period.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T00:01:14.483032Z",
     "start_time": "2019-03-31T00:01:14.478726Z"
    }
   },
   "source": [
    "<font size=\"5\">__Symptoms prediction__<font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T08:31:17.193384Z",
     "start_time": "2019-03-31T08:29:21.809Z"
    }
   },
   "source": [
    "In this notebook, we use data given by Pslove about their user's periods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T08:31:17.181699Z",
     "start_time": "2019-03-31T08:31:17.171739Z"
    }
   },
   "source": [
    "# Data mining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:27:26.104666Z",
     "start_time": "2019-03-31T09:27:26.091799Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from joblib import dump, load\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:47:46.231382Z",
     "start_time": "2019-03-31T09:47:37.412911Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import files\n",
    "user = pd.read_csv('User.csv', index_col=0)\n",
    "period = pd.read_csv('Period.csv', index_col=0)\n",
    "symptoms = pd.read_csv('Symptom.csv', index_col=0)\n",
    "\n",
    "# Change data types to nice date types\n",
    "user.cycle_length_initial = pd.to_timedelta(user.cycle_length_initial, unit='D').dt.days\n",
    "user.period_length_initial = pd.to_timedelta(user.period_length_initial, unit='D').dt.days\n",
    "user.dob = pd.to_datetime(user.dob, dayfirst=True, errors='coerce')\n",
    "period.start_date = pd.to_datetime(period.start_date, dayfirst=True, errors='coerce')\n",
    "period.end_date = pd.to_datetime(period.end_date, dayfirst=True, errors='coerce')\n",
    "symptoms.date = pd.to_datetime(symptoms.date, dayfirst=True, errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:28:52.043374Z",
     "start_time": "2019-03-31T09:28:52.034661Z"
    }
   },
   "source": [
    "## Cycle variations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:48:17.514168Z",
     "start_time": "2019-03-31T09:47:56.014949Z"
    }
   },
   "outputs": [],
   "source": [
    "# Calculate period length\n",
    "period = period.join(user, on='User_id')\n",
    "period['length'] = (period.end_date-period.start_date).dt.days + 1\n",
    "period = period.drop(period[period.length<2].index)\n",
    "period = period.drop(period[period.length>11].index)\n",
    "\n",
    "# Calculate the rank of the user's period\n",
    "period['order'] = 0\n",
    "for user_id in user.index:\n",
    "    index = period[period.User_id == user_id].sort_values('start_date').index\n",
    "    period.loc[index, 'order'] = pd.Series(range(len(index)), index=index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:48:26.243644Z",
     "start_time": "2019-03-31T09:48:26.203557Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Calculate days between periods\n",
    "diff = period.loc[:, ['User_id', 'start_date']].sort_values(['User_id', 'start_date']).diff()\n",
    "period['days_since_last'] = ((1 - diff.User_id) * diff.start_date).sort_index().dt.days\n",
    "# If no previous period saved, fix length at 'cycle_length_initial'\n",
    "index = period.loc[period.days_since_last==0].index\n",
    "period.loc[index, 'days_since_last'] = period.cycle_length_initial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:48:27.194894Z",
     "start_time": "2019-03-31T09:48:27.185177Z"
    }
   },
   "outputs": [],
   "source": [
    "def last_period(x):\n",
    "    # Return last first day of period\n",
    "    user_period = period[period.User_id == x.user_id]\n",
    "    last_date = user_period[user_period.start_date <= x.date].start_date.max()\n",
    "    return last_date\n",
    "\n",
    "def last_period_order(x):\n",
    "    # Return last period order\n",
    "    user_period = period[period.User_id == x.user_id]\n",
    "    last_order = user_period[user_period.start_date <= x.date].order.max()\n",
    "    return last_order\n",
    "\n",
    "def last_len_period(x):\n",
    "    # Return last period length\n",
    "    liste = period[(period.User_id==x.user_id)&(period.start_date==x.last_period)].length\n",
    "    if len(liste)==0:\n",
    "        return 0\n",
    "    else:\n",
    "        return liste.iloc[0]\n",
    "\n",
    "def last_len_cycle(x):\n",
    "    # Return last cycle length\n",
    "    liste = period[(period.User_id==x.user_id)&(period.start_date==x.last_period)].days_since_last\n",
    "    if len(liste)==0:\n",
    "        return 0\n",
    "    else:\n",
    "        return liste.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:49:15.474477Z",
     "start_time": "2019-03-31T09:49:15.431041Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Calculate mean cycle length\n",
    "mean_cycle = period[period.days_since_last<50].groupby('User_id').mean()['days_since_last']\n",
    "symptoms = symptoms.join(mean_cycle.rename('mean_cycle'), on='user_id')\n",
    "# Calculate mean period length\n",
    "mean_period = period.groupby('User_id').mean()['length']\n",
    "symptoms = symptoms.join(mean_period.rename('mean_period'), on='user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T08:23:24.047455Z",
     "start_time": "2019-03-31T08:22:56.305460Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "symptoms2 = symptoms.copy()\n",
    "# Get features about last period\n",
    "symptoms2['last_period'] = symptoms2.apply(last_period, axis=1)\n",
    "symptoms2['last_order'] = symptoms2.apply(last_period_order, axis=1)\n",
    "symptoms22['last_len_period'] = symptoms2.apply(last_len_period, axis=1)\n",
    "symptoms['last_len_cycle'] = symptoms2.apply(last_len_cycle, axis=1)\n",
    "symptoms2 = symptoms2.join(user, on='user_id')\n",
    "# Days since period begin\n",
    "symptoms2['period_day'] = (symptoms2.date - symptoms2.last_period).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T10:14:12.640673Z",
     "start_time": "2019-03-31T10:14:12.573644Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>acne</th>\n",
       "      <th>backache</th>\n",
       "      <th>bloating</th>\n",
       "      <th>cramp</th>\n",
       "      <th>diarrhea</th>\n",
       "      <th>dizzy</th>\n",
       "      <th>headache</th>\n",
       "      <th>mood</th>\n",
       "      <th>nausea</th>\n",
       "      <th>sore</th>\n",
       "      <th>differenceDays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.5</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>-8.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>28.5</td>\n",
       "      <td>-8.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>93.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>76.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>76.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>-5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>63.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>32.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>42</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>49.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>46</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>47</td>\n",
       "      <td>26.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>54.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>53</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>56</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>58</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>-8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>61</td>\n",
       "      <td>43.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>20.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>64</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>66</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>56.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>56.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>231 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index   acne  backache  bloating  cramp  diarrhea  dizzy  headache  mood  \\\n",
       "0        0   33.5       0.0      21.5    0.0       0.0    0.0       0.0   0.0   \n",
       "1        1    0.0       0.0      30.5   95.0       0.0    0.0       0.0  98.0   \n",
       "2        2    0.0       0.0       0.0    0.0       0.0    0.0       0.0   0.0   \n",
       "3        3   61.0       0.0      32.5   81.0       0.0    0.0       0.0   0.0   \n",
       "4        4    0.0      77.5      93.5  100.0       0.0    0.0      69.5  30.0   \n",
       "5        5   29.0     100.0      97.5  100.0       0.0   32.0      42.5   0.0   \n",
       "6        6    0.0      20.5      37.0    0.0       0.0    0.0      34.5   0.0   \n",
       "7        7    0.0      13.0       0.0   28.0       6.5    0.0      32.0   9.5   \n",
       "8        8    8.0      30.0       0.0   16.5       0.0    0.0       0.0   0.0   \n",
       "9        9    0.0       0.0       0.0    0.0       0.0    0.0       0.0  41.0   \n",
       "10      10    0.0       0.0       0.0    0.0       0.0    0.0       0.0  41.0   \n",
       "11      11   23.0       0.0       0.0   19.5       0.0    0.0       0.0   0.0   \n",
       "12      12    0.0      11.5       0.0   35.0       0.0    0.0       0.0   0.0   \n",
       "13      13    5.0       0.0      27.5   73.0       4.0   12.5       9.0  25.0   \n",
       "14      14   24.0       4.0       0.0   55.0       0.0    0.0      12.0   0.0   \n",
       "15      15    0.0       0.0       0.0   60.0      35.5   33.0      82.0   0.0   \n",
       "16      16    0.0       0.0       0.0   37.0       0.0    0.0       0.0   0.0   \n",
       "17      17    0.0      11.5      76.0   89.5       0.0    0.0       0.0  29.5   \n",
