data_to_plot.ipynb

{
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/neurobit-ai/eyeForm/blob/main/data_to_plot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 讀eyeForm.db.xlsx"
      ],
      "metadata": {
        "id": "lnnflL76jrMK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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          "base_uri": "https://localhost:8080/",
          "height": 73
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        "id": "NRfo5Uqgi7Dm",
        "outputId": "c543a8d1-60f1-4530-98cf-188bfb999d1b"
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              "     <input type=\"file\" id=\"files-1b9f7288-2f0f-4dd8-b705-0790ffe5753d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1b9f7288-2f0f-4dd8-b705-0790ffe5753d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
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              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
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              "    return next.value.response;\n",
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              "\n",
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              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
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              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
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              "      resolve(null);\n",
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              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
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              "          action: 'append',\n",
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              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving eyeForm.db.xlsx to eyeForm.db.xlsx\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "assert files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 軸長危險度分析data"
      ],
      "metadata": {
        "id": "4LdQvL8igEH0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "v6giYQ0gGOf2",
        "outputId": "86183fb2-adb4-417f-997b-0884f8c5c10c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     性別     實歲   右眼軸長   左眼軸長   眼鏡     散瞳劑 角膜塑型片\n",
              "0     男  6歲11月  25.46  25.13  NaN  0.0005   NaN\n",
              "1     男   7歲2月  25.48   25.2  NaN     NaN   NaN\n",
              "2     男   7歲4月  25.56  25.26  NaN     NaN   NaN\n",
              "3     女   7歲7月  23.66   23.5  NaN  0.0003   NaN\n",
              "5     女   8歲3月     24  23.77  NaN  0.0005   NaN\n",
              "...  ..    ...    ...    ...  ...     ...   ...\n",
              "4341  女  6歲5個月  20.89  20.81  NaN  0.0010   NaN\n",
              "4342  男  8歲6個月  24.07  24.18  NaN     NaN   NaN\n",
              "4343  女  6歲1個月  22.04  22.26  NaN     NaN   NaN\n",
              "4344  女  6歲7個月  22.24  22.48  NaN     NaN   NaN\n",
              "4345  男  6歲0個月  21.62   21.5  NaN     NaN   NaN\n",
              "\n",
              "[2057 rows x 7 columns]"
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              "      <th></th>\n",
              "      <th>性別</th>\n",
              "      <th>實歲</th>\n",
              "      <th>右眼軸長</th>\n",
              "      <th>左眼軸長</th>\n",
              "      <th>眼鏡</th>\n",
              "      <th>散瞳劑</th>\n",
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              "      <td>NaN</td>\n",
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              "      <th>1</th>\n",
              "      <td>男</td>\n",
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              "      <td>25.48</td>\n",
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              "      <td>25.56</td>\n",
              "      <td>25.26</td>\n",
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              "      <td>23.66</td>\n",
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              "      <td>女</td>\n",
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              "      <td>24</td>\n",
              "      <td>23.77</td>\n",
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              "      <td>20.81</td>\n",
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              "      <td>0.0010</td>\n",
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              "    <tr>\n",
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              "      <td>男</td>\n",
              "      <td>8歲6個月</td>\n",
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              "      <td>24.18</td>\n",
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              "      <td>女</td>\n",
              "      <td>6歲1個月</td>\n",
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              "    <tr>\n",
              "      <th>4344</th>\n",
              "      <td>女</td>\n",
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              "      <td>男</td>\n",
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              "      <td>21.5</td>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import pandas as pd\n",
        "sheet = pd.read_excel('eyeForm.db.xlsx', sheet_name='三總', header=2)\n",
        "db_version = f\"v{sheet.iloc[-1]['時間戳記']}\".split()[0].replace('-', '')\n",
        "df = sheet[['性別', '實歲', '右眼軸長', '左眼軸長', '眼鏡', '散瞳劑', '角膜塑型片']].dropna(subset=['性別', '實歲', '右眼軸長', '左眼軸長'])\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "BsLQTB4M5N7P",
        "outputId": "2bb4ed38-6504-435b-b7c0-24381d1b31f9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     性別     實歲     軸長   眼鏡     散瞳劑 角膜塑型片\n",
              "0     男  6歲11月  25.46  NaN  0.0005   NaN\n",
              "1     男   7歲2月  25.48  NaN     NaN   NaN\n",
              "2     男   7歲4月  25.56  NaN     NaN   NaN\n",
              "3     女   7歲7月  23.66  NaN  0.0003   NaN\n",
              "5     女   8歲3月     24  NaN  0.0005   NaN\n",
              "...  ..    ...    ...  ...     ...   ...\n",
              "4341  女  6歲5個月  20.81  NaN  0.0010   NaN\n",
              "4342  男  8歲6個月  24.18  NaN     NaN   NaN\n",
              "4343  女  6歲1個月  22.26  NaN     NaN   NaN\n",
              "4344  女  6歲7個月  22.48  NaN     NaN   NaN\n",
              "4345  男  6歲0個月   21.5  NaN     NaN   NaN\n",
              "\n",
              "[4114 rows x 6 columns]"
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4342</th>\n",
              "      <td>男</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>女</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4345</th>\n",
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              "<p>4114 rows × 6 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "od = df.drop(columns='左眼軸長')\n",
        "os = df.drop(columns='右眼軸長')\n",
        "od.columns = ['性別', '實歲', '軸長', '眼鏡', '散瞳劑', '角膜塑型片']\n",
        "os.columns = ['性別', '實歲', '軸長', '眼鏡', '散瞳劑', '角膜塑型片']\n",
        "data = pd.concat([od, os])\n",
        "data"
      ]
    },
    {
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      "execution_count": 4,
      "metadata": {
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        "id": "J-sTG_nvtrs0",
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            "text/plain": [
              "     性別     實歲     軸長   眼鏡     散瞳劑 角膜塑型片\n",
              "0     男  6歲11月  25.46  NaN  0.0005   NaN\n",
              "1     男   7歲2月  25.48  NaN     NaN   NaN\n",
              "2     男   7歲4月  25.56  NaN     NaN   NaN\n",
              "3     女   7歲7月  23.66  NaN  0.0003   NaN\n",
              "5     女   8歲3月     24  NaN  0.0005   NaN\n",
              "...  ..    ...    ...  ...     ...   ...\n",
              "4341  女  6歲5個月  20.81  NaN  0.0010   NaN\n",
              "4342  男  8歲6個月  24.18  NaN     NaN   NaN\n",
