{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 947,
     "status": "ok",
     "timestamp": 1699354862142,
     "user": {
      "displayName": "Laurent Lefort",
      "userId": "10454990283758645335"
     },
     "user_tz": -60
    },
    "id": "zgPHBoEzveMr"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5VShoE4nAHgp"
   },
   "source": [
    "# REAXYS + SCIFINDER SEARCH v3.3 (match based on ligand/substrate/solvent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3168\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Data group_jnj</th>\n",
       "      <th>Starting material_jnj</th>\n",
       "      <th>Solvent_jnj</th>\n",
       "      <th>Ligand#_jnj</th>\n",
       "      <th>Temperature [°C]_jnj</th>\n",
       "      <th>Hydrogen [bar]_jnj</th>\n",
       "      <th>Time [h]_jnj</th>\n",
       "      <th>Conversion [-]_jnj</th>\n",
       "      <th>Enantiomeric excess [-]_jnj</th>\n",
       "      <th>|ee| [-]_jnj</th>\n",
       "      <th>|ee| [-] (2digits)_jnj</th>\n",
       "      <th>ΔΔG‡ [kJ/mol]_jnj</th>\n",
       "      <th>ΔΔG‡ [kJ/mol] with sign_jnj</th>\n",
       "      <th>Absolute stereochemistry [-]_jnj</th>\n",
       "      <th>Product_jnj</th>\n",
       "      <th>Product SMILES_jnj</th>\n",
       "      <th>Comment_jnj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Main data set</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>L1</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.346869</td>\n",
       "      <td>0.346869</td>\n",
       "      <td>0.347</td>\n",
       "      <td>1.794176</td>\n",
       "      <td>1.79</td>\n",
       "      <td>(R)</td>\n",
       "      <td>(R)-P1</td>\n",
       "      <td>O=C(OC)[C@H](NC(C)=O)CC1=CC=CC=C1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Main data set</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>L2</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000289</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001431</td>\n",
       "      <td>0.00</td>\n",
       "      <td>(S)</td>\n",
       "      <td>(S)-P1</td>\n",
       "      <td>O=C(OC)[C@@H](NC(C)=O)CC1=CC=CC=C1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data group_jnj Starting material_jnj Solvent_jnj Ligand#_jnj  \\\n",
       "0  Main data set                   SM1    Methanol          L1   \n",
       "1  Main data set                   SM1    Methanol          L2   \n",
       "\n",
       "   Temperature [°C]_jnj  Hydrogen [bar]_jnj  Time [h]_jnj  Conversion [-]_jnj  \\\n",
       "0                    25                   5             1                 1.0   \n",
       "1                    25                   5             1                 1.0   \n",
       "\n",
       "   Enantiomeric excess [-]_jnj  |ee| [-]_jnj  |ee| [-] (2digits)_jnj  \\\n",
       "0                     0.346869      0.346869                   0.347   \n",
       "1                    -0.000289      0.000289                   0.000   \n",
       "\n",
       "   ΔΔG‡ [kJ/mol]_jnj  ΔΔG‡ [kJ/mol] with sign_jnj  \\\n",
       "0           1.794176                         1.79   \n",
       "1           0.001431                         0.00   \n",
       "\n",
       "  Absolute stereochemistry [-]_jnj Product_jnj  \\\n",
       "0                              (R)      (R)-P1   \n",
       "1                              (S)      (S)-P1   \n",
       "\n",
       "                   Product SMILES_jnj Comment_jnj  \n",
       "0   O=C(OC)[C@H](NC(C)=O)CC1=CC=CC=C1         NaN  \n",
       "1  O=C(OC)[C@@H](NC(C)=O)CC1=CC=CC=C1         NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#IMPORT JNJ EXP DATA\n",
    "jnj_exp = pd.read_excel('C=C_AH_dataset_v3.6red.xlsx','All data')\n",
    "jnj_exp = jnj_exp.drop(['Ligand','Rh [mol%]','L [mol%]'], axis = 1)\n",
    "jnj_exp = jnj_exp[jnj_exp['Data group'].isin(['Main data set','Additional set', 'Pressure effect'])]\n",
    "jnj_exp.columns = [f'{i}_jnj' for i in jnj_exp.columns]\n",
    "\n",
    "all_jnj_conv = jnj_exp['Conversion [-]_jnj'].values\n",
    "all_jnj_ee = jnj_exp['|ee| [-] (2digits)_jnj'].values\n",
    "all_jnj_ddg = jnj_exp['ΔΔG‡ [kJ/mol]_jnj'].values\n",
    "\n",
    "print(len(jnj_exp))\n",
    "jnj_exp.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "236\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Data group</th>\n",
       "      <th>Ligand#</th>\n",
       "      <th>Substrate</th>\n",
       "      <th>Solvent</th>\n",
       "      <th>Temperature [°C]</th>\n",
       "      <th>Hydrogen [bar]</th>\n",
       "      <th>Time [h]</th>\n",
       "      <th>Yield [-]</th>\n",
       "      <th>|ee| [-]</th>\n",
       "      <th>Enantiomeric excess [-]</th>\n",
       "      <th>ΔΔG‡ [kJ/mol]</th>\n",
       "      <th>Absolute stereochemistry [-]</th>\n",
       "      <th>ΔΔG‡ [kJ/mol] with sign</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cleaned Reaxys export</td>\n",
       "      <td>L64</td>\n",
       "      <td>Methyl (Z)-2-acetylamino-3-phenylpropenoate</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.990</td>\n",
       "      <td>13.34199</td>\n",
       "      <td>(S)</td>\n",
       "      <td>-13.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Cleaned Reaxys export</td>\n",
       "      <td>L67</td>\n",
       "      <td>Methyl (Z)-2-acetylamino-3-phenylpropenoate</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(R)</td>\n",
       "      <td>19.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Data group Ligand#  \\\n",
       "28  Cleaned Reaxys export     L64   \n",
       "61  Cleaned Reaxys export     L67   \n",
       "\n",
       "                                      Substrate   Solvent  Temperature [°C]  \\\n",
       "28  Methyl (Z)-2-acetylamino-3-phenylpropenoate  Methanol                30   \n",
       "61  Methyl (Z)-2-acetylamino-3-phenylpropenoate  Methanol                35   \n",
       "\n",
       "    Hydrogen [bar]  Time [h]  Yield [-]  |ee| [-]  Enantiomeric excess [-]  \\\n",
       "28              10       NaN        NaN     0.990                    0.990   \n",
       "61               3       NaN        NaN     0.999                    0.999   \n",
       "\n",
       "    ΔΔG‡ [kJ/mol] Absolute stereochemistry [-]  ΔΔG‡ [kJ/mol] with sign  \n",
       "28       13.34199                          (S)                   -13.34  \n",
       "61            NaN                          (R)                    19.47  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#IMPORT REAXYS DATA\n",
    "reaxys = pd.read_excel('C=C_AH_dataset_v3.6red.xlsx','Cleaned Reaxys export')\n",
    "\n",
    "#save raw values to compare distributions (not only overlapping ligands)\n",
    "raw_reaxys_yield = reaxys['Yield [-]'].values\n",
    "raw_reaxys_ee = reaxys['|ee| [-]'].values\n",
    "raw_reaxys_ddg = reaxys['ΔΔG‡ [kJ/mol]'].values\n",
    "\n",
    "reaxys = reaxys[reaxys['Ligand#'].notnull()]\n",
    "reaxys = reaxys[reaxys.Solvent.isin(['Methanol','1,2-Dichloroethane'])]\n",
    "reaxys = reaxys.loc[:,['Data group','Ligand#','Substrate', 'Solvent','Temperature [°C]', \n",
    "                       'Hydrogen [bar]','Time [h]', 'Yield [-]','|ee| [-]','Enantiomeric excess [-]', \n",
    "                       'ΔΔG‡ [kJ/mol]','Absolute stereochemistry [-]']]\n",
    "\n",
    "#replace with median\n",
    "reaxys['Temperature [°C]'] = reaxys['Temperature [°C]'].fillna(np.nanmedian(np.array(reaxys['Temperature [°C]'].values)))\n",
    "reaxys['Hydrogen [bar]'] = reaxys['Hydrogen [bar]'].fillna(np.nanmedian(np.array(reaxys['Hydrogen [bar]'].values)))\n",
    "\n",
    "reaxys['Temperature [°C]'] = reaxys['Temperature [°C]'].astype(int)\n",
    "reaxys['Hydrogen [bar]'] = reaxys['Hydrogen [bar]'].astype(int)\n",
    "#=ROUND(IF(R2=\"(S)\";(8.3145*(I2+273.15)*LN((O2+1)/(1-O2))*0.001)*(-1);8.3145*(I2+273.15)*LN((O2+1)/(1-O2))*0.001);2)\n",
    "reaxys['ΔΔG‡ [kJ/mol] with sign'] = np.round((-1 + 2*(reaxys['Absolute stereochemistry [-]']==\"(R)\"))*8.3145*(reaxys['Temperature [°C]']+273.15)*np.log((reaxys['|ee| [-]']+1)/(1-reaxys['|ee| [-]']))*0.001, decimals = 2)\n",
    "print(len(reaxys))\n",
    "reaxys.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# COMPARE DISTRIBUTIONS\n",
    "import seaborn as sns\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize = (8,4))\n",
    "\n",
    "for pair, ax, lab in zip([(all_jnj_conv, raw_reaxys_yield),\n",
    "                     #(all_jnj_ee, raw_reaxys_ee),\n",
    "                     (all_jnj_ee, raw_reaxys_ee)], axes.flatten(), \n",
    "                    ['Conversion','|ee| [-]']):\n",
    "    \n",
    "    dftmp = pd.DataFrame({lab:list(pair[0])+list(pair[1]), \n",
    "                          'group':['HTE Data']*len(pair[0])+['Reaxys+SciFinder']*len(pair[1])})\n",
    "    sns.histplot(data=dftmp, x=lab, kde=True, hue = 'group', ax=ax, bins = 15,\n",
    "                 stat='percent', common_norm=False)\n",
    "    ax.set_title(lab)\n",
    "    \n",
    "plt.savefig('hist_lit.svg')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# COMPARE DISTRIBUTIONS\n",
    "import seaborn as sns\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize = (6,3))\n",
    "\n",
    "for pair, ax, lab in zip([(all_jnj_conv, raw_reaxys_yield),\n",
    "                     #(all_jnj_ee, raw_reaxys_ee),\n",
    "                     (all_jnj_ee, raw_reaxys_ee)], axes.flatten(), \n",
    "                    ['Conversion','|ee|']):\n",
    "    \n",
    "    dftmp = pd.DataFrame({lab:list(pair[0])+list(pair[1]), \n",
    "                          'group':['HTE Data']*len(pair[0])+['Reaxys+SciFinder']*len(pair[1])})\n",
    "    sns.histplot(data=dftmp, x=lab, kde=True, hue = 'group', ax=ax, bins = 15,\n",
    "                 stat='percent', common_norm=False)\n",
    "    #ax.set_title(lab)\n",
    "    \n",
    "plt.savefig('hist_lit_small.svg')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "subs_name_df= pd.DataFrame({'id':['SM1','SM1','SM1','SM2','SM3','SM4','SM4','SM5'],\n",
    "                            'name':['Methyl (Z)-2-acetylamino-3-phenylpropenoate',\n",
