HOMPA-example.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jjmatthiesen/homeOffice/blob/main/HOMPA-example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6avSxIdEGY0"
      },
      "source": [
        "# Introduction to the Home Office Calculation\n",
        "\n",
        "The COVID pandemic has made working from home necessary, and many employees want to continue doing so even after the pandemic. There are advantages and drawbacks to the home office trend from both employer and employee perspectives. Determining the ideal proportion of home office for each employee is important but there is a research gap in how to do so. This work presents an algorithm that considers multiple perspectives to determine the ideal proportion of home office, including the employer's view, demographic and social factors of the employee, and the employee's preferred proportion of home office. The algorithm combines findings from several studies and can identify discrepancies between these perspectives."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w2X73Qjm84ub",
        "outputId": "81e74d00-4c20-461c-b147-ebb9e9b31227"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
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      "source": [
        "# dtale requires statsmodels to be updated; see https://stackoverflow.com/questions/71106940/cannot-import-name-centered-from-scipy-signal-signaltools\n",
        "\n",
        "!pip install statsmodels --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_GZ5SlNPB7Va",
        "outputId": "43a0c5e5-5943-4ffe-90e3-2f2e6f1a06e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.9/dist-packages (from pexpect->ipython->pygwalker) (0.7.0)\n",
            "Installing collected packages: jedi, pygwalker\n",
            "Successfully installed jedi-0.18.2 pygwalker-0.1.4.8\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: seaborn[stats] in /usr/local/lib/python3.9/dist-packages (0.11.2)\n",
            "\u001b[33mWARNING: seaborn 0.11.2 does not provide the extra 'stats'\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.9/dist-packages (from seaborn[stats]) (3.6.0)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.9/dist-packages (from seaborn[stats]) (1.3.5)\n",
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            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=2.2->seaborn[stats]) (1.0.7)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=2.2->seaborn[stats]) (3.0.9)\n",
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            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=2.2->seaborn[stats]) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=2.2->seaborn[stats]) (23.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib>=2.2->seaborn[stats]) (4.39.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.23->seaborn[stats]) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn[stats]) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install dtale\n",
        "!pip install pygwalker\n",
        "!pip install seaborn[stats]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jWAfGeSCBv2U"
      },
      "outputs": [],
      "source": [
        "# Just for checking the required package versions\n",
        "\n",
        "#!pip list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CM6ghnwI8_Tf"
      },
      "outputs": [],
      "source": [
        "import pygwalker as pyg\n",
        "import pandas as pd\n",
        "\n",
        "import dtale\n",
        "import dtale.app as dtale_app\n",
        "\n",
        "from IPython.display import Image\n",
        "import numpy as np\n",
        "import datetime\n",
        "import warnings\n",
        "import pathlib\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "#import seaborn.objects as so"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TmBW3MpefkcP"
      },
      "outputs": [],
      "source": [
        "def load_data(comp):\n",
        "    d_employees = pd.read_csv('./data/' + comp + '/employees_' + comp + '.csv', sep=',')\n",
        "    d_tasks = pd.read_csv('./data/' + comp + '/tasks_' + comp + '.csv', sep=',')\n",
        "    d_employee_input = pd.read_csv('./data/' + comp + '/employee_input_' + comp + '.csv', sep=',')\n",
        "    return d_employees, d_tasks, d_employee_input\n",
        "\n",
        "# calculate diff between today and the start date at the company\n",
        "def tenure(start_date):\n",
        "    date_now = datetime.datetime.utcnow()\n",
        "    date_diff = int((date_now - start_date).days)\n",
        "    return date_diff\n",
        "\n",
        "# probationary period\n",
        "def ho_prob(days):\n",
        "    if days < 180:\n",
        "        home_office_prob = 0\n",
        "    else:\n",
        "        home_office_prob = 100\n",
        "    return home_office_prob\n",
        "\n",
        "\n",
        "# calculate the home office proportion based on the age (gen-preference)\n",
        "def ho_generation(year):\n",
        "    if 1946 <= year <= 1964:\n",
        "        return 48\n",
        "    elif 1965 <= year <= 1980:\n",
        "        return 50\n",
        "    elif 1981 <= year <= 1994:\n",
        "        return 44\n",
        "    else:\n",
        "        return 28\n",
        "\n",
        "\n",
        "# calculate the home office proportion based on the education\n",
        "def ho_degree(degree):\n",
        "    if degree == 'Hochschule':\n",
        "        return 48\n",
        "    elif degree == 'mittlere Reife':\n",
        "        return 17\n",
        "    else:\n",
        "        return 8\n",
        "\n",
        "\n",
        "# calculate the home office proportion based on the commute time\n",
        "def ho_commute(time):\n",
        "    if time > 40:\n",
        "        return 46\n",
        "    elif 20 <= time <= 40:\n",
        "        return 2.3 * time -46 # linear decline\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "\n",
        "# calculate the home office proportion based on caring responsibility\n",
        "def ho_gender_resp(employees):\n",
        "    if employees['Caring Responsibility'] and employees['Gender'] == 'female':\n",
        "        return 56.1\n",
        "    elif employees['Caring Responsibility'] and employees['Gender'] == 'male':\n",
