{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# supp-2: 심슨의 역설 (2)\n",
        "\n",
        "최규빈  \n",
        "2023-10-15\n",
        "\n",
        "<a href=\"https://colab.research.google.com/github/guebin/DV2023/blob/main/posts/06wk-supp.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"text-align: left\"></a>\n",
        "\n",
        "# 1. 강의영상\n",
        "\n",
        "<https://youtu.be/playlist?list=PLQqh36zP38-xAatJ9BdzK2ASQnPRmvO8J&si=-64H-NnvEw_bMBTd>\n",
        "\n",
        "# 2. Imports"
      ],
      "id": "73dacc89-8b41-48b7-a20d-01c1bbdeb1a5"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from plotnine import *"
      ],
      "id": "f4734f21-7690-4703-a1a9-8639e2fc3555"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 3. 심슨의 역설 – 이어서\n",
        "\n",
        "`-` 버클리대학교의 입학데이터\n",
        "\n",
        "-   <https://github.com/guebin/DV2023/blob/main/posts/ds.pdf>\n",
        "\n",
        "`-` 주장: 버클리대학에 gender bias가 존재한다.\n",
        "\n",
        "-   1973년 가을학기의 입학통계에 따르면 지원하는 남성이 여성보다 훨씬\n",
        "    많이 합격했고, 그 차이가 너무 커서 우연의 일치라 보기 어렵다."
      ],
      "id": "0a7c7b27-bf40-44fd-9b0c-69a04d99748a"
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "df=pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1])\\\n",
        ".stack().stack().reset_index()\\\n",
        ".rename({'level_0':'department','level_1':'result','level_2':'gender',0:'count'},axis=1)\n",
        "df"
      ],
      "id": "a57f7905-3265-4ab4-b849-e8819a5740fb"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## A. 시각화1: 전체합격률 시각화 – pandas 초보\n",
        "\n",
        "## B. 시각화1: 전체합격률 시각화 – pandas 고수"
      ],
      "id": "346a93f0-735a-44cf-8aba-4af057cf4e57"
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_1831005/1840989269.py:1: FutureWarning: The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead."
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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1dXWWLl260v8w+/fvn4svvniluxEDAAAANMXSpUtzxhln5MYbb1zp+cmTJ2fy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\nSy18ww035IADDsiLL76YW26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          }
        }
      ],
      "source": [
        "tidydata = df.pivot_table(index='gender', columns='result', values='count', aggfunc=sum)\\\n",
        ".assign(rate = lambda _df: _df['pass'] / (_df['fail'] + _df['pass']))\\\n",
        ".reset_index()\n",
        "\n",
        "fig = ggplot(tidydata) \n",
        "col = geom_col(aes(x='gender',y='rate',fill='gender'))\n",
        "fig + col "
      ],
      "id": "1467356c-8c1a-4495-94c4-adba5834e263"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## C. 시각화2: 학과별합격률 시각화 – pandas 고수"
      ],
      "id": "5212fb99-aa91-4488-99a8-3a8dd08e9eb1"
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_1831005/1491763288.py:1: FutureWarning: The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead."
          ]
        }
      ],
      "source": [
        "df.pivot_table(index=['gender','department'], columns='result',values='count',aggfunc=sum)\\\n",
        ".assign(rate = lambda _df: _df['pass']/(_df['fail']+_df['pass']))\\\n",
        ".reset_index()"
      ],
      "id": "249ba67c-22b3-4ba3-a74f-ee95d1944937"
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_1831005/2462720722.py:1: FutureWarning: The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead."
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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AACgwASAAAAAAFJgAEAAAAAAKTAAIAAAAAAUmAAQAAACAAhMAAgAAAECBCQAB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\n7//+75x99tnNKwRXVFRk6623TlVVVZLk1VdfTX19ferr61t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          }
        }
      ],
      "source": [
        "tidydata = df.pivot_table(index=['gender','department'], columns='result',values='count',aggfunc=sum)\\\n",
        ".assign(rate = lambda _df: _df['pass']/(_df['fail']+_df['pass']))\\\n",
        ".reset_index()\n",
        "\n",
        "fig = ggplot(tidydata) \n",
        "facet = facet_wrap('department')\n",
        "col = geom_col(aes(x='gender',y='rate',fill='gender'))\n",
        "fig + facet + col "
      ],
      "id": "59dad38e-8ab9-4b11-ae77-a1ffb7392641"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## D. 해석\n",
        "\n",
        "`-` 시각화1: 남자의 합격률이 더 높다. $\\to$ 성차별이 있어보인다(?)\n",
        "\n",
        "`-` 시각화2: 학과별로 살펴보니 오히려 A,B,F,D의 경우 여성의 합격률이\n",
        "높다.\n",
        "\n",
        "`-` 교재에서 설명한 이유: 여성이 합격률이 낮은 학과에만 많이 지원하였기\n",
        "때문"
      ],
      "id": "0e3e1192-bf38-45d9-b485-b058c7c0b0db"
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "df.pivot_table(index='department', columns='gender', values='count',aggfunc='sum')\\\n",
        ".stack().reset_index().rename({0:'count'},axis=1)"
      ],
      "id": "650b8766-d85b-4145-a630-274fa6a4336b"
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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AQIEJgAAAAABQYAIgAAAAABSYAAgAAAAABSYAAgAAAECBCYAAAAAAUGACIAAA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\nzenTp08Xzoy3i7ccAL/85S/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517velWOOOSYf/ehHM2DAgI6eylpeeOGF3HzzzRk8ePBG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\njY2N2XPPPTN58uRUVKx7vttbeUDHtttum1deeSV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          }
        }
      ],
      "source": [
        "tidydata = df.pivot_table(index='department', columns='gender', values='count',aggfunc='sum')\\\n",
        ".stack().reset_index().rename({0:'count'},axis=1)\n",
        "\n",
        " \n",
        "fig = ggplot(tidydata) \n",
        "col = geom_col(aes(x='department',y='count',fill='gender'),position='dodge')\n",
        "fig+col"
      ],
      "id": "a78a4350-0757-43a6-b02c-5a2ef7abf8fc"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 4. HW"
      ],
      "id": "42d67659-86ba-4263-8469-2f8b26a76d6a"
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson2.csv\")\n",
        "df"
      ],
      "id": "b9933517-e39b-44e9-a278-8ee45699ab2e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`-` 시각화1: 성별합격률 시각화"
      ],
      "id": "f0b24df9-009a-4b38-9eef-fb4462d75a10"
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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/uMS1PmpJ73GttdZKu3btlmu9VVHxAfDD/4B9+B+8j1qwbebMmcu8dps2bbLP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\nv/nNjBgx4mOv+2FNdgrwh22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          }
        }
      ],
      "source": [
        "# 시각화예시"
      ],
      "id": "b69f363c-a103-4399-a191-6cae475e0055"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`-` 시각화2: (학과,성별) 합격률 시각화"
      ],
      "id": "c089c3c8-6a3d-43e7-9d7f-c885e7867996"
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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          }
        }
      ],
      "source": [
        "# 시각화예시"
      ],
      "id": "f713ac9c-3a0d-4ca6-89dc-93bdedfabbeb"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "codemirror_mode": {
        "name": "ipython",
        "version": "3"
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.13"
    }
  }
}