</td>\n",
              "      <td>20580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 3</th>\n",
              "      <td>45.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>1237.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 4</th>\n",
              "      <td>613.0</td>\n",
              "      <td>A</td>\n",
              "      <td>400</td>\n",
              "      <td>24520.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 5</th>\n",
              "      <td>618.0</td>\n",
              "      <td>A</td>\n",
              "      <td>400</td>\n",
              "      <td>24720.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ebfd804-e1fa-4875-b057-316e37b7d5f9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ebfd804-e1fa-4875-b057-316e37b7d5f9 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-1ebfd804-e1fa-4875-b057-316e37b7d5f9');\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": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Nb56dXytczJz",
        "outputId": "88e77e84-2485-41d5-f2e8-1256aa67d6d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Total tasks Rating  Remuneration_per_task    Bonus\n",
              "Agent 1           563.0      B                    350  19705.0\n",
              "Agent 2           588.0      B                    350  20580.0\n",
              "Agent 3            45.0      C                    275   1237.5\n",
              "Agent 4           613.0      A                    400  24520.0\n",
              "Agent 5           618.0      A                    400  24720.0\n",
              "...                 ...    ...                    ...      ...\n",
              "Agent 1195         26.0      D                    175    455.0\n",
              "Agent 1196        175.0      C                    275   4812.5\n",
              "Agent 1197        144.0      C                    275   3960.0\n",
              "Agent 1198        612.0      A                    400  24480.0\n",
              "Agent 1199         50.0      C                    275   1375.0\n",
              "\n",
              "[1199 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ecb57aae-8729-40b9-baa1-445d36a82ed2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total tasks</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Remuneration_per_task</th>\n",
              "      <th>Bonus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Agent 1</th>\n",
              "      <td>563.0</td>\n",
              "      <td>B</td>\n",
              "      <td>350</td>\n",
              "      <td>19705.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 2</th>\n",
              "      <td>588.0</td>\n",
              "      <td>B</td>\n",
              "      <td>350</td>\n",
              "      <td>20580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 3</th>\n",
              "      <td>45.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>1237.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 4</th>\n",