        "        return 52\n",
        "    else:\n",
        "        return 50\n",
        "\n",
        "\n",
        "# calculate the preferred proportion of home office\n",
        "def ho_prefer(employee_input):\n",
        "    if employee_input['ho_wish'] == True:\n",
        "        return (employee_input['desired_days'] / 5) * 100\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "# calculate derivations\n",
        "def deviation(ho_shares):\n",
        "    ho_shares['difference_to_max'] = int(ho_shares['ho_max'] - ho_shares['ho_prefer'])\n",
        "    ho_shares['difference_to_opt'] = int(ho_shares['ho_opt'] - ho_shares['ho_prefer'])\n",
        "    ho_shares['difference_to_social'] = int(ho_shares['ho_social'] - ho_shares['ho_prefer'])\n",
        "    return ho_shares"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WqhLmAbc-wBu"
      },
      "source": [
        "# see Kawakubo, S., & Arata, S. (2022). Study on residential environment and workers’ personality traits on productivity while working from home. Building and Environment, 212, 108787. \n",
        "\n",
        "![homeoffice_tipi_j.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X_BUhMo8-dLJ"
      },
      "outputs": [],
      "source": [
        "# calculate the preferred proportion of home office based on personality traits\n",
        "# see Kawakubo, S., & Arata, S. (2022). Study on residential environment and workers’ personality traits on productivity while working from home. Building and Environment, 212, 108787. \n",
        "def ho_personality_openness(employee_input):\n",
        "    if 2 <= employee_input['openness'] <= 8:\n",
        "        return 1.2\n",
        "    else:\n",
        "        return 6.1\n",
        "\n",
        "def ho_personality_neuroticism(employee_input):\n",
        "    if 2 <= employee_input['neuroticism'] <= 7:\n",
        "        return 6.3\n",
        "    else:\n",
        "        return 2.2\n",
        "\n",
        "def ho_personality_perseverance_and_passion(employee_input):\n",
        "    if 1 <= employee_input['perseverance_and_grit'] <= 3:\n",
        "        return 1.7\n",
        "    else:\n",
        "        return 6.5\n",
        "\n",
        "# the other three factorsare not significant, therefore, we currently avoid these\n",
        "#Conscientiousness \n",
        "#Extraversion \n",
        "##Agreeableness \n",
        "\n",
        "\n",
        "# we calculate the percentage based on the single value\n",
        "def ho_personality__complete(employee_input):\n",
        "  return ((ho_personality_openness(employee_input) + ho_personality_neuroticism(employee_input) + ho_personality_perseverance_and_passion(employee_input))/(6.1+6.3+6.5)) * 100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dE1XQ2LVNoO4"
      },
      "source": [
        "# Visualize Different Functions using NumPy (not complete)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "4UHOj2htNs0k",
        "outputId": "22c0c074-33b6-4a81-cb8b-38cfcd08a62e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9e1c2fbee0>]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x252 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "### visualize different functions\n",
        "\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = [7.50, 3.50]\n",
        "plt.rcParams[\"figure.autolayout\"] = True\n",
        "\n",
        "\n",
        "# Use np for arrays; see https://stackoverflow.com/a/47195696 \n",
        "\n",
        "def ho_generation_conditions(year):\n",
        "    if   1946 <= year <= 1964:   return 48\n",
        "    elif 1965 <= year <= 1980:   return 50\n",
        "    elif 1981 <= year <= 1994:    return 44\n",
        "    else:           return 28\n",
        "ho_generation_np = np.vectorize(ho_generation_conditions)\n",
        "\n",
        "x = np.linspace(1920, 2005, 100, dtype='int')\n",
        "plt.plot(x, ho_generation_np(x), color='red')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "4_thJszaNvq8",
        "outputId": "e3e5ac76-666c-4944-8181-be7f2d627346"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9de8247af0>]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x252 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "def ho_prob_conditions(days):\n",
        "    if   days < 180:   return 0\n",
        "    else:              return 100\n",
        "ho_prob_np = np.vectorize(ho_prob_conditions)\n",
        "\n",
        "\n",
        "\n",
        "# one year\n",
        "x = np.linspace(0, 360, 100, dtype='int')\n",
        "plt.plot(x, ho_prob_np(x), color='g')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "picy8zg7Fu5Q",
        "outputId": "77270636-13f0-473b-a5e7-9786346f7d52"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9de81bb3d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x252 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "def ho_personality_openness_conditions(openness):\n",
        "    if   2 <= openness <= 8:   return 1.2\n",
        "    else:           return 6.1\n",
        "ho_personality_openness_np = np.vectorize(ho_personality_openness_conditions)\n",
        "\n",
        "\n",
        "\n",
        "x = np.linspace(2, 14, 20, dtype='int')\n",
        "plt.plot(x, ho_personality_openness_np(x), color='y')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "uxS_pyKwOzRo",
        "outputId": "5ec57373-625f-4938-9c84-95c58fc2e0cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9de8197c70>]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x252 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "def ho_personality_neuroticism_conditions(neuroticism):\n",
        "    if   2 <= neuroticism <= 7:   return 6.3\n",
        "    else:           return 2.2\n",
        "ho_personality_neuroticism_np = np.vectorize(ho_personality_neuroticism_conditions)\n",
        "\n",
        "\n",
        "\n",
        "x = np.linspace(2, 14, 20, dtype='int')\n",
        "plt.plot(x, ho_personality_neuroticism_np(x), color='b')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "VnyDwQBROyu8",