       "18      18    0.0      11.5      76.0   89.5       0.0    0.0       0.0  29.5   \n",
       "19      19    0.0      37.0       0.0   28.0      51.0    0.0       0.0   0.0   \n",
       "20      20    0.0      37.0       0.0   28.0      51.0    0.0       0.0   0.0   \n",
       "21      21   17.0       0.0      35.5   69.5       0.0    0.0       0.0  28.0   \n",
       "22      22    0.0       0.0      11.5    0.0       0.0    0.0       0.0   0.0   \n",
       "23      23   92.0       0.0      98.5   65.0       0.0    0.0       0.0   0.0   \n",
       "24      24    0.0       0.0      75.5   38.0       0.0    0.0       0.0  19.5   \n",
       "25      25   22.0       0.0      73.0   63.5       0.0    0.0       0.0   0.0   \n",
       "26      26    0.0       0.0       0.0   31.5       0.0    0.0       1.5  32.0   \n",
       "27      27    2.0      23.5       0.0    0.0      28.0    0.0       0.0   0.0   \n",
       "28      28    7.5       0.0      26.5   32.5      17.5    0.0      23.5  12.0   \n",
       "29      29   28.5       0.0       0.0    0.0       0.0    0.0       0.0   0.0   \n",
       "..     ...    ...       ...       ...    ...       ...    ...       ...   ...   \n",
       "201     40    0.0       0.0       0.0   12.0       0.0    0.0       0.0   0.0   \n",
       "202     41    0.0       0.0      14.0   68.5       0.0    0.0       0.0   0.0   \n",
       "203     42    9.0       6.5       4.0   18.0       0.0    0.0       0.0   0.0   \n",
       "204     43    0.0      11.0       0.0   14.5      16.5    0.0       0.0   0.0   \n",
       "205     44    0.0      43.0      20.5   49.5      23.0   42.5      50.0  50.0   \n",
       "206     45    0.0       0.0      11.0   44.0       0.0    0.0       0.0   0.0   \n",
       "207     46   22.5       0.0      19.5   39.5      23.0    0.0       0.0  22.5   \n",
       "208     47   26.0      29.0      49.5   54.0      27.0    0.0       0.0   0.0   \n",
       "209     48    0.0      59.5      21.0   71.5       0.0    0.0       0.0  19.5   \n",
       "210     49    0.0      31.0      42.5   70.0      14.5    0.0      10.0   0.0   \n",
       "211     50    0.0      41.0      26.5   64.0       0.0    0.0       0.0   0.0   \n",
       "212     51    0.0       0.0      27.5   21.0      22.5    0.0      45.5   0.0   \n",
       "213     52    0.0       0.0       0.0    0.0       0.0    0.0       0.0   0.0   \n",
       "214     53  100.0       0.0      68.5    0.0       0.0    0.0       0.0  48.0   \n",
       "215     54    0.0      33.5      25.0   22.5      43.0    0.0       0.0   9.0   \n",
       "216     55    0.0      33.5      25.0   22.5      43.0    0.0       0.0   9.0   \n",
       "217     56   47.0       0.0      17.0   50.0       9.5   30.5      42.5   0.0   \n",
       "218     57    0.0      32.5       0.0   76.5       0.0    0.0       0.0  50.0   \n",
       "219     58   28.0       0.0      19.0    3.5       0.0    0.0       8.5   0.0   \n",
       "220     59    0.0       0.0      37.5   58.0       0.0   35.5       0.0   0.0   \n",
       "221     60    0.0      24.5       0.0   58.5       0.0   13.5       0.0  51.5   \n",
       "222     61   43.0      38.5      20.5    9.5      31.0    0.0       0.0   7.0   \n",
       "223     62    0.0       0.0       8.5    0.0       0.0    0.0       0.0  64.0   \n",
       "224     63    0.0       0.0       0.0    9.0       0.0    0.0      11.0  25.0   \n",
       "225     64   12.0      28.5      20.0   60.5       4.0    0.0      30.5  42.0   \n",
       "226     65    0.0      62.0       0.0   45.0       0.0    0.0       0.0  57.0   \n",
       "227     66   30.5       0.0      23.5    0.0       0.0    0.0       0.0   0.0   \n",
       "228     67    0.0       0.0       0.0    0.0       0.0    0.0       0.0   0.0   \n",
       "229     68    0.0       0.0       0.0   33.0       0.0    0.0      29.5   0.0   \n",
       "230     69    0.0      43.5      25.0   56.5       0.0    0.0      29.5  56.5   \n",
       "\n",
       "     nausea   sore  differenceDays  \n",
       "0       0.0   48.5            -9.0  \n",
       "1      50.0   50.0             1.5  \n",
       "2       0.0   42.0            -8.5  \n",
       "3      32.5   28.5            -8.5  \n",
       "4      48.0  100.0            -2.0  \n",
       "5       0.0   75.0            -6.0  \n",
       "6      47.0    0.0             0.5  \n",
       "7       0.0   16.5            -0.5  \n",
       "8       0.0    0.0             1.5  \n",
       "9       0.0    0.0           -10.5  \n",
       "10      0.0    0.0            -5.5  \n",
       "11      0.0    0.0            -2.0  \n",
       "12      0.0   17.0             1.0  \n",
       "13      8.0    0.0             2.5  \n",
       "14     11.5    0.0            -0.5  \n",
       "15      0.0    0.0             1.0  \n",
       "16      0.0   72.5             3.0  \n",
       "17      0.0   50.0             6.5  \n",
       "18      0.0   50.0             3.5  \n",
       "19      0.0    0.0             0.5  \n",
       "20      0.0    0.0             0.5  \n",
       "21     32.5    0.0             0.5  \n",
       "22      0.0   17.0            -0.5  \n",
       "23      0.0   84.0            -5.5  \n",
       "24      0.0   37.0            -4.0  \n",
       "25      0.0    0.0            -2.5  \n",
       "26     31.0    0.0             3.5  \n",
       "27      0.0   27.0             2.0  \n",
       "28     11.0    0.0             0.5  \n",
       "29      0.0    0.0            -4.5  \n",
       "..      ...    ...             ...  \n",
       "201    11.5    0.0            -5.0  \n",
       "202     0.0    0.0            -0.5  \n",
       "203    10.0    5.0            -4.5  \n",
       "204     0.0    0.0            -6.0  \n",
       "205    48.0   26.5             0.5  \n",
       "206     0.0   41.5            -1.5  \n",
       "207     0.0    0.0             1.5  \n",
       "208     0.0    0.0             0.5  \n",
       "209    13.0   59.5            -3.0  \n",
       "210     0.0    0.0             0.5  \n",
       "211     0.0    0.0             0.5  \n",
       "212     0.0   22.0             4.5  \n",
       "213     0.0   25.0            -2.0  \n",
       "214     0.0    0.0            -2.0  \n",
       "215    28.0    0.0            -4.5  \n",
       "216    28.0    0.0             1.5  \n",
       "217    80.5    0.0             0.5  \n",
       "218    27.0    0.0             0.5  \n",
       "219     0.0   28.0            -6.5  \n",
       "220     0.0   42.5            -8.5  \n",
       "221     0.0   39.0             0.5  \n",
       "222     0.0    0.0            -1.0  \n",
       "223     9.5    0.0            -1.5  \n",
       "224     0.0   54.0            -2.5  \n",
       "225    15.0   40.5             0.5  \n",
       "226     8.5    0.0             2.5  \n",
       "227     0.0    0.0            -7.0  \n",
       "228     0.0    0.0            -7.0  \n",
       "229     0.0    0.0            -6.5  \n",
       "230    45.0   35.5             0.5  \n",
       "\n",
       "[231 rows x 12 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFinalePropreX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T10:13:11.151296Z",
     "start_time": "2019-03-31T10:13:11.088524Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>acne</th>\n",
       "      <th>backache</th>\n",
       "      <th>bloating</th>\n",
       "      <th>cramp</th>\n",
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       "      <th>mean_period</th>\n",
       "    </tr>\n",
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       "      <th>id</th>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>257</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>46</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-10</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>257</td>\n",
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       "      <td>0</td>\n",
       "      <td>2015-04-11</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>5.500000</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>263</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-11</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>269</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-11</td>\n",
       "      <td>30.833333</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-11</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>271</td>\n",
       "      <td>0</td>\n",
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       "      <td>22</td>\n",
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       "      <td>47</td>\n",
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       "      <td>37</td>\n",
       "      <td>2015-04-11</td>\n",
       "      <td>31.500000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-11</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>262</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-12</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>257</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-12</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-12</td>\n",
       "      <td>32.937500</td>\n",
       "      <td>6.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>284</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>36</td>\n",
       "      <td>76</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>79</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>252</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>268</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>262</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>295</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-14</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-14</td>\n",
