statistical_analysis_over_time.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "This jupiter notebook transfomr weight data in long format (tidy) by day of infection. It also normalize the data from the day 0 (Infection)\n",
    "\n",
    "Statistical tests:\n",
    "1. Compute general stats per day of infection: Mean, Median, Std, Ci95, Count = Number of mice still alive at this day\n",
    "2. Median time to death\n",
    "3. Median time to death for mice that will not survive\n",
    "4. Mean time of survival"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np\n",
    "from statannotations.Annotator import Annotator\n",
    "from lifelines import KaplanMeierFitter\n",
    "from lifelines.utils import median_survival_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"./data/df_for_analysis.xlsx\",index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utils function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keep_weight_post_infection(x,weight_end = \"weight_T13\"):\n",
    "    \"\"\"\n",
    "    Given a DataFrame `x`, returns a Series containing the weights post-infection.\n",
    "\n",
    "    *Arguments*\n",
    "    - x: DataFrame containing the data.\n",
    "    - weight_end: Column name of the last weight measurement.\n",
    "\n",
    "    *Returns*\n",
    "    - shifted_series: Series containing the weights post-infection.\n",
    "    \"\"\"\n",
    "    # Extract relevant columns from the DataFrame\n",
    "    dates = x['Dates']\n",
    "    t_infection = x['Time_infection']\n",
    "    datas = x['weight_T_infection':weight_end]\n",
    "\n",
    "    # Find the date closest to the infection time\n",
    "    new_time_infection = dates[dates <= t_infection][-1]\n",
    "    location_of_TI = dates.get_loc(new_time_infection)\n",
    "\n",
    "    # Return the original series if the data at the infection time is NaN\n",
    "    if np.isnan(datas[location_of_TI]):\n",
    "        return datas\n",
    "    \n",
    "    # Shift the values of the input series by the specified index\n",
    "    shifted_series = pd.Series([np.nan] * len(datas), index=datas.index)\n",
    "    if location_of_TI == 0:\n",
    "        return datas\n",
    "    else:\n",
    "        shifted_series[:-location_of_TI] = datas.values.tolist()[location_of_TI:]\n",
    "    # Shift the values of the input series by the specified index\n",
    "    return shifted_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform data to long format\n",
    "\n",
    "Keep only data that are after the time of infeciton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ma7054\\AppData\\Local\\Temp\\ipykernel_17880\\2938565056.py:2: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:,\"weight_T_infection\":\"weight_T13\"] = df.loc[:,\"weight_T_infection\":\"weight_T13\"].apply(pd.to_numeric,errors='coerce')\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight_T_infection</th>\n",
       "      <th>weight_T1</th>\n",
       "      <th>weight_T2</th>\n",
       "      <th>weight_T3</th>\n",
       "      <th>weight_T4</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.92</td>\n",
       "      <td>21.72</td>\n",
       "      <td>20.96</td>\n",
       "      <td>19.38</td>\n",
       "      <td>18.16</td>\n",
       "      <td>16.44</td>\n",
       "      <td>15.49</td>\n",
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       "      <th>1</th>\n",
       "      <td>21.40</td>\n",
       "      <td>19.45</td>\n",
       "      <td>18.84</td>\n",
       "      <td>17.82</td>\n",
       "      <td>16.80</td>\n",
       "      <td>15.02</td>\n",
       "      <td>14.14</td>\n",
       "      <td>14.40</td>\n",
       "      <td>14.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.56</td>\n",
       "      <td>21.45</td>\n",
       "      <td>20.83</td>\n",
       "      <td>18.67</td>\n",
       "      <td>16.82</td>\n",
       "      <td>15.30</td>\n",
       "      <td>14.74</td>\n",
       "      <td>14.80</td>\n",
       "      <td>14.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>20.39</td>\n",
       "      <td>18.69</td>\n",
       "      <td>16.60</td>\n",
       "      <td>15.58</td>\n",
       "      <td>14.17</td>\n",
       "      <td>13.54</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>23.72</td>\n",
       "      <td>21.74</td>\n",
       "      <td>20.29</td>\n",
       "      <td>19.56</td>\n",
       "      <td>18.50</td>\n",
       "      <td>16.80</td>\n",
       "      <td>16.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2352</th>\n",
       "      <td>27.70</td>\n",
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       "      <td>21.10</td>\n",
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       "      <th>2353</th>\n",
       "      <td>26.10</td>\n",
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       "      <td>22.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>24.60</td>\n",
       "      <td>25.80</td>\n",
       "      <td>25.70</td>\n",
       "      <td>24.90</td>\n",
       "      <td>22.80</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>27.40</td>\n",
       "      <td>27.30</td>\n",
       "      <td>28.20</td>\n",
       "      <td>27.80</td>\n",
       "      <td>27.70</td>\n",
       "      <td>27.80</td>\n",
       "      <td>27.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>24.20</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "<p>2357 rows × 14 columns</p>\n",
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      ],
      "text/plain": [
       "      weight_T_infection  weight_T1  weight_T2  weight_T3  weight_T4  \\\n",
       "0                  23.92      21.72      20.96      19.38      18.16   \n",
       "1                  21.40      19.45      18.84      17.82      16.80   \n",
       "2                  22.56      21.45      20.83      18.67      16.82   \n",
       "3                  20.39      18.69      16.60      15.58      14.17   \n",
       "4                  23.72      21.74      20.29      19.56      18.50   \n",
       "...                  ...        ...        ...        ...        ...   \n",
       "2352               27.70      25.00      22.80      21.10        NaN   \n",
       "2353               26.10      24.90      22.60        NaN        NaN   \n",
       "2354               24.60      25.80      25.70      24.90      22.80   \n",
       "2355               27.40      27.30      28.20      27.80      27.70   \n",
       "2356               27.20      27.90      24.20        NaN        NaN   \n",
       "\n",
       "      weight_T5  weight_T6  weight_T7  weight_T8  weight_T9  weight_T10  \\\n",
