goodcents.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "required_desc = [\n",
    "'alcoholic', 'aldehydic', 'alliaceous', 'almond', 'amber', 'animal',\n",
    "'anisic', 'apple', 'apricot', 'aromatic', 'balsamic', 'banana', 'beefy',\n",
    "'bergamot', 'berry', 'bitter', 'black currant', 'brandy', 'burnt',\n",
    "'buttery', 'cabbage', 'camphoreous', 'caramellic', 'cedar', 'celery',\n",
    "'chamomile', 'cheesy', 'cherry', 'chocolate', 'cinnamon', 'citrus', 'clean',\n",
    "'clove', 'cocoa', 'coconut', 'coffee', 'cognac', 'cooked', 'cooling',\n",
    "'cortex', 'coumarinic', 'creamy', 'cucumber', 'dairy', 'dry', 'earthy',\n",
    "'ethereal', 'fatty', 'fermented', 'fishy', 'floral', 'fresh', 'fruit skin',\n",
    "'fruity', 'garlic', 'gassy', 'geranium', 'grape', 'grapefruit', 'grassy',\n",
    "'green', 'hawthorn', 'hay', 'hazelnut', 'herbal', 'honey', 'hyacinth',\n",
    "'jasmin', 'juicy', 'ketonic', 'lactonic', 'lavender', 'leafy', 'leathery',\n",
    "'lemon', 'lily', 'malty', 'meaty', 'medicinal', 'melon', 'metallic',\n",
    "'milky', 'mint', 'muguet', 'mushroom', 'musk', 'musty', 'natural', 'nutty',\n",
    "'odorless', 'oily', 'onion', 'orange', 'orangeflower', 'orris', 'ozone',\n",
    "'peach', 'pear', 'phenolic', 'pine', 'pineapple', 'plum', 'popcorn',\n",
    "'potato', 'powdery', 'pungent', 'radish', 'raspberry', 'ripe', 'roasted',\n",
    "'rose', 'rummy', 'sandalwood', 'savory', 'sharp', 'smoky', 'soapy',\n",
    "'solvent', 'sour', 'spicy', 'strawberry', 'sulfurous', 'sweaty', 'sweet',\n",
    "'tea', 'terpenic', 'tobacco', 'tomato', 'tropical', 'vanilla', 'vegetable',\n",
    "'vetiver', 'violet', 'warm', 'waxy', 'weedy', 'winey', 'woody'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(required_desc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aryanab/anaconda3/envs/deepchem/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pyrfume\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[raw]\n",
    "\"data_rw_opl.csv\" = \"Odor labels for molecules as originally compiled by John Leffingwell and cleaned by Sanchez-Lengeling et al\"\n",
    "\"data_rw_odor.csv\" = \"Information about the dataset\"\n",
    "\"data_rw_contents.csv\" = \"...\"\n",
    "LICENSE = \"Licensing information and use restrictions according to the terms of The Goodscents Company\"\n",
    "\"README.md\" = \"Additional information about this archive\"\n",
    "\n",
    "[parsed]\n",
    "\"opl.csv\" = \"Molecular structure data\"\n",
    "\n",
    "[processed]\n",
    "\"molecules.csv\" = \"Information about odorant molecules used\"\n",
    "\"stimuli.csv\" = \"Mappings between PubChem IDs and GoodScents IDs, Concentration, and Solvent\"\n",
    "\"behavior.csv\" = \"Odor descriptors for each molecule\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "molecules = pyrfume.load_data('goodscents/molecules.csv', remote=True)\n",
    "behavior = pyrfume.load_data('goodscents/behavior.csv', remote=True)\n",
    "stimuli = pyrfume.load_data('goodscents/stimuli.csv', remote=True)\n",
    "opl = pyrfume.load_data('goodscents/opl.csv', remote=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "possible tests: molecules\n",
    "- duplicate smiles?\n",
    "- convert all smiles to canonical smiles then check\n",
    "- check if all are valid smiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MolecularWeight</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>IUPACName</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75.11</td>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>1-aminopropan-2-ol</td>\n",
       "      <td>1-aminopropan-2-ol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>116.11</td>\n",
       "      <td>CC(C)C(=O)C(=O)O</td>\n",
       "      <td>3-methyl-2-oxobutanoic acid</td>\n",
       "      <td>3-methyl-2-oxobutanoic acid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>146.10</td>\n",
       "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
       "      <td>2-oxopentanedioic acid</td>\n",
       "      <td>2-ketoglutaric acid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>102.09</td>\n",
       "      <td>CCC(=O)C(=O)O</td>\n",
       "      <td>2-oxobutanoic acid</td>\n",
       "      <td>2-oxobutanoic acid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>130.14</td>\n",
       "      <td>CC(C)CC(=O)C(=O)O</td>\n",
       "      <td>4-methyl-2-oxopentanoic acid</td>\n",
       "      <td>4-methyl-2-oxopentanoic acid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     MolecularWeight         IsomericSMILES                     IUPACName   \n",
       "CID                                                                         \n",
       "4              75.11                CC(CN)O            1-aminopropan-2-ol  \\\n",
       "49            116.11       CC(C)C(=O)C(=O)O   3-methyl-2-oxobutanoic acid   \n",
       "51            146.10  C(CC(=O)O)C(=O)C(=O)O        2-oxopentanedioic acid   \n",
       "58            102.09          CCC(=O)C(=O)O            2-oxobutanoic acid   \n",
       "70            130.14      CC(C)CC(=O)C(=O)O  4-methyl-2-oxopentanoic acid   \n",
       "\n",
       "                             name  \n",
       "CID                                \n",
       "4              1-aminopropan-2-ol  \n",
       "49    3-methyl-2-oxobutanoic acid  \n",
       "51            2-ketoglutaric acid  \n",
       "58             2-oxobutanoic acid  \n",
       "70   4-methyl-2-oxopentanoic acid  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molecules.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MolecularWeight</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>IUPACName</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
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       "      <th>CID</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41570</th>\n",
       "      <td>212.31</td>\n",
       "      <td>CC1=CC(=C(O1)C)SC(=O)CC(C)C</td>\n",
       "      <td>S-(2,5-dimethylfuran-3-yl) 3-methylbutanethioate</td>\n",
       "      <td>2,5-dimethyl-3-thioisovalerylfuran</td>\n",
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      "text/plain": [
       "       MolecularWeight               IsomericSMILES   \n",
       "CID                                                   \n",
       "41570           212.31  CC1=CC(=C(O1)C)SC(=O)CC(C)C  \\\n",
       "\n",
       "                                              IUPACName   \n",
       "CID                                                       \n",
       "41570  S-(2,5-dimethylfuran-3-yl) 3-methylbutanethioate  \\\n",
       "\n",
       "                                     name  \n",
       "CID                                        \n",
       "41570  2,5-dimethyl-3-thioisovalerylfuran  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molecules[molecules['name'].str.match('.*2,5-dimethyl-3-thioisovalerylfuran.*')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4565 entries, 4 to 152743294\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   MolecularWeight  4565 non-null   float64\n",
      " 1   IsomericSMILES   4565 non-null   object \n",
      " 2   IUPACName        4559 non-null   object \n",
      " 3   name             4565 non-null   object \n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 178.3+ KB\n"
     ]
    }
   ],
   "source": [
    "molecules.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4565"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(molecules['IsomericSMILES'].drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4565"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_molecules = len(molecules['IsomericSMILES'].value_counts())\n",
    "total_molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "\n",
    "def canonical_smiles(smiles):\n",
    "    return Chem.MolToSmiles(Chem.MolFromSmiles(smiles), isomericSmiles = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4565/4565 [00:00<00:00, 7547.21it/s]\n"
     ]
    }
   ],
   "source": [
    "molecules['canonicalSMILES'] = molecules['IsomericSMILES'].progress_apply(lambda x: canonical_smiles(x))\n",
    "assert len(molecules['canonicalSMILES'].value_counts()) == total_molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "molecules = molecules.drop(columns=['canonicalSMILES'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MolecularWeight', 'IsomericSMILES', 'IUPACName', 'name'], dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molecules.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CC(CN)O'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molecules['IsomericSMILES'][4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "possible tests: behavior\n",
    "- basic\n",
    "- count of unique behaviours available, maybe pivot table\n",
    "- % count of each behaviour (sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Descriptors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100-06-1</th>\n",
       "      <td>sweet;vanilla;cherry maraschino cherry;powdery...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-09-4</th>\n",
       "      <td>phenolic;animal;fecal;medicinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-42-5</th>\n",
       "      <td>sweet;plastic;floral;balsamic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-51-6</th>\n",
       "      <td>sweet;floral;rose;fruity;phenolic;balsamic;che...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-52-7</th>\n",
       "      <td>sweet;cherry;cherry maraschino cherry;nutty;fr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                Descriptors\n",
       "Stimulus                                                   \n",
       "100-06-1  sweet;vanilla;cherry maraschino cherry;powdery...\n",
       "100-09-4                    phenolic;animal;fecal;medicinal\n",
       "100-42-5                      sweet;plastic;floral;balsamic\n",
       "100-51-6  sweet;floral;rose;fruity;phenolic;balsamic;che...\n",
       "100-52-7  sweet;cherry;cherry maraschino cherry;nutty;fr..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4626 entries, 100-06-1 to NF0825\n",
      "Data columns (total 1 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Descriptors  4622 non-null   object\n",
      "dtypes: object(1)\n",
      "memory usage: 72.3+ KB\n"
     ]
    }
   ],
   "source": [
    "behavior.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1523-19-9</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53584-56-8</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6698-82-0</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>78183-56-9</th>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "           Descriptors\n",
       "Stimulus              \n",
       "1523-19-9          NaN\n",
       "53584-56-8         NaN\n",
       "6698-82-0          NaN\n",
       "78183-56-9         NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior_nan = behavior[behavior.isna().any(axis=1)]\n",
    "behavior_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4622 entries, 100-06-1 to NF0825\n",
      "Data columns (total 1 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Descriptors  4622 non-null   object\n",
      "dtypes: object(1)\n",
      "memory usage: 72.2+ KB\n"
     ]
    }
   ],
   "source": [
    "behavior_clean = behavior.dropna()\n",
    "behavior_clean.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "original dataset analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Descriptors</th>\n",
       "      <th>TGSC ID</th>\n",
       "      <th>CID</th>\n",
       "      <th>Concentration %</th>\n",
       "      <th>Solvent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100-06-1</th>\n",
       "      <td>sweet;vanilla;cherry maraschino cherry;powdery...</td>\n",
       "      <td>1000111</td>\n",
       "      <td>7476</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-09-4</th>\n",
       "      <td>phenolic;animal;fecal;medicinal</td>\n",
       "      <td>1031871</td>\n",
       "      <td>7478</td>\n",
       "      <td>10.0</td>\n",
       "      <td>dipropylene glycol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-42-5</th>\n",
       "      <td>sweet;plastic;floral;balsamic</td>\n",
       "      <td>1009281</td>\n",
       "      <td>7501</td>\n",
       "      <td>0.1</td>\n",
       "      <td>triacetin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Descriptors  TGSC ID   CID   \n",
       "Stimulus                                                                     \n",
       "100-06-1  sweet;vanilla;cherry maraschino cherry;powdery...  1000111  7476  \\\n",
       "100-09-4                    phenolic;animal;fecal;medicinal  1031871  7478   \n",
       "100-42-5                      sweet;plastic;floral;balsamic  1009281  7501   \n",
       "\n",
       "          Concentration %             Solvent  \n",
       "Stimulus                                       \n",
       "100-06-1            100.0                 NaN  \n",
       "100-09-4             10.0  dipropylene glycol  \n",
       "100-42-5              0.1           triacetin  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_cid_behaviors = pd.merge(behavior_clean['Descriptors'], stimuli, how='inner', left_index=True, right_index=True)\n",
    "og_cid_behaviors.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Descriptors</th>\n",
       "      <th>TGSC ID</th>\n",
       "      <th>CID</th>\n",
       "      <th>Concentration %</th>\n",
       "      <th>Solvent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55764-28-8</th>\n",
       "      <td>roasted</td>\n",
       "      <td>1036451</td>\n",
       "      <td>41570</td>\n",
       "      <td>0.1</td>\n",
       "      <td>propylene glycol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Descriptors  TGSC ID    CID  Concentration %           Solvent\n",
       "Stimulus                                                                 \n",
       "55764-28-8     roasted  1036451  41570              0.1  propylene glycol"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_cid_behaviors[og_cid_behaviors['CID']==41570]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['2623-23-6', '89-48-5'],\n",
       " ['91069-39-5', '91069-40-8'],\n",
       " ['137886-38-5', '22451-50-9'],\n",
       " ['29759-11-3', '93-92-5'],\n",
       " ['110-93-0', '409-02-9'],\n",
       " ['141-25-3', '26489-01-0'],\n",
       " ['1321-89-7', '3085-26-5'],\n",
       " ['25773-40-4', '93905-03-4'],\n",
       " ['6728-26-3', '85761-70-2'],\n",
       " ['7704-34-9', '7783-06-4'],\n",
       " ['41199-19-3', '71832-76-3'],\n",
       " ['1334-82-3', '4996-48-9'],\n",
       " ['55764-31-3', '65505-16-0'],\n",
       " ['1118-39-4', '69103-01-1'],\n",
       " ['15537-55-0', '546-79-2'],\n",
       " ['1329-99-3', '99-83-2'],\n",
       " ['334-49-6', '72881-27-7'],\n",
       " ['1365-19-1', '60047-17-8'],\n",
       " ['138-86-3', '9003-73-0'],\n",
       " ['1337-83-3', '143-14-6'],\n",
       " ['13952-84-6', '513-49-5'],\n",
       " ['67859-99-8', '67874-69-5'],\n",
       " ['13828-37-0', '5502-75-0'],\n",
       " ['39872-57-6', '70266-48-7'],\n",
       " ['63450-30-6', '68378-13-2'],\n",
       " ['1335-86-0', '591-49-1'],\n",
       " ['139-33-3', '6381-92-6'],\n",
       " ['25524-95-2', '34686-71-0'],\n",
       " ['75640-26-5', '80417-97-6'],\n",
       " ['111-60-4', '9004-99-3'],\n",
       " ['23696-85-7', '23726-93-4'],\n",
       " ['80722-28-7', '80957-74-0'],\n",
       " ['16423-19-1', '5173-69-3'],\n",
       " ['1334-78-7', '620-23-5'],\n",
       " ['10458-14-7', '36977-92-1', '89-80-5'],\n",
       " ['127-42-4', '1335-46-2'],\n",
       " ['1197-06-4', '2102-59-2'],\n",
       " ['67883-79-8', '84060-80-0'],\n",
       " ['68259-31-4', '93904-56-4'],\n",
       " ['8000-41-7', '98-55-5'],\n",
       " ['5524-05-0', '5948-04-9'],\n",
       " ['1653-30-1', '30207-98-8'],\n",
       " ['1866-31-5N', '1866-31-5Z'],\n",
       " ['39638-67-0', '80041-01-6'],\n",
       " ['21862-63-5', '98-52-2'],\n",
       " ['23787-90-8', '29461-13-0'],\n",
       " ['1490-04-6', '491-02-1', '89-78-1'],\n",
       " ['58985-18-5', '80-25-1'],\n",
       " ['124071-42-7', '134769-33-8'],\n",
       " ['133-37-9', '526-83-0'],\n",
       " ['106-21-8', '1333-49-9'],\n",
       " ['10411-92-4', '1900-69-2', '32210-23-4'],\n",
       " ['58625-95-9', '85508-08-3'],\n",
       " ['18368-91-7', '67952-68-5'],\n",
       " ['13092-66-5', '7757-87-1'],\n",
       " ['2882-20-4', '68378-12-1'],\n",
       " ['104-46-1', '4180-23-8'],\n",
       " ['2396-77-2', '2396-78-3']]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup = og_cid_behaviors[og_cid_behaviors['CID'].duplicated(keep=False)]\n",
    "index_set = set()\n",
    "index_set.update(dup['CID'].to_list())\n",
    "list_idx_of_duplicates = []\n",
    "for i in index_set:\n",
    "    # print(list(dup[dup['CID'] == i].index))\n",
    "    list_idx_of_duplicates.append(list(dup[dup['CID'] == i].index))\n",
    "list_idx_of_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4561 entries, 100-06-1 to NF0825\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Descriptors      4561 non-null   object \n",
      " 1   TGSC ID          4561 non-null   int64  \n",
      " 2   CID              4561 non-null   int64  \n",
      " 3   Concentration %  4080 non-null   float64\n",
      " 4   Solvent          1823 non-null   object \n",
      " 5   Descriptors_old  4561 non-null   object \n",
      "dtypes: float64(1), int64(2), object(3)\n",
      "memory usage: 249.4+ KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/767019178.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n"
     ]
    }
   ],
   "source": [
    "og_cid_behaviors['Descriptors_old'] = og_cid_behaviors.loc[:, 'Descriptors']\n",
    "og_cid_behaviors.head()\n",
    "\n",
    "for dup_set in list_idx_of_duplicates:\n",
    "    desc_set = set()\n",
    "    desc_string = ';'.join(og_cid_behaviors['Descriptors'][dup_set].to_list())\n",
    "    for desc in desc_string.split(';'):\n",
    "        if desc == 'odorless':\n",
    "            continue\n",
    "        desc_set.update([desc])\n",
    "    if len(desc_set) == 0:\n",
    "        desc_set.update(['odorless'])\n",
    "    new_desc_string = ';'.join(desc_set)\n",
    "    og_cid_behaviors['Descriptors'][dup_set] = new_desc_string\n",
    "\n",
    "og_cid_behaviors = og_cid_behaviors.drop_duplicates('CID', keep='first')\n",
    "og_cid_behaviors.info()"
   ]
  },
  {
   "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>CID</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Descriptors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>fishy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>CC(C)C(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
       "      <td>odorless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>CCC(=O)C(=O)O</td>\n",
       "      <td>sweet;caramellic;creamy;brown;lactonic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>CC(C)CC(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CID         IsomericSMILES                             Descriptors\n",
       "0    4                CC(CN)O                                   fishy\n",
       "1   49       CC(C)C(=O)C(=O)O                                  fruity\n",
       "2   51  C(CC(=O)O)C(=O)C(=O)O                                odorless\n",
       "3   58          CCC(=O)C(=O)O  sweet;caramellic;creamy;brown;lactonic\n",
       "4   70      CC(C)CC(=O)C(=O)O                                  fruity"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_cid_behaviors_mol = pd.merge(molecules['IsomericSMILES'], og_cid_behaviors[['Descriptors', 'CID']],how='inner', on='CID')\n",
    "og_cid_behaviors_mol.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4561 entries, 0 to 4560\n",
      "Columns: 667 entries, absinthe to zesty\n",
      "dtypes: int64(667)\n",
      "memory usage: 23.2 MB\n"
     ]
    }
   ],
   "source": [
    "og_encoded = og_cid_behaviors_mol['Descriptors'].str.get_dummies(sep=';')\n",
    "og_encoded.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "1     721\n",
       "2     674\n",
       "3     612\n",
       "4     594\n",
       "5     531\n",
       "6     405\n",
       "7     308\n",
       "8     257\n",
       "9     159\n",
       "10    141\n",
       "11     83\n",
       "12     36\n",
       "13     14\n",
       "14     13\n",
       "15      7\n",
       "16      4\n",
       "17      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(og_encoded.sum(axis=1).sort_values()).groupby(by=0).size()\n",
    "# number of desc | number of molecules"
   ]
  },
  {
   "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>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [count]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# molecules with no descriptors\n",
    "pd.DataFrame(og_encoded.sum(axis=1).sort_values(), columns=['count']).query('count==0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fruity': 1243,\n",
       " 'green': 1064,\n",
       " 'sweet': 1004,\n",
       " 'floral': 900,\n",
       " 'woody': 616,\n",
       " 'herbal': 551,\n",
       " 'fatty': 401,\n",
       " 'fresh': 380,\n",
       " 'waxy': 376,\n",
       " 'spicy': 351,\n",
       " 'citrus': 293,\n",
       " 'rose': 270,\n",
       " 'sulfurous': 251,\n",
       " 'earthy': 244,\n",
       " 'nutty': 236,\n",
       " 'tropical': 234,\n",
       " 'odorless': 221,\n",
       " 'balsamic': 220,\n",
       " 'oily': 217,\n",
       " 'minty': 187,\n",
       " 'vegetable': 182,\n",
       " 'powdery': 171,\n",
       " 'musty': 158,\n",
       " 'meaty': 158,\n",
       " 'creamy': 148,\n",
       " 'pineapple': 147,\n",
       " 'apple': 145,\n",
       " 'roasted': 138,\n",
       " 'aldehydic': 138,\n",
       " 'phenolic': 134,\n",
       " 'ethereal': 134,\n",
       " 'caramellic': 133,\n",
       " 'dry': 129,\n",
       " 'berry': 121,\n",
       " 'metallic': 117,\n",
       " 'pungent': 110,\n",
       " 'honey': 110,\n",
       " 'camphoreous': 110,\n",
       " 'onion': 96,\n",
       " 'leafy': 96,\n",
       " 'animal': 95,\n",
       " 'jasmin': 93,\n",
       " 'clean': 90,\n",
       " 'pear': 90,\n",
       " 'winey': 90,\n",
       " 'bland': 90,\n",
       " 'musk': 89,\n",
       " 'melon': 88,\n",
       " 'coconut': 86,\n",
       " 'cheesy': 85,\n",
       " 'amber': 84,\n",
       " 'mushroom': 80,\n",
       " 'banana': 80,\n",
       " 'coffee': 76,\n",
       " 'cooling': 75,\n",
       " 'buttery': 75,\n",
       " 'vanilla': 73,\n",
       " 'garlic': 73,\n",