       "      <td>32.937500</td>\n",
       "      <td>6.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>286</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-14</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>297</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>2015-04-14</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-14</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>264</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-14</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>297</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-15</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>283</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-15</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-15</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-16</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>262</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-16</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>5.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13703</th>\n",
       "      <td>2289</td>\n",
       "      <td>22</td>\n",
       "      <td>47</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>2019-03-16</td>\n",
       "      <td>29.300000</td>\n",
       "      <td>5.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13704</th>\n",
       "      <td>5950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-17</td>\n",
       "      <td>28.375000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13705</th>\n",
       "      <td>6618</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-17</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13706</th>\n",
       "      <td>3978</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-17</td>\n",
       "      <td>29.766667</td>\n",
       "      <td>6.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13707</th>\n",
       "      <td>6924</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>5.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13708</th>\n",
       "      <td>5982</td>\n",
       "      <td>37</td>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>57</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-17</td>\n",
       "      <td>27.941176</td>\n",
       "      <td>4.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13709</th>\n",
       "      <td>6653</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-17</td>\n",
       "      <td>24.545455</td>\n",
       "      <td>5.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13710</th>\n",
       "      <td>6276</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>52</td>\n",
       "      <td>100</td>\n",
       "      <td>79</td>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13711</th>\n",
       "      <td>6305</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13712</th>\n",
       "      <td>6305</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-17</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13713</th>\n",
       "      <td>6669</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>25.333333</td>\n",
       "      <td>5.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13714</th>\n",
       "      <td>6742</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>4.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13715</th>\n",
       "      <td>6846</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13716</th>\n",
       "      <td>807</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>56</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>29.812500</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13717</th>\n",
       "      <td>6515</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-19</td>\n",
       "      <td>25.666667</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13718</th>\n",
       "      <td>6515</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>25.666667</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13719</th>\n",
       "      <td>4022</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-19</td>\n",
       "      <td>25.241379</td>\n",
       "      <td>5.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13720</th>\n",
       "      <td>5419</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-19</td>\n",
       "      <td>34.600000</td>\n",
       "      <td>6.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13721</th>\n",
       "      <td>6427</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>33.777778</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13722</th>\n",
       "      <td>6713</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>28.687500</td>\n",
       "      <td>6.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13723</th>\n",
       "      <td>6924</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>5.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13724</th>\n",
       "      <td>6712</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>73</td>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>3.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13725</th>\n",
       "      <td>6923</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13726</th>\n",
       "      <td>7045</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>29.200000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13727</th>\n",
       "      <td>1542</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>36</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>32.228571</td>\n",
       "      <td>5.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13728</th>\n",
       "      <td>6953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>8.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13729</th>\n",
       "      <td>6453</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>34.444444</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13730</th>\n",
       "      <td>6713</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>28.687500</td>\n",
       "      <td>6.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13731</th>\n",
       "      <td>2253</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>25.444444</td>\n",
       "      <td>6.906977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13732</th>\n",
       "      <td>4592</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>32.629630</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13512 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  acne  backache  bloating  cramp  diarrhea  dizzy  headache  \\\n",
       "id                                                                           \n",
       "127        252     0         0         0      0         0      0         0   \n",
       "128        257     0         0         0     46         0      0         0   \n",
       "129        257     0         0         0      0         0     27        48   \n",
       "130        263     0         0         0      0         0      0         0   \n",
       "132        269     0         0         0      0         0      0         0   \n",
       "133        270     0         0         0      0         0      0         0   \n",
       "134        271     0         0        15     22         0      0         0   \n",
       "135        254     0         0         0      0         0      0         0   \n",
       "136        273     0         0         0     20         0      0         0   \n",
       "137        275     0         0         0      0         0      0         0   \n",
       "138        262    71         0        45      0         0      0         0   \n",
       "139        257     0         0         0      0         0      0        47   \n",
       "140        278     0         0         0      0         0      0         0   \n",
       "141        284     0         0        93     36        76     43         0   \n",
       "142        252    50        50         0      0         0      0         0   \n",
       "143        286     0         0         0      0         0      0        22   \n",
       "144        268    13         0         0      0         0      0         0   \n",
       "147        289     0         0         0      0         0      0         0   \n",
       "149        262    43         0         0      0         0     34         0   \n",
       "150        295     0         0         0      0         0      0         0   \n",
       "151        278     0        20        15      0         0     17        15   \n",
       "152        286     3         0         0      0         0      6        34   \n",
       "153        297     0         0         0      0         0      0        29   \n",
       "154        298     0         0         0      0         0      0         0   \n",
       "155        264    45         0         0      0         0      0        34   \n",
       "156        297    12         0        27     53         0      0        17   \n",
       "157        283     4         0        19     50         0      0         0   \n",
       "158        255     0         0         0      0         0      0         0   \n",
       "159        283     0         0         0      0         0      0         0   \n",
       "160        262    39         0         0      0         0      0         0   \n",
       "...        ...   ...       ...       ...    ...       ...    ...       ...   \n",
       "13703     2289    22        47        31      0         0     12        23   \n",
       "13704     5950     0         0         0      0         0      0         0   \n",