              "      <td>613.0</td>\n",
              "      <td>A</td>\n",
              "      <td>400</td>\n",
              "      <td>24520.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 5</th>\n",
              "      <td>618.0</td>\n",
              "      <td>A</td>\n",
              "      <td>400</td>\n",
              "      <td>24720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1195</th>\n",
              "      <td>26.0</td>\n",
              "      <td>D</td>\n",
              "      <td>175</td>\n",
              "      <td>455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1196</th>\n",
              "      <td>175.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>4812.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1197</th>\n",
              "      <td>144.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>3960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1198</th>\n",
              "      <td>612.0</td>\n",
              "      <td>A</td>\n",
              "      <td>400</td>\n",
              "      <td>24480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agent 1199</th>\n",
              "      <td>50.0</td>\n",
              "      <td>C</td>\n",
              "      <td>275</td>\n",
              "      <td>1375.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1199 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ecb57aae-8729-40b9-baa1-445d36a82ed2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ecb57aae-8729-40b9-baa1-445d36a82ed2 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-ecb57aae-8729-40b9-baa1-445d36a82ed2');\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": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[\"Bonus\"]=df2[\"Total tasks\"] * df2[\"Remuneration_per_task\"] * 0.1"
      ],
      "metadata": {
        "id": "Qm6wdI1U1j4w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.relplot(data=df2, x=\"Bonus\", y=\"Total tasks\",hue=\"Rating\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "U-IizgXY15-G",
        "outputId": "01b56394-202b-4472-a216-220ca17b9ff4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f2a8a66fdd0>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 403.75x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "fig = px.scatter(df2, x=\"Bonus\", y=\"Total tasks\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ChZ-p2ng13df",
        "outputId": "fccecb84-b7cf-4ab1-c06c-9a35a4482d1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"4517c5cb-6b03-4739-8fc2-1348dad5ac01\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"4517c5cb-6b03-4739-8fc2-1348dad5ac01\")) {                    Plotly.newPlot(                        \"4517c5cb-6b03-4739-8fc2-1348dad5ac01\",                        [{\"hovertemplate\":\"Bonus=%{x}<br>Total