       " 'alliaceous': 72,\n",
       " 'pine': 72,\n",
       " 'natural': 68,\n",
       " 'peach': 68,\n",
       " 'mild': 68,\n",
       " 'dairy': 67,\n",
       " 'tobacco': 66,\n",
       " 'burnt': 66,\n",
       " 'cherry': 65,\n",
       " 'cocoa': 63,\n",
       " 'warm': 63,\n",
       " 'apricot': 60,\n",
       " 'orange': 59,\n",
       " 'rummy': 58,\n",
       " 'cortex': 58,\n",
       " 'savory': 57,\n",
       " 'violet': 57,\n",
       " 'hyacinth': 56,\n",
       " 'geranium': 56,\n",
       " 'milky': 56,\n",
       " 'medicinal': 55,\n",
       " 'orris': 53,\n",
       " 'muguet': 52,\n",
       " 'grapefruit': 52,\n",
       " 'grape': 52,\n",
       " 'sharp': 52,\n",
       " 'fermented': 51,\n",
       " 'soapy': 50,\n",
       " 'cucumber': 49,\n",
       " 'brown': 49,\n",
       " 'chocolate': 49,\n",
       " 'terpenic': 47,\n",
       " 'lactonic': 47,\n",
       " 'grassy': 47,\n",
       " 'cooked': 46,\n",
       " 'estery': 46,\n",
       " 'plum': 46,\n",
       " 'cognac': 45,\n",
       " 'cinnamon': 44,\n",
       " 'weedy': 44,\n",
       " 'ripe': 44,\n",
       " 'lily': 44,\n",
       " 'lavender': 42,\n",
       " 'lemon': 42,\n",
       " 'leathery': 41,\n",
       " 'smoky': 40,\n",
       " 'cedar': 40,\n",
       " 'hay': 39,\n",
       " 'coumarinic': 38,\n",
       " 'celery': 38,\n",
       " 'bready': 38,\n",
       " 'raspberry': 37,\n",
       " 'acidic': 37,\n",
       " 'almond': 37,\n",
       " 'fishy': 37,\n",
       " 'ozone': 37,\n",
       " 'sandalwood': 35,\n",
       " 'strawberry': 35,\n",
       " 'sour': 35,\n",
       " 'anisic': 34,\n",
       " 'juicy': 34,\n",
       " 'mentholic': 34,\n",
       " 'alcoholic': 34,\n",
       " 'chemical': 33,\n",
       " 'hawthorn': 33,\n",
       " 'vetiver': 33,\n",
       " 'bergamot': 32,\n",
       " 'jammy': 31,\n",
       " 'clove': 31,\n",
       " 'marine': 31,\n",
       " 'orangeflower': 30,\n",
       " 'cabbage': 30,\n",
       " 'peppery': 30,\n",
       " 'anise': 30,\n",
       " 'hazelnut': 29,\n",
       " 'sweaty': 29,\n",
       " 'licorice': 29,\n",
       " 'ambergris': 29,\n",
       " 'gardenia': 29,\n",
       " 'plastic': 29,\n",
       " 'neroli': 28,\n",
       " 'orchid': 27,\n",
       " 'watery': 27,\n",
       " 'cinnamyl': 27,\n",
       " 'tonka': 27,\n",
       " 'balsam': 26,\n",
       " 'dusty': 26,\n",
       " 'narcissus': 26,\n",
       " 'patchouli': 26,\n",
       " 'chicken': 26,\n",
       " 'cumin': 26,\n",
       " 'potato': 26,\n",
       " 'fruit tropical fruit': 25,\n",
       " 'meaty roasted meaty': 25,\n",
       " 'nut skin': 24,\n",
       " 'galbanum': 24,\n",
       " 'rooty': 24,\n",
       " 'lime': 23,\n",
       " 'aromatic': 23,\n",
       " 'foliage': 23,\n",
       " 'ammoniacal': 23,\n",
       " 'tutti frutti': 22,\n",
       " 'fusel': 22,\n",
       " 'maple': 22,\n",
       " 'solvent': 21,\n",
       " 'tomato': 21,\n",
       " 'tea': 21,\n",
       " 'rubbery': 21,\n",
       " 'radish': 21,\n",
       " 'lilac': 20,\n",
       " 'popcorn': 20,\n",
       " 'ylang': 20,\n",
       " 'toasted': 20,\n",
       " 'pepper bell pepper': 19,\n",
       " 'naphthyl': 19,\n",
       " 'carnation': 19,\n",
       " 'fungal': 19,\n",
       " 'horseradish': 19,\n",
       " 'onion green onion': 19,\n",
       " 'ketonic': 18,\n",
       " 'fruit dried fruit': 18,\n",
       " 'brothy': 18,\n",
       " 'violet leaf': 18,\n",
       " 'blueberry': 18,\n",
       " 'resinous': 18,\n",
       " 'rhubarb': 18,\n",
       " 'fir needle': 18,\n",
       " 'almond bitter almond': 18,\n",
       " 'mossy': 17,\n",
       " 'spearmint': 17,\n",
       " 'magnolia': 17,\n",
       " 'ambrette': 17,\n",
       " 'currant bud black currant bud': 17,\n",
       " 'seedy': 17,\n",
       " 'greasy': 17,\n",
       " 'cloth laundered cloth': 17,\n",
       " 'yeasty': 16,\n",
       " 'mandarin': 16,\n",
       " 'lily of the valley': 16,\n",
       " 'coffee roasted coffee': 16,\n",
       " 'brandy': 16,\n",
       " 'apple green apple': 16,\n",
       " 'mimosa': 16,\n",
       " 'currant black currant': 16,\n",
       " 'beefy': 16,\n",
       " 'petal': 15,\n",
       " 'bitter': 15,\n",
       " 'passion fruit': 15,\n",
       " 'mustard': 15,\n",
       " 'rose red rose': 15,\n",
       " 'eucalyptus': 15,\n",
       " 'chamomile': 14,\n",
       " 'rancid': 14,\n",
       " 'dirty': 14,\n",
       " 'candy': 14,\n",
       " 'thujonic': 14,\n",
       " 'walnut': 14,\n",
       " 'eggy': 13,\n",
       " 'fennel': 13,\n",
       " 'beany': 13,\n",
       " 'camphor': 13,\n",
       " 'flowery': 13,\n",
       " 'faint': 13,\n",
       " 'sage clary sage': 13,\n",
       " 'mango': 13,\n",
       " 'buchu': 13,\n",
       " 'cyclamen': 13,\n",
       " 'chrysanthemum': 13,\n",
       " 'corn': 13,\n",
       " 'petitgrain': 13,\n",
       " 'oakmoss': 12,\n",
       " 'onion cooked onion': 12,\n",
       " 'bois de rose': 12,\n",
       " 'rosemary': 12,\n",
       " 'privet': 12,\n",
       " 'sugar': 12,\n",
       " 'bacon': 12,\n",
       " 'corn chip': 12,\n",
       " 'heliotrope': 12,\n",
       " 'gassy': 12,\n",
       " 'peppermint': 11,\n",
       " 'tallow': 11,\n",
       " 'peanut': 11,\n",
       " 'fried': 11,\n",
       " 'tuberose': 11,\n",
       " 'acetic': 11,\n",
       " 'humus': 11,\n",
       " 'wintergreen': 10,\n",
       " 'whiskey': 10,\n",
       " 'incense': 10,\n",
       " 'basil': 10,\n",
       " 'melon rind': 10,\n",
       " 'malty': 10,\n",
       " 'raw': 10,\n",
       " 'nasturtium': 10,\n",
       " 'caraway': 10,\n",
       " 'banana unripe banana': 10,\n",
       " 'peony': 10,\n",
       " 'hazelnut roasted hazelnut': 10,\n",
       " 'apple skin': 9,\n",
       " 'tangerine': 9,\n",
       " 'honeysuckle': 9,\n",
       " 'acrylate': 9,\n",
       " 'kiwi': 9,\n",
       " 'labdanum': 9,\n",
       " 'clover': 9,\n",
       " 'thyme': 9,\n",
       " 'papaya': 9,\n",
       " 'fecal': 9,\n",
       " 'woody old wood': 8,\n",
       " 'peanut roasted peanut': 8,\n",
       " 'cologne': 8,\n",
       " 'cassia': 8,\n",
       " 'seaweed': 8,\n",
       " 'myrrh': 8,\n",
       " 'guaiacol': 8,\n",
       " 'peely': 8,\n",
       " 'orange peel': 8,\n",
       " 'castoreum': 8,\n",
       " 'cilantro': 8,\n",
       " 'catty': 8,\n",
       " 'goaty': 8,\n",
       " 'cedarwood': 8,\n",
       " 'chicken fat': 8,\n",
       " 'sugar brown sugar': 8,\n",
       " 'sugar burnt sugar': 8,\n",
       " 'tart': 8,\n",
       " 'sulfury': 8,\n",
       " 'wet': 8,\n",
       " 'watermelon': 8,\n",
       " 'custard': 7,\n",
       " 'sawdust': 7,\n",
       " 'soft': 7,\n",
       " 'tomato leaf': 7,\n",
       " 'ammonia': 7,\n",
       " 'cereal': 7,\n",
       " 'costus': 7,\n",
       " 'spice': 7,\n",
       " 'wasabi': 7,\n",
       " 'moldy': 7,\n",
       " 'fruit overripe fruit': 7,\n",
       " 'civet': 7,\n",
       " 'pork': 7,\n",
       " 'blackberry': 7,\n",
       " 'flower': 7,\n",
       " 'bubble gum': 7,\n",
       " 'cotton candy': 7,\n",
       " 'honeydew': 7,\n",
       " 'storax': 7,\n",
       " 'watercress': 7,\n",
       " 'saffron': 7,\n",
       " 'raisin': 7,\n",
       " 'cherry maraschino cherry': 6,\n",
       " 'lamb': 6,\n",
       " 'seafood': 6,\n",
       " 'truffle': 6,\n",
       " 'opoponax': 6,\n",
       " 'coriander': 6,\n",
       " 'nutmeg': 6,\n",
       " 'acacia': 6,\n",
       " 'fresh outdoors': 6,\n",
       " 'almost odorless': 6,\n",
       " 'potato baked potato': 6,\n",
       " 'root beer': 6,\n",
       " 'cranberry': 6,\n",
       " 'sage': 6,\n",
       " 'potato raw potato': 6,\n",
       " 'fenugreek': 6,\n",
       " 'seashore': 6,\n",
       " 'cantaloupe': 6,\n",
       " 'cypress': 6,\n",
       " 'sassafrass': 6,\n",
       " 'dewy': 6,\n",
       " 'bean green bean': 6,\n",
       " 'barley roasted barley': 6,\n",
       " 'mutton': 5,\n",
       " 'skunk': 5,\n",
       " 'rose tea rose': 5,\n",
       " 'reseda': 5,\n",
       " 'lemon peel': 5,\n",
       " 'lemongrass': 5,\n",
       " 'pea green pea': 5,\n",
       " 'lovage': 5,\n",
       " 'painty': 5,\n",
       " 'parsley': 5,\n",
       " 'scallion': 5,\n",
       " 'juicy fruit': 5,\n",
       " 'grape skin': 5,\n",
       " 'fig': 5,\n",
       " 'gooseberry': 5,\n",
       " 'sweet pea': 5,\n",
       " 'ginger': 5,\n",
       " 'guava': 5,\n",
       " 'citronella': 5,\n",
       " 'bread baked': 5,\n",
       " 'rue': 5,\n",
       " 'baked': 5,\n",
       " 'fleshy': 5,\n",
       " 'dill': 5,\n",
       " 'acorn': 5,\n",
       " 'carrot': 5,\n",
       " 'beefy roasted beefy': 5,\n",
       " 'cardamom': 5,\n",
       " 'strong': 5,\n",
       " 'acetone': 5,\n",
       " 'citrus peel': 5,\n",
       " 'forest': 4,\n",
       " 'nitromusk': 4,\n",
       " 'soup': 4,\n",
       " 'gourmand': 4,\n",
       " 'cashew': 4,\n",
       " 'banana ripe banana': 4,\n",
       " 'tagette': 4,\n",
       " 'pulpy': 4,\n",
       " 'molasses': 4,\n",
       " 'fish': 4,\n",
       " 'banana peel': 4,\n",
       " 'citrus rind': 4,\n",
       " 'gasoline': 4,\n",
       " 'shellfish': 4,\n",
       " 'autumn': 4,\n",
       " 'cool': 4,\n",
       " 'grain toasted grain': 4,\n",
       " 'blossom': 4,\n",
       " 'durian': 4,\n",
       " 'valerian root': 4,\n",
       " 'pear skin': 4,\n",
       " 'almond roasted almond': 4,\n",
       " 'cananga': 4,\n",
       " 'algae': 4,\n",
       " 'chicken coup': 4,\n",
       " 'butterscotch': 4,\n",
       " 'buttermilk': 4,\n",
       " 'licorice black licorice': 4,\n",
       " 'verbena': 4,\n",
       " 'petroleum': 4,\n",
       " 'leek': 4,\n",
       " 'ham': 4,\n",
       " 'guaiacwood': 4,\n",
       " 'grilled': 4,\n",
       " 'vinegar': 4,\n",
       " 'grapefruit peel': 4,\n",
       " 'asparagus': 4,\n",
       " 'allspice': 4,\n",
       " 'beeswax': 3,\n",
       " 'potato chip': 3,\n",
       " 'deep': 3,\n",
       " 'paper': 3,\n",
       " 'shrimp': 3,\n",
       " 'pepper black pepper': 3,\n",
       " 'oil': 3,\n",
       " 'nut flesh': 3,\n",
       " 'broccoli': 3,\n",
       " 'cider': 3,\n",
       " 'cookie': 3,\n",
       " 'clam': 3,\n",
       " 'bread crust': 3,\n",
       " 'lard': 3,\n",
       " 'slight': 3,\n",
       " 'peanut butter': 3,\n",
       " 'powerful': 3,\n",
       " 'liver': 3,\n",
       " 'menthol': 3,\n",
       " 'graham cracker': 3,\n",
       " 'grain': 3,\n",
       " 'taco': 3,\n",
       " 'hairy': 3,\n",
       " 'hay new mown hay': 3,\n",
       " 'rich': 3,\n",
       " 'fruit': 3,\n",
       " 'linden flower': 3,\n",
       " 'apple cooked apple': 3,\n",
       " 'heavy': 3,\n",
       " 'rose dried rose': 3,\n",
       " 'lettuce': 3,\n",
       " 'juniper': 3,\n",
       " 'metal': 3,\n",
       " 'mahogany': 3,\n",
       " 'tea green tea': 3,\n",
       " 'pumpkin': 3,\n",
       " 'freesia': 3,\n",
       " 'beef juice': 3,\n",
       " 'tarragon': 3,\n",
       " 'prune': 3,\n",
       " 'angelica': 2,\n",
       " 'amine': 2,\n",
       " 'caprylic': 2,\n",
       " 'caramel': 2,\n",
       " 'pleasant': 2,\n",
       " 'agarwood': 2,\n",
       " 'salty': 2,\n",
       " 'woody burnt wood': 2,\n",
       " 'rotten': 2,\n",
       " 'rubber': 2,\n",
       " 'peel': 2,\n",
       " 'bloody': 2,\n",
       " 'astringent': 2,\n",
       " 'watermelon rind': 2,\n",
       " 'quince': 2,\n",
       " 'arrack': 2,\n",
       " 'wine': 2,\n",
       " 'bread rye bread': 2,\n",
       " 'artichoke': 2,\n",
       " 'sappy': 2,\n",
       " 'bouillon': 2,\n",
       " 'praline': 2,\n",
       " 'plum skin': 2,\n",
       " 'wild': 2,\n",
       " 'leaf': 2,\n",
       " 'fruit ripe fruit': 2,\n",
       " 'cauliflower': 2,\n",
       " 'linalool': 2,\n",
       " 'marzipan': 2,\n",
       " 'toffee': 2,\n",
       " 'davana': 2,\n",
       " 'immortelle': 2,\n",
       " 'thujone': 2,\n",
       " 'hydrocarbon': 2,\n",
       " 'lily-of-the-valley': 2,\n",
       " 'egg nog': 2,\n",
       " 'elderberry': 2,\n",
       " 'cucumber skin': 2,\n",
       " 'elderflower': 2,\n",
       " 'musky': 2,\n",
       " 'terpene': 2,\n",
       " 'spruce': 2,\n",
       " 'tequila': 2,\n",
       " 'filbert': 2,\n",
       " 'methyl': 2,\n",
       " 'loganberry': 2,\n",
       " 'nitrile': 2,\n",
       " 'lychee': 2,\n",
       " 'coumarin': 2,\n",
       " 'orange bitter orange': 2,\n",
       " 'lactone': 2,\n",
       " 'cheese': 2,\n",
       " 'cheesy bleu cheese': 2,\n",
       " 'cheesy parmesan cheese': 2,\n",
       " 'sausage smoked sausage': 2,\n",
       " 'oriental': 2,\n",
       " 'chocolate dark chocolate': 2,\n",
       " 'urine': 2,\n",
       " 'cistus': 2,\n",
       " 'citral': 2,\n",
       " 'turnup': 2,\n",
       " 'citric': 2,\n",
       " 'cotton': 2,\n",
       " 'sweat': 1,\n",
       " 'yogurt': 1,\n",
       " 'styrene': 1,\n",
       " 'stalk': 1,\n",
       " 'straw': 1,\n",
       " 'rum': 1,\n",
       " 'worty': 1,\n",
       " 'styrax': 1,\n",
       " 'spinach': 1,\n",
       " 'wormwood': 1,\n",
       " 'starfruit': 1,\n",
       " 'sandy': 1,\n",
       " 'salmon': 1,\n",
       " 'soil': 1,\n",
       " 'verdox': 1,\n",
       " 'sauerkraut': 1,\n",
       " 'vegetables': 1,\n",
       " 'umami': 1,\n",
       " 'sassafras': 1,\n",
       " 'sarsaparilla': 1,\n",
       " 'tolu balsam': 1,\n",
       " 'wood': 1,\n",
       " 'shell': 1,\n",
       " 'thiamine': 1,\n",
       " 'terpentine': 1,\n",
       " 'skin': 1,\n",
       " 'vinyl': 1,\n",
       " 'woody-lactone': 1,\n",
       " 'tea black tea': 1,\n",
       " 'smelling': 1,\n",
       " 'tar': 1,\n",
       " 'turkey': 1,\n",
       " 'absinthe': 1,\n",
       " 'rotting': 1,\n",
       " 'flowers': 1,\n",
       " 'cheesy limburger cheese': 1,\n",
       " 'cheesy roquefort cheese': 1,\n",
       " 'chervil': 1,\n",
       " 'chicken roasted chicken': 1,\n",
       " 'chicory': 1,\n",
       " 'chlorine': 1,\n",
       " 'chloroform': 1,\n",
       " 'citralva': 1,\n",
       " 'citronellal': 1,\n",
       " 'coal': 1,\n",
       " 'concord grape': 1,\n",
       " 'cornmint': 1,\n",
       " 'creosote': 1,\n",
       " 'cresol': 1,\n",
       " 'damascone': 1,\n",
       " 'dank': 1,\n",
       " 'date': 1,\n",
       " 'de': 1,\n",
       " 'dead': 1,\n",
       " 'diffuse': 1,\n",
       " 'dihydrojasmonate': 1,\n",
       " 'dried': 1,\n",
       " 'dull': 1,\n",
       " 'egg': 1,\n",
       " 'egg yolk': 1,\n",
       " 'elemi': 1,\n",
       " 'ether': 1,\n",
       " 'ethyl': 1,\n",
       " 'evil': 1,\n",
       " 'cheesy feta cheese': 1,\n",
       " 'chavicol': 1,\n",
       " 'charred': 1,\n",
       " 'bonbon': 1,\n",
       " 'acetaldehyde': 1,\n",
       " 'alkane': 1,\n",
       " 'almond toasted almond': 1,\n",
       " 'apple dried apple': 1,\n",
       " 'armoise': 1,\n",
       " 'bay': 1,\n",
       " 'beef': 1,\n",
       " 'benzaldehyde': 1,\n",
       " 'benzoate': 1,\n",
       " 'benzoin': 1,\n",
       " 'berry ripe berry': 1,\n",
       " 'boiled': 1,\n",
       " 'bois': 1,\n",
       " 'borneol': 1,\n",
       " 'characteristic': 1,\n",
       " 'boronia': 1,\n",
       " 'bread': 1,\n",
       " 'bud': 1,\n",
       " 'butter': 1,\n",
       " 'butter rancid': 1,\n",
       " 'cacao': 1,\n",
       " 'capers': 1,\n",
       " 'cardboard': 1,\n",
       " 'carrot seed': 1,\n",
       " 'carvone': 1,\n",
       " 'cassie': 1,\n",
       " 'catechu': 1,\n",
       " 'cellar': 1,\n",
       " 'flat': 1,\n",
       " 'fragrant': 1,\n",
       " 'rosy': 1,\n",
       " 'frankincense': 1,\n",
       " 'milk': 1,\n",
       " 'mint': 1,\n",
       " 'moss': 1,\n",
       " 'mouldy': 1,\n",
       " 'muttony': 1,\n",
       " 'naphtha': 1,\n",
       " 'needle': 1,\n",
       " 'needles': 1,\n",
       " 'nerol': 1,\n",
       " 'new mown hay': 1,\n",
       " 'nice': 1,\n",
       " 'nut': 1,\n",
       " 'ocean': 1,\n",
       " 'old': 1,\n",
       " 'orange rind': 1,\n",
       " 'osmanthus': 1,\n",
       " 'oxide': 1,\n",
       " 'palmarosa': 1,\n",
       " 'pastry': 1,\n",
       " 'pistachio': 1,\n",
       " 'plowed': 1,\n",
       " 'pomegranate': 1,\n",
       " 'privetblossom': 1,\n",
       " 'pyrazine': 1,\n",
       " 'pyridine': 1,\n",
       " 'red': 1,\n",
       " 'resin': 1,\n",
       " 'rindy': 1,\n",
       " 'rosey': 1,\n",
       " 'mignonette': 1,\n",
       " 'methyl heptine carbonate': 1,\n",
       " 'mercaptan': 1,\n",
       " 'jackfruit': 1,\n",
       " 'freshly': 1,\n",
       " 'genet': 1,\n",
       " 'goats': 1,\n",
       " 'grapfruit': 1,\n",
       " 'grass': 1,\n",
       " 'gravy': 1,\n",
       " 'harsh': 1,\n",
       " 'herb': 1,\n",
       " 'herbaceous': 1,\n",
       " 'hesperidic': 1,\n",
       " 'impacting': 1,\n",
       " 'indole': 1,\n",
       " 'iodine': 1,\n",
       " 'jam': 1,\n",
       " 'melon unripe melon': 1,\n",
       " 'absolute': 1,\n",
       " 'kernel': 1,\n",
       " 'kimchi': 1,\n",
       " 'leather': 1,\n",
       " 'light': 1,\n",
       " 'lilial': 1,\n",
       " 'linden': 1,\n",
       " 'liquid': 1,\n",
       " 'mace': 1,\n",
       " 'marjoram': 1,\n",
       " 'may': 1,\n",
       " 'meat': 1,\n",
       " 'medical': 1,\n",
       " 'zesty': 1}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols_per_desc = dict(og_encoded.sum().sort_values(ascending=False))\n",
    "mols_per_desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols_per_desc['medical']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols_per_desc['medicinal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CID                              7924\n",
      "IsomericSMILES           CC1COC(=O)O1\n",
      "Descriptors       minty;spicy;medical\n",
      "Name: 424, dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        \n",
       "1        \n",
       "2        \n",
       "3        \n",
       "4        \n",
       "       ..\n",
       "4556     \n",
       "4557     \n",
       "4558     \n",
       "4559     \n",
       "4560     \n",
       "Length: 4561, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_cid_behaviors_mol.apply(lambda x: print(x) if ('medical' in x['Descriptors'].split(';')) else '', axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "handling labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_odors(desc, odor_list):\n",
    "    odors = desc.split(';')\n",
    "    for i in odors:\n",
    "        if i not in odor_list:\n",
    "            odor_list.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['absinthe',\n",
       " 'absolute',\n",
       " 'acacia',\n",
       " 'acetaldehyde',\n",
       " 'acetic',\n",
       " 'acetone',\n",
       " 'acidic',\n",
       " 'acorn',\n",
       " 'acrylate',\n",
       " 'agarwood',\n",
       " 'alcoholic',\n",
       " 'aldehydic',\n",
       " 'algae',\n",
       " 'alkane',\n",
       " 'alliaceous',\n",
       " 'allspice',\n",
       " 'almond',\n",
       " 'almond bitter almond',\n",
       " 'almond roasted almond',\n",
       " 'almond toasted almond',\n",
       " 'almost odorless',\n",
       " 'amber',\n",
       " 'ambergris',\n",
       " 'ambrette',\n",
       " 'amine',\n",
       " 'ammonia',\n",
       " 'ammoniacal',\n",
       " 'angelica',\n",
       " 'animal',\n",
       " 'anise',\n",
       " 'anisic',\n",
       " 'apple',\n",
       " 'apple cooked apple',\n",
       " 'apple dried apple',\n",
       " 'apple green apple',\n",
       " 'apple skin',\n",
       " 'apricot',\n",
       " 'armoise',\n",
       " 'aromatic',\n",
       " 'arrack',\n",
       " 'artichoke',\n",
       " 'asparagus',\n",
       " 'astringent',\n",
       " 'autumn',\n",
       " 'bacon',\n",
       " 'baked',\n",
       " 'balsam',\n",
       " 'balsamic',\n",
       " 'banana',\n",
       " 'banana peel',\n",
       " 'banana ripe banana',\n",
       " 'banana unripe banana',\n",
       " 'barley roasted barley',\n",
       " 'basil',\n",
       " 'bay',\n",
       " 'bean green bean',\n",
       " 'beany',\n",
       " 'beef',\n",
       " 'beef juice',\n",
       " 'beefy',\n",
       " 'beefy roasted beefy',\n",
       " 'beeswax',\n",
       " 'benzaldehyde',\n",
       " 'benzoate',\n",
       " 'benzoin',\n",
       " 'bergamot',\n",
       " 'berry',\n",
       " 'berry ripe berry',\n",
       " 'bitter',\n",
       " 'blackberry',\n",
       " 'bland',\n",
       " 'bloody',\n",
       " 'blossom',\n",
       " 'blueberry',\n",
       " 'boiled',\n",
       " 'bois',\n",
       " 'bois de rose',\n",
       " 'bonbon',\n",
       " 'borneol',\n",
       " 'boronia',\n",
       " 'bouillon',\n",
       " 'brandy',\n",
       " 'bread',\n",
       " 'bread baked',\n",
       " 'bread crust',\n",
       " 'bread rye bread',\n",
       " 'bready',\n",
       " 'broccoli',\n",
       " 'brothy',\n",
       " 'brown',\n",
       " 'bubble gum',\n",
       " 'buchu',\n",
       " 'bud',\n",
       " 'burnt',\n",
       " 'butter',\n",
       " 'butter rancid',\n",
       " 'buttermilk',\n",
       " 'butterscotch',\n",
       " 'buttery',\n",
       " 'cabbage',\n",
       " 'cacao',\n",
       " 'camphor',\n",
       " 'camphoreous',\n",
       " 'cananga',\n",
       " 'candy',\n",
       " 'cantaloupe',\n",
       " 'capers',\n",
       " 'caprylic',\n",
       " 'caramel',\n",
       " 'caramellic',\n",
       " 'caraway',\n",
       " 'cardamom',\n",
       " 'cardboard',\n",
       " 'carnation',\n",
       " 'carrot',\n",
       " 'carrot seed',\n",
       " 'carvone',\n",
       " 'cashew',\n",
       " 'cassia',\n",
       " 'cassie',\n",
       " 'castoreum',\n",
       " 'catechu',\n",
       " 'catty',\n",
       " 'cauliflower',\n",
       " 'cedar',\n",
       " 'cedarwood',\n",
       " 'celery',\n",
       " 'cellar',\n",
       " 'cereal',\n",
       " 'chamomile',\n",
       " 'characteristic',\n",
       " 'charred',\n",
       " 'chavicol',\n",
       " 'cheese',\n",
       " 'cheesy',\n",
       " 'cheesy bleu cheese',\n",
       " 'cheesy feta cheese',\n",
       " 'cheesy limburger cheese',\n",
       " 'cheesy parmesan cheese',\n",
       " 'cheesy roquefort cheese',\n",
       " 'chemical',\n",
       " 'cherry',\n",
       " 'cherry maraschino cherry',\n",
       " 'chervil',\n",
       " 'chicken',\n",
       " 'chicken coup',\n",
       " 'chicken fat',\n",
       " 'chicken roasted chicken',\n",
       " 'chicory',\n",
       " 'chlorine',\n",
       " 'chloroform',\n",
       " 'chocolate',\n",
       " 'chocolate dark chocolate',\n",
       " 'chrysanthemum',\n",
       " 'cider',\n",
       " 'cilantro',\n",
