{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 13wk-1: Plotly – `pio`, `go` 를 활용한 시각화\n",
        "\n",
        "최규빈  \n",
        "2023-11-27\n",
        "\n",
        "<a href=\"https://colab.research.google.com/github/guebin/DV2023/blob/main/posts/13wk-1.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-zQL5qH6iFWWd7EDmVE9eTm&si=CcNvj-WpJBID8tsM>\n",
        "\n",
        "# 2. Imports"
      ],
      "id": "34b73bdc-cc2a-4017-8f6b-42ee54645474"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "#---#\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import plotly.io as pio"
      ],
      "id": "b9465fad-cc6b-46ad-813b-c5ac7199696c"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "pd.options.plotting.backend = \"plotly\"\n",
        "pio.templates.default = \"plotly_white\""
      ],
      "id": "75b8b10c-43cf-4f6c-b3d5-0d4054d76bee"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 3. Intro\n",
        "\n",
        "## A. 궁금해\n",
        "\n",
        "12wk-2 강의노트중.."
      ],
      "id": "6ad6cde4-b2c1-4a94-9f20-b441ec32b11e"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "df_sample = pd.DataFrame(\n",
        "    {'path':['A','A','B','B','B'],\n",
        "     'lon':[-73.986420,-73.995300,-73.975922,-73.988922,-73.962654],\n",
        "     'lat':[40.756569,40.740059,40.754192,40.762859,40.772449]}\n",
        ")\n",
        "fig = px.line_mapbox(\n",
        "    data_frame=df_sample,\n",
        "    lat = 'lat',\n",
        "    lon = 'lon',\n",
        "    color = 'path',\n",
        "    line_group = 'path',\n",
        "    #---#\n",
        "    mapbox_style = 'carto-positron',\n",
        "    zoom=12,\n",
        "    width = 750,\n",
        "    height = 600    \n",
        ")\n",
        "scatter_data = px.scatter_mapbox(\n",
        "    data_frame=df_sample,\n",
        "    lat = 'lat',\n",
        "    lon = 'lon',\n",
        "    color = 'path',\n",
        "    #---#\n",
        "    mapbox_style = 'carto-positron',\n",
        "    zoom=12,\n",
        "    width = 750,\n",
        "    height = 600    \n",
        ").data \n",
        "fig.add_trace(scatter_data[0])\n",
        "fig.add_trace(scatter_data[1])\n",
        "fig.show(config={'scrollZoom':False})"
      ],
      "id": "fdf90cc9-8ea5-4126-b3a3-4c347434fde2"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "도데체 저런코드는 어떻게 알아내는 걸까?\n",
        "\n",
        "## B. 심슨의 역설 데이터\n",
        "\n",
        "`-` 아래의 자료를 관찰하자."
      ],
      "id": "9d7740c3-c089-443c-b5bc-9a90183cd29d"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1]).reset_index().melt(id_vars='index').set_axis(['department','gender','result','count'],axis=1)\n",
        "df.head()"
      ],
      "id": "224fd99d-bbb3-481f-92fe-450fc77a7bfe"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## C. `plotly`의 시각화구조\n",
        "\n",
        "`-` 아래와 같은 방법이 가능하다.\n",
        "\n",
        "-   pandas backend\n",
        "-   `px`, 즉 `plotly.express`를 이용한 시각화\n",
        "-   `go`, 즉 `plotly.graph_objects`를 이용한 시각화\n",
        "-   `pio`, 즉 `plotly.io`를 이용한 시각화\n",
        "\n",
        "`-` 예시1: pandas backend"
      ],
      "id": "3e11afbf-5836-4b69-b026-b3aecf3d0786"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "df.pivot_table(index='gender',columns='result',values='count',aggfunc='sum')\\\n",
        ".assign(rate = lambda df:  df['pass']/(df['fail']+df['pass']))\\\n",
        ".assign(rate = lambda df:  np.round(df['rate'],2))\\\n",
        ".loc[:,'rate'].reset_index()\\\n",
        ".plot.bar(\n",
        "    x='gender',\n",
        "    y='rate',\n",
        "    color='gender',\n",
        "    #---#\n",
        "    title = '버클리대학교 성별합격률',\n",
        "    width = 600\n",
        ")"
      ],
      "id": "8027f2b1-5e58-4cdb-a7a8-56462190855a"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`-` 예시2: `px.bar`를 이용한 plot"
      ],
      "id": "c00d765a-e555-4bab-9d6d-cf85c5e9fe70"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "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",
        ".assign(rate = lambda df:  np.round(df['rate'],2))\\\n",
        ".loc[:,'rate'].reset_index()\n",
        "#---#\n",
        "px.bar(\n",
        "    tidydata,\n",
