ferritin.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1VjXSMYQXMwqq80sRALv3bjOr3Aw2nIxt",
      "authorship_tag": "ABX9TyNCIsjirKI7ZrurJvuiyx6t",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ivcvy/boxplot-without-dataset/blob/main/ferritin.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_path = '/content/drive/MyDrive/ferritin/all.csv'"
      ],
      "metadata": {
        "id": "PydpKS2ZTV9K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ia3TGOLQTFtf"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "all = pd.read_csv(all_path)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6BTdyVz0TRXd",
        "outputId": "430a318b-babc-425f-a6da-b2cde15f4baa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      # ferritin statistics     timing     overall statistics       med  \\\n",
              "0    69        median (IQR)       peak        49 (17.2-153.3)     49.00   \n",
              "1    45           mean ± SD  not given                28 ± 32     28.00   \n",
              "2    11           mean ± SD   baseline                45 ± 30     45.00   \n",
              "3    54          mean (IQR)   baseline     141.3 (27.9-188.0)    141.30   \n",
              "4    88           mean ± SD   baseline              143 ± 126    143.00   \n",
              "5   109        median (IQR)  not given         144 (80.1-290)    144.00   \n",
              "6    32           mean ± SD  not given          167.2 ± 256.6    167.20   \n",
              "7    19           mean ± SD  not given        424.35 ± 763.85    424.35   \n",
              "8    47           mean ± SD   baseline            1229 ± 1689   1229.00   \n",
              "9    15        median (IQR)  not given          413 (95-1500)    413.00   \n",
              "10   37        median (IQR)  not given          309 (156-696)    309.00   \n",
              "11   55        median (IQR)       peak          350 (170-733)    350.00   \n",
              "12   60        median (IQR)   baseline          438 (420-846)    438.00   \n",
              "13   97        median (IQR)       peak          463 (225-911)    463.00   \n",
              "14   26        median (IQR)   baseline     535 (326.8-1533.1)    535.00   \n",
              "15   25        median (IQR)   baseline         915 (475-1650)    915.00   \n",
              "16    9        median (IQR)   baseline         668 (420-2714)    668.00   \n",
              "17   66        median (IQR)  not given        1067 (272-1709)   1067.00   \n",
              "18   44      median (range)   baseline  4825 (2000.0-10324.5)   4825.00   \n",
              "19   78           mean ± SD  not given          41785 ± 41448  41785.00   \n",
              "20   89      median (range)  not given      6341 (765-100000)   6341.00   \n",
              "21   80        median (IQR)  not given      150.5 (82.25-362)    150.50   \n",
              "22   77        median (IQR)       peak     149.5 (81.7-377.2)    149.50   \n",
              "23   74        median (IQR)   baseline        172 (118.3-514)    172.00   \n",
              "24   43        median (IQR)   baseline         798 (378-3205)    798.00   \n",
              "25   85           mean ± SD       peak            2025 ± 6233   2025.00   \n",
              "26   38           mean ± SD  not given        4989.3 ± 1978.3   4989.30   \n",
              "27  113        median (IQR)   baseline          303 (21-2210)    303.00   \n",
              "28  103        median (IQR)   baseline         581 (287-1283)    581.00   \n",
              "29   65        median (IQR)       peak   60214 (12000-130000)  60214.00   \n",
              "\n",
              "      whislo        q1        q3    whishi  \\\n",
              "0      17.20     49.00     49.00     153.3   \n",
              "1       0.00     28.00     28.00      60.0   \n",
              "2      15.00     45.00     45.00      75.0   \n",
              "3      27.90    141.30    141.30     188.0   \n",
              "4      17.00    143.00    143.00     269.0   \n",
              "5      80.10    144.00    144.00     290.0   \n",
              "6       0.00    167.20    167.20     423.8   \n",
              "7       0.00    424.35    424.35    1188.2   \n",
              "8       0.00   1229.00   1229.00    2918.0   \n",
              "9      95.00    413.00    413.00    1500.0   \n",
              "10    156.00    309.00    309.00     696.0   \n",
              "11    170.00    350.00    350.00     733.0   \n",
              "12    420.00    438.00    438.00     846.0   \n",
              "13    225.00    463.00    463.00     911.0   \n",
              "14    326.80    535.00    535.00    1533.1   \n",
              "15    475.00    915.00    915.00    1650.0   \n",
