{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/home/dwip.dalal/AIISC/ScratchIMP/data/data_ytrain.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Intent Of Lie (Gaining Advantage/Gaining Esteem/Avoiding Punishment/Avoiding Embarrassment/Protecting Themselves)</th>\n",
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       "    ordered_list_1         ordered_list_3   ordered_list_4  \\\n",
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       "4  [1, 0, 0, 0, 0]  [1, 1, 0, 0, 0, 0, 0]  [0, 0, 0, 1, 0]   \n",
       "\n",
       "  Intent Of Lie (Gaining Advantage/Gaining Esteem/Avoiding Punishment/Avoiding Embarrassment/Protecting Themselves)  \\\n",
       "0                                               Nan4                                                                  \n",
       "1                                  Gaining Advantage                                                                  \n",
       "2                                    Defaming Esteem                                                                  \n",
       "3                                  Gaining Advantage                                                                  \n",
       "4                                     Gaining Esteem                                                                  \n",
       "\n",
       "          ordered_list_7  \n",
       "0  [0, 0, 1, 0, 0, 0, 0]  \n",
       "1  [1, 0, 0, 0, 0, 0, 0]  \n",
       "2  [1, 0, 0, 0, 0, 0, 0]  \n",
       "3  [0, 0, 0, 0, 0, 0, 1]  \n",
       "4  [0, 0, 0, 0, 0, 0, 1]  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['ordered_list_1', 'ordered_list_3', 'ordered_list_4', 'ordered_list_7']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>[0, 0, 1, 0, 0]</td>\n",
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       "      <td>[1, 0, 0, 0, 0]</td>\n",
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      "text/plain": [
       "    ordered_list_1         ordered_list_3   ordered_list_4  \\\n",
       "0  [1, 0, 0, 0, 0]  [1, 1, 0, 0, 0, 0, 0]  [0, 0, 0, 0, 1]   \n",
       "1  [0, 0, 1, 0, 0]  [0, 1, 0, 0, 0, 1, 0]  [1, 0, 0, 0, 0]   \n",
       "2  [0, 0, 0, 0, 1]  [1, 1, 0, 0, 0, 0, 0]  [0, 1, 0, 0, 0]   \n",
       "3  [0, 0, 0, 0, 1]  [1, 1, 0, 0, 0, 1, 0]  [0, 0, 0, 1, 0]   \n",
       "4  [1, 0, 0, 0, 0]  [1, 1, 0, 0, 0, 0, 0]  [0, 0, 0, 1, 0]   \n",
       "\n",
       "          ordered_list_7  \n",
       "0  [0, 0, 1, 0, 0, 0, 0]  \n",
       "1  [1, 0, 0, 0, 0, 0, 0]  \n",
       "2  [1, 0, 0, 0, 0, 0, 0]  \n",
       "3  [0, 0, 0, 0, 0, 0, 1]  \n",
       "4  [0, 0, 0, 0, 0, 0, 1]  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ordered_list_1'] = df['ordered_list_1'].apply(ast.literal_eval).apply(np.array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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