        "outputId": "5c5f64c4-bf9a-497b-95de-d9cfd8dadcbc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9de810f040>]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x252 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "def ho_personality_perseverance_and_passion_conditions(perseverance_and_grit):\n",
        "    if   1 <= perseverance_and_grit <= 3:   return 1.7\n",
        "    else:           return 6.5\n",
        "ho_personality_perseverance_and_passion_np = np.vectorize(ho_personality_perseverance_and_passion_conditions)\n",
        "\n",
        "\n",
        "\n",
        "x = np.linspace(1, 5, 20, dtype='float')\n",
        "plt.plot(x, ho_personality_perseverance_and_passion_np(x), color='orange')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WEb9pYIG9HmV"
      },
      "outputs": [],
      "source": [
        "# this will work once we have the data public\n",
        "#employees, tasks, employee_input = load_data('example)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "hwHpmUr4jOhg",
        "outputId": "656ce804-5c3a-4a36-ab3c-6597574f8475"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Personal number Employee  Birth date  Entry date  Gender  \\\n",
              "0           111111       AA  1962-02-23  2000-07-01    male   \n",
              "1           111112       BB  1996-09-16  2021-02-01  female   \n",
              "2           111113       CC  1988-04-12  2018-10-01  female   \n",
              "3           111114       DD  1973-12-31  1995-01-01  female   \n",
              "4           111115       EE  1986-07-03  2010-12-01    male   \n",
              "5           111116       FF  1965-04-19  2003-04-01    male   \n",
              "6           111117       GG  1977-09-23  2008-05-01    male   \n",
              "7           111118       HH  1971-10-26  2014-06-01  female   \n",
              "8           111119       II  1994-11-02  2020-08-01  female   \n",
              "9           111120       JJ  1958-05-16  1996-11-01  female   \n",
              "10          111121       KK  1981-04-23  2021-06-01    male   \n",
              "11          111122       LL  1970-09-21  2010-06-01  female   \n",
              "12          111123       MM  1988-12-22  2020-03-01    male   \n",
              "13          111124       NN  1993-08-02  1999-11-01  female   \n",
              "14          111125       OO  1959-06-16  2021-06-01    male   \n",
              "15          111126       PP  1983-01-23  2020-04-01    male   \n",
              "16          111127       QQ  1990-02-21  1996-12-01  female   \n",
              "\n",
              "                 Degree               Activity  \n",
              "0            Hochschule             HR Manager  \n",
              "1        mittlere Reife     Purchasing Manager  \n",
              "2            Hochschule              Secretary  \n",
              "3   Hauptschulabschluss                Janitor  \n",
              "4   Hauptschulabschluss         Cleaning staff  \n",
              "5            Hochschule        IT specialist 1  \n",
              "6            Hochschule             Controller  \n",
              "7            Hochschule        IT specialist 2  \n",
              "8            Hochschule      Marketing Manager  \n",
              "9            Hochschule  Team Leader - Finance  \n",
              "10       mittlere Reife   Sales representative  \n",
              "11       mittlere Reife          Store manager  \n",
              "12  Hauptschulabschluss          Store manager  \n",
              "13           Hochschule                 Seller  \n",
              "14           Hochschule                 Seller  \n",
              "15       mittlere Reife                 Seller  \n",
              "16  Hauptschulabschluss                 Seller  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec849c96-97c2-4be1-8f68-fdbc05da23e4\">\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>Personal number</th>\n",
              "      <th>Employee</th>\n",
              "      <th>Birth date</th>\n",
              "      <th>Entry date</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Degree</th>\n",
              "      <th>Activity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111111</td>\n",
              "      <td>AA</td>\n",
              "      <td>1962-02-23</td>\n",
              "      <td>2000-07-01</td>\n",
              "      <td>male</td>\n",
              "      <td>Hochschule</td>\n",
              "      <td>HR Manager</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>111112</td>\n",
              "      <td>BB</td>\n",
              "      <td>1996-09-16</td>\n",
              "      <td>2021-02-01</td>\n",
              "      <td>female</td>\n",
              "      <td>mittlere Reife</td>\n",
              "      <td>Purchasing Manager</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>111113</td>\n",
              "      <td>CC</td>\n",
              "      <td>1988-04-12</td>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>female</td>\n",
              "      <td>Hochschule</td>\n",
              "      <td>Secretary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>111114</td>\n",
              "      <td>DD</td>\n",
              "      <td>1973-12-31</td>\n",
              "      <td>1995-01-01</td>\n",
              "      <td>female</td>\n",
              "      <td>Hauptschulabschluss</td>\n",
              "      <td>Janitor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>111115</td>\n",
              "      <td>EE</td>\n",
              "      <td>1986-07-03</td>\n",
              "      <td>2010-12-01</td>\n",
              "      <td>male</td>\n",
              "      <td>Hauptschulabschluss</td>\n",
              "      <td>Cleaning staff</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>111116</td>\n",
              "      <td>FF</td>\n",
              "      <td>1965-04-19</td>\n",
              "      <td>2003-04-01</td>\n",
              "      <td>male</td>\n",
              "      <td>Hochschule</td>\n",
              "      <td>IT specialist 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>111117</td>\n",
              "      <td>GG</td>\n",
              "      <td>1977-09-23</td>\n",
              "      <td>2008-05-01</td>\n",
              "      <td>male</td>\n",
              "      <td>Hochschule</td>\n",