       " 'cinnamon',\n",
       " 'cinnamyl',\n",
       " 'cistus',\n",
       " 'citral',\n",
       " 'citralva',\n",
       " 'citric',\n",
       " 'citronella',\n",
       " 'citronellal',\n",
       " 'citrus',\n",
       " 'citrus peel',\n",
       " 'citrus rind',\n",
       " 'civet',\n",
       " 'clam',\n",
       " 'clean',\n",
       " 'cloth laundered cloth',\n",
       " 'clove',\n",
       " 'clover',\n",
       " 'coal',\n",
       " 'cocoa',\n",
       " 'coconut',\n",
       " 'coffee',\n",
       " 'coffee roasted coffee',\n",
       " 'cognac',\n",
       " 'cologne',\n",
       " 'concord grape',\n",
       " 'cooked',\n",
       " 'cookie',\n",
       " 'cool',\n",
       " 'cooling',\n",
       " 'coriander',\n",
       " 'corn',\n",
       " 'corn chip',\n",
       " 'cornmint',\n",
       " 'cortex',\n",
       " 'costus',\n",
       " 'cotton',\n",
       " 'cotton candy',\n",
       " 'coumarin',\n",
       " 'coumarinic',\n",
       " 'cranberry',\n",
       " 'creamy',\n",
       " 'creosote',\n",
       " 'cresol',\n",
       " 'cucumber',\n",
       " 'cucumber skin',\n",
       " 'cumin',\n",
       " 'currant black currant',\n",
       " 'currant bud black currant bud',\n",
       " 'custard',\n",
       " 'cyclamen',\n",
       " 'cypress',\n",
       " 'dairy',\n",
       " 'damascone',\n",
       " 'dank',\n",
       " 'date',\n",
       " 'davana',\n",
       " 'de',\n",
       " 'dead',\n",
       " 'deep',\n",
       " 'dewy',\n",
       " 'diffuse',\n",
       " 'dihydrojasmonate',\n",
       " 'dill',\n",
       " 'dirty',\n",
       " 'dried',\n",
       " 'dry',\n",
       " 'dull',\n",
       " 'durian',\n",
       " 'dusty',\n",
       " 'earthy',\n",
       " 'egg',\n",
       " 'egg nog',\n",
       " 'egg yolk',\n",
       " 'eggy',\n",
       " 'elderberry',\n",
       " 'elderflower',\n",
       " 'elemi',\n",
       " 'estery',\n",
       " 'ether',\n",
       " 'ethereal',\n",
       " 'ethyl',\n",
       " 'eucalyptus',\n",
       " 'evil',\n",
       " 'faint',\n",
       " 'fatty',\n",
       " 'fecal',\n",
       " 'fennel',\n",
       " 'fenugreek',\n",
       " 'fermented',\n",
       " 'fig',\n",
       " 'filbert',\n",
       " 'fir needle',\n",
       " 'fish',\n",
       " 'fishy',\n",
       " 'flat',\n",
       " 'fleshy',\n",
       " 'floral',\n",
       " 'flower',\n",
       " 'flowers',\n",
       " 'flowery',\n",
       " 'foliage',\n",
       " 'forest',\n",
       " 'fragrant',\n",
       " 'frankincense',\n",
       " 'freesia',\n",
       " 'fresh',\n",
       " 'fresh outdoors',\n",
       " 'freshly',\n",
       " 'fried',\n",
       " 'fruit',\n",
       " 'fruit dried fruit',\n",
       " 'fruit overripe fruit',\n",
       " 'fruit ripe fruit',\n",
       " 'fruit tropical fruit',\n",
       " 'fruity',\n",
       " 'fungal',\n",
       " 'fusel',\n",
       " 'galbanum',\n",
       " 'gardenia',\n",
       " 'garlic',\n",
       " 'gasoline',\n",
       " 'gassy',\n",
       " 'genet',\n",
       " 'geranium',\n",
       " 'ginger',\n",
       " 'goats',\n",
       " 'goaty',\n",
       " 'gooseberry',\n",
       " 'gourmand',\n",
       " 'graham cracker',\n",
       " 'grain',\n",
       " 'grain toasted grain',\n",
       " 'grape',\n",
       " 'grape skin',\n",
       " 'grapefruit',\n",
       " 'grapefruit peel',\n",
       " 'grapfruit',\n",
       " 'grass',\n",
       " 'grassy',\n",
       " 'gravy',\n",
       " 'greasy',\n",
       " 'green',\n",
       " 'grilled',\n",
       " 'guaiacol',\n",
       " 'guaiacwood',\n",
       " 'guava',\n",
       " 'hairy',\n",
       " 'ham',\n",
       " 'harsh',\n",
       " 'hawthorn',\n",
       " 'hay',\n",
       " 'hay new mown hay',\n",
       " 'hazelnut',\n",
       " 'hazelnut roasted hazelnut',\n",
       " 'heavy',\n",
       " 'heliotrope',\n",
       " 'herb',\n",
       " 'herbaceous',\n",
       " 'herbal',\n",
       " 'hesperidic',\n",
       " 'honey',\n",
       " 'honeydew',\n",
       " 'honeysuckle',\n",
       " 'horseradish',\n",
       " 'humus',\n",
       " 'hyacinth',\n",
       " 'hydrocarbon',\n",
       " 'immortelle',\n",
       " 'impacting',\n",
       " 'incense',\n",
       " 'indole',\n",
       " 'iodine',\n",
       " 'jackfruit',\n",
       " 'jam',\n",
       " 'jammy',\n",
       " 'jasmin',\n",
       " 'juicy',\n",
       " 'juicy fruit',\n",
       " 'juniper',\n",
       " 'kernel',\n",
       " 'ketonic',\n",
       " 'kimchi',\n",
       " 'kiwi',\n",
       " 'labdanum',\n",
       " 'lactone',\n",
       " 'lactonic',\n",
       " 'lamb',\n",
       " 'lard',\n",
       " 'lavender',\n",
       " 'leaf',\n",
       " 'leafy',\n",
       " 'leather',\n",
       " 'leathery',\n",
       " 'leek',\n",
       " 'lemon',\n",
       " 'lemon peel',\n",
       " 'lemongrass',\n",
       " 'lettuce',\n",
       " 'licorice',\n",
       " 'licorice black licorice',\n",
       " 'light',\n",
       " 'lilac',\n",
       " 'lilial',\n",
       " 'lily',\n",
       " 'lily of the valley',\n",
       " 'lily-of-the-valley',\n",
       " 'lime',\n",
       " 'linalool',\n",
       " 'linden',\n",
       " 'linden flower',\n",
       " 'liquid',\n",
       " 'liver',\n",
       " 'loganberry',\n",
       " 'lovage',\n",
       " 'lychee',\n",
       " 'mace',\n",
       " 'magnolia',\n",
       " 'mahogany',\n",
       " 'malty',\n",
       " 'mandarin',\n",
       " 'mango',\n",
       " 'maple',\n",
       " 'marine',\n",
       " 'marjoram',\n",
       " 'marzipan',\n",
       " 'may',\n",
       " 'meat',\n",
       " 'meaty',\n",
       " 'meaty roasted meaty',\n",
       " 'medical',\n",
       " 'medicinal',\n",
       " 'melon',\n",
       " 'melon rind',\n",
       " 'melon unripe melon',\n",
       " 'menthol',\n",
       " 'mentholic',\n",
       " 'mercaptan',\n",
       " 'metal',\n",
       " 'metallic',\n",
       " 'methyl',\n",
       " 'methyl heptine carbonate',\n",
       " 'mignonette',\n",
       " 'mild',\n",
       " 'milk',\n",
       " 'milky',\n",
       " 'mimosa',\n",
       " 'mint',\n",
       " 'minty',\n",
       " 'molasses',\n",
       " 'moldy',\n",
       " 'moss',\n",
       " 'mossy',\n",
       " 'mouldy',\n",
       " 'muguet',\n",
       " 'mushroom',\n",
       " 'musk',\n",
       " 'musky',\n",
       " 'mustard',\n",
       " 'musty',\n",
       " 'mutton',\n",
       " 'muttony',\n",
       " 'myrrh',\n",
       " 'naphtha',\n",
       " 'naphthyl',\n",
       " 'narcissus',\n",
       " 'nasturtium',\n",
       " 'natural',\n",
       " 'needle',\n",
       " 'needles',\n",
       " 'nerol',\n",
       " 'neroli',\n",
       " 'new mown hay',\n",
       " 'nice',\n",
       " 'nitrile',\n",
       " 'nitromusk',\n",
       " 'nut',\n",
       " 'nut flesh',\n",
       " 'nut skin',\n",
       " 'nutmeg',\n",
       " 'nutty',\n",
       " 'oakmoss',\n",
       " 'ocean',\n",
       " 'odorless',\n",
       " 'oil',\n",
       " 'oily',\n",
       " 'old',\n",
       " 'onion',\n",
       " 'onion cooked onion',\n",
       " 'onion green onion',\n",
       " 'opoponax',\n",
       " 'orange',\n",
       " 'orange bitter orange',\n",
       " 'orange peel',\n",
       " 'orange rind',\n",
       " 'orangeflower',\n",
       " 'orchid',\n",
       " 'oriental',\n",
       " 'orris',\n",
       " 'osmanthus',\n",
       " 'oxide',\n",
       " 'ozone',\n",
       " 'painty',\n",
       " 'palmarosa',\n",
       " 'papaya',\n",
       " 'paper',\n",
       " 'parsley',\n",
       " 'passion fruit',\n",
       " 'pastry',\n",
       " 'patchouli',\n",
       " 'pea green pea',\n",
       " 'peach',\n",
       " 'peanut',\n",
       " 'peanut butter',\n",
       " 'peanut roasted peanut',\n",
       " 'pear',\n",
       " 'pear skin',\n",
       " 'peel',\n",
       " 'peely',\n",
       " 'peony',\n",
       " 'pepper bell pepper',\n",
       " 'pepper black pepper',\n",
       " 'peppermint',\n",
       " 'peppery',\n",
       " 'petal',\n",
       " 'petitgrain',\n",
       " 'petroleum',\n",
       " 'phenolic',\n",
       " 'pine',\n",
       " 'pineapple',\n",
       " 'pistachio',\n",
       " 'plastic',\n",
       " 'pleasant',\n",
       " 'plowed',\n",
       " 'plum',\n",
       " 'plum skin',\n",
       " 'pomegranate',\n",
       " 'popcorn',\n",
       " 'pork',\n",
       " 'potato',\n",
       " 'potato baked potato',\n",
       " 'potato chip',\n",
       " 'potato raw potato',\n",
       " 'powdery',\n",
       " 'powerful',\n",
       " 'praline',\n",
       " 'privet',\n",
       " 'privetblossom',\n",
       " 'prune',\n",
       " 'pulpy',\n",
       " 'pumpkin',\n",
       " 'pungent',\n",
       " 'pyrazine',\n",
       " 'pyridine',\n",
       " 'quince',\n",
       " 'radish',\n",
       " 'raisin',\n",
       " 'rancid',\n",
       " 'raspberry',\n",
       " 'raw',\n",
       " 'red',\n",
       " 'reseda',\n",
       " 'resin',\n",
       " 'resinous',\n",
       " 'rhubarb',\n",
       " 'rich',\n",
       " 'rindy',\n",
       " 'ripe',\n",
       " 'roasted',\n",
       " 'root beer',\n",
       " 'rooty',\n",
       " 'rose',\n",
       " 'rose dried rose',\n",
       " 'rose red rose',\n",
       " 'rose tea rose',\n",
       " 'rosemary',\n",
       " 'rosey',\n",
       " 'rosy',\n",
       " 'rotten',\n",
       " 'rotting',\n",
       " 'rubber',\n",
       " 'rubbery',\n",
       " 'rue',\n",
       " 'rum',\n",
       " 'rummy',\n",
       " 'saffron',\n",
       " 'sage',\n",
       " 'sage clary sage',\n",
       " 'salmon',\n",
       " 'salty',\n",
       " 'sandalwood',\n",
       " 'sandy',\n",
       " 'sappy',\n",
       " 'sarsaparilla',\n",
       " 'sassafras',\n",
       " 'sassafrass',\n",
       " 'sauerkraut',\n",
       " 'sausage smoked sausage',\n",
       " 'savory',\n",
       " 'sawdust',\n",
       " 'scallion',\n",
       " 'seafood',\n",
       " 'seashore',\n",
       " 'seaweed',\n",
       " 'seedy',\n",
       " 'sharp',\n",
       " 'shell',\n",
       " 'shellfish',\n",
       " 'shrimp',\n",
       " 'skin',\n",
       " 'skunk',\n",
       " 'slight',\n",
       " 'smelling',\n",
       " 'smoky',\n",
       " 'soapy',\n",
       " 'soft',\n",
       " 'soil',\n",
       " 'solvent',\n",
       " 'soup',\n",
       " 'sour',\n",
       " 'spearmint',\n",
       " 'spice',\n",
       " 'spicy',\n",
       " 'spinach',\n",
       " 'spruce',\n",
       " 'stalk',\n",
       " 'starfruit',\n",
       " 'storax',\n",
       " 'straw',\n",
       " 'strawberry',\n",
       " 'strong',\n",
       " 'styrax',\n",
       " 'styrene',\n",
       " 'sugar',\n",
       " 'sugar brown sugar',\n",
       " 'sugar burnt sugar',\n",
       " 'sulfurous',\n",
       " 'sulfury',\n",
       " 'sweat',\n",
       " 'sweaty',\n",
       " 'sweet',\n",
       " 'sweet pea',\n",
       " 'taco',\n",
       " 'tagette',\n",
       " 'tallow',\n",
       " 'tangerine',\n",
       " 'tar',\n",
       " 'tarragon',\n",
       " 'tart',\n",
       " 'tea',\n",
       " 'tea black tea',\n",
       " 'tea green tea',\n",
       " 'tequila',\n",
       " 'terpene',\n",
       " 'terpenic',\n",
       " 'terpentine',\n",
       " 'thiamine',\n",
       " 'thujone',\n",
       " 'thujonic',\n",
       " 'thyme',\n",
       " 'toasted',\n",
       " 'tobacco',\n",
       " 'toffee',\n",
       " 'tolu balsam',\n",
       " 'tomato',\n",
       " 'tomato leaf',\n",
       " 'tonka',\n",
       " 'tropical',\n",
       " 'truffle',\n",
       " 'tuberose',\n",
       " 'turkey',\n",
       " 'turnup',\n",
       " 'tutti frutti',\n",
       " 'umami',\n",
       " 'urine',\n",
       " 'valerian root',\n",
       " 'vanilla',\n",
       " 'vegetable',\n",
       " 'vegetables',\n",
       " 'verbena',\n",
       " 'verdox',\n",
       " 'vetiver',\n",
       " 'vinegar',\n",
       " 'vinyl',\n",
       " 'violet',\n",
       " 'violet leaf',\n",
       " 'walnut',\n",
       " 'warm',\n",
       " 'wasabi',\n",
       " 'watercress',\n",
       " 'watermelon',\n",
       " 'watermelon rind',\n",
       " 'watery',\n",
       " 'waxy',\n",
       " 'weedy',\n",
       " 'wet',\n",
       " 'whiskey',\n",
       " 'wild',\n",
       " 'wine',\n",
       " 'winey',\n",
       " 'wintergreen',\n",
       " 'wood',\n",
       " 'woody',\n",
       " 'woody burnt wood',\n",
       " 'woody old wood',\n",
       " 'woody-lactone',\n",
       " 'wormwood',\n",
       " 'worty',\n",
       " 'yeasty',\n",
       " 'ylang',\n",
       " 'yogurt',\n",
       " 'zesty']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odor_list = []\n",
    "behavior_clean['Descriptors'].apply(lambda x: get_odors(x, odor_list))\n",
    "odor_list.sort()\n",
    "odor_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "667"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(odor_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>absinthe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>absolute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>acacia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>acetaldehyde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acetic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>worty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>yeasty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>ylang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>yogurt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>zesty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>667 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             desc\n",
       "0        absinthe\n",
       "1        absolute\n",
       "2          acacia\n",
       "3    acetaldehyde\n",
       "4          acetic\n",
       "..            ...\n",
       "662         worty\n",
       "663        yeasty\n",
       "664         ylang\n",
       "665        yogurt\n",
       "666         zesty\n",
       "\n",
       "[667 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odors_df = pd.DataFrame(odor_list, columns=['desc'])\n",
    "odors_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>amber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ambergris</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         desc\n",
       "21      amber\n",
       "22  ambergris"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odors_df[odors_df['desc'].str.match('amber.*')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def desc_analyser():\n",
    "    for desc in required_desc:\n",
    "        odor_set = odors_df[odors_df['desc'].str.match(f'.* {desc}.*|.*{desc} .*|{desc}')]\n",
    "        if len(odor_set) > 1:\n",
    "            yield(desc, odor_set)\n",
    "gen = desc_analyser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'almond': ['almond',\n",
       "  'almond bitter almond',\n",
       "  'almond roasted almond',\n",
       "  'almond toasted almond'],\n",
       " 'amber': ['amber', 'ambergris'],\n",
       " 'apple': ['apple',\n",
       "  'apple cooked apple',\n",
       "  'apple dried apple',\n",
       "  'apple green apple',\n",
       "  'apple skin'],\n",
       " 'banana': ['banana',\n",
       "  'banana peel',\n",
       "  'banana ripe banana',\n",
       "  'banana unripe banana'],\n",
       " 'beefy': ['beefy', 'beefy roasted beefy'],\n",
       " 'berry': ['berry', 'berry ripe berry'],\n",
       " 'bitter': ['almond bitter almond', 'bitter', 'orange bitter orange'],\n",
       " 'black currant': ['currant black currant', 'currant bud black currant bud'],\n",
       " 'burnt': ['burnt', 'sugar burnt sugar', 'woody burnt wood'],\n",
       " 'cedar': ['cedar', 'cedarwood'],\n",
       " 'cheesy': ['cheesy',\n",
       "  'cheesy bleu cheese',\n",
       "  'cheesy feta cheese',\n",
       "  'cheesy limburger cheese',\n",
       "  'cheesy parmesan cheese',\n",
       "  'cheesy roquefort cheese'],\n",
       " 'cherry': ['cherry', 'cherry maraschino cherry'],\n",
       " 'chocolate': ['chocolate', 'chocolate dark chocolate'],\n",
       " 'citrus': ['citrus', 'citrus peel', 'citrus rind'],\n",
       " 'clove': ['clove', 'clover'],\n",
       " 'coffee': ['coffee', 'coffee roasted coffee'],\n",
       " 'cooked': ['apple cooked apple', 'cooked', 'onion cooked onion'],\n",
       " 'cucumber': ['cucumber', 'cucumber skin'],\n",
       " 'fresh': ['fresh', 'fresh outdoors', 'freshly'],\n",
       " 'grape': ['concord grape',\n",
       "  'grape',\n",
       "  'grape skin',\n",
       "  'grapefruit',\n",
       "  'grapefruit peel'],\n",
       " 'grapefruit': ['grapefruit', 'grapefruit peel'],\n",
       " 'green': ['apple green apple',\n",
       "  'bean green bean',\n",
       "  'green',\n",
       "  'onion green onion',\n",
       "  'pea green pea',\n",
       "  'tea green tea'],\n",
       " 'hay': ['hay', 'hay new mown hay', 'new mown hay'],\n",
       " 'hazelnut': ['hazelnut', 'hazelnut roasted hazelnut'],\n",
       " 'honey': ['honey', 'honeydew', 'honeysuckle'],\n",
       " 'juicy': ['juicy', 'juicy fruit'],\n",
       " 'lemon': ['lemon', 'lemon peel', 'lemongrass'],\n",
       " 'lily': ['lily', 'lily of the valley', 'lily-of-the-valley'],\n",
       " 'meaty': ['meaty', 'meaty roasted meaty'],\n",
       " 'melon': ['melon', 'melon rind', 'melon unripe melon', 'watermelon rind'],\n",
       " 'mint': ['mint', 'minty'],\n",
       " 'musk': ['musk', 'musky'],\n",
       " 'odorless': ['almost odorless', 'odorless'],\n",
       " 'onion': ['onion', 'onion cooked onion', 'onion green onion'],\n",
       " 'orange': ['orange',\n",
       "  'orange bitter orange',\n",
       "  'orange peel',\n",
       "  'orange rind',\n",
       "  'orangeflower'],\n",
       " 'pear': ['pear', 'pear skin'],\n",
       " 'pine': ['pine', 'pineapple'],\n",
       " 'plum': ['plum', 'plum skin'],\n",
       " 'potato': ['potato',\n",
       "  'potato baked potato',\n",
       "  'potato chip',\n",
       "  'potato raw potato'],\n",
       " 'ripe': ['banana ripe banana',\n",
       "  'banana unripe banana',\n",
       "  'berry ripe berry',\n",
       "  'fruit overripe fruit',\n",
       "  'fruit ripe fruit',\n",
       "  'melon unripe melon',\n",
       "  'ripe'],\n",
       " 'roasted': ['almond roasted almond',\n",
       "  'barley roasted barley',\n",
       "  'beefy roasted beefy',\n",
       "  'chicken roasted chicken',\n",
       "  'coffee roasted coffee',\n",
       "  'hazelnut roasted hazelnut',\n",
       "  'meaty roasted meaty',\n",
       "  'peanut roasted peanut',\n",
       "  'roasted'],\n",
       " 'rose': ['bois de rose',\n",
       "  'rose',\n",
       "  'rose dried rose',\n",
       "  'rose red rose',\n",
       "  'rose tea rose',\n",
       "  'rosemary',\n",
       "  'rosey'],\n",
       " 'sweet': ['sweet', 'sweet pea'],\n",
       " 'tea': ['rose tea rose', 'tea', 'tea black tea', 'tea green tea'],\n",
       " 'tomato': ['tomato', 'tomato leaf'],\n",
       " 'tropical': ['fruit tropical fruit', 'tropical'],\n",
       " 'vegetable': ['vegetable', 'vegetables'],\n",
       " 'violet': ['violet', 'violet leaf'],\n",
       " 'woody': ['woody', 'woody burnt wood', 'woody old wood', 'woody-lactone']}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merger_dict = {}\n",
    "for out in desc_analyser():\n",
    "    merger_dict[out[0]] = list(out[1]['desc'])\n",
    "merger_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# manual correction\n",
    "merger_dict['orange'] = ['orange', 'orange bitter orange', 'orange peel', 'orange rind']\n",
    "merger_dict['grape'] = ['concord grape', 'grape', 'grape skin']\n",
    "merger_dict['grassy'] = ['lemongrass']\n",
    "merger_dict['lactonic'] = ['woody-lactone']\n",
    "merger_dict['leafy'] = ['tomato leaf', 'violet leaf']\n",
    "merger_dict['fruit skin'] = ['apple skin', 'grape skin', 'pear skin', 'plum skin', 'orange peel', 'banana peel', 'citrus peel', 'grapefruit peel', 'lemon peel']\n",
    "merger_dict['fruity'] = ['fruit tropical fruit', 'juicy fruit', 'fruit overripe fruit', 'fruit ripe fruit']\n",
    "merger_dict['dry'] = ['apple dried apple', 'rose dried rose']\n",
    "\n",
    "\n",
    "del merger_dict['pine']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "reverse_merger_dict = defaultdict(list)\n",
    "for key, values in merger_dict.items():\n",
    "    for value in values:\n",
    "        reverse_merger_dict[value].append(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'apple skin' in reverse_merger_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_desc(desc_string):\n",
    "    desc_list = desc_string.split(';')\n",
    "    new_odors = set()\n",
    "    flag = 0\n",
    "    for desc in desc_list:\n",
    "        flag = 0\n",
    "        for old_desc, new_desc in reverse_merger_dict.items():\n",
    "            if desc == old_desc:\n",
    "                new_odors.update(new_desc)\n",
    "                flag = 1\n",
    "                break\n",
    "        if flag==0:\n",
    "            new_odors.update([desc])\n",
    "    return ';'.join(new_odors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'phenolic;animal;fecal;medicinal'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "update_desc(\"phenolic;animal;fecal;medicinal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nutty;cherry;sweet'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "update_desc(\"sweet;cherry;cherry maraschino cherry;nutty\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Descriptors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100-06-1</th>\n",
       "      <td>sweet;vanilla;cherry maraschino cherry;powdery...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-09-4</th>\n",
       "      <td>phenolic;animal;fecal;medicinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-42-5</th>\n",
       "      <td>sweet;plastic;floral;balsamic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-51-6</th>\n",
       "      <td>sweet;floral;rose;fruity;phenolic;balsamic;che...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-52-7</th>\n",
       "      <td>sweet;cherry;cherry maraschino cherry;nutty;fr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Descriptors\n",
       "Stimulus                                                   \n",
       "100-06-1  sweet;vanilla;cherry maraschino cherry;powdery...\n",
       "100-09-4                    phenolic;animal;fecal;medicinal\n",
       "100-42-5                      sweet;plastic;floral;balsamic\n",
       "100-51-6  sweet;floral;rose;fruity;phenolic;balsamic;che...\n",
       "100-52-7  sweet;cherry;cherry maraschino cherry;nutty;fr..."