    "                                    'Methyl (E/Z)-2-acetylamino-3-phenylpropenoate',\n",
    "                                    'Methyl (E)-2-acetylamino-3-phenylpropenoate',\n",
    "                                    'Methyl 2-acetylamino-2-propenoate',\n",
    "                                    '2-Acetylamino-3-phenylpropenoic acid',\n",
    "                                    '(E)-2-methyl-3-phenylacrylic acid',\n",
    "                                    '2-Methyl-3-phenylacrylic acid',\n",
    "                                    '2-Phenylacrylic acid']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 199
    },
    "executionInfo": {
     "elapsed": 3602,
     "status": "ok",
     "timestamp": 1699019655737,
     "user": {
      "displayName": "Cecile Valsecchi",
      "userId": "07365043477514203846"
     },
     "user_tz": -60
    },
    "id": "XDJ4tIlauXbz",
    "outputId": "8d182598-63cf-4f17-f068-ecda167baf09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144 85\n"
     ]
    },
    {
     "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>Data group_rea</th>\n",
       "      <th>Ligand#_rea</th>\n",
       "      <th>Substrate_rea</th>\n",
       "      <th>Solvent_rea</th>\n",
       "      <th>Temperature [°C]_rea</th>\n",
       "      <th>Hydrogen [bar]_rea</th>\n",
       "      <th>Time [h]_rea</th>\n",
       "      <th>Yield [-]_rea</th>\n",
       "      <th>|ee| [-]_rea</th>\n",
       "      <th>Enantiomeric excess [-]_rea</th>\n",
       "      <th>ΔΔG‡ [kJ/mol]_rea</th>\n",
       "      <th>Absolute stereochemistry [-]_rea</th>\n",
       "      <th>ΔΔG‡ [kJ/mol] with sign_rea</th>\n",
       "      <th>id_rea</th>\n",
       "      <th>name_rea</th>\n",
       "      <th>Ligand#_v1_rea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cleaned Reaxys export</td>\n",
       "      <td>L64</td>\n",
       "      <td>Methyl (Z)-2-acetylamino-3-phenylpropenoate</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.990</td>\n",
       "      <td>13.34199</td>\n",
       "      <td>(S)</td>\n",
       "      <td>-13.34</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methyl (Z)-2-acetylamino-3-phenylpropenoate</td>\n",
       "      <td>L64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cleaned Reaxys export</td>\n",
       "      <td>L67</td>\n",
       "      <td>Methyl (Z)-2-acetylamino-3-phenylpropenoate</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(R)</td>\n",
       "      <td>19.47</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methyl (Z)-2-acetylamino-3-phenylpropenoate</td>\n",
       "      <td>L67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Data group_rea Ligand#_rea  \\\n",
       "0  Cleaned Reaxys export         L64   \n",
       "1  Cleaned Reaxys export         L67   \n",
       "\n",
       "                                 Substrate_rea Solvent_rea  \\\n",
       "0  Methyl (Z)-2-acetylamino-3-phenylpropenoate    Methanol   \n",
       "1  Methyl (Z)-2-acetylamino-3-phenylpropenoate    Methanol   \n",
       "\n",
       "   Temperature [°C]_rea  Hydrogen [bar]_rea  Time [h]_rea  Yield [-]_rea  \\\n",
       "0                    30                  10           NaN            NaN   \n",
       "1                    35                   3           NaN            NaN   \n",
       "\n",
       "   |ee| [-]_rea  Enantiomeric excess [-]_rea  ΔΔG‡ [kJ/mol]_rea  \\\n",
       "0         0.990                        0.990           13.34199   \n",
       "1         0.999                        0.999                NaN   \n",
       "\n",
       "  Absolute stereochemistry [-]_rea  ΔΔG‡ [kJ/mol] with sign_rea id_rea  \\\n",
       "0                              (S)                       -13.34    SM1   \n",
       "1                              (R)                        19.47    SM1   \n",
       "\n",
       "                                      name_rea Ligand#_v1_rea  \n",
       "0  Methyl (Z)-2-acetylamino-3-phenylpropenoate            L64  \n",
       "1  Methyl (Z)-2-acetylamino-3-phenylpropenoate            L67  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add substrate label to reaxys\n",
    "reaxys = pd.merge(reaxys, subs_name_df, left_on='Substrate', right_on='name', how='inner')\n",
    "# ignore enantiomers\n",
    "reaxys['Ligand#_v1']=[i.replace(' (Enant)','') for i in reaxys['Ligand#']]\n",
    "# split reaxys and scifinder data and add suffix _rea and _sci to their columns\n",
    "scifinder = reaxys[reaxys['Data group']=='SciFinder']\n",
    "scifinder.columns = [f'{i}_sci' for i in scifinder.columns]\n",
    "reaxys = reaxys[reaxys['Data group']!='SciFinder']\n",
    "reaxys.columns = [f'{i}_rea' for i in reaxys.columns]\n",
    "\n",
    "print(len(reaxys), len(scifinder))\n",
    "reaxys.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 719
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1699019656982,
     "user": {
      "displayName": "Cecile Valsecchi",
      "userId": "07365043477514203846"
     },
     "user_tz": -60
    },
    "id": "ShcxqvBwBIRK",
    "outputId": "c389524f-be09-40d9-f75b-e8ea716789b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "566\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>Ligand#_v1_sci</th>\n",
       "      <th>id_sci</th>\n",
       "      <th>Solvent_sci</th>\n",
       "      <th>Temperature [°C]_sci</th>\n",
       "      <th>Hydrogen [bar]_sci</th>\n",
       "      <th>Time [h]_sci</th>\n",
       "      <th>|ee| [-]_sci</th>\n",
       "      <th>Absolute stereochemistry [-]_sci</th>\n",
       "      <th>Ligand#_v1_rea</th>\n",
       "      <th>id_rea</th>\n",
       "      <th>...</th>\n",
       "      <th>|ee| [-]_rea</th>\n",
       "      <th>Absolute stereochemistry [-]_rea</th>\n",
       "      <th>Ligand#_jnj</th>\n",
       "      <th>Starting material_jnj</th>\n",
       "      <th>Solvent_jnj</th>\n",
       "      <th>Temperature [°C]_jnj</th>\n",
       "      <th>Hydrogen [bar]_jnj</th>\n",
       "      <th>Time [h]_jnj</th>\n",
       "      <th>|ee| [-]_jnj</th>\n",
       "      <th>Absolute stereochemistry [-]_jnj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L64</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>(S)</td>\n",
       "      <td>L64</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.993098</td>\n",
       "      <td>(S)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L64</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>(S)</td>\n",
       "      <td>L64</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.980832</td>\n",
       "      <td>(S)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ligand#_v1_sci id_sci Solvent_sci  Temperature [°C]_sci  Hydrogen [bar]_sci  \\\n",
       "0            NaN    NaN         NaN                   NaN                 NaN   \n",
       "1            NaN    NaN         NaN                   NaN                 NaN   \n",
       "\n",
       "   Time [h]_sci  |ee| [-]_sci Absolute stereochemistry [-]_sci Ligand#_v1_rea  \\\n",
       "0           NaN           NaN                              NaN            L64   \n",
       "1           NaN           NaN                              NaN            L64   \n",
       "\n",
       "  id_rea  ... |ee| [-]_rea  Absolute stereochemistry [-]_rea  Ligand#_jnj  \\\n",
       "0    SM1  ...         0.99                               (S)          L64   \n",
       "1    SM1  ...         0.99                               (S)          L64   \n",
       "\n",
       "   Starting material_jnj  Solvent_jnj Temperature [°C]_jnj Hydrogen [bar]_jnj  \\\n",
       "0                    SM1     Methanol                   25                  5   \n",
       "1                    SM1     Methanol                   25                  5   \n",
       "\n",
       "  Time [h]_jnj |ee| [-]_jnj  Absolute stereochemistry [-]_jnj  \n",
       "0            1     0.993098                               (S)  \n",
       "1           16     0.980832                               (S)  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge jnj, reaxys and scifinder dataframes on ligand, substrate and solvent, filtering out some columns for readbility\n",
    "df_merged = pd.merge(reaxys.loc[:,['Ligand#_v1_rea','id_rea',\n",
    "                                   'Solvent_rea','Temperature [°C]_rea',\n",
    "                                   'Hydrogen [bar]_rea','Time [h]_rea',\n",
    "                                   '|ee| [-]_rea',\n",
    "                                   'Absolute stereochemistry [-]_rea']],\n",
    "                  jnj_exp.loc[:,['Ligand#_jnj','Starting material_jnj',\n",
    "                                 'Solvent_jnj','Temperature [°C]_jnj',\n",
    "                                 'Hydrogen [bar]_jnj','Time [h]_jnj',\n",
    "                                 '|ee| [-]_jnj',\n",
    "                                 'Absolute stereochemistry [-]_jnj']],\n",
    "                  left_on=['Ligand#_v1_rea','id_rea','Solvent_rea'],\n",
    "                  right_on=['Ligand#_jnj','Starting material_jnj','Solvent_jnj'], how='inner')\n",
    "\n",
    "df_merged = pd.merge(scifinder.loc[:,['Ligand#_v1_sci','id_sci','Solvent_sci',\n",
    "                                      'Temperature [°C]_sci','Hydrogen [bar]_sci',\n",
    "                                      'Time [h]_sci',\n",
    "                                      '|ee| [-]_sci',\n",
    "                                      'Absolute stereochemistry [-]_sci']],\n",
    "                  df_merged,\n",
    "                  left_on=['Ligand#_v1_sci','id_sci','Solvent_sci'],\n",
    "                  right_on=['Ligand#_jnj','Starting material_jnj','Solvent_jnj'], how='right')\n",
    "\n",
    "print(len(df_merged))\n",
    "df_merged.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Ligand#_jnj</th>\n",
       "      <th>Starting material_jnj</th>\n",