       "13705     6618     0         0         0      0         0      0         0   \n",
       "13706     3978    44         0         0    100         0      0         0   \n",
       "13707     6924     0         0        15      0         0      0         0   \n",
       "13708     5982    37        22        67    100         3     61        57   \n",
       "13709     6653     0        28         0     21        72      0        55   \n",
       "13710     6276     5        80        79     88        77     52       100   \n",
       "13711     6305    27         0         0      0         0      0         0   \n",
       "13712     6305    40         0         0      0         0      0        46   \n",
       "13713     6669     0         0         0      0         0      0         0   \n",
       "13714     6742     0        31        35     42         0      0         0   \n",
       "13715     6846     8        30         0     39         0      0         0   \n",
       "13716      807     0        99        56     95         0     94         0   \n",
       "13717     6515     0         0        21      0         0      0         0   \n",
       "13718     6515     0         0         0     84        64      0        38   \n",
       "13719     4022     0         0         0      0         0      0         0   \n",
       "13720     5419     0         0        39     53         0      0         0   \n",
       "13721     6427     7         0         0      0        62      0         0   \n",
       "13722     6713     0         0        28      0         0      0         0   \n",
       "13723     6924     0         0        26      0         8      0         0   \n",
       "13724     6712     0         0        51      0         0     30        71   \n",
       "13725     6923     0         0         0     77         0      0        49   \n",
       "13726     7045     0         0         0      0         0     32        41   \n",
       "13727     1542     0        62        36     10         0      0         0   \n",
       "13728     6953     0         0         0      0         0      0         0   \n",
       "13729     6453    12        22         0     79         0      0         0   \n",
       "13730     6713     0        31        23     36         0      0        25   \n",
       "13731     2253     0         0         0      0         0      0        36   \n",
       "13732     4592     0         0         0     68         0      0         0   \n",
       "\n",
       "       mood  nausea  sore       date  mean_cycle  mean_period  \n",
       "id                                                             \n",
       "127       0       0     0 2015-04-11         NaN     6.000000  \n",
       "128       0       0     0 2015-04-10   31.000000     5.500000  \n",
       "129       0       0     0 2015-04-11   31.000000     5.500000  \n",
       "130       0       0     0 2015-04-11   28.000000     4.000000  \n",
       "132       0       0     0 2015-04-11   30.833333     6.500000  \n",
       "133       0       0     0 2015-04-11   28.000000     6.000000  \n",
       "134      47       0    37 2015-04-11   31.500000     6.000000  \n",
       "135       0       0     0 2015-04-12   28.333333     6.000000  \n",
       "136       0      42    21 2015-04-12   30.000000     7.000000  \n",
       "137       0       0     0 2015-04-11   40.000000     5.000000  \n",
       "138      53       0     0 2015-04-12   30.500000     5.000000  \n",
       "139       0       0     0 2015-04-12   31.000000     5.500000  \n",
       "140       0       0     0 2015-04-12   32.937500     6.916667  \n",
       "141       0      60    79 2015-04-13   28.000000     6.000000  \n",
       "142       0       0     0 2015-04-13         NaN     6.000000  \n",
       "143      15       0     0 2015-04-13   30.000000     6.000000  \n",
       "144       0       0     0 2015-04-13   26.000000     5.000000  \n",
       "147       0       0     0 2015-04-13   30.500000     6.000000  \n",
       "149       0       0     0 2015-04-13   30.500000     5.000000  \n",
       "150       0       0     0 2015-04-14   32.000000     5.000000  \n",
       "151       0       0     0 2015-04-14   32.937500     6.916667  \n",
       "152       7       0     0 2015-04-14   30.000000     6.000000  \n",
       "153      28       0    31 2015-04-14   20.500000     6.000000  \n",
       "154       0       0     0 2015-04-14   26.000000     7.000000  \n",
       "155       0       0     0 2015-04-14   28.000000     7.000000  \n",
       "156       0       0     0 2015-04-15   20.500000     6.000000  \n",
       "157       0       0     0 2015-04-15   28.000000     5.000000  \n",
       "158     100       0     0 2015-04-15   30.000000     4.000000  \n",
       "159       0       0     0 2015-04-16   28.000000     5.000000  \n",
       "160       0       0     0 2015-04-16   30.500000     5.000000  \n",
       "...     ...     ...   ...        ...         ...          ...  \n",
       "13703     0       0    74 2019-03-16   29.300000     5.125000  \n",
       "13704     0       0     0 2019-03-17   28.375000     6.500000  \n",
       "13705     0       0     0 2019-03-17   33.250000     6.500000  \n",
       "13706     0       0     0 2019-03-17   29.766667     6.793103  \n",
       "13707     0       0     0 2019-03-18   26.333333     5.400000  \n",
       "13708    69       0     0 2019-03-17   27.941176     4.882353  \n",
       "13709    70       0     0 2019-03-17   24.545455     5.100000  \n",
       "13710    79      44    19 2019-03-18   32.000000     7.727273  \n",
       "13711     0       0     0 2019-03-18   28.000000     5.000000  \n",
       "13712     0       0     0 2019-03-17   28.000000     5.000000  \n",
       "13713     0       0     0 2019-03-18   25.333333     5.428571  \n",
       "13714     0       0     0 2019-03-18   34.000000     4.333333  \n",
       "13715   100       0     0 2019-03-18   28.500000     7.500000  \n",
       "13716     0      51     0 2019-03-18   29.812500     4.666667  \n",
       "13717     0       0     0 2019-03-19   25.666667     6.500000  \n",
       "13718    56       0     0 2019-03-15   25.666667     6.500000  \n",
       "13719    49       0     0 2019-03-19   25.241379     5.791667  \n",
       "13720     0      23     0 2019-03-19   34.600000     6.333333  \n",
       "13721    23       0     0 2019-03-20   33.777778     7.000000  \n",
       "13722    17       0    38 2019-03-20   28.687500     6.333333  \n",
       "13723     0       0    52 2019-03-20   26.333333     5.400000  \n",
       "13724     0      44    73 2019-03-20   26.400000     3.777778  \n",
       "13725     0       0     0 2019-03-21   29.000000     6.666667  \n",
       "13726     0       0     0 2019-03-21   29.200000     6.200000  \n",
       "13727     0       0     0 2019-03-21   32.228571     5.888889  \n",
       "13728     0       0     0 2019-03-22   24.666667     8.142857  \n",
       "13729    55       2    59 2019-03-22   34.444444     7.500000  \n",
       "13730     0       0    17 2019-03-22   28.687500     6.333333  \n",
       "13731     0       0    13 2019-03-22   25.444444     6.906977  \n",
       "13732     0       0     0 2019-03-22   32.629630     6.000000  \n",
       "\n",
       "[13512 rows x 14 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symptoms.join(dataFinalePropreX, on='user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T08:25:58.988105Z",
     "start_time": "2019-03-31T08:25:58.966240Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create features and labels matrices\n",
    "X = symptoms2.loc[:, ['period_day', 'last_len_period', 'last_len_cycle', 'mean_cycle', 'mean_period']]\n",
    "Y = symptoms2.loc[:, ['acne', 'backache', 'bloating', 'cramp', 'diarrhea', 'dizzy', 'headache', 'mood', 'nausea', 'sore']]\n",
    "#\n",
    "X.last_len_period = X.last_len_period.fillna(X.mean_period)\n",
    "X = X.dropna()\n",
    "Y = Y.loc[X.index]\n",
    "# Split traint test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:29:16.126447Z",
     "start_time": "2019-03-31T09:29:16.119673Z"
    }
   },
   "source": [
    "## Previous period's symptoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:49:27.403630Z",
     "start_time": "2019-03-31T09:49:27.393345Z"
    }
   },
   "outputs": [],
   "source": [
    "### The first thing is to create a dataset of users who ansswered more than 3 times\n",
    "SymptomeAnswer=symptoms.groupby('user_id').size().reset_index(name='size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:27:31.087707Z",