       "0         16.44      15.49      15.44      15.05        NaN         NaN   \n",
       "1         15.02      14.14      14.40      14.73        NaN         NaN   \n",
       "2         15.30      14.74      14.80      14.42        NaN         NaN   \n",
       "3         13.54        NaN        NaN        NaN        NaN         NaN   \n",
       "4         16.80      16.78        NaN        NaN        NaN         NaN   \n",
       "...         ...        ...        ...        ...        ...         ...   \n",
       "2352        NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "2353        NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "2354        NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "2355      27.80      27.60        NaN        NaN        NaN         NaN   \n",
       "2356        NaN        NaN        NaN        NaN        NaN         NaN   \n",
       "\n",
       "      weight_T11  weight_T12  weight_T13  \n",
       "0            NaN         NaN         NaN  \n",
       "1            NaN         NaN         NaN  \n",
       "2            NaN         NaN         NaN  \n",
       "3            NaN         NaN         NaN  \n",
       "4            NaN         NaN         NaN  \n",
       "...          ...         ...         ...  \n",
       "2352         NaN         NaN         NaN  \n",
       "2353         NaN         NaN         NaN  \n",
       "2354         NaN         NaN         NaN  \n",
       "2355         NaN         NaN         NaN  \n",
       "2356         NaN         NaN         NaN  \n",
       "\n",
       "[2357 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change dates column to datetimindex and transform weight datas to numeric only\n",
    "df.loc[:,\"weight_T_infection\":\"weight_T13\"] = df.loc[:,\"weight_T_infection\":\"weight_T13\"].apply(pd.to_numeric,errors='coerce')\n",
    "serie_dates = df['Time_point'].apply(lambda x: pd.to_datetime(x.split(','),dayfirst=True))\n",
    "df['Dates'] = serie_dates\n",
    "data = df.apply(lambda x: keep_weight_post_infection(x),axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalization by weight at T infection and replace the normalize data into the original dataframe\n",
    "\n",
    "--> we obtain percentage of weight loss from the initial day of infeciton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse_ID</th>\n",
       "      <th>ID_Experiment</th>\n",
       "      <th>Cage</th>\n",
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       "      <th>survival_0.05</th>\n",
       "      <th>time_original</th>\n",
       "      <th>survival_original</th>\n",
       "      <th>max_loss_weight_percentage</th>\n",
       "      <th>exp</th>\n",
       "      <th>sub_exp</th>\n",
       "      <th>min_weight</th>\n",
       "      <th>t_origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRO-05432</td>\n",
       "      <td>ID_001</td>\n",
       "      <td>A</td>\n",
       "      <td>BALB/cByJ</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>Candida/Propionate</td>\n",
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       "      <td>Propionate / 2*10^5</td>\n",
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       "      <td>Propionate / 2*10^5</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
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       "      <td>9.0</td>\n",
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       "      <td>0.639184</td>\n",
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       "      <td>63.918440</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>Propionate / 2*10^5</td>\n",
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       "      <td>propionate</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.707420</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>70.741990</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>TRO-028337</td>\n",
       "      <td>ID_096</td>\n",
       "      <td>ETRO-01911</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>Pneumococcus/Training/Cross-fostering/male</td>\n",
       "      <td>3</td>\n",
       "      <td>D. Zy-Zy</td>\n",
       "      <td>1</td>\n",
       "      <td>training/cross-fostering</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.761733</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>76.173285</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>TRO-028338</td>\n",
       "      <td>ID_096</td>\n",
       "      <td>ETRO-01911</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>Pneumococcus/Training/Cross-fostering/male</td>\n",
       "      <td>3</td>\n",
       "      <td>D. Zy-Zy</td>\n",
       "      <td>1</td>\n",
       "      <td>training/cross-fostering</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.865900</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>86.590038</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>TRO-028339</td>\n",
       "      <td>ID_096</td>\n",
       "      <td>ETRO-01911</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>Pneumococcus/Training/Cross-fostering/male</td>\n",
       "      <td>3</td>\n",
       "      <td>D. Zy-Zy</td>\n",
       "      <td>1</td>\n",
       "      <td>training/cross-fostering</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>92.682927</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>TRO-028342</td>\n",
       "      <td>ID_096</td>\n",
       "      <td>ETRO-01911</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>Pneumococcus/Training/Cross-fostering/male</td>\n",
       "      <td>3</td>\n",
       "      <td>D. Zy-Zy</td>\n",
       "      <td>1</td>\n",
       "      <td>training/cross-fostering</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.996350</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>99.635036</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2356</th>\n",
       "      <td>TRO-028343</td>\n",
       "      <td>ID_096</td>\n",
       "      <td>ETRO-01911</td>\n",
       "      <td>C57BL/6J</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>Pneumococcus/Training/Cross-fostering/male</td>\n",
       "      <td>3</td>\n",
       "      <td>D. Zy-Zy</td>\n",
       "      <td>1</td>\n",
       "      <td>training/cross-fostering</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.889706</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>88.970588</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2357 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Mouse_ID ID_Experiment        Cage     Strain       Date  \\\n",
       "0      TRO-05432        ID_001           A  BALB/cByJ 2014-06-05   \n",