              "16    420.00    668.00    668.00    2714.0   \n",
              "17    272.00   1067.00   1067.00    1709.0   \n",
              "18   2000.00   4825.00   4825.00   10324.5   \n",
              "19    337.00  41785.00  41785.00   83233.0   \n",
              "20    765.00   6341.00   6341.00  100000.0   \n",
              "21     82.25    150.50    150.50     362.0   \n",
              "22     81.70    149.50    149.50     377.2   \n",
              "23    118.30    172.00    172.00     514.0   \n",
              "24    378.00    798.00    798.00    3205.0   \n",
              "25      0.00   2025.00   2025.00    8258.0   \n",
              "26   3011.00   4989.30   4989.30    6967.6   \n",
              "27     21.00    303.00    303.00    2210.0   \n",
              "28    287.00    581.00    581.00    1283.0   \n",
              "29  12000.00  60214.00  60214.00  130000.0   \n",
              "\n",
              "                                                label  \n",
              "0                                   Hospitalized [21]  \n",
              "1                                Febrile seizure [24]  \n",
              "2         HF secondary to dilated cardiomyopathy [25]  \n",
              "3                                            IPH [26]  \n",
              "4                  Very low birth weight infants [74]  \n",
              "5                                       COVID-19 [33]  \n",
              "6                                       COVID-19 [32]  \n",
              "7                                       COVID-19 [31]  \n",
              "8                                       COVID-19 [34]  \n",
              "9                                COVID-19 in IEI [35]  \n",
              "10                                         MIS-C [55]  \n",
              "11                                         MIS-C [54]  \n",
              "12                                         MIS-C [56]  \n",
              "13                                         MIS-C [57]  \n",
              "14                                         MIS-C [60]  \n",
              "15                                       PIMS-TS [50]  \n",
              "16                                  Scrub typhus [37]  \n",
              "17                                 Kawa-COVID-19 [45]  \n",
              "18                                           HLH [41]  \n",
              "19                                           MAS [47]  \n",
              "20                 Secondary HLH/sepsis/MODS/MAS [43]  \n",
              "21                                        Sepsis [69]  \n",
              "22                                        Sepsis [68]  \n",
              "23                                        Sepsis [66]  \n",
              "24                                        Sepsis [67]  \n",
              "25                                        Sepsis [63]  \n",
              "26  Severe sepsis in transfusion dependent thalass...  \n",
              "27                   Severe sepsis, septic shock [72]  \n",
              "28                                  Septic shock [71]  \n",
              "29                     Cytokine release syndrome [62]  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6fbfec6-931b-4726-a794-5a4cbbac0368\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>#</th>\n",
              "      <th>ferritin statistics</th>\n",
              "      <th>timing</th>\n",
              "      <th>overall statistics</th>\n",
              "      <th>med</th>\n",
              "      <th>whislo</th>\n",
              "      <th>q1</th>\n",
              "      <th>q3</th>\n",
              "      <th>whishi</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>69</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>peak</td>\n",
              "      <td>49 (17.2-153.3)</td>\n",
              "      <td>49.00</td>\n",
              "      <td>17.20</td>\n",
              "      <td>49.00</td>\n",
              "      <td>49.00</td>\n",
              "      <td>153.3</td>\n",
              "      <td>Hospitalized [21]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45</td>\n",
              "      <td>mean ± SD</td>\n",
              "      <td>not given</td>\n",
              "      <td>28 ± 32</td>\n",
              "      <td>28.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>28.00</td>\n",
              "      <td>28.00</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Febrile seizure [24]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11</td>\n",
              "      <td>mean ± SD</td>\n",
              "      <td>baseline</td>\n",
              "      <td>45 ± 30</td>\n",