              "4343  女  6歲1個月  22.26  NaN     NaN   NaN\n",
              "4344  女  6歲7個月  22.48  NaN     NaN   NaN\n",
              "4345  男  6歲0個月   21.5  NaN     NaN   NaN\n",
              "\n",
              "[4108 rows x 6 columns]"
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              "      <td>男</td>\n",
              "      <td>8歲6個月</td>\n",
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              "      <td>22.26</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <th>4345</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "<p>4108 rows × 6 columns</p>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6c811983-bfd4-4507-89a5-39be683a5371');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data = data.drop(data[data['軸長'] == '< 20'].index)\n",
        "data = data.drop(data[data['軸長'] == '> 30'].index)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "pTI_XHknMXDi",
        "outputId": "fda7ea2b-3126-4502-b629-c14ce484bc52"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      0   1\n",
              "0     6  11\n",
              "1     7   2\n",
              "2     7   4\n",
              "3     7   7\n",
              "5     8   3\n",
              "...  ..  ..\n",
              "4341  6   5\n",
              "4342  8   6\n",
              "4343  6   1\n",
              "4344  6   7\n",
              "4345  6   0\n",
              "\n",
              "[4108 rows x 2 columns]"
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4341</th>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4342</th>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4343</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4344</th>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4345</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4108 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c13c7dd2-9d5e-42d7-8779-f11c33eaf73d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c13c7dd2-9d5e-42d7-8779-f11c33eaf73d 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-c13c7dd2-9d5e-42d7-8779-f11c33eaf73d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "age = data['實歲'].str.extract('(\\d+)歲(\\d+)')\n",
        "age"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "nwLiOaNmPxXi",
        "outputId": "1b02ae32-02f6-44f7-a2fe-5e5bbf4b038c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     性別        實歲     軸長   眼鏡     散瞳劑 角膜塑型片  年歲\n",
              "0     男  6.916667  25.46  NaN  0.0005   NaN   7\n",
              "1     男  7.166667  25.48  NaN     NaN   NaN   7\n",
              "2     男  7.333333  25.56  NaN     NaN   NaN   7\n",
              "3     女  7.583333  23.66  NaN  0.0003   NaN   8\n",
              "5     女  8.250000  24.00  NaN  0.0005   NaN   8\n",
              "...  ..       ...    ...  ...     ...   ...  ..\n",
              "4341  女  6.416667  20.81  NaN  0.0010   NaN   6\n",
              "4342  男  8.500000  24.18  NaN     NaN   NaN   9\n",
              "4343  女  6.083333  22.26  NaN     NaN   NaN   6\n",
              "4344  女  6.583333  22.48  NaN     NaN   NaN   7\n",
              "4345  男  6.000000  21.50  NaN     NaN   NaN   6\n",
              "\n",
              "[4108 rows x 7 columns]"
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d015a50e-bd35-4108-81c0-9e042a32e365');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "data['實歲'] = age[0].astype(int) + age[1].astype(int) / 12\n",
        "data['年歲'] = age[0].astype(int) + (age[1].astype(int) >= 6).astype(int) # round() could be wrong\n",
        "data['軸長'] = data['軸長'].astype(float)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "10jaEDY0LyHz",
        "outputId": "189689ac-6de4-4d81-9312-8fd8a9fa9bdd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "不處置          1994\n",
              "散瞳劑           868\n",
              "一般眼鏡          610\n",
              "一般眼鏡散瞳劑       460\n",
              "角膜塑型片         154\n",
              "散瞳劑角膜塑型片       16\n",
              "一般眼鏡角膜塑型片       6\n",
              "Name: 治療處置分類, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "data.loc[data['散瞳劑'] > 0, '散瞳劑'] = '散瞳劑'\n",
        "data['治療處置分類'] = data['眼鏡'].fillna('') + data['散瞳劑'].fillna('') + data['角膜塑型片'].fillna('')\n",
        "data['治療處置分類'] = data['治療處置分類'].replace({'': '不處置'})\n",
        "data['治療處置分類'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "t71-LKO9TiVu"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "def percentile(q):\n",
        "    # def function(a):\n",
        "    #     return np.percentile(a, q)\n",
        "    function = lambda a: np.percentile(a, q)\n",
        "    function.__name__ = f'P{q}'\n",
        "    return function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "2XUwvRKS1nxu"
      },
      "outputs": [],
      "source": [
        "quartile = data.groupby(['性別', '年歲'])['軸長'].aggregate([percentile(25), percentile(75)])\n",
        "IQR = quartile['P75'] - quartile['P25']\n",
        "lower_fence = quartile['P25'] - IQR * 1.5\n",
        "upper_fence = quartile['P75'] + IQR * 1.5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "sP-GjRqBK_7y",
        "outputId": "0593e25a-a7c2-457d-8ecf-f124d72de9e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     性別        實歲     軸長   眼鏡  散瞳劑 角膜塑型片  年歲 治療處置分類  lower_fence  upper_fence\n",
              "0     男  6.916667  25.46  NaN  散瞳劑   NaN   7    散瞳劑     20.55125     26.20125\n",
              "1     男  7.166667  25.48  NaN  NaN   NaN   7    不處置     20.55125     26.20125\n",
              "2     男  7.333333  25.56  NaN  NaN   NaN   7    不處置     20.55125     26.20125\n",
              "3     女  7.583333  23.66  NaN  散瞳劑   NaN   8    散瞳劑     21.33250     25.61250\n",
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          "metadata": {},
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      ],
      "source": [
        "for i in quartile.index:\n",
        "    data.loc[(data['性別'] == i[0]) & (data['年歲'] == i[1]), 'lower_fence'] = lower_fence[i]\n",
        "    data.loc[(data['性別'] == i[0]) & (data['年歲'] == i[1]), 'upper_fence'] = upper_fence[i]\n",
        "data"
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    {
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      "execution_count": 11,
      "metadata": {
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        "id": "W8agHf7fhnXN",
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              "     性別        實歲     軸長   眼鏡  散瞳劑 角膜塑型片  年歲 治療處置分類  lower_fence  upper_fence\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "data_without_outliers = data[(data['軸長'] > data['lower_fence']) & (data['軸長'] < data['upper_fence'])]\n",
        "data_without_outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDZ716xI_giJ",
        "outputId": "b94b9a84-1c7b-4591-b993-4be967764663"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "性別  治療處置分類   \n",
              "女   一般眼鏡         0.350750\n",
              "    一般眼鏡散瞳劑      0.216851\n",
              "    一般眼鏡角膜塑型片    1.830000\n",
              "    不處置          0.262043\n",
              "    散瞳劑          0.157267\n",
              "    散瞳劑角膜塑型片     0.000000\n",
              "    角膜塑型片        0.053746\n",
              "男   一般眼鏡         0.329154\n",
              "    一般眼鏡散瞳劑      0.189347\n",
              "    一般眼鏡角膜塑型片    0.000000\n",
              "    不處置          0.258378\n",
              "    散瞳劑          0.109909\n",
              "    散瞳劑角膜塑型片    -0.077909\n",
              "    角膜塑型片        0.060966\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "slope_groupby = data_without_outliers.groupby(['性別', '治療處置分類']).apply(lambda group: LinearRegression().fit(group['實歲'].to_numpy().reshape(-1, 1), group['軸長'].to_numpy().reshape(-1, 1)).coef_[0][0])\n",