       "      <th>Solvent_jnj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L64</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L67</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>L22</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L20</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>L170</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>L165</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>L134</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>L56</td>\n",
       "      <td>SM3</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM4</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ligand#_jnj Starting material_jnj Solvent_jnj\n",
       "0           L64                   SM1    Methanol\n",
       "3           L67                   SM1    Methanol\n",
       "9           L22                   SM1    Methanol\n",
       "12          L20                   SM1    Methanol\n",
       "15         L170                   SM1    Methanol\n",
       "..          ...                   ...         ...\n",
       "536        L165                   SM2    Methanol\n",
       "540        L134                   SM2    Methanol\n",
       "544        L102                   SM2    Methanol\n",
       "560         L56                   SM3    Methanol\n",
       "562         L25                   SM4    Methanol\n",
       "\n",
       "[75 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unique triplets\n",
    "df_merged.loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>|ee| [-]_sci</th>\n",
       "      <th>|ee| [-]_rea</th>\n",
       "      <th>|ee| [-]_jnj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>|ee| [-]_sci</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.283927</td>\n",
       "      <td>0.431674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>|ee| [-]_rea</th>\n",
       "      <td>0.283927</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.643189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>|ee| [-]_jnj</th>\n",
       "      <td>0.431674</td>\n",
       "      <td>0.643189</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              |ee| [-]_sci  |ee| [-]_rea  |ee| [-]_jnj\n",
       "|ee| [-]_sci      1.000000      0.283927      0.431674\n",
       "|ee| [-]_rea      0.283927      1.000000      0.643189\n",
       "|ee| [-]_jnj      0.431674      0.643189      1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first correlation (without any refinement)\n",
    "df_merged.loc[:,['|ee| [-]_sci','|ee| [-]_rea','|ee| [-]_jnj']].corr(method = 'spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1699019657255,
     "user": {
      "displayName": "Cecile Valsecchi",
      "userId": "07365043477514203846"
     },
     "user_tz": -60
    },
    "id": "v3Zmks8qJEma",
    "outputId": "6d317302-8488-4f3c-e2f3-a1d6e2c6ec9c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SM1    324\n",
       "SM2    236\n",
       "SM4      4\n",
       "SM3      2\n",
       "Name: Starting material_jnj, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mostly sm1 and sm2 in the overlap\n",
    "df_merged['Starting material_jnj'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add columns with absolute difference between rea/jnj, sci/jnj and rea/sci\n",
    "df_merged['DELTA_REA_JNJ']=np.abs(df_merged['|ee| [-]_rea'].values - df_merged['|ee| [-]_jnj'].values)\n",
    "df_merged['DELTA_REA_SCI']=np.abs(df_merged['|ee| [-]_rea'].values - df_merged['|ee| [-]_sci'].values)\n",
    "df_merged['DELTA_SCI_JNJ']=np.abs(df_merged['|ee| [-]_sci'].values - df_merged['|ee| [-]_jnj'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['L25', 'L56', 'L120', 'L1', 'L3', 'L9', 'L147', 'L65', 'L102',\n",
       "       'L52', 'L2', 'L7', 'L70', 'L75', 'L69', 'L68', 'L167', 'L121',\n",
       "       'L66', 'L77', 'L108', 'L80', 'L143', 'L78', 'L134'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0.2 = thr for discrepancy in rea vs jnj\n",
    "# which ligands have discrepancies?\n",
    "df_merged[(df_merged['DELTA_REA_JNJ']>0.2)]['Ligand#_jnj'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ligand#_v1_sci</th>\n",
       "      <th>id_sci</th>\n",
       "      <th>Solvent_sci</th>\n",
       "      <th>Temperature [°C]_sci</th>\n",
       "      <th>Hydrogen [bar]_sci</th>\n",
       "      <th>Time [h]_sci</th>\n",
       "      <th>|ee| [-]_sci</th>\n",
       "      <th>Absolute stereochemistry [-]_sci</th>\n",
       "      <th>Ligand#_v1_rea</th>\n",
       "      <th>id_rea</th>\n",
       "      <th>...</th>\n",
       "      <th>Starting material_jnj</th>\n",
       "      <th>Solvent_jnj</th>\n",
       "      <th>Temperature [°C]_jnj</th>\n",
       "      <th>Hydrogen [bar]_jnj</th>\n",
       "      <th>Time [h]_jnj</th>\n",
       "      <th>|ee| [-]_jnj</th>\n",
       "      <th>Absolute stereochemistry [-]_jnj</th>\n",
       "      <th>DELTA_REA_JNJ</th>\n",
       "      <th>DELTA_REA_SCI</th>\n",
       "      <th>DELTA_SCI_JNJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.537949</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.342051</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.062051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>(S)</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.537949</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.342051</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.092051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>(R)</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.537949</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.342051</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.062051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.525981</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.354019</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.074019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>(S)</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.525981</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.354019</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.104019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.887125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>...</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.855030</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.725030</td>\n",
       "      <td>0.757125</td>\n",
       "      <td>0.032095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>...</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.866353</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.736353</td>\n",
       "      <td>0.770500</td>\n",
       "      <td>0.034147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.902313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>...</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.866353</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.736353</td>\n",
       "      <td>0.772313</td>\n",
       "      <td>0.035959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.888000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>...</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.866353</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.736353</td>\n",
       "      <td>0.758000</td>\n",
       "      <td>0.021647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.887125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>...</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.866353</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.736353</td>\n",
       "      <td>0.757125</td>\n",
       "      <td>0.020772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ligand#_v1_sci id_sci Solvent_sci  Temperature [°C]_sci  \\\n",
       "27             L25    SM1    Methanol                  25.0   \n",
       "28             L25    SM1    Methanol                  20.0   \n",
       "29             L25    SM1    Methanol                  25.0   \n",
       "30             L25    SM1    Methanol                  25.0   \n",
       "31             L25    SM1    Methanol                  20.0   \n",
       "..             ...    ...         ...                   ...   \n",
       "555           L102    SM2    Methanol                  25.0   \n",
       "556           L102    SM2    Methanol                  25.0   \n",
       "557           L102    SM2    Methanol                  25.0   \n",
       "558           L102    SM2    Methanol                  25.0   \n",
       "559           L102    SM2    Methanol                  25.0   \n",
       "\n",
       "     Hydrogen [bar]_sci  Time [h]_sci  |ee| [-]_sci  \\\n",
       "27                  1.0           1.0      0.600000   \n",
       "28                  1.0          16.0      0.630000   \n",
       "29                  1.0          20.5      0.600000   \n",
       "30                  1.0           1.0      0.600000   \n",
       "31                  1.0          16.0      0.630000   \n",
       "..                  ...           ...           ...   \n",
       "555                 2.0           1.0      0.887125   \n",
       "556                 2.0           1.0      0.900500   \n",
       "557                 2.0           1.0      0.902313   \n",
       "558                 2.0           1.0      0.888000   \n",
       "559                 2.0           1.0      0.887125   \n",
       "\n",
       "    Absolute stereochemistry [-]_sci Ligand#_v1_rea id_rea  ...  \\\n",
       "27                               NaN            L25    SM1  ...   \n",
       "28                               (S)            L25    SM1  ...   \n",
       "29                               (R)            L25    SM1  ...   \n",
       "30                               NaN            L25    SM1  ...   \n",
       "31                               (S)            L25    SM1  ...   \n",
       "..                               ...            ...    ...  ...   \n",
       "555                              NaN           L102    SM2  ...   \n",
       "556                              NaN           L102    SM2  ...   \n",
       "557                              NaN           L102    SM2  ...   \n",
       "558                              NaN           L102    SM2  ...   \n",
       "559                              NaN           L102    SM2  ...   \n",