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior_clean.head(\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_67313/483659658.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  behavior_clean['Updated_Desc'] = behavior_clean['Descriptors'].apply(lambda x: update_desc(x))\n"
     ]
    }
   ],
   "source": [
    "behavior_clean['Updated_Desc'] = behavior_clean['Descriptors'].apply(lambda x: update_desc(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Descriptors</th>\n",
       "      <th>Updated_Desc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100-06-1</th>\n",
       "      <td>sweet;vanilla;cherry maraschino cherry;powdery...</td>\n",
       "      <td>hawthorn;balsamic;cherry;anisic;powdery;vanill...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-09-4</th>\n",
       "      <td>phenolic;animal;fecal;medicinal</td>\n",
       "      <td>phenolic;animal;fecal;medicinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-42-5</th>\n",
       "      <td>sweet;plastic;floral;balsamic</td>\n",
       "      <td>balsamic;plastic;sweet;floral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-51-6</th>\n",
       "      <td>sweet;floral;rose;fruity;phenolic;balsamic;che...</td>\n",
       "      <td>balsamic;fruity;floral;chemical;rose;phenolic;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-52-7</th>\n",
       "      <td>sweet;cherry;cherry maraschino cherry;nutty;fr...</td>\n",
       "      <td>nutty;cherry;sharp;fruity;powdery;almond;bitte...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Descriptors   \n",
       "Stimulus                                                      \n",
       "100-06-1  sweet;vanilla;cherry maraschino cherry;powdery...  \\\n",
       "100-09-4                    phenolic;animal;fecal;medicinal   \n",
       "100-42-5                      sweet;plastic;floral;balsamic   \n",
       "100-51-6  sweet;floral;rose;fruity;phenolic;balsamic;che...   \n",
       "100-52-7  sweet;cherry;cherry maraschino cherry;nutty;fr...   \n",
       "\n",
       "                                               Updated_Desc  \n",
       "Stimulus                                                     \n",
       "100-06-1  hawthorn;balsamic;cherry;anisic;powdery;vanill...  \n",
       "100-09-4                    phenolic;animal;fecal;medicinal  \n",
       "100-42-5                      balsamic;plastic;sweet;floral  \n",
       "100-51-6  balsamic;fruity;floral;chemical;rose;phenolic;...  \n",
       "100-52-7  nutty;cherry;sharp;fruity;powdery;almond;bitte...  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4622 entries, 100-06-1 to NF0825\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Descriptors   4622 non-null   object\n",
      " 1   Updated_Desc  4622 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 237.4+ KB\n"
     ]
    }
   ],
   "source": [
    "behavior_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['absinthe',\n",
       " 'absolute',\n",
       " 'acacia',\n",
       " 'acetaldehyde',\n",
       " 'acetic',\n",
       " 'acetone',\n",
       " 'acidic',\n",
       " 'acorn',\n",
       " 'acrylate',\n",
       " 'agarwood',\n",
       " 'alcoholic',\n",
       " 'aldehydic',\n",
       " 'algae',\n",
       " 'alkane',\n",
       " 'alliaceous',\n",
       " 'allspice',\n",
       " 'almond',\n",
       " 'amber',\n",
       " 'ambrette',\n",
       " 'amine',\n",
       " 'ammonia',\n",
       " 'ammoniacal',\n",
       " 'angelica',\n",
       " 'animal',\n",
       " 'anise',\n",
       " 'anisic',\n",
       " 'apple',\n",
       " 'apricot',\n",
       " 'armoise',\n",
       " 'aromatic',\n",
       " 'arrack',\n",
       " 'artichoke',\n",
       " 'asparagus',\n",
       " 'astringent',\n",
       " 'autumn',\n",
       " 'bacon',\n",
       " 'baked',\n",
       " 'balsam',\n",
       " 'balsamic',\n",
       " 'banana',\n",
       " 'basil',\n",
       " 'bay',\n",
       " 'beany',\n",
       " 'beef',\n",
       " 'beef juice',\n",
       " 'beefy',\n",
       " 'beeswax',\n",
       " 'benzaldehyde',\n",
       " 'benzoate',\n",
       " 'benzoin',\n",
       " 'bergamot',\n",
       " 'berry',\n",
       " 'bitter',\n",
       " 'black currant',\n",
       " 'blackberry',\n",
       " 'bland',\n",
       " 'bloody',\n",
       " 'blossom',\n",
       " 'blueberry',\n",
       " 'boiled',\n",
       " 'bois',\n",
       " 'bonbon',\n",
       " 'borneol',\n",
       " 'boronia',\n",
       " 'bouillon',\n",
       " 'brandy',\n",
       " 'bread',\n",
       " 'bread baked',\n",
       " 'bread crust',\n",
       " 'bread rye bread',\n",
       " 'bready',\n",
       " 'broccoli',\n",
       " 'brothy',\n",
       " 'brown',\n",
       " 'bubble gum',\n",
       " 'buchu',\n",
       " 'bud',\n",
       " 'burnt',\n",
       " 'butter',\n",
       " 'butter rancid',\n",
       " 'buttermilk',\n",
       " 'butterscotch',\n",
       " 'buttery',\n",
       " 'cabbage',\n",
       " 'cacao',\n",
       " 'camphor',\n",
       " 'camphoreous',\n",
       " 'cananga',\n",
       " 'candy',\n",
       " 'cantaloupe',\n",
       " 'capers',\n",
       " 'caprylic',\n",
       " 'caramel',\n",
       " 'caramellic',\n",
       " 'caraway',\n",
       " 'cardamom',\n",
       " 'cardboard',\n",
       " 'carnation',\n",
       " 'carrot',\n",
       " 'carrot seed',\n",
       " 'carvone',\n",
       " 'cashew',\n",
       " 'cassia',\n",
       " 'cassie',\n",
       " 'castoreum',\n",
       " 'catechu',\n",
       " 'catty',\n",
       " 'cauliflower',\n",
       " 'cedar',\n",
       " 'celery',\n",
       " 'cellar',\n",
       " 'cereal',\n",
       " 'chamomile',\n",
       " 'characteristic',\n",
       " 'charred',\n",
       " 'chavicol',\n",
       " 'cheese',\n",
       " 'cheesy',\n",
       " 'chemical',\n",
       " 'cherry',\n",
       " 'chervil',\n",
       " 'chicken',\n",
       " 'chicken coup',\n",
       " 'chicken fat',\n",
       " 'chicory',\n",
       " 'chlorine',\n",
       " 'chloroform',\n",
       " 'chocolate',\n",
       " 'chrysanthemum',\n",
       " 'cider',\n",
       " 'cilantro',\n",
       " 'cinnamon',\n",
       " 'cinnamyl',\n",
       " 'cistus',\n",
       " 'citral',\n",
       " 'citralva',\n",
       " 'citric',\n",
       " 'citronella',\n",
       " 'citronellal',\n",
       " 'citrus',\n",
       " 'civet',\n",
       " 'clam',\n",
       " 'clean',\n",
       " 'cloth laundered cloth',\n",
       " 'clove',\n",
       " 'coal',\n",
       " 'cocoa',\n",
       " 'coconut',\n",
       " 'coffee',\n",
       " 'cognac',\n",
       " 'cologne',\n",
       " 'cooked',\n",
       " 'cookie',\n",
       " 'cool',\n",
       " 'cooling',\n",
       " 'coriander',\n",
       " 'corn',\n",
       " 'corn chip',\n",
       " 'cornmint',\n",
       " 'cortex',\n",
       " 'costus',\n",
       " 'cotton',\n",
       " 'cotton candy',\n",
       " 'coumarin',\n",
       " 'coumarinic',\n",
       " 'cranberry',\n",
       " 'creamy',\n",
       " 'creosote',\n",
       " 'cresol',\n",
       " 'cucumber',\n",
       " 'cumin',\n",
       " 'custard',\n",
       " 'cyclamen',\n",
       " 'cypress',\n",
       " 'dairy',\n",
       " 'damascone',\n",
       " 'dank',\n",
       " 'date',\n",
       " 'davana',\n",
       " 'de',\n",
       " 'dead',\n",
       " 'deep',\n",
       " 'dewy',\n",
       " 'diffuse',\n",
       " 'dihydrojasmonate',\n",
       " 'dill',\n",
       " 'dirty',\n",
       " 'dried',\n",
       " 'dry',\n",
       " 'dull',\n",
       " 'durian',\n",
       " 'dusty',\n",
       " 'earthy',\n",
       " 'egg',\n",
       " 'egg nog',\n",
       " 'egg yolk',\n",
       " 'eggy',\n",
       " 'elderberry',\n",
       " 'elderflower',\n",
       " 'elemi',\n",
       " 'estery',\n",
       " 'ether',\n",
       " 'ethereal',\n",
       " 'ethyl',\n",
       " 'eucalyptus',\n",
       " 'evil',\n",
       " 'faint',\n",
       " 'fatty',\n",
       " 'fecal',\n",
       " 'fennel',\n",
       " 'fenugreek',\n",
       " 'fermented',\n",
       " 'fig',\n",
       " 'filbert',\n",
       " 'fir needle',\n",
       " 'fish',\n",
       " 'fishy',\n",
       " 'flat',\n",
       " 'fleshy',\n",
       " 'floral',\n",
       " 'flower',\n",
       " 'flowers',\n",
       " 'flowery',\n",
       " 'foliage',\n",
       " 'forest',\n",
       " 'fragrant',\n",
       " 'frankincense',\n",
       " 'freesia',\n",
       " 'fresh',\n",
       " 'fried',\n",
       " 'fruit',\n",
       " 'fruit dried fruit',\n",
       " 'fruit skin',\n",
       " 'fruity',\n",
       " 'fungal',\n",
       " 'fusel',\n",
       " 'galbanum',\n",
       " 'gardenia',\n",
       " 'garlic',\n",
       " 'gasoline',\n",
       " 'gassy',\n",
       " 'genet',\n",
       " 'geranium',\n",
       " 'ginger',\n",
       " 'goats',\n",
       " 'goaty',\n",
       " 'gooseberry',\n",
       " 'gourmand',\n",
       " 'graham cracker',\n",
       " 'grain',\n",
       " 'grain toasted grain',\n",
       " 'grape',\n",
       " 'grapefruit',\n",
       " 'grapfruit',\n",
       " 'grass',\n",
       " 'grassy',\n",
       " 'gravy',\n",
       " 'greasy',\n",
       " 'green',\n",
       " 'grilled',\n",
       " 'guaiacol',\n",
       " 'guaiacwood',\n",
       " 'guava',\n",
       " 'hairy',\n",
       " 'ham',\n",
       " 'harsh',\n",
       " 'hawthorn',\n",
       " 'hay',\n",
       " 'hazelnut',\n",
       " 'heavy',\n",
       " 'heliotrope',\n",
       " 'herb',\n",
       " 'herbaceous',\n",
       " 'herbal',\n",
       " 'hesperidic',\n",
       " 'honey',\n",
       " 'horseradish',\n",
       " 'humus',\n",
       " 'hyacinth',\n",
       " 'hydrocarbon',\n",
       " 'immortelle',\n",
       " 'impacting',\n",
       " 'incense',\n",
       " 'indole',\n",
       " 'iodine',\n",
       " 'jackfruit',\n",
       " 'jam',\n",
       " 'jammy',\n",
       " 'jasmin',\n",
       " 'juicy',\n",
       " 'juniper',\n",
       " 'kernel',\n",
       " 'ketonic',\n",
       " 'kimchi',\n",
       " 'kiwi',\n",
       " 'labdanum',\n",
       " 'lactone',\n",
       " 'lactonic',\n",
       " 'lamb',\n",
       " 'lard',\n",
       " 'lavender',\n",
       " 'leaf',\n",
       " 'leafy',\n",
       " 'leather',\n",
       " 'leathery',\n",
       " 'leek',\n",
       " 'lemon',\n",
       " 'lettuce',\n",
       " 'licorice',\n",
       " 'licorice black licorice',\n",
       " 'light',\n",
       " 'lilac',\n",
       " 'lilial',\n",
       " 'lily',\n",
       " 'lime',\n",
       " 'linalool',\n",
       " 'linden',\n",
       " 'linden flower',\n",
       " 'liquid',\n",
       " 'liver',\n",
       " 'loganberry',\n",
       " 'lovage',\n",
       " 'lychee',\n",
       " 'mace',\n",
       " 'magnolia',\n",
       " 'mahogany',\n",
       " 'malty',\n",
       " 'mandarin',\n",
       " 'mango',\n",
       " 'maple',\n",
       " 'marine',\n",
       " 'marjoram',\n",
       " 'marzipan',\n",
       " 'may',\n",
       " 'meat',\n",
       " 'meaty',\n",
       " 'medical',\n",
       " 'medicinal',\n",
       " 'melon',\n",
       " 'menthol',\n",
       " 'mentholic',\n",
       " 'mercaptan',\n",
       " 'metal',\n",
       " 'metallic',\n",
       " 'methyl',\n",
       " 'methyl heptine carbonate',\n",
       " 'mignonette',\n",
       " 'mild',\n",
       " 'milk',\n",
       " 'milky',\n",
       " 'mimosa',\n",
       " 'mint',\n",
       " 'molasses',\n",
       " 'moldy',\n",
       " 'moss',\n",
       " 'mossy',\n",
       " 'mouldy',\n",
       " 'muguet',\n",
       " 'mushroom',\n",
       " 'musk',\n",
       " 'mustard',\n",
       " 'musty',\n",
       " 'mutton',\n",
       " 'muttony',\n",
       " 'myrrh',\n",
       " 'naphtha',\n",
       " 'naphthyl',\n",
       " 'narcissus',\n",
       " 'nasturtium',\n",
       " 'natural',\n",
       " 'needle',\n",
       " 'needles',\n",
       " 'nerol',\n",
       " 'neroli',\n",
       " 'nice',\n",
       " 'nitrile',\n",
       " 'nitromusk',\n",
       " 'nut',\n",
       " 'nut flesh',\n",
       " 'nut skin',\n",
       " 'nutmeg',\n",
       " 'nutty',\n",
       " 'oakmoss',\n",
       " 'ocean',\n",
       " 'odorless',\n",
       " 'oil',\n",
       " 'oily',\n",
       " 'old',\n",
       " 'onion',\n",
       " 'opoponax',\n",
       " 'orange',\n",
       " 'orangeflower',\n",
       " 'orchid',\n",
       " 'oriental',\n",
       " 'orris',\n",
       " 'osmanthus',\n",
       " 'oxide',\n",
       " 'ozone',\n",
       " 'painty',\n",
       " 'palmarosa',\n",
       " 'papaya',\n",
       " 'paper',\n",
       " 'parsley',\n",
       " 'passion fruit',\n",
       " 'pastry',\n",
       " 'patchouli',\n",
       " 'peach',\n",
       " 'peanut',\n",
       " 'peanut butter',\n",
       " 'pear',\n",
       " 'peel',\n",
       " 'peely',\n",
       " 'peony',\n",
       " 'pepper bell pepper',\n",
       " 'pepper black pepper',\n",
       " 'peppermint',\n",
       " 'peppery',\n",
       " 'petal',\n",
       " 'petitgrain',\n",
       " 'petroleum',\n",
       " 'phenolic',\n",
       " 'pine',\n",
       " 'pineapple',\n",
       " 'pistachio',\n",
       " 'plastic',\n",
       " 'pleasant',\n",
       " 'plowed',\n",
       " 'plum',\n",
       " 'pomegranate',\n",
       " 'popcorn',\n",
       " 'pork',\n",
       " 'potato',\n",
       " 'powdery',\n",
       " 'powerful',\n",
       " 'praline',\n",
       " 'privet',\n",
       " 'privetblossom',\n",
       " 'prune',\n",
       " 'pulpy',\n",
       " 'pumpkin',\n",
       " 'pungent',\n",
       " 'pyrazine',\n",
       " 'pyridine',\n",
       " 'quince',\n",
       " 'radish',\n",
       " 'raisin',\n",
       " 'rancid',\n",
       " 'raspberry',\n",
       " 'raw',\n",
       " 'red',\n",
       " 'reseda',\n",
       " 'resin',\n",
       " 'resinous',\n",
       " 'rhubarb',\n",
       " 'rich',\n",
       " 'rindy',\n",
       " 'ripe',\n",
       " 'roasted',\n",
       " 'root beer',\n",
       " 'rooty',\n",
       " 'rose',\n",
       " 'rosy',\n",
       " 'rotten',\n",
       " 'rotting',\n",
       " 'rubber',\n",
       " 'rubbery',\n",
       " 'rue',\n",
       " 'rum',\n",
       " 'rummy',\n",
       " 'saffron',\n",
       " 'sage',\n",
       " 'sage clary sage',\n",
       " 'salmon',\n",
       " 'salty',\n",
       " 'sandalwood',\n",
       " 'sandy',\n",
       " 'sappy',\n",
       " 'sarsaparilla',\n",
       " 'sassafras',\n",
       " 'sassafrass',\n",
       " 'sauerkraut',\n",
       " 'sausage smoked sausage',\n",
       " 'savory',\n",
       " 'sawdust',\n",
       " 'scallion',\n",
       " 'seafood',\n",
       " 'seashore',\n",
       " 'seaweed',\n",
       " 'seedy',\n",
       " 'sharp',\n",
       " 'shell',\n",
       " 'shellfish',\n",
       " 'shrimp',\n",
       " 'skin',\n",
       " 'skunk',\n",
       " 'slight',\n",
       " 'smelling',\n",
       " 'smoky',\n",
       " 'soapy',\n",
       " 'soft',\n",
       " 'soil',\n",
       " 'solvent',\n",
       " 'soup',\n",
       " 'sour',\n",
       " 'spearmint',\n",
       " 'spice',\n",
       " 'spicy',\n",
       " 'spinach',\n",
       " 'spruce',\n",
       " 'stalk',\n",
       " 'starfruit',\n",
       " 'storax',\n",
       " 'straw',\n",
       " 'strawberry',\n",
       " 'strong',\n",
       " 'styrax',\n",
       " 'styrene',\n",
       " 'sugar',\n",
       " 'sugar brown sugar',\n",
       " 'sulfurous',\n",
       " 'sulfury',\n",
       " 'sweat',\n",
       " 'sweaty',\n",
       " 'sweet',\n",
       " 'taco',\n",
       " 'tagette',\n",
       " 'tallow',\n",
       " 'tangerine',\n",
       " 'tar',\n",
       " 'tarragon',\n",
       " 'tart',\n",
       " 'tea',\n",
       " 'tequila',\n",
       " 'terpene',\n",
       " 'terpenic',\n",
       " 'terpentine',\n",
       " 'thiamine',\n",
       " 'thujone',\n",
       " 'thujonic',\n",
       " 'thyme',\n",
       " 'toasted',\n",
       " 'tobacco',\n",
       " 'toffee',\n",
       " 'tolu balsam',\n",
       " 'tomato',\n",
       " 'tonka',\n",
       " 'tropical',\n",
       " 'truffle',\n",
       " 'tuberose',\n",
       " 'turkey',\n",
       " 'turnup',\n",
       " 'tutti frutti',\n",
       " 'umami',\n",
       " 'urine',\n",
       " 'valerian root',\n",
       " 'vanilla',\n",
       " 'vegetable',\n",
       " 'verbena',\n",
       " 'verdox',\n",
       " 'vetiver',\n",
       " 'vinegar',\n",
       " 'vinyl',\n",
       " 'violet',\n",
       " 'walnut',\n",
       " 'warm',\n",
       " 'wasabi',\n",
       " 'watercress',\n",
       " 'watermelon',\n",
       " 'watery',\n",
       " 'waxy',\n",
       " 'weedy',\n",
       " 'wet',\n",
       " 'whiskey',\n",
       " 'wild',\n",
       " 'wine',\n",
       " 'winey',\n",
       " 'wintergreen',\n",
       " 'wood',\n",
       " 'woody',\n",
       " 'wormwood',\n",
       " 'worty',\n",
       " 'yeasty',\n",
       " 'ylang',\n",
       " 'yogurt',\n",
       " 'zesty']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odor_list = []\n",
    "behavior_clean['Updated_Desc'].apply(lambda x: get_odors(x, odor_list))\n",
    "odor_list.sort()\n",
    "odor_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "585"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(odor_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>TGSC ID</th>\n",
       "      <th>CID</th>\n",
       "      <th>Concentration %</th>\n",
       "      <th>Solvent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100-06-1</th>\n",
       "      <td>1000111</td>\n",
       "      <td>7476</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-09-4</th>\n",
       "      <td>1031871</td>\n",
       "      <td>7478</td>\n",
       "      <td>10.0</td>\n",
       "      <td>dipropylene glycol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-42-5</th>\n",
       "      <td>1009281</td>\n",
       "      <td>7501</td>\n",
       "      <td>0.1</td>\n",
       "      <td>triacetin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-51-6</th>\n",
       "      <td>1001651</td>\n",
       "      <td>244</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-52-7</th>\n",
       "      <td>1001491</td>\n",
       "      <td>240</td>\n",
       "      <td>10.0</td>\n",
       "      <td>dipropylene glycol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          TGSC ID   CID  Concentration %             Solvent\n",
       "Stimulus                                                    \n",
       "100-06-1  1000111  7476            100.0                 NaN\n",
       "100-09-4  1031871  7478             10.0  dipropylene glycol\n",
       "100-42-5  1009281  7501              0.1           triacetin\n",
       "100-51-6  1001651   244            100.0                 NaN\n",
       "100-52-7  1001491   240             10.0  dipropylene glycol"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stimuli.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           TGSC ID     CID  Concentration % Solvent\n",
      "Stimulus                                           \n",
      "2623-23-6  1002031  220674            100.0     NaN\n",
      "89-48-5    1046271  220674            100.0     NaN\n",
      "\n",
      "            TGSC ID       CID  Concentration %             Solvent\n",
      "Stimulus                                                          \n",
      "91069-39-5  1537621  18598146              1.0  dipropylene glycol\n",
      "91069-40-8  1537631  18598146              1.0  dipropylene glycol\n",
      "\n",
      "             TGSC ID     CID  Concentration % Solvent\n",
      "Stimulus                                             \n",
      "137886-38-5  1587131  527108            100.0     NaN\n",
      "22451-50-9   1593881  527108            100.0     NaN\n",
      "\n",
      "            TGSC ID    CID  Concentration %             Solvent\n",
      "Stimulus                                                       \n",
      "29759-11-3  1412121  62341              1.0  dipropylene glycol\n",
      "93-92-5     1011091  62341             10.0  dipropylene glycol\n",
      "\n",
      "          TGSC ID   CID  Concentration %             Solvent\n",
      "Stimulus                                                    \n",
      "110-93-0  1008311  9862             10.0  dipropylene glycol\n",
      "409-02-9  1337381  9862              1.0    propylene glycol\n",
      "\n",
      "            TGSC ID   CID  Concentration % Solvent\n",
      "Stimulus                                          \n",
      "141-25-3    1010451  8842              1.0     NaN\n",
      "26489-01-0  1007031  8842            100.0     NaN\n",
      "\n",
      "           TGSC ID    CID  Concentration %             Solvent\n",
      "Stimulus                                                      \n",
      "1321-89-7  1016951  14862              1.0  dipropylene glycol\n",
      "3085-26-5  1094441  14862              1.0  dipropylene glycol\n",
      "\n",
      "            TGSC ID    CID  Concentration %             Solvent\n",
      "Stimulus                                                       \n",
      "25773-40-4  1107761  33166              0.1  dipropylene glycol\n",
      "93905-03-4  1008661  33166              0.1  dipropylene glycol\n",
      "\n",
      "            TGSC ID      CID  Concentration %             Solvent\n",
      "Stimulus                                                         \n",
      "6728-26-3   1013831  5281168              1.0  dipropylene glycol\n",
      "85761-70-2  1552391  5281168              0.1  dipropylene glycol\n",
      "\n",
      "           TGSC ID  CID  Concentration % Solvent\n",
      "Stimulus                                        \n",
      "7704-34-9  1285181  402            100.0     NaN\n",
      "7783-06-4  1038291  402              NaN     NaN\n",
      "\n",
      "            TGSC ID     CID  Concentration %             Solvent\n",
      "Stimulus                                                        \n",
      "41199-19-3  1039181  162452             10.0  dipropylene glycol\n",
      "71832-76-3  1024191  162452             10.0  dipropylene glycol\n",
      "\n",
      "           TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                          \n",
      "1334-82-3  1003551  74008            100.0     NaN\n",
      "4996-48-9  1035831  74008            100.0     NaN\n",
      "\n",
      "            TGSC ID    CID  Concentration %             Solvent\n",
      "Stimulus                                                       \n",
      "55764-31-3  1475701  62106              0.1    propylene glycol\n",
      "65505-16-0  1036441  62106              0.1  dipropylene glycol\n",
      "\n",
      "            TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                           \n",
      "1118-39-4   1004301  14235            100.0     NaN\n",
      "69103-01-1  1017191  14235            100.0     NaN\n",
      "\n",
      "            TGSC ID    CID  Concentration %             Solvent\n",
      "Stimulus                                                       \n",
      "15537-55-0  1532211  62367            100.0                 NaN\n",
      "546-79-2    1035671  62367              1.0  dipropylene glycol\n",
      "\n",
      "           TGSC ID   CID  Concentration %             Solvent\n",
      "Stimulus                                                     \n",
      "1329-99-3  1399431  7460              NaN                 NaN\n",
      "99-83-2    1009891  7460             10.0  dipropylene glycol\n",
      "\n",
      "            TGSC ID      CID  Concentration %             Solvent\n",
      "Stimulus                                                         \n",
      "334-49-6    1042171  5282724            100.0                 NaN\n",
      "72881-27-7  1037801  5282724             10.0  dipropylene glycol\n",
      "\n",
      "            TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                           \n",
      "1365-19-1   1014331  22310            100.0     NaN\n",
      "60047-17-8  1030501  22310            100.0     NaN\n",
      "\n",
      "           TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                          \n",
      "138-86-3   1240221  22311            100.0     NaN\n",
      "9003-73-0  1049481  22311            100.0     NaN\n",
      "\n",
      "           TGSC ID    CID  Concentration %             Solvent\n",
      "Stimulus                                                      \n",
      "1337-83-3  1016731  61097              1.0  dipropylene glycol\n",
      "143-14-6   1035131  61097              1.0  dipropylene glycol\n",
      "\n",
      "            TGSC ID    CID  Concentration %           Solvent\n",
      "Stimulus                                                     \n",
      "13952-84-6  1056631  24874              0.1  propylene glycol\n",
      "513-49-5    1056641  24874              NaN               NaN\n",
      "\n",
      "            TGSC ID      CID  Concentration % Solvent\n",
      "Stimulus                                             \n",
      "67859-99-8  1384091  6437163              NaN     NaN\n",
      "67874-69-5  1024861  6437163            100.0     NaN\n",
      "\n",
      "            TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                           \n",
      "13828-37-0  1596941  83763            100.0     NaN\n",
      "5502-75-0   1015951  83763            100.0     NaN\n",
      "\n",
      "            TGSC ID      CID  Concentration %             Solvent\n",
      "Stimulus                                                         \n",
      "39872-57-6  1023021  6438067             10.0  dipropylene glycol\n",
      "70266-48-7  1596181  6438067             10.0  dipropylene glycol\n",
      "\n",
      "            TGSC ID     CID  Concentration %             Solvent\n",
      "Stimulus                                                        \n",
      "63450-30-6  1376761  113204              1.0  dipropylene glycol\n",