tasks=%{y}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"\",\"showlegend\":false,\"x\":[19705.0,20580.0,1237.5,24520.0,24720.0,1155.0,700.0,7630.0,25000.0,20020.0,1485.0,3685.0,20580.0,595.0,1237.5,23960.0,24600.0,8750.0,1347.5,7805.0,717.5,647.5,332.5,24160.0,12425.0,25080.0,20510.0,507.5,630.0,560.0,1265.0,19600.0,9135.0,3712.5,595.0,24440.0,682.5,682.5,1182.5,20230.0,612.5,9485.0,19950.0,1155.0,700.0,682.5,577.5,19845.0,612.5,1155.0,542.5,3465.0,13020.0,20405.0,10500.0,25000.0,24240.0,24040.0,24400.0,14980.0,17850.0,665.0,5280.0,24120.0,700.0,350.0,25080.0,24240.0,20370.0,542.5,717.5,665.0,700.0,3355.0,717.5,717.5,1402.5,717.5,24400.0,20580.0,630.0,612.5,7525.0,542.5,525.0,20790.0,26000.0,595.0,595.0,20335.0,7595.0,437.5,700.0,1210.0,1237.5,20475.0,24160.0,24200.0,682.5,647.5,4400.0,1182.5,700.0,20685.0,472.5,15785.0,24440.0,9800.0,20615.0,3987.5,1320.0,24480.0,5390.0,1210.0,612.5,24240.0,20160.0,630.0,717.5,560.0,7315.0,1375.0,560.0,24960.0,5252.5,647.5,700.0,20755.0,20650.0,1237.5,5252.5,25040.0,25400.0,1155.0,3877.5,20685.0,19950.0,24560.0,24080.0,560.0,12320.0,1155.0,20720.0,5032.5,1292.5,25360.0,10955.0,24840.0,647.5,1210.0,25360.0,717.5,630.0,1292.5,7630.0,647.5,23920.0,20755.0,24520.0,24520.0,20475.0,25280.0,8295.0,24280.0,2392.5,665.0,560.0,7875.0,10710.0,612.5,1237.5,24280.0,20790.0,682.5,20545.0,24080.0,4730.0,24640.0,24520.0,647.5,4262.5,595.0,23920.0,24320.0,19845.0,7735.0,5060.0,595.0,24520.0,647.5,20755.0,24120.0,507.5,647.5,1155.0,23880.0,20755.0,25600.0,24280.0,717.5,560.0,595.0,7420.0,3437.5,665.0,24720.0,20195.0,10255.0,24320.0,4290.0,717.5,1155.0,630.0,10885.0,23840.0,1265.0,24600.0,20685.0,682.5,665.0,1210.0,20020.0,630.0,24640.0,7910.0,23880.0,20720.0,2942.5,682.5,24080.0,647.5,20090.0,24240.0,20790.0,717.5,8400.0,700.0,23920.0,20545.0,20755.0,24440.0,20755.0,20475.0,507.5,26400.0,1182.5,26160.0,24840.0,385.0,8015.0,24640.0,19635.0,10745.0,595.0,1320.0,682.5,20720.0,24480.0,23880.0,612.5,25240.0,24680.0,1457.5,665.0,24920.0,16380.0,1265.0,20545.0,630.0,20720.0,8505.0,19985.0,4015.0,4757.5,15505.0,2612.5,24400.0,717.5,4757.5,542.5,20615.0,490.0,4785.0,25200.0,525.0,1182.5,560.0,437.5,525.0,682.5,25160.0,1237.5,10010.0,25120.0,4647.5,1155.0,12600.0,2942.5,612.5,612.5,507.5,525.0,24720.0,20090.0,24200.0,24360.0,24840.0,1182.5,9485.0,25640.0,20755.0,20440.0,1347.5,4730.0,4042.5,542.5,24040.0,1155.0,24440.0,700.0,25880.0,12670.0,24600.0,1265.0,24120.0,1155.0,24240.0,8925.0,24800.0,23960.0,717.5,3547.5,9135.0,1265.0,612.5,4565.0,24080.0,24040.0,717.5,1182.5,647.5,630.0,665.0,20545.0,4537.5,1155.0,4207.5,595.0,4647.5,24240.0,612.5,1237.5,23960.0,630.0,20440.0,612.5,24240.0,560.0,1210.0,24360.0,25320.0,1320.0,24840.0,24400.0,1237.5,542.5,23880.0,647.5,1265.0,23880.0,9660.0,20405.0,1430.0,9415.0,24360.0,8610.0,4317.5,24240.0,560.0,20335.0,20650.0,630.0,1402.5,682.5,20125.0,20580.0,13580.0,5335.0,2887.5,24200.0,3025.0,1155.0,9975.0,5527.5,612.5,24400.0,647.5,24840.0,630.0,455.0,19775.0,4455.0,8785.0,9870.0,12635.0,7385.0,630.0,560.0,23800.0,1237.5,7525.0,24680.0,1292