        "slope_groupby"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "f4APhLvYklAw",
        "outputId": "d1185a00-f3c0-4ada-e265-17563b163b7e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          P0     P50      P75     P90   P100\n",
              "性別 年歲                                       \n",
              "女  0   20.50  20.520  20.5300  20.536  20.54\n",
              "   1   20.55  20.690  20.7450  20.765  20.77\n",
              "   2   20.86  20.905  20.9700  21.024  21.06\n",
              "   3   20.26  21.310  21.7850  22.194  23.06\n",
              "   4   20.35  21.720  22.0200  22.789  23.33\n",
              "   5   19.73  22.130  22.6175  23.015  24.50\n",
              "   6   19.64  22.105  22.9225  23.314  24.57\n",
              "   7   20.86  22.955  23.4125  24.000  24.84\n",
              "   8   21.35  23.460  23.9100  24.590  25.54\n",
              "   9   21.51  23.690  24.0800  24.584  25.57\n",
              "   10  21.81  23.840  24.5100  25.010  25.89\n",
              "   11  21.63  24.040  24.7100  25.076  25.90\n",
              "   12  22.51  24.330  24.6700  25.030  25.33\n",
              "   13  22.81  24.380  24.9400  25.530  26.66\n",
              "   14  22.92  24.840  25.5900  25.890  26.20\n",
              "   15  22.39  24.980  25.3975  26.062  26.44\n",
              "   16  23.29  24.560  25.1100  25.800  26.54\n",
              "   17  23.63  25.290  25.5900  25.872  26.01\n",
              "   18  22.42  23.700  25.1400  25.806  26.25\n",
              "   21  24.69  24.915  25.0275  25.095  25.14\n",
              "男  2   21.88  22.060  22.0800  22.296  22.44\n",
              "   3   20.99  22.610  23.2450  23.825  24.29\n",
              "   4   20.37  22.170  22.7000  23.134  24.14\n",
              "   5   20.48  22.650  23.3225  23.935  25.10\n",
              "   6   20.13  22.780  23.7150  24.332  25.53\n",
              "   7   21.10  23.460  24.0875  24.599  26.14\n",
              "   8   21.45  23.960  24.4300  24.982  25.76\n",
              "   9   22.45  24.250  24.6400  24.994  25.93\n",
              "   10  21.99  24.390  25.1000  25.490  26.28\n",
              "   11  22.36  24.435  25.0475  25.750  26.77\n",
              "   12  23.02  24.650  25.1450  25.826  26.93\n",
              "   13  23.08  24.840  25.1600  25.603  26.63\n",
              "   14  23.21  24.820  25.1000  25.832  26.56\n",
              "   15  22.83  24.735  25.2450  26.839  27.28\n",
              "   16  22.40  25.410  26.6525  27.197  27.86\n",
              "   17  26.55  27.010  27.4675  27.715  27.88\n",
              "   18  25.46  25.695  25.8125  25.883  25.93"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ac2d2e42-fcf4-45bf-bcae-47600940cc0a\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>P0</th>\n",
              "      <th>P50</th>\n",
              "      <th>P75</th>\n",
              "      <th>P90</th>\n",
              "      <th>P100</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>性別</th>\n",
              "      <th>年歲</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"20\" valign=\"top\">女</th>\n",
              "      <th>0</th>\n",
              "      <td>20.50</td>\n",
              "      <td>20.520</td>\n",
              "      <td>20.5300</td>\n",
              "      <td>20.536</td>\n",
              "      <td>20.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.55</td>\n",
              "      <td>20.690</td>\n",
              "      <td>20.7450</td>\n",
              "      <td>20.765</td>\n",
              "      <td>20.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20.86</td>\n",
              "      <td>20.905</td>\n",
              "      <td>20.9700</td>\n",
              "      <td>21.024</td>\n",
              "      <td>21.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.26</td>\n",
              "      <td>21.310</td>\n",
              "      <td>21.7850</td>\n",
              "      <td>22.194</td>\n",
              "      <td>23.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.35</td>\n",
              "      <td>21.720</td>\n",
              "      <td>22.0200</td>\n",
              "      <td>22.789</td>\n",
              "      <td>23.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>19.73</td>\n",
              "      <td>22.130</td>\n",
              "      <td>22.6175</td>\n",
              "      <td>23.015</td>\n",
              "      <td>24.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>19.64</td>\n",
              "      <td>22.105</td>\n",
              "      <td>22.9225</td>\n",
              "      <td>23.314</td>\n",
              "      <td>24.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>20.86</td>\n",
              "      <td>22.955</td>\n",
              "      <td>23.4125</td>\n",
              "      <td>24.000</td>\n",
              "      <td>24.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>21.35</td>\n",
              "      <td>23.460</td>\n",
              "      <td>23.9100</td>\n",
              "      <td>24.590</td>\n",
              "      <td>25.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>21.51</td>\n",
              "      <td>23.690</td>\n",
              "      <td>24.0800</td>\n",
              "      <td>24.584</td>\n",
              "      <td>25.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>21.81</td>\n",
              "      <td>23.840</td>\n",
              "      <td>24.5100</td>\n",
              "      <td>25.010</td>\n",
              "      <td>25.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>21.63</td>\n",
              "      <td>24.040</td>\n",
              "      <td>24.7100</td>\n",
              "      <td>25.076</td>\n",
              "      <td>25.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>22.51</td>\n",
              "      <td>24.330</td>\n",
              "      <td>24.6700</td>\n",
              "      <td>25.030</td>\n",
              "      <td>25.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>22.81</td>\n",
              "      <td>24.380</td>\n",
              "      <td>24.9400</td>\n",
              "      <td>25.530</td>\n",
              "      <td>26.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>22.92</td>\n",
              "      <td>24.840</td>\n",
              "      <td>25.5900</td>\n",
              "      <td>25.890</td>\n",
              "      <td>26.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>22.39</td>\n",
              "      <td>24.980</td>\n",
              "      <td>25.3975</td>\n",
              "      <td>26.062</td>\n",
              "      <td>26.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>23.29</td>\n",
              "      <td>24.560</td>\n",
              "      <td>25.1100</td>\n",
              "      <td>25.800</td>\n",
              "      <td>26.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>23.63</td>\n",
              "      <td>25.290</td>\n",
              "      <td>25.5900</td>\n",
              "      <td>25.872</td>\n",
              "      <td>26.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>22.42</td>\n",
              "      <td>23.700</td>\n",
              "      <td>25.1400</td>\n",
              "      <td>25.806</td>\n",
              "      <td>26.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>24.69</td>\n",
              "      <td>24.915</td>\n",
              "      <td>25.0275</td>\n",
              "      <td>25.095</td>\n",
              "      <td>25.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"17\" valign=\"top\">男</th>\n",
              "      <th>2</th>\n",
              "      <td>21.88</td>\n",
              "      <td>22.060</td>\n",
              "      <td>22.0800</td>\n",
              "      <td>22.296</td>\n",