       "\n",
       "    Starting material_jnj  Solvent_jnj  Temperature [°C]_jnj  \\\n",
       "27                    SM1     Methanol                    25   \n",
       "28                    SM1     Methanol                    25   \n",
       "29                    SM1     Methanol                    25   \n",
       "30                    SM1     Methanol                    25   \n",
       "31                    SM1     Methanol                    25   \n",
       "..                    ...          ...                   ...   \n",
       "555                   SM2     Methanol                    25   \n",
       "556                   SM2     Methanol                    25   \n",
       "557                   SM2     Methanol                    25   \n",
       "558                   SM2     Methanol                    25   \n",
       "559                   SM2     Methanol                    25   \n",
       "\n",
       "     Hydrogen [bar]_jnj  Time [h]_jnj |ee| [-]_jnj  \\\n",
       "27                    5             1     0.537949   \n",
       "28                    5             1     0.537949   \n",
       "29                    5             1     0.537949   \n",
       "30                    5            16     0.525981   \n",
       "31                    5            16     0.525981   \n",
       "..                  ...           ...          ...   \n",
       "555                   5             1     0.855030   \n",
       "556                   5            16     0.866353   \n",
       "557                   5            16     0.866353   \n",
       "558                   5            16     0.866353   \n",
       "559                   5            16     0.866353   \n",
       "\n",
       "    Absolute stereochemistry [-]_jnj DELTA_REA_JNJ DELTA_REA_SCI  \\\n",
       "27                               (S)      0.342051      0.280000   \n",
       "28                               (S)      0.342051      0.250000   \n",
       "29                               (S)      0.342051      0.280000   \n",
       "30                               (S)      0.354019      0.280000   \n",
       "31                               (S)      0.354019      0.250000   \n",
       "..                               ...           ...           ...   \n",
       "555                              (S)      0.725030      0.757125   \n",
       "556                              (S)      0.736353      0.770500   \n",
       "557                              (S)      0.736353      0.772313   \n",
       "558                              (S)      0.736353      0.758000   \n",
       "559                              (S)      0.736353      0.757125   \n",
       "\n",
       "     DELTA_SCI_JNJ  \n",
       "27        0.062051  \n",
       "28        0.092051  \n",
       "29        0.062051  \n",
       "30        0.074019  \n",
       "31        0.104019  \n",
       "..             ...  \n",
       "555       0.032095  \n",
       "556       0.034147  \n",
       "557       0.035959  \n",
       "558       0.021647  \n",
       "559       0.020772  \n",
       "\n",
       "[202 rows x 27 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how many discrepancies?\n",
    "df_merged[(df_merged['DELTA_REA_JNJ']>0.2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ligand#_v1_sci</th>\n",
       "      <th>id_sci</th>\n",
       "      <th>Solvent_sci</th>\n",
       "      <th>Temperature [°C]_sci</th>\n",
       "      <th>Hydrogen [bar]_sci</th>\n",
       "      <th>Time [h]_sci</th>\n",
       "      <th>|ee| [-]_sci</th>\n",
       "      <th>Absolute stereochemistry [-]_sci</th>\n",
       "      <th>Ligand#_v1_rea</th>\n",
       "      <th>id_rea</th>\n",
       "      <th>...</th>\n",
       "      <th>Starting material_jnj</th>\n",
       "      <th>Solvent_jnj</th>\n",
       "      <th>Temperature [°C]_jnj</th>\n",
       "      <th>Hydrogen [bar]_jnj</th>\n",
       "      <th>Time [h]_jnj</th>\n",
       "      <th>|ee| [-]_jnj</th>\n",
       "      <th>Absolute stereochemistry [-]_jnj</th>\n",
       "      <th>DELTA_REA_JNJ</th>\n",
       "      <th>DELTA_REA_SCI</th>\n",
       "      <th>DELTA_SCI_JNJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L64</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.993098</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.003098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L64</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.980832</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.009168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L64</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>0.988250</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.001750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L67</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.989575</td>\n",
       "      <td>(R)</td>\n",
       "      <td>0.009425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L67</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.993033</td>\n",
       "      <td>(R)</td>\n",
       "      <td>0.005967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L56</td>\n",
       "      <td>SM3</td>\n",
       "      <td>...</td>\n",
       "      <td>SM3</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.866461</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.043539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM4</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM4</td>\n",
       "      <td>...</td>\n",
       "      <td>SM4</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.840631</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.010631</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.020631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM4</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM4</td>\n",
       "      <td>...</td>\n",
       "      <td>SM4</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.840631</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.010631</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.029369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM4</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>(S)</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM4</td>\n",
       "      <td>...</td>\n",
       "      <td>SM4</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.840631</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.010631</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.010631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM4</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.82</td>\n",
       "      <td>(R)</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM4</td>\n",
       "      <td>...</td>\n",
       "      <td>SM4</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.840631</td>\n",
       "      <td>(S)</td>\n",
       "      <td>0.010631</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.020631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ligand#_v1_sci id_sci Solvent_sci  Temperature [°C]_sci  \\\n",
       "0              NaN    NaN         NaN                   NaN   \n",
       "1              NaN    NaN         NaN                   NaN   \n",
       "2              NaN    NaN         NaN                   NaN   \n",
       "3              NaN    NaN         NaN                   NaN   \n",
       "4              NaN    NaN         NaN                   NaN   \n",
       "..             ...    ...         ...                   ...   \n",
       "561            NaN    NaN         NaN                   NaN   \n",
       "562            L25    SM4    Methanol                  25.0   \n",
       "563            L25    SM4    Methanol                  25.0   \n",
       "564            L25    SM4    Methanol                  20.0   \n",
       "565            L25    SM4    Methanol                  25.0   \n",
       "\n",
       "     Hydrogen [bar]_sci  Time [h]_sci  |ee| [-]_sci  \\\n",
       "0                   NaN           NaN           NaN   \n",
       "1                   NaN           NaN           NaN   \n",
       "2                   NaN           NaN           NaN   \n",
       "3                   NaN           NaN           NaN   \n",
       "4                   NaN           NaN           NaN   \n",
       "..                  ...           ...           ...   \n",
       "561                 NaN           NaN           NaN   \n",
       "562                 5.0          20.0          0.82   \n",
       "563                 5.0          16.0          0.87   \n",
       "564                20.0          16.0          0.83   \n",
       "565                 5.0           0.2          0.82   \n",
       "\n",
       "    Absolute stereochemistry [-]_sci Ligand#_v1_rea id_rea  ...  \\\n",
       "0                                NaN            L64    SM1  ...   \n",
       "1                                NaN            L64    SM1  ...   \n",
       "2                                NaN            L64    SM1  ...   \n",
       "3                                NaN            L67    SM1  ...   \n",
       "4                                NaN            L67    SM1  ...   \n",
       "..                               ...            ...    ...  ...   \n",
       "561                              NaN            L56    SM3  ...   \n",
       "562                              NaN            L25    SM4  ...   \n",
       "563                              NaN            L25    SM4  ...   \n",
       "564                              (S)            L25    SM4  ...   \n",
       "565                              (R)            L25    SM4  ...   \n",
       "\n",
       "    Starting material_jnj  Solvent_jnj  Temperature [°C]_jnj  \\\n",
       "0                     SM1     Methanol                    25   \n",
       "1                     SM1     Methanol                    25   \n",
       "2                     SM1     Methanol                    25   \n",
       "3                     SM1     Methanol                    25   \n",