       "1      TRO-05433        ID_001           A  BALB/cByJ 2014-06-05   \n",
       "2      TRO-05434        ID_001           A  BALB/cByJ 2014-06-05   \n",
       "3      TRO-05435        ID_001           A  BALB/cByJ 2014-06-05   \n",
       "4      TRO-05456        ID_001           B  BALB/cByJ 2014-06-05   \n",
       "...          ...           ...         ...        ...        ...   \n",
       "2352  TRO-028337        ID_096  ETRO-01911   C57BL/6J 2023-03-03   \n",
       "2353  TRO-028338        ID_096  ETRO-01911   C57BL/6J 2023-03-03   \n",
       "2354  TRO-028339        ID_096  ETRO-01911   C57BL/6J 2023-03-03   \n",
       "2355  TRO-028342        ID_096  ETRO-01911   C57BL/6J 2023-03-03   \n",
       "2356  TRO-028343        ID_096  ETRO-01911   C57BL/6J 2023-03-03   \n",
       "\n",
       "                                      Experiment Group           Group_info  \\\n",
       "0                             Candida/Propionate    1A  Propionate / 2*10^5   \n",
       "1                             Candida/Propionate    1A  Propionate / 2*10^5   \n",
       "2                             Candida/Propionate    1A  Propionate / 2*10^5   \n",
       "3                             Candida/Propionate    1A  Propionate / 2*10^5   \n",
       "4                             Candida/Propionate    1A  Propionate / 2*10^5   \n",
       "...                                          ...   ...                  ...   \n",
       "2352  Pneumococcus/Training/Cross-fostering/male     3             D. Zy-Zy   \n",
       "2353  Pneumococcus/Training/Cross-fostering/male     3             D. Zy-Zy   \n",
       "2354  Pneumococcus/Training/Cross-fostering/male     3             D. Zy-Zy   \n",
       "2355  Pneumococcus/Training/Cross-fostering/male     3             D. Zy-Zy   \n",
       "2356  Pneumococcus/Training/Cross-fostering/male     3             D. Zy-Zy   \n",
       "\n",
       "     H0             Pre_traitment  ... survival_0.06 time_0.05 survival_0.05  \\\n",
       "0     1                propionate  ...             1       1.5             1   \n",
       "1     1                propionate  ...             1       1.5             1   \n",
       "2     1                propionate  ...             1       2.5             1   \n",
       "3     1                propionate  ...             1       1.5             1   \n",
       "4     1                propionate  ...             1       1.5             1   \n",
       "...  ..                       ...  ...           ...       ...           ...   \n",
       "2352  1  training/cross-fostering  ...             1       1.5             1   \n",
       "2353  1  training/cross-fostering  ...             1       2.5             1   \n",
       "2354  1  training/cross-fostering  ...             1       5.5             1   \n",
       "2355  1  training/cross-fostering  ...             0       8.0             0   \n",
       "2356  1  training/cross-fostering  ...             1       2.5             1   \n",
       "\n",
       "     time_original survival_original max_loss_weight_percentage  exp  sub_exp  \\\n",
       "0              9.0                 1                   0.629181    1        A   \n",
       "1              9.0                 1                   0.660748    1        A   \n",
       "2              9.0                 1                   0.639184    1        A   \n",
       "3              6.0                 1                   0.664051    1        A   \n",
       "4              7.0                 1                   0.707420    1        A   \n",
       "...            ...               ...                        ...  ...      ...   \n",
       "2352           5.0                 1                   0.761733    3       no   \n",
       "2353           4.0                 1                   0.865900    3       no   \n",
       "2354           6.0                 1                   0.926829    3       no   \n",
       "2355           8.0                 0                   0.996350    3       no   \n",
       "2356           5.0                 1                   0.889706    3       no   \n",
       "\n",
       "      min_weight  t_origin  \n",
       "0      62.918060       9.0  \n",
       "1      66.074766       9.0  \n",
       "2      63.918440       9.0  \n",
       "3      66.405101       6.0  \n",
       "4      70.741990       7.0  \n",
       "...          ...       ...  \n",
       "2352   76.173285       5.0  \n",
       "2353   86.590038       4.0  \n",
       "2354   92.682927       6.0  \n",
       "2355   99.635036       8.0  \n",
       "2356   88.970588       5.0  \n",
       "\n",
       "[2357 rows x 90 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize = data.div(data['weight_T_infection'],axis=0)*100\n",
    "df_normalize = df.copy()\n",
    "df_normalize.loc[:,\"weight_T_infection\":\"weight_T13\"] = normalize\n",
    "\n",
    "df_normalize['min_weight'] = df_normalize.loc[:,\"weight_T_infection\":\"weight_T13\"].min(axis=1)\n",
    "df_normalize['t_origin'] = df_normalize['time_original']\n",
    "df_normalize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select relevant columns:\n",
    "1. 'ID_Experiment'\n",
    "2. 'Mouse_ID'\n",
    "3. 'Date'\n",
    "4. 'Infection'\n",
    "5. 'Group'\n",
    "6. 'exp'\n",
    "7. 'survival_original'\n",
    "8. 't_origin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = df_normalize.loc[:,\"weight_T_infection\":\"weight_T13\"].columns.tolist()\n",
    "columns_index = df_normalize.loc[:, ~df_normalize.columns.isin(columns)]\n",
    "column_time = [n for n in df_normalize.columns.tolist() if \"time_\" in n]\n",
    "column_time = [column_time[-1]] + column_time[:-1]\n",
    "columns_index_time = ['ID_Experiment','Mouse_ID','Date','Infection','Group','exp','survival_original','t_origin']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform to tidy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>ID_Experiment</th>\n",
       "      <th>Mouse_ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Infection</th>\n",
       "      <th>Group</th>\n",
       "      <th>exp</th>\n",
       "      <th>survival_original</th>\n",
       "      <th>t_origin</th>\n",