.5,560.0,10045.0,665.0,630.0,20545.0,24720.0,665.0,23920.0,19845.0,630.0,23960.0,24760.0,10815.0,24040.0,20580.0,20545.0,1265.0,24800.0,472.5,612.5,20685.0,20405.0,5005.0,8295.0,1402.5,2365.0,25640.0,630.0,24280.0,717.5,490.0,24520.0,14455.0,4922.5,10395.0,542.5,577.5,472.5,23960.0,20160.0,682.5,630.0,1237.5,595.0,20265.0,23800.0,20335.0,1320.0,595.0,24640.0,24720.0,577.5,24560.0,700.0,717.5,1292.5,1237.5,24240.0,665.0,24160.0,20545.0,700.0,20790.0,2557.5,24200.0,1210.0,19775.0,24120.0,19810.0,20580.0,24280.0,630.0,24120.0,24760.0,612.5,1320.0,3272.5,9310.0,5527.5,595.0,1292.5,8260.0,19950.0,24120.0,4317.5,560.0,12600.0,560.0,24440.0,20755.0,577.5,612.5,665.0,542.5,24960.0,9345.0,23920.0,24880.0,665.0,23880.0,24680.0,19915.0,20755.0,1210.0,24960.0,19565.0,23840.0,24760.0,25240.0,717.5,1155.0,19285.0,25320.0,472.5,23880.0,24200.0,23880.0,700.0,20195.0,19110.0,24480.0,24200.0,1237.5,25000.0,20335.0,5555.0,8050.0,25680.0,3657.5,1210.0,647.5,472.5,20405.0,612.5,612.5,647.5,577.5,717.5,1347.5,24000.0,20440.0,24520.0,23880.0,24080.0,25880.0,11690.0,24480.0,2695.0,25000.0,1237.5,1347.5,20790.0,717.5,682.5,24720.0,647.5,10360.0,595.0,665.0,1430.0,1210.0,24120.0,577.5,4620.0,700.0,9485.0,25520.0,507.5,24480.0,20580.0,7210.0,25680.0,10955.0,25240.0,1182.5,3767.5,24640.0,3520.0,1485.0,9485.0,24960.0,1512.5,630.0,5335.0,595.0,8750.0,700.0,24480.0,24200.0,647.5,665.0,10745.0,8610.0,10115.0,9765.0,542.5,24160.0,5252.5,20160.0,24080.0,1265.0,12040.0,4510.0,24240.0,24800.0,3987.5,20405.0,20685.0,507.5,24960.0,24080.0,647.5,647.5,20580.0,19740.0,25600.0,3712.5,4785.0,8155.0,560.0,9310.0,595.0,20335.0,4482.5,1457.5,560.0,717.5,24640.0,24560.0,24520.0,20510.0,20790.0,577.5,612.5,1347.5,717.5,630.0,7910.0,24080.0,20475.0,24600.0,25040.0,682.5,8715.0,1182.5,1320.0,420.0,717.5,19915.0,525.0,682.5,542.5,4537.5,11970.0,490.0,1237.5,20580.0,11935.0,1155.0,577.5,24040.0,1210.0,25640.0,25560.0,560.0,4675.0,23840.0,8505.0,24360.0,24240.0,23800.0,612.5,3300.0,1402.5,19880.0,23880.0,1182.5,2475.0,25160.0,1155.0,700.0,2475.0,490.0,612.5,4317.5,7175.0,1457.5,7455.0,1210.0,630.0,20685.0,24600.0,19880.0,24240.0,595.0,18655.0,507.5,717.5,15190.0,1485.0,19950.0,630.0,23840.0,3052.5,717.5,25360.0,10850.0,25080.0,10780.0,20230.0,542.5,7910.0,20265.0,24160.0,20090.0,3795.0,25080.0,1182.5,1237.5,612.5,1237.5,1237.5,5032.5,20545.0,700.0,8820.0,4785.0,24240.0,665.0,20755.0,560.0,24600.0,595.0,665.0,682.5,20545.0,1292.5,700.0,1237.5,23880.0,16275.0,1402.5,1897.5,25040.0,19950.0,9800.0,19915.0,682.5,1210.0,1292.5,24400.0,612.5,24320.0,24400.0,717.5,20755.0,1677.5,525.0,612.5,717.5,11340.0,25320.0,1347.5,612.5,1375.0,1237.5,20195.0,24000.0,19845.0,25240.0,23800.0,23800.0,647.5,23960.0,7315.0,682.5,10745.0,19705.0,1155.0,24440.0,437.5,682.5,8225.0,24040.0,13440.0,717.5,25000.0,20195.0,700.0,2750.0,647.5,20370.0,23880.0,9450.0,1210.0,24040.0,25240.0,1347.5,560.0,7805.0,24640.0,24080.0,25680.0,717.5,10815.0,19565.0,12180.0,4812.5,23800.0,24000.0,24360.0