      "68378-13-2  1008701  113204              0.1    propylene glycol\n",
      "\n",
      "           TGSC ID    CID  Concentration %             Solvent\n",
      "Stimulus                                                      \n",
      "1335-86-0  1506631  11574             10.0  dipropylene glycol\n",
      "591-49-1   1046721  11574              1.0  dipropylene glycol\n",
      "\n",
      "           TGSC ID   CID  Concentration % Solvent\n",
      "Stimulus                                         \n",
      "139-33-3   1131691  8759            100.0     NaN\n",
      "6381-92-6  1246321  8759              NaN     NaN\n",
      "\n",
      "            TGSC ID      CID  Concentration %             Solvent\n",
      "Stimulus                                                         \n",
      "25524-95-2  1038021  6438070              5.0  dipropylene glycol\n",
      "34686-71-0  1023991  6438070            100.0                 NaN\n",
      "\n",
      "            TGSC ID      CID  Concentration % Solvent\n",
      "Stimulus                                             \n",
      "75640-26-5  1428841  3018936            100.0     NaN\n",
      "80417-97-6  1038101  3018936            100.0     NaN\n",
      "\n",
      "           TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                          \n",
      "111-60-4   1299941  24762            100.0     NaN\n",
      "9004-99-3  1097891  24762            100.0     NaN\n",
      "\n",
      "            TGSC ID      CID  Concentration %             Solvent\n",
      "Stimulus                                                         \n",
      "23696-85-7  1021911  5366074             10.0  dipropylene glycol\n",
      "23726-93-4  1441071  5366074              1.0  dipropylene glycol\n",
      "\n",
      "            TGSC ID      CID  Concentration % Solvent\n",
      "Stimulus                                             \n",
      "80722-28-7  1065761  6428985              NaN     NaN\n",
      "80957-74-0  1600471  6428985              NaN     NaN\n",
      "\n",
      "            TGSC ID       CID  Concentration %             Solvent\n",
      "Stimulus                                                          \n",
      "16423-19-1  1462671  15559490             0.01  dipropylene glycol\n",
      "5173-69-3   1557971  15559490             0.01  dipropylene glycol\n",
      "\n",
      "           TGSC ID    CID  Concentration %             Solvent\n",
      "Stimulus                                                      \n",
      "1334-78-7  1004561  12105              1.0  dipropylene glycol\n",
      "620-23-5   1051061  12105            100.0                 NaN\n",
      "\n",
      "            TGSC ID   CID  Concentration %             Solvent\n",
      "Stimulus                                                      \n",
      "10458-14-7  1399161  6986             10.0  dipropylene glycol\n",
      "36977-92-1  1003111  6986             10.0  dipropylene glycol\n",
      "89-80-5     1398981  6986             10.0  dipropylene glycol\n",
      "\n",
      "           TGSC ID      CID  Concentration %             Solvent\n",
      "Stimulus                                                        \n",
      "127-42-4   1011971  5371084             10.0  dipropylene glycol\n",
      "1335-46-2  1006171  5371084             10.0  dipropylene glycol\n",
      "\n",
      "           TGSC ID     CID  Concentration % Solvent\n",
      "Stimulus                                           \n",
      "1197-06-4  1551481  330573            100.0     NaN\n",
      "2102-59-2  1053801  330573            100.0     NaN\n",
      "\n",
      "            TGSC ID       CID  Concentration %           Solvent\n",
      "Stimulus                                                        \n",
      "67883-79-8  1002611  16220109              1.0  propylene glycol\n",
      "84060-80-0  1498571  16220109            100.0               NaN\n",
      "\n",
      "            TGSC ID     CID  Concentration %             Solvent\n",
      "Stimulus                                                        \n",
      "68259-31-4  1548051  109261            100.0                 NaN\n",
      "93904-56-4  1039651  109261             10.0  dipropylene glycol\n",
      "\n",
      "           TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                          \n",
      "8000-41-7  1311481  17100            100.0     NaN\n",
      "98-55-5    1011251  17100            100.0     NaN\n",
      "\n",
      "           TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                          \n",
      "5524-05-0  1106571  22227            100.0     NaN\n",
      "5948-04-9  1511171  22227            100.0     NaN\n",
      "\n",
      "            TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                           \n",
      "1653-30-1   1035681  15448            100.0     NaN\n",
      "30207-98-8  1022111  15448            100.0     NaN\n",
      "\n",
      "            TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                           \n",
      "1866-31-5N  1002311  15834            100.0     NaN\n",
      "1866-31-5Z  1117241  15834            100.0     NaN\n",
      "\n",
      "            TGSC ID       CID  Concentration % Solvent\n",
      "Stimulus                                              \n",
      "39638-67-0  1552111  11084123              1.0     NaN\n",
      "80041-01-6  1552351  11084123              NaN     NaN\n",
      "\n",
      "            TGSC ID   CID  Concentration %             Solvent\n",
      "Stimulus                                                      \n",
      "21862-63-5  1053371  7391             10.0  dipropylene glycol\n",
      "98-52-2     1013261  7391            100.0                 NaN\n",
      "\n",
      "            TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                           \n",
      "23787-90-8  1007011  90978            100.0     NaN\n",
      "29461-13-0  1442141  90978            100.0     NaN\n",
      "\n",
      "           TGSC ID   CID  Concentration %             Solvent\n",
      "Stimulus                                                     \n",
      "1490-04-6  1310491  1254             10.0  dipropylene glycol\n",
      "491-02-1   1374441  1254             10.0  dipropylene glycol\n",
      "89-78-1    1029671  1254             10.0  dipropylene glycol\n",
      "\n",
      "            TGSC ID   CID  Concentration % Solvent\n",
      "Stimulus                                          \n",
      "58985-18-5  1006131  6631            100.0     NaN\n",
      "80-25-1     1399001  6631            100.0     NaN\n",
      "\n",
      "             TGSC ID      CID  Concentration %             Solvent\n",
      "Stimulus                                                          \n",
      "124071-42-7  1123891  6450538              1.0  dipropylene glycol\n",
      "134769-33-8  1030521  6450538             10.0  dipropylene glycol\n",
      "\n",
      "          TGSC ID  CID  Concentration % Solvent\n",
      "Stimulus                                       \n",
      "133-37-9  1060791  875            100.0     NaN\n",
      "526-83-0  1482521  875            100.0     NaN\n",
      "\n",
      "           TGSC ID   CID  Concentration % Solvent\n",
      "Stimulus                                         \n",
      "106-21-8   1000591  7792            100.0     NaN\n",
      "1333-49-9  1424091  7792            100.0     NaN\n",
      "\n",
      "            TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                           \n",
      "10411-92-4  1096661  36081            100.0     NaN\n",
      "1900-69-2   1489891  36081            100.0     NaN\n",
      "32210-23-4  1001371  36081            100.0     NaN\n",
      "\n",
      "            TGSC ID      CID  Concentration % Solvent\n",
      "Stimulus                                             \n",
      "58625-95-9  1037451  6366072              NaN     NaN\n",
      "85508-08-3  1446151  6366072            100.0     NaN\n",
      "\n",
      "            TGSC ID     CID  Concentration %             Solvent\n",
      "Stimulus                                                        \n",
      "18368-91-7  1024171  106997              1.0  dipropylene glycol\n",
      "67952-68-5  1130961  106997             10.0  dipropylene glycol\n",
      "\n",
      "            TGSC ID    CID  Concentration % Solvent\n",
      "Stimulus                                           \n",
      "13092-66-5  1609991  24439            100.0     NaN\n",
      "7757-87-1   1301301  24439            100.0     NaN\n",
      "\n",
      "            TGSC ID    CID  Concentration %           Solvent\n",
      "Stimulus                                                     \n",
      "2882-20-4   1046991  76152              0.1  propylene glycol\n",
      "68378-12-1  1381361  76152              NaN               NaN\n",
      "\n",
      "           TGSC ID     CID  Concentration %             Solvent\n",
      "Stimulus                                                       \n",
      "104-46-1   1375541  637563            100.0                 NaN\n",
      "4180-23-8  1001151  637563             10.0  dipropylene glycol\n",
      "\n",
      "           TGSC ID    CID  Concentration %             Solvent\n",
      "Stimulus                                                      \n",
      "2396-77-2  1032721  61310            100.0                 NaN\n",
      "2396-78-3  1035941  61310             10.0  dipropylene glycol\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dup = stimuli[stimuli['CID'].duplicated(keep=False)]\n",
    "index_set = set()\n",
    "index_set.update(dup['CID'].to_list())\n",
    "for i in index_set:\n",
    "    print(dup[dup['CID'] == i])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dup['CID'].to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4626 entries, 100-06-1 to NF0825\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   TGSC ID          4626 non-null   int64  \n",
      " 1   CID              4626 non-null   int64  \n",
      " 2   Concentration %  4137 non-null   float64\n",
      " 3   Solvent          1850 non-null   object \n",
      "dtypes: float64(1), int64(2), object(1)\n",
      "memory usage: 309.7+ KB\n"
     ]
    }
   ],
   "source": [
    "stimuli.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Updated_Desc</th>\n",
       "      <th>TGSC ID</th>\n",
       "      <th>CID</th>\n",
       "      <th>Concentration %</th>\n",
       "      <th>Solvent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100-06-1</th>\n",
       "      <td>hawthorn;balsamic;cherry;anisic;powdery;vanill...</td>\n",
       "      <td>1000111</td>\n",
       "      <td>7476</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-09-4</th>\n",
       "      <td>phenolic;animal;fecal;medicinal</td>\n",
       "      <td>1031871</td>\n",
       "      <td>7478</td>\n",
       "      <td>10.0</td>\n",
       "      <td>dipropylene glycol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-42-5</th>\n",
       "      <td>balsamic;plastic;sweet;floral</td>\n",
       "      <td>1009281</td>\n",
       "      <td>7501</td>\n",
       "      <td>0.1</td>\n",
       "      <td>triacetin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Updated_Desc  TGSC ID   CID   \n",
       "Stimulus                                                                     \n",
       "100-06-1  hawthorn;balsamic;cherry;anisic;powdery;vanill...  1000111  7476  \\\n",
       "100-09-4                    phenolic;animal;fecal;medicinal  1031871  7478   \n",
       "100-42-5                      balsamic;plastic;sweet;floral  1009281  7501   \n",
       "\n",
       "          Concentration %             Solvent  \n",
       "Stimulus                                       \n",
       "100-06-1            100.0                 NaN  \n",
       "100-09-4             10.0  dipropylene glycol  \n",
       "100-42-5              0.1           triacetin  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid_behaviors = pd.merge(behavior_clean['Updated_Desc'], stimuli, how='inner', left_index=True, right_index=True)\n",
    "cid_behaviors.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4622 entries, 100-06-1 to NF0825\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Updated_Desc     4622 non-null   object \n",
      " 1   TGSC ID          4622 non-null   int64  \n",
      " 2   CID              4622 non-null   int64  \n",
      " 3   Concentration %  4135 non-null   float64\n",
      " 4   Solvent          1850 non-null   object \n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 216.7+ KB\n"
     ]
    }
   ],
   "source": [
    "cid_behaviors.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['2623-23-6', '89-48-5'],\n",
       " ['91069-39-5', '91069-40-8'],\n",
       " ['137886-38-5', '22451-50-9'],\n",
       " ['29759-11-3', '93-92-5'],\n",
       " ['110-93-0', '409-02-9'],\n",
       " ['141-25-3', '26489-01-0'],\n",
       " ['1321-89-7', '3085-26-5'],\n",
       " ['25773-40-4', '93905-03-4'],\n",
       " ['6728-26-3', '85761-70-2'],\n",
       " ['7704-34-9', '7783-06-4'],\n",
       " ['41199-19-3', '71832-76-3'],\n",
       " ['1334-82-3', '4996-48-9'],\n",
       " ['55764-31-3', '65505-16-0'],\n",
       " ['1118-39-4', '69103-01-1'],\n",
       " ['15537-55-0', '546-79-2'],\n",
       " ['1329-99-3', '99-83-2'],\n",
       " ['334-49-6', '72881-27-7'],\n",
       " ['1365-19-1', '60047-17-8'],\n",
       " ['138-86-3', '9003-73-0'],\n",
       " ['1337-83-3', '143-14-6'],\n",
       " ['13952-84-6', '513-49-5'],\n",
       " ['67859-99-8', '67874-69-5'],\n",
       " ['13828-37-0', '5502-75-0'],\n",
       " ['39872-57-6', '70266-48-7'],\n",
       " ['63450-30-6', '68378-13-2'],\n",
       " ['1335-86-0', '591-49-1'],\n",
       " ['139-33-3', '6381-92-6'],\n",
       " ['25524-95-2', '34686-71-0'],\n",
       " ['75640-26-5', '80417-97-6'],\n",
       " ['111-60-4', '9004-99-3'],\n",
       " ['23696-85-7', '23726-93-4'],\n",
       " ['80722-28-7', '80957-74-0'],\n",
       " ['16423-19-1', '5173-69-3'],\n",
       " ['1334-78-7', '620-23-5'],\n",
       " ['10458-14-7', '36977-92-1', '89-80-5'],\n",
       " ['127-42-4', '1335-46-2'],\n",
       " ['1197-06-4', '2102-59-2'],\n",
       " ['67883-79-8', '84060-80-0'],\n",
       " ['68259-31-4', '93904-56-4'],\n",
       " ['8000-41-7', '98-55-5'],\n",
       " ['5524-05-0', '5948-04-9'],\n",
       " ['1653-30-1', '30207-98-8'],\n",
       " ['1866-31-5N', '1866-31-5Z'],\n",
       " ['39638-67-0', '80041-01-6'],\n",
       " ['21862-63-5', '98-52-2'],\n",
       " ['23787-90-8', '29461-13-0'],\n",
       " ['1490-04-6', '491-02-1', '89-78-1'],\n",
       " ['58985-18-5', '80-25-1'],\n",
       " ['124071-42-7', '134769-33-8'],\n",
       " ['133-37-9', '526-83-0'],\n",
       " ['106-21-8', '1333-49-9'],\n",
       " ['10411-92-4', '1900-69-2', '32210-23-4'],\n",
       " ['58625-95-9', '85508-08-3'],\n",
       " ['18368-91-7', '67952-68-5'],\n",
       " ['13092-66-5', '7757-87-1'],\n",
       " ['2882-20-4', '68378-12-1'],\n",
       " ['104-46-1', '4180-23-8'],\n",
       " ['2396-77-2', '2396-78-3']]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup = cid_behaviors[cid_behaviors['CID'].duplicated(keep=False)]\n",
    "index_set = set()\n",
    "index_set.update(dup['CID'].to_list())\n",
    "list_idx_of_duplicates = []\n",
    "for i in index_set:\n",
    "    # print(list(dup[dup['CID'] == i].index))\n",
    "    list_idx_of_duplicates.append(list(dup[dup['CID'] == i].index))\n",
    "list_idx_of_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Updated_Desc</th>\n",
       "      <th>TGSC ID</th>\n",
       "      <th>CID</th>\n",
       "      <th>Concentration %</th>\n",
       "      <th>Solvent</th>\n",
       "      <th>Updated_Desc_old</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100-06-1</th>\n",
       "      <td>hawthorn;balsamic;cherry;anisic;powdery;vanill...</td>\n",
       "      <td>1000111</td>\n",
       "      <td>7476</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hawthorn;balsamic;cherry;anisic;powdery;vanill...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-09-4</th>\n",
       "      <td>phenolic;animal;fecal;medicinal</td>\n",
       "      <td>1031871</td>\n",
       "      <td>7478</td>\n",
       "      <td>10.0</td>\n",
       "      <td>dipropylene glycol</td>\n",
       "      <td>phenolic;animal;fecal;medicinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-42-5</th>\n",
       "      <td>balsamic;plastic;sweet;floral</td>\n",
       "      <td>1009281</td>\n",
       "      <td>7501</td>\n",
       "      <td>0.1</td>\n",
       "      <td>triacetin</td>\n",
       "      <td>balsamic;plastic;sweet;floral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-51-6</th>\n",
       "      <td>balsamic;fruity;floral;chemical;rose;phenolic;...</td>\n",
       "      <td>1001651</td>\n",
       "      <td>244</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>balsamic;fruity;floral;chemical;rose;phenolic;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-52-7</th>\n",
       "      <td>nutty;cherry;sharp;fruity;powdery;almond;bitte...</td>\n",
       "      <td>1001491</td>\n",
       "      <td>240</td>\n",
       "      <td>10.0</td>\n",
       "      <td>dipropylene glycol</td>\n",
       "      <td>nutty;cherry;sharp;fruity;powdery;almond;bitte...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Updated_Desc  TGSC ID   CID   \n",
       "Stimulus                                                                     \n",
       "100-06-1  hawthorn;balsamic;cherry;anisic;powdery;vanill...  1000111  7476  \\\n",
       "100-09-4                    phenolic;animal;fecal;medicinal  1031871  7478   \n",
       "100-42-5                      balsamic;plastic;sweet;floral  1009281  7501   \n",
       "100-51-6  balsamic;fruity;floral;chemical;rose;phenolic;...  1001651   244   \n",
       "100-52-7  nutty;cherry;sharp;fruity;powdery;almond;bitte...  1001491   240   \n",
       "\n",
       "          Concentration %             Solvent   \n",
       "Stimulus                                        \n",
       "100-06-1            100.0                 NaN  \\\n",
       "100-09-4             10.0  dipropylene glycol   \n",
       "100-42-5              0.1           triacetin   \n",
       "100-51-6            100.0                 NaN   \n",
       "100-52-7             10.0  dipropylene glycol   \n",
       "\n",
       "                                           Updated_Desc_old  \n",
       "Stimulus                                                     \n",
       "100-06-1  hawthorn;balsamic;cherry;anisic;powdery;vanill...  \n",
       "100-09-4                    phenolic;animal;fecal;medicinal  \n",
       "100-42-5                      balsamic;plastic;sweet;floral  \n",
       "100-51-6  balsamic;fruity;floral;chemical;rose;phenolic;...  \n",
       "100-52-7  nutty;cherry;sharp;fruity;powdery;almond;bitte...  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid_behaviors['Updated_Desc_old'] = cid_behaviors.loc[:, 'Updated_Desc']\n",
    "cid_behaviors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n",
      "/tmp/ipykernel_67313/1922628141.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n"
     ]
    }
   ],
   "source": [
    "for dup_set in list_idx_of_duplicates:\n",
    "    desc_set = set()\n",
    "    desc_string = ';'.join(cid_behaviors['Updated_Desc'][dup_set].to_list())\n",
    "    for desc in desc_string.split(';'):\n",
    "        if desc == 'odorless':\n",
    "            continue\n",
    "        desc_set.update([desc])\n",
    "    if len(desc_set) == 0:\n",
    "        desc_set.update(['odorless'])\n",
    "    new_desc_string = ';'.join(desc_set)\n",
    "    cid_behaviors['Updated_Desc'][dup_set] = new_desc_string\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "cid_behaviors = cid_behaviors.drop_duplicates('CID', keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4561 entries, 100-06-1 to NF0825\n",
      "Data columns (total 6 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Updated_Desc      4561 non-null   object \n",
      " 1   TGSC ID           4561 non-null   int64  \n",
      " 2   CID               4561 non-null   int64  \n",
      " 3   Concentration %   4080 non-null   float64\n",
      " 4   Solvent           1823 non-null   object \n",
      " 5   Updated_Desc_old  4561 non-null   object \n",
      "dtypes: float64(1), int64(2), object(3)\n",
      "memory usage: 249.4+ KB\n"
     ]
    }
   ],
   "source": [
    "cid_behaviors.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup = cid_behaviors[cid_behaviors['CID'].duplicated(keep=False)]\n",
    "# index_set = set()\n",
    "# index_set.update(dup['CID'].to_list())\n",
    "# list_idx_of_duplicates = []\n",
    "# for i in index_set:\n",
    "#     print(dup[dup['CID'] == i])\n",
    "len(dup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>CID</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Updated_Desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>fishy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>CC(C)C(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
       "      <td>odorless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>CCC(=O)C(=O)O</td>\n",
       "      <td>creamy;lactonic;caramellic;brown;sweet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>CC(C)CC(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CID         IsomericSMILES                            Updated_Desc\n",
       "0    4                CC(CN)O                                   fishy\n",
       "1   49       CC(C)C(=O)C(=O)O                                  fruity\n",
       "2   51  C(CC(=O)O)C(=O)C(=O)O                                odorless\n",
       "3   58          CCC(=O)C(=O)O  creamy;lactonic;caramellic;brown;sweet\n",
       "4   70      CC(C)CC(=O)C(=O)O                                  fruity"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid_behaviors_mol = pd.merge(molecules['IsomericSMILES'], cid_behaviors[['Updated_Desc', 'CID']],how='inner', on='CID')\n",
    "cid_behaviors_mol.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4561 entries, 0 to 4560\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   CID             4561 non-null   int64 \n",
      " 1   IsomericSMILES  4561 non-null   object\n",
      " 2   Updated_Desc    4561 non-null   object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 107.0+ KB\n"
     ]
    }
   ],
   "source": [
    "cid_behaviors_mol.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>CID</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Updated_Desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [CID, IsomericSMILES, Updated_Desc]\n",
       "Index: []"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid_behaviors_mol[cid_behaviors_mol.duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cid_behaviors_mol = cid_behaviors_mol.drop_duplicates(keep='first').reset_index(drop=True)\n",
    "# cid_behaviors_mol.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>CID</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Updated_Desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>fishy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>CC(C)C(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
       "      <td>odorless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>CCC(=O)C(=O)O</td>\n",
       "      <td>creamy;lactonic;caramellic;brown;sweet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>CC(C)CC(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CID         IsomericSMILES                            Updated_Desc\n",
       "0    4                CC(CN)O                                   fishy\n",
       "1   49       CC(C)C(=O)C(=O)O                                  fruity\n",
       "2   51  C(CC(=O)O)C(=O)C(=O)O                                odorless\n",
       "3   58          CCC(=O)C(=O)O  creamy;lactonic;caramellic;brown;sweet\n",
       "4   70      CC(C)CC(=O)C(=O)O                                  fruity"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid_behaviors_mol.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "Name: fruity, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odor_dummies = cid_behaviors_mol['Updated_Desc'].str.get_dummies(sep=';')\n",
    "odor_dummies['fruity'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "cid_behaviors_mol_encoded = pd.concat([cid_behaviors_mol, odor_dummies], axis=1)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updated behavior mol dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CID</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Updated_Desc</th>\n",
       "      <th>absinthe</th>\n",
       "      <th>absolute</th>\n",
       "      <th>acacia</th>\n",
       "      <th>acetaldehyde</th>\n",
       "      <th>acetic</th>\n",
       "      <th>acetone</th>\n",
       "      <th>acidic</th>\n",
       "      <th>...</th>\n",
       "      <th>winey</th>\n",
       "      <th>wintergreen</th>\n",
       "      <th>wood</th>\n",
       "      <th>woody</th>\n",
       "      <th>wormwood</th>\n",
       "      <th>worty</th>\n",
       "      <th>yeasty</th>\n",
       "      <th>ylang</th>\n",
       "      <th>yogurt</th>\n",
       "      <th>zesty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
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       "      <td>fruity</td>\n",
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       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
       "      <td>odorless</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>CCC(=O)C(=O)O</td>\n",