       "      <th>Time</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05432</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Tinfection</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05433</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Tinfection</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05434</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Tinfection</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05435</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tinfection</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05456</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Tinfection</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32993</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028337</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>T13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32994</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028338</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>T13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32995</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028339</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>T13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32996</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028342</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>T13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32997</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028343</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>T13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32998 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID_Experiment    Mouse_ID       Date      Infection Group  exp  \\\n",
       "0            ID_001   TRO-05432 2014-06-05    C. albicans    1A    1   \n",
       "1            ID_001   TRO-05433 2014-06-05    C. albicans    1A    1   \n",
       "2            ID_001   TRO-05434 2014-06-05    C. albicans    1A    1   \n",
       "3            ID_001   TRO-05435 2014-06-05    C. albicans    1A    1   \n",
       "4            ID_001   TRO-05456 2014-06-05    C. albicans    1A    1   \n",
       "...             ...         ...        ...            ...   ...  ...   \n",
       "32993        ID_096  TRO-028337 2023-03-03  S. pneumoniae     3    3   \n",
       "32994        ID_096  TRO-028338 2023-03-03  S. pneumoniae     3    3   \n",
       "32995        ID_096  TRO-028339 2023-03-03  S. pneumoniae     3    3   \n",
       "32996        ID_096  TRO-028342 2023-03-03  S. pneumoniae     3    3   \n",
       "32997        ID_096  TRO-028343 2023-03-03  S. pneumoniae     3    3   \n",
       "\n",
       "       survival_original  t_origin        Time  weight  \n",
       "0                      1       9.0  Tinfection   100.0  \n",
       "1                      1       9.0  Tinfection   100.0  \n",
       "2                      1       9.0  Tinfection   100.0  \n",
       "3                      1       6.0  Tinfection   100.0  \n",
       "4                      1       7.0  Tinfection   100.0  \n",
       "...                  ...       ...         ...     ...  \n",
       "32993                  1       5.0         T13     NaN  \n",
       "32994                  1       4.0         T13     NaN  \n",
       "32995                  1       6.0         T13     NaN  \n",
       "32996                  0       8.0         T13     NaN  \n",
       "32997                  1       5.0         T13     NaN  \n",
       "\n",
       "[32998 rows x 10 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_longer_weight = df_normalize.melt(id_vars=columns_index_time,value_vars=columns,var_name=\"Time\",value_name=\"weight\")\n",
    "df_longer_weight['Time'] = df_longer_weight['Time'].apply(lambda x: \"\".join(x.split(\"_\")[1:]))\n",
    "df_longer_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "save the datas in long format. Use in graphiques.ipynb for visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_longer_weight.to_excel(\"./data/df_long_format_for_analysis.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight loss by day of infection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infection</th>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">C. albicans</th>\n",
       "      <th>T1</th>\n",
       "      <td>93.279207</td>\n",
       "      <td>93.953488</td>\n",
       "      <td>251</td>\n",
       "      <td>3.862355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T10</th>\n",
       "      <td>88.030905</td>\n",
       "      <td>90.481172</td>\n",
       "      <td>117</td>\n",
       "      <td>11.002648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T11</th>\n",
       "      <td>89.093038</td>\n",
       "      <td>91.262136</td>\n",
       "      <td>109</td>\n",
       "      <td>11.051094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T12</th>\n",
       "      <td>89.512350</td>\n",
       "      <td>92.839506</td>\n",
       "      <td>103</td>\n",
       "      <td>11.733767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T13</th>\n",
       "      <td>91.540737</td>\n",
       "      <td>94.016363</td>\n",
       "      <td>36</td>\n",
       "      <td>8.440398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>87.984191</td>\n",
       "      <td>88.369218</td>\n",
       "      <td>234</td>\n",
       "      <td>5.431728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>85.058591</td>\n",
       "      <td>83.976864</td>\n",
       "      <td>232</td>\n",
       "      <td>7.532595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>85.233445</td>\n",
       "      <td>83.467694</td>\n",
       "      <td>230</td>\n",
       "      <td>9.461354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T5</th>\n",
       "      <td>85.575387</td>\n",
       "      <td>85.300926</td>\n",
       "      <td>224</td>\n",
       "      <td>11.491712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T6</th>\n",
       "      <td>84.637715</td>\n",
       "      <td>85.512367</td>\n",
       "      <td>153</td>\n",
       "      <td>12.198206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T7</th>\n",
       "      <td>85.421056</td>\n",
       "      <td>88.261368</td>\n",
       "      <td>138</td>\n",
       "      <td>10.780553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T8</th>\n",
       "      <td>86.044889</td>\n",
       "      <td>89.134910</td>\n",
       "      <td>134</td>\n",
       "      <td>11.449223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T9</th>\n",
       "      <td>85.792915</td>\n",
       "      <td>88.042474</td>\n",
       "      <td>124</td>\n",
       "      <td>10.148711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tinfection</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>252</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">H1N1</th>\n",
       "      <th>T1</th>\n",
       "      <td>98.327784</td>\n",
       "      <td>98.698475</td>\n",
       "      <td>336</td>\n",
       "      <td>4.051930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T10</th>\n",