        "    x='gender',\n",
        "    y='rate',\n",
        "    color='gender',\n",
        "    #---#\n",
        "    title = '버클리대학교 성별합격률',\n",
        "    width = 600\n",
        ")"
      ],
      "id": "c1f68132-0809-4dc3-90e8-7481c351ca78"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`-` 예시3: `px.bar`를 이용한 플랏 (pandas Series를 입력) – 결과가 조금\n",
        "다름"
      ],
      "id": "e5480bdf-2e6d-42f3-a838-97a32d89a18b"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "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",
        ".assign(rate = lambda df:  np.round(df['rate'],2))\\\n",
        ".loc[:,'rate'].reset_index()\n",
        "#---#\n",
        "px.bar(\n",
        "    x=tidydata.gender,\n",
        "    y=tidydata.rate,\n",
        "    color=tidydata.gender,\n",
        "    #---#\n",
        "    title = '버클리대학교 성별합격률',\n",
        "    width = 600\n",
        ")"
      ],
      "id": "065eeb7d-99e3-4c8c-b228-06bc5b661467"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "-   x축,y축,legend의 제목이 살짝 달라지긴 했음..\n",
        "\n",
        "`-` 예시4: `px.bar`를 이용한 플랏 (list를 입력) – 결과가 조금 다름"
      ],
      "id": "c2d43c0a-30bc-4e00-95eb-13686ca9a9fc"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "px.bar(\n",
        "    x=['female', 'male'],\n",
        "    y=[0.42, 0.52],\n",
        "    color=['female', 'male'],\n",
        "    #---#\n",
        "    title = '버클리대학교 성별합격률',\n",
        "    width = 600\n",
        ")"
      ],
      "id": "3cce185f-144a-46bd-acdc-7873c344865e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`# 예시5`: `go`를 이용한 시각화 – 색깔시각화가 불가능\n",
        "\n",
        "`-` ggplot() + geom_col() 의 느낌으로!"
      ],
      "id": "d9547907-3711-4f58-8e55-be5d90e75e02"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure()\n",
        "bar = go.Bar(\n",
        "    x=['female', 'male'],\n",
        "    y=[0.42, 0.52]\n",
        ")\n",
        "layout = {'title':'버클리대학교의 남녀합격률','width':600}\n",
        "fig.add_trace(bar).update_layout(layout)"
      ],
      "id": "f4297dff-3dc4-4180-98e2-df6d8f698b41"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "-   색을 어떻게 구분하냐?\n",
        "\n",
        "`#`\n",
        "\n",
        "`# 예시6`: `go`를 이용한 시각화 – matplotlib의 겹쳐그리기 감성으로\n",
        "색깔시각화 ($\\star$)\n",
        "\n",
        "`(예비학습)` – 이런느낌이 있었지"
      ],
      "id": "094ceb81-084a-4122-b3da-5f9bcf5ab95a"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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121idyK2p6IiIiJSVH6fB2lcBG/SYDPU6WZ3I7anoiIiIlIXdn8NXfzV/7vwuNHrI2jzl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\nqeiIiIiI21LREREREbf1/wGubkhSzLNRNAAAAABJRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "plt.plot([1,2,3],[3,4,1],label='A')\n",
        "plt.plot([1,2,3],[4,2,5],label='B')\n",
        "plt.legend()"
      ],
      "id": "314efa57-21dd-4b4f-a1cf-a3d6661c92a9"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "이걸 응용하면"
      ],
      "id": "21c3d78d-c9bd-4deb-a9ea-d4f71491b8c9"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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5Y9OmTRozZky0x8AAEtEvDARO5bXXXtPOnTtVXV3d7W++BgDgXOMdHfSZQ4cO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\n"
          }
        }
      ],
      "source": [
        "plt.bar(['female'],[0.42],label='female')\n",
        "plt.bar(['male'],[0.52],label='male')\n",
        "plt.legend()"
      ],
      "id": "28f2c1fa-6085-4684-84f4-115125552879"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "예비학습에서 힌트를 얻으면!"
      ],
      "id": "e1db23fb-2634-4412-a81e-521386ba2e09"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure()\n",
        "bar_female = go.Bar(\n",
        "    x=['female'],\n",
        "    y=[0.42]\n",
        ")\n",
        "bar_male = go.Bar(\n",
        "    x=['male'],\n",
        "    y=[0.52]\n",
        ")\n",
        "layout = {'title':'버클리대학교의 남녀합격률','width':600}\n",
        "fig.add_trace(bar_female).add_trace(bar_male)\\\n",
        ".update_layout(layout)"
      ],
      "id": "42e45259-0ec3-4f98-97bc-1dd6c7f541cc"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`# 예시7`: `go`를 이용한 시각화 – 색상의 변경\n",
        "\n",
        "-   여자는 빨강, 남자는 파랑으로?"