,19775.0,507.5,682.5,1210.0,525.0,1182.5,24720.0,1265.0,665.0,1265.0,17640.0,20685.0,1320.0,647.5,1265.0,1512.5,647.5,1265.0,8820.0,12810.0,25640.0,19915.0,23840.0,595.0,24800.0,25440.0,15050.0,595.0,23840.0,20755.0,20685.0,577.5,20055.0,1512.5,700.0,4565.0,595.0,3630.0,647.5,9905.0,4977.5,24760.0,24640.0,682.5,700.0,20370.0,1265.0,24440.0,525.0,5225.0,595.0,525.0,20230.0,5555.0,23960.0,4840.0,717.5,24240.0,20160.0,1155.0,10675.0,2557.5,560.0,665.0,24000.0,1182.5,665.0,25080.0,595.0,24640.0,8050.0,1787.5,1485.0,20720.0,3850.0,24040.0,20335.0,25240.0,682.5,20265.0,612.5,595.0,682.5,15260.0,20650.0,542.5,20160.0,717.5,24000.0,665.0,717.5,24240.0,1237.5,11865.0,24320.0,3245.0,682.5,1210.0,24360.0,20125.0,3877.5,2777.5,2007.5,647.5,23840.0,20020.0,595.0,19810.0,612.5,1182.5,7210.0,4400.0,24520.0,24920.0,2200.0,9030.0,20720.0,577.5,1292.5,24040.0,25080.0,24400.0,24720.0,525.0,20405.0,665.0,11270.0,20265.0,647.5,560.0,20405.0,20790.0,11515.0,20020.0,577.5,24280.0,24160.0,612.5,1347.5,647.5,1237.5,717.5,472.5,4455.0,1210.0,20580.0,24280.0,4317.5,20055.0,5280.0,20650.0,20440.0,24640.0,682.5,560.0,19985.0,577.5,8575.0,560.0,665.0,560.0,717.5,25200.0,3987.5,630.0,25120.0,1155.0,12005.0,665.0,717.5,1650.0,20265.0,20685.0,437.5,3877.5,8155.0,1457.5,20755.0,4812.5,682.5,20545.0,542.5,630.0,1155.0,665.0,14770.0,24920.0,1182.5,700.0,1210.0,24960.0,630.0,25640.0,20545.0,4565.0,560.0,700.0,665.0,23800.0,630.0,1292.5,23800.0,630.0,24480.0,25040.0,700.0,20580.0,595.0,525.0,20020.0,25240.0,24400.0,1237.5,1292.5,24360.0,1210.0,665.0,24360.0,682.5,3877.5,8925.0,11900.0,8365.0,717.5,25480.0,10850.0,24440.0,24760.0,20370.0,577.5,577.5,24720.0,612.5,717.5,20160.0,1430.0,1155.0,24440.0,1347.5,24680.0,1375.0,24040.0,717.5,4400.0,490.0,682.5,5087.5,542.5,577.5,20335.0,700.0,577.5,472.5,20685.0,630.0,20755.0,20580.0,1237.5,717.5,1292.5,682.5,10115.0,4097.5,1182.5,20720.0,24040.0,20755.0,1182.5,1265.0,23960.0,595.0,5115.0,25520.0,24000.0,8225.0,1237.5,595.0,665.0,5445.0,24400.0,20615.0,630.0,24920.0,5252.5,13195.0,15050.0,24880.0,9555.0,542.5,23960.0,24680.0,24440.0,20160.0,7175.0,24680.0,20685.0,16345.0,23920.0,1155.0,20230.0,19845.0,612.5,24000.0,24240.0,1265.0,560.0,1375.0,24560.0,24640.0,682.5,23960.0,23880.0,19670.0,20160.0,647.5,23840.0,20685.0,1375.0,20125.0,20685.0,24160.0,23920.0,4592.5,24880.0,23840.0,1155.0,24320.0,13335.0,1155.0,24880.0,8610.0,4730.0,577.5,1265.0,24360.0,20405.0,23960.0,20650.0,24040.0,4950.0,5252.5,560.0,525.0,10780.0,24000.0,24480.0,577.5,1237.5,4152.5,24480.0,5307.5,612.5,542.5,700.0,1292.5,595.0,11130.0,19600.0,280.0,1347.5,20615.0,1485.0,665.0,23800.0,20755.0,560.0,24120.0,25080.0,7910.0,20545.0,455.0,4812.5,3960.0,24480.0,1375.0],\"xaxis\":\"x\",\"y\":[563.0,588.0,45.0,613.0,618.0,42.0,40.0,218.0,625.0,572.0,54.0,134.0,588.0,34.0,45.0,599.0,615.0,250.0,49.0,223.0,41.0,37.0,19.0,604.0,355.0,627.0,586.0,29.0,36.0,32.0,46.0,560.0,261.0,135.0,34.0,611.0,39.0,39.0,43.0,578.0,35.0,271.0,570.0,42.0,40.0,39.0,33