       "      <td>90.377142</td>\n",
       "      <td>93.536400</td>\n",
       "      <td>122</td>\n",
       "      <td>13.040054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T11</th>\n",
       "      <td>89.270627</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>29</td>\n",
       "      <td>16.077912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T12</th>\n",
       "      <td>83.984279</td>\n",
       "      <td>81.724263</td>\n",
       "      <td>14</td>\n",
       "      <td>13.535254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>94.544187</td>\n",
       "      <td>94.143483</td>\n",
       "      <td>336</td>\n",
       "      <td>5.621029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>91.327386</td>\n",
       "      <td>89.600000</td>\n",
       "      <td>336</td>\n",
       "      <td>7.909140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>88.642446</td>\n",
       "      <td>86.001697</td>\n",
       "      <td>322</td>\n",
       "      <td>9.671188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T5</th>\n",
       "      <td>86.413411</td>\n",
       "      <td>85.786802</td>\n",
       "      <td>277</td>\n",
       "      <td>11.124952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T6</th>\n",
       "      <td>85.320512</td>\n",
       "      <td>84.883721</td>\n",
       "      <td>243</td>\n",
       "      <td>12.475680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T7</th>\n",
       "      <td>88.083057</td>\n",
       "      <td>91.011236</td>\n",
       "      <td>201</td>\n",
       "      <td>12.482347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T8</th>\n",
       "      <td>90.518798</td>\n",
       "      <td>94.331984</td>\n",
       "      <td>167</td>\n",
       "      <td>12.787637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T9</th>\n",
       "      <td>90.454694</td>\n",
       "      <td>93.582888</td>\n",
       "      <td>153</td>\n",
       "      <td>12.703328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tinfection</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>336</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">Listeria</th>\n",
       "      <th>T1</th>\n",
       "      <td>97.493993</td>\n",
       "      <td>97.481092</td>\n",
       "      <td>1038</td>\n",
       "      <td>3.717083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T10</th>\n",
       "      <td>102.765257</td>\n",
       "      <td>102.544006</td>\n",
       "      <td>16</td>\n",
       "      <td>4.244996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T11</th>\n",
       "      <td>103.749123</td>\n",
       "      <td>102.834704</td>\n",
       "      <td>16</td>\n",
       "      <td>4.050310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>90.230473</td>\n",
       "      <td>89.417024</td>\n",
       "      <td>1018</td>\n",
       "      <td>5.395146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>87.391896</td>\n",
       "      <td>85.145716</td>\n",
       "      <td>800</td>\n",
       "      <td>8.319945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>89.187612</td>\n",
       "      <td>89.108911</td>\n",
       "      <td>533</td>\n",
       "      <td>9.967525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T5</th>\n",
       "      <td>91.972894</td>\n",
       "      <td>93.750000</td>\n",
       "      <td>331</td>\n",
       "      <td>9.975515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T6</th>\n",
       "      <td>94.862222</td>\n",
       "      <td>96.644331</td>\n",
       "      <td>218</td>\n",
       "      <td>9.358876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T7</th>\n",
       "      <td>95.709996</td>\n",
       "      <td>96.634615</td>\n",
       "      <td>155</td>\n",
       "      <td>7.958578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T8</th>\n",
       "      <td>96.455673</td>\n",
       "      <td>97.396208</td>\n",
       "      <td>98</td>\n",
       "      <td>8.140584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T9</th>\n",
       "      <td>95.848173</td>\n",
       "      <td>95.831754</td>\n",
       "      <td>40</td>\n",
       "      <td>10.562874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tinfection</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1048</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">S. pneumoniae</th>\n",
       "      <th>T1</th>\n",
       "      <td>96.972586</td>\n",
       "      <td>97.461929</td>\n",
       "      <td>700</td>\n",
       "      <td>5.301085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T10</th>\n",
       "      <td>97.522693</td>\n",
       "      <td>100.469484</td>\n",
       "      <td>81</td>\n",
       "      <td>11.921876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T11</th>\n",
       "      <td>100.135090</td>\n",
       "      <td>102.204637</td>\n",
       "      <td>68</td>\n",
       "      <td>12.040936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T12</th>\n",
       "      <td>98.831040</td>\n",
       "      <td>99.130435</td>\n",
       "      <td>7</td>\n",
       "      <td>6.552113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>95.570449</td>\n",
       "      <td>96.875000</td>\n",
       "      <td>697</td>\n",
       "      <td>6.940353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>94.442641</td>\n",
       "      <td>96.995708</td>\n",
       "      <td>635</td>\n",
       "      <td>8.759953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>94.927780</td>\n",
       "      <td>97.714286</td>\n",
       "      <td>482</td>\n",
       "      <td>8.938534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T5</th>\n",
       "      <td>97.754325</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>374</td>\n",
       "      <td>7.805807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T6</th>\n",
       "      <td>98.260455</td>\n",
       "      <td>100.453573</td>\n",
       "      <td>302</td>\n",
       "      <td>8.208155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T7</th>\n",
       "      <td>98.912146</td>\n",
       "      <td>100.588235</td>\n",
       "      <td>263</td>\n",
       "      <td>8.633481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T8</th>\n",
       "      <td>99.071403</td>\n",
       "      <td>100.993933</td>\n",
       "      <td>200</td>\n",
       "      <td>9.178562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T9</th>\n",
       "      <td>98.906629</td>\n",
       "      <td>100.978703</td>\n",
       "      <td>152</td>\n",