      ],
      "id": "45611ed1-b264-4900-9550-2dd9e13bea42"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure()\n",
        "bar_female = go.Bar(\n",
        "    x=['female'],\n",
        "    y=[0.42],\n",
        "    marker= {'color':'red'}\n",
        ")\n",
        "bar_male = go.Bar(\n",
        "    x=['male'],\n",
        "    y=[0.52],\n",
        "    marker= {'color':'blue'}    \n",
        ")\n",
        "layout = {'title':'버클리대학교의 남녀합격률','width':600}\n",
        "fig.add_trace(bar_female).add_trace(bar_male)\\\n",
        ".update_layout(layout)"
      ],
      "id": "2b977537-d785-4964-8f74-b92d5fcf989d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예시8`: `go`를 이용한 시각화 – 색상재설정 + x축, y축, legend의\n",
        "title의 설정 + hover 설정\n",
        "\n",
        "-   색상설정: `#EF553B`,`#636efa`\n",
        "-   hovertemplate: `'gender=%{x}<br>rate=%{text}<extra></extra>'`"
      ],
      "id": "6b39d617-83f1-4c65-b284-02d06ae54095"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure()\n",
        "bar_female = go.Bar(\n",
        "    x=['female'],\n",
        "    y=[0.42],\n",
        "    marker= {'color':'#EF553B'},\n",
        "    text =[0.42],\n",
        "    hovertemplate='gender=%{x}<br>rate=%{text}<extra></extra>',\n",
        "    name='female'\n",
        ")\n",
        "bar_male = go.Bar(\n",
        "    x=['male'],\n",
        "    y=[0.52],\n",
        "    marker= {'color':'#636efa'},\n",
        "    text= [0.52],\n",
        "    hovertemplate='gender=%{x}<br>rate=%{text}<extra></extra>',\n",
        "    name='male'\n",
        ")\n",
        "layout = {\n",
        "    'title':'버클리대학교의 남녀합격률',\n",
        "    'width':600,\n",
        "    'xaxis':{'title':'gender'},\n",
        "    'yaxis':{'title':'rate'}, \n",
        "    'legend':{'title':'gender'},     \n",
        "}\n",
        "fig.add_trace(bar_female).add_trace(bar_male)\\\n",
        ".update_layout(layout)"
      ],
      "id": "7df7cecd-c560-4b06-aa82-e6457b29819e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "***궁금: `#EF553B` 이런거 어떻게 알았어?…***"
      ],
      "id": "0920a50e-1d66-41f0-a3ac-160bd2a07441"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "_fig = df.pivot_table(index='gender',columns='result',values='count',aggfunc='sum')\\\n",
        ".assign(rate = lambda df:  df['pass']/(df['fail']+df['pass']))\\\n",
        ".assign(rate = lambda df:  np.round(df['rate'],2))\\\n",
        ".loc[:,'rate'].reset_index()\\\n",
        ".plot.bar(\n",
        "    x='gender',\n",
        "    y='rate',\n",
        "    color='gender',\n",
        "    #---#\n",
        "    title = '버클리대학교 성별합격률',\n",
        "    width = 600\n",
        ")"
      ],
      "id": "98a9565f-b970-4409-b134-ae7600de9c1b"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "_fig.data"
      ],
      "id": "5e180bf4-78a0-48d7-a816-009a99b1ef80"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "_fig.layout"
      ],
      "id": "79614f9e-80e3-4ade-8dc2-273154588afc"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## D. `px` vs `go`\n",
        "\n",
        "`-` `go`는 핸드메이드 제품을 `px`는 양산품을 만든다고 이해하면 편리하다.\n",
        "\n",
        "-   `go`의 특징: 유저의 자유도가 매우 높음 (내가 직접 하는 느낌). 이는\n",
        "    그림의 크기, 색상 등을 선호에 맞게 조정하기 유리. 생산성이 낮음.\n",
        "-   `px`의 특징: 유저의 자유도가 낮음 (알아서 해주는 느낌). 원하는\n",
        "    그림을 빠르게 생산할 수 있음. 다만 내가 원하는 디자인이 나오지 않을\n",
        "    수 있음.\n",
        "\n",