       "4                     SM1     Methanol                    25   \n",
       "..                    ...          ...                   ...   \n",
       "561                   SM3     Methanol                    25   \n",
       "562                   SM4     Methanol                    50   \n",
       "563                   SM4     Methanol                    50   \n",
       "564                   SM4     Methanol                    50   \n",
       "565                   SM4     Methanol                    50   \n",
       "\n",
       "     Hydrogen [bar]_jnj  Time [h]_jnj |ee| [-]_jnj  \\\n",
       "0                     5             1     0.993098   \n",
       "1                     5            16     0.980832   \n",
       "2                    30            16     0.988250   \n",
       "3                     5             1     0.989575   \n",
       "4                     5            16     0.993033   \n",
       "..                  ...           ...          ...   \n",
       "561                   5            16     0.866461   \n",
       "562                   5            16     0.840631   \n",
       "563                   5            16     0.840631   \n",
       "564                   5            16     0.840631   \n",
       "565                   5            16     0.840631   \n",
       "\n",
       "    Absolute stereochemistry [-]_jnj DELTA_REA_JNJ DELTA_REA_SCI  \\\n",
       "0                                (S)      0.003098           NaN   \n",
       "1                                (S)      0.009168           NaN   \n",
       "2                                (S)      0.001750           NaN   \n",
       "3                                (R)      0.009425           NaN   \n",
       "4                                (R)      0.005967           NaN   \n",
       "..                               ...           ...           ...   \n",
       "561                              (S)      0.043539           NaN   \n",
       "562                              (S)      0.010631          0.01   \n",
       "563                              (S)      0.010631          0.04   \n",
       "564                              (S)      0.010631          0.00   \n",
       "565                              (S)      0.010631          0.01   \n",
       "\n",
       "     DELTA_SCI_JNJ  \n",
       "0              NaN  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  \n",
       "..             ...  \n",
       "561            NaN  \n",
       "562       0.020631  \n",
       "563       0.029369  \n",
       "564       0.010631  \n",
       "565       0.020631  \n",
       "\n",
       "[364 rows x 27 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how many consistencies?\n",
    "df_merged[(df_merged['DELTA_REA_JNJ']<=0.2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how many triplets in total\n",
    "len(df_merged.loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how many triplets ligand/substrate/solvent have at least one discrepancy?\n",
    "len(df_merged[(df_merged['DELTA_REA_JNJ']>0.2)].loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how many triplets ligand/substrate/solvent have at least one consistency?\n",
    "len(df_merged[(df_merged['DELTA_REA_JNJ']<=0.2)].loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how many triplets ligand/substrate/solvent have consistencies&discrepancies?\n",
    "len(df_merged[(df_merged['DELTA_REA_JNJ']>0.2)].loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates().merge(df_merged[(df_merged['DELTA_REA_JNJ']<=0.2)].loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates(), on = ['Ligand#_jnj','Starting material_jnj','Solvent_jnj']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Ligand#_jnj</th>\n",
       "      <th>Starting material_jnj</th>\n",
       "      <th>Solvent_jnj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L56</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L120</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L1</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L3</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L9</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L147</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L65</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L52</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>L7</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>L9</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>L70</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L66</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>L77</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L80</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ligand#_jnj Starting material_jnj Solvent_jnj\n",
       "0          L56                   SM1    Methanol\n",
       "1         L120                   SM1    Methanol\n",
       "2           L1                   SM1    Methanol\n",
       "3           L3                   SM1    Methanol\n",
       "4           L9                   SM1    Methanol\n",
       "5         L147                   SM1    Methanol\n",
       "6          L65                   SM1    Methanol\n",
       "7         L102                   SM1    Methanol\n",
       "8          L52                   SM2    Methanol\n",
       "9           L7                   SM2    Methanol\n",
       "10          L9                   SM2    Methanol\n",
       "11         L70                   SM2    Methanol\n",
       "12         L66                   SM2    Methanol\n",
       "13         L77                   SM2    Methanol\n",
       "14         L80                   SM2    Methanol"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged[(df_merged['DELTA_REA_JNJ']>0.2)].loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates().merge(df_merged[(df_merged['DELTA_REA_JNJ']<=0.2)].loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates(), on = ['Ligand#_jnj','Starting material_jnj','Solvent_jnj'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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>Ligand#_v1_sci_x</th>\n",
       "      <th>id_sci_x</th>\n",
       "      <th>Solvent_sci_x</th>\n",
       "      <th>Temperature [°C]_sci_x</th>\n",
       "      <th>Hydrogen [bar]_sci_x</th>\n",
       "      <th>Time [h]_sci_x</th>\n",
       "      <th>|ee| [-]_sci_x</th>\n",
       "      <th>Absolute stereochemistry [-]_sci_x</th>\n",
       "      <th>Ligand#_v1_rea_x</th>\n",
       "      <th>id_rea_x</th>\n",
       "      <th>...</th>\n",
       "      <th>|ee| [-]_rea_y</th>\n",
       "      <th>Absolute stereochemistry [-]_rea_y</th>\n",
       "      <th>Temperature [°C]_jnj_y</th>\n",
       "      <th>Hydrogen [bar]_jnj_y</th>\n",
       "      <th>Time [h]_jnj_y</th>\n",
       "      <th>|ee| [-]_jnj_y</th>\n",
       "      <th>Absolute stereochemistry [-]_jnj_y</th>\n",
       "      <th>DELTA_REA_JNJ_y</th>\n",
       "      <th>DELTA_REA_SCI_y</th>\n",
       "      <th>DELTA_SCI_JNJ_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>(S)</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>(R)</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>(S)</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.887125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.902313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.888000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.887125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ligand#_v1_sci_x id_sci_x Solvent_sci_x  Temperature [°C]_sci_x  \\\n",
       "0                L25      SM1      Methanol                    25.0   \n",
       "1                L25      SM1      Methanol                    20.0   \n",
       "2                L25      SM1      Methanol                    25.0   \n",
       "3                L25      SM1      Methanol                    25.0   \n",
       "4                L25      SM1      Methanol                    20.0   \n",
       "..               ...      ...           ...                     ...   \n",
       "377             L102      SM2      Methanol                    25.0   \n",
       "378             L102      SM2      Methanol                    25.0   \n",
       "379             L102      SM2      Methanol                    25.0   \n",
       "380             L102      SM2      Methanol                    25.0   \n",
       "381             L102      SM2      Methanol                    25.0   \n",
       "\n",
       "     Hydrogen [bar]_sci_x  Time [h]_sci_x  |ee| [-]_sci_x  \\\n",
       "0                     1.0             1.0        0.600000   \n",
       "1                     1.0            16.0        0.630000   \n",
       "2                     1.0            20.5        0.600000   \n",
       "3                     1.0             1.0        0.600000   \n",
       "4                     1.0            16.0        0.630000   \n",
       "..                    ...             ...             ...   \n",
       "377                   2.0             1.0        0.887125   \n",
       "378                   2.0             1.0        0.900500   \n",
       "379                   2.0             1.0        0.902313   \n",
       "380                   2.0             1.0        0.888000   \n",
       "381                   2.0             1.0        0.887125   \n",
       "\n",
       "    Absolute stereochemistry [-]_sci_x Ligand#_v1_rea_x id_rea_x  ...  \\\n",
       "0                                  NaN              L25      SM1  ...   \n",
       "1                                  (S)              L25      SM1  ...   \n",
       "2                                  (R)              L25      SM1  ...   \n",
       "3                                  NaN              L25      SM1  ...   \n",
       "4                                  (S)              L25      SM1  ...   \n",
       "..                                 ...              ...      ...  ...   \n",
       "377                                NaN             L102      SM2  ...   \n",
       "378                                NaN             L102      SM2  ...   \n",