              "      <td>45.00</td>\n",
              "      <td>15.00</td>\n",
              "      <td>45.00</td>\n",
              "      <td>45.00</td>\n",
              "      <td>75.0</td>\n",
              "      <td>HF secondary to dilated cardiomyopathy [25]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>54</td>\n",
              "      <td>mean (IQR)</td>\n",
              "      <td>baseline</td>\n",
              "      <td>141.3 (27.9-188.0)</td>\n",
              "      <td>141.30</td>\n",
              "      <td>27.90</td>\n",
              "      <td>141.30</td>\n",
              "      <td>141.30</td>\n",
              "      <td>188.0</td>\n",
              "      <td>IPH [26]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>88</td>\n",
              "      <td>mean ± SD</td>\n",
              "      <td>baseline</td>\n",
              "      <td>143 ± 126</td>\n",
              "      <td>143.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>143.00</td>\n",
              "      <td>143.00</td>\n",
              "      <td>269.0</td>\n",
              "      <td>Very low birth weight infants [74]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>109</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>not given</td>\n",
              "      <td>144 (80.1-290)</td>\n",
              "      <td>144.00</td>\n",
              "      <td>80.10</td>\n",
              "      <td>144.00</td>\n",
              "      <td>144.00</td>\n",
              "      <td>290.0</td>\n",
              "      <td>COVID-19 [33]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>32</td>\n",
              "      <td>mean ± SD</td>\n",
              "      <td>not given</td>\n",
              "      <td>167.2 ± 256.6</td>\n",
              "      <td>167.20</td>\n",
              "      <td>0.00</td>\n",
              "      <td>167.20</td>\n",
              "      <td>167.20</td>\n",
              "      <td>423.8</td>\n",
              "      <td>COVID-19 [32]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>19</td>\n",
              "      <td>mean ± SD</td>\n",
              "      <td>not given</td>\n",
              "      <td>424.35 ± 763.85</td>\n",
              "      <td>424.35</td>\n",
              "      <td>0.00</td>\n",
              "      <td>424.35</td>\n",
              "      <td>424.35</td>\n",
              "      <td>1188.2</td>\n",
              "      <td>COVID-19 [31]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>47</td>\n",
              "      <td>mean ± SD</td>\n",
              "      <td>baseline</td>\n",
              "      <td>1229 ± 1689</td>\n",
              "      <td>1229.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1229.00</td>\n",
              "      <td>1229.00</td>\n",
              "      <td>2918.0</td>\n",
              "      <td>COVID-19 [34]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>15</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>not given</td>\n",
              "      <td>413 (95-1500)</td>\n",
              "      <td>413.00</td>\n",
              "      <td>95.00</td>\n",
              "      <td>413.00</td>\n",
              "      <td>413.00</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>COVID-19 in IEI [35]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>37</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>not given</td>\n",
              "      <td>309 (156-696)</td>\n",
              "      <td>309.00</td>\n",
              "      <td>156.00</td>\n",
              "      <td>309.00</td>\n",
              "      <td>309.00</td>\n",
              "      <td>696.0</td>\n",
              "      <td>MIS-C [55]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>55</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>peak</td>\n",
              "      <td>350 (170-733)</td>\n",
              "      <td>350.00</td>\n",
              "      <td>170.00</td>\n",
              "      <td>350.00</td>\n",
              "      <td>350.00</td>\n",
              "      <td>733.0</td>\n",
              "      <td>MIS-C [54]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>60</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>baseline</td>\n",
              "      <td>438 (420-846)</td>\n",
              "      <td>438.00</td>\n",
              "      <td>420.00</td>\n",
              "      <td>438.00</td>\n",
              "      <td>438.00</td>\n",
              "      <td>846.0</td>\n",
              "      <td>MIS-C [56]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>97</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>peak</td>\n",
              "      <td>463 (225-911)</td>\n",
              "      <td>463.00</td>\n",
              "      <td>225.00</td>\n",
