{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.493</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.667</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.783</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.739</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.101</td>\n",
       "      <td>SK276-DMSO-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>17072.232</td>\n",
       "      <td>SK282-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>13123.899</td>\n",
       "      <td>SK282-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>14312.464</td>\n",
       "      <td>SK282-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>13796.087</td>\n",
       "      <td>SK282-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>11355.536</td>\n",
       "      <td>SK282-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean        sample\n",
       "                           \n",
       "1      50.493  SK276-DMSO-1\n",
       "2      36.667  SK276-DMSO-1\n",
       "3      37.783  SK276-DMSO-1\n",
       "4      34.739  SK276-DMSO-1\n",
       "5      25.101  SK276-DMSO-1\n",
       "..        ...           ...\n",
       "46  17072.232       SK282-1\n",
       "47  13123.899       SK282-1\n",
       "48  14312.464       SK282-1\n",
       "49  13796.087       SK282-1\n",
       "50  11355.536       SK282-1\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\"SK276-DMSO-1\",\"SK276-PCB-2\",\"SK276YG503-1\",\"SK282-1\"]\n",
    "df_tmp = pd.DataFrame()\n",
    "\n",
    "for i in range(4):\n",
    "    data_tmp = pd.read_csv(\"quantification/\"+data[i]+\".csv\", index_col=0)\n",
    "    data_tmp[\"sample\"] = np.full(50,data[i])\n",
    "    df_tmp = pd.concat([df_tmp,data_tmp])\n",
    "\n",
    "df_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfc91efc10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "sns.boxplot(data=df_tmp[(df_tmp[\"sample\"]==\"SK276-DMSO-1\")|(df_tmp[\"sample\"]==\"SK276-PCB-2\")|(df_tmp[\"sample\"]==\"SK276YG503-1\")|(df_tmp[\"sample\"]==\"SK282-1\")], x='sample', y='Mean', color ='lightgrey',fliersize=0 , width=0.6)\n",
    "sns.swarmplot(x=\"sample\", y=\"Mean\", data=df_tmp[(df_tmp[\"sample\"]==\"SK276-DMSO-1\")|(df_tmp[\"sample\"]==\"SK276-PCB-2\")|(df_tmp[\"sample\"]==\"SK276YG503-1\")|(df_tmp[\"sample\"]==\"SK282-1\")], color=\"cornflowerblue\", size=4, alpha =0.5)\n",
    "#plt.savefig(\"leakagePCB-3.pdf\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}