        "`-` 뭘 써야 할까?\n",
        "\n",
        "-   `px`를 쓰는게 좋다.\n",
        "-   그런데 `go`를 이용하여 그림이 그려지는 원리를 이해하면 이후에 `px`를\n",
        "    이용한 그림을 수정하기 용이하다.\n",
        "-   전략: `px`로 그림을 그린다. + `go`로 수정한다.\n",
        "\n",
        "# 4. `pio`를 이용한 시각화\n",
        "\n",
        "## A. 함수의 입력 (예비학습)\n",
        "\n",
        "`예제1` – 두 벡터 x,y가 주어졌을때 `R`에서 cbind와 같은 역할을 하는\n",
        "함수를 구현하라.\n",
        "\n",
        "![](attachment:13wk-1_files/figure-ipynb/24ea9555-553e-4f7f-a586-1f02f07f15da-1-4a202b51-fe9c-4c61-bcc6-2442f586450d.png)"
      ],
      "attachments": {
        "13wk-1_files/figure-ipynb/24ea9555-553e-4f7f-a586-1f02f07f15da-1-4a202b51-fe9c-4c61-bcc6-2442f586450d.png": {
          "image/png": 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80uZMv9x9R0CW2EHzlZ3Md0lHLjdrFZbF9uSI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\n6qL3DaaH/t1523XXpu590vIlGl572wXnpe783vOyjAACCCC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rzUZKeTFw4MD4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\nAQIECBAgQKDWBf4f6NiG7VRB6bsAAAAASUVORK5CYII=\n"
        }
      },
      "id": "1c2d63ab-f4a7-4c10-8f5a-57d19f64ad66"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "def cbind(x,y):\n",
        "    rslt = np.stack([x,y],axis=1)\n",
        "    return rslt "
      ],
      "id": "5a61c474-2423-4bd3-86fe-5c575023b3c3"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "cbind([2,3,4],[5,4,2])"
      ],
      "id": "5fc393cc-0beb-4771-890c-f9b2e192da17"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`예제2` – 세개이상의 벡터가 온다면?\n",
        "\n",
        "`-` args를 이용하여 이후 입력을 받음"
      ],
      "id": "0058bf56-4afa-420a-aae6-aada0c8806ae"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "def _cbind(x,y,*args):\n",
        "    print(args)\n",
        "    rslt = np.stack([x,y],axis=1)\n",
        "    return rslt "
      ],
      "id": "4af64988-610b-4875-bdac-c275f7d25c07"
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "([3, 3, 3], [4, 4, 4])"
          ]
        }
      ],
      "source": [
        "_cbind([1,1,1],[2,2,2],[3,3,3],[4,4,4])"
      ],
      "id": "c7318e66-db09-44d3-bb44-c2696d941a61"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "-   args는 함수내부에서 “튜플”로 취급된다!!\n",
        "\n",
        "`-` args를 이용한 수정"
      ],
      "id": "c517cdb8-8835-41eb-a7c8-c20890ee50a3"
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "def cbind(x,y,*args):\n",
        "    rslt = np.stack([x,y]+list(args),axis=1)\n",
        "    return rslt "
      ],
      "id": "170d0c0a-8e22-4fdf-a639-9dd1e6f8d678"
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "cbind([1,1,1],[2,2,2],[3,3,3],[4,4,4])"
      ],
      "id": "767b7476-24e6-4d1a-93f2-27fb7972bdc1"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제3` – 기본적으로는 cbind의 동작을 하지만 경우에 따라서 rbind처럼\n",
        "동작하길 원한다면?\n",
        "\n",
        "`-` `axis`라는 변수를 따로 생성하여 입력으로 처리, 기본값은 1"
      ],
      "id": "ae3481c2-29f8-46e9-94ba-29d2959dbf30"
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "def bind(x,y,*args,axis=1):\n",
        "    rslt = np.stack([x,y]+list(args),axis=axis)\n",
        "    return rslt "
      ],
      "id": "ada66e66-2c12-45a0-b383-8e8537883faf"
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "bind([1,1,1],[2,2,2],[3,3,3],axis=1)"
      ],
      "id": "4f1f9993-7b8d-4c62-a179-96041be5197e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제4` – 여러가지 추가옵션을 사용하여 print를 통제하고 싶다면?"