     "start_time": "2019-03-31T09:27:30.407394Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qalGSq5JsmN6gqtZNm/zOJF+evxIBAAA4VR3xN6WttfGquibJh5MMJnlPa+2WqnpbkptaaxuSXFNVL0oylmRbkteeyKIBAAA4NcxloKO01q5Pcv0h894y7fJPznNdAAAAfB2Yt4GOAAAA4GgJpQAAAHRGKAUAAKAzQikAAACdEUoBAADojFAKAABAZ4RSAAAAOiOUAgAA0BmhFAAAgM4IpQAAAHRGKAUAAKAzQikAAACdEUoBAADojFAKAABAZ4RSAAAAOiOUAgAA0BmhFAAAgM4IpQAAAHRGKAUAAKAzQikAAACdEUoBAADojFAKAABAZ4RSAAAAOiOUAgAA0BmhFAAAgM4IpQAAAHRGKAUAAKAzQikAAACdEUoBAADojFAKAABAZ4RSAAAAOiOUAgAA0BmhFAAAgM4IpQAAAHRGKAUAAKAzQikAAACdEUoBAADojFAKAABAZ4RSAAAAOiOUAgAA0BmhFAAAgM4IpQAAAHRGKAUAAKAzQikAAACdEUoBAADozJxCaVVdUVW3V9XGqnrjDMvfUFW3VtXnquofquri+S8VAACAU80RQ2lVDSa5NslLk1yW5FVVddkhzT6TZH1r7WlJPpjk7fNdKAAAAKeeufSUPjvJxtbaHa210STvT/Ly6Q1aax9pre3pT34iyQXzWyYAAACnormE0vOT3DNtelN/3mx+OMnfHE9RAAAAfH0Yms+VVdUPJFmf5FtnWX51kquT5KKLLprPmwYAAGABmktP6b1JLpw2fUF/3kGq6kVJfj7Jla21/TOtqLX2ztba+tba+tWrVx9LvQAAAJxC5hJKb0yyrqouqapFSa5KsmF6g6p6RpLfTS+QPjj/ZQIAAHAqOmIoba2NJ7kmyYeT3JbkA621W6rqbVV1Zb/ZryZZluRPq+rmqtowy+oAAADggDn9prS1dn2S6w+Z95Zpl180z3UBAADwdWAuh+8CAADACSGUAgAA0BmhFAAAgM4IpQAAAHRGKAUAAKAzQikAAACdEUoBAADozJzOU8rMrrvh7oOmX335RR1VAgAAsDDpKQUAAKAzQikAAACdEUoBAADojFAKAABAZ4RSAAAAOiOUAgAA0BmhFAAAgM4IpQAAAHRGKAUAAKAzQikAAACdEUoBAADojFAKAABAZ4RSAAAAOiOUAgAA0BmhFAAAgM4IpQAAAHRGKAUAAKAzQikAAACdEUoBAADojFAKAABAZ4RSAAAAOiOUAgAA0BmhFAAAgM4IpQAAAHRGKAUAAKAzQikAAACdEUoBAADojFAKAABAZ4RSAAAAOiOUAgAA0BmhFAAAgM4IpQAAAHRGKAUAAKAzQikAAACdEUoBAADojFAKAABAZ4RSAAAAOjOnUFpVV1TV7VW1sareOMPyb6mqT1fVeFW9Yv7LBAAA4FR0xFBaVYNJrk3y0iSXJXlVVV12SLO7k7wuyXXzXSAAAACnrqE5tHl2ko2ttTuSpKren+TlSW6datBau6u/bPIE1AgAAMApai6H756f5J5p05v68wAAAOC4PKYDHVXV1VV1U1XdtGXLlsfypgEAADgJzSWU3pvkwmnTF/TnHbXW2jtba+tba+tXr159LKsAAADgFDKXUHpjknVVdUlVLUpyVZINJ7YsAAAAvh4ccaCj1tp4VV2T5MNJBpO8p7V2S1W9LclNrbUNVfVNSf48yRlJvquqfqG19pQTWvlJZu/oRH7w//9kvu2JZ2dosJf1X335RR1XBQAAcHKby+i7aa1dn+T6Q+a9ZdrlG9M7rPfr1m2bd+Yjt2/J2rOWZt05y7suBwAAYEF4TAc6OpVt3TOaJNm8Y1/HlQAAACwcQuk82ba7F0rv27G340oAAAAWDqF0nmzbM5Yk2bxdTykAAMBcCaXzZFv/8N2Hdu3P6Phkx9UAAAAsDELpPBifnMzOvWNZs2IkLckDO/WWAgAAzIVQOg927BlLS3LZmtOT+F0pAADAXAml82Bq5N1LVi3NyPCA35UCAADMkVA6D7bt7g1ydObSRVmzYkk26ykFAACYE6F0HmzbM5rBqpy+ZDjnrRjJ/Tv3ZbK1rssCAAA46Qml82Dr7tGsOG04A1VZs3JJxiZaHnpkf9dlAQAAnPSE0nmwbc9ozjxtUZJkzYqRJMnmHX5XCgAAcCRC6TzYtns0ZywdTpKcvXwkgwNlBF4AAIA5EEqP0/7xiewencgZ/Z7SwYHKOacv1lMKAAAwB0Lpcdq2pzfy7hlLFx2Yt2bFkmzevjfNYEcAAACHJZQep227e+confpNadL7Xenu0Yk8sNNgRwAAAIcjlB6nbXt6oXR6T+l5K5YkSW7dvKOTmgAAABaKoa4LWOi27R7N8GBl6aLBA/PO7Y/A+75P3pP7d3ytt/TVl1/0mNcHAABwMtNTepy27RnLGactSlUdmDcyPJgzly7K5u1G4AUAADgcofQ4bdszemDk3enOWzGS+4zACwAAcFhC6XForWXr7tGDfk86Zc3KJdm6ezT7xiY6qAwAAGBhEEqPw96xiewfn8yZpw0/atma/u9K79dbCgAAMCuh9Dhs2/3oc5ROmRqB974dflcKAAAwG6H0OGydOh3MDL8pXT4ylKWLBrNZTykAAMCshNLjsG13L5SeOUNPaVVlzcol2aynFAAAYFZC6XHYtmc0S4YHMzI8OOPy81aM5IGd+zMx2R7jygAAABYGofQ4bNszmjOWPnqQoylrVizJxGTLg484hBcAAGAmQulx2Lp7bMbfk06ZGoHX70oBAABmJpQeo8nWsn3PaM48TChdtXxxhgcrm7f7XSkAAMBMhrouYKHatW8845NtxtPBTBmoyrmnj+S+fk/pdTfcfdDyV19+0QmtEQAA4GSnp/QYbTvM6WCmW7OiNwJvawcPdnT/jn255rpP5/t+9+PZNzZxwuoEAAA4mekpPUZfC6WzD3SUJGtWjuSTd01m+97e708379ibf/zig7nlvp05bdFg9oxO5N0fuzM/9m2PfyzKBgAAOKkIpcdo6+6xJDns4btJct6KJUmSz9y9Lfdt35dbN+/M4qGBfNsTz87/88pvzM988HO59iMb84pnXZBzTh854XUDAACcTBy+e4y27RnN8sVDGR48/EN4zukjqSR/f9uDueOhXfn2J52dn3nJk/Liy87JytMW5ee/88kZn2h5+4duf2wKBwAAOInoKT1G23aPHrGXNEkWDQ3k2598dqoqz7n0rIwMDx60/OKzluaHnndJfuefv5LXPOfiPP3ClSeqZAAAgJOOntJjtG3P6BF/TzrlhU86J9/2xLMfFUinXPPCx2f18sX5hb+85VEDIgEAAJzK9JQeg4nJlh17x+bUU3o4008R8y3rVufPPr0pP/tnn8vTLzwjiVPGAAAApz49pcdgx96xTLbkzCOcDuZoPOOilTl/5ZJ86Av3Z3R8ct7WCwAAcDITSo/BgdPBHGdP6XQDVXnZ09Zk577x/POXtszbegEAAE5mQukx2LZ76hyl8xdKk96gR0+7YEU++uUtB25juk3b9uSDn9qUG+/aOq+3CwAA0BW/KT0GW/eMZqCSFUvmNtDR0bjiKefmts078ze33J/BwcodW3bnji27csdDu7O1H1Srkp976ZPz+udfkqqa9xoAAAAeK0LpMdi2ezQrlgxncGD+A+HK0xblW9atzj988cF84d4dSZKR4YFcumpZfuKFj8/6tWfmt/9pY37p+ttyx0O787aXP+WI50oFAAA4WQmls9i+ZzQrZzk8d9uesXk/dHe6569bnT1jE1m5ZDiXrl6WNStGMtDvEf3cph35N49bld37J/K+T96de7buybXf/8wT0msLAABwoulim8FdD+3OC3/9n/Puj9054/Jtu0fndZCjQy0aGsh3Pe28PH/d6py/csmBQDploCovecq5efsrnpYb7nw4//63/zV3P7znhNUDAABwougpncGalSO5/JIz81/+6tbs3DuWn3rRugO/3RybmMwj+8dzxmnd90yOT7S89t+szR9/4u5c8Y5/ybteuz6XrlqWvWMT2TM6nn1jE9kzOpF9Y5N5ynmn57yVS7ouGQAA4CBC6QwWDw3mt171jLzxv38+7/iHL+eRfeN583c+Ocm008GcwMN3j8alq5blR7/1cXnvx+/Kq3/vhlnbVZLnPn5VXvGsC/KSp5ybJYsGZ227ecfebHxwV77xwpU5faT78A0AAJy65hRKq+qKJO9IMpjkXa21Xz5k+eIkf5DkWUkeTvLK1tpd81vqY2tocCBv/56nZfnIUN7zr3fmkX1jedoFK7Nt91iS5MwTePju0Vq1fHF+9AWPy8jwYAaqsmTRQJYMD+YTd2zN8OBABiv50oO78pm7t+VjGx/K4qGBPPX8FfnZK56Up5x3er5w74585p7tufnu7bn5nu25f+e+JMnioYG85Cnn5nvXX5B/87hVhx3Yad/YRB7ePZrzVowYERgAAI5Day033Lk1f3zD3Vk+MpQfeu7aPP7s5V2XdcIcMZRW1WCSa5O8OMmmJDdW1YbW2q3Tmv1wkm2ttcdX1VVJfiXJK09EwY+lgYHKW152WU4fGc47/uHLuXXzzqw9a2mSk6endMppi4by6ssvOmje1n6ATpKLzlqaFz7p7Nz10O58+u7t+fymHfm+3/34Qe3PXLooF56xJOvXnpGzli7OF+/fmb+99f5s+Ox9WbFkOD/wzRflFc+6MKuXL86t9+3MF+7dkS/ctyO33rczX35wVyYmW1YtW5T1F5+Zb7rkzDx77Zl58prlGeqPDjw2MZk7tuzObZt35rbNO3Pr5p0ZHZ/Msy4+I990yZl51sVnnJCe2W27R7NsZMgoxQAAPOYmJ1tuu39nPnHH1uzeP55nX3JmnnHRyiweevSRi2MTk/nrz23Ouz52R75w786sPG04e0cnct0Nd+cFT1yd1z/v0jz38Wedcp1A1Vo7fIOq5yR5a2vtJf3pNyVJa+2/Tmvz4X6bj1fVUJL7k6xuh1n5+vXr20033TQPd+Gx8a6P3pFf/OvbMtTvLXzrlU951ABEXTs0lF53w92ztt0/PpFb7tuZbbtHc/4ZS3LBGadl2eJHf0cxNjGZ2zbvzKfv3pYvP7ArLb1Dgac27LLFQzl/5ZKct3Iky0aGs2nrntz18O5s29MLxEsXDeYbL1yZnfvG8sXNj2R8snfNwYHKk85dnqGByi337cz4ZEslOXfFSNaetTQXnrkkz77krOzcO5ad+8ayc+94//9YliwazHkrl+S8lUtyQf//+WcsyaLBgWx8cFdu27wzX7x/Z27b/Ei+eP/OPLRrNMODlUtWLc26s5fn8Wcvy7pzlmXd2cuzbGQo9+/Y1/vbuS/379ib+3fuz/Y9o1m1bHHOWzly4LbOX7kka1aMZGKyZcsj+7Plkf15sP9/y6792Ts6kdXLF+fc00dy9umLc87pIzn39JGsPG04ky15ZN9YHtk3nh3T7lPScvrIcE5fMtz/P5Rli4cyNDiQ1lr2j09m7+hE9o71/0YnMjhQWTI8mNMWDWZk0WCWDA8eFLjHJyYzOjGZsfGW/RMTGZtoGRqoLBocyKKh3t/QQB30O+k9+yeye3Q8e0Z7v0ceHZ/MyPBgli4eymmLBvt/QxkcqLTWsmd0Irv3j+eR/ePZtW88u/aPZ3yyZdniwSxbPJxlI737sWzx0IEe9tZaxidbxidar76JyVR6RyUsGhzI8GBlcFpdU1prmWzJ+ORkJiZbBqrXbmiGtjNpraW1ZLK/nsmDpnvz2rRlk60lLamqDFRvYLGBqtTA1OXe/5q2bKDymL45TE62jE1OZmyiZWy891hOtmSgkkzVl6/VWdNqHKikMjV/2v1LDrR9LExtlzZ9etryuTyuk5MtE61lov+6MnDo/TzG+9JmeF4c6TkzWJXhoYEMD8z+XD50/ZXel58ni0Pfsk/Uc2Fq2x/N8+14rjPXx3j6dpnr7RzLtpy6ztE8Rycne/vHY/laM/35MP2p0WZrc9D86e1nXs/Bt3Xk9lXpv/YPzPo4TL3PTEy23ntM9d4rDveeMdV2fLJlYqKlBpLhgYEMDlSGB2d+TxqbaBnvvwZPTE7t/73aZrrO1G3sn3q9nmwZHhzovWYMVoYHBg56/ky9/49OTGZ0vPfXkgPv44vg40nSAAAMMElEQVSHeu+b068z3l9/728i+8cmM1CVxcO99ouHBrN46GvXmf4ZY9947/PF/vHJDA9WRoZ7ny1G+n9T7+NjE5PZM9pru6f/mWH/+GRGhgdy2qKhLF00mCWHfF7YOzaRXft6nxd29z8zjE5M9j4j9D8rLF88nKWLBzM0OJCxicns2Dt20N/Ovb3PlKcvGc6KJcNZ2f9/+pLhDA1Udu0fz8O7RvPw7v15aNdoHtq1P1t3jWZkeDBnLVuUVcsWH/h/5tJFqSQPPrI/923fm3u378192/flvu1789Cu/Tlj6aIDn2vPW9H7/HfuipHsHZvIxgd35csPPJIvP7ArX3pwVzY+8Ei27NqftWctzRPO6X2+fMI5y7PunGVZe9bS3PnQ7nz8Kw/l43c8nBvu3Jrt/c/GVb3n9uKhgTzzojPynMedlec87qysPWtpPvipTXnv/7wr9+/cl8etXprXP//SfPczzs+e0Yn88Se+mvd+/Kt5aNf+POnc5fnh512SK59+3ozB9mRSVZ9qra0/Yrs5hNJXJLmitfb6/vRrklzeWrtmWpsv9Nts6k9/pd/modnWu9BCaZK84U9uzp9/5t6ctWxx3vDiJ3RdzqMcTSg9Fjv3juWzm7ZnbGLyQFCbrWdzx96x3PXw7tz10O7cs21PTls0lDWnj+TcFSNZs3JJVi9bnNc85+IkyZ7R8Xzm7u1598fuzF0P7849W/dkbOLg5+XioYEDL5CjE5PZsWcsE4d+gMrX3hSHBupAKDz79MXZMzqRB3fuy4OP7M/W3aOZ7Vk/NFA5fclwTls0mNaS+3fuO/CB93AWDQ1ksCp7xyZmXOf4HNZx6P0dnZic9Q18ptsYGqyMjvfCyZFU9d7cJvtvrkdT11QAmquR4YFMTiajE5Nzqqt3yHkdCBuHe/wH+h9SBgd6wWqmwDDXx3A+HBRip4XXo/0I+bWgNjXdDkyPH+ExmQ8zBdip+zJVR0ubVt/Xip6aP1PYPJZtUZUMTntMp7bxXB+DA7VnekjvXW5t5sA5H6aey0MDlYnJ3m1M9G/j0Mdh6nlcVRk8hufM9Mf3aLfLXLfJVE1TH7YPBLepSqfVXNOeN0kO3P/J1vsS4dCgMTDtfk992Tt9exz6mB36nOh9+E1/3TM/Pw7smwNf+2JpsrVMTs5c12zXOdpteeh9mel5Ozhw8Bdgydfuy8Tko5+Thz5mU/c/efR2Tjv8PtmbflRJC8LUa/9c3y+mrjPUf78Yn+wF0SPd/68F2t4Ak3N5P596T24tc37PnLpOL8DObaNMXWcqHM/F8GDv/u8fP/J78pRF/S++5/I+fuA6QwMZP8rPC4sGB47qNpLe/TmazzFJbz89tK4VS4azatmibNszlq27Rw9aNhUipyweGjgQQFctW5Q7H9qTLz/4SO7eumfG59MFZyzJcy7tBc9vvvSsLF08lE/euTWfuOPhfPwrD+e2+3cedL3nPv6svP55l+Zbn7D6UV927R+fyIab78u7P3Znvnj/I1m1bHH+8sefmzUrTt7BTE/KUFpVVye5uj/5xCS3z/0uPeZWJZk1VHNSs+0WLttu4bLtFi7bbuGy7RYu227hsu2OzsWttdVHajSXgY7uTXLhtOkL+vNmarOpf/juivQGPDpIa+2dSd45h9vsXFXdNJdUz8nHtlu4bLuFy7ZbuGy7hcu2W7hsu4XLtjsx5jLyy41J1lXVJVW1KMlVSTYc0mZDktf2L78iyT8e7vekAAAAkMyhp7S1Nl5V1yT5cHqnhHlPa+2WqnpbkptaaxuSvDvJH1bVxiRb0wuuAAAAcFhzOk9pa+36JNcfMu8t0y7vS/K981ta5xbEYcbMyLZbuGy7hcu2W7hsu4XLtlu4bLuFy7Y7AY440BEAAACcKHP5TSkAAACcEELpDKrqiqq6vao2VtUbu66H2VXVhVX1kaq6tapuqaqf7M9/a1XdW1U39/++o+taebSququqPt/fRjf1551ZVX9XVV/u/z+j6zo5WFU9cdq+dXNV7ayqn7LfnZyq6j1V9WD/9G1T82bcz6rnN/vvf5+rqmd2VzmzbLtfraov9rfPn1fVyv78tVW1d9r+9zvdVc4s227W18iqelN/v7u9ql7STdUks267P5m23e6qqpv78+1388Thu4eoqsEkX0ry4iSb0ht9+FWttVs7LYwZVdWaJGtaa5+uquVJPpXk3yX5viS7Wmu/1mmBHFZV3ZVk/fRzGlfV25Nsba39cv9LoTNaaz/bVY0cXv81894klyf5wdjvTjpV9S1JdiX5g9baU/vzZtzP+h+SfzzJd6S3Td/RWru8q9q/3s2y7f5temc5GK+qX0mS/rZbm+SvptrRrVm23Vszw2tkVV2W5H1Jnp3kvCR/n+QJrbWJx7Roksy87Q5Z/utJdrTW3ma/mz96Sh/t2Uk2ttbuaK2NJnl/kpd3XBOzaK1tbq19un/5kSS3JTm/26o4Ti9P8t7+5fem9yUDJ69vT/KV1tpXuy6EmbXW/iW9kfGnm20/e3l6H8Raa+0TSVb2v/yjAzNtu9ba37bWxvuTn0jv/PGcZGbZ72bz8iTvb63tb63dmWRjep9H6cDhtl1VVXodH+97TIv6OiCUPtr5Se6ZNr0pQs6C0P+26hlJbujPuqZ/eNN7HAJ60mpJ/raqPlVVV/fnndNa29y/fH+Sc7opjTm6Kge/OdvvFobZ9jPvgQvLDyX5m2nTl1TVZ6rqn6vq+V0VxWHN9Bppv1s4np/kgdbal6fNs9/NA6GUU0JVLUvyZ0l+qrW2M8n/l+RxSZ6eZHOSX++wPGb3vNbaM5O8NMmP9Q+ZOaD1fl/gNwYnqapalOTKJH/an2W/W4DsZwtTVf18kvEkf9yftTnJRa21ZyR5Q5Lrqur0rupjRl4jF75X5eAvYu1380QofbR7k1w4bfqC/jxOUlU1nF4g/ePW2n9PktbaA621idbaZJLfi8NgTkqttXv7/x9M8ufpbacHpg4X7P9/sLsKOYKXJvl0a+2BxH63wMy2n3kPXACq6nVJXpbk+/tfKqR/6OfD/cufSvKVJE/orEge5TCvkfa7BaCqhpL8+yR/MjXPfjd/hNJHuzHJuqq6pN8LcFWSDR3XxCz6x/a/O8ltrbXfmDZ/+m+gvjvJFw69Lt2qqqX9walSVUuT/Nv0ttOGJK/tN3ttkv/RTYXMwUHfGNvvFpTZ9rMNSf5DfxTeb05vMI/NM62AblTVFUl+JsmVrbU90+av7g88lqq6NMm6JHd0UyUzOcxr5IYkV1XV4qq6JL1t98nHuj6O6EVJvtha2zQ1w343f4a6LuBk0x/N7pokH04ymOQ9rbVbOi6L2T03yWuSfH5qeO4kP5fkVVX19PQOSbsryX/qpjwO45wkf977XiFDSa5rrX2oqm5M8oGq+uEkX01vQAFOMv0vEl6cg/ett9vvTj5V9b4kL0iyqqo2Jfm/kvxyZt7Prk9v5N2NSfakN6IyHZll270pyeIkf9d//fxEa+1HknxLkrdV1ViSySQ/0lqb60A7zLNZtt0LZnqNbK3dUlUfSHJreodk/5iRd7sz07Zrrb07jx5DIbHfzRunhAEAAKAzDt8FAACgM0IpAAAAnRFKAQAA6IxQCgAAQGeEUgAAADojlALACVBV76qqy7quAwBOdk4JAwAAQGf0lALAcaqqpVX111X12ar6QlW9sqr+qarWV9WVVXVz/+/2qrqzf51nVdU/V9WnqurDVbWm6/sBAF0QSgHg+F2R5L7W2je21p6a5ENTC1prG1prT2+tPT3JZ5P8WlUNJ/mtJK9orT0ryXuS/FIXhQNA14a6LgAATgGfT/LrVfUrSf6qtfbRqjqoQVX9TJK9rbVrq+qpSZ6a5O/67QaTbH6MawaAk4JQCgDHqbX2pap6ZpLvSPKLVfUP05dX1YuSfG+Sb5maleSW1tpzHttKAeDk4/BdADhOVXVekj2ttT9K8qtJnjlt2cVJrk3yva21vf3ZtydZXVXP6bcZrqqnPMZlA8BJQU8pABy/b0jyq1U1mWQsyY8m+bX+stclOSvJX/QP1b2vtfYdVfWKJL9ZVSvSez/+f5Pc8lgXDgBdc0oYAAAAOuPwXQAAADojlAIAANAZoRQAAIDOCKUAAAB0RigFAACgM0IpAAAAnRFKAQAA6IxQCgAAQGf+F2GxIiXDkGI2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e3709e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plt.title(\"Distribution of size\")\n",
    "sns.distplot(SymptomeAnswer['size'],bins=200)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:49:59.340318Z",