       "      <td>creamy;lactonic;caramellic;brown;sweet</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>CC(C)CC(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 588 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CID         IsomericSMILES                            Updated_Desc   \n",
       "0    4                CC(CN)O                                   fishy  \\\n",
       "1   49       CC(C)C(=O)C(=O)O                                  fruity   \n",
       "2   51  C(CC(=O)O)C(=O)C(=O)O                                odorless   \n",
       "3   58          CCC(=O)C(=O)O  creamy;lactonic;caramellic;brown;sweet   \n",
       "4   70      CC(C)CC(=O)C(=O)O                                  fruity   \n",
       "\n",
       "   absinthe  absolute  acacia  acetaldehyde  acetic  acetone  acidic  ...   \n",
       "0         0         0       0             0       0        0       0  ...  \\\n",
       "1         0         0       0             0       0        0       0  ...   \n",
       "2         0         0       0             0       0        0       0  ...   \n",
       "3         0         0       0             0       0        0       0  ...   \n",
       "4         0         0       0             0       0        0       0  ...   \n",
       "\n",
       "   winey  wintergreen  wood  woody  wormwood  worty  yeasty  ylang  yogurt   \n",
       "0      0            0     0      0         0      0       0      0       0  \\\n",
       "1      0            0     0      0         0      0       0      0       0   \n",
       "2      0            0     0      0         0      0       0      0       0   \n",
       "3      0            0     0      0         0      0       0      0       0   \n",
       "4      0            0     0      0         0      0       0      0       0   \n",
       "\n",
       "   zesty  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  \n",
       "\n",
       "[5 rows x 588 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid_behaviors_mol_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "585"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odor_list = []\n",
    "cid_behaviors_mol_encoded['Updated_Desc'].apply(lambda x: get_odors(x, odor_list))\n",
    "odor_list.sort()\n",
    "len(odor_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_odors_df = pd.DataFrame(odor_list, columns=['desc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>fruit skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>nut skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>skin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           desc\n",
       "232  fruit skin\n",
       "379    nut skin\n",
       "493        skin"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_odors_df[new_odors_df['desc'].str.match('.*skin*')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package snowball_data to\n",
      "[nltk_data]     /home/aryanab/nltk_data...\n",
      "[nltk_data]   Package snowball_data is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('snowball_data')\n",
    "from nltk.stem import SnowballStemmer\n",
    "\n",
    "# Initialize the SnowballStemmer for English\n",
    "stemmer = SnowballStemmer('english')\n",
    "\n",
    "def desc_stem_analyser():\n",
    "    for desc in required_desc:\n",
    "        root = stemmer.stem(desc)\n",
    "        odor_set = new_odors_df[new_odors_df['desc'].str.match(f'.*{root}.*')]\n",
    "        if len(odor_set) > 1:\n",
    "            yield(desc, odor_set)\n",
    "gen = desc_stem_analyser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('aldehydic',\n",
       "             desc\n",
       " 3   acetaldehyde\n",
       " 11     aldehydic\n",
       " 47  benzaldehyde)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aldehydic': ['acetaldehyde', 'aldehydic', 'benzaldehyde'],\n",
       " 'anisic': ['anise', 'anisic'],\n",
       " 'apple': ['apple', 'pineapple'],\n",
       " 'balsamic': ['balsam', 'balsamic', 'tolu balsam'],\n",
       " 'camphoreous': ['camphor', 'camphoreous'],\n",
       " 'caramellic': ['caramel', 'caramellic'],\n",
       " 'cooked': ['cooked', 'cookie'],\n",
       " 'cooling': ['cool', 'cooling'],\n",
       " 'coumarinic': ['coumarin', 'coumarinic'],\n",
       " 'dry': ['dried', 'fruit dried fruit'],\n",
       " 'ethereal': ['ether', 'ethereal'],\n",
       " 'grape': ['grape', 'grapefruit'],\n",
       " 'green': ['green', 'wintergreen'],\n",
       " 'lactonic': ['lactone', 'lactonic'],\n",
       " 'melon': ['melon', 'watermelon'],\n",
       " 'metallic': ['metal', 'metallic'],\n",
       " 'mint': ['cornmint', 'mint', 'peppermint', 'spearmint'],\n",
       " 'musk': ['musk', 'nitromusk'],\n",
       " 'orange': ['orange', 'orangeflower'],\n",
       " 'pear': ['pear', 'spearmint'],\n",
       " 'pine': ['pine', 'pineapple'],\n",
       " 'radish': ['horseradish', 'radish'],\n",
       " 'rose': ['rose', 'tuberose'],\n",
       " 'sulfurous': ['sulfurous', 'sulfury'],\n",
       " 'terpenic': ['terpene', 'terpenic', 'terpentine']}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merger_dict_v2 = {}\n",
    "for out in desc_stem_analyser():\n",
    "    merger_dict_v2[out[0]] = list(out[1]['desc'])\n",
    "merger_dict_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'anisic': ['anise', 'anisic'],\n",
       " 'balsamic': ['balsam', 'balsamic', 'tolu balsam'],\n",
       " 'camphoreous': ['camphor', 'camphoreous'],\n",
       " 'caramellic': ['caramel', 'caramellic'],\n",
       " 'cooling': ['cool', 'cooling'],\n",
       " 'coumarinic': ['coumarin', 'coumarinic'],\n",
       " 'ethereal': ['ether', 'ethereal'],\n",
       " 'lactonic': ['lactone', 'lactonic'],\n",
       " 'metallic': ['metal', 'metallic'],\n",
       " 'musk': ['musk', 'nitromusk', 'ambrette'],\n",
       " 'sulfurous': ['sulfurous', 'sulfury'],\n",
       " 'terpenic': ['terpene', 'terpenic', 'terpentine']}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys_to_remove = ['aldehydic', 'apple', 'cooked', 'dry', 'grape', 'green', 'melon', 'mint', 'orange', 'pear', 'pine', 'radish', 'rose']\n",
    "for key in keys_to_remove:\n",
    "    del merger_dict_v2[key]\n",
    "merger_dict_v2['musk'] = ['musk', 'nitromusk', 'ambrette']\n",
    "merger_dict_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def desc_suffix_analyser():\n",
    "    for desc in required_desc:\n",
    "        root = desc[:-2]\n",
    "        odor_set = new_odors_df[new_odors_df['desc'].str.match(f'.*{root}.*')]\n",
    "        if len(odor_set) > 1:\n",
    "            yield(desc, odor_set)\n",
    "gen = desc_suffix_analyser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('aldehydic',\n",
       "             desc\n",
       " 3   acetaldehyde\n",
       " 11     aldehydic\n",
       " 47  benzaldehyde)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_be_checked = ['beefy', 'buttery', 'cheesy', 'cinnamon', 'citrus', 'fatty', 'fishy', 'fruity', 'grape', 'grassy', 'hay', 'herbal', 'leafy', 'leathery', 'meaty', 'milky', 'nutty', 'oily', 'radish', 'rose', 'rummy', 'smoky', 'spicy', 'sweaty', 'sweet', 'winey', 'woody']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'beefy': ['beef', 'beef juice', 'beefy', 'beeswax', 'root beer'],\n",
       " 'buttery': ['butter',\n",
       "  'butter rancid',\n",
       "  'buttermilk',\n",
       "  'butterscotch',\n",
       "  'buttery',\n",
       "  'peanut butter'],\n",
       " 'cheesy': ['cheese', 'cheesy', 'lychee'],\n",
       " 'cinnamon': ['cinnamon', 'cinnamyl'],\n",
       " 'citrus': ['citral',\n",
       "  'citralva',\n",
       "  'citric',\n",
       "  'citronella',\n",
       "  'citronellal',\n",
       "  'citrus'],\n",
       " 'fatty': ['chicken fat', 'fatty'],\n",
       " 'fishy': ['fish', 'fishy', 'shellfish'],\n",
       " 'fruity': ['fruit',\n",
       "  'fruit dried fruit',\n",
       "  'fruit skin',\n",
       "  'fruity',\n",
       "  'grapefruit',\n",
       "  'grapfruit',\n",
       "  'jackfruit',\n",
       "  'passion fruit',\n",
       "  'starfruit'],\n",
       " 'grape': ['fragrant',\n",
       "  'graham cracker',\n",
       "  'grain',\n",
       "  'grain toasted grain',\n",
       "  'grape',\n",
       "  'grapefruit',\n",
       "  'grapfruit',\n",
       "  'grass',\n",
       "  'grassy',\n",
       "  'gravy',\n",
       "  'petitgrain',\n",
       "  'pomegranate'],\n",
       " 'grassy': ['grass', 'grassy'],\n",
       " 'hay': ['absinthe',\n",
       "  'acetaldehyde',\n",
       "  'alcoholic',\n",
       "  'aldehydic',\n",
       "  'artichoke',\n",
       "  'benzaldehyde',\n",
       "  'brothy',\n",
       "  'buchu',\n",
       "  'butterscotch',\n",
       "  'camphor',\n",
       "  'camphoreous',\n",
       "  'cashew',\n",
       "  'catechu',\n",
       "  'chamomile',\n",
       "  'characteristic',\n",
       "  'charred',\n",
       "  'chavicol',\n",
       "  'cheese',\n",
       "  'cheesy',\n",
       "  'chemical',\n",
       "  'cherry',\n",
       "  'chervil',\n",
       "  'chicken',\n",
       "  'chicken coup',\n",
       "  'chicken fat',\n",
       "  'chicory',\n",
       "  'chlorine',\n",
       "  'chloroform',\n",
       "  'chocolate',\n",
       "  'chrysanthemum',\n",
       "  'cloth laundered cloth',\n",
       "  'corn chip',\n",
       "  'dihydrojasmonate',\n",
       "  'earthy',\n",
       "  'ether',\n",
       "  'ethereal',\n",
       "  'ethyl',\n",
       "  'fish',\n",
       "  'fishy',\n",
       "  'fleshy',\n",
       "  'fresh',\n",
       "  'graham cracker',\n",
       "  'hairy',\n",
       "  'ham',\n",
       "  'harsh',\n",
       "  'hawthorn',\n",
       "  'hay',\n",
       "  'hazelnut',\n",
       "  'heavy',\n",
       "  'heliotrope',\n",
       "  'herb',\n",
       "  'herbaceous',\n",
       "  'herbal',\n",
       "  'hesperidic',\n",
       "  'honey',\n",
       "  'horseradish',\n",
       "  'humus',\n",
       "  'hyacinth',\n",
       "  'hydrocarbon',\n",
       "  'kimchi',\n",
       "  'leather',\n",
       "  'leathery',\n",
       "  'light',\n",
       "  'lychee',\n",
       "  'mahogany',\n",
       "  'menthol',\n",
       "  'mentholic',\n",
       "  'methyl',\n",
       "  'methyl heptine carbonate',\n",
       "  'mushroom',\n",
       "  'myrrh',\n",
       "  'naphtha',\n",
       "  'naphthyl',\n",
       "  'nut flesh',\n",
       "  'orchid',\n",
       "  'osmanthus',\n",
       "  'patchouli',\n",
       "  'peach',\n",
       "  'phenolic',\n",
       "  'pistachio',\n",
       "  'radish',\n",
       "  'rhubarb',\n",
       "  'rich',\n",
       "  'seashore',\n",
       "  'sharp',\n",
       "  'shell',\n",
       "  'shellfish',\n",
       "  'shrimp',\n",
       "  'slight',\n",
       "  'spinach',\n",
       "  'thiamine',\n",
       "  'thujone',\n",
       "  'thujonic',\n",
       "  'thyme',\n",
       "  'whiskey'],\n",
       " 'herbal': ['herb', 'herbaceous', 'herbal'],\n",
       " 'leafy': ['clean', 'leaf', 'leafy', 'leather', 'leathery', 'pleasant'],\n",
       " 'leathery': ['leather', 'leathery'],\n",
       " 'meaty': ['meat', 'meaty'],\n",
       " 'milky': ['buttermilk', 'chamomile', 'mild', 'milk', 'milky'],\n",
       " 'nutty': ['coconut',\n",
       "  'hazelnut',\n",
       "  'nut',\n",
       "  'nut flesh',\n",
       "  'nut skin',\n",
       "  'nutmeg',\n",
       "  'nutty',\n",
       "  'peanut',\n",
       "  'peanut butter',\n",
       "  'walnut'],\n",
       " 'oily': ['armoise', 'benzoin', 'boiled', 'bois', 'oil', 'oily', 'soil'],\n",
       " 'radish': ['horseradish', 'radish'],\n",
       " 'rose': ['aromatic',\n",
       "  'boronia',\n",
       "  'broccoli',\n",
       "  'brothy',\n",
       "  'brown',\n",
       "  'carrot',\n",
       "  'carrot seed',\n",
       "  'chloroform',\n",
       "  'cilantro',\n",
       "  'citronella',\n",
       "  'citronellal',\n",
       "  'dihydrojasmonate',\n",
       "  'heliotrope',\n",
       "  'hydrocarbon',\n",
       "  'mushroom',\n",
       "  'nerol',\n",
       "  'neroli',\n",
       "  'nitromusk',\n",
       "  'palmarosa',\n",
       "  'petroleum',\n",
       "  'roasted',\n",
       "  'root beer',\n",
       "  'rooty',\n",
       "  'rose',\n",
       "  'rosy',\n",
       "  'rotten',\n",
       "  'rotting',\n",
       "  'saffron',\n",
       "  'strong',\n",
       "  'sugar brown sugar',\n",
       "  'sulfurous',\n",
       "  'tropical',\n",
       "  'tuberose',\n",
       "  'valerian root'],\n",
       " 'rummy': ['rum', 'rummy'],\n",
       " 'smoky': ['dihydrojasmonate', 'sausage smoked sausage', 'smoky'],\n",
       " 'spicy': ['allspice', 'spice', 'spicy', 'spinach'],\n",
       " 'sweaty': ['sweat', 'sweaty'],\n",
       " 'sweet': ['sweat', 'sweaty', 'sweet'],\n",
       " 'winey': ['wine', 'winey', 'wintergreen'],\n",
       " 'woody': ['agarwood',\n",
       "  'guaiacwood',\n",
       "  'sandalwood',\n",
       "  'wood',\n",
       "  'woody',\n",
       "  'wormwood']}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merger_dict_v3 = {}\n",
    "for out in desc_suffix_analyser():\n",
    "    # # manual selection\n",
    "    # choice = input(out)\n",
    "    # if choice == '1':\n",
    "    if out[0] in to_be_checked:\n",
    "        merger_dict_v3[out[0]] = list(out[1]['desc'])\n",
    "merger_dict_v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sweat', 'sweaty']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merger_dict_v3['sweaty']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# manually update the merger_dict_v3\n",
    "merger_dict_v3['beefy'] = ['beef', 'beef juice', 'beefy']\n",
    "merger_dict_v3['buttery'] = ['butter', 'buttery']\n",
    "merger_dict_v3['cheesy'] = ['cheese', 'cheesy']\n",
    "merger_dict_v3['grapefruit'] = ['grapefruit', 'grapfruit']\n",
    "merger_dict_v3['grassy'] = ['grass', 'grassy']\n",
    "del merger_dict_v3['grape']\n",
    "merger_dict_v3['floral'] = ['floral','flower','flowers','flowery']\n",
    "merger_dict_v3['fruity'] = ['fruit', 'fruit dried fruit','fruity']\n",
    "merger_dict_v3['dry'] = ['dry', 'fruit dried fruit', 'dried']\n",
    "merger_dict_v3['grain'] = ['grain','grain toasted grain']\n",
    "del merger_dict_v3['hay']\n",
    "merger_dict_v3['leafy'] = ['leaf', 'leafy']\n",
    "merger_dict_v3['lemon'] = ['lemon', 'lime']\n",
    "merger_dict_v3['lily'] = ['lily', 'lilial']\n",
    "merger_dict_v3['milky'] = ['milk', 'milky']\n",
    "\n",
    "# to check later after model training\n",
    "# merger_dict_v3['medicinal'] = ['medical', 'medicinal']\n",
    "\n",
    "merger_dict_v3['nutty'] = ['nut', 'nut flesh','nut skin','nutty']\n",
    "merger_dict_v3['oily'] = ['oil', 'oily']\n",
    "merger_dict_v3['rose'] = ['rose', 'rosy']\n",
    "merger_dict_v3['smoky'] = ['sausage smoked sausage', 'smoky']\n",
    "merger_dict_v3['spicy'] = ['allspice', 'spice', 'spicy']\n",
    "del merger_dict_v3['sweet']\n",
    "merger_dict_v3['winey'] = ['wine', 'winey']\n",
    "merger_dict_v3['woody'] = ['wood', 'woody']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'beefy': ['beef', 'beef juice', 'beefy'],\n",
       " 'buttery': ['butter', 'buttery'],\n",
       " 'cheesy': ['cheese', 'cheesy'],\n",
       " 'cinnamon': ['cinnamon', 'cinnamyl'],\n",
       " 'citrus': ['citral',\n",
       "  'citralva',\n",
       "  'citric',\n",
       "  'citronella',\n",
       "  'citronellal',\n",
       "  'citrus'],\n",
       " 'fatty': ['chicken fat', 'fatty'],\n",
       " 'fishy': ['fish', 'fishy', 'shellfish'],\n",
       " 'fruity': ['fruit', 'fruit dried fruit', 'fruity'],\n",
       " 'grassy': ['grass', 'grassy'],\n",
       " 'herbal': ['herb', 'herbaceous', 'herbal'],\n",
       " 'leafy': ['leaf', 'leafy'],\n",
       " 'leathery': ['leather', 'leathery'],\n",
       " 'meaty': ['meat', 'meaty'],\n",
       " 'milky': ['milk', 'milky'],\n",
       " 'nutty': ['nut', 'nut flesh', 'nut skin', 'nutty'],\n",
       " 'oily': ['oil', 'oily'],\n",
       " 'radish': ['horseradish', 'radish'],\n",
       " 'rose': ['rose', 'rosy'],\n",
       " 'rummy': ['rum', 'rummy'],\n",
       " 'smoky': ['sausage smoked sausage', 'smoky'],\n",
       " 'spicy': ['allspice', 'spice', 'spicy'],\n",
       " 'sweaty': ['sweat', 'sweaty'],\n",
       " 'winey': ['wine', 'winey'],\n",
       " 'woody': ['wood', 'woody'],\n",
       " 'grapefruit': ['grapefruit', 'grapfruit'],\n",
       " 'floral': ['floral', 'flower', 'flowers', 'flowery'],\n",
       " 'dry': ['dry', 'fruit dried fruit', 'dried'],\n",
       " 'grain': ['grain', 'grain toasted grain'],\n",
       " 'lemon': ['lemon', 'lime'],\n",
       " 'lily': ['lily', 'lilial']}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merger_dict_v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merger_dict_v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "merger_dict_v2.update(merger_dict_v3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merger_dict_v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "reverse_merger_dict_v2 = defaultdict(list)\n",
    "for key, values in merger_dict_v2.items():\n",
    "    for value in values:\n",
    "        reverse_merger_dict_v2[value].append(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'terpentine' in reverse_merger_dict_v2.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'grapfruit' in reverse_merger_dict_v2.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_desc_v2(desc_string):\n",
    "    desc_list = desc_string.split(';')\n",
    "    new_odors = set()\n",
    "    flag = 0\n",
    "    for desc in desc_list:\n",
    "        flag = 0\n",
    "        for old_desc, new_desc in reverse_merger_dict_v2.items():\n",
    "            if desc == old_desc:\n",
    "                new_odors.update(new_desc)\n",
    "                flag = 1\n",
    "                break\n",
    "        if flag==0:\n",
    "            new_odors.update([desc])\n",
    "    return ';'.join(new_odors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>CID</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Updated_Desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>fishy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>CC(C)C(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
       "      <td>odorless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>CCC(=O)C(=O)O</td>\n",
       "      <td>creamy;lactonic;caramellic;brown;sweet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>CC(C)CC(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>72</td>\n",
       "      <td>C1=CC(=C(C=C1C(=O)O)O)O</td>\n",
       "      <td>phenolic;balsamic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>107</td>\n",
       "      <td>C1=CC=C(C=C1)CCC(=O)O</td>\n",
       "      <td>cinnamon;floral;rose;fatty;sweet;musk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>119</td>\n",
       "      <td>C(CC(=O)O)CN</td>\n",
       "      <td>meaty;savory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>125</td>\n",
       "      <td>C1=CC(=CC=C1CO)O</td>\n",
       "      <td>fruity;almond;bitter;coconut;sweet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>126</td>\n",
       "      <td>C1=CC(=CC=C1C=O)O</td>\n",
       "      <td>balsamic;nutty;almond;metallic;woody;vanilla;h...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CID           IsomericSMILES   \n",
       "0    4                  CC(CN)O  \\\n",
       "1   49         CC(C)C(=O)C(=O)O   \n",
       "2   51    C(CC(=O)O)C(=O)C(=O)O   \n",
       "3   58            CCC(=O)C(=O)O   \n",
       "4   70        CC(C)CC(=O)C(=O)O   \n",
       "5   72  C1=CC(=C(C=C1C(=O)O)O)O   \n",
       "6  107    C1=CC=C(C=C1)CCC(=O)O   \n",
       "7  119             C(CC(=O)O)CN   \n",
       "8  125         C1=CC(=CC=C1CO)O   \n",
       "9  126        C1=CC(=CC=C1C=O)O   \n",
       "\n",
       "                                        Updated_Desc  \n",
       "0                                              fishy  \n",
       "1                                             fruity  \n",
       "2                                           odorless  \n",
       "3             creamy;lactonic;caramellic;brown;sweet  \n",
       "4                                             fruity  \n",
       "5                                  phenolic;balsamic  \n",
       "6              cinnamon;floral;rose;fatty;sweet;musk  \n",
       "7                                       meaty;savory  \n",
       "8                 fruity;almond;bitter;coconut;sweet  \n",
       "9  balsamic;nutty;almond;metallic;woody;vanilla;h...  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid_behaviors_mol.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fruity;grassy;almond;metallic;woody;vanilla;sweet'"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "update_desc_v2(\"vanilla;metal;sweet;almond;wood;fruit;grass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "cid_behaviors_mol['Updated_Desc_v2'] = cid_behaviors_mol['Updated_Desc'].apply(lambda x: update_desc_v2(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>CID</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Updated_Desc</th>\n",
       "      <th>Updated_Desc_v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>fishy</td>\n",
       "      <td>fishy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>CC(C)C(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
       "      <td>odorless</td>\n",
       "      <td>odorless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>CCC(=O)C(=O)O</td>\n",
       "      <td>creamy;lactonic;caramellic;brown;sweet</td>\n",
       "      <td>creamy;lactonic;caramellic;brown;sweet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>CC(C)CC(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CID         IsomericSMILES                            Updated_Desc   \n",
       "0    4                CC(CN)O                                   fishy  \\\n",
       "1   49       CC(C)C(=O)C(=O)O                                  fruity   \n",
       "2   51  C(CC(=O)O)C(=O)C(=O)O                                odorless   \n",
       "3   58          CCC(=O)C(=O)O  creamy;lactonic;caramellic;brown;sweet   \n",
       "4   70      CC(C)CC(=O)C(=O)O                                  fruity   \n",
       "\n",
       "                          Updated_Desc_v2  \n",
       "0                                   fishy  \n",
       "1                                  fruity  \n",
       "2                                odorless  \n",
       "3  creamy;lactonic;caramellic;brown;sweet  \n",
       "4                                  fruity  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid_behaviors_mol.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4561 entries, 0 to 4560\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   CID              4561 non-null   int64 \n",
      " 1   IsomericSMILES   4561 non-null   object\n",
      " 2   Updated_Desc     4561 non-null   object\n",
      " 3   Updated_Desc_v2  4561 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 142.7+ KB\n"
     ]
    }
   ],
   "source": [
    "cid_behaviors_mol.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['absinthe',\n",
       " 'absolute',\n",
       " 'acacia',\n",
       " 'acetaldehyde',\n",
       " 'acetic',\n",
       " 'acetone',\n",
       " 'acidic',\n",
       " 'acorn',\n",
       " 'acrylate',\n",
       " 'agarwood',\n",
       " 'alcoholic',\n",
       " 'aldehydic',\n",
       " 'algae',\n",
       " 'alkane',\n",
       " 'alliaceous',\n",
       " 'almond',\n",
       " 'amber',\n",
       " 'amine',\n",
       " 'ammonia',\n",
       " 'ammoniacal',\n",
       " 'angelica',\n",
       " 'animal',\n",
       " 'anisic',\n",
       " 'apple',\n",
       " 'apricot',\n",
       " 'armoise',\n",
       " 'aromatic',\n",
       " 'arrack',\n",
       " 'artichoke',\n",
       " 'asparagus',\n",
       " 'astringent',\n",
       " 'autumn',\n",
       " 'bacon',\n",
       " 'baked',\n",
       " 'balsamic',\n",
       " 'banana',\n",
       " 'basil',\n",
       " 'bay',\n",
       " 'beany',\n",
       " 'beefy',\n",
       " 'beeswax',\n",
       " 'benzaldehyde',\n",
       " 'benzoate',\n",
       " 'benzoin',\n",
       " 'bergamot',\n",
       " 'berry',\n",
       " 'bitter',\n",
       " 'black currant',\n",
       " 'blackberry',\n",
       " 'bland',\n",
       " 'bloody',\n",
       " 'blossom',\n",
       " 'blueberry',\n",
       " 'boiled',\n",
       " 'bois',\n",
       " 'bonbon',\n",
       " 'borneol',\n",
       " 'boronia',\n",
       " 'bouillon',\n",
       " 'brandy',\n",
       " 'bread',\n",
       " 'bread baked',\n",
       " 'bread crust',\n",
       " 'bread rye bread',\n",
       " 'bready',\n",
       " 'broccoli',\n",
       " 'brothy',\n",
       " 'brown',\n",
       " 'bubble gum',\n",
       " 'buchu',\n",
       " 'bud',\n",
       " 'burnt',\n",
       " 'butter rancid',\n",
       " 'buttermilk',\n",
       " 'butterscotch',\n",
       " 'buttery',\n",
       " 'cabbage',\n",
       " 'cacao',\n",
       " 'camphoreous',\n",
       " 'cananga',\n",
       " 'candy',\n",
       " 'cantaloupe',\n",
       " 'capers',\n",
       " 'caprylic',\n",
       " 'caramellic',\n",
       " 'caraway',\n",
       " 'cardamom',\n",
       " 'cardboard',\n",
       " 'carnation',\n",
       " 'carrot',\n",
       " 'carrot seed',\n",
       " 'carvone',\n",
       " 'cashew',\n",
       " 'cassia',\n",
       " 'cassie',\n",
       " 'castoreum',\n",
       " 'catechu',\n",
       " 'catty',\n",
       " 'cauliflower',\n",
       " 'cedar',\n",
       " 'celery',\n",
       " 'cellar',\n",
       " 'cereal',\n",
       " 'chamomile',\n",
       " 'characteristic',\n",
       " 'charred',\n",
       " 'chavicol',\n",
       " 'cheesy',\n",
       " 'chemical',\n",
       " 'cherry',\n",
       " 'chervil',\n",
       " 'chicken',\n",
       " 'chicken coup',\n",
       " 'chicory',\n",
       " 'chlorine',\n",
       " 'chloroform',\n",