              "      <td>463.00</td>\n",
              "      <td>463.00</td>\n",
              "      <td>911.0</td>\n",
              "      <td>MIS-C [57]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>26</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>baseline</td>\n",
              "      <td>535 (326.8-1533.1)</td>\n",
              "      <td>535.00</td>\n",
              "      <td>326.80</td>\n",
              "      <td>535.00</td>\n",
              "      <td>535.00</td>\n",
              "      <td>1533.1</td>\n",
              "      <td>MIS-C [60]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>25</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>baseline</td>\n",
              "      <td>915 (475-1650)</td>\n",
              "      <td>915.00</td>\n",
              "      <td>475.00</td>\n",
              "      <td>915.00</td>\n",
              "      <td>915.00</td>\n",
              "      <td>1650.0</td>\n",
              "      <td>PIMS-TS [50]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>9</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>baseline</td>\n",
              "      <td>668 (420-2714)</td>\n",
              "      <td>668.00</td>\n",
              "      <td>420.00</td>\n",
              "      <td>668.00</td>\n",
              "      <td>668.00</td>\n",
              "      <td>2714.0</td>\n",
              "      <td>Scrub typhus [37]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>66</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>not given</td>\n",
              "      <td>1067 (272-1709)</td>\n",
              "      <td>1067.00</td>\n",
              "      <td>272.00</td>\n",
              "      <td>1067.00</td>\n",
              "      <td>1067.00</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>Kawa-COVID-19 [45]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>44</td>\n",
              "      <td>median (range)</td>\n",
              "      <td>baseline</td>\n",
              "      <td>4825 (2000.0-10324.5)</td>\n",
              "      <td>4825.00</td>\n",
              "      <td>2000.00</td>\n",
              "      <td>4825.00</td>\n",
              "      <td>4825.00</td>\n",
              "      <td>10324.5</td>\n",
              "      <td>HLH [41]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>78</td>\n",
              "      <td>mean ± SD</td>\n",
              "      <td>not given</td>\n",
              "      <td>41785 ± 41448</td>\n",
              "      <td>41785.00</td>\n",
              "      <td>337.00</td>\n",
              "      <td>41785.00</td>\n",
              "      <td>41785.00</td>\n",
              "      <td>83233.0</td>\n",
              "      <td>MAS [47]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>89</td>\n",
              "      <td>median (range)</td>\n",
              "      <td>not given</td>\n",
              "      <td>6341 (765-100000)</td>\n",
              "      <td>6341.00</td>\n",
              "      <td>765.00</td>\n",
              "      <td>6341.00</td>\n",
              "      <td>6341.00</td>\n",
              "      <td>100000.0</td>\n",
              "      <td>Secondary HLH/sepsis/MODS/MAS [43]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>80</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>not given</td>\n",
              "      <td>150.5 (82.25-362)</td>\n",
              "      <td>150.50</td>\n",
              "      <td>82.25</td>\n",
              "      <td>150.50</td>\n",
              "      <td>150.50</td>\n",
              "      <td>362.0</td>\n",
              "      <td>Sepsis [69]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>77</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>peak</td>\n",
              "      <td>149.5 (81.7-377.2)</td>\n",
              "      <td>149.50</td>\n",
              "      <td>81.70</td>\n",
              "      <td>149.50</td>\n",
              "      <td>149.50</td>\n",
              "      <td>377.2</td>\n",
              "      <td>Sepsis [68]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>74</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>baseline</td>\n",
              "      <td>172 (118.3-514)</td>\n",
              "      <td>172.00</td>\n",
              "      <td>118.30</td>\n",
              "      <td>172.00</td>\n",
              "      <td>172.00</td>\n",
              "      <td>514.0</td>\n",
              "      <td>Sepsis [66]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>43</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>baseline</td>\n",
              "      <td>798 (378-3205)</td>\n",
              "      <td>798.00</td>\n",
              "      <td>378.00</td>\n",