      ],
      "id": "980203e9-6c9c-464b-8b77-9412cafaad58"
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "def _bind(x,y,*args,axis=1,**kwargs):\n",
        "    print(kwargs)\n",
        "    rslt = np.stack([x,y]+list(args),axis=axis)\n",
        "    return rslt "
      ],
      "id": "856d7aeb-bd79-4313-9d41-09409219e1f2"
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{}"
          ]
        }
      ],
      "source": [
        "_bind([1,1,1],[2,2,2],[3,3,3],axis=1)"
      ],
      "id": "795750d2-6c89-49fd-9132-a3a2a28c4c9a"
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'vb1': True, 'vb2': True, 'vb3': False, 'vb4': False}"
          ]
        }
      ],
      "source": [
        "_bind([1,1,1],[2,2,2],[3,3,3],axis=1,vb1=True,vb2=True,vb3=False,vb4=False)"
      ],
      "id": "d6409220-7ce4-4c0b-931f-79f684f10bde"
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "def bind(x,y, *args, axis=1, **kwargs):\n",
        "    if ('vb1' in kwargs) and (kwargs['vb1'] == True):\n",
        "        print(f'위치인자: {x,y}')\n",
        "    if ('vb2' in kwargs) and (kwargs['vb2'] == True): \n",
        "        print(f'가변위치인자: {args}')\n",
        "    if ('vb3' in kwargs) and (kwargs['vb3'] == True): \n",
        "        print(f'키워드인자: {axis}')\n",
        "    if ('vb4' in kwargs) and (kwargs['vb4'] == True): \n",
        "        print(f'가변키워드인자: {kwargs}')   \n",
        "    rslt = np.stack([x,y]+list(args),axis=axis)\n",
        "    return rslt "
      ],
      "id": "2de50d78-2dfd-4847-981c-1ca3a446c27c"
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "위치인자: ([1, 1, 1], [2, 2, 2])\n",
            "가변위치인자: ([3, 3, 3],)\n",
            "키워드인자: 0\n",
            "가변키워드인자: {'vb1': True, 'vb2': True, 'vb3': True, 'vb4': True}"
          ]
        }
      ],
      "source": [
        "bind(\n",
        "    [1,1,1],[2,2,2],\n",
        "    [3,3,3],\n",
        "    axis=0,\n",
        "    vb1=True,vb2=True,vb3=True,vb4=True\n",
        ") "
      ],
      "id": "99f9f069-463c-47dc-af60-03e4d3bb3678"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제5` – 위치인자를 키워드인자보다 뒤에 넣을 경우?"
      ],
      "id": "c6aa858a-43f6-4a4c-b763-a8877f82a03c"
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "bind(axis=0,[1,2,3],[2,3,4])"
      ],
      "id": "8ddbe374-1ad8-4421-a3ea-34e19aaeb90f"
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "bind([1,2,3],[2,3,4],axis=0)"
      ],
      "id": "739f6584-fe16-4837-8551-ba596de22a9d"
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "bind([1,2,3],[2,3,4],axis=0,[3,4,5])"
      ],
      "id": "141344e7-122d-4c73-b23a-09910acf7742"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제6` – 가변키워드인자가 존재할 때, 키워드인자의 키를 잘못 입력할\n",
        "경우?"
      ],
      "id": "51bcd54d-3ab4-49b7-9c30-637f37aa7666"
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "위치인자: ([1, 2, 3], [2, 3, 4])\n",
            "가변위치인자: ()\n",
            "키워드인자: 1\n",
            "가변키워드인자: {'ax': 0, 'vb1': True, 'vb2': True, 'vb3': True, 'vb4': True}"
          ]
        }
      ],
      "source": [
        "bind([1,2,3],[2,3,4],ax=0,vb1=True,vb2=True,vb3=True,vb4=True)"
      ],
      "id": "43507636-ce26-4fc9-8ef3-33a4057e8ac2"
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "bind([1,2,3],[2,3,4], verbose = True)"
      ],
      "id": "4d6ee15c-13f9-468d-ad65-badf26cd445c"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "-   아무일없음"
      ],
      "id": "cf6a55aa-7bde-4f60-984a-8fc2139336cc"
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "bind([1,2,3],[2,3,4],axis=3)"
      ],
      "id": "bf47ebe8-c0eb-403a-8123-fcc1f68182af"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "-   이건 문제가 있음\n",
        "\n",
        "`-` 요약\n",
        "\n",
        "-   함수의 입력은 꽤 복잡한 방식으로 동작한다.\n",