       "      <td>9.825548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tinfection</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>721</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                mean      median  count        std\n",
       "Infection     Time                                                \n",
       "C. albicans   T1           93.279207   93.953488    251   3.862355\n",
       "              T10          88.030905   90.481172    117  11.002648\n",
       "              T11          89.093038   91.262136    109  11.051094\n",
       "              T12          89.512350   92.839506    103  11.733767\n",
       "              T13          91.540737   94.016363     36   8.440398\n",
       "              T2           87.984191   88.369218    234   5.431728\n",
       "              T3           85.058591   83.976864    232   7.532595\n",
       "              T4           85.233445   83.467694    230   9.461354\n",
       "              T5           85.575387   85.300926    224  11.491712\n",
       "              T6           84.637715   85.512367    153  12.198206\n",
       "              T7           85.421056   88.261368    138  10.780553\n",
       "              T8           86.044889   89.134910    134  11.449223\n",
       "              T9           85.792915   88.042474    124  10.148711\n",
       "              Tinfection  100.000000  100.000000    252   0.000000\n",
       "H1N1          T1           98.327784   98.698475    336   4.051930\n",
       "              T10          90.377142   93.536400    122  13.040054\n",
       "              T11          89.270627   88.000000     29  16.077912\n",
       "              T12          83.984279   81.724263     14  13.535254\n",
       "              T13                NaN         NaN      0        NaN\n",
       "              T2           94.544187   94.143483    336   5.621029\n",
       "              T3           91.327386   89.600000    336   7.909140\n",
       "              T4           88.642446   86.001697    322   9.671188\n",
       "              T5           86.413411   85.786802    277  11.124952\n",
       "              T6           85.320512   84.883721    243  12.475680\n",
       "              T7           88.083057   91.011236    201  12.482347\n",
       "              T8           90.518798   94.331984    167  12.787637\n",
       "              T9           90.454694   93.582888    153  12.703328\n",
       "              Tinfection  100.000000  100.000000    336   0.000000\n",
       "Listeria      T1           97.493993   97.481092   1038   3.717083\n",
       "              T10         102.765257  102.544006     16   4.244996\n",
       "              T11         103.749123  102.834704     16   4.050310\n",
       "              T12                NaN         NaN      0        NaN\n",
       "              T13                NaN         NaN      0        NaN\n",
       "              T2           90.230473   89.417024   1018   5.395146\n",
       "              T3           87.391896   85.145716    800   8.319945\n",
       "              T4           89.187612   89.108911    533   9.967525\n",
       "              T5           91.972894   93.750000    331   9.975515\n",
       "              T6           94.862222   96.644331    218   9.358876\n",
       "              T7           95.709996   96.634615    155   7.958578\n",
       "              T8           96.455673   97.396208     98   8.140584\n",
       "              T9           95.848173   95.831754     40  10.562874\n",
       "              Tinfection  100.000000  100.000000   1048   0.000000\n",
       "S. pneumoniae T1           96.972586   97.461929    700   5.301085\n",
       "              T10          97.522693  100.469484     81  11.921876\n",
       "              T11         100.135090  102.204637     68  12.040936\n",
       "              T12          98.831040   99.130435      7   6.552113\n",
       "              T13                NaN         NaN      0        NaN\n",
       "              T2           95.570449   96.875000    697   6.940353\n",
       "              T3           94.442641   96.995708    635   8.759953\n",
       "              T4           94.927780   97.714286    482   8.938534\n",
       "              T5           97.754325  100.000000    374   7.805807\n",
       "              T6           98.260455  100.453573    302   8.208155\n",
       "              T7           98.912146  100.588235    263   8.633481\n",
       "              T8           99.071403  100.993933    200   9.178562\n",
       "              T9           98.906629  100.978703    152   9.825548\n",
       "              Tinfection  100.000000  100.000000    721   0.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_longer_weight.groupby([\"Infection\",'Time'])['weight'].agg(['mean',\"median\",\"count\",\"std\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### weight at day 6 non survivor mice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dead = df_longer_weight[df_longer_weight['survival_original']==1]\n",
    "df_survive = df_longer_weight[df_longer_weight['survival_original']==0]\n",
    "df_dead_T6 = df_dead[df_dead['Time'] == 'T6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "      <th>std</th>\n",
       "      <th>ci95_hi</th>\n",
       "      <th>ci95_low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infection</th>\n",
       "      <th></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>C. albicans</th>\n",
       "      <td>72.756838</td>\n",
       "      <td>70.700637</td>\n",
       "      <td>51</td>\n",
       "      <td>7.882190</td>\n",
       "      <td>72.863943</td>\n",
       "      <td>68.537331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H1N1</th>\n",
       "      <td>74.404462</td>\n",
       "      <td>72.855544</td>\n",
       "      <td>66</td>\n",
       "      <td>6.899504</td>\n",
       "      <td>74.520114</td>\n",
       "      <td>71.190974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Listeria</th>\n",
       "      <td>74.637131</td>\n",
       "      <td>74.015748</td>\n",
       "      <td>7</td>\n",
       "      <td>3.225896</td>\n",
       "      <td>76.405525</td>\n",
       "      <td>71.625971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S. pneumoniae</th>\n",
       "      <td>91.887905</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>57</td>\n",
       "      <td>8.133820</td>\n",
       "      <td>93.778274</td>\n",
       "      <td>89.555059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean     median  count       std    ci95_hi   ci95_low\n",