.0,567.0,35.0,42.0,31.0,126.0,372.0,583.0,300.0,625.0,606.0,601.0,610.0,428.0,510.0,38.0,192.0,603.0,40.0,20.0,627.0,606.0,582.0,31.0,41.0,38.0,40.0,122.0,41.0,41.0,51.0,41.0,610.0,588.0,36.0,35.0,215.0,31.0,30.0,594.0,650.0,34.0,34.0,581.0,217.0,25.0,40.0,44.0,45.0,585.0,604.0,605.0,39.0,37.0,160.0,43.0,40.0,591.0,27.0,451.0,611.0,280.0,589.0,145.0,48.0,612.0,196.0,44.0,35.0,606.0,576.0,36.0,41.0,32.0,209.0,50.0,32.0,624.0,191.0,37.0,40.0,593.0,590.0,45.0,191.0,626.0,635.0,42.0,141.0,591.0,570.0,614.0,602.0,32.0,352.0,42.0,592.0,183.0,47.0,634.0,313.0,621.0,37.0,44.0,634.0,41.0,36.0,47.0,218.0,37.0,598.0,593.0,613.0,613.0,585.0,632.0,237.0,607.0,87.0,38.0,32.0,225.0,306.0,35.0,45.0,607.0,594.0,39.0,587.0,602.0,172.0,616.0,613.0,37.0,155.0,34.0,598.0,608.0,567.0,221.0,184.0,34.0,613.0,37.0,593.0,603.0,29.0,37.0,42.0,597.0,593.0,640.0,607.0,41.0,32.0,34.0,212.0,125.0,38.0,618.0,577.0,293.0,608.0,156.0,41.0,42.0,36.0,311.0,596.0,46.0,615.0,591.0,39.0,38.0,44.0,572.0,36.0,616.0,226.0,597.0,592.0,107.0,39.0,602.0,37.0,574.0,606.0,594.0,41.0,240.0,40.0,598.0,587.0,593.0,611.0,593.0,585.0,29.0,660.0,43.0,654.0,621.0,22.0,229.0,616.0,561.0,307.0,34.0,48.0,39.0,592.0,612.0,597.0,35.0,631.0,617.0,53.0,38.0,623.0,468.0,46.0,587.0,36.0,592.0,243.0,571.0,146.0,173.0,443.0,95.0,610.0,41.0,173.0,31.0,589.0,28.0,174.0,630.0,30.0,43.0,32.0,25.0,30.0,39.0,629.0,45.0,286.0,628.0,169.0,42.0,360.0,107.0,35.0,35.0,29.0,30.0,618.0,574.0,605.0,609.0,621.0,43.0,271.0,641.0,593.0,584.0,49.0,172.0,147.0,31.0,601.0,42.0,611.0,40.0,647.0,362.0,615.0,46.0,603.0,42.0,606.0,255.0,620.0,599.0,41.0,129.0,261.0,46.0,35.0,166.0,602.0,601.0,41.0,43.0,37.0,36.0,38.0,587.0,165.0,42.0,153.0,34.0,169.0,606.0,35.0,45.0,599.0,36.0,584.0,35.0,606.0,32.0,44.0,609.0,633.0,48.0,621.0,610.0,45.0,31.0,597.0,37.0,46.0,597.0,276.0,583.0,52.0,269.0,609.0,246.0,157.0,606.0,32.0,581.0,590.0,36.0,51.0,39.0,575.0,588.0,388.0,194.0,105.0,605.0,110.0,42.0,285.0,201.0,35.0,610.0,37.0,621.0,36.0,26.0,565.0,162.0,251.0,282.0,361.0,211.0,36.0,32.0,595.0,45.0,215.0,617.0,47.0,32.0,287.0,38.0,36.0,587.0,618.0,38.0,598.0,567.0,36.0,599.0,619.0,309.0,601.0,588.0,587.0,46.0,620.0,27.0,35.0,591.0,583.0,182.0,237.0,51.0,86.0,641.0,36.0,607.0,41.0,28.0,613.0,413.0,179.0,297.0,31.0,33.0,27.0,599.0,576.0,39.0,36.0,45.0,34.0,579.0,595.0,581.0,48.0,34.0,616.0,618.0,33.0,614.0,40.0,41.0,47.0,45.0,606.0,38.0,604.0,587.0,40.0,594.0,93.0,605.0,44.0,565.0,603.0,566.0,588.0,607.0,36.0,603.0,619.0,35.0,48.0,119.0,266.0,201.0,34.0,47.0,236.0,570.0,603.0,157.0,32.0,360.0,32.0,611.0,593.0,33.0,35.0,38.0,31.0,624.0,267.0,598.0,622.0,38.0,597.0,617.0,569.0,593.0,44.0,624.0,559.0,596.0,619.0,631.0,41.0,42.0,551.0,633.0,27.0,597.0,605.0,597.0,40.0,577.0,546.0,612.0,605.0,45.0,625.0,581.0,202.0,230.0,642.0,133.0,44.0,37.0,27.0,583.0,35.0,35.0,37.0,33.0,41.0,49.0,600.0,584.0,613.0,597.0,602.0,647.0,334.0,612.0,98.0,625.0,45.0,49.0,594.0,41.0,39.0,618.0,37.0,296.0,34.0,38.0,52.0,44.0,603.0,33.0