       " 'chocolate',\n",
       " 'chrysanthemum',\n",
       " 'cider',\n",
       " 'cilantro',\n",
       " 'cinnamon',\n",
       " 'cistus',\n",
       " 'citrus',\n",
       " 'civet',\n",
       " 'clam',\n",
       " 'clean',\n",
       " 'cloth laundered cloth',\n",
       " 'clove',\n",
       " 'coal',\n",
       " 'cocoa',\n",
       " 'coconut',\n",
       " 'coffee',\n",
       " 'cognac',\n",
       " 'cologne',\n",
       " 'cooked',\n",
       " 'cookie',\n",
       " 'cooling',\n",
       " 'coriander',\n",
       " 'corn',\n",
       " 'corn chip',\n",
       " 'cornmint',\n",
       " 'cortex',\n",
       " 'costus',\n",
       " 'cotton',\n",
       " 'cotton candy',\n",
       " 'coumarinic',\n",
       " 'cranberry',\n",
       " 'creamy',\n",
       " 'creosote',\n",
       " 'cresol',\n",
       " 'cucumber',\n",
       " 'cumin',\n",
       " 'custard',\n",
       " 'cyclamen',\n",
       " 'cypress',\n",
       " 'dairy',\n",
       " 'damascone',\n",
       " 'dank',\n",
       " 'date',\n",
       " 'davana',\n",
       " 'de',\n",
       " 'dead',\n",
       " 'deep',\n",
       " 'dewy',\n",
       " 'diffuse',\n",
       " 'dihydrojasmonate',\n",
       " 'dill',\n",
       " 'dirty',\n",
       " 'dry',\n",
       " 'dull',\n",
       " 'durian',\n",
       " 'dusty',\n",
       " 'earthy',\n",
       " 'egg',\n",
       " 'egg nog',\n",
       " 'egg yolk',\n",
       " 'eggy',\n",
       " 'elderberry',\n",
       " 'elderflower',\n",
       " 'elemi',\n",
       " 'estery',\n",
       " 'ethereal',\n",
       " 'ethyl',\n",
       " 'eucalyptus',\n",
       " 'evil',\n",
       " 'faint',\n",
       " 'fatty',\n",
       " 'fecal',\n",
       " 'fennel',\n",
       " 'fenugreek',\n",
       " 'fermented',\n",
       " 'fig',\n",
       " 'filbert',\n",
       " 'fir needle',\n",
       " 'fishy',\n",
       " 'flat',\n",
       " 'fleshy',\n",
       " 'floral',\n",
       " 'foliage',\n",
       " 'forest',\n",
       " 'fragrant',\n",
       " 'frankincense',\n",
       " 'freesia',\n",
       " 'fresh',\n",
       " 'fried',\n",
       " 'fruit skin',\n",
       " 'fruity',\n",
       " 'fungal',\n",
       " 'fusel',\n",
       " 'galbanum',\n",
       " 'gardenia',\n",
       " 'garlic',\n",
       " 'gasoline',\n",
       " 'gassy',\n",
       " 'genet',\n",
       " 'geranium',\n",
       " 'ginger',\n",
       " 'goats',\n",
       " 'goaty',\n",
       " 'gooseberry',\n",
       " 'gourmand',\n",
       " 'graham cracker',\n",
       " 'grain',\n",
       " 'grape',\n",
       " 'grapefruit',\n",
       " 'grassy',\n",
       " 'gravy',\n",
       " 'greasy',\n",
       " 'green',\n",
       " 'grilled',\n",
       " 'guaiacol',\n",
       " 'guaiacwood',\n",
       " 'guava',\n",
       " 'hairy',\n",
       " 'ham',\n",
       " 'harsh',\n",
       " 'hawthorn',\n",
       " 'hay',\n",
       " 'hazelnut',\n",
       " 'heavy',\n",
       " 'heliotrope',\n",
       " 'herbal',\n",
       " 'hesperidic',\n",
       " 'honey',\n",
       " 'humus',\n",
       " 'hyacinth',\n",
       " 'hydrocarbon',\n",
       " 'immortelle',\n",
       " 'impacting',\n",
       " 'incense',\n",
       " 'indole',\n",
       " 'iodine',\n",
       " 'jackfruit',\n",
       " 'jam',\n",
       " 'jammy',\n",
       " 'jasmin',\n",
       " 'juicy',\n",
       " 'juniper',\n",
       " 'kernel',\n",
       " 'ketonic',\n",
       " 'kimchi',\n",
       " 'kiwi',\n",
       " 'labdanum',\n",
       " 'lactonic',\n",
       " 'lamb',\n",
       " 'lard',\n",
       " 'lavender',\n",
       " 'leafy',\n",
       " 'leathery',\n",
       " 'leek',\n",
       " 'lemon',\n",
       " 'lettuce',\n",
       " 'licorice',\n",
       " 'licorice black licorice',\n",
       " 'light',\n",
       " 'lilac',\n",
       " 'lily',\n",
       " 'linalool',\n",
       " 'linden',\n",
       " 'linden flower',\n",
       " 'liquid',\n",
       " 'liver',\n",
       " 'loganberry',\n",
       " 'lovage',\n",
       " 'lychee',\n",
       " 'mace',\n",
       " 'magnolia',\n",
       " 'mahogany',\n",
       " 'malty',\n",
       " 'mandarin',\n",
       " 'mango',\n",
       " 'maple',\n",
       " 'marine',\n",
       " 'marjoram',\n",
       " 'marzipan',\n",
       " 'may',\n",
       " 'meaty',\n",
       " 'medical',\n",
       " 'medicinal',\n",
       " 'melon',\n",
       " 'menthol',\n",
       " 'mentholic',\n",
       " 'mercaptan',\n",
       " 'metallic',\n",
       " 'methyl',\n",
       " 'methyl heptine carbonate',\n",
       " 'mignonette',\n",
       " 'mild',\n",
       " 'milky',\n",
       " 'mimosa',\n",
       " 'mint',\n",
       " 'molasses',\n",
       " 'moldy',\n",
       " 'moss',\n",
       " 'mossy',\n",
       " 'mouldy',\n",
       " 'muguet',\n",
       " 'mushroom',\n",
       " 'musk',\n",
       " 'mustard',\n",
       " 'musty',\n",
       " 'mutton',\n",
       " 'muttony',\n",
       " 'myrrh',\n",
       " 'naphtha',\n",
       " 'naphthyl',\n",
       " 'narcissus',\n",
       " 'nasturtium',\n",
       " 'natural',\n",
       " 'needle',\n",
       " 'needles',\n",
       " 'nerol',\n",
       " 'neroli',\n",
       " 'nice',\n",
       " 'nitrile',\n",
       " 'nutmeg',\n",
       " 'nutty',\n",
       " 'oakmoss',\n",
       " 'ocean',\n",
       " 'odorless',\n",
       " 'oily',\n",
       " 'old',\n",
       " 'onion',\n",
       " 'opoponax',\n",
       " 'orange',\n",
       " 'orangeflower',\n",
       " 'orchid',\n",
       " 'oriental',\n",
       " 'orris',\n",
       " 'osmanthus',\n",
       " 'oxide',\n",
       " 'ozone',\n",
       " 'painty',\n",
       " 'palmarosa',\n",
       " 'papaya',\n",
       " 'paper',\n",
       " 'parsley',\n",
       " 'passion fruit',\n",
       " 'pastry',\n",
       " 'patchouli',\n",
       " 'peach',\n",
       " 'peanut',\n",
       " 'peanut butter',\n",
       " 'pear',\n",
       " 'peel',\n",
       " 'peely',\n",
       " 'peony',\n",
       " 'pepper bell pepper',\n",
       " 'pepper black pepper',\n",
       " 'peppermint',\n",
       " 'peppery',\n",
       " 'petal',\n",
       " 'petitgrain',\n",
       " 'petroleum',\n",
       " 'phenolic',\n",
       " 'pine',\n",
       " 'pineapple',\n",
       " 'pistachio',\n",
       " 'plastic',\n",
       " 'pleasant',\n",
       " 'plowed',\n",
       " 'plum',\n",
       " 'pomegranate',\n",
       " 'popcorn',\n",
       " 'pork',\n",
       " 'potato',\n",
       " 'powdery',\n",
       " 'powerful',\n",
       " 'praline',\n",
       " 'privet',\n",
       " 'privetblossom',\n",
       " 'prune',\n",
       " 'pulpy',\n",
       " 'pumpkin',\n",
       " 'pungent',\n",
       " 'pyrazine',\n",
       " 'pyridine',\n",
       " 'quince',\n",
       " 'radish',\n",
       " 'raisin',\n",
       " 'rancid',\n",
       " 'raspberry',\n",
       " 'raw',\n",
       " 'red',\n",
       " 'reseda',\n",
       " 'resin',\n",
       " 'resinous',\n",
       " 'rhubarb',\n",
       " 'rich',\n",
       " 'rindy',\n",
       " 'ripe',\n",
       " 'roasted',\n",
       " 'root beer',\n",
       " 'rooty',\n",
       " 'rose',\n",
       " 'rotten',\n",
       " 'rotting',\n",
       " 'rubber',\n",
       " 'rubbery',\n",
       " 'rue',\n",
       " 'rummy',\n",
       " 'saffron',\n",
       " 'sage',\n",
       " 'sage clary sage',\n",
       " 'salmon',\n",
       " 'salty',\n",
       " 'sandalwood',\n",
       " 'sandy',\n",
       " 'sappy',\n",
       " 'sarsaparilla',\n",
       " 'sassafras',\n",
       " 'sassafrass',\n",
       " 'sauerkraut',\n",
       " 'savory',\n",
       " 'sawdust',\n",
       " 'scallion',\n",
       " 'seafood',\n",
       " 'seashore',\n",
       " 'seaweed',\n",
       " 'seedy',\n",
       " 'sharp',\n",
       " 'shell',\n",
       " 'shrimp',\n",
       " 'skin',\n",
       " 'skunk',\n",
       " 'slight',\n",
       " 'smelling',\n",
       " 'smoky',\n",
       " 'soapy',\n",
       " 'soft',\n",
       " 'soil',\n",
       " 'solvent',\n",
       " 'soup',\n",
       " 'sour',\n",
       " 'spearmint',\n",
       " 'spicy',\n",
       " 'spinach',\n",
       " 'spruce',\n",
       " 'stalk',\n",
       " 'starfruit',\n",
       " 'storax',\n",
       " 'straw',\n",
       " 'strawberry',\n",
       " 'strong',\n",
       " 'styrax',\n",
       " 'styrene',\n",
       " 'sugar',\n",
       " 'sugar brown sugar',\n",
       " 'sulfurous',\n",
       " 'sweaty',\n",
       " 'sweet',\n",
       " 'taco',\n",
       " 'tagette',\n",
       " 'tallow',\n",
       " 'tangerine',\n",
       " 'tar',\n",
       " 'tarragon',\n",
       " 'tart',\n",
       " 'tea',\n",
       " 'tequila',\n",
       " 'terpenic',\n",
       " 'thiamine',\n",
       " 'thujone',\n",
       " 'thujonic',\n",
       " 'thyme',\n",
       " 'toasted',\n",
       " 'tobacco',\n",
       " 'toffee',\n",
       " 'tomato',\n",
       " 'tonka',\n",
       " 'tropical',\n",
       " 'truffle',\n",
       " 'tuberose',\n",
       " 'turkey',\n",
       " 'turnup',\n",
       " 'tutti frutti',\n",
       " 'umami',\n",
       " 'urine',\n",
       " 'valerian root',\n",
       " 'vanilla',\n",
       " 'vegetable',\n",
       " 'verbena',\n",
       " 'verdox',\n",
       " 'vetiver',\n",
       " 'vinegar',\n",
       " 'vinyl',\n",
       " 'violet',\n",
       " 'walnut',\n",
       " 'warm',\n",
       " 'wasabi',\n",
       " 'watercress',\n",
       " 'watermelon',\n",
       " 'watery',\n",
       " 'waxy',\n",
       " 'weedy',\n",
       " 'wet',\n",
       " 'whiskey',\n",
       " 'wild',\n",
       " 'winey',\n",
       " 'wintergreen',\n",
       " 'woody',\n",
       " 'wormwood',\n",
       " 'worty',\n",
       " 'yeasty',\n",
       " 'ylang',\n",
       " 'yogurt',\n",
       " 'zesty']"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v2_odor_list = []\n",
    "cid_behaviors_mol['Updated_Desc_v2'].apply(lambda x: get_odors(x, v2_odor_list))\n",
    "v2_odor_list.sort()\n",
    "v2_odor_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "527"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(v2_odor_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_v2_odors_df = pd.DataFrame(v2_odor_list, columns=['desc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\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>desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>absinthe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>absolute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>acacia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>acetaldehyde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acetic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>worty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>yeasty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>ylang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>yogurt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>zesty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>527 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             desc\n",
       "0        absinthe\n",
       "1        absolute\n",
       "2          acacia\n",
       "3    acetaldehyde\n",
       "4          acetic\n",
       "..            ...\n",
       "522         worty\n",
       "523        yeasty\n",
       "524         ylang\n",
       "525        yogurt\n",
       "526         zesty\n",
       "\n",
       "[527 rows x 1 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_v2_odors_df[new_v2_odors_df['desc'].str.match('.*a*')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "Name: fruity, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odor_dummies = cid_behaviors_mol['Updated_Desc_v2'].str.get_dummies(sep=';')\n",
    "odor_dummies['fruity'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "cid_behaviors_mol_encoded_v2 = pd.concat([cid_behaviors_mol, odor_dummies], axis=1)\n",
    "cid_behaviors_mol_encoded_v2= cid_behaviors_mol_encoded_v2.drop(columns=['CID', 'Updated_Desc'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2nd version of encoded mol behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Updated_Desc_v2</th>\n",
       "      <th>absinthe</th>\n",
       "      <th>absolute</th>\n",
       "      <th>acacia</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>fishy</td>\n",
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       "      <th>1</th>\n",
       "      <td>CC(C)C(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
       "      <td>odorless</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCC(=O)C(=O)O</td>\n",
       "      <td>creamy;lactonic;caramellic;brown;sweet</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CC(C)CC(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 529 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          IsomericSMILES                         Updated_Desc_v2  absinthe   \n",
       "0                CC(CN)O                                   fishy         0  \\\n",
       "1       CC(C)C(=O)C(=O)O                                  fruity         0   \n",
       "2  C(CC(=O)O)C(=O)C(=O)O                                odorless         0   \n",
       "3          CCC(=O)C(=O)O  creamy;lactonic;caramellic;brown;sweet         0   \n",
       "4      CC(C)CC(=O)C(=O)O                                  fruity         0   \n",
       "\n",
       "   absolute  acacia  acetaldehyde  acetic  acetone  acidic  acorn  ...  wild   \n",
       "0         0       0             0       0        0       0      0  ...     0  \\\n",
       "1         0       0             0       0        0       0      0  ...     0   \n",
       "2         0       0             0       0        0       0      0  ...     0   \n",
       "3         0       0             0       0        0       0      0  ...     0   \n",
       "4         0       0             0       0        0       0      0  ...     0   \n",
       "\n",
       "   winey  wintergreen  woody  wormwood  worty  yeasty  ylang  yogurt  zesty  \n",
       "0      0            0      0         0      0       0      0       0      0  \n",
       "1      0            0      0         0      0       0      0       0      0  \n",
       "2      0            0      0         0      0       0      0       0      0  \n",
       "3      0            0      0         0      0       0      0       0      0  \n",
       "4      0            0      0         0      0       0      0       0      0  \n",
       "\n",
       "[5 rows x 529 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid_behaviors_mol_encoded_v2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_behavior = cid_behaviors_mol_encoded_v2.drop(columns=['IsomericSMILES', 'Updated_Desc_v2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "1     724\n",
       "2     665\n",
       "3     621\n",
       "4     591\n",
       "5     539\n",
       "6     408\n",
       "7     305\n",
       "8     251\n",
       "9     169\n",
       "10    136\n",
       "11     73\n",
       "12     34\n",
       "13     20\n",
       "14     13\n",
       "15      7\n",
       "16      4\n",
       "17      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(encoded_behavior.sum(axis=1).sort_values()).groupby(by=0).size()\n",
    "# number of desc | number of molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [count]\n",
       "Index: []"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# molecules with no descriptors\n",
    "pd.DataFrame(encoded_behavior.sum(axis=1).sort_values(), columns=['count']).query('count==0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fruity': 1258,\n",
       " 'green': 1090,\n",
       " 'sweet': 1009,\n",
       " 'floral': 921,\n",
       " 'woody': 621,\n",
       " 'herbal': 553,\n",
       " 'fatty': 401,\n",
       " 'fresh': 386,\n",
       " 'waxy': 376,\n",
       " 'spicy': 358,\n",
       " 'rose': 310,\n",
       " 'citrus': 304,\n",
       " 'sulfurous': 259,\n",
       " 'tropical': 251,\n",
       " 'balsamic': 247,\n",
       " 'nutty': 245,\n",
       " 'earthy': 244,\n",
       " 'odorless': 225,\n",
       " 'oily': 220,\n",
       " 'mint': 188,\n",
       " 'vegetable': 182,\n",
       " 'roasted': 180,\n",
       " 'powdery': 171,\n",
       " 'meaty': 168,\n",
       " 'apple': 165,\n",
       " 'musty': 158,\n",
       " 'dry': 151,\n",
       " 'creamy': 148,\n",
       " 'pineapple': 147,\n",
       " 'aldehydic': 138,\n",
       " 'caramellic': 135,\n",
       " 'ethereal': 135,\n",
       " 'phenolic': 134,\n",
       " 'camphoreous': 123,\n",
       " 'honey': 122,\n",
       " 'berry': 121,\n",
       " 'metallic': 120,\n",
       " 'leafy': 118,\n",
       " 'onion': 111,\n",
       " 'pungent': 110,\n",
       " 'amber': 102,\n",
       " 'melon': 99,\n",
       " 'musk': 95,\n",
       " 'banana': 95,\n",
       " 'animal': 95,\n",
       " 'pear': 94,\n",
       " 'jasmin': 93,\n",
       " 'winey': 92,\n",
       " 'clean': 90,\n",
       " 'bland': 90,\n",
       " 'cheesy': 88,\n",
       " 'coconut': 86,\n",
       " 'coffee': 81,\n",
       " 'mushroom': 80,\n",
       " 'cooling': 79,\n",
       " 'burnt': 76,\n",
       " 'buttery': 76,\n",
       " 'vanilla': 73,\n",
       " 'garlic': 73,\n",
       " 'alliaceous': 72,\n",
       " 'pine': 72,\n",
       " 'violet': 71,\n",
       " 'peach': 68,\n",
       " 'orange': 68,\n",
       " 'mild': 68,\n",
       " 'natural': 68,\n",
       " 'dairy': 67,\n",
       " 'cherry': 67,\n",
       " 'tobacco': 66,\n",
       " 'lemon': 66,\n",
       " 'cinnamon': 65,\n",
       " 'cocoa': 63,\n",
       " 'warm': 63,\n",
       " 'ripe': 62,\n",
       " 'lily': 62,\n",
       " 'apricot': 60,\n",
       " 'rummy': 59,\n",
       " 'cooked': 59,\n",
       " 'almond': 59,\n",
       " 'cortex': 58,\n",
       " 'anisic': 58,\n",
       " 'milky': 57,\n",
       " 'savory': 57,\n",
       " 'grape': 57,\n",
       " 'geranium': 56,\n",
       " 'hyacinth': 56,\n",
       " 'medicinal': 55,\n",
       " 'grapefruit': 55,\n",
       " 'grassy': 53,\n",
       " 'orris': 53,\n",
       " 'sharp': 52,\n",
       " 'muguet': 52,\n",
       " 'fermented': 51,\n",
       " 'chocolate': 50,\n",
       " 'cucumber': 50,\n",
       " 'lactonic': 50,\n",
       " 'soapy': 50,\n",
       " 'terpenic': 50,\n",
       " 'brown': 49,\n",
       " 'cedar': 48,\n",
       " 'plum': 48,\n",
       " 'estery': 46,\n",
       " 'cognac': 45,\n",
       " 'weedy': 44,\n",
       " 'fishy': 43,\n",
       " 'fruit skin': 43,\n",
       " 'smoky': 42,\n",
       " 'lavender': 42,\n",
       " 'leathery': 42,\n",
       " 'hay': 42,\n",
       " 'clove': 40,\n",
       " 'coumarinic': 40,\n",
       " 'celery': 38,\n",
       " 'potato': 38,\n",
       " 'bready': 38,\n",
       " 'juicy': 37,\n",
       " 'acidic': 37,\n",
       " 'raspberry': 37,\n",
       " 'ozone': 37,\n",
       " 'radish': 37,\n",
       " 'hazelnut': 36,\n",
       " 'strawberry': 35,\n",
       " 'sour': 35,\n",
       " 'sandalwood': 35,\n",
       " 'bitter': 35,\n",
       " 'mentholic': 34,\n",
       " 'alcoholic': 34,\n",
       " 'vetiver': 33,\n",
       " 'hawthorn': 33,\n",
       " 'black currant': 33,\n",
       " 'chemical': 33,\n",
       " 'bergamot': 32,\n",
       " 'jammy': 31,\n",
       " 'marine': 31,\n",
       " 'orangeflower': 30,\n",
       " 'tea': 30,\n",
       " 'peppery': 30,\n",
       " 'sweaty': 30,\n",
       " 'cabbage': 30,\n",
       " 'plastic': 29,\n",
       " 'licorice': 29,\n",
       " 'gardenia': 29,\n",
       " 'neroli': 28,\n",
       " 'watery': 27,\n",
       " 'tomato': 27,\n",
       " 'tonka': 27,\n",
       " 'orchid': 27,\n",
       " 'cumin': 26,\n",
       " 'narcissus': 26,\n",
       " 'dusty': 26,\n",
       " 'patchouli': 26,\n",
       " 'chicken': 26,\n",
       " 'rooty': 24,\n",
       " 'galbanum': 24,\n",
       " 'foliage': 23,\n",
       " 'aromatic': 23,\n",
       " 'beefy': 23,\n",
       " 'ammoniacal': 23,\n",
       " 'tutti frutti': 22,\n",
       " 'fusel': 22,\n",
       " 'maple': 22,\n",
       " 'rubbery': 21,\n",
       " 'solvent': 21,\n",
       " 'toasted': 20,\n",
       " 'lilac': 20,\n",
       " 'ylang': 20,\n",
       " 'popcorn': 20,\n",
       " 'naphthyl': 19,\n",
       " 'fungal': 19,\n",
       " 'carnation': 19,\n",
       " 'pepper bell pepper': 19,\n",
       " 'brothy': 18,\n",
       " 'blueberry': 18,\n",
       " 'rhubarb': 18,\n",
       " 'resinous': 18,\n",
       " 'ketonic': 18,\n",
       " 'fir needle': 18,\n",
       " 'seedy': 17,\n",
       " 'spearmint': 17,\n",
       " 'greasy': 17,\n",
       " 'mossy': 17,\n",
       " 'cloth laundered cloth': 17,\n",
       " 'magnolia': 17,\n",
       " 'brandy': 16,\n",
       " 'mandarin': 16,\n",
       " 'yeasty': 16,\n",
       " 'mimosa': 16,\n",
       " 'eucalyptus': 15,\n",
       " 'mustard': 15,\n",
       " 'passion fruit': 15,\n",
       " 'petal': 15,\n",
       " 'thujonic': 14,\n",
       " 'dirty': 14,\n",
       " 'candy': 14,\n",
       " 'chamomile': 14,\n",
       " 'walnut': 14,\n",
       " 'rancid': 14,\n",
       " 'buchu': 13,\n",
       " 'sage clary sage': 13,\n",
       " 'chrysanthemum': 13,\n",
       " 'faint': 13,\n",
       " 'corn': 13,\n",
       " 'eggy': 13,\n",
       " 'cyclamen': 13,\n",
       " 'petitgrain': 13,\n",
       " 'fennel': 13,\n",
       " 'mango': 13,\n",
       " 'beany': 13,\n",
       " 'sugar': 12,\n",
       " 'privet': 12,\n",
       " 'heliotrope': 12,\n",
       " 'bacon': 12,\n",
       " 'oakmoss': 12,\n",
       " 'gassy': 12,\n",
       " 'corn chip': 12,\n",
       " 'acetic': 11,\n",
       " 'tallow': 11,\n",
       " 'peppermint': 11,\n",
       " 'fried': 11,\n",
       " 'peanut': 11,\n",
       " 'tuberose': 11,\n",
       " 'humus': 11,\n",
       " 'wintergreen': 10,\n",
       " 'incense': 10,\n",
       " 'raw': 10,\n",
       " 'peony': 10,\n",
       " 'caraway': 10,\n",
       " 'basil': 10,\n",
       " 'whiskey': 10,\n",
       " 'malty': 10,\n",
       " 'nasturtium': 10,\n",
       " 'papaya': 9,\n",
       " 'acrylate': 9,\n",
       " 'thyme': 9,\n",
       " 'kiwi': 9,\n",
       " 'fecal': 9,\n",
       " 'tangerine': 9,\n",
       " 'labdanum': 9,\n",
       " 'watermelon': 8,\n",
       " 'seaweed': 8,\n",
       " 'tart': 8,\n",
       " 'catty': 8,\n",
       " 'castoreum': 8,\n",
       " 'wet': 8,\n",
       " 'sugar brown sugar': 8,\n",
       " 'peely': 8,\n",
       " 'cilantro': 8,\n",
       " 'cologne': 8,\n",
       " 'guaiacol': 8,\n",
       " 'myrrh': 8,\n",
       " 'goaty': 8,\n",
       " 'cassia': 8,\n",
       " 'pork': 7,\n",
       " 'raisin': 7,\n",
       " 'sawdust': 7,\n",
       " 'civet': 7,\n",
       " 'blackberry': 7,\n",
       " 'storax': 7,\n",
       " 'ammonia': 7,\n",
       " 'watercress': 7,\n",
       " 'custard': 7,\n",
       " 'cereal': 7,\n",
       " 'grain': 7,\n",
       " 'wasabi': 7,\n",
       " 'saffron': 7,\n",
       " 'bubble gum': 7,\n",
       " 'soft': 7,\n",
       " 'moldy': 7,\n",
       " 'cotton candy': 7,\n",
       " 'costus': 7,\n",
       " 'cranberry': 6,\n",
       " 'seafood': 6,\n",
       " 'fenugreek': 6,\n",
       " 'seashore': 6,\n",
       " 'truffle': 6,\n",
       " 'dewy': 6,\n",
       " 'coriander': 6,\n",
       " 'cypress': 6,\n",
       " 'opoponax': 6,\n",
       " 'nutmeg': 6,\n",
       " 'sassafrass': 6,\n",
       " 'acacia': 6,\n",
       " 'root beer': 6,\n",
       " 'sage': 6,\n",
       " 'lamb': 6,\n",
       " 'cantaloupe': 6,\n",
       " 'fig': 5,\n",
       " 'rue': 5,\n",
       " 'guava': 5,\n",
       " 'painty': 5,\n",
       " 'scallion': 5,\n",
       " 'carrot': 5,\n",
       " 'acetone': 5,\n",
       " 'mutton': 5,\n",
       " 'fleshy': 5,\n",
       " 'dill': 5,\n",
       " 'strong': 5,\n",
       " 'bread baked': 5,\n",
       " 'parsley': 5,\n",
       " 'lovage': 5,\n",
       " 'skunk': 5,\n",
       " 'ginger': 5,\n",
       " 'baked': 5,\n",
       " 'gooseberry': 5,\n",
       " 'cardamom': 5,\n",
       " 'acorn': 5,\n",
       " 'reseda': 5,\n",
       " 'ham': 4,\n",
       " 'verbena': 4,\n",
       " 'guaiacwood': 4,\n",
       " 'grilled': 4,\n",
       " 'durian': 4,\n",
       " 'cananga': 4,\n",
       " 'gasoline': 4,\n",
       " 'gourmand': 4,\n",
       " 'pulpy': 4,\n",
       " 'tagette': 4,\n",
       " 'soup': 4,\n",
       " 'valerian root': 4,\n",
       " 'algae': 4,\n",
       " 'licorice black licorice': 4,\n",
       " 'leek': 4,\n",
       " 'cashew': 4,\n",
       " 'asparagus': 4,\n",
       " 'chicken coup': 4,\n",
       " 'petroleum': 4,\n",
       " 'forest': 4,\n",
       " 'vinegar': 4,\n",
       " 'blossom': 4,\n",
       " 'buttermilk': 4,\n",
       " 'butterscotch': 4,\n",
       " 'molasses': 4,\n",
       " 'autumn': 4,\n",
       " 'shrimp': 3,\n",
       " 'beeswax': 3,\n",
       " 'paper': 3,\n",
       " 'peanut butter': 3,\n",
       " 'pepper black pepper': 3,\n",
       " 'powerful': 3,\n",
       " 'pumpkin': 3,\n",
       " 'cookie': 3,\n",
       " 'rich': 3,\n",
       " 'cider': 3,\n",
       " 'prune': 3,\n",
       " 'clam': 3,\n",
       " 'deep': 3,\n",
       " 'slight': 3,\n",
       " 'taco': 3,\n",
       " 'heavy': 3,\n",
       " 'hairy': 3,\n",
       " 'menthol': 3,\n",
       " 'mahogany': 3,\n",
       " 'graham cracker': 3,\n",
       " 'lettuce': 3,\n",
       " 'liver': 3,\n",
       " 'linden flower': 3,\n",
       " 'bread crust': 3,\n",
       " 'juniper': 3,\n",
       " 'broccoli': 3,\n",
       " 'lard': 3,\n",
       " 'freesia': 3,\n",
       " 'tarragon': 3,\n",
       " 'rotten': 2,\n",
       " 'tequila': 2,\n",
       " 'lychee': 2,\n",
       " 'praline': 2,\n",
       " 'loganberry': 2,\n",
       " 'astringent': 2,\n",
       " 'filbert': 2,\n",
       " 'hydrocarbon': 2,\n",
       " 'linalool': 2,\n",
       " 'bouillon': 2,\n",
       " 'marzipan': 2,\n",
       " 'agarwood': 2,\n",
       " 'quince': 2,\n",
       " 'urine': 2,\n",
       " 'bread rye bread': 2,\n",