              "      <td>22.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.99</td>\n",
              "      <td>22.610</td>\n",
              "      <td>23.2450</td>\n",
              "      <td>23.825</td>\n",
              "      <td>24.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.37</td>\n",
              "      <td>22.170</td>\n",
              "      <td>22.7000</td>\n",
              "      <td>23.134</td>\n",
              "      <td>24.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>20.48</td>\n",
              "      <td>22.650</td>\n",
              "      <td>23.3225</td>\n",
              "      <td>23.935</td>\n",
              "      <td>25.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>20.13</td>\n",
              "      <td>22.780</td>\n",
              "      <td>23.7150</td>\n",
              "      <td>24.332</td>\n",
              "      <td>25.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>21.10</td>\n",
              "      <td>23.460</td>\n",
              "      <td>24.0875</td>\n",
              "      <td>24.599</td>\n",
              "      <td>26.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>21.45</td>\n",
              "      <td>23.960</td>\n",
              "      <td>24.4300</td>\n",
              "      <td>24.982</td>\n",
              "      <td>25.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>22.45</td>\n",
              "      <td>24.250</td>\n",
              "      <td>24.6400</td>\n",
              "      <td>24.994</td>\n",
              "      <td>25.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>21.99</td>\n",
              "      <td>24.390</td>\n",
              "      <td>25.1000</td>\n",
              "      <td>25.490</td>\n",
              "      <td>26.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>22.36</td>\n",
              "      <td>24.435</td>\n",
              "      <td>25.0475</td>\n",
              "      <td>25.750</td>\n",
              "      <td>26.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>23.02</td>\n",
              "      <td>24.650</td>\n",
              "      <td>25.1450</td>\n",
              "      <td>25.826</td>\n",
              "      <td>26.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>23.08</td>\n",
              "      <td>24.840</td>\n",
              "      <td>25.1600</td>\n",
              "      <td>25.603</td>\n",
              "      <td>26.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>23.21</td>\n",
              "      <td>24.820</td>\n",
              "      <td>25.1000</td>\n",
              "      <td>25.832</td>\n",
              "      <td>26.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>22.83</td>\n",
              "      <td>24.735</td>\n",
              "      <td>25.2450</td>\n",
              "      <td>26.839</td>\n",
              "      <td>27.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>22.40</td>\n",
              "      <td>25.410</td>\n",
              "      <td>26.6525</td>\n",
              "      <td>27.197</td>\n",
              "      <td>27.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>26.55</td>\n",
              "      <td>27.010</td>\n",
              "      <td>27.4675</td>\n",
              "      <td>27.715</td>\n",
              "      <td>27.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>25.46</td>\n",
              "      <td>25.695</td>\n",
              "      <td>25.8125</td>\n",
              "      <td>25.883</td>\n",
              "      <td>25.93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac2d2e42-fcf4-45bf-bcae-47600940cc0a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ac2d2e42-fcf4-45bf-bcae-47600940cc0a 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-ac2d2e42-fcf4-45bf-bcae-47600940cc0a');\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": 13
        }
      ],
      "source": [
        "stacked_area = data_without_outliers.groupby(['性別', '年歲'])['軸長'].aggregate([percentile(0), percentile(50), percentile(75), percentile(90), percentile(100)])\n",
        "stacked_area"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "MVJ5A3odm1Sy"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def plot(sex):\n",
        "    area = stacked_area.loc[sex].loc[3:16]\n",
        "    MorF = {'男': 'Male', '女': 'Female'}[sex]\n",
        "    plt.fill_between(area.index, area['P100'], area['P90'], color='red', alpha=0.6, label='90~100%')\n",
        "    plt.fill_between(area.index, area['P90'], area['P75'], color='orange', alpha=0.6, label='75~90%')\n",
        "    plt.fill_between(area.index, area['P75'], area['P50'], color='yellow', alpha=0.6, label='50~75%')\n",
        "    plt.fill_between(area.index, area['P50'], area['P0'], color='lightgreen', alpha=0.6, label='0~50%')\n",
        "    plt.title(f'Trend of {MorF} Children in Taiwan', fontsize=12)\n",
        "    plt.legend(loc='lower right')\n",
        "    plt.xticks(range(3, 17))\n",
        "    plt.yticks(range(20, 30))\n",
        "    plt.xlabel('Age', fontsize=12)\n",
        "    plt.ylabel('Axial Length', fontsize=12)\n",
        "    plt.margins(0)\n",
        "    plt.text(16, 18.5, f'{db_version}', horizontalalignment='right', fontsize=8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "z2irvP1rziii",
        "outputId": "ac6900e0-5135-49d7-b420-2bdcc13d2449"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot('男')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "Yj9Qtc3hzpOi",
        "outputId": "5c6ffccd-2030-4c3a-d8ca-83a40f8529dc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot('女')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_to_plot = dict()\n",
        "data_to_plot['軸長'] = [db_version, slope_groupby.copy(), stacked_area.copy()]\n",
        "del slope_groupby, stacked_area"
      ],
      "metadata": {
        "id": "kV4R9i_Bl4Te"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 球面度數危險度分析data"
      ],
      "metadata": {
        "id": "pGd0YzXSgtAt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "6a35c840-8dc9-4627-d01e-75433c42cc29",
        "id": "kV3Uhq3SgtAu"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     性別     實歲 右眼球面度數  左眼球面度數    眼鏡     散瞳劑 角膜塑型片\n",
              "0     男  6歲11月   -1.5   -1.50   NaN  0.0005   NaN\n",
              "1     男   7歲2月   -1.5   -1.50   NaN     NaN   NaN\n",
              "2     男   7歲4月   -1.5   -2.00   NaN     NaN   NaN\n",
              "3     女   7歲7月     -1   -1.00   NaN  0.0003   NaN\n",
              "4     女   7歲8月   -1.5   -1.50  一般眼鏡  0.0003   NaN\n",
              "...  ..    ...    ...     ...   ...     ...   ...\n",
              "4339  男  6歲7個月    0.5    1.00   NaN     NaN   NaN\n",
              "4341  女  6歲5個月    0.5    1.25   NaN  0.0010   NaN\n",
              "4343  女  6歲1個月      1    0.50   NaN     NaN   NaN\n",
              "4344  女  6歲7個月      0   -0.75   NaN     NaN   NaN\n",
              "4345  男  6歲0個月      1    1.25   NaN     NaN   NaN\n",
              "\n",
              "[2473 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-daa2f56b-f686-4198-93a5-a6c1a871f684\">\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>性別</th>\n",
              "      <th>實歲</th>\n",
              "      <th>右眼球面度數</th>\n",
              "      <th>左眼球面度數</th>\n",
              "      <th>眼鏡</th>\n",
              "      <th>散瞳劑</th>\n",
              "      <th>角膜塑型片</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>男</td>\n",
              "      <td>6歲11月</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-1.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>男</td>\n",
              "      <td>7歲2月</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-1.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>男</td>\n",