        "-   위치인자의 위치를 잘못 넣으면 동작하지 않는다.\n",
        "-   가변키워드 인자가 존재할 때, 키워드인자의 키를 다른이름으로 넣으면\n",
        "    에러가 발생하지 않는다. (그냥 무시)\n",
        "\n",
        "`#`\n",
        "\n",
        "`# 예제7` – 은근히 짜증났던 `plt.plot()`"
      ],
      "id": "cfbf70ac-fe82-4f80-b655-20990763892a"
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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O7OWXzTBkl8usO9a3r+1EIhXr88+hfXszO/WuXZoiwg9poVYRKbt5807NxzJr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\nrkJggg==\n"
          }
        }
      ],
      "source": [
        "plt.plot([1,2,3,4],[2,3,4,2],'r--')"
      ],
      "id": "46a64e91-8997-493c-af18-640fc18fb943"
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "plt.plot([1,2,3,4],[2,3,4,2],color='lime','--')"
      ],
      "id": "fc34d458-c491-4889-aac3-cfcc3196e072"
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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9eueNSUtLY+fOnRQVFZGfn8+SJUv44IMP6N+/v+trIyIVysJiLnOxYwfAhk1/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\nICc+a/NSAAAAAElFTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "plt.plot([1,2,3,4],[2,3,4,2],'--',color='lime')"
      ],
      "id": "ed66ff6a-bbac-4cc5-83e3-25e985e52051"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "## B. dictionary + `pio.show()`\n",
        "\n",
        "`# 예제1` – dictionary + `pio.show()`"
      ],
      "id": "69da20d9-0ba0-4736-8d90-c5be4f760bdf"
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = dict()\n",
        "fig['data'] = [\n",
        "    {'type':'bar','x':['female'],'y':[0.42]},\n",
        "    {'type':'bar','x':['male'],'y':[0.52]},\n",
        "]\n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "pio.show(fig) # pio.show에 필요한 입력들을 fig라는 이름의 딕셔너리로 전달하는 느낌"
      ],
      "id": "1bb6d9f1-6fcf-42e4-9e36-871ecc0e8d79"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "기묘하다.. 마치 `pio.show()`에 필요한 kwargs를 fig라는 이름의 dict로\n",
        "전달하는 느낌임!!\n",
        "\n",
        "**요약: fig는 dictionary와 본질이 비슷하고, 이는 pio.show()에 전달할\n",
        "kwargs를 모아놓은 집합이다.**\n",
        "\n",
        "`#`\n",
        "\n",
        "`# 예제2` – `female`의 rate를 0.62로 수정\n",
        "\n",
        "`-` 아래의 그림을 그렸음."
      ],
      "id": "6d68c9e3-8316-47f8-96a3-adfb8078923c"
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = dict()\n",
        "fig['data'] = [\n",
        "    {'type':'bar','x':['female'],'y':[0.42]},\n",
        "    {'type':'bar','x':['male'],'y':[0.52]},\n",
        "]\n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "pio.show(fig) # pio.show에 필요한 입력들을 fig라는 이름의 딕셔너리로 전달하는 느낌"
      ],
      "id": "37781080-7e99-422f-82b2-6cd70dd2b570"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`-` female의 rate을 0.42에서 0.62로 바꾸고 싶음."
      ],
      "id": "f76fb95b-2eb3-4af0-b52a-bfb89e39869c"
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig['data'][0]['y'] = [0.62]\n",
        "fig"
      ],
      "id": "ef47b52a-3c20-49c4-8253-6f9397677ea5"
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "pio.show(fig)"
      ],
      "id": "ca3bea0f-0943-403c-82c9-5a66e2d1b85d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제3` – `fig`에 정리된 arg들이 전부는 아님\n",
        "\n",
        "`-` 아래의 그림을 다시 관찰하자."
      ],
      "id": "c10f24d5-09a5-4216-a234-2764b4755fd9"
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = dict()\n",
        "fig['data'] = [\n",
        "    {'type':'bar','x':['female'],'y':[0.42]},\n",
        "    {'type':'bar','x':['male'],'y':[0.52]},\n",
        "]\n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "pio.show(fig) # pio.show에 필요한 입력들을 fig라는 이름의 딕셔너리로 전달하는 느낌"
      ],
      "id": "f19c5839-25e0-46c3-af4c-411c9358d2ce"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`-` 아래의 코드는 위와 같은 결과를 준다."