       "Infection                                                                 \n",
       "C. albicans    72.756838  70.700637     51  7.882190  72.863943  68.537331\n",
       "H1N1           74.404462  72.855544     66  6.899504  74.520114  71.190974\n",
       "Listeria       74.637131  74.015748      7  3.225896  76.405525  71.625971\n",
       "S. pneumoniae  91.887905  91.666667     57  8.133820  93.778274  89.555059"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = df_dead_T6.groupby(['Infection'])['weight'].agg(['mean','median','count','std'])\n",
    "stats['ci95_hi'] = stats['median'] + 1.96*stats['std']/np.sqrt(stats['count'])\n",
    "stats['ci95_low'] = stats['median'] - 1.96*stats['std']/np.sqrt(stats['count'])\n",
    "stats\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Median time to death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"./data/df_long_format_for_analysis.xlsx\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_Experiment</th>\n",
       "      <th>Mouse_ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Infection</th>\n",
       "      <th>Group</th>\n",
       "      <th>exp</th>\n",
       "      <th>survival_original</th>\n",
       "      <th>t_origin</th>\n",
       "      <th>Time</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05432</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Tinfection</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05433</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Tinfection</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05434</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Tinfection</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05435</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tinfection</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05456</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Tinfection</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32993</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028337</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>T13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32994</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028338</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>T13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32995</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028339</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>T13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32996</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028342</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>T13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32997</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028343</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>T13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32998 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID_Experiment    Mouse_ID       Date      Infection Group  exp  \\\n",
       "0            ID_001   TRO-05432 2014-06-05    C. albicans    1A    1   \n",
       "1            ID_001   TRO-05433 2014-06-05    C. albicans    1A    1   \n",
       "2            ID_001   TRO-05434 2014-06-05    C. albicans    1A    1   \n",
       "3            ID_001   TRO-05435 2014-06-05    C. albicans    1A    1   \n",
       "4            ID_001   TRO-05456 2014-06-05    C. albicans    1A    1   \n",
       "...             ...         ...        ...            ...   ...  ...   \n",
       "32993        ID_096  TRO-028337 2023-03-03  S. pneumoniae     3    3   \n",
       "32994        ID_096  TRO-028338 2023-03-03  S. pneumoniae     3    3   \n",
       "32995        ID_096  TRO-028339 2023-03-03  S. pneumoniae     3    3   \n",
       "32996        ID_096  TRO-028342 2023-03-03  S. pneumoniae     3    3   \n",
       "32997        ID_096  TRO-028343 2023-03-03  S. pneumoniae     3    3   \n",
       "\n",
       "       survival_original  t_origin        Time  weight  \n",
       "0                      1       9.0  Tinfection   100.0  \n",
       "1                      1       9.0  Tinfection   100.0  \n",
       "2                      1       9.0  Tinfection   100.0  \n",
       "3                      1       6.0  Tinfection   100.0  \n",
       "4                      1       7.0  Tinfection   100.0  \n",
       "...                  ...       ...         ...     ...  \n",
       "32993                  1       5.0         T13     NaN  \n",
       "32994                  1       4.0         T13     NaN  \n",
       "32995                  1       6.0         T13     NaN  \n",
       "32996                  0       8.0         T13     NaN  \n",
       "32997                  1       5.0         T13     NaN  \n",
       "\n",
       "[32998 rows x 10 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cleaning datas for computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Group</th>\n",
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       "      <th>Mice</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05432</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
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       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05433</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
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       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05434</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>Dead</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05435</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>Dead</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_001</td>\n",
       "      <td>TRO-05456</td>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>C. albicans</td>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>Dead</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32993</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028337</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Dead</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32994</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028338</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Dead</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32995</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028339</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Dead</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32996</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028342</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Alive</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32997</th>\n",
       "      <td>ID_096</td>\n",