       "379                                NaN             L102      SM2  ...   \n",
       "380                                NaN             L102      SM2  ...   \n",
       "381                                NaN             L102      SM2  ...   \n",
       "\n",
       "    |ee| [-]_rea_y  Absolute stereochemistry [-]_rea_y  \\\n",
       "0              NaN                                 NaN   \n",
       "1              NaN                                 NaN   \n",
       "2              NaN                                 NaN   \n",
       "3              NaN                                 NaN   \n",
       "4              NaN                                 NaN   \n",
       "..             ...                                 ...   \n",
       "377            NaN                                 NaN   \n",
       "378            NaN                                 NaN   \n",
       "379            NaN                                 NaN   \n",
       "380            NaN                                 NaN   \n",
       "381            NaN                                 NaN   \n",
       "\n",
       "     Temperature [°C]_jnj_y  Hydrogen [bar]_jnj_y  Time [h]_jnj_y  \\\n",
       "0                       NaN                   NaN             NaN   \n",
       "1                       NaN                   NaN             NaN   \n",
       "2                       NaN                   NaN             NaN   \n",
       "3                       NaN                   NaN             NaN   \n",
       "4                       NaN                   NaN             NaN   \n",
       "..                      ...                   ...             ...   \n",
       "377                     NaN                   NaN             NaN   \n",
       "378                     NaN                   NaN             NaN   \n",
       "379                     NaN                   NaN             NaN   \n",
       "380                     NaN                   NaN             NaN   \n",
       "381                     NaN                   NaN             NaN   \n",
       "\n",
       "    |ee| [-]_jnj_y Absolute stereochemistry [-]_jnj_y DELTA_REA_JNJ_y  \\\n",
       "0              NaN                                NaN             NaN   \n",
       "1              NaN                                NaN             NaN   \n",
       "2              NaN                                NaN             NaN   \n",
       "3              NaN                                NaN             NaN   \n",
       "4              NaN                                NaN             NaN   \n",
       "..             ...                                ...             ...   \n",
       "377            NaN                                NaN             NaN   \n",
       "378            NaN                                NaN             NaN   \n",
       "379            NaN                                NaN             NaN   \n",
       "380            NaN                                NaN             NaN   \n",
       "381            NaN                                NaN             NaN   \n",
       "\n",
       "    DELTA_REA_SCI_y  DELTA_SCI_JNJ_y  \n",
       "0               NaN              NaN  \n",
       "1               NaN              NaN  \n",
       "2               NaN              NaN  \n",
       "3               NaN              NaN  \n",
       "4               NaN              NaN  \n",
       "..              ...              ...  \n",
       "377             NaN              NaN  \n",
       "378             NaN              NaN  \n",
       "379             NaN              NaN  \n",
       "380             NaN              NaN  \n",
       "381             NaN              NaN  \n",
       "\n",
       "[153 rows x 51 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how many triplets ligand/substrate/solvent have only discrepancies?\n",
    "df1 = df_merged[(df_merged['DELTA_REA_JNJ']>0.2)]\n",
    "df2 = df_merged[(df_merged['DELTA_REA_JNJ']<=0.2)]\n",
    "# Specify the columns to match on\n",
    "common_columns = ['Ligand#_jnj','Starting material_jnj','Solvent_jnj']\n",
    "# Merge with an indicator, specifying the columns to match on, and filter out the rows present in df2\n",
    "result = pd.merge(df1, df2, on=common_columns, how='outer', indicator=True).query('_merge == \"left_only\"').drop(columns=['_merge'])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result.loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Ligand#_jnj</th>\n",
       "      <th>Starting material_jnj</th>\n",
       "      <th>Solvent_jnj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>L68</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>L167</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>L121</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ligand#_jnj Starting material_jnj Solvent_jnj\n",
       "258         L68                   SM2    Methanol\n",
       "270        L167                   SM2    Methanol\n",
       "288        L121                   SM2    Methanol"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which ligands have discrepancies sci/jnj in result?\n",
    "result[(result['DELTA_SCI_JNJ_x']>0.2)].loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Ligand#_jnj</th>\n",
       "      <th>Starting material_jnj</th>\n",
       "      <th>Solvent_jnj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>L69</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>L68</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>L167</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>L121</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>L65</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>L108</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ligand#_jnj Starting material_jnj Solvent_jnj\n",
       "0           L25                   SM1    Methanol\n",
       "221         L69                   SM2    Methanol\n",
       "257         L68                   SM2    Methanol\n",
       "269        L167                   SM2    Methanol\n",
       "281        L121                   SM2    Methanol\n",
       "343         L65                   SM2    Methanol\n",
       "347        L108                   SM2    Methanol\n",
       "366        L102                   SM2    Methanol"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which ligands have discrepancies sci/jnj in result?\n",
    "result[(result['DELTA_SCI_JNJ_x']<=0.2)].loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>|ee| [-]_sci</th>\n",
       "      <th>|ee| [-]_rea</th>\n",
       "      <th>Ligand#_jnj</th>\n",
       "      <th>Starting material_jnj</th>\n",
       "      <th>Solvent_jnj</th>\n",
       "      <th>|ee| [-]_jnj</th>\n",
       "      <th>DELTA_REA_JNJ</th>\n",
       "      <th>DELTA_REA_SCI</th>\n",
       "      <th>DELTA_SCI_JNJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.88</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0.537949</td>\n",
       "      <td>0.342051</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.062051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.88</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0.537949</td>\n",
       "      <td>0.342051</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.092051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.88</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0.537949</td>\n",
       "      <td>0.342051</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.062051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.88</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0.525981</td>\n",
       "      <td>0.354019</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.074019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.88</td>\n",
       "      <td>L25</td>\n",
       "      <td>SM1</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0.525981</td>\n",
       "      <td>0.354019</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.104019</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>0.887125</td>\n",
       "      <td>0.13</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0.855030</td>\n",
       "      <td>0.725030</td>\n",
       "      <td>0.757125</td>\n",
       "      <td>0.032095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>0.900500</td>\n",
       "      <td>0.13</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0.866353</td>\n",
       "      <td>0.736353</td>\n",
       "      <td>0.770500</td>\n",
       "      <td>0.034147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>0.902313</td>\n",
       "      <td>0.13</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0.866353</td>\n",
       "      <td>0.736353</td>\n",
       "      <td>0.772313</td>\n",
       "      <td>0.035959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>0.888000</td>\n",
       "      <td>0.13</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0.866353</td>\n",
       "      <td>0.736353</td>\n",
       "      <td>0.758000</td>\n",
       "      <td>0.021647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>0.887125</td>\n",
       "      <td>0.13</td>\n",
       "      <td>L102</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "      <td>0.866353</td>\n",
       "      <td>0.736353</td>\n",
       "      <td>0.757125</td>\n",
       "      <td>0.020772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     |ee| [-]_sci  |ee| [-]_rea Ligand#_jnj Starting material_jnj Solvent_jnj  \\\n",
       "0        0.600000          0.88         L25                   SM1    Methanol   \n",
       "1        0.630000          0.88         L25                   SM1    Methanol   \n",
       "2        0.600000          0.88         L25                   SM1    Methanol   \n",
       "3        0.600000          0.88         L25                   SM1    Methanol   \n",
       "4        0.630000          0.88         L25                   SM1    Methanol   \n",
       "..            ...           ...         ...                   ...         ...   \n",
       "377      0.887125          0.13        L102                   SM2    Methanol   \n",