              "      <td>7歲4月</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>女</td>\n",
              "      <td>7歲7月</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>4</th>\n",
              "      <td>女</td>\n",
              "      <td>7歲8月</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-1.50</td>\n",
              "      <td>一般眼鏡</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4339</th>\n",
              "      <td>男</td>\n",
              "      <td>6歲7個月</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4341</th>\n",
              "      <td>女</td>\n",
              "      <td>6歲5個月</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4343</th>\n",
              "      <td>女</td>\n",
              "      <td>6歲1個月</td>\n",
              "      <td>1</td>\n",
              "      <td>0.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4344</th>\n",
              "      <td>女</td>\n",
              "      <td>6歲7個月</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4345</th>\n",
              "      <td>男</td>\n",
              "      <td>6歲0個月</td>\n",
              "      <td>1</td>\n",
              "      <td>1.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2473 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-daa2f56b-f686-4198-93a5-a6c1a871f684')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-daa2f56b-f686-4198-93a5-a6c1a871f684 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-daa2f56b-f686-4198-93a5-a6c1a871f684');\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": 18
        }
      ],
      "source": [
        "import pandas as pd\n",
        "sheet = pd.read_excel('eyeForm.db.xlsx', sheet_name='三總', header=2)\n",
        "db_version = f\"v{sheet.iloc[-1]['時間戳記']}\".split()[0].replace('-', '')\n",
        "df = sheet[['性別', '實歲', '右眼球面度數', '左眼球面度數', '眼鏡', '散瞳劑', '角膜塑型片']].dropna(subset=['性別', '實歲', '右眼球面度數', '左眼球面度數'])\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "4b55e6b6-a9bb-45fd-8d7a-c1792b2a629b",
        "id": "2nzDdm6VgtAu"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     性別     實歲  球面度數    眼鏡     散瞳劑 角膜塑型片\n",
              "0     男  6歲11月  -1.5   NaN  0.0005   NaN\n",
              "1     男   7歲2月  -1.5   NaN     NaN   NaN\n",
              "2     男   7歲4月  -1.5   NaN     NaN   NaN\n",
              "3     女   7歲7月    -1   NaN  0.0003   NaN\n",
              "4     女   7歲8月  -1.5  一般眼鏡  0.0003   NaN\n",
              "...  ..    ...   ...   ...     ...   ...\n",
              "4339  男  6歲7個月   1.0   NaN     NaN   NaN\n",
              "4341  女  6歲5個月  1.25   NaN  0.0010   NaN\n",
              "4343  女  6歲1個月   0.5   NaN     NaN   NaN\n",
              "4344  女  6歲7個月 -0.75   NaN     NaN   NaN\n",
              "4345  男  6歲0個月  1.25   NaN     NaN   NaN\n",
              "\n",
              "[4946 rows x 6 columns]"
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              "      <td>7歲4月</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
              "      <td>女</td>\n",
              "      <td>7歲7月</td>\n",
              "      <td>-1</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4339</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4344</th>\n",
              "      <td>女</td>\n",
              "      <td>6歲7個月</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>4345</th>\n",
              "      <td>男</td>\n",
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              "      <td>1.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "<p>4946 rows × 6 columns</p>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5da1d2f4-1178-4649-966d-9fc2cd73ddf4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "od = df.drop(columns='左眼球面度數')\n",
        "os = df.drop(columns='右眼球面度數')\n",
        "od.columns = ['性別', '實歲', '球面度數', '眼鏡', '散瞳劑', '角膜塑型片']\n",
        "os.columns = ['性別', '實歲', '球面度數', '眼鏡', '散瞳劑', '角膜塑型片']\n",
        "data = pd.concat([od, os])\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "ed4c63cd-871d-4f76-a70f-e8fa42b9e549",
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        {
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          "data": {
            "text/plain": [
              "     性別     實歲  球面度數    眼鏡     散瞳劑 角膜塑型片\n",
              "0     男  6歲11月  -1.5   NaN  0.0005   NaN\n",
              "1     男   7歲2月  -1.5   NaN     NaN   NaN\n",
              "2     男   7歲4月  -1.5   NaN     NaN   NaN\n",
              "3     女   7歲7月    -1   NaN  0.0003   NaN\n",
              "4     女   7歲8月  -1.5  一般眼鏡  0.0003   NaN\n",
              "...  ..    ...   ...   ...     ...   ...\n",
              "4339  男  6歲7個月   1.0   NaN     NaN   NaN\n",
              "4341  女  6歲5個月  1.25   NaN  0.0010   NaN\n",
              "4343  女  6歲1個月   0.5   NaN     NaN   NaN\n",
              "4344  女  6歲7個月 -0.75   NaN     NaN   NaN\n",
              "4345  男  6歲0個月  1.25   NaN     NaN   NaN\n",
              "\n",
              "[4946 rows x 6 columns]"
            ],
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              "\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "data = data.drop(data[data['球面度數'] == '< 20'].index)\n",
        "data = data.drop(data[data['球面度數'] == '> 30'].index)\n",
        "data"
      ]
    },
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          "metadata": {},
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      ],
      "source": [
        "age = data['實歲'].str.extract('(\\d+)歲(\\d+)')\n",
        "age"
      ]
    },
    {
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              "     性別        實歲  球面度數    眼鏡     散瞳劑 角膜塑型片  年歲\n",
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              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4341</th>\n",
              "      <td>女</td>\n",
              "      <td>6.416667</td>\n",
              "      <td>1.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4343</th>\n",
              "      <td>女</td>\n",
              "      <td>6.083333</td>\n",
              "      <td>0.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4344</th>\n",
              "      <td>女</td>\n",
              "      <td>6.583333</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4345</th>\n",
              "      <td>男</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4946 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ba0c640-ab30-43d9-90a4-b0e0fa5739c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3ba0c640-ab30-43d9-90a4-b0e0fa5739c4 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-3ba0c640-ab30-43d9-90a4-b0e0fa5739c4');\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": 22
        }
      ],
      "source": [
        "data['實歲'] = age[0].astype(int) + age[1].astype(int) / 12\n",
        "data['年歲'] = age[0].astype(int) + (age[1].astype(int) >= 6).astype(int) # round() could be wrong\n",
        "data['球面度數'] = data['球面度數'].astype(float)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b0f0943-c8d0-43a4-f6c9-ca148b380c66",
        "id": "MOu4p955gtAx"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "不處置          2336\n",
              "散瞳劑          1034\n",
              "一般眼鏡          792\n",
              "一般眼鏡散瞳劑       516\n",
              "角膜塑型片         242\n",
              "散瞳劑角膜塑型片       20\n",
              "一般眼鏡角膜塑型片       6\n",
              "Name: 治療處置分類, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "data.loc[data['散瞳劑'] > 0, '散瞳劑'] = '散瞳劑'\n",
        "data['治療處置分類'] = data['眼鏡'].fillna('') + data['散瞳劑'].fillna('') + data['角膜塑型片'].fillna('')\n",
        "data['治療處置分類'] = data['治療處置分類'].replace({'': '不處置'})\n",
        "data['治療處置分類'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "9fTo1N0HgtAx"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "def percentile(q):\n",