      ],
      "id": "6413a86b-ecd9-4bd0-b737-4fd8bc069551"
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = dict()\n",
        "fig['data'] = [\n",
        "    {'type':'bar','x':['female'],'y':[0.42],'marker':{'color':'#636efa'}},\n",
        "    {'type':'bar','x':['male'],'y':[0.52]},\n",
        "]\n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "pio.show(fig) # pio.show에 필요한 입력들을 fig라는 이름의 딕셔너리로 전달하는 느낌"
      ],
      "id": "eca9ad20-c3ac-4c47-b057-66df753480d8"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`-` `fig`에 아무것도 정의하지 않아도 함수는 동작함"
      ],
      "id": "08fbbfa0-9ba2-4d9a-a3ff-b3dc81fbd71a"
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "pio.show(fig=dict())"
      ],
      "id": "59aaa72c-bb23-499f-a03d-f046514619c5"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "-   내부적으로 어떠한 값이 저장되어 있는 것임\n",
        "\n",
        "`#`\n",
        "\n",
        "# 5. `go`를 이용한 시각화\n",
        "\n",
        "## A. `pio`와 `go`의 연결"
      ],
      "id": "0425ef40-0f60-4992-b152-e25ecf2d6a41"
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = dict()\n",
        "fig['data'] = [\n",
        "    {'type':'bar','x':['female'],'y':[0.42]},\n",
        "    {'type':'bar','x':['male'],'y':[0.52]},\n",
        "]\n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "pio.show(fig) # pio.show에 필요한 입력들을 fig라는 이름의 딕셔너리로 전달하는 느낌"
      ],
      "id": "9a3c48a5-1d6a-4a79-8a10-9a9d876eb6cc"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "위의 코드와 동일한 효과를 주는 코드를 알아보자.\n",
        "\n",
        "`# 예제1` – data의 원소를 dict로 정리하여 추가"
      ],
      "id": "3e5db347-6746-4f6b-a1a9-78ad82cca03f"
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = dict()\n",
        "fig['data'] = list()\n",
        "bar_female = {'type':'bar','x':['female'],'y':[0.42]}\n",
        "bar_male = {'type':'bar','x':['male'],'y':[0.52]}\n",
        "fig['data'].append(bar_female)\n",
        "fig['data'].append(bar_male)\n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "pio.show(fig) # pio.show에 필요한 입력들을 fig라는 이름의 딕셔너리로 전달하는 느낌"
      ],
      "id": "00985b06-7370-4077-a968-7b8e289e4976"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제2` – `go.Bar()`를 이용"
      ],
      "id": "79b9cf41-a0a6-40f7-84b0-a40aa083d036"
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = dict()\n",
        "fig['data'] = list()\n",
        "bar_female = go.Bar({'x':['female'],'y':[0.42]}) # bar_female = {'type':'bar','x':['female'],'y':[0.42]}\n",
        "bar_male = go.Bar({'x':['male'],'y':[0.52]}) # bar_male = {'type':'bar','x':['male'],'y':[0.52]}\n",
        "fig['data'].append(bar_female)\n",
        "fig['data'].append(bar_male)\n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "pio.show(fig) # pio.show에 필요한 입력들을 fig라는 이름의 딕셔너리로 전달하는 느낌"
      ],
      "id": "4befa51c-4258-4c04-8cbc-82a9c4f8a5a1"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제3` – `go.Bar()`를 이용, `go.Figure()`+`add_trace()`이용"
      ],
      "id": "a1d9ae04-ab12-4277-b049-da4671ae42ba"
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure() # fig = dict()\n",
        "fig['data'] = tuple()\n",
        "bar_female = go.Bar({'x':['female'],'y':[0.42]}) # bar_female = {'type':'bar','x':['female'],'y':[0.42]}\n",
        "bar_male = go.Bar({'x':['male'],'y':[0.52]}) # bar_male = {'type':'bar','x':['male'],'y':[0.52]}\n",
        "fig.add_trace(bar_female) # fig['data'].append(bar_female)\n",
        "fig.add_trace(bar_male) # fig['data'].append(bar_male)\n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "fig # fig.show() # pio.show(fig) # pio.show에 필요한 입력들을 fig라는 이름의 딕셔너리로 전달하는 느낌"
      ],
      "id": "0562e928-9b8c-4939-bae0-9fbe5d139f10"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`# 예제4` – `go.Bar()`를 다르게 선언하여 이용,\n",
        "`go.Figure()`+`add_trace()`이용"
      ],
      "id": "5f5c982e-b0ea-47cf-aa5f-b42449d9aefd"
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure() # fig = dict()\n",
        "bar_female = go.Bar(x=['female'],y=[0.42])  # bar_female = go.Bar({'x':['female'],'y':[0.42]}) \n",
        "bar_male = go.Bar(x=['male'],y=[0.52])  # bar_male = go.Bar({'x':['male'],'y':[0.52]}) \n",
        "fig.add_trace(bar_female)\n",
        "fig.add_trace(bar_male) \n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "fig # fig.show() # pio.show(fig) # pio.show에 필요한 입력들을 fig라는 이름의 딕셔너리로 전달하는 느낌"