       "378      0.900500          0.13        L102                   SM2    Methanol   \n",
       "379      0.902313          0.13        L102                   SM2    Methanol   \n",
       "380      0.888000          0.13        L102                   SM2    Methanol   \n",
       "381      0.887125          0.13        L102                   SM2    Methanol   \n",
       "\n",
       "     |ee| [-]_jnj  DELTA_REA_JNJ  DELTA_REA_SCI  DELTA_SCI_JNJ  \n",
       "0        0.537949       0.342051       0.280000       0.062051  \n",
       "1        0.537949       0.342051       0.250000       0.092051  \n",
       "2        0.537949       0.342051       0.280000       0.062051  \n",
       "3        0.525981       0.354019       0.280000       0.074019  \n",
       "4        0.525981       0.354019       0.250000       0.104019  \n",
       "..            ...            ...            ...            ...  \n",
       "377      0.855030       0.725030       0.757125       0.032095  \n",
       "378      0.866353       0.736353       0.770500       0.034147  \n",
       "379      0.866353       0.736353       0.772313       0.035959  \n",
       "380      0.866353       0.736353       0.758000       0.021647  \n",
       "381      0.866353       0.736353       0.757125       0.020772  \n",
       "\n",
       "[153 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = result.loc[:,['|ee| [-]_sci_x','|ee| [-]_rea_x','Ligand#_jnj',\n",
    "              'Starting material_jnj', 'Solvent_jnj', '|ee| [-]_jnj_x',\n",
    "             'DELTA_REA_JNJ_x','DELTA_REA_SCI_x', 'DELTA_SCI_JNJ_x']]\n",
    "result.columns = ['|ee| [-]_sci','|ee| [-]_rea','Ligand#_jnj',\n",
    "              'Starting material_jnj', 'Solvent_jnj', '|ee| [-]_jnj',\n",
    "             'DELTA_REA_JNJ','DELTA_REA_SCI', 'DELTA_SCI_JNJ']\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Ligand#_jnj</th>\n",
       "      <th>Starting material_jnj</th>\n",
       "      <th>Solvent_jnj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>L120</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>L2</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>L3</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>L75</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>L143</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>L78</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>L134</td>\n",
       "      <td>SM2</td>\n",
       "      <td>Methanol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ligand#_jnj Starting material_jnj Solvent_jnj\n",
       "194        L120                   SM2    Methanol\n",
       "198          L2                   SM2    Methanol\n",
       "205          L3                   SM2    Methanol\n",
       "217         L75                   SM2    Methanol\n",
       "354        L143                   SM2    Methanol\n",
       "358         L78                   SM2    Methanol\n",
       "362        L134                   SM2    Methanol"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[result['|ee| [-]_sci'].isna()].loc[:,['Ligand#_jnj','Starting material_jnj','Solvent_jnj']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Methanol'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['Solvent_jnj'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SHOW DISCREPANCY FOR SM1\n",
    "import matplotlib.pyplot as plt\n",
    "df_discr = result.groupby(['Ligand#_jnj', 'Starting material_jnj']).agg({'|ee| [-]_jnj': list, \n",
    "                                                              '|ee| [-]_rea': list, \n",
    "                                                              '|ee| [-]_sci': list}).reset_index()\n",
    "\n",
    "# Create a temporary column to hold the first element of each list in 'C'\n",
    "df_discr['temp_sort'] = df_discr['|ee| [-]_sci'].apply(lambda x: x[0] if x else None)\n",
    "df_discr = df_discr.sort_values(by='temp_sort').drop(columns=['temp_sort'])\n",
    "# Extract data and plot\n",
    "\n",
    "plt.figure(figsize=(10,3))\n",
    "for idx, row in df_discr.iterrows():\n",
    "    x = row['Ligand#_jnj']+'_'+row['Starting material_jnj']\n",
    "    y1 = row['|ee| [-]_jnj']\n",
    "    y2 = row['|ee| [-]_rea']\n",
    "    y3 = row['|ee| [-]_sci']\n",
    "    \n",
    "    plt.scatter([x] * len(y1), y1, c='tab:blue', marker='o', label='JNJ', alpha=0.5)\n",
    "    plt.scatter([x] * len(y2), y2, c='tab:orange', marker='o', label='Reaxys', alpha=0.5)\n",
    "    plt.scatter([x] * len(y3), y3, c='darkred', marker='x', label='SciFinder', alpha=0.5)\n",
    "\n",
    "plt.ylabel('|EE|')\n",
    "\n",
    "# Show the plot\n",
    "plt.xticks(df_discr['Ligand#_jnj']+'_'+df_discr['Starting material_jnj'], rotation = 60)\n",
    "\n",
    "#plt.title('SM1')\n",
    "#plt.grid(True)\n",
    "#plt.savefig('data_analysis_EE_v3.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_mergedred = df_merged.groupby(['Ligand#_jnj','Starting material_jnj', 'Solvent_jnj']).agg({'|ee| [-]_jnj': list, \n",
    "                                                              '|ee| [-]_rea': list, \n",
    "                                                              '|ee| [-]_sci': list}).reset_index()\n",
    "\n",
    "x_jnj_rea = []\n",
    "y_jnj_rea = []\n",
    "x_jnj_sci = []\n",
    "y_jnj_sci = []\n",
    "delta_rea = []\n",
    "delta_sci = []\n",
    "lab_jnj_sci=[]\n",
    "lab_jnj_rea = []\n",
    "\n",
    "for idx, row in df_mergedred.iterrows():\n",
    "    \n",
    "    for eei in row['|ee| [-]_jnj']:\n",
    "        idxmin_rea = np.abs(np.array(row['|ee| [-]_rea'])-eei).argmin() \n",
    "        if sum(row['|ee| [-]_sci'])>0:\n",
    "            idxmin_sci = np.abs(np.array(row['|ee| [-]_sci'])-eei).argmin()\n",
    "\n",
    "            if abs(np.array(row['|ee| [-]_rea'])[idxmin_rea]-eei)<abs(np.array(row['|ee| [-]_sci'])[idxmin_sci]-eei):\n",
    "                y_jnj_rea.append(eei)\n",
    "                x_jnj_rea.append(np.array(row['|ee| [-]_rea'])[idxmin_rea])\n",
    "                delta_rea.append(abs(np.array(row['|ee| [-]_rea'])[idxmin_rea]-eei)<=0.2)\n",
    "                lab_jnj_rea.append(row)\n",
    "            else:\n",
    "                x_jnj_sci.append(eei)\n",
    "                y_jnj_sci.append(np.array(row['|ee| [-]_sci'])[idxmin_sci])\n",
    "                delta_sci.append(abs(np.array(row['|ee| [-]_sci'])[idxmin_sci]-eei)<=0.2)\n",
    "                lab_jnj_sci.append(row)\n",
    "            \n",
    "        else:\n",
    "            y_jnj_rea.append(eei)\n",
    "            x_jnj_rea.append(np.array(row['|ee| [-]_rea'])[idxmin_rea])\n",
    "            delta_rea.append(abs(np.array(row['|ee| [-]_rea'])[idxmin_rea]-eei)<=0.2)\n",
    "            lab_jnj_rea.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mergedred = df_merged.groupby(['Ligand#_jnj','Starting material_jnj', 'Solvent_jnj']).agg({'|ee| [-]_jnj': list, \n",
    "                                                              '|ee| [-]_rea': list, \n",
    "                                                              '|ee| [-]_sci': list}).reset_index()\n",
    "\n",
    "x_jnj_rea = []\n",
    "y_jnj_rea = []\n",
    "x_jnj_sci = []\n",
    "y_jnj_sci = []\n",
    "delta_rea = []\n",
    "delta_sci = []\n",
    "lab_jnj_sci=[]\n",
    "lab_jnj_rea = []\n",
    "\n",
    "for idx, row in df_mergedred.iterrows():\n",
    "    flag_sci = False\n",
    "    min_diff = np.inf\n",
    "    min_indices = (-1, -1)\n",
    "    # Calculate the absolute difference between each pair\n",
    "    for i, a_val in enumerate(row['|ee| [-]_jnj']):\n",
    "        if sum(row['|ee| [-]_sci'])>0:\n",
    "            for j, b_val in enumerate(row['|ee| [-]_rea']):\n",
    "                diff = abs(a_val - b_val)\n",
    "                if diff < min_diff:\n",
    "                    min_diff = diff\n",
    "                    min_indices = (i, j)\n",
    "                    flag_sci = False\n",
    "            for j, b_val in enumerate(row['|ee| [-]_sci']):\n",
    "                diff = abs(a_val - b_val)\n",
    "                if diff < min_diff:\n",
    "                    min_diff = diff\n",
    "                    min_indices = (i, j)\n",
    "                    flag_sci = True\n",
    "                \n",
    "        else:\n",
    "            for j, b_val in enumerate(row['|ee| [-]_rea']):\n",
    "                diff = abs(a_val - b_val)\n",
    "                if diff < min_diff:\n",
    "                    min_diff = diff\n",
    "                    min_indices = (i, j)\n",
    "                    flag_sci = False\n",
    "                    \n",
    "    if flag_sci:\n",
    "        y_jnj_sci.append(row['|ee| [-]_jnj'][min_indices[0]])\n",
    "        x_jnj_sci.append(row['|ee| [-]_sci'][min_indices[1]])\n",
    "        delta_sci.append(min_diff<=0.2)\n",
    "        lab_jnj_sci.append(row)\n",
    "    else:\n",
    "        y_jnj_rea.append(row['|ee| [-]_jnj'][min_indices[0]])\n",
    "        x_jnj_rea.append(row['|ee| [-]_rea'][min_indices[1]])\n",
    "        delta_rea.append(min_diff<=0.2)\n",
    "        lab_jnj_rea.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mergedred = df_merged.groupby(['Ligand#_jnj','Starting material_jnj', 'Solvent_jnj']).agg({'|ee| [-]_jnj': list, \n",
    "                                                              '|ee| [-]_rea': list, \n",
    "                                                              '|ee| [-]_sci': list}).reset_index()\n",
    "\n",
    "x_jnj_rea = []\n",
    "y_jnj_rea = []\n",
    "x_jnj_sci = []\n",
    "y_jnj_sci = []\n",
    "delta_rea = []\n",
    "delta_sci = []\n",
    "lab_jnj_sci=[]\n",
    "lab_jnj_rea = []\n",
    "\n",
    "for idx, row in df_mergedred.iterrows():\n",
    "    flag_sci = False\n",
    "    min_diff = np.inf\n",
    "    min_indices = (-1, -1)\n",
    "    # Calculate the absolute difference between each pair\n",
    "    for i, a_val in enumerate(row['|ee| [-]_jnj']):\n",
    "        for j, b_val in enumerate(row['|ee| [-]_rea']):\n",