     "start_time": "2019-03-31T09:49:59.191608Z"
    }
   },
   "outputs": [],
   "source": [
    "SymptomeAnswer=SymptomeAnswer[(SymptomeAnswer['size']>3)]\n",
    "Symptom=symptoms.merge(SymptomeAnswer,on='user_id',how='inner')\n",
    "Symptom=Symptom.merge(period,left_on='user_id',right_on='User_id')\n",
    "Symptom['differenceDays']=(Symptom['date']-Symptom['start_date']).dt.days\n",
    "### Select only the data with 13days of difference between the start of period and the date of symptoms\n",
    "Symptom13days=Symptom[abs(Symptom['differenceDays'])<13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:50:28.018810Z",
     "start_time": "2019-03-31T09:50:28.006551Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### The first thing is to create a dataset of users who ansswered more than 3 times\n",
    "SymptomNbOrder=Symptom13days.groupby('user_id').agg({'order':'nunique'}).reset_index().rename(columns={'order':'uniqueOrder'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:50:37.311716Z",
     "start_time": "2019-03-31T09:50:36.691462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a209fe4e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plt.title(\"Distribution of number of different order\")\n",
    "sns.distplot(SymptomNbOrder['uniqueOrder'],bins=200)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:50:47.004538Z",
     "start_time": "2019-03-31T09:50:46.994517Z"
    }
   },
   "outputs": [],
   "source": [
    "SymptomNbOrderValid=SymptomNbOrder[(SymptomNbOrder['uniqueOrder']>3)&(SymptomNbOrder['uniqueOrder']<10)]\n",
    "indexValid=SymptomNbOrderValid.user_id\n",
    "\n",
    "Symptom13daysValid=Symptom13days[Symptom13days.user_id.isin(indexValid)].reset_index()\n",
    "Symptom13daysValidDays=Symptom13daysValid.groupby(['user_id','order']).size().reset_index(name='sizeDaysInPeriod')\n",
    "Symptom13daysValid=Symptom13daysValid.drop(columns=['index','User_id'])\n",
    "\n",
    "Symptom13daysValid['orderInsideOrder'] = 0\n",
    "for user_id in indexValid:\n",
    "    index = Symptom13daysValid[Symptom13daysValid.user_id == user_id].sort_values('date').index\n",
    "    Symptom13daysValid.loc[index, 'orderInsideOrder'] = pd.Series(range(len(index)), index=index)\n",
    "    \n",
    "SymptInt=Symptom13daysValid.groupby(['user_id','order'],as_index=False)['user_id','acne','backache','bloating','cramp','diarrhea','dizzy','headache','mood','nausea','sore'].mean()\n",
    "SymptMean=SymptInt.groupby('user_id',as_index=False)['user_id','acne','backache','bloating','cramp','diarrhea','dizzy','headache','mood','nausea','sore'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:51:21.261558Z",
     "start_time": "2019-03-31T09:51:20.865563Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wDaEUAAAAAOAbQikAAAAAwDeEUgAAAACAbwilAAAAAADfEEoBAAAAAL4hlAIAAAAAfEMoBQAAAAD4hlAKAAAAAPANoRQAAAAA4BtCKQAAAADAN4RSAAAAAIBvCKUAAAAAAN8QSgEAAAAAviGUAgAAAAB8QygFAAAAAPiGUAoAAAAA8A2hFAAAAADgG0IpAAAAAMA3hFIAAAAAgG8IpQAAAAAA3xBKAQAAAAC+IZQCAAAAAHxDKAUAAAAA+IZQCgAAAADwDaEUAAAAAOAbQikAAAAAwDeEUgAAAACAbwilAAAAAADfEEoBAAAAAL4hlAIAAAAAfEMoBQAAAAD4hlAKAAAAAPANoRQAAAAA4BtCKQAAAADAN4RSAAAAAIBvCKUAAAAAAN8QSgEAAAAAviGUAgAAAAB8QygFAAAAAPiGUAoAAAAA8M2YodTM5pjZ42a2ycw2mtlnRljGzOy/zGy7mb1kZielplwAAAAAwFQSGscyEUnXO+fWmlmxpDVm9qhzbtOQZd4haZF3OVXS/3g/AQAAAAAY1Zg9pc65RufcWu96p6TNkmqGLXaxpFtd3HOSysxsVtKrBQAAAABMKUd0TqmZzZd0oqRVwx6qkVQ75HadXh9cZWbXmdlqM1vd0tJyZJUCAAAAAKaccYdSMyuSdK+kzzrnOiayMufcTc65lc65lVVVVRNpAgAAAAAwhYwrlJpZjuKB9Hbn3K9HWKRe0pwht2d79wEAAAAAMKrxjL5rkn4qabNz7gejLHa/pA97o/CeJmm/c64xiXUCAAAAAKag8Yy+e6akD0l62czWefd9SdJcSXLO3SjpAUnvlLRdUo+kjyS/VAAAAADAVDNmKHXOPSXJxljGSfpUsooCAAAAALwxHNHouwAAAAAAJBOhFAAAAADgG0IpAAAAAMA3hFIAAAAAgG8IpQAAAAAA3xBKAQAAAAC+IZQCAAAAAHxDKAUAAAAA+IZQCgAAAADwDaEUAAAAAOAbQikAAAAAwDeEUgAAAACAbwilAAAAAADfEEoBAAAAAL4hlAIAAAAAfEMoBQAAAAD4hlAKAAAAAPANoRQAAAAA4BtCKQAAAADAN4RSAAAAAIBvCKUAAAAAAN8QSgEAAAAAviGUAgAAAAB8QygFAAAAAPiGUAoAAAAA8A2hFAAAAADgG0IpAAAAAMA3hFIAAAAAgG8IpQAAAAAA3xBKAQAAAAC+IZQCAAAAAHxDKAUAAAAA+IZQCgAAAADwDaEUAAAAAOAbQikAAAAAwDeEUgAAAACAbwilAAAAAADfEEoBAAAAAL4hlAIAAAAAfEMoBQAAAAD4hlAKAAAAAPANoRQAAAAA4BtCKQAAAADAN2OGUjP7mZk1m9mGUR4/28z2m9k67/LV5JcJAAAAAJiKQuNY5mZJN0i69TDLPOmcuzApFQEAAAAA3jDG7Cl1zj0haV8aagEAAAAAvMEk65zS081svZk9aGbLRlvIzK4zs9VmtrqlpSVJqwYAAAAAZKtkhNK1kuY555ZL+pGk3462oHPuJufcSufcyqqqqiSseup6qa5dnX2DfpcBAAAAACk16VDqnOtwznV51x+QlGNmlZOu7A2sqz+i9/33M7ry/1apg2AKAAAAYAqbdCg1s5lmZt71U7w2Wyfb7hvZ1qYORWJOL9fv17U3v6CBSMzvkgAAAAAgJcYcfdfM7pR0tqRKM6uT9DVJOZLknLtR0qWSPmFmEUm9kq5wzrmUVfwGsLmxU5L0pXcu1nce3KK2nkF9+LR5CgWZVhYAAADA1DJmKHXOfWCMx29QfMoYJMmWpg4V54X0sbMWqqwgrM/f85J++UKtPnDKXAUD5nd5AAAAAJA0dL1loM2NnVoys0RmpstXztGFx8/SpsYO3bu2TjE6oQEAAABMIYTSDBOLOW1t6tTiWcUH7jvjqEqdt3SG1tW26/51DeLoaAAAAABTxZiH7yK96tt71dUf0eKZJYfc/5ZjqtQ3GNMT21pUkBvUeUtn+lQhAAAAACQPPaUZZnNjhyRpyZCeUkkyM52/bIaWzirRc6+10lsKAAAAYEoglGaYzY2dMpOOmVH8usfMTAsqC9U3GFP3QNSH6gAAAAAguQilGWZLU4fmVRSoMHfkI6sri8KSpNau/nSWBQAAAAApQSjNMFuaOl93PulQlUW5kqS9XQPpKgkAAAAAUoZQmkF6BiLa2dqtJbNGD6VlBWEFTNpLTykAAACAKYBQmkG2NnXKOR0yHcxwwYCpojBMKAUAAAAwJRBKM8iWpk5J0pLDHL4rxQ/hbeXwXQAAAABTAKE0g2xp7FBhOKjZ5fmHXa6yKFet3f2KMS0MAAAAgCxHKM0gm5s6tXhWiQIBO+xy04rCGow6dfQOpqkyAAAAAEgNQmmGcM5pc2OHFs8c/XzSBEbgBQAAADBVEEozRMP+PnX2RbT4MCPvJhwMpQx2BAAAACC7EUozxJbGDknSknH0lBbnhZQTNLUSSgEAAABkOUJphkiMvHvsOEJpwEyVRbkcvgsAAAAg6xFKM8Smxg7NqchXcV7OuJafxlylAAAAAKYAQmmG2NLYocVjzE86VGVRrtp6BhSNMS0MAAAAgOxFKM0AfYNR7djbPa7zSRMqi3IVc1JbD4fwAgAAAMhehNIMsG1Pl2JOWjKOkXcTKovCkhiBFwAAAEB2I5RmgM1N8ZF3xzMdTMI05ioFAAAAMAUQSjPA5sYO5ecENbeiYNzPKQgHlZ8TpKcUAAAAQFYjlGaALY2dOmZmsYIBG/dzzEyVRYzACwAAACC7EUp95pzTlqYOLZ01/kGOEiqLctXK4bsAAAAAshih1GfNnf1q6xk8oulgEqYVhbW/d1ADkVgKKgMAAACA1COU+mxTozfI0RFMB5NQ6Q121NrNIbwAAAAAshOh1GdbGjslaUI9pZWMwAsAAAAgyxFKfbalqUM1ZfkqLcg54udO8+YqbWWwIwAAAABZilDqsy2NnRM6dFeSckNBFeeF6CkFAAAAkLUIpT7qj0T1akuXFk9g5N2EyqJcpoUBAAAAkLUIpT7a3tylSMxN6HzSBOYqBQAAAJDNCKU+SgxytGSSPaU9A1H1DkSTVRYAAAAApA2h1Ee79vXITJpbUTjhNqYVJkbgpbcUAAAAQPYhlPqovq1XM0vyFA5N/G2o9EbgJZQCAAAAyEaEUh/VtfWopix/Um1UFIZlYq5SAAAAANmJUOqj+vZezS6fXCgNBQMqLwyrtZueUgAAAADZh1Dqk0g0psb9fZpdXjDptqYVMgIvAAAAgOxEKPVJU0efojGnmkn2lEqJuUoH5JxLQmUAAAAAkD6EUp/Ut/VK0qQP35Xigx0NRGLq7I9Mui0AAAAASCdCqU/qDoTSyR++W1nEtDAAAAAAshOh1CeJUDqrNG/SbU3zQmkrI/ACAAAAyDKEUp/Ut/doenGu8nKCk26rrCBHwYDRUwoAAAAg6xBKfVLXNvnpYBICZt4IvPSUAgAAAMguY4ZSM/uZmTWb2YZRHjcz+y8z225mL5nZSckvc+qpa+tVTRLOJ02Ij8BLTykAAACA7DKentKbJV1wmMffIWmRd7lO0v9MvqypLRpzatyfvJ5SSZpWFNa+7gHFmBYGAAAAQBYZM5Q6556QtO8wi1ws6VYX95ykMjOblawCp6Lmzj4NRl1SQ2llUa6iMaf2nsGktQkAAAAAqZaMc0prJNUOuV3n3YdRJEberSlLbk+pxLQwAAAAALJLWgc6MrPrzGy1ma1uaWlJ56ozSn0S5yhNqCiIh9K2HgY7AgAAAJA9khFK6yXNGXJ7tnff6zjnbnLOrXTOrayqqkrCqrNTXVuPJCX18N2S/BwFzdTWzeG7AAAAALJHMkLp/ZI+7I3Ce5qk/c65xiS0O2XVtfWqsiiclDlKEwJmKi3IoacUAAAAQFYJjbWAmd0p6WxJlWZWJ+lrknIkyTl3o6QHJL1T0nZJPZI+kqpip4r69uROB5NQURAmlAIAAADIKmOGUufcB8Z43En6VNIqegOoa+vV0uqSpLdbXpijTQ29SW8XAAAAAFJlzFCK5IrFnOrbenXe0hlJb7u8IKzugaj6I1HlhpJ3aHC63bFq94Sed+Wpc5NcCQAAAIBUS+vou4hP2TIQjSV1kKOE8gMj8DLYEQAAAIDsQChNs9oUTAeTUF7ohdJuzisFAAAAkB0IpWmWmA6mJiU9pTmSmKsUAAAAQPYglKZZfXu8p7SmLPmhtCg3pJyg0VMKAAAAIGsQStOsrq1XFYVhFeYmf4wpM1N5QZhzSgEAAABkDUJpmtW19aaklzShnLlKAQAAAGQRQmma1bf1pGTk3YTywhxCKQAAAICsQShNI+ec6tt7UxtKC8LqG4ypdyCasnUAAAAAQLIQStOotXtAfYOxlB++K0n76C0FAAAAkAUIpWlUl8I5ShOYqxQAAABANiGUplFijtLZFanrKa3weko5rxQAAABANiCUplF9W+rmKE3IDweVlxMglAIAAADICoTSNKpr61Vpfo6K83JSup7ygrDaupmrFAAAAEDmI5SmUV2Kp4NJYK5SAAAAANmCUJpG9e29KT10N6G8ID5XqXMu5esCAAAAgMkglKaJc051bb0pHXk3obwwrMGoU1d/JOXrAgAAAIDJIJSmSVvPoHoGomk5fPfgCLycVwoAAAAgsxFK0+TAyLtpCKVlhUwLAwAAACA7EErT5MAcpWkZ6Cg+um9bN6EUAAAAQGYjlKZJnddTmo5zSnNDQRWGg/SUAgAAAMh4hNI0qW/vVXFuSKX5qZ2jNKG8kLlKAQAAAGQ+Qmma1LX1pOV80gTmKgUAAACQDQilaZKu6WASygvCau8ZVDTGXKUAAAAAMhehNA2cc6pv603LIEcJ5YU5ijqnPR19aVsnAAAAABwpQmkadPRG1NkfSWsoTcxVWruvJ23rBAAAAIAjRShNg9o0TgeTUO7NVZoY9RcAAAAAMhGhNA3q2+PBsKYsfeeUluXnyHQwEAMAAABAJiKUpsHBOUrT11MaCgZUnBdS7T56SgEAAABkLkJpGtS19agwHFRZQXrmKE0oLwzTUwoAAAAgoxFK06C+rVc15fkys7Sut6IgrHrOKQUAAACQwQilabB7X09a5yhNKCsIq3F/rwajsbSvGwAAAADGg1CaYtGY02t7u3VUVWHa111RmKOYkxra6S0FAAAAkJkIpSlW19ajgUhMR08vSvu6ywuYFgYAAABAZiOUptj25i5J8ieUenOV1u5jsCMAAAAAmYlQmmIHQmlVcdrXXZKXo2DAGIEXAAAAQMYilKbY9uYuVRXnqjTN08FIUjBgqi7LY65SAAAAABmLUJpi25q7dHRV+g/dTZhTXqA6ekoBAAAAZChCaQo55/Rqc5cv55MmzCkvUC0DHQEAAADIUITSFGru7Fdnf8TXUDq7PF8tnf3qG4z6VgMAAAAAjIZQmkKJQY4W+dlTWlEgSRzCCwAAACAjEUpTaNueTkn+TAeTMKciX5I4hBcAAABARiKUptD2li4V54VUVZzrWw1zyr2eUuYqBQAAAJCBCKUptN0b5MjMfKuhsihX4VCAnlIAAAAAGWlcodTMLjCzrWa23cz+aYTHrzGzFjNb513+OvmlZp/tzd2+nk8qSYGAaW5FgXbs7fa1DgAAAAAYSWisBcwsKOnHks6VVCfpBTO73zm3adiidznnPp2CGrNSe8+A9nb1+3o+acLimcVaV9vudxkAAAAA8Drj6Sk9RdJ259xrzrkBSb+UdHFqy8p+iZF3MyGULq0uUV1br/b3DvpdyqTtbu3Way1dfpcBAAAAIEnGE0prJNUOuV3n3TfcJWb2kpndY2ZzRmrIzK4zs9VmtrqlpWUC5WaPA6G0qtjnSqRl1aWSpE0NHT5XMjmvtXTpJ0/t0M3P7FRDO+fIAgAAAFNBsgY6+p2k+c654yU9KumWkRZyzt3knFvpnFtZVVWVpFVnpu3NXcoNBVRTnu93KVo6q0SStKkxe0NpfXuvbntul8oLwyoIB/XLF3arfzDqd1kAAAAAJmk8obRe0tCez9nefQc451qdc/3ezZ9IWpGc8rLX9pYuHVVVpGDAv5F3E6qKczW9OFcbG/b7XcqE7O3s181P71B+OKiPnrlAl588R61dA7pvfYNeeQmIAAAgAElEQVScc36XBwAAAGASxhNKX5C0yMwWmFlY0hWS7h+6gJnNGnLzIkmbk1didkpMB5MpllWXZOXhu/t7B/Wzp3dIkj56xgKV5udoYWWR3r5kutbVtmvt7jafKwQAAAAwGWOGUudcRNKnJT2seNj8lXNuo5l908wu8hb7OzPbaGbrJf2dpGtSVXA26BmIqK6tN6NC6dLqEm1v7lJ/JHsOee3pj+jnT+9Q72BU15y5QJXFuQceO/vY6VpYVaj71zdoT0efj1UCAAAAmIxxnVPqnHvAOXeMc+4o59y3vfu+6py737v+RefcMufccufcW51zW1JZdKZ7rSU+J2gmhdJl1aWKxJy27cmOkWv7I1Hd8uxO7ese0IdOm6easkPPzQ2Y6fKVcxQOBXXn87s1EIn5UygAAACASUnWQEcYIpOmg0lIDHaUDeeVxpzTHat2q66tV1ecPFcLq0bejiV5Obp85Wy1dPbr9y81pLlKAAAAAMlAKE2B7c1dCgZM86cV+l3KAXMrClSUG9LGLDivdHtzl7Y1d+nC5dVaWl1y2GUXTS/WW46t0updbbpvXf1hlwUAAACQeQilKbCtuVPzphUoHMqczRsImJbMKs6KwY7W7GpTQTiok+eXj2v5ty+eoXnTCvSlX7+sZs4vBQAAALJK5qSmKWR7c5eOHuWQUz8tqy7V5sYOxWKZO41Ke8+ANjV26IQ5ZQoFxrd7BgOmS06cre6BqH79Ir2lAAAAQDYhlCbZYDSmXa09GXU+acLSWSXqHohq174ev0sZ1X3rGhSNOa2YN75e0oTK4lytmFeue9fUMXcpAAAAkEUIpUm2q7VbkZjLzFBanfmDHd29plbVpXmaVZo/9sLDXHLSbG1r7tKG+sw/RBkAAABAHKE0yRIj7y6aXuxzJa+3aEaRQgHL2PNKNzV0aEN9xxH3kia86/hZCocCundtXZIrAwAAAJAqhNIkS8wDetT0zBl5NyE3FNSiGcUZOwLv3WtqFQ4GtHxO2YSeX5qfo3OXztB96+qZtxQAAADIEoTSJNve0qWasnwVhEN+lzKipbNKtKkx80LpQCSm375Yr3OXzpjUtrv0pNlq6xnUn7c2J7E6AAAAAKlCKE2y7c1dOioDzydNWFZdopbOfjV3ZtbUKX/avEdtPYO6bOXsSbVz1qJKVRblcggvAAAAkCUIpUkUizm92pKZ08EkJAY7yrTzSu9eU6eZJXk6a1HVpNoJBQN6zwnVemxLs9q6B5JUHQAAAIBUycxjTLNUfXuv+gZjWjQj80PpxoYOnX3sdJ+ridvT0ac/b23Wx99ylIIBm3R7l6yYrZ88tUO/e6lBHz59/uQLzFB3rNo94edeeercJFYCAAAATBw9pUmUGHk3E6eDSSjJy9GcivyMOq/012vrFXPSZSvnJKW9JbNKtGRWie5dwyG8AAAAQKYjlCbRgVCawYfvStKyWaUZc/iuc053r67VyfPLtaAyeSMWX3JSjdbX7df25s6ktQkAAAAg+QilSbS9uUvTCsMqLwz7XcphLa0u0c7WbnX1R/wuRWt3t+m1vd1J6yVNuPiEGgUDpnvX1ie1XQAAAADJRShNou0tmT3ybsKy6hI5J23JgEN4715dp4JwUO9606yktltVnKu3HFOl36ytVzTmkto2AAAAgOQhlCaJc07b9nRqURaE0gMj8PocSnsGIvrd+ga9802zVJib/DG33ndSjZo6+vTsq61JbxsAAABAchBKk6Slq18dfZGMHuQoYWZJnioKw9pY728offDlJnUPRHV5kg/dTThnyQyV5IWYsxQAAADIYITSJHl+xz5J0nE1pT5XMjYz09JZJb73lN6+apcWVhbq5PnlKWk/LyeoC5dX66ENTRlx/iwAAACA1yOUJsmDG5pUWRTWSXNTE7CSbVl1ibY2dWowGvNl/Rsb9mvt7nZdddo8mU1+btLRXHJSjXoHo3rg5caUrQMAAADAxBFKk6BvMKrHtzTr3KUzFQykLmAl09LqEg1EY3q1pcuX9f/iud3Kywno0pNmp3Q9J80t18KqQt2xandK1wMAAABgYgilSfDktr3qGYjqHcfN9LuUcVvmDXbkx3mlHX2D+u2L9bpoebVKC3JSui4z09Wnz9e62natq21P6boAAAAAHDlCaRI8tKFJJXkhnbZwmt+ljNuCyiLl5QR8Oa/0N2vr1TsY1QdPm5eW9V2yYraKckO65ZmdaVkfAAAAgPEjlE7SYDSmP27eo3OWzlA4lD2bMxgwLZ5Zoo0N+9O6Xuecbntul5bPLtXxs8vSss6i3JAuXTFbv3+pQc2dfWlZJwAAAIDxyZ4UlaGee61V+3sHdcGy7Dl0N2FZdYk2NXTIOZe2da7asU/bm7t0VZp6SROuPmO+BqOOc0sBAACADEMonaQHNzSpIBzUm4+p8ruUI3by/Ap19EX07GutaVvnbc/tUml+jt59fHXa1ilJCyoLdfaxVbp91W4NRPwZcRgAAADA6xFKJyEac3pkY5Peuni68nKCfpdzxC44bqbKC3J06zO70rK+5o4+PbyhSZeumK38cPq31zVnzFdLZ78e3MD0MAAAAECmIJROwppdbdrbNZCVh+5KUl5OUFecMlePbGpSfXtvytd31wu1isScrjp1bsrXNZI3L6rSwspC/fzpnb6sHwAAAMDrEUon4cENjQqHAnrr4ul+lzJhiYB4x6rU9pZGojHd8fxunbWoUgurilK6rtEEAqYPnz4vq6eHeamuXXe9sFu/fH63XtixT8/v2KdVO1q1akerXq7fr1gazw8GAAAAkiHkdwHZyjmnhzc06c2LKlWUm72bcXZ5gd6+ZIbufL5Wf/u2RSk7DPmxLc1q3N+nr717WUraH69LVszW9x55Rbc8s1MnvP8EX2s5EgORmL7/6Fbd9MRrOlzuXFBZqMtWzFZZQTh9xQEAAACTkL1pymcv1e1Xw/4+/f15x/pdyqRdffp8Pbppjx54uVHvO2l2StZx23O7NLMkT+cs8bdXuTgvR5eumK3bV+3SF9+5WNOL83ytZzy2N3fps3e9qA31HfrAKXP0ybOPVjBgum9dgyTJvH9eaerU719u1H89tk0Xn1Cj5WmacgcAAACYDELpBD20sUmhgPkespLhzKOnaWFVoW55dldKQunOvd16cttefe6cYxQK+n/E+IdPn6ebn9mpO1fV6jPnLPK7nFE553T7qt361h82KT8nqP/90AqdP+T85dL8nEOWXzm/QgsqC3X3mjrd9UKttjZ16t3HV/syqFSyTWYqnyt9OocZAAAA4+N/QshCzjk9tKFJpx81bUocJmlmuvr0+VqfonMtb1+1S6GA6YpT5iS97YlYWFWks4+t0i9W7crY6WH2dvXrY7eu1ld+u0Enz6/QQ5998yGBdDTTinL1sbMW6pwl0/VSXbt+9Ng2vba3Kw0VAwAAABNDKJ2AV/Z0acfebl1wXHaOujuS951Uo8JwULc+uzOp7e7Y2607Vu3W+ctmakZJ5hwqm8nTw6yrbdcFP3xST2zbq3++cKlu+cgpR7TtggHT2xbP0N+8+SgFA6afPrlDT7zSIscgSAAAAMhAhNIJeHBDo8ykc5fO8LuUpCnOy9ElK2br9+sb1drVn5Q2+yNRffqOtQoFA/rSu5Ykpc1kefOiKi2oLNRPn9qhWCxzwtoTr7Toyv97TvnhgO771Jm69q8WKBCwCbU1p6JAn37b0TquplQPbWzSgxuaGJ0XAAAAGYdQOgEPbWjSyfMqsmKQnCPx4dPnaSAa0y9fqE1Ke//6wBZtbOjQ9y5brpqy/KS0mSyBgOlTbz1aL9Xt13/88RW/y5Ek3beuXh+9+QXNm1aoez9+hpbMKpl0m7mhoN5/8hydvnCantq+V/euqVM0g0I4AAAAQCg9Qjv2dmtLU6fOn0KH7iYcPb1YZx49TXes2q1IdHLnWj60oVE3P7NT1/7VgoztUb7kpBpdcfIc/eix7XrwZX8P4/350zv0mV+u00nzynXX35ym6Uk81DlgpguPn6Vzl87Qi7Xt+sVzu9Q7EE1a+wAAAMBkEEqP0L1r6iRpSp1POtSHT5+v+vZe/WlL84TbqN3Xo3+85yUdP7tUX7hgcRKrSy4z0zcuXqYT55bp+rvXa2tTZ9prcM7pew9v1Td+t0nnLZ2hWz96ikrycsZ+4hEyM7312Ol6zwk1emVPp676yXNq7xlI+nr84pyb9BcpAAAA8AdTwhyB375Yrxse3653vWlWxh2OmixvXzxd1aV5uvXZneMa7XW4gUhMn77zRclJN3zgJIVDmf29R24oqBs/uELv/tFT+titq3X/p89M24jKkWhM/3zfBt35fK3ev3KOvv3e41I+Zc4pCypUEA7qnjV1uuzGZ3XrtadoVmn27ctd/RHVt/Wotq1XdW09qmvrVe9AVGUFOZpenKfpJbma4f3s6o+oKJf/6gAAADIVn9TG6Y+b9uj6u9fr9IXT9P3Ll/tdTsqEggFdddo8/fvDW7WpoUNLq4/svMbvPbJV62vb9d9XnaS50wpSVGVyzSjJ040fWqEr/vc5/e2dL+rmj5yi4AQHFxqv3a09+sK9L+nZ11r1qbcepX8471iZpXadCcfVlOrC5bN03a1rdNENT+vb7zlO503gC4h0cs6ptq1Xz+/Ypx17u9TWMyhJMsXfv6WzSlScl6O9Xf1q6ezX9pauA+fO/s9fXtVZi6p05Slzdc6S6RkxVy4AAAAOIpSOwzOv7tUn71ir42pK9X9Xr1ReTtDvklLqipPn6Ma/vKrLbnxG1593rK4+Y/64QtpjW/bopide04dOm6d3vmlWGipNnpPmluubFy/TP/36ZX334S364jtSM1pwNOZ0yzM79e8Pb1UwYPruJcfr8pPTP3/rGUdV6p5PnK7P3bVe1922Rhctr9bXL1qmisLMmne3dyCq361v0I8f366G/X3KDQW0aHqRTls4TbPLC1Rdlqfc0Ot/H6Mxp7buATV39qm0IEf3rqnXx3+xRjNKcvX+lXP0/lPmTtmjHQAAALINoXQM62rb9bFbVmv+tALdfM3Jb4jDAKcV5eqBvztLX/ntBn3z95v0mxfr9a/ve5OOqykdcfnOvkE9vHGPvv2HTVoyq0RfzrDpX8brilPmakPDfv3vX17TsupSXbS8Oqntb2/u0hfufUlrdrXprcdW6dvvfZOqfQxGi2eW6L5Pnan/+fOr+tFj2/TMq3v1Lxcfp3dkwBcKu1q79YvndulXq+u0v3dQ04tzddHyap04p0y54/hSKBgwVRbnqrI4V1eeOlefO+cYPb61Rbev2qUfPb5dNzy+XW89drquOGWuzj62Sjn0ngIAAPhm6iesSdja1Klrfv68phXl6rZrT1V5hvUipdKcigLd/JGT9fuXGvWN323SRTc8pY+euUCfO/cYFeaG1DcY1eNbmnX/+gb9aUuzBiIxLags1I+vPDGre5K/euEybW3q1OfvWa/2ngG976TZk/4iIhKN6aYnX9MP/7hN+TlB/eDy5XrviTVpO1z3cMKhgD5zziKdt2yG/vGe9frE7Wv1rjfN0jcuXqbKoty01rK/d1APvtyo37xYr1U79ikUMJ2/bKY+dPo8vdrcNantFQoGdO7SGTp36QzV7uvRXS/U6q7VtfrTratVWZSr955YrUtXzNGxM4uT+Ir8cceq3RN63pWnzk1yJQAAAONjzo09Z6GZXSDpPyUFJf3EOfedYY/nSrpV0gpJrZLe75zbebg2V65c6VavXj3BslNvd2uPLr3xGUnSPR8/w9fzIyf6IVNKzgfN/b2D+reHtuiOVbtVXZqnUxdO0x837VFnf0SVRbm68PhZuuiEeC9WMoKW3x+qWzr7dd1tq/Xi7nYV54Z0yYrZuvqM+VpQWTjuNmIxp5fr9+uxLc164OVGbWvu0gXLZuqb71mWtPltk71fDEZjuumJ1/Sff9ymgtygLl5erfOWzdQpCypS1pPYH4nq8S0t+u2L9XpsS7MGojEtrCrUe0+o0eUnz9EMb2qcVPwODEZj+vPWFt2zplZ/2tysSMzpTTWlunTFbF20vDrjv4Ryzqm9Z1D17b2qa+tVW8+AIjGnVa+1Khpzirn4YcxOTgU5QRXl5agoN3TgkpcTOOT3NRtDqd//NwIAgMMzszXOuZVjLjdWKDWzoKRXJJ0rqU7SC5I+4JzbNGSZT0o63jn3cTO7QtJ7nXPvP1y7mRxK27oHdNGPn1JnX0S/+pvTdcwMf3tPMuWD15pd+/Tl32xQfVuvLjhupi46oVqnL5yW9IFj/A6lCS/ubtMtz+zUH15u1GDU6S3HVOmaM+brrEWVCgbsdQG8s29QT23bq8e2NOvxrS3a29WvgEknzi3XtX+1IOnn2aZqv3hlT6e+/8hW/eWVFvUNxlSan6O3L56u85bN1JuPqVRBeGI9x845NXf2a1NDhzY1dmhTQ4ee3Naijr74lxvvXj5L7z2xRm+qKX3dtk3170BrV7/uW9ege9bUaVNjh4IB0zEzinXCnDKdMKdUJ8wp19HTi1I+ANZQkWhMezr71dDeq/q2XtW3exfvekN7r3omMd9sKGAqyc9RRWFYFQVhvW3JdM2tKNDcigLNKS9QSX4oI3rzRzMQielnT+9Qz0BUPQMR9fRHD14fOHi9bzCqgJlCQVPQTMFgQKGA6ZgZRSrJy9G0olxNKwqrsiisaYWJ67lZfcTHcLGYU9Q5RWNOkZhTNOoUicUUCgaUlxNQOBjI6PcaAJC9khlKT5f0defc+d7tL0qSc+5fhyzzsLfMs2YWktQkqcodpvFMDqWxmNO/PbxF7zxulpbPKfO7nIwJpQmxmFMghR/OMyWUJjR39unOVbW6fdUuNXf2H/JYMGAKWHwe0Eg0ppiTSvJCesux0/W2xVV6yzHTUzZ4UKr3i96BqJ7Y1qJHNu7Rn7bsUXvPoHJDAR0zo1iV3gf3yuJcTSsMq6o4V8V5IfUOxNQ9EFHvkFDQ2RfRqy1d2tTQodbug3OjzptWoBVzy3XxiTU686jDf7mRzt+BjQ379dCGJq2rbde62nZ19kUkSYXhoI6fXaa5FQWqKIoHuYrC+KW8MKzivFA89ARMZvF9I2gmWTxA9Q3G1DcYVX8kpv7BqPoiUXX0RrS3q1+t3QNq7erXvu4B7e0aUEtnv5o6+g6MIJxQURhWdVmeasryVVMWH+hpdnn8ekVRWDlB0/0vNigQMG/fjP+e9gxE1NXvXfoOXm/vGVRbz4D2dQ+8LuDm5QQ0oyTvwNQ6M0ryNKMkVyV5OSr0elsLc0MqzA2qKDek/JygAgFTKGAHfia2QcxJMee8i3c95jQQial3MBq/DMR/9g1G1dUfVbtXV6LGtp5BtXUPqK0nfl9Xf2TU9zAnaCoIh1QQDiovJ6iYF8gOhLKYU24ooP29g6MG+8JwcEhgzVVlUfy9LsnLUVFe/PUX54VUlBvvgc4PB5UTNOUEA94lfj0UMDnFX3PiL2Li+iH3e9vFSYrEYuofjKk/ElXfkJ/xbXPwfezuj6hz2Hva6d1/YJmBiGJjHBBlkkJBUygQrzscCig3FFRuKKCjphep2HuvE6/7wGXI7fjrP/j8sLcdQkGT6dC/F8Pz7/C/JjEX3waD0fj8w5GY02A0fju+nxz84iGx38Svx+9/sbZdg5GYBqKxAz8HIvHnH7weO1CLybyfOvA68sNBFYSDys8JKj8cUkGOdztxv7d/HVwmcX3I/d7+F7D474EFpKDFfy8T/0cE7ODfkMNxI+wzMW+HGnrbSXIxySn+u+bcwZ/RxO9f7ODyMXfocon/c8xev23Mhl0fvv1slPvHem5gjDaHt6OD6xppGyW2w9Dtkvh/xw15rYnXH7/v0NsjPcfpYA0B04H3cbSfpoPLBbwXMOrzNOR5GfwF0dCP9YmrbrTHD7l/yHXvkdESwljLjrW+Q9odoS1Jh/wdSnxhF4vF74sOu995vxcH7veuJ+6PORf/HU/87feuB7zf76CZAoGhv+8H/zYe+Kww5P+CEZ9rpkjMHfhcdfAzVvz2aQunqTCDx7wZbygdzyuokVQ75HadpFNHW8Y5FzGz/ZKmSdo7vnIzSyBgKRt9dSpIZSDNRNOL8/SZcxbpE2cfpUc37dGrLV3D/pA7RWNSbiigM46aphXzyqfEtCP54aDOXzZT5y+bqUg0pud37tOjm/Zox95utXT1a3Njp1q7+zUYHf0Tr5lUkBPUgqpCvX3JdC2dVaKl1aVaMqtYxXk5aXw147esulTLquODesViTjtau7Vud7vW17VrfW27Ht/arLaegcO+7iMVCpimDempW1BZqJqyfFWX5aumPF81ZXmqLssfVy91wQh/mIrzcsbc3hcun6XafT2q3Ref93VPR5/2dPRrT0efNjZ06E+bm9U7OPGe2YkqzgupvCCs8oIcTSsK6+jpRSoryFF5QVjbm7u8ABAPAoW58Z/jOdw88WVFz0BErV0DB74YaO0aUIv3s7U7/rN2X4/W1bZrX/fA674o8JOZVBQeEg7z4kG5uixPhd79BeGgtjR2HviQc/BDsg6E9ETgG4zGFPGCW38k/gVK7b4edfUfDLrJ3O9TIeh9IZIIxuFQPBzn5gRVnHcwNOcEAzLFP7PGw178Q+ZR04s0GImpx/uipGcgov09A2oaPBiCe7wgnEyJD7OJ8OLcoWERI0uE1cT7OBUkXtPwMJv4cmdouBotrI0UxiYc7JDxHv7sm6fEmBhpjdVmdp2k67ybXWa29TCLVypLQ20mucrvAlJjxH1jir7WlEj3ttok6Q/pWdXr9o1s2S9e9buAqe+QfSNb9gukBZ83MBr2DYwmY/aNxf/mdwVjmjeehcYTSuslDZ1IcbZ330jL1HmH75YqPuDRIZxzN0m6aTyFmdnq8XT14o2HfQOjYd/AaNg3MBr2DYyGfQOjYd9IvvEcY/iCpEVmtsDMwpKukHT/sGXul3S1d/1SSY8d7nxSAAAAAACkcfSUeueIflrSw4pPCfMz59xGM/umpNXOufsl/VTSbWa2XdI+xYMrAAAAAACHNa5zSp1zD0h6YNh9Xx1yvU/SZcktbXyH+eINiX0Do2HfwGjYNzAa9g2Mhn0Do2HfSLIxp4QBAAAAACBVsn/eCgAAAABA1sq4UGpmF5jZVjPbbmb/5Hc9yAxmNsfMHjezTWa20cw+43dNyCxmFjSzF83s937XgsxhZmVmdo+ZbTGzzWZ2ut81ITOY2ee8vycbzOxOM8vzuyb4x8x+ZmbNZrZhyH0VZvaomW3zfpb7WSP8Mcq+8e/e35WXzOw3ZlbmZ41TQUaFUjMLSvqxpHdIWirpA2a21N+qkCEikq53zi2VdJqkT/3/9u49ZuuyjuP4+5PgBCvbijEOJbAppsgpO5Dl8kAhMmgrdUubRLZOarWVk1l/uMrZcGV5oDkTLJmHEJXWFAlSW8ssDnIUMjFBIGgaNjWQ/PTH72J7fOQoyPXj4fPant3377qv63d/fvdz77nv73P9DnlvRCffAFbWDhGt81PgQdsnAMPIeyQASf2Ay4BTbA+hOZFjTtJ4eJsOjOnUdgUwz/ZxwLyyHIef6bzxvTEXGGJ7KLAamHywQ3U1rSpKgQ8BT9l+2vY24E5gQuVM0QK2N9heWO7/h+aLZb+6qaItJPUHzgFuqZ0l2kPSMcBpNGeIx/Y22/+umypapBvQo1xfvSewvnKeqMj2ozRXkOhoAnBbuX8b8OmDGipaYWfvDdsP2d5eFh8D+h/0YF1M24rSfsDaDsvrSOERnUgaAIwA/lw3SbTIdcDlwGu1g0SrDAQ2A9PKrt23SDq6dqioz/ZzwLXAs8AGYIvth+qmihbqbXtDub8R6F0zTLTWJOCB2iEOdW0rSiN2S9LbgXuAb9p+sXaeqE/SOGCT7QW1s0TrdANGAlNtjwBeIrvfBVCODZxA84+LvsDRki6smyrazM3lKnLJingdSVfSHGI2o3aWQ13bitLngPd2WO5f2iKQ1J2mIJ1he1btPNEapwLjJT1Ds8v/GZJurxspWmIdsM72jr0qZtIUqRFnAWtsb7b9KjAL+GjlTNE+/5TUB6DcbqqcJ1pE0kRgHHCBc43N/da2ovQvwHGSBko6kuakA7MrZ4oWkCSa48JW2v5x7TzRHrYn2+5vewDN34z5tjPjEdjeCKyVNLg0nQmsqBgp2uNZ4COSepbPlzPJSbDijWYDF5X7FwH3V8wSLSJpDM1hQ+Ntv1w7T1fQqqK0HDB8CTCH5sPhbtvL66aKljgV+DzNLNji8jO2dqiIaL1LgRmSlgDDgasr54kWKLPnM4GFwFKa70M3Vw0VVUm6A/gTMFjSOklfBK4BRkv6G83s+jU1M0Ydu3hv3AC8A5hbvpP+vGrILkCZbY6IiIiIiIhaWjVTGhEREREREYeXFKURERERERFRTYrSiIiIiIiIqCZFaURERERERFSTojQiIiIiIiKqSVEaERFdkqRbJJ34JsZNl7RG0hOSVkv6paT+BzjbdEmf3UOfiZI2l8sNrJD0pX18