              "      <td>Controller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>111118</td>\n",
              "      <td>HH</td>\n",
              "      <td>1971-10-26</td>\n",
              "      <td>2014-06-01</td>\n",
              "      <td>female</td>\n",
              "      <td>Hochschule</td>\n",
              "      <td>IT specialist 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>111119</td>\n",
              "      <td>II</td>\n",
              "      <td>1994-11-02</td>\n",
              "      <td>2020-08-01</td>\n",
              "      <td>female</td>\n",
              "      <td>Hochschule</td>\n",
              "      <td>Marketing Manager</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>111120</td>\n",
              "      <td>JJ</td>\n",
              "      <td>1958-05-16</td>\n",
              "      <td>1996-11-01</td>\n",
              "      <td>female</td>\n",
              "      <td>Hochschule</td>\n",
              "      <td>Team Leader - Finance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>111121</td>\n",
              "      <td>KK</td>\n",
              "      <td>1981-04-23</td>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>male</td>\n",
              "      <td>mittlere Reife</td>\n",
              "      <td>Sales representative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>111122</td>\n",
              "      <td>LL</td>\n",
              "      <td>1970-09-21</td>\n",
              "      <td>2010-06-01</td>\n",
              "      <td>female</td>\n",
              "      <td>mittlere Reife</td>\n",
              "      <td>Store manager</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>111123</td>\n",
              "      <td>MM</td>\n",
              "      <td>1988-12-22</td>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>male</td>\n",
              "      <td>Hauptschulabschluss</td>\n",
              "      <td>Store manager</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>111124</td>\n",
              "      <td>NN</td>\n",
              "      <td>1993-08-02</td>\n",
              "      <td>1999-11-01</td>\n",
              "      <td>female</td>\n",
              "      <td>Hochschule</td>\n",
              "      <td>Seller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>111125</td>\n",
              "      <td>OO</td>\n",
              "      <td>1959-06-16</td>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>male</td>\n",
              "      <td>Hochschule</td>\n",
              "      <td>Seller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>111126</td>\n",
              "      <td>PP</td>\n",
              "      <td>1983-01-23</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>male</td>\n",
              "      <td>mittlere Reife</td>\n",
              "      <td>Seller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>111127</td>\n",
              "      <td>QQ</td>\n",
              "      <td>1990-02-21</td>\n",
              "      <td>1996-12-01</td>\n",
              "      <td>female</td>\n",
              "      <td>Hauptschulabschluss</td>\n",
              "      <td>Seller</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "employees = pd.DataFrame([['111111', 'AA', '1962-02-23', '2000-07-01', 'male', 'Hochschule', 'HR Manager'], ['111112', 'BB', '1996-09-16', '2021-02-01', 'female', 'mittlere Reife', 'Purchasing Manager'], ['111113', 'CC', '1988-04-12', '2018-10-01', 'female', 'Hochschule', 'Secretary'], ['111114', 'DD', '1973-12-31', '1995-01-01', 'female', 'Hauptschulabschluss', 'Janitor'], ['111115', 'EE', '1986-07-03', '2010-12-01', 'male', 'Hauptschulabschluss', 'Cleaning staff'], ['111116', 'FF', '1965-04-19', '2003-04-01', 'male', 'Hochschule', 'IT specialist 1'], ['111117', 'GG', '1977-09-23', '2008-05-01', 'male', 'Hochschule', 'Controller'], ['111118', 'HH', '1971-10-26', '2014-06-01', 'female', 'Hochschule', 'IT specialist 2'], ['111119', 'II', '1994-11-02', '2020-08-01', 'female', 'Hochschule', 'Marketing Manager'], ['111120', 'JJ', '1958-05-16', '1996-11-01', 'female', 'Hochschule', 'Team Leader - Finance'], ['111121', 'KK', '1981-04-23', '2021-06-01', 'male', 'mittlere Reife', 'Sales representative'], ['111122', 'LL', '1970-09-21', '2010-06-01', 'female', 'mittlere Reife', 'Store manager'], ['111123', 'MM', '1988-12-22', '2020-03-01', 'male', 'Hauptschulabschluss', 'Store manager'], ['111124', 'NN', '1993-08-02', '1999-11-01', 'female', 'Hochschule', 'Seller'], ['111125', 'OO', '1959-06-16', '2021-06-01', 'male', 'Hochschule', 'Seller'], ['111126', 'PP', '1983-01-23', '2020-04-01', 'male', 'mittlere Reife', 'Seller'], ['111127', 'QQ', '1990-02-21', '1996-12-01', 'female', 'Hauptschulabschluss', 'Seller']], columns=['Personal number', 'Employee', 'Birth date', 'Entry date', 'Gender', 'Degree', 'Activity'])\n",
        "employees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "Ob6dwtO3jND1",
        "outputId": "60f78438-2b7a-41d9-9891-209f55a94a58"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Activity T01 T02 T03 T04 T05 T06 T07  T08 T09  ... T13 T14  \\\n",
              "0              HR Manager   0   0   0   0   0   0   0    0  90  ...   0   0   \n",
              "1      Purchasing Manager   0   0   0   0   0   0   0    0   0  ...   0  10   \n",
              "2               Secretary   0   0   0   0   0   0   0    0   0  ...  50  10   \n",
              "3                 Janitor   0  80   0   0   0   0   0   10   0  ...   0   0   \n",
              "4          Cleaning staff   0   0   0   0   0   0   0  100   0  ...   0  10   \n",
              "5         IT specialist 1   0   0   0   0   0   0   0    0  20  ...   0  50   \n",
              "6              Controller   0   0   0   0   0   0   0    0   0  ...  40  30   \n",
              "7         IT specialist 2   0   0   0   0   0   0   0    0   0  ...   0  65   \n",
              "8       Marketing Manager   0   0   0   0   0   0   0    0   0  ...   0   5   \n",
              "9   Team Leader - Finance   0   0   0   0   0   0   0    0  30  ...  10  10   \n",
              "10   Sales representative   0   0   0   0   0   0   0    0   0  ...  10  10   \n",
              "11          Store manager   0  15   0   0   0   0   0    0  20  ...   5   5   \n",