      ],
      "id": "b8a55d91-f76d-4153-81ed-7f882ab1bd09"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제5` – `go.Bar()`를 사용X, `go.Figure()`+`add_trace()`이용"
      ],
      "id": "998efaf7-5e66-49b2-8aa8-299c9c4ff4ab"
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure() # fig = dict()\n",
        "bar_female = {'type':'bar','x':['female'],'y':[0.42]}\n",
        "bar_male = {'type':'bar','x':['male'],'y':[0.52]}\n",
        "fig.add_trace(bar_female)\n",
        "fig.add_trace(bar_male) \n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "fig # fig.show() # pio.show(fig) # pio.show에 필요한 입력들을 fig라는 이름의 딕셔너리로 전달하는 느낌"
      ],
      "id": "376f05de-5319-43b4-a32d-cb88c7e5cdee"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제6` – `go.Bar()`를 이용, `go.Figure()`+`add_traces()`이용"
      ],
      "id": "6ff78bb9-f72b-4242-9fdd-6c3795958cbd"
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure() # fig = dict()\n",
        "bar_female = go.Bar(x=['female'],y=[0.42])\n",
        "bar_male = go.Bar(x=['male'],y=[0.52])\n",
        "fig.add_traces([bar_female,bar_male])\n",
        "fig['layout'] = {\n",
        "    'title':{'text': '버클리대학교 성별합격률'},\n",
        "    'width':600\n",
        "}\n",
        "fig "
      ],
      "id": "7b238e1d-9744-474e-b6bf-e0a0966dcd40"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제7` – `go.Bar()`를 이용,`go.Figure()`+`add_traces()`이용\n",
        ",`update_layout()`이용"
      ],
      "id": "7a7d66a8-2b99-43e2-92b1-00fddd8114ed"
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure() # fig = dict()\n",
        "bar_female = go.Bar(x=['female'],y=[0.42])\n",
        "bar_male = go.Bar(x=['male'],y=[0.52])\n",
        "fig.add_traces([bar_female,bar_male])\n",
        "fig.update_layout(\n",
        "     {'title':{'text': '버클리대학교 성별합격률'},'width':600}\n",
        ")\n",
        "fig "
      ],
      "id": "dc675e4e-fcd8-4854-9390-182bd6ffa833"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "`# 예제8` – `go.Bar()`를 이용, `go.Figure()`+`add_traces()`이용,\n",
        "`update_layout()`의 다른이용"
      ],
      "id": "e1770386-df55-47bd-adf7-192ca9687799"
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure() # fig = dict()\n",
        "bar_female = go.Bar(x=['female'],y=[0.42])\n",
        "bar_male = go.Bar(x=['male'],y=[0.52])\n",
        "fig.add_traces([bar_female,bar_male])\n",
        "fig.update_layout(\n",
        "    title={'text': '버클리대학교 성별합격률'},\n",
        "    width=600\n",
        ")\n",
        "fig "
      ],
      "id": "00bce9bb-58d5-4532-ad7e-0900c998eb2d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`#`\n",
        "\n",
        "## B. `go`를 이용하는 추천포맷"
      ],
      "id": "b7462f84-59c2-4536-ac20-9d8823b2c26d"
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "fig = go.Figure()\n",
        "fig.add_traces(\n",
        "    [go.Bar(x=['female'], y= [0.42], name='female'),\n",
        "     go.Bar(x=['male'], y= [0.52], name='male')]\n",
        ")\n",
        "fig.update_layout(\n",
        "    width = 600,\n",
        "    legend = {'title':{'text':'gender'}}\n",
        ")"
      ],
      "id": "bd573078-c911-4693-80c0-ea10670b7a3b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 6. HW\n",
        "\n",
        "``` python\n",
        "fig = go.Figure()\n",
        "fig.add_traces(\n",
        "    [go.Bar(x=['female'], y= [0.42], name='female'),\n",
        "     go.Bar(x=['male'], y= [0.52], name='male')]\n",
        ")\n",
        "fig.update_layout(\n",
        "    width = 600,\n",
        "    legend = {'title':{'text':'gender'}}\n",
        ")\n",
        "```\n",
        "\n",
        "위의 코드를 적절하게 수정하여 아래의 결과를 만들어라.\n",
        "\n",
        "![](attachment:13wk-1_files/figure-ipynb/f9478762-112c-40c2-a2eb-335368114911-1-ad3ff330-260c-483c-8d09-6efda90b0a96.png)\n",
        "\n",
        "> Note: x,y축의 이름이 수정되었음."
      ],
      "attachments": {
        "13wk-1_files/figure-ipynb/f9478762-112c-40c2-a2eb-335368114911-1-ad3ff330-260c-483c-8d09-6efda90b0a96.png": {
          "image/png": 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TQm/TH/z88fnGH2TdV9/Jjzv3yvfbd8uNGTNI8dsLSIliBaVx3SckV44sfo4S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\nqS3nFDtDz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