jr6SZu7jmIclnbIvYyIiIvZXitKIiOiSbF9se8WbHP4d28OAwcAiYL6kIw9cur12l+3hwCeAqyX13ptBkrrZXm97t4VvREREG6QojYiIQ56koyX9tsxuLpN0/o5ZP0njy2zjYkmrJK0pYz4g6RFJCyTNkdSn83rd+AmwETi7jJsq6a+Slku6qrSdIem+DnlGS7pX0hFlVnSZpKWSvrWT7M9IukrSwtLnhJ3k2AT8HTi2bOutkh6XtEjShLKeiZJmS5oPzJM0QNKy8thRkqaV9S+SdHpp7yHpTkkrJd0L9Njf30VERMS+6lY7QERExAEwBlhv+xwASccAXwWwPRuYXdrvBh6R1B24Hphge7Ok84EfApN2sf6FwAnA/cCVtp+XdARN8TcU+D1wk6RetjcDXwBuBYYD/WwPKc//rl2s/1+2R0r6GvBt4OKODyN3ZKsAAAKgSURBVEoaBAwCngKuBObbnlTW97ik35WuI4GhJd+ADqv4evNS+ORS9D4k6fjyGr1s+/1lOxbuIl9ERMRbJjOlERHRFSwFRkv6kaSP297SuYOky4FXbN9Is1vuEGCupMXAd4HdHTeqDvfPk7SQZrfek4ATbRv4FXBhKRRHAQ8ATwODJF0vaQzw4i7WP6vcLgAGdGg/v+S7A/iy7eeBTwJXlPaHgaOA95X+c0ufzj4G3A5g+0ngH8DxwGkd2pcAS3bzGkRERLwlMlMaERGHPNurJY0ExgI/kDSv4+OSzgLOpSnCoCkyl9setZdPMYJmVnQgzUzmB22/IGk6TVEIMA34DfBf4Ne2twMvSBoGfAr4CnAeO5+N3Vpu/8frP5vvsn1Jp74CPmN7Vadt/DDw0l5uT0RERGtkpjQiIg55kvrS7IZ6OzCFZjfWHY8dC9wInGv7ldK8CuglaVTp013SSTtZryRdBvQBHgTeSVP4bSknHTp7R1/b64H1NLOu08r49wBvs31PaR/J/psDXCpJ5TlG7MWYPwAXlP7H08ysrgIeBT5X2ocAQw9AvoiIiH2SmdKIiOgKTgamSHoNeJXmWMlry2MTgXcD95U6br3tseWSLD8rx592A64DlpcxUyR9D+gJPAacbnsb8ISkRcCTwFrgj51yzAB62V5ZlvsB0yTt+Cfw5AOwrd8vWZeU9a4Bxu1hzE3AVElLge3ARNtbJU0t+VYCK2l2H46IiDio1BwGExEREftL0g3AItu/qJ0lIiLiUJGiNCIi4gCQtIBm197RtrfuqX9EREQ0UpRGRERERERENTnRUURERERERFSTojQiIiIiIiKqSVEaERERERER1aQojYiIiIiIiGpSlEZEREREREQ1KUojIiIiIiKimv8DOR/+0N2fSYsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20383588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plt.title(\"Distribution of number of different order\")\n",
    "sns.distplot(Symptom13daysValidDays['sizeDaysInPeriod'],bins=50)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T09:53:27.735131Z",
     "start_time": "2019-03-31T09:53:27.711749Z"
    }
   },
   "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>user_id</th>\n",
       "      <th>acne</th>\n",
       "      <th>backache</th>\n",
       "      <th>bloating</th>\n",
       "      <th>cramp</th>\n",
       "      <th>diarrhea</th>\n",
       "      <th>dizzy</th>\n",
       "      <th>headache</th>\n",
       "      <th>mood</th>\n",
       "      <th>nausea</th>\n",
       "      <th>sore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>278</td>\n",
       "      <td>5.142857</td>\n",
       "      <td>10.285714</td>\n",
       "      <td>4.928571</td>\n",
       "      <td>36.571429</td>\n",
       "      <td>12.285714</td>\n",
       "      <td>25.0</td>\n",
       "      <td>27.214286</td>\n",
       "      <td>5.357143</td>\n",
       "      <td>18.214286</td>\n",
       "      <td>5.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>405</td>\n",
       "      <td>73.187500</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>70.812500</td>\n",
       "      <td>53.125000</td>\n",
       "      <td>17.125000</td>\n",
       "      <td>49.0</td>\n",
       "      <td>51.437500</td>\n",
       "      <td>33.187500</td>\n",
       "      <td>37.750000</td>\n",
       "      <td>67.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>490</td>\n",
       "      <td>7.428571</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>55.571429</td>\n",
       "      <td>61.357143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.571429</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>26.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>506</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>569</td>\n",
       "      <td>7.066667</td>\n",
       "      <td>11.466667</td>\n",
       "      <td>2.866667</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.766667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id       acne   backache   bloating      cramp   diarrhea  dizzy  \\\n",
       "0      278   5.142857  10.285714   4.928571  36.571429  12.285714   25.0   \n",
       "1      405  73.187500  62.500000  70.812500  53.125000  17.125000   49.0   \n",
       "2      490   7.428571  45.500000  55.571429  61.357143   0.000000    0.0   \n",
       "3      506   0.000000  46.250000   0.000000   0.000000  26.000000    8.0   \n",
       "4      569   7.066667  11.466667   2.866667   5.700000   0.000000    0.0   \n",
       "\n",
       "    headache       mood     nausea       sore  \n",
       "0  27.214286   5.357143  18.214286   5.571429  \n",
       "1  51.437500  33.187500  37.750000  67.937500  \n",
       "2  27.571429  18.000000   3.428571  26.357143  \n",
       "3   9.000000  13.750000   0.000000  16.000000  \n",
       "4   0.000000   7.700000   0.000000  18.766667  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SymptMean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T10:15:49.138767Z",
     "start_time": "2019-03-31T10:15:48.131183Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Symptom13daysValidInt=Symptom13daysValid.groupby(['user_id','order']).size().reset_index(name='sizeNbPeriods')\n",
    "Symptom13daysValidFinal3=Symptom13daysValidInt[Symptom13daysValidInt['sizeNbPeriods']==3]\n",
    "Symptom13daysValidFinal4=Symptom13daysValidInt[Symptom13daysValidInt['sizeNbPeriods']==4]\n",
    "DataFinal0_3=Symptom13daysValid.merge(Symptom13daysValidFinal3,on=['user_id','order'],how='inner')\n",
    "DataFinal0_4=Symptom13daysValid.merge(Symptom13daysValidFinal4,on=['user_id','order'],how='inner')\n",
    "DataFinal1=DataFinal0_3.drop(columns=['date','size','start_date','end_date','order','orderInsideOrder','sizeNbPeriods'])\n",
    "DataFinal2=DataFinal0_4.drop(columns=['date','size','start_date','end_date','order','orderInsideOrder','sizeNbPeriods'])\n",
    "DataFinal1 = DataFinal1.drop(['mean_cycle', 'mean_period', 'dob', 'cycle_length_initial', 'period_length_initial', 'length', 'days_since_last'], axis=1)\n",
    "DataFinal2 = DataFinal2.drop(['mean_cycle', 'mean_period', 'dob', 'cycle_length_initial', 'period_length_initial', 'length', 'days_since_last'], axis=1)\n",
    "\n",
    "columns=DataFinal1.columns\n",
    "columns1=[x+'_1' for x in columns]\n",
    "columns2=[x+'_2' for x in columns]\n",
    "columnsTrain=np.concatenate((columns1, columns2))\n",
    "\n",
    "n1=len(DataFinal1)//3\n",
    "n2=len(DataFinal2)//4\n",
    "dataFinaleX_1=pd.DataFrame(columns=columns)\n",
    "dataFinaleY_1=pd.DataFrame(columns=columns)\n",
    "dataFinaleX_2=pd.DataFrame(columns=columns)\n",
    "dataFinaleY_2=pd.DataFrame(columns=columns)\n",
    "# DataFinal1.iloc[0].append(DataFinal1.iloc[1])\n",
    "\n",
    "for i in range(0,n1):\n",
    "    a=(DataFinal1.iloc[3*i]+DataFinal1.iloc[3*i+1])/2\n",
    "    a.index=columns\n",
    "    dataFinaleX_1.loc[i]=a\n",
    "    dataFinaleY_1.loc[i]=DataFinal1.iloc[3*i+2]\n",
    "for i in range(0,n2):\n",
    "    a=(DataFinal2.iloc[4*i]+DataFinal2.iloc[4*i+1])/2\n",
    "    a.index=columns\n",
    "    dataFinaleX_2.loc[i]=a\n",
    "    dataFinaleY_2.loc[i]=DataFinal1.iloc[4*i+2]\n",
    "\n",
    "dataFinalePropreX_1=dataFinaleX_1.drop(columns='user_id')\n",
    "dataFinalePropreY_1=dataFinaleY_1.drop(columns=['user_id','differenceDays'])\n",
    "\n",
    "dataFinalePropreX_2=dataFinaleX_2.drop(columns='user_id')\n",
    "dataFinalePropreY_2=dataFinaleY_2.drop(columns=['user_id','differenceDays'])\n",
    "\n",
    "dataFinalePropreX=pd.concat([dataFinalePropreX_1, dataFinalePropreX_2]).reset_index()\n",
    "dataFinalePropreY=pd.concat([dataFinalePropreY_1, dataFinalePropreY_2]).reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T10:17:45.196532Z",
     "start_time": "2019-03-31T10:17:45.190305Z"
    }
   },
   "outputs": [],
   "source": [
    "# Split traint test\n",
    "X_train, X_test, y_train, y_test = train_test_split(dataFinalePropreX, dataFinalePropreY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T10:18:09.650407Z",
     "start_time": "2019-03-31T10:18:09.645939Z"
    }
   },
   "outputs": [],
   "source": [
    "# Here the merge between the two datasets remains to be done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T08:26:37.036862Z",
     "start_time": "2019-03-31T08:26:37.030696Z"
    }
   },
   "source": [
    "# Machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T08:31:16.815536Z",
     "start_time": "2019-03-31T08:27:38.504287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'max_depth': 10,\n",
       " 'max_features': 3,\n",
       " 'min_samples_split': 10,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid search to get the best model using RandomForest\n",
    "# Specify parameters to sample from\n",
    "param_grid = {\"n_estimators\" : [20, 50, 100],\n",
    "              \"max_depth\": [5, 10, None],\n",
    "              \"max_features\": [2, 3, 4],\n",
    "              \"min_samples_split\": [5, 10, 20],\n",
    "              \"bootstrap\": [True, False]}\n",
    "#\n",
    "rfr = RandomForestRegressor()\n",
    "# run grid search\n",
    "grid_search = GridSearchCV(rfr, param_grid, cv=3, n_jobs=-1)\n",
    "grid_search.fit(X, Y)\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T08:34:25.786837Z",
     "start_time": "2019-03-31T08:34:25.684682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model1.joblib']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save model\n",
    "dump(rfr, 'model1.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-31T10:19:10.597772Z",
     "start_time": "2019-03-31T10:19:10.423932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2892642355253314"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the model (from grid search or load it)\n",
    "#rfr = grid_search.best_estimator_\n",
    "rfr = load('model1.joblib')\n",
    "\n",
    "# Fit and get score\n",
    "rfr.fit(X_train, y_train)\n",
    "rfr.score(X_test, y_test)"
   ]
  }
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