              "      <td>798.00</td>\n",
              "      <td>798.00</td>\n",
              "      <td>3205.0</td>\n",
              "      <td>Sepsis [67]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>85</td>\n",
              "      <td>mean ± SD</td>\n",
              "      <td>peak</td>\n",
              "      <td>2025 ± 6233</td>\n",
              "      <td>2025.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2025.00</td>\n",
              "      <td>2025.00</td>\n",
              "      <td>8258.0</td>\n",
              "      <td>Sepsis [63]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>38</td>\n",
              "      <td>mean ± SD</td>\n",
              "      <td>not given</td>\n",
              "      <td>4989.3 ± 1978.3</td>\n",
              "      <td>4989.30</td>\n",
              "      <td>3011.00</td>\n",
              "      <td>4989.30</td>\n",
              "      <td>4989.30</td>\n",
              "      <td>6967.6</td>\n",
              "      <td>Severe sepsis in transfusion dependent thalass...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>113</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>baseline</td>\n",
              "      <td>303 (21-2210)</td>\n",
              "      <td>303.00</td>\n",
              "      <td>21.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>303.00</td>\n",
              "      <td>2210.0</td>\n",
              "      <td>Severe sepsis, septic shock [72]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>103</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>baseline</td>\n",
              "      <td>581 (287-1283)</td>\n",
              "      <td>581.00</td>\n",
              "      <td>287.00</td>\n",
              "      <td>581.00</td>\n",
              "      <td>581.00</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>Septic shock [71]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>65</td>\n",
              "      <td>median (IQR)</td>\n",
              "      <td>peak</td>\n",
              "      <td>60214 (12000-130000)</td>\n",
              "      <td>60214.00</td>\n",
              "      <td>12000.00</td>\n",
              "      <td>60214.00</td>\n",
              "      <td>60214.00</td>\n",
              "      <td>130000.0</td>\n",
              "      <td>Cytokine release syndrome [62]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6fbfec6-931b-4726-a794-5a4cbbac0368')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b6fbfec6-931b-4726-a794-5a4cbbac0368 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-b6fbfec6-931b-4726-a794-5a4cbbac0368');\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
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert to float\n",
        "\n",
        "all[[\"med\"]] = all[[\"med\"]].astype(float)\n",
        "all[[\"q1\"]] = all[[\"q1\"]].astype(float)\n",
        "all[[\"q3\"]] = all[[\"q3\"]].astype(float)\n",
        "all[[\"whislo\"]] = all[[\"whislo\"]].astype(float)\n",
        "all[[\"whishi\"]] = all[[\"whishi\"]].astype(float)"
      ],
      "metadata": {
        "id": "eBcTn0txT2MU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# logarithmic scale for better visual\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "all[[\"med\"]] = np.log10(all[[\"med\"]])\n",
        "all[[\"q1\"]] = np.log10(all[[\"q1\"]])\n",
        "all[[\"q3\"]] = np.log10(all[[\"q3\"]])\n",
        "all[[\"whislo\"]] = np.log10(all[[\"whislo\"]])\n",
        "all[[\"whishi\"]] = np.log10(all[[\"whishi\"]])"
      ],
      "metadata": {
        "id": "4GaLeaJYcIYI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert the columns to dictionary\n",
        "\n",
        "all1 = all[[\"med\",\"q1\",\"q3\",\"whislo\",\"whishi\"]]\n",
        "dict1 = all1.to_dict(orient='index')\n",
        "allfinal = dict1.values()\n",
        "allfinal"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGPuTvLIcFYL",
        "outputId": "c5eb87c4-c4eb-4c36-9613-b71eabc432bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values([{'med': 1.6901960800285136, 'q1': 1.6901960800285136, 'q3': 1.6901960800285136, 'whislo': 1.235528446907549, 'whishi': 2.1855421548543754}, {'med': 1.4471580313422192, 'q1': 1.4471580313422192, 'q3': 1.4471580313422192, 'whislo': -inf, 'whishi': 1.7781512503836436}, {'med': 1.6532125137753437, 'q1': 1.6532125137753437, 'q3': 1.6532125137753437, 'whislo': 1.1760912590556813, 'whishi': 1.8750612633917}, {'med': 2.1501421618485588, 'q1': 2.1501421618485588, 'q3': 2.1501421618485588, 'whislo': 1.4456042032735976, 'whishi': 2.27415784926368}, {'med': 2.155336037465062, 'q1': 2.155336037465062, 'q3': 2.155336037465062, 'whislo': 1.2304489213782739, 'whishi': 2.429752280002408}, {'med': 