    "            diff = abs(a_val - b_val)\n",
    "            if diff < min_diff:\n",
    "                min_diff = diff\n",
    "                min_indices = (i, j)\n",
    "                flag_sci = False\n",
    "\n",
    "    y_jnj_rea.append(row['|ee| [-]_jnj'][min_indices[0]])\n",
    "    x_jnj_rea.append(row['|ee| [-]_rea'][min_indices[1]])\n",
    "    delta_rea.append(min_diff<=0.2)\n",
    "    lab_jnj_rea.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(349, 217, 349, 217, 349, 217, 217, 349)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_jnj_rea), len(x_jnj_sci),len(y_jnj_rea), len(y_jnj_sci),len(delta_rea), len(delta_sci), len(lab_jnj_sci), len(lab_jnj_rea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(349, 301)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(delta_rea), sum(delta_rea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_jnj_rea = np.array(x_jnj_rea)\n",
    "y_jnj_rea = np.array(y_jnj_rea)\n",
    "x_jnj_sci = np.array(x_jnj_sci)\n",
    "y_jnj_sci = np.array(y_jnj_sci)\n",
    "delta_rea = np.array(delta_rea)\n",
    "delta_sci = np.array(delta_sci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "x_rea = x_jnj_rea\n",
    "y_rea =  y_jnj_rea\n",
    "\n",
    "x_sci = x_jnj_sci\n",
    "y_sci =  y_jnj_sci\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "gs = GridSpec(4,4)\n",
    "\n",
    "ax_joint = fig.add_subplot(gs[1:4,0:3])\n",
    "ax_marg_x = fig.add_subplot(gs[0,0:3])\n",
    "ax_marg_y = fig.add_subplot(gs[1:4,3])\n",
    "\n",
    "ax_joint.scatter(x_rea[delta_rea], y_rea[delta_rea], marker = '.', c = 'green')\n",
    "ax_joint.scatter(x_rea[np.array(delta_rea)==0], y_rea[np.array(delta_rea)==0], marker = '.', c = 'red')\n",
    "#ax_joint.scatter(x_sci[delta_sci], y_sci[delta_sci], marker = 'x',c = 'darkgreen')\n",
    "#ax_joint.scatter(x_sci[np.array(delta_sci)==0], y_sci[np.array(delta_sci)==0], marker = 'x',c = 'orange')\n",
    "\n",
    "ax_marg_x.hist(list(x_rea), bins= 15, alpha =0.5, color = 'tab:orange')\n",
    "#ax_marg_x.hist(x_discr_rea, bins= 15, alpha =0.5, color = 'green')\n",
    "#ax_marg_x.hist(x_sci, bins= 15, alpha =0.3, color = 'blue')\n",
    "\n",
    "ax_marg_y.hist(list(y_rea),orientation=\"horizontal\", bins= 15, alpha =0.5, color = 'tab:blue')\n",
    "#ax_marg_y.hist(y_discr_rea,orientation=\"horizontal\", bins= 15, alpha =0.5, color = 'green')\n",
    "#ax_marg_y.hist(y_sci,orientation=\"horizontal\", bins= 15, alpha =0.3, color = 'blue')\n",
    "\n",
    "xlin = np.linspace(-0.05, 1.05, 11)\n",
    "y_est = xlin\n",
    "y_err = [0.2]*11\n",
    "ax_joint.plot(xlin, y_est, 'k:', alpha = 0.5)\n",
    "ax_joint.fill_between(xlin, y_est - y_err, y_est + y_err, alpha=0.1, color = 'grey')\n",
    "ax_joint.set_xlim((-0.05,1.05))\n",
    "ax_joint.set_ylim((-0.05,1.05))\n",
    "\n",
    "# Turn off tick labels on marginals\n",
    "plt.setp(ax_marg_x.get_xticklabels(), visible=False)\n",
    "plt.setp(ax_marg_y.get_yticklabels(), visible=False)\n",
    "\n",
    "# Set labels on joint\n",
    "ax_joint.set_xlabel('Reaxys+SciFinder |EE|')\n",
    "ax_joint.set_ylabel('JNJ |EE|')\n",
    "# Set labels on marginals\n",
    "ax_marg_y.set_xlabel('#')\n",
    "ax_marg_x.set_ylabel('#')\n",
    "\n",
    "#plt.savefig('data_analysis_ref_EE_v3.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "x_rea = x_jnj_rea\n",
    "y_rea =  y_jnj_rea\n",
    "\n",
    "x_sci = x_jnj_sci\n",
    "y_sci =  y_jnj_sci\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "gs = GridSpec(4,4)\n",
    "\n",
    "ax_joint = fig.add_subplot(gs[1:4,0:3])\n",
    "ax_marg_x = fig.add_subplot(gs[0,0:3])\n",
    "ax_marg_y = fig.add_subplot(gs[1:4,3])\n",
    "\n",
    "ax_joint.scatter(x_rea[delta_rea], y_rea[delta_rea], marker = '.', c = 'green')\n",
    "ax_joint.scatter(x_rea[np.array(delta_rea)==0], y_rea[np.array(delta_rea)==0], marker = '.', c = 'red')\n",
    "ax_joint.scatter(x_sci[delta_sci], y_sci[delta_sci], marker = 'x',c = 'darkgreen')\n",
    "ax_joint.scatter(x_sci[np.array(delta_sci)==0], y_sci[np.array(delta_sci)==0], marker = 'x',c = 'orange')\n",
    "\n",
    "ax_marg_x.hist(list(x_rea)+list(x_sci), bins= 15, alpha =0.5, color = 'tab:orange')\n",
    "#ax_marg_x.hist(x_discr_rea, bins= 15, alpha =0.5, color = 'green')\n",
    "#ax_marg_x.hist(x_sci, bins= 15, alpha =0.3, color = 'blue')\n",
    "\n",
    "ax_marg_y.hist(list(y_rea)+list(y_sci),orientation=\"horizontal\", bins= 15, alpha =0.5, color = 'tab:blue')\n",
    "#ax_marg_y.hist(y_discr_rea,orientation=\"horizontal\", bins= 15, alpha =0.5, color = 'green')\n",
    "#ax_marg_y.hist(y_sci,orientation=\"horizontal\", bins= 15, alpha =0.3, color = 'blue')\n",
    "\n",
    "xlin = np.linspace(-0.05, 1.05, 11)\n",
    "y_est = xlin\n",
    "y_err = [0.2]*11\n",
    "ax_joint.plot(xlin, y_est, 'k:', alpha = 0.5)\n",
    "ax_joint.fill_between(xlin, y_est - y_err, y_est + y_err, alpha=0.1, color = 'grey')\n",
    "ax_joint.set_xlim((-0.05,1.05))\n",
    "ax_joint.set_ylim((-0.05,1.05))\n",
    "\n",
    "# Turn off tick labels on marginals\n",
    "plt.setp(ax_marg_x.get_xticklabels(), visible=False)\n",
    "plt.setp(ax_marg_y.get_yticklabels(), visible=False)\n",
    "\n",
    "# Set labels on joint\n",
    "ax_joint.set_xlabel('Reaxys+SciFinder |EE|')\n",
    "ax_joint.set_ylabel('JNJ |EE|')\n",
    "# Set labels on marginals\n",
    "ax_marg_y.set_xlabel('#')\n",
    "ax_marg_x.set_ylabel('#')\n",
    "\n",
    "#plt.savefig('data_analysis_ref_EE_v3.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_jnj_rea = list(x_jnj_rea)\n",
    "y_jnj_rea = list(y_jnj_rea)\n",
    "x_jnj_sci = list(x_jnj_sci)\n",
    "y_jnj_sci = list(y_jnj_sci)\n",
    "delta_rea = list(delta_rea)\n",
    "delta_sci = list(delta_sci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, row in df_mergedred.iterrows():\n",
    "    for eei in row['|ee| [-]_jnj']:\n",
    "        idxmin_rea = np.abs(np.array(row['|ee| [-]_rea'])-eei).argmin() \n",
    "        if sum(row['|ee| [-]_sci'])>0:\n",
    "            idxmin_sci = np.abs(np.array(row['|ee| [-]_sci'])-eei).argmin()\n",
    "\n",
    "            if abs(np.array(row['|ee| [-]_rea'])[idxmin_rea]-eei)<abs(np.array(row['|ee| [-]_sci'])[idxmin_sci]-eei):\n",
    "                y_jnj_rea.append(eei)\n",
    "                x_jnj_rea.append(np.array(row['|ee| [-]_rea'])[idxmin_rea])\n",
    "                delta_rea.append(abs(np.array(row['|ee| [-]_rea'])[idxmin_rea]-eei)<=0.2)\n",
    "            else:\n",
    "                x_jnj_sci.append(eei)\n",
    "                y_jnj_sci.append(np.array(row['|ee| [-]_sci'])[idxmin_sci])\n",
    "                delta_sci.append(abs(np.array(row['|ee| [-]_sci'])[idxmin_sci]-eei)<=0.2)\n",
    "            \n",
    "        else:\n",
    "            y_jnj_rea.append(eei)\n",
    "            x_jnj_rea.append(np.array(row['|ee| [-]_rea'])[idxmin_rea])\n",
    "            delta_rea.append(abs(np.array(row['|ee| [-]_rea'])[idxmin_rea]-eei)<=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "4K_oeBupKV6x"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7753829564337722"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(x_jnj_rea,y_jnj_rea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9441994208645842"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(x_jnj_sci,y_jnj_sci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n",
      "C:\\Users\\cvalsecc\\AppData\\Local\\Temp\\10\\ipykernel_273828\\2892962854.py:47: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.savefig('fig5.svg')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axd = plt.figure(layout=\"constrained\", figsize = (12,6)).subplot_mosaic(\n",
    "        [[\"barpl1\", \"barpl2\",\"scattpl\"],[\"empty\", \"barpl3\",\"barpl3\"]], \n",
    "    empty_sentinel=None, gridspec_kw={'width_ratios': [1,1,1],'height_ratios': [1,0.7]})\n",
    "\n",
    "for pair, ax, lab, nm in zip([(all_jnj_conv, raw_reaxys_yield),\n",
    "                     #(all_jnj_ee, raw_reaxys_ee),\n",
    "                     (all_jnj_ee, raw_reaxys_ee)], axes.flatten(), \n",
    "                    ['Conversion','|ee|'], [\"barpl1\", \"barpl2\"]):\n",
    "    \n",
    "    dftmp = pd.DataFrame({lab:list(pair[0])+list(pair[1]), \n",
    "                          'group':['HTE Data']*len(pair[0])+['Reaxys+SciFinder']*len(pair[1])})\n",
    "    sns.histplot(data=dftmp, x=lab, kde=True, hue = 'group', ax=axd[nm], bins = 15,\n",
    "                 stat='percent', common_norm=False)\n",
    "    ax.set_title(lab)\n",
    "\n",
    "\n",
    "\n",
    "df_scttpl = pd.DataFrame({'Literature |ee|':list(x_jnj_rea)+list(x_jnj_sci),\n",
    "                          'HTE data |ee|':list(y_jnj_rea)+list(y_jnj_sci),\n",
    "                          'cl':list(delta_rea)+list(delta_sci)})\n",
    "    \n",
    "\n",
    "\n",
    "# Scatter plot\n",
    "sns.scatterplot(data=df_scttpl, x='Literature |ee|', y='HTE data |ee|', hue=\"cl\", \n",
    "                palette=['tab:red','tab:green'], ax=axd['scattpl'])\n",
    "\n",
    "\n",
    "\n",
    "for idx, row in df_discr.iterrows():\n",
    "    x = row['Ligand#_jnj']+'_'+row['Starting material_jnj']\n",
    "    y1 = row['|ee| [-]_jnj']\n",
    "    y2 = row['|ee| [-]_rea']\n",
    "    y3 = row['|ee| [-]_sci']\n",
    "    \n",
    "    axd[\"barpl3\"].axvspan(-0.5, 7.5, facecolor='b', alpha=0.005)\n",
    "    axd[\"barpl3\"].scatter([x] * len(y1), y1, c='tab:blue', marker='o', label='JNJ', alpha=0.5)\n",
    "    axd[\"barpl3\"].scatter([x] * len(y2), y2, c='tab:orange', marker='o', label='Reaxys', alpha=0.5)\n",
    "    axd[\"barpl3\"].scatter([x] * len(y3), y3, c='darkred', marker='x', label='SciFinder', alpha=0.5)\n",
    "    axd[\"barpl3\"].set_xlim([-0.5, 14.5])\n",
    "    \n",
    "\n",
    "axd[\"barpl3\"].set_ylabel('|ee|')\n",
    "\n",
    "# Show the plot\n",
    "plt.xticks(df_discr['Ligand#_jnj']+' '+df_discr['Starting material_jnj'], rotation = 60)\n",
    "plt.savefig('fig5.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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