        "    # def function(a):\n",
        "    #     return np.percentile(a, q)\n",
        "    function = lambda a: np.percentile(a, q)\n",
        "    function.__name__ = f'P{q}'\n",
        "    return function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "jLJRhHy3gtAx"
      },
      "outputs": [],
      "source": [
        "quartile = data.groupby(['性別', '年歲'])['球面度數'].aggregate([percentile(25), percentile(75)])\n",
        "IQR = quartile['P75'] - quartile['P25']\n",
        "lower_fence = quartile['P25'] - IQR * 1.5\n",
        "upper_fence = quartile['P75'] + IQR * 1.5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "fa8924f0-bbf7-4c21-d79b-20e0d21054fb",
        "id": "DroeC_lugtAy"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     性別        實歲  球面度數    眼鏡  散瞳劑 角膜塑型片  年歲   治療處置分類  lower_fence  \\\n",
              "0     男  6.916667 -1.50   NaN  散瞳劑   NaN   7      散瞳劑     -3.75000   \n",
              "1     男  7.166667 -1.50   NaN  NaN   NaN   7      不處置     -3.75000   \n",
              "2     男  7.333333 -1.50   NaN  NaN   NaN   7      不處置     -3.75000   \n",
              "3     女  7.583333 -1.00   NaN  散瞳劑   NaN   8      散瞳劑     -5.00000   \n",
              "4     女  7.666667 -1.50  一般眼鏡  散瞳劑   NaN   8  一般眼鏡散瞳劑     -5.00000   \n",
              "...  ..       ...   ...   ...  ...   ...  ..      ...          ...   \n",
              "4339  男  6.583333  1.00   NaN  NaN   NaN   7      不處置     -3.75000   \n",
              "4341  女  6.416667  1.25   NaN  散瞳劑   NaN   6      散瞳劑     -3.09375   \n",
              "4343  女  6.083333  0.50   NaN  NaN   NaN   6      不處置     -3.09375   \n",
              "4344  女  6.583333 -0.75   NaN  NaN   NaN   7      不處置     -3.25000   \n",
              "4345  男  6.000000  1.25   NaN  NaN   NaN   6      不處置     -3.37500   \n",
              "\n",
              "      upper_fence  \n",
              "0         2.25000  \n",
              "1         2.25000  \n",
              "2         2.25000  \n",
              "3         3.00000  \n",
              "4         3.00000  \n",
              "...           ...  \n",
              "4339      2.25000  \n",
              "4341      4.65625  \n",
              "4343      4.65625  \n",
              "4344      2.75000  \n",
              "4345      3.62500  \n",
              "\n",
              "[4946 rows x 10 columns]"
            ],
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              "\n",
              "  <div id=\"df-f27dd024-0a24-4342-b4a9-c0f35a995deb\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>性別</th>\n",
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              "      <th>4</th>\n",
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              "      <th>4339</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>7</td>\n",
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              "      <td>4.65625</td>\n",
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              "    <tr>\n",
              "      <th>4343</th>\n",
              "      <td>女</td>\n",
              "      <td>6.083333</td>\n",
              "      <td>0.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
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              "      <td>-3.09375</td>\n",
              "      <td>4.65625</td>\n",
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              "    <tr>\n",
              "      <th>4344</th>\n",
              "      <td>女</td>\n",
              "      <td>6.583333</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>不處置</td>\n",
              "      <td>-3.25000</td>\n",
              "      <td>2.75000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>-3.37500</td>\n",
              "      <td>3.62500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4946 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f27dd024-0a24-4342-b4a9-c0f35a995deb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f27dd024-0a24-4342-b4a9-c0f35a995deb 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-f27dd024-0a24-4342-b4a9-c0f35a995deb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "for i in quartile.index:\n",
        "    data.loc[(data['性別'] == i[0]) & (data['年歲'] == i[1]), 'lower_fence'] = lower_fence[i]\n",
        "    data.loc[(data['性別'] == i[0]) & (data['年歲'] == i[1]), 'upper_fence'] = upper_fence[i]\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "ae907cfc-98f3-4260-807f-416a6170e167",
        "id": "i70_AJTGgtAy"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     性別        實歲  球面度數    眼鏡  散瞳劑 角膜塑型片  年歲   治療處置分類  lower_fence  \\\n",
              "0     男  6.916667 -1.50   NaN  散瞳劑   NaN   7      散瞳劑     -3.75000   \n",
              "1     男  7.166667 -1.50   NaN  NaN   NaN   7      不處置     -3.75000   \n",
              "2     男  7.333333 -1.50   NaN  NaN   NaN   7      不處置     -3.75000   \n",
              "3     女  7.583333 -1.00   NaN  散瞳劑   NaN   8      散瞳劑     -5.00000   \n",
              "4     女  7.666667 -1.50  一般眼鏡  散瞳劑   NaN   8  一般眼鏡散瞳劑     -5.00000   \n",
              "...  ..       ...   ...   ...  ...   ...  ..      ...          ...   \n",
              "4339  男  6.583333  1.00   NaN  NaN   NaN   7      不處置     -3.75000   \n",
              "4341  女  6.416667  1.25   NaN  散瞳劑   NaN   6      散瞳劑     -3.09375   \n",
              "4343  女  6.083333  0.50   NaN  NaN   NaN   6      不處置     -3.09375   \n",
              "4344  女  6.583333 -0.75   NaN  NaN   NaN   7      不處置     -3.25000   \n",
              "4345  男  6.000000  1.25   NaN  NaN   NaN   6      不處置     -3.37500   \n",
              "\n",
              "      upper_fence  \n",
              "0         2.25000  \n",
              "1         2.25000  \n",
              "2         2.25000  \n",
              "3         3.00000  \n",
              "4         3.00000  \n",
              "...           ...  \n",
              "4339      2.25000  \n",
              "4341      4.65625  \n",
              "4343      4.65625  \n",
              "4344      2.75000  \n",
              "4345      3.62500  \n",
              "\n",
              "[4521 rows x 10 columns]"
            ],
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              "      <th></th>\n",
              "      <th>性別</th>\n",
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              "      <th>3</th>\n",
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              "      <td>7.583333</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>散瞳劑</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>散瞳劑</td>\n",
              "      <td>-5.00000</td>\n",
              "      <td>3.00000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>女</td>\n",
              "      <td>7.666667</td>\n",
              "      <td>-1.50</td>\n",
              "      <td>一般眼鏡</td>\n",
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              "      <td>-5.00000</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4339</th>\n",
              "      <td>男</td>\n",
              "      <td>6.583333</td>\n",
              "      <td>1.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>不處置</td>\n",
              "      <td>-3.75000</td>\n",
              "      <td>2.25000</td>\n",
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              "    <tr>\n",
              "      <th>4341</th>\n",
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              "      <td>6.416667</td>\n",
              "      <td>1.25</td>\n",
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              "      <td>4.65625</td>\n",
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              "    <tr>\n",
              "      <th>4343</th>\n",
              "      <td>女</td>\n",