,168.0,40.0,271.0,638.0,29.0,612.0,588.0,206.0,642.0,313.0,631.0,43.0,137.0,616.0,128.0,54.0,271.0,624.0,55.0,36.0,194.0,34.0,250.0,40.0,612.0,605.0,37.0,38.0,307.0,246.0,289.0,279.0,31.0,604.0,191.0,576.0,602.0,46.0,344.0,164.0,606.0,620.0,145.0,583.0,591.0,29.0,624.0,602.0,37.0,37.0,588.0,564.0,640.0,135.0,174.0,233.0,32.0,266.0,34.0,581.0,163.0,53.0,32.0,41.0,616.0,614.0,613.0,586.0,594.0,33.0,35.0,49.0,41.0,36.0,226.0,602.0,585.0,615.0,626.0,39.0,249.0,43.0,48.0,24.0,41.0,569.0,30.0,39.0,31.0,165.0,342.0,28.0,45.0,588.0,341.0,42.0,33.0,601.0,44.0,641.0,639.0,32.0,170.0,596.0,243.0,609.0,606.0,595.0,35.0,120.0,51.0,568.0,597.0,43.0,90.0,629.0,42.0,40.0,90.0,28.0,35.0,157.0,205.0,53.0,213.0,44.0,36.0,591.0,615.0,568.0,606.0,34.0,533.0,29.0,41.0,434.0,54.0,570.0,36.0,596.0,111.0,41.0,634.0,310.0,627.0,308.0,578.0,31.0,226.0,579.0,604.0,574.0,138.0,627.0,43.0,45.0,35.0,45.0,45.0,183.0,587.0,40.0,252.0,174.0,606.0,38.0,593.0,32.0,615.0,34.0,38.0,39.0,587.0,47.0,40.0,45.0,597.0,465.0,51.0,69.0,626.0,570.0,280.0,569.0,39.0,44.0,47.0,610.0,35.0,608.0,610.0,41.0,593.0,61.0,30.0,35.0,41.0,324.0,633.0,49.0,35.0,50.0,45.0,577.0,600.0,567.0,631.0,595.0,595.0,37.0,599.0,209.0,39.0,307.0,563.0,42.0,611.0,25.0,39.0,235.0,601.0,384.0,41.0,625.0,577.0,40.0,100.0,37.0,582.0,597.0,270.0,44.0,601.0,631.0,49.0,32.0,223.0,616.0,602.0,642.0,41.0,309.0,559.0,348.0,175.0,595.0,600.0,609.0,565.0,29.0,39.0,44.0,30.0,43.0,618.0,46.0,38.0,46.0,504.0,591.0,48.0,37.0,46.0,55.0,37.0,46.0,252.0,366.0,641.0,569.0,596.0,34.0,620.0,636.0,430.0,34.0,596.0,593.0,591.0,33.0,573.0,55.0,40.0,166.0,34.0,132.0,37.0,283.0,181.0,619.0,616.0,39.0,40.0,582.0,46.0,611.0,30.0,190.0,34.0,30.0,578.0,202.0,599.0,176.0,41.0,606.0,576.0,42.0,305.0,93.0,32.0,38.0,600.0,43.0,38.0,627.0,34.0,616.0,230.0,65.0,54.0,592.0,140.0,601.0,581.0,631.0,39.0,579.0,35.0,34.0,39.0,436.0,590.0,31.0,576.0,41.0,600.0,38.0,41.0,606.0,45.0,339.0,608.0,118.0,39.0,44.0,609.0,575.0,141.0,101.0,73.0,37.0,596.0,572.0,34.0,566.0,35.0,43.0,206.0,160.0,613.0,623.0,80.0,258.0,592.0,33.0,47.0,601.0,627.0,610.0,618.0,30.0,583.0,38.0,322.0,579.0,37.0,32.0,583.0,594.0,329.0,572.0,33.0,607.0,604.0,35.0,49.0,37.0,45.0,41.0,27.0,162.0,44.0,588.0,607.0,157.0,573.0,192.0,590.0,584.0,616.0,39.0,32.0,571.0,33.0,245.0,32.0,38.0,32.0,41.0,630.0,145.0,36.0,628.0,42.0,343.0,38.0,41.0,60.0,579.0,591.0,25.0,141.0,233.0,53.0,593.0,175.0,39.0,587.0,31.0,36.0,42.0,38.0,422.0,623.0,43.0,40.0,44.0,624.0,36.0,641.0,587.0,166.0,32.0,40.0,38.0,595.0,36.0,47.0,595.0,36.0,612.0,626.0,40.0,588.0,34.0,30.0,572.0,631.0,610.0,45.0,47.0,609.0,44.0,38.0,609.0,39.0,141.0,255.0,340.0,239.0,41.0,637.0,310.0,611.0,619.0,582.0,33.0,33.0,618.0,35.0,41.0,576.0,52.0,42.0,611.0,49.0,617.0,50.0,601.0,41.0,160.0,28.0,39.0,185.0,31.0,33.0,581.0,40.0,33.0,27.0,591.0,36.0,593.0,588.0,45.0,41.0,47.0,39.0,289.0,149.0,43.0,592.0,601.0,593.0,43.0,46.0,599.0,34.0,186.0,638.0,600.0,235.0,45.0,34.0,38.0,198.0,610.0,589.0,36.0,623.0,191.0,377