              "12                 Seller   0  20   0   0   0   0   0    0  70  ...   0   5   \n",
              "\n",
              "   T15 T16 Q01 Q02 Q03 Q41 Q42     it  \n",
              "0   10   0  10  40  30  10   0   True  \n",
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              "7   30   5   5   0   0   0   0   True  \n",
              "8    0   0   5   0   0   0   0   True  \n",
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              "10  10  70   5  10  50   5  10   True  \n",
              "11  10   0  50  10  50  10   0   True  \n",
              "12   5   0   0   0  50   0  40  False  \n",
              "\n",
              "[13 rows x 23 columns]"
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Controller</td>\n",
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              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>70</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Store manager</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Seller</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13 rows × 23 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "tasks = pd.DataFrame([['HR Manager', '0', '0', '0', '0', '0', '0', '0', '0', '90', '0', '0', '0', '0', '0', '10', '0', '10', '40', '30', '10', '0', 'True'], ['Purchasing Manager', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '10', '10', '80', '30', '20', '30', '0', '0', 'True'], ['Secretary', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '50', '10', '40', '0', '40', '10', '0', '0', '0', 'True'], ['Janitor', '0', '80', '0', '0', '0', '0', '0', '10', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', 'False'], ['Cleaning staff', '0', '0', '0', '0', '0', '0', '0', '100', '0', '0', '0', '0', '0', '10', '0', '0', '0', '0', '0', '0', '0', 'False'], ['IT specialist 1', '0', '0', '0', '0', '0', '0', '0', '0', '20', '0', '0', '0', '0', '50', '30', '0', '10', '10', '0', '0', '0', 'True'], ['Controller', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '10', '0', '40', '30', '20', '0', '30', '20', '0', '0', '0', 'False'], ['IT specialist 2', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '65', '30', '5', '5', '0', '0', '0', '0', 'True'], ['Marketing Manager', '0', '0', '0', '0', '0', '0', '0', '0', '0', '80', '15', '0', '0', '5', '0', '0', '5', '0', '0', '0', '0', 'True'], ['Team Leader - Finance', '0', '0', '0', '0', '0', '0', '0', '0', '30', '0', '40', '0', '10', '10', '10', '0', '30', '20', '30', '0', '0', 'True'], ['Sales representative', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '10', '10', '10', '70', '5', '10', '50', '5', '10', 'True'], ['Store manager', '0', '15', '0', '0', '0', '0', '0', '0', '20', '0', '40', '0', '5', '5', '10', '0', '50', '10', '50', '10', '0', 'True'], ['Seller', '0', '20', '0', '0', '0', '0', '0', '0', '70', '0', '0', '0', '0', '5', '5', '0', '0', '0', '50', '0', '40', 'False']], columns=['Activity', 'T01', 'T02', 'T03', 'T04', 'T05', 'T06', 'T07', 'T08', 'T09', 'T10', 'T11', 'T12', 'T13', 'T14', 'T15', 'T16', 'Q01', 'Q02', 'Q03', 'Q41', 'Q42', 'it'])\n",
        "tasks.loc[:, \"T01\":\"Q42\"] = tasks.loc[:,\"T01\":\"Q42\"].apply(pd.to_numeric)\n",
        "tasks[[\"it\"]] = tasks[[\"it\"]].replace({'True': True, 'False': False})\n",
        "tasks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "n1NPQlhai3kH",
        "outputId": "a6a8b57c-d4ea-415c-9301-4b82cdbf67cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Personal number Employee  ho_wish  desired_days  Caring Responsibility  \\\n",
              "0           111111       AA     True             2                   True   \n",
              "1           111112       BB    False             3                   True   \n",
              "2           111113       CC     True             3                   True   \n",
              "3           111114       DD    False             0                  False   \n",
              "4           111115       EE    False             0                  False   \n",
              "5           111116       FF     True             4                   True   \n",
              "6           111117       GG     True             2                   True   \n",
              "7           111118       HH     True             5                  False   \n",
              "8           111119       II     True             2                   True   \n",
              "9           111120       JJ    False             0                  False   \n",
              "10          111121       KK    False             4                   True   \n",
              "11          111122       LL     True             1                   True   \n",
              "12          111123       MM     True             2                  False   \n",
              "13          111124       NN    False             2                  False   \n",
              "14          111125       OO    False             1                  False   \n",
              "15          111126       PP     True             0                   True   \n",
              "16          111127       QQ    False             2                   True   \n",
              "\n",
              "    Commute  openness  neuroticism  perseverance_and_grit  \n",
              "0        40         2           12                      2  \n",
              "1       120         4           10                      2  \n",
              "2        10        12           12                      2  \n",
              "3        20         5            9                      3  \n",
              "4        50         4            5                      2  \n",
              "5        30         2            5                      3  \n",
              "6        25         5            9                      3  \n",
              "7        60         5            9                      3  \n",
              "8        45         5            9                      3  \n",
              "9        70         5            9                      3  \n",
              "10       30         5            9                      3  \n",
              "11       10         5            9                      3  \n",
              "12       40         5            9                      3  \n",
              "13       20         5            9                      3  \n",