              "      <td>6.083333</td>\n",
              "      <td>0.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
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              "      <td>-3.09375</td>\n",
              "      <td>4.65625</td>\n",
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              "    <tr>\n",
              "      <th>4344</th>\n",
              "      <td>女</td>\n",
              "      <td>6.583333</td>\n",
              "      <td>-0.75</td>\n",
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              "      <td>7</td>\n",
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              "<p>4521 rows × 10 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "data_without_outliers = data[(data['球面度數'] > data['lower_fence']) & (data['球面度數'] < data['upper_fence'])]\n",
        "data_without_outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "775d9662-f6a6-42e5-ca2d-a9e8dc564365",
        "id": "pECRmCixgtAy"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "性別  治療處置分類   \n",
              "女   一般眼鏡        -0.330001\n",
              "    一般眼鏡散瞳劑     -0.282083\n",
              "    一般眼鏡角膜塑型片    0.500000\n",
              "    不處置         -0.269612\n",
              "    散瞳劑         -0.096504\n",
              "    散瞳劑角膜塑型片     0.000000\n",
              "    角膜塑型片       -0.488897\n",
              "男   一般眼鏡        -0.343222\n",
              "    一般眼鏡散瞳劑     -0.332038\n",
              "    一般眼鏡角膜塑型片    0.000000\n",
              "    不處置         -0.237713\n",
              "    散瞳劑         -0.248002\n",
              "    散瞳劑角膜塑型片     0.297210\n",
              "    角膜塑型片       -0.214276\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "slope_groupby = data_without_outliers.groupby(['性別', '治療處置分類']).apply(lambda group: LinearRegression().fit(group['實歲'].to_numpy().reshape(-1, 1), group['球面度數'].to_numpy().reshape(-1, 1)).coef_[0][0])\n",
        "slope_groupby"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a9034a07-186a-46b0-c4ac-c1661e9fedf6",
        "id": "wUCl7zBOgtAy"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       P100    P50     P25    P10    P0\n",
              "性別 年歲                                  \n",
              "女  0   7.00  6.250  3.7500  2.750  2.50\n",
              "   1   3.00  2.000  1.0000  0.550  0.50\n",
              "   2   7.00  2.000  1.2500  0.075  0.00\n",
              "   3   4.50  0.250 -0.2500 -3.500 -4.25\n",
              "   4   5.50  0.625  0.0000 -0.500 -3.50\n",
              "   5   3.50  0.500  0.0000  0.000 -1.75\n",
              "   6   4.50  0.125  0.0000 -0.750 -3.00\n",
              "   7   2.50 -0.250 -1.0000 -1.825 -3.00\n",
              "   8   2.75 -0.750 -2.0000 -3.225 -4.75\n",
              "   9   1.75 -1.000 -1.5000 -2.500 -3.50\n",
              "   10  1.25 -1.000 -2.0000 -2.750 -4.00\n",
              "   11  1.00 -1.250 -2.0000 -2.500 -3.75\n",
              "   12  2.25 -1.375 -2.2500 -3.250 -4.25\n",
              "   13  1.00 -2.750 -3.2500 -3.875 -5.50\n",
              "   14  1.25 -3.000 -3.7500 -4.250 -4.50\n",
              "   15  1.50 -2.500 -4.0000 -5.000 -6.75\n",
              "   16  0.75 -2.375 -4.0000 -6.500 -7.00\n",
              "   17 -3.50 -3.500 -3.6250 -3.700 -3.75\n",
              "   18  0.50  0.375  0.3125  0.275  0.25\n",
              "   19  0.00 -1.875 -4.1875 -4.975 -5.50\n",
              "   28  0.50 -0.125 -0.4375 -0.625 -0.75\n",
              "男  0   3.00  2.500  2.1250  2.000  2.00\n",
              "   1   5.50  3.750  1.7500  0.500  0.00\n",
              "   2   5.00  2.000  1.0000  1.000  0.50\n",
              "   3   2.50  0.250  0.0000 -0.450 -1.50\n",
              "   4   3.50  0.750  0.0000 -0.500 -2.00\n",
              "   5   2.75  0.000 -0.2500 -1.000 -2.00\n",
              "   6   3.50  0.000 -0.7500 -1.500 -3.25\n",
              "   7   2.00 -0.500 -1.2500 -2.000 -3.50\n",
              "   8   2.00 -0.500 -1.2500 -2.000 -3.50\n",
              "   9   1.50 -0.750 -1.2500 -2.000 -3.25\n",
              "   10  1.50 -1.000 -2.0000 -3.000 -4.00\n",
              "   11  4.00 -1.250 -3.0000 -4.150 -7.25\n",
              "   12  3.50 -1.750 -3.5000 -5.075 -8.00\n",
              "   13  4.25 -1.750 -4.0000 -5.000 -9.00\n",
              "   14  3.75 -1.625 -3.8125 -5.000 -8.50\n",
              "   15  0.50 -1.250 -3.2500 -4.250 -5.75\n",
              "   16  0.00 -4.500 -6.2500 -6.500 -8.75\n",
              "   17 -3.00 -5.125 -5.8750 -6.175 -6.25\n",
              "   18  0.00 -2.250 -4.7500 -5.875 -6.25\n",
              "   44 -8.25 -9.000 -9.3750 -9.600 -9.75"
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>性別</th>\n",
              "      <th>年歲</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"21\" valign=\"top\">女</th>\n",
              "      <th>0</th>\n",
              "      <td>7.00</td>\n",
              "      <td>6.250</td>\n",
              "      <td>3.7500</td>\n",
              "      <td>2.750</td>\n",
              "      <td>2.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.00</td>\n",
              "      <td>2.000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.00</td>\n",
              "      <td>2.000</td>\n",
              "      <td>1.2500</td>\n",
              "      <td>0.075</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.50</td>\n",
              "      <td>0.250</td>\n",
              "      <td>-0.2500</td>\n",
              "      <td>-3.500</td>\n",
              "      <td>-4.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.50</td>\n",
              "      <td>0.625</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.500</td>\n",
              "      <td>-3.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.50</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.50</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.750</td>\n",
              "      <td>-3.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.50</td>\n",
              "      <td>-0.250</td>\n",
              "      <td>-1.0000</td>\n",
              "      <td>-1.825</td>\n",
              "      <td>-3.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.75</td>\n",
              "      <td>-0.750</td>\n",
              "      <td>-2.0000</td>\n",
              "      <td>-3.225</td>\n",
              "      <td>-4.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.75</td>\n",
              "      <td>-1.000</td>\n",
              "      <td>-1.5000</td>\n",
              "      <td>-2.500</td>\n",
              "      <td>-3.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1.25</td>\n",
              "      <td>-1.000</td>\n",
              "      <td>-2.0000</td>\n",
              "      <td>-2.750</td>\n",
              "      <td>-4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.00</td>\n",
              "      <td>-1.250</td>\n",
              "      <td>-2.0000</td>\n",
              "      <td>-2.500</td>\n",
              "      <td>-3.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2.25</td>\n",
              "      <td>-1.375</td>\n",
              "      <td>-2.2500</td>\n",
              "      <td>-3.250</td>\n",
              "      <td>-4.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.00</td>\n",
              "      <td>-2.750</td>\n",
              "      <td>-3.2500</td>\n",
              "      <td>-3.875</td>\n",
              "      <td>-5.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.25</td>\n",
              "      <td>-3.000</td>\n",
              "      <td>-3.7500</td>\n",
              "      <td>-4.250</td>\n",
              "      <td>-4.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1.50</td>\n",
              "      <td>-2.500</td>\n",
              "      <td>-4.0000</td>\n",