.0,430.0,622.0,273.0,31.0,599.0,617.0,611.0,576.0,205.0,617.0,591.0,467.0,598.0,42.0,578.0,567.0,35.0,600.0,606.0,46.0,32.0,50.0,614.0,616.0,39.0,599.0,597.0,562.0,576.0,37.0,596.0,591.0,50.0,575.0,591.0,604.0,598.0,167.0,622.0,596.0,42.0,608.0,381.0,42.0,622.0,246.0,172.0,33.0,46.0,609.0,583.0,599.0,590.0,601.0,180.0,191.0,32.0,30.0,308.0,600.0,612.0,33.0,45.0,151.0,612.0,193.0,35.0,31.0,40.0,47.0,34.0,318.0,560.0,16.0,49.0,589.0,54.0,38.0,595.0,593.0,32.0,603.0,627.0,226.0,587.0,26.0,175.0,144.0,612.0,50.0],\"yaxis\":\"y\",\"type\":\"scattergl\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Bonus\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Total tasks\"}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('4517c5cb-6b03-4739-8fc2-1348dad5ac01');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.set_theme(style=\"ticks\")\n",
        "\n",
        "df2 = sns.load_dataset(\"penguins\")\n",
        "sns.pairplot(df2, hue=\"species\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "-WLpljj0EZAL",
        "outputId": "ddffb6f8-ebf5-4785-bb73-cf2619eb230f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f44dc8b8490>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 812.85x720 with 20 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8XUx8uE9Daj-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "payment_per_task = []\n",
        "\n",
        "for i in df2[\"Rating\"]:\n",
        "  if i==\"A\":\n",
        "    payment_per_task.append(400)\n",
        "  elif i==\"B\":\n",
        "    payment_per_task.append(350)\n",
        "  elif i==\"C\":\n",
        "    payment_per_task.append(275)\n",
        "  else:\n",
        "    payment_per_task.append(175)\n",
        "df2['Remuneration_per_task']=payment_per_task"
      ],
      "metadata": {
        "id": "V8ugTGSMyk-A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.to_csv('remuneration.csv')\n",
        "!cp remuneration.csv \"drive/My Drive/\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bdxw2dQ17XKO",
        "outputId": "39017892-2c87-4a44-f51e-d9699da94c91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: cannot create regular file 'drive/My Drive/': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAWIUaST1io2",
        "outputId": "8d468b5e-b333-444d-f266-dada7f02062b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Day             3486.0\n",
              "Agent 0          583.0\n",
              "Agent 1          563.0\n",
              "Agent 2          588.0\n",
              "Agent 3           45.0\n",
              "                ...   \n",
              "Agent 1196       175.0\n",
              "Agent 1197       144.0\n",
              "Agent 1198       612.0\n",
              "Agent 1199        50.0\n",
              "Total         360606.0\n",
              "Length: 1202, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QEcFKN9YAjrJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}