              "14       80         5            9                      3  \n",
              "15       35         7            3                      4  \n",
              "16       90         2           13                      1  "
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            "text/html": [
              "\n",
              "  <div id=\"df-7b78591c-985a-4d8a-94cf-25a4e15b87dc\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Personal number</th>\n",
              "      <th>Employee</th>\n",
              "      <th>ho_wish</th>\n",
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              "      <th>perseverance_and_grit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111111</td>\n",
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              "      <td>40</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
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              "      <th>1</th>\n",
              "      <td>111112</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
              "      <td>111115</td>\n",
              "      <td>EE</td>\n",
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              "      <td>50</td>\n",
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              "      <td>111116</td>\n",
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              "      <td>30</td>\n",
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              "      <td>5</td>\n",
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              "    </tr>\n",
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              "      <th>6</th>\n",
              "      <td>111117</td>\n",
              "      <td>GG</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>25</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>111118</td>\n",
              "      <td>HH</td>\n",
              "      <td>True</td>\n",
              "      <td>5</td>\n",
              "      <td>False</td>\n",
              "      <td>60</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
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              "      <th>8</th>\n",
              "      <td>111119</td>\n",
              "      <td>II</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>45</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>111120</td>\n",
              "      <td>JJ</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>70</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>111121</td>\n",
              "      <td>KK</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "      <td>True</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
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              "      <th>11</th>\n",
              "      <td>111122</td>\n",
              "      <td>LL</td>\n",
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              "      \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-7b78591c-985a-4d8a-94cf-25a4e15b87dc 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-7b78591c-985a-4d8a-94cf-25a4e15b87dc');\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": 15
        }
      ],
      "source": [
        "#Scales:\n",
        "#'openness': 2-14\n",
        "#'neuroticism' : 2-14\n",
        "#'perseverance_and_grit' : 1-5\n",
        "\n",
        "employee_input = pd.DataFrame([['111111', 'AA', 'True', '2', 'True', '40', '2', '12', '2'], ['111112', 'BB', 'False', '3', 'True', '120', '4', '10', '2'], ['111113', 'CC', 'True', '3', 'True', '10', '12', '12', '2'], ['111114', 'DD', 'False', '0', 'False', '20', '5', '9', '3'], ['111115', 'EE', 'False', '0', 'False', '50', '4', '5', '2'], ['111116', 'FF', 'True', '4', 'True', '30', '2', '5', '3'], ['111117', 'GG', 'True', '2', 'True', '25', '5', '9', '3'], ['111118', 'HH', 'True', '5', 'False', '60', '5', '9', '3'], ['111119', 'II', 'True', '2', 'True', '45', '5', '9', '3'], ['111120', 'JJ', 'False', '0', 'False', '70', '5', '9', '3'], ['111121', 'KK', 'False', '4', 'True', '30', '5', '9', '3'], ['111122', 'LL', 'True', '1', 'True', '10', '5', '9', '3'], ['111123', 'MM', 'True', '2', 'False', '40', '5', '9', '3'], ['111124', 'NN', 'False', '2', 'False', '20', '5', '9', '3'], ['111125', 'OO', 'False', '1', 'False', '80', '5', '9', '3'], ['111126', 'PP', 'True', '0', 'True', '35', '7', '3', '4'], ['111127', 'QQ', 'False', '2', 'True', '90', '2', '13', '1']], columns=['Personal number', 'Employee', 'ho_wish', 'desired_days', 'Caring Responsibility', 'Commute', 'openness', 'neuroticism', 'perseverance_and_grit'])\n",
        "\n",
        "employee_input[[\"desired_days\", \"Commute\", \"openness\", \"neuroticism\", \"perseverance_and_grit\"]] = employee_input[[\"desired_days\", \"Commute\", \"openness\", \"neuroticism\", \"perseverance_and_grit\"]].apply(pd.to_numeric)\n",
        "\n",
        "# make everything in these two columns to a boolean value\n",
        "employee_input[[\"ho_wish\", \"Caring Responsibility\"]] = employee_input[[\"ho_wish\", \"Caring Responsibility\"]].replace({'True':True,'False':False})\n",
        "employee_input[[\"ho_wish\", \"Caring Responsibility\"]] = employee_input[[\"ho_wish\", \"Caring Responsibility\"]].where(employee_input[[\"ho_wish\", \"Caring Responsibility\"]].applymap(type) == bool)\n",
        "\n",
        "\n",
        "employee_input\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qPmso90p0UA8"
      },
      "source": [
        "# You Can Add Own Data (Optional, no checks are included)\n",
        "\n",
        "## Employees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "ac0-jAUR0Shg",
        "outputId": "d4b8bdff-733b-47a5-f511-de62bbce2c1e",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-bb782a0cfd11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#employees: ['Personal number', 'Employee', 'Birth date', 'Entry date', 'Gender', 'Degree', 'Activity']\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpersonal_number\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Personal number \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0memployee\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Employee \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mbirth_date\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Birth date (e.g., 1965-02-02) \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    859\u001b[0m             )\n\u001b[0;32m--> 860\u001b[0;31m         return self._input_request(str(prompt),\n\u001b[0m\u001b[1;32m    861\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    902\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 904\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    905\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