       "      <td>TRO-028343</td>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>S. pneumoniae</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Dead</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32998 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID_Experiment    Mouse_ID       Date      Infection Group  exp   Mice  \\\n",
       "0            ID_001   TRO-05432 2014-06-05    C. albicans    1A    1   Dead   \n",
       "1            ID_001   TRO-05433 2014-06-05    C. albicans    1A    1   Dead   \n",
       "2            ID_001   TRO-05434 2014-06-05    C. albicans    1A    1   Dead   \n",
       "3            ID_001   TRO-05435 2014-06-05    C. albicans    1A    1   Dead   \n",
       "4            ID_001   TRO-05456 2014-06-05    C. albicans    1A    1   Dead   \n",
       "...             ...         ...        ...            ...   ...  ...    ...   \n",
       "32993        ID_096  TRO-028337 2023-03-03  S. pneumoniae     3    3   Dead   \n",
       "32994        ID_096  TRO-028338 2023-03-03  S. pneumoniae     3    3   Dead   \n",
       "32995        ID_096  TRO-028339 2023-03-03  S. pneumoniae     3    3   Dead   \n",
       "32996        ID_096  TRO-028342 2023-03-03  S. pneumoniae     3    3  Alive   \n",
       "32997        ID_096  TRO-028343 2023-03-03  S. pneumoniae     3    3   Dead   \n",
       "\n",
       "       t_origin  Time  weight  \n",
       "0           9.0     0   100.0  \n",
       "1           9.0     0   100.0  \n",
       "2           9.0     0   100.0  \n",
       "3           6.0     0   100.0  \n",
       "4           7.0     0   100.0  \n",
       "...         ...   ...     ...  \n",
       "32993       5.0    13     NaN  \n",
       "32994       4.0    13     NaN  \n",
       "32995       6.0    13     NaN  \n",
       "32996       8.0    13     NaN  \n",
       "32997       5.0    13     NaN  \n",
       "\n",
       "[32998 rows x 10 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean = df.copy()\n",
    "status_mapping = {0: 'Alive', 1: 'Dead'}\n",
    "df_clean[\"survival_original\"] = df_clean['survival_original'].replace(status_mapping)\n",
    "\n",
    "infection_mapping = {'Listeria':\"L. monocytogenes\"}\n",
    "df_clean['Infection'] =df_clean['Infection'].replace(infection_mapping)\n",
    "\n",
    "df_clean.rename(columns={'survival_original':'Mice'},inplace=True)\n",
    "\n",
    "time_rename = list(np.arange(0,15,1))\n",
    "time_unique = df_clean['Time'].unique()\n",
    "time_mapping = dict(zip(time_unique,time_rename))\n",
    "df_clean['Time'] = df_clean['Time'].replace(time_mapping)\n",
    "df_clean\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median time to death all mice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inf\n",
      "     Total_lower_0.95  Total_upper_0.95\n",
      "0.5               inf               inf\n",
      "C. albicans\n",
      "inf\n",
      "     C. albicans_lower_0.95  C. albicans_upper_0.95\n",
      "0.5                     inf                     inf\n",
      "H1N1\n",
      "inf\n",
      "     H1N1_lower_0.95  H1N1_upper_0.95\n",
      "0.5              inf              inf\n",
      "L. monocytogenes\n",
      "inf\n",
      "     L. monocytogenes_lower_0.95  L. monocytogenes_upper_0.95\n",
      "0.5                          inf                          inf\n",
      "S. pneumoniae\n",
      "11.0\n",
      "     S. pneumoniae_lower_0.95  S. pneumoniae_upper_0.95\n",
      "0.5                       9.0                      12.0\n"
     ]
    }
   ],
   "source": [
    "kmf = KaplanMeierFitter()\n",
    "# A4 page dimensions in inches\n",
    "\n",
    "df_KP = df_clean.copy()\n",
    "df_KP['Mice'] = df_KP['Mice'].replace({'Dead':1,'Alive':0})\n",
    "df_KP.loc[df_KP['Mice'] == 0, 't_origin'] = 14\n",
    "\n",
    "kmf.fit(df_KP[\"t_origin\"], df_KP[\"Mice\"], label='Total')\n",
    "median_ = kmf.median_survival_time_\n",
    "median_confidence_interval_ = median_survival_times(kmf.confidence_interval_)\n",
    "print(median_)\n",
    "print(median_confidence_interval_)\n",
    "\n",
    "for name, grouped_df in df_KP.groupby('Infection'):\n",
    "    print(name)\n",
    "    kmf.fit(grouped_df[\"t_origin\"], grouped_df[\"Mice\"], label=name)\n",
    "    median_ = kmf.median_survival_time_\n",
    "    median_confidence_interval_ = median_survival_times(kmf.confidence_interval_)\n",
    "    print(median_)\n",
    "    print(median_confidence_interval_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median time to death non survivor mice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "     Total_lower_0.95  Total_upper_0.95\n",
      "0.5               5.0               5.0\n",
      "8.0\n",
      "     C. albicans_lower_0.95  C. albicans_upper_0.95\n",
      "0.5                     8.0                     8.0\n",
      "7.0\n",
      "     H1N1_lower_0.95  H1N1_upper_0.95\n",
      "0.5              7.0              7.0\n",
      "4.0\n",
      "     L. monocytogenes_lower_0.95  L. monocytogenes_upper_0.95\n",
      "0.5                          4.0                          4.0\n",
      "5.0\n",
      "     S. pneumoniae_lower_0.95  S. pneumoniae_upper_0.95\n",
      "0.5                       5.0                       5.0\n"
     ]
    }
   ],
   "source": [
    "kmf = KaplanMeierFitter()\n",
    "\n",
    "df_KP = df_clean.copy()\n",
    "df_KP['Mice'] = df_KP['Mice'].replace({'Dead':1,'Alive':0})\n",
    "df_dead = df_KP[df_KP['Mice']==1]\n",
    "\n",
    "kmf.fit(df_dead[\"t_origin\"], df_dead[\"Mice\"], label='Total')\n",
    "median_ = kmf.median_survival_time_\n",
    "median_confidence_interval_ = median_survival_times(kmf.confidence_interval_)\n",
    "print(median_)\n",
    "print(median_confidence_interval_)\n",
    "\n",
    "for name, grouped_df in df_dead.groupby('Infection'):\n",
    "    kmf.fit(grouped_df[\"t_origin\"], grouped_df[\"Mice\"], label=name)\n",
    "    median_ = kmf.median_survival_time_\n",
    "    median_confidence_interval_ = median_survival_times(kmf.confidence_interval_)\n",
    "    print(median_)\n",
    "    print(median_confidence_interval_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Infection\n",
       "C. albicans      11.472222\n",
       "H1N1              9.764881\n",
       "Listeria          5.665076\n",
       "S. pneumoniae     7.554785\n",
       "Name: time_original, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"./data/df_for_analysis.xlsx\",index_col=0)\n",
    "df.groupby('Infection')['time_original'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.448451421298261"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['time_original'].mean()"
   ]
  }
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