2.1583624920952498, 'q1': 2.1583624920952498, 'q3': 2.1583624920952498, 'whislo': 1.9036325160842376, 'whishi': 2.462397997898956}, {'med': 2.2232362731029975, 'q1': 2.2232362731029975, 'q3': 2.2232362731029975, 'whislo': -inf, 'whishi': 2.627160952374776}, {'med': 2.627724206512672, 'q1': 2.627724206512672, 'q3': 2.627724206512672, 'whislo': -inf, 'whishi': 3.074889548040668}, {'med': 3.089551882886454, 'q1': 3.089551882886454, 'q3': 3.089551882886454, 'whislo': -inf, 'whishi': 3.4650852875574327}, {'med': 2.615950051656401, 'q1': 2.615950051656401, 'q3': 2.615950051656401, 'whislo': 1.9777236052888478, 'whishi': 3.1760912590556813}, {'med': 2.4899584794248346, 'q1': 2.4899584794248346, 'q3': 2.4899584794248346, 'whislo': 2.1931245983544616, 'whishi': 2.842609239610562}, {'med': 2.5440680443502757, 'q1': 2.5440680443502757, 'q3': 2.5440680443502757, 'whislo': 2.230448921378274, 'whishi': 2.8651039746411278}, {'med': 2.6414741105040993, 'q1': 2.6414741105040993, 'q3': 2.6414741105040993, 'whislo': 2.6232492903979003, 'whishi': 2.9273703630390235}, {'med': 2.6655809910179533, 'q1': 2.6655809910179533, 'q3': 2.6655809910179533, 'whislo': 2.3521825181113627, 'whishi': 2.9595183769729982}, {'med': 2.7283537820212285, 'q1': 2.7283537820212285, 'q3': 2.7283537820212285, 'whislo': 2.514282047860378, 'whishi': 3.1855704836422203}, {'med': 2.9614210940664485, 'q1': 2.9614210940664485, 'q3': 2.9614210940664485, 'whislo': 2.6766936096248664, 'whishi': 3.2174839442139063}, {'med': 2.824776462475546, 'q1': 2.824776462475546, 'q3': 2.824776462475546, 'whislo': 2.6232492903979003, 'whishi': 3.433609843323718}, {'med': 3.0281644194244697, 'q1': 3.0281644194244697, 'q3': 3.0281644194244697, 'whislo': 2.4345689040341987, 'whishi': 3.232742062720737}, {'med': 3.6834973176798114, 'q1': 3.6834973176798114, 'q3': 3.6834973176798114, 'whislo': 3.3010299956639813, 'whishi': 4.013869028609415}, {'med': 4.621020406503193, 'q1': 4.621020406503193, 'q3': 4.621020406503193, 'whislo': 2.5276299008713385, 'whishi': 4.920295548363157}, {'med': 3.8021577531869615, 'q1': 3.8021577531869615, 'q3': 3.8021577531869615, 'whislo': 2.8836614351536176, 'whishi': 5.0}, {'med': 2.1775364999298623, 'q1': 2.1775364999298623, 'q3': 2.1775364999298623, 'whislo': 1.915135906622012, 'whishi': 2.558708570533166}, {'med': 2.1746411926604483, 'q1': 2.1746411926604483, 'q3': 2.1746411926604483, 'whislo': 1.9122220565324155, 'whishi': 2.5765716840652906}, {'med': 2.2355284469075487, 'q1': 2.2355284469075487, 'q3': 2.2355284469075487, 'whislo': 2.0729847446279304, 'whishi': 2.710963118995276}, {'med': 2.9020028913507296, 'q1': 2.9020028913507296, 'q3': 2.9020028913507296, 'whislo': 2.577491799837225, 'whishi': 3.5058280338548364}, {'med': 3.3064250275506875, 'q1': 3.3064250275506875, 'q3': 3.3064250275506875, 'whislo': -inf, 'whishi': 3.9168748785386835}, {'med': 3.698039618276215, 'q1': 3.698039618276215, 'q3': 3.698039618276215, 'whislo': 3.4787107555127594, 'whishi': 3.843083210487105}, {'med': 2.481442628502305, 'q1': 2.481442628502305, 'q3': 2.481442628502305, 'whislo': 1.3222192947339193, 'whishi': 3.3443922736851106}, {'med': 2.7641761323903307, 'q1': 2.7641761323903307, 'q3': 2.7641761323903307, 'whislo': 2.4578818967339924, 'whishi': 3.1082266563749283}, {'med': 4.77969747823233, 'q1': 4.77969747823233, 'q3': 4.77969747823233, 'whislo': 4.079181246047625, 'whishi': 5.113943352306837}])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set up condition/disease names as y tick labels\n",
        "\n",
        "ylabel = all[[\"label\"]]\n",
        "ser1 = ylabel.squeeze()"
      ],
      "metadata": {
        "id": "ZlBOT1qhnsub"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# generate customized whisker plot\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "ylabel_list = ser1.tolist()\n",
        "xlabel_list = [\"1\", \"10\", \"100\", \"1,000\", \"10,000\", \"100,000\"]\n",
        "\n",
        "fig, ax = plt.subplots();\n",
        "ax.bxp(allfinal, showfliers=False, vert=False);\n",
        "\n",
        "x = ax.get_xticks()\n",
        "x_tick = np.arange(0, max(x), 1)\n",
        "plt.xticks(ticks = x_tick, labels = xlabel_list)\n",
        "ax.set_xlabel(\"Serum ferritin (ng/mL)\")\n",
        "\n",
        "y = ax.get_yticks()\n",
        "plt.yticks(ticks = y, labels = ylabel_list)\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = [1, 1]\n",
        "plt.rcParams[\"figure.autolayout\"] = True"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "hiH4QHSqT6Pa",
        "outputId": "a8496178-5068-4b8b-d3d1-c8a73805777d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig.savefig(\"all1.png\")"
      ],
      "metadata": {
        "id": "q65dRcj3dp9q"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}