          ]
        }
      ],
      "source": [
        "#@title \n",
        "#employees: ['Personal number', 'Employee', 'Birth date', 'Entry date', 'Gender', 'Degree', 'Activity']\n",
        "\n",
        "personal_number = int(input(\"Personal number \"))\n",
        "employee = input(\"Employee \")\n",
        "birth_date = input(\"Birth date (e.g., 1965-02-02) \")\n",
        "entry_date = input(\"Entry date (e.g., 2005-02-02) \")\n",
        "gender = input(\"Gender \")\n",
        "degree = input(\"Degree \")\n",
        "activity = input(\"Activity \")\n",
        "\n",
        "new_employee = pd.DataFrame([[personal_number, employee, birth_date, entry_date, gender, degree, activity]], columns=['Personal number', 'Employee', 'Birth date', 'Entry date', 'Gender', 'Degree', 'Activity'])\n",
        "\n",
        "employees = pd.concat([employees, new_employee])\n",
        "#employees"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pM0LrWMA2E5r"
      },
      "source": [
        "##  Tasks (necessary for new tasks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j39XzjEx2AzU",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title \n",
        "#tasks: ['Activity', 'T01', 'T02', 'T03', 'T04', 'T05', 'T06', 'T07', 'T08', 'T09', 'T10', 'T11', 'T12', 'T13', 'T14', 'T15', 'T16', 'Q01', 'Q02', 'Q03', 'Q41', 'Q42', 'it']\n",
        "\n",
        "activity = input(\"Activity \")\n",
        "T01 = int(input(\"T01 \"))\n",
        "T02 = int(input(\"T02 \"))\n",
        "T03 = int(input(\"T03 \"))\n",
        "T04 = int(input(\"T04 \"))\n",
        "T05 = int(input(\"T05 \"))\n",
        "T06 = int(input(\"T06 \"))\n",
        "T07 = int(input(\"T07 \"))\n",
        "T08 = int(input(\"T08 \"))\n",
        "T09 = int(input(\"T09 \"))\n",
        "T10 = int(input(\"T10 \"))\n",
        "T11 = int(input(\"T11 \"))\n",
        "T12 = int(input(\"T12 \"))\n",
        "T13 = int(input(\"T13 \"))\n",
        "T14 = int(input(\"T14 \"))\n",
        "T15 = int(input(\"T15 \"))\n",
        "T16 = int(input(\"T16 \"))\n",
        "Q01 = int(input(\"Q01 \"))\n",
        "Q02 = int(input(\"Q02 \"))\n",
        "Q03 = int(input(\"Q03 \"))\n",
        "Q41 = int(input(\"Q41 \"))\n",
        "Q42 = int(input(\"Q42 \"))\n",
        "it = input(\"it (True/False) \")\n",
        "\n",
        "new_task = pd.DataFrame([[T01, T02, T03, T04, T05, T06, T07, T08, T09, T10, T11, T12, T13, T14, T15, T16, Q01, Q02, Q03, Q41, Q42, it]], columns=['Activity', 'T01', 'T02', 'T03', 'T04', 'T05', 'T06', 'T07', 'T08', 'T09', 'T10', 'T11', 'T12', 'T13', 'T14', 'T15', 'T16', 'Q01', 'Q02', 'Q03', 'Q41', 'Q42', 'it'])\n",
        "\n",
        "tasks = pd.concat([tasks, new_task])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V8N2YXFp2Grp"
      },
      "source": [
        "## Employee Input (necessary if you added an employee)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ih92JfCP2JPW",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title \n",
        "#employee_input: ['Personal number', 'Employee', 'ho_wish', 'desired_days', 'Caring Responsibility', 'Commute', 'openness', 'neuroticism', 'perseverance_and_grit']\n",
        "\n",
        "personal_number = int(input(\"Personal number (must match) \"))\n",
        "employee = input(\"Employee \")\n",
        "ho_wish = input(\"Home office wish (True/False) \")\n",
        "desired_days = int(input(\"Desired days (1-5) \"))\n",
        "caring = input(\"Caring Responsibility (True/False) \")\n",
        "commute = int(input(\"Commute time (in min) \"))\n",
        "openness = int(input(\"Openness (2-15)) \"))\n",
        "neuroticism = int(input(\"Neuroticism (2-15)) \"))\n",
        "perseverance_and_grit = int(input(\"Openness (1-5)) \"))\n",
        "\n",
        "\n",
        "new_employee_input = pd.DataFrame([[personal_number, employee, ho_wish, desired_days, caring, commute, openness, neuroticism, perseverance_and_grit]], columns=['Personal number', 'Employee', 'ho_wish', 'desired_days', 'Caring Responsibility', 'Commute', 'openness', 'neuroticism', 'perseverance_and_grit'])\n",
        "\n",
        "employee_input = pd.concat([employee_input, new_employee_input])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iq5ZZsaojMdO"
      },
      "outputs": [],
      "source": [
        "# -------------------------\n",
        "# Employer's point of view\n",
        "# -------------------------\n",
        "\n",
        "# take sum of tasks\n",
        "# 3.2.1 The Teleworkability-Index\n",
        "ho_tasks = tasks['T09'] + tasks['T10'] + tasks['T11'] + tasks['T12'] + tasks['T13'] + tasks['T14'] + tasks['T15'] +  tasks['T16']\n",
        "\n",
        "# add new column to df\n",
        "tasks['ho_max'] = ho_tasks\n",
        "tasks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5tDfsEENj2uY"
      },
      "outputs": [],
      "source": [
        "# 3.2.2 Infrastructure.\n",
        "tasks.loc[tasks.it == False, ['ho_max']] = 0\n",
        "\n",
        "# 3.2.3 Sense of Belonging to Company.\n",
        "employees['Entry date'] = pd.to_datetime(employees['Entry date'])\n",
        "employees['Company affiliation duration'] = employees['Entry date'].apply(tenure)\n",
        "employees['ho_prob'] = employees['Company affiliation duration'].apply(ho_prob)\n",
        "\n",
        "tasks = pd.merge(tasks, employees[['Activity', 'ho_prob']], on='Activity', how='left')\n",
        "tasks.loc[tasks.ho_prob == 0, ['ho_max']] = 0\n",
        "# calculate the mean\n",
        "ho_max_total = int((int(tasks['ho_max'].sum())) / len(tasks.index))\n",
        "\n",
        "# results after first 3 steps (mean)\n",
        "print('The maximum proportion of home office at the company after the first three points is: ' + str(ho_max_total) + '%.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qVmlc81YkDZI"