              "      <td>-5.000</td>\n",
              "      <td>-6.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.75</td>\n",
              "      <td>-2.375</td>\n",
              "      <td>-4.0000</td>\n",
              "      <td>-6.500</td>\n",
              "      <td>-7.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>-3.50</td>\n",
              "      <td>-3.500</td>\n",
              "      <td>-3.6250</td>\n",
              "      <td>-3.700</td>\n",
              "      <td>-3.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.50</td>\n",
              "      <td>0.375</td>\n",
              "      <td>0.3125</td>\n",
              "      <td>0.275</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-1.875</td>\n",
              "      <td>-4.1875</td>\n",
              "      <td>-4.975</td>\n",
              "      <td>-5.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.50</td>\n",
              "      <td>-0.125</td>\n",
              "      <td>-0.4375</td>\n",
              "      <td>-0.625</td>\n",
              "      <td>-0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"20\" valign=\"top\">男</th>\n",
              "      <th>0</th>\n",
              "      <td>3.00</td>\n",
              "      <td>2.500</td>\n",
              "      <td>2.1250</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.50</td>\n",
              "      <td>3.750</td>\n",
              "      <td>1.7500</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.00</td>\n",
              "      <td>2.000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.50</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.450</td>\n",
              "      <td>-1.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.50</td>\n",
              "      <td>0.750</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.500</td>\n",
              "      <td>-2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.75</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.2500</td>\n",
              "      <td>-1.000</td>\n",
              "      <td>-2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3.50</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.7500</td>\n",
              "      <td>-1.500</td>\n",
              "      <td>-3.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.00</td>\n",
              "      <td>-0.500</td>\n",
              "      <td>-1.2500</td>\n",
              "      <td>-2.000</td>\n",
              "      <td>-3.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.00</td>\n",
              "      <td>-0.500</td>\n",
              "      <td>-1.2500</td>\n",
              "      <td>-2.000</td>\n",
              "      <td>-3.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.50</td>\n",
              "      <td>-0.750</td>\n",
              "      <td>-1.2500</td>\n",
              "      <td>-2.000</td>\n",
              "      <td>-3.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1.50</td>\n",
              "      <td>-1.000</td>\n",
              "      <td>-2.0000</td>\n",
              "      <td>-3.000</td>\n",
              "      <td>-4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4.00</td>\n",
              "      <td>-1.250</td>\n",
              "      <td>-3.0000</td>\n",
              "      <td>-4.150</td>\n",
              "      <td>-7.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3.50</td>\n",
              "      <td>-1.750</td>\n",
              "      <td>-3.5000</td>\n",
              "      <td>-5.075</td>\n",
              "      <td>-8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>4.25</td>\n",
              "      <td>-1.750</td>\n",
              "      <td>-4.0000</td>\n",
              "      <td>-5.000</td>\n",
              "      <td>-9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>3.75</td>\n",
              "      <td>-1.625</td>\n",
              "      <td>-3.8125</td>\n",
              "      <td>-5.000</td>\n",
              "      <td>-8.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.50</td>\n",
              "      <td>-1.250</td>\n",
              "      <td>-3.2500</td>\n",
              "      <td>-4.250</td>\n",
              "      <td>-5.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-4.500</td>\n",
              "      <td>-6.2500</td>\n",
              "      <td>-6.500</td>\n",
              "      <td>-8.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>-3.00</td>\n",
              "      <td>-5.125</td>\n",
              "      <td>-5.8750</td>\n",
              "      <td>-6.175</td>\n",
              "      <td>-6.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.00</td>\n",
              "      <td>-2.250</td>\n",
              "      <td>-4.7500</td>\n",
              "      <td>-5.875</td>\n",
              "      <td>-6.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>-8.25</td>\n",
              "      <td>-9.000</td>\n",
              "      <td>-9.3750</td>\n",
              "      <td>-9.600</td>\n",
              "      <td>-9.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d4e31e3-6ea5-4941-a4ff-460875d0abb3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4d4e31e3-6ea5-4941-a4ff-460875d0abb3 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-4d4e31e3-6ea5-4941-a4ff-460875d0abb3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "stacked_area = data_without_outliers.groupby(['性別', '年歲'])['球面度數'].aggregate([percentile(100), percentile(50), percentile(25), percentile(10), percentile(0)])\n",
        "stacked_area"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "YhlN9oy8gtAz"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def plot(sex):\n",
        "    area = stacked_area.loc[sex].loc[3:16]\n",
        "    MorF = {'男': 'Male', '女': 'Female'}[sex]\n",
        "    plt.fill_between(area.index, area['P50'], area['P100'], color='lightgreen', alpha=0.6, label='50~100%')\n",
        "    plt.fill_between(area.index, area['P25'], area['P50'], color='yellow', alpha=0.6, label='25~50%')\n",
        "    plt.fill_between(area.index, area['P10'], area['P25'], color='orange', alpha=0.6, label='10~25%')\n",
        "    plt.fill_between(area.index, area['P0'], area['P10'], color='red', alpha=0.6, label='0~10%')\n",
        "    plt.title(f'Trend of {MorF} Children in Taiwan', fontsize=12)\n",
        "    plt.legend(loc='lower right')\n",
        "    plt.xticks(range(3, 17))\n",
        "    plt.yticks(range(-8, 7))\n",
        "    plt.xlabel('Age', fontsize=12)\n",
        "    plt.ylabel('SPH', fontsize=12)\n",
        "    plt.margins(0)\n",
        "    plt.text(16, -10, f'{db_version}', horizontalalignment='right', fontsize=8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "edd2cc32-ddda-4ad6-d2dc-0c2360e5a02c",
        "id": "UCM8pfq5gtAz"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot('男')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "b4c27dd1-7a2f-4895-cdf2-c921166e1df9",
        "id": "VNcfWH1ZgtA0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot('女')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_to_plot['球面度數'] = [db_version, slope_groupby.copy(), stacked_area.copy()]"
      ],
      "metadata": {
        "id": "MszIHOAMnxre"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 寫data_to_plot.pkl"
      ],
      "metadata": {
        "id": "KJxpSjeTj50F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('data_to_plot.pkl', 'wb') as f:\n",
        "    pickle.dump(data_to_plot, f)\n",
        "files.download('data_to_plot.pkl')"
      ],
      "metadata": {
        "id": "1rd7umm6kLwA",
        "outputId": "851b8f74-be32-4409-aafa-b4511e8db423",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
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