       " 'immortelle': 2,\n",
       " 'amine': 2,\n",
       " 'rubber': 2,\n",
       " 'pleasant': 2,\n",
       " 'turnup': 2,\n",
       " 'oriental': 2,\n",
       " 'salty': 2,\n",
       " 'davana': 2,\n",
       " 'sappy': 2,\n",
       " 'artichoke': 2,\n",
       " 'toffee': 2,\n",
       " 'arrack': 2,\n",
       " 'nitrile': 2,\n",
       " 'cistus': 2,\n",
       " 'caprylic': 2,\n",
       " 'cauliflower': 2,\n",
       " 'peel': 2,\n",
       " 'egg nog': 2,\n",
       " 'thujone': 2,\n",
       " 'elderberry': 2,\n",
       " 'elderflower': 2,\n",
       " 'bloody': 2,\n",
       " 'cotton': 2,\n",
       " 'methyl': 2,\n",
       " 'angelica': 2,\n",
       " 'wild': 2,\n",
       " 'spruce': 2,\n",
       " 'sandy': 1,\n",
       " 'yogurt': 1,\n",
       " 'sarsaparilla': 1,\n",
       " 'sassafras': 1,\n",
       " 'starfruit': 1,\n",
       " 'umami': 1,\n",
       " 'styrene': 1,\n",
       " 'skin': 1,\n",
       " 'shell': 1,\n",
       " 'smelling': 1,\n",
       " 'worty': 1,\n",
       " 'soil': 1,\n",
       " 'wormwood': 1,\n",
       " 'turkey': 1,\n",
       " 'thiamine': 1,\n",
       " 'spinach': 1,\n",
       " 'stalk': 1,\n",
       " 'vinyl': 1,\n",
       " 'verdox': 1,\n",
       " 'straw': 1,\n",
       " 'sauerkraut': 1,\n",
       " 'styrax': 1,\n",
       " 'tar': 1,\n",
       " 'absinthe': 1,\n",
       " 'salmon': 1,\n",
       " 'characteristic': 1,\n",
       " 'chavicol': 1,\n",
       " 'chervil': 1,\n",
       " 'chicory': 1,\n",
       " 'chlorine': 1,\n",
       " 'chloroform': 1,\n",
       " 'coal': 1,\n",
       " 'cornmint': 1,\n",
       " 'creosote': 1,\n",
       " 'cresol': 1,\n",
       " 'damascone': 1,\n",
       " 'dank': 1,\n",
       " 'date': 1,\n",
       " 'de': 1,\n",
       " 'dead': 1,\n",
       " 'diffuse': 1,\n",
       " 'dihydrojasmonate': 1,\n",
       " 'dull': 1,\n",
       " 'egg': 1,\n",
       " 'egg yolk': 1,\n",
       " 'elemi': 1,\n",
       " 'ethyl': 1,\n",
       " 'charred': 1,\n",
       " 'cellar': 1,\n",
       " 'flat': 1,\n",
       " 'catechu': 1,\n",
       " 'acetaldehyde': 1,\n",
       " 'alkane': 1,\n",
       " 'armoise': 1,\n",
       " 'bay': 1,\n",
       " 'benzaldehyde': 1,\n",
       " 'benzoate': 1,\n",
       " 'benzoin': 1,\n",
       " 'boiled': 1,\n",
       " 'bois': 1,\n",
       " 'bonbon': 1,\n",
       " 'borneol': 1,\n",
       " 'boronia': 1,\n",
       " 'bread': 1,\n",
       " 'bud': 1,\n",
       " 'butter rancid': 1,\n",
       " 'cacao': 1,\n",
       " 'capers': 1,\n",
       " 'cardboard': 1,\n",
       " 'carrot seed': 1,\n",
       " 'carvone': 1,\n",
       " 'cassie': 1,\n",
       " 'evil': 1,\n",
       " 'fragrant': 1,\n",
       " 'rotting': 1,\n",
       " 'moss': 1,\n",
       " 'muttony': 1,\n",
       " 'naphtha': 1,\n",
       " 'needle': 1,\n",
       " 'needles': 1,\n",
       " 'nerol': 1,\n",
       " 'nice': 1,\n",
       " 'ocean': 1,\n",
       " 'old': 1,\n",
       " 'osmanthus': 1,\n",
       " 'oxide': 1,\n",
       " 'palmarosa': 1,\n",
       " 'pastry': 1,\n",
       " 'pistachio': 1,\n",
       " 'plowed': 1,\n",
       " 'pomegranate': 1,\n",
       " 'privetblossom': 1,\n",
       " 'pyrazine': 1,\n",
       " 'pyridine': 1,\n",
       " 'red': 1,\n",
       " 'resin': 1,\n",
       " 'rindy': 1,\n",
       " 'mouldy': 1,\n",
       " 'mignonette': 1,\n",
       " 'frankincense': 1,\n",
       " 'methyl heptine carbonate': 1,\n",
       " 'genet': 1,\n",
       " 'goats': 1,\n",
       " 'gravy': 1,\n",
       " 'harsh': 1,\n",
       " 'hesperidic': 1,\n",
       " 'impacting': 1,\n",
       " 'indole': 1,\n",
       " 'iodine': 1,\n",
       " 'jackfruit': 1,\n",
       " 'jam': 1,\n",
       " 'kernel': 1,\n",
       " 'kimchi': 1,\n",
       " 'absolute': 1,\n",
       " 'light': 1,\n",
       " 'linden': 1,\n",
       " 'liquid': 1,\n",
       " 'mace': 1,\n",
       " 'marjoram': 1,\n",
       " 'may': 1,\n",
       " 'medical': 1,\n",
       " 'mercaptan': 1,\n",
       " 'zesty': 1}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(encoded_behavior.sum().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2403647 entries, 0 to 2403646\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Dtype \n",
      "---  ------    ----- \n",
      " 0   variable  object\n",
      " 1   value     int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 36.7+ MB\n"
     ]
    }
   ],
   "source": [
    "behavior_wise = pd.melt(encoded_behavior)\n",
    "behavior_wise.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value\n",
       "0    2383360\n",
       "1      20287\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior_wise['value'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_earthy_df = encoded_behavior.drop(columns=['earthy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_df = pd.DataFrame(non_earthy_df[encoded_behavior['earthy']==1].sum().sort_values(ascending=False), columns=['sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All descriptors that are mentioned along with \"earthy\" more than 10 times.\n",
    "analysis_df.query('sum>10').plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get required labels only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_columns = list(encoded_behavior.columns)\n",
    "# elements of required desc not in og_columns\n",
    "missing_desc = []\n",
    "for req in required_desc:\n",
    "    if req not in og_columns:\n",
    "        missing_desc.append(req)\n",
    "missing_desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .remove('fruit skin') # fruit skin from required_desc missing in the goodscents dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_req_desc(desc_string):\n",
    "    desc_list = desc_string.split(';')\n",
    "    desc_set = set()\n",
    "    for desc in desc_list:\n",
    "        if desc in required_desc:\n",
    "            desc_set.update([desc])\n",
    "    return ';'.join(desc_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'creamy;sweet;caramellic'"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_req_desc('sweet;creamy;brown;caramellic;testing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "cid_behaviors_mol_encoded_v2['Updated_Desc_v3'] = cid_behaviors_mol_encoded_v2['Updated_Desc_v2'].apply(lambda x: get_req_desc(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>IsomericSMILES</th>\n",
       "      <th>Updated_Desc_v2</th>\n",
       "      <th>absinthe</th>\n",
       "      <th>absolute</th>\n",
       "      <th>acacia</th>\n",
       "      <th>acetaldehyde</th>\n",
       "      <th>acetic</th>\n",
       "      <th>acetone</th>\n",
       "      <th>acidic</th>\n",
       "      <th>acorn</th>\n",
       "      <th>...</th>\n",
       "      <th>winey</th>\n",
       "      <th>wintergreen</th>\n",
       "      <th>woody</th>\n",
       "      <th>wormwood</th>\n",
       "      <th>worty</th>\n",
       "      <th>yeasty</th>\n",
       "      <th>ylang</th>\n",
       "      <th>yogurt</th>\n",
       "      <th>zesty</th>\n",
       "      <th>Updated_Desc_v3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>fishy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fishy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CC(C)C(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fruity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
       "      <td>odorless</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>odorless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCC(=O)C(=O)O</td>\n",
       "      <td>creamy;lactonic;caramellic;brown;sweet</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>creamy;lactonic;sweet;caramellic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CC(C)CC(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "          IsomericSMILES                         Updated_Desc_v2  absinthe   \n",
       "0                CC(CN)O                                   fishy         0  \\\n",
       "1       CC(C)C(=O)C(=O)O                                  fruity         0   \n",
       "2  C(CC(=O)O)C(=O)C(=O)O                                odorless         0   \n",
       "3          CCC(=O)C(=O)O  creamy;lactonic;caramellic;brown;sweet         0   \n",
       "4      CC(C)CC(=O)C(=O)O                                  fruity         0   \n",
       "\n",
       "   absolute  acacia  acetaldehyde  acetic  acetone  acidic  acorn  ...  winey   \n",
       "0         0       0             0       0        0       0      0  ...      0  \\\n",
       "1         0       0             0       0        0       0      0  ...      0   \n",
       "2         0       0             0       0        0       0      0  ...      0   \n",
       "3         0       0             0       0        0       0      0  ...      0   \n",
       "4         0       0             0       0        0       0      0  ...      0   \n",
       "\n",
       "   wintergreen  woody  wormwood  worty  yeasty  ylang  yogurt  zesty   \n",
       "0            0      0         0      0       0      0       0      0  \\\n",
       "1            0      0         0      0       0      0       0      0   \n",
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       "3            0      0         0      0       0      0       0      0   \n",
       "4            0      0         0      0       0      0       0      0   \n",
       "\n",
       "                    Updated_Desc_v3  \n",
       "0                             fishy  \n",
       "1                            fruity  \n",
       "2                          odorless  \n",
       "3  creamy;lactonic;sweet;caramellic  \n",
       "4                            fruity  \n",
       "\n",
       "[5 rows x 530 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid_behaviors_mol_encoded_v2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "required_goodscents_dataset = cid_behaviors_mol_encoded_v2[['IsomericSMILES', 'Updated_Desc_v3']+required_desc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
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       "          IsomericSMILES                   Updated_Desc_v3  alcoholic   \n",
       "0                CC(CN)O                             fishy          0  \\\n",
       "1       CC(C)C(=O)C(=O)O                            fruity          0   \n",
       "2  C(CC(=O)O)C(=O)C(=O)O                          odorless          0   \n",
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     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "required_goodscents_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4561 entries, 0 to 4560\n",
      "Columns: 140 entries, IsomericSMILES to woody\n",
      "dtypes: int64(138), object(2)\n",
      "memory usage: 4.9+ MB\n"
     ]
    }
   ],
   "source": [
    "required_goodscents_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4561 rows × 138 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      alcoholic  aldehydic  alliaceous  almond  amber  animal  anisic  apple   \n",
       "0             0          0           0       0      0       0       0      0  \\\n",
       "1             0          0           0       0      0       0       0      0   \n",
       "2             0          0           0       0      0       0       0      0   \n",
       "3             0          0           0       0      0       0       0      0   \n",
       "4             0          0           0       0      0       0       0      0   \n",
       "...         ...        ...         ...     ...    ...     ...     ...    ...   \n",
       "4556          0          0           0       0      0       0       0      0   \n",
       "4557          0          0           0       0      0       0       0      0   \n",
       "4558          0          0           0       0      0       0       0      0   \n",
       "4559          0          0           1       0      0       0       0      0   \n",
       "4560          0          0           0       0      0       0       0      0   \n",
       "\n",
       "      apricot  aromatic  ...  tropical  vanilla  vegetable  vetiver  violet   \n",
       "0           0         0  ...         0        0          0        0       0  \\\n",
       "1           0         0  ...         0        0          0        0       0   \n",
       "2           0         0  ...         0        0          0        0       0   \n",
       "3           0         0  ...         0        0          0        0       0   \n",
       "4           0         0  ...         0        0          0        0       0   \n",
       "...       ...       ...  ...       ...      ...        ...      ...     ...   \n",
       "4556        0         0  ...         0        0          0        0       0   \n",
       "4557        0         0  ...         0        0          0        0       0   \n",
       "4558        0         0  ...         0        0          0        0       0   \n",
       "4559        0         0  ...         0        0          0        0       0   \n",
       "4560        0         0  ...         0        0          0        0       0   \n",
       "\n",
       "      warm  waxy  weedy  winey  woody  \n",
       "0        0     0      0      0      0  \n",
       "1        0     0      0      0      0  \n",
       "2        0     0      0      0      0  \n",
       "3        0     0      0      0      0  \n",
       "4        0     0      0      0      0  \n",
       "...    ...   ...    ...    ...    ...  \n",
       "4556     0     0      0      0      0  \n",
       "4557     0     0      0      0      0  \n",
       "4558     0     0      0      0      0  \n",
       "4559     0     0      0      0      0  \n",
       "4560     0     0      0      0      0  \n",
       "\n",
       "[4561 rows x 138 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "required_encoded = required_goodscents_dataset.drop(columns=['IsomericSMILES', 'Updated_Desc_v3'])\n",
    "required_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <th>3713</th>\n",
       "      <td>0</td>\n",
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       "      <th>3968</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1573</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2762</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2459</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      count\n",
       "3713      0\n",
       "3968      0\n",
       "425       0\n",
       "1160      0\n",
       "909       0\n",
       "...     ...\n",
       "1573      0\n",
       "616       0\n",
       "2762      0\n",
       "93        0\n",
       "2459      0\n",
       "\n",
       "[168 rows x 1 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# molecules with no descriptors\n",
    "no_desc_df = pd.DataFrame(required_encoded.sum(axis=1).sort_values(), columns=['count']).query('count==0')\n",
    "no_desc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>IsomericSMILES</th>\n",
       "      <th>Updated_Desc_v3</th>\n",
       "      <th>alcoholic</th>\n",
       "      <th>aldehydic</th>\n",
       "      <th>alliaceous</th>\n",
       "      <th>almond</th>\n",
       "      <th>amber</th>\n",
       "      <th>animal</th>\n",
       "      <th>anisic</th>\n",
       "      <th>apple</th>\n",
       "      <th>...</th>\n",
       "      <th>tropical</th>\n",
       "      <th>vanilla</th>\n",
       "      <th>vegetable</th>\n",
       "      <th>vetiver</th>\n",
       "      <th>violet</th>\n",
       "      <th>warm</th>\n",
       "      <th>waxy</th>\n",
       "      <th>weedy</th>\n",
       "      <th>winey</th>\n",
       "      <th>woody</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>fishy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CC(C)C(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
       "      <td>odorless</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCC(=O)C(=O)O</td>\n",
       "      <td>creamy;lactonic;sweet;caramellic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CC(C)CC(=O)C(=O)O</td>\n",
       "      <td>fruity</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          IsomericSMILES                   Updated_Desc_v3  alcoholic   \n",
       "0                CC(CN)O                             fishy          0  \\\n",
       "1       CC(C)C(=O)C(=O)O                            fruity          0   \n",
       "2  C(CC(=O)O)C(=O)C(=O)O                          odorless          0   \n",
       "3          CCC(=O)C(=O)O  creamy;lactonic;sweet;caramellic          0   \n",
       "4      CC(C)CC(=O)C(=O)O                            fruity          0   \n",
       "\n",
       "   aldehydic  alliaceous  almond  amber  animal  anisic  apple  ...  tropical   \n",
       "0          0           0       0      0       0       0      0  ...         0  \\\n",
       "1          0           0       0      0       0       0      0  ...         0   \n",
       "2          0           0       0      0       0       0      0  ...         0   \n",
       "3          0           0       0      0       0       0      0  ...         0   \n",
       "4          0           0       0      0       0       0      0  ...         0   \n",
       "\n",
       "   vanilla  vegetable  vetiver  violet  warm  waxy  weedy  winey  woody  \n",
       "0        0          0        0       0     0     0      0      0      0  \n",
       "1        0          0        0       0     0     0      0      0      0  \n",
       "2        0          0        0       0     0     0      0      0      0  \n",
       "3        0          0        0       0     0     0      0      0      0  \n",
       "4        0          0        0       0     0     0      0      0      0  \n",
       "\n",
       "[5 rows x 140 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "required_goodscents_dataset_v2 = required_goodscents_dataset.drop(no_desc_df.index).reset_index(drop=True)\n",
    "required_goodscents_dataset_v2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Updated_Desc_v3</th>\n",
       "      <th>alcoholic</th>\n",
       "      <th>aldehydic</th>\n",
       "      <th>alliaceous</th>\n",
       "      <th>almond</th>\n",
       "      <th>amber</th>\n",
       "      <th>animal</th>\n",
       "      <th>anisic</th>\n",
       "      <th>apple</th>\n",
       "      <th>...</th>\n",
       "      <th>tropical</th>\n",
       "      <th>vanilla</th>\n",
       "      <th>vegetable</th>\n",
       "      <th>vetiver</th>\n",
       "      <th>violet</th>\n",
       "      <th>warm</th>\n",
       "      <th>waxy</th>\n",
       "      <th>weedy</th>\n",
       "      <th>winey</th>\n",
       "      <th>woody</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [IsomericSMILES, Updated_Desc_v3, alcoholic, aldehydic, alliaceous, almond, amber, animal, anisic, apple, apricot, aromatic, balsamic, banana, beefy, bergamot, berry, bitter, black currant, brandy, burnt, buttery, cabbage, camphoreous, caramellic, cedar, celery, chamomile, cheesy, cherry, chocolate, cinnamon, citrus, clean, clove, cocoa, coconut, coffee, cognac, cooked, cooling, cortex, coumarinic, creamy, cucumber, dairy, dry, earthy, ethereal, fatty, fermented, fishy, floral, fresh, fruit skin, fruity, garlic, gassy, geranium, grape, grapefruit, grassy, green, hawthorn, hay, hazelnut, herbal, honey, hyacinth, jasmin, juicy, ketonic, lactonic, lavender, leafy, leathery, lemon, lily, malty, meaty, medicinal, melon, metallic, milky, mint, muguet, mushroom, musk, musty, natural, nutty, odorless, oily, onion, orange, orangeflower, orris, ozone, peach, pear, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 140 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# required_goodscents_dataset_v2.query(\"Updated_Desc_v3 == None\")\n",
    "required_goodscents_dataset_v2[required_goodscents_dataset_v2['Updated_Desc_v3'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4393 entries, 0 to 4392\n",
      "Columns: 140 entries, IsomericSMILES to woody\n",
      "dtypes: int64(138), object(2)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "required_goodscents_dataset_v2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "required_encoded_v2 = required_goodscents_dataset_v2.drop(columns=['IsomericSMILES', 'Updated_Desc_v3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead 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>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [count]\n",
       "Index: []"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# molecules with no descriptors\n",
    "pd.DataFrame(required_encoded_v2.sum(axis=1).sort_values(), columns=['count']).query('count==0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fruity       1258\n",
       "green        1090\n",
       "sweet        1009\n",
       "floral        921\n",
       "woody         621\n",
       "             ... \n",
       "ketonic        18\n",
       "brandy         16\n",
       "chamomile      14\n",
       "gassy          12\n",
       "malty          10\n",
       "Length: 138, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "required_encoded_v2.sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Updated_Desc_v3</th>\n",
       "      <th>alcoholic</th>\n",
       "      <th>aldehydic</th>\n",
       "      <th>alliaceous</th>\n",
       "      <th>almond</th>\n",
       "      <th>amber</th>\n",
       "      <th>animal</th>\n",
       "      <th>anisic</th>\n",
       "      <th>apple</th>\n",
       "      <th>...</th>\n",
       "      <th>tropical</th>\n",
       "      <th>vanilla</th>\n",
       "      <th>vegetable</th>\n",
       "      <th>vetiver</th>\n",
       "      <th>violet</th>\n",
       "      <th>warm</th>\n",
       "      <th>waxy</th>\n",
       "      <th>weedy</th>\n",
       "      <th>winey</th>\n",
       "      <th>woody</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [IsomericSMILES, Updated_Desc_v3, alcoholic, aldehydic, alliaceous, almond, amber, animal, anisic, apple, apricot, aromatic, balsamic, banana, beefy, bergamot, berry, bitter, black currant, brandy, burnt, buttery, cabbage, camphoreous, caramellic, cedar, celery, chamomile, cheesy, cherry, chocolate, cinnamon, citrus, clean, clove, cocoa, coconut, coffee, cognac, cooked, cooling, cortex, coumarinic, creamy, cucumber, dairy, dry, earthy, ethereal, fatty, fermented, fishy, floral, fresh, fruit skin, fruity, garlic, gassy, geranium, grape, grapefruit, grassy, green, hawthorn, hay, hazelnut, herbal, honey, hyacinth, jasmin, juicy, ketonic, lactonic, lavender, leafy, leathery, lemon, lily, malty, meaty, medicinal, melon, metallic, milky, mint, muguet, mushroom, musk, musty, natural, nutty, odorless, oily, onion, orange, orangeflower, orris, ozone, peach, pear, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 140 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "required_goodscents_dataset_v2[required_goodscents_dataset_v2['IsomericSMILES'].duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "required_goodscents_dataset_v2.to_csv('curated_goodcents.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking nan associated values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Descriptors</th>\n",
       "      <th>TGSC ID</th>\n",
       "      <th>CID</th>\n",
       "      <th>Concentration %</th>\n",
       "      <th>Solvent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stimulus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1523-19-9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1010921</td>\n",
       "      <td>137059</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53584-56-8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1660331</td>\n",
       "      <td>439314</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6698-82-0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1025691</td>\n",
       "      <td>532739</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78183-56-9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1660341</td>\n",
       "      <td>447765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Descriptors  TGSC ID     CID  Concentration % Solvent\n",
       "Stimulus                                                        \n",
       "1523-19-9          NaN  1010921  137059            100.0     NaN\n",
       "53584-56-8         NaN  1660331  439314              NaN     NaN\n",
       "6698-82-0          NaN  1025691  532739            100.0     NaN\n",
       "78183-56-9         NaN  1660341  447765              NaN     NaN"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_behaviors = pd.merge(behavior_nan, stimuli, how='inner', left_index=True, right_index=True)\n",
    "nan_behaviors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CID</th>\n",
       "      <th>IsomericSMILES</th>\n",
       "      <th>Descriptors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>137059</td>\n",
       "      <td>COC1=CC=C(C=C1)OC(=O)C2=CC=CC=C2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>439314</td>\n",
       "      <td>C[C@H](C(=O)C)O</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>447765</td>\n",
       "      <td>C[C@@H](C(=O)C)O</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>532739</td>\n",
       "      <td>CC(C)C(=O)OC1=CC=C(C=C1)OC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CID                    IsomericSMILES Descriptors\n",
       "0  137059  COC1=CC=C(C=C1)OC(=O)C2=CC=CC=C2         NaN\n",
       "1  439314                   C[C@H](C(=O)C)O         NaN\n",
       "2  447765                  C[C@@H](C(=O)C)O         NaN\n",
       "3  532739        CC(C)C(=O)OC1=CC=C(C=C1)OC         NaN"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_behaviors_mol = pd.merge(molecules['IsomericSMILES'], nan_behaviors[['Descriptors', 'CID']],how='inner', on='CID')\n",
    "nan_behaviors_mol"
   ]
  }
 ],
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