      },
      "outputs": [],
      "source": [
        "# 3.2.4 Task-Media-Fit Model\n",
        "grouptasks_ho = tasks['Q01'] + tasks['Q02'] + tasks['Q42']\n",
        "grouptasks_office = tasks['Q03'] + tasks['Q41']\n",
        "\n",
        "tasks['grouptasks_ho'] = grouptasks_ho\n",
        "tasks['grouptasks_office'] = grouptasks_office\n",
        "tasks[['Activity','grouptasks_ho', 'grouptasks_office']]\n",
        "\n",
        "opt_tasks = tasks['ho_max'] - tasks['grouptasks_office']\n",
        "tasks['ho_opt'] = opt_tasks\n",
        "\n",
        "# correct if mox amount of home office was already zero:\n",
        "tasks.loc[tasks.ho_opt <= 0, ['ho_opt']] = 0\n",
        "\n",
        "# calculate the mean\n",
        "ho_opt_total = int((int(tasks['ho_opt'].sum())) / len(tasks.index))\n",
        "print('The optimal amount of home office based on the media fit model is: ' + str(ho_opt_total) + '%.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TG6gnVEbkIe5"
      },
      "outputs": [],
      "source": [
        "# -------------------------\n",
        "# Social factors\n",
        "# -------------------------\n",
        "\n",
        "# 3.3.1 Different generations\n",
        "employees['Birth date'] = pd.to_datetime(employees['Birth date'])\n",
        "employees['birth_year'] = employees['Birth date'].dt.year\n",
        "employees['ho_generation'] = employees['birth_year'].apply(ho_generation)\n",
        "\n",
        "# 3.3.2 Education.\n",
        "employees['ho_degree'] = employees['Degree'].apply(ho_degree)\n",
        "\n",
        "# 3.3.3 Commute time\n",
        "employees['ho_commute'] = employee_input['Commute'].apply(ho_commute)\n",
        "\n",
        "# 3.3.4 Caring Responsibility\n",
        "employees = employees.join(employee_input['Caring Responsibility'])\n",
        "employees['ho_responsibility'] = employees.apply(ho_gender_resp, axis=1)\n",
        "\n",
        "# 3.3.5 Personality factors\n",
        "employees = employees.join(employee_input['openness'])\n",
        "employees = employees.join(employee_input['neuroticism'])\n",
        "employees = employees.join(employee_input['perseverance_and_grit'])\n",
        "\n",
        "employees['personality_factor'] = employees.apply(ho_personality__complete, axis=1)\n",
        "\n",
        "\n",
        "# Social factors subset\n",
        "employees_subset = employees[['ho_generation', 'ho_degree', 'ho_commute', 'ho_responsibility', 'personality_factor']]\n",
        "\n",
        "\n",
        "\n",
        "# calculate the mean\n",
        "average_value_ho = np.average(employees_subset, axis=1)\n",
        "employees_subset['ho_social'] = average_value_ho\n",
        "ho_social_total = round((int(employees_subset['ho_social'].sum()) / len(employees.index)), 2)\n",
        "print('The optimal proportion of home office based on the social factors is: ' + str(ho_social_total) + '%.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "db3bEwvokMap"
      },
      "outputs": [],
      "source": [
        "# %%\n",
        "# -------------------------\n",
        "# Employee Requests\n",
        "# -------------------------\n",
        "\n",
        "# 3.4 Employee Requests\n",
        "employee_input['ho_prefer'] = employee_input.apply(ho_prefer, axis=1)\n",
        "\n",
        "# calculate the mean\n",
        "ho_prefer_total = round((int(employee_input['ho_prefer'].sum()) / len(employee_input.index)), 2)\n",
        "print('The mean of the employees wishes for home office is: ' + str(ho_prefer_total) + '%.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SHeq1zXKkTZJ"
      },
      "outputs": [],
      "source": [
        "# %%\n",
        "# -------------------------\n",
        "# Difference Between the Calculated proportion of Home Office and Preference\n",
        "# -------------------------\n",
        "\n",
        "employees = employees.join(employee_input['ho_prefer'])\n",
        "employees = employees.join(employees_subset['ho_social'])\n",
        "employees = pd.merge(employees, tasks[['Activity', 'ho_max']], on='Activity', how='left')\n",
        "employees = pd.merge(employees, tasks[['Activity', 'ho_opt']], on='Activity', how='left')\n",
        "\n",
        "ho_shares = employees[['Personal number', 'Activity', 'Employee', 'ho_social', 'ho_opt', 'ho_max', 'ho_prefer']]\n",
        "ho_shares = ho_shares.drop_duplicates()\n",
        "employees = employees.drop_duplicates()\n",
        "\n",
        "ho_shares = ho_shares.apply(deviation, axis=1)\n",
        "ho_shares"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t_BI0S00K3ZD"
      },
      "source": [
        "# Exploratory Data Analysis\n",
        "\n",
        "This section allows you to evaluate the data in a visual manner either via [Pygwalker](https://github.com/Kanaries/pygwalker) or [Dtale](https://github.com/man-group/dtale)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bDRtFJJHfhf_"
      },
      "outputs": [],
      "source": [
        "#pyg.walk(ho_shares, vegaTheme = 'vega')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cR0scIweD6Zn"
      },
      "source": [
        "# Example: Correlation\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0-0VuHbsmu73"
      },
      "outputs": [],
      "source": [
        "dtale_app.USE_COLAB = True\n",
        "\n",
        "dtale.show(ho_shares)\n",
        "\n",
        "# explore for yourself, e.g. correlation; see before"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gw321ZQV3Z02"
      },
      "source": [
        "# Exploring via [Seaborn](https://seaborn.pydata.org/) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NDvhW2n52cB_"
      },
      "outputs": [],
      "source": [
        "sns.catplot(data=ho_shares, kind=\"violin\", x=\"Activity\", y=\"ho_prefer\", hue=\"ho_max\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gSHLcz923ANL"
      },
      "outputs": [],
      "source": [
        "sns.jointplot(data=ho_shares, x=\"ho_social\", y=\"ho_prefer\", hue=\"ho_max\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mjRntOIS3O4D"
      },
      "outputs": [],
      "source": [
        "sns.pairplot(data=ho_shares, hue=\"ho_social\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}