spotify_multivariate_label_creation_large_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.read_csv(r'E:\\logdata\\test_set\\log_prehistory_20180715_000000000000.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "                               session_id  session_position  session_length  \\\n",
       "0  0_00001e52-55c0-470c-8d32-85c691c2019a                 1              16   \n",
       "1  0_00001e52-55c0-470c-8d32-85c691c2019a                 2              16   \n",
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       "3  0_00001e52-55c0-470c-8d32-85c691c2019a                 4              16   \n",
       "4  0_00001e52-55c0-470c-8d32-85c691c2019a                 5              16   \n",
       "\n",
       "                           track_id_clean  skip_1  skip_2  skip_3  \\\n",
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       "4  t_b10f5a2a-ee7b-47c3-9789-4ce0f96c6989    True    True    True   \n",
       "\n",
       "   not_skipped  context_switch  no_pause_before_play  ...  \\\n",
       "0        False               0                     0  ...   \n",
       "1        False               1                     1  ...   \n",
       "2         True               0                     0  ...   \n",
       "3        False               0                     0  ...   \n",
       "4        False               0                     1  ...   \n",
       "\n",
       "   long_pause_before_play  hist_user_behavior_n_seekfwd  \\\n",
       "0                       0                             1   \n",
       "1                       0                             0   \n",
       "2                       1                             0   \n",
       "3                       1                             0   \n",
       "4                       0                             0   \n",
       "\n",
       "   hist_user_behavior_n_seekback  hist_user_behavior_is_shuffle  hour_of_day  \\\n",
       "0                              0                          False           21   \n",
       "1                              0                          False           21   \n",
       "2                              1                          False           21   \n",
       "3                              0                          False           21   \n",
       "4                              0                          False           21   \n",
       "\n",
       "         date premium     context_type hist_user_behavior_reason_start  \\\n",
       "0  2018-07-14   False          catalog                         appload   \n",
       "1  2018-07-14   False  user_collection                        clickrow   \n",
       "2  2018-07-14   False  user_collection                        clickrow   \n",
       "3  2018-07-14   False  user_collection                       trackdone   \n",
       "4  2018-07-14   False  user_collection                          fwdbtn   \n",
       "\n",
       "  hist_user_behavior_reason_end  \n",
       "0                       endplay  \n",
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       "2                     trackdone  \n",
       "3                        fwdbtn  \n",
       "4                        fwdbtn  \n",
       "\n",
       "[5 rows x 21 columns]"
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['session_id', 'session_position', 'session_length', 'track_id_clean',\n",
       "       'skip_1', 'skip_2', 'skip_3', 'not_skipped', 'context_switch',\n",
       "       'no_pause_before_play', 'short_pause_before_play',\n",
       "       'long_pause_before_play', 'hist_user_behavior_n_seekfwd',\n",
       "       'hist_user_behavior_n_seekback', 'hist_user_behavior_is_shuffle',\n",
       "       'hour_of_day', 'date', 'premium', 'context_type',\n",
       "       'hist_user_behavior_reason_start', 'hist_user_behavior_reason_end'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dat[['session_id','track_id_clean', 'skip_1', 'skip_2', 'skip_3', 'not_skipped','context_switch']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "                               session_id  \\\n",
       "0  0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "1  0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "2  0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "3  0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "4  0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "5  0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "6  0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "7  0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "8  0_00006529-2d8d-41f8-9f13-7ee65e53ae9a   \n",
       "9  0_00006529-2d8d-41f8-9f13-7ee65e53ae9a   \n",
       "\n",
       "                           track_id_clean  skip_1  skip_2  skip_3  \\\n",
       "0  t_a0b2a533-caad-4a72-af14-e647bd7e9c1f   False    True    True   \n",
       "1  t_a998907c-e11c-4a4e-b9d2-b34846bfb839   False   False    True   \n",
       "2  t_977b3ee2-e7a8-4357-b164-f663446c1a25   False   False   False   \n",
       "3  t_e2dda38d-f826-481b-b49a-a1d9051f5c6d   False    True    True   \n",
       "4  t_b10f5a2a-ee7b-47c3-9789-4ce0f96c6989    True    True    True   \n",
       "5  t_a998907c-e11c-4a4e-b9d2-b34846bfb839   False    True    True   \n",
       "6  t_977b3ee2-e7a8-4357-b164-f663446c1a25   False   False    True   \n",
       "7  t_e2dda38d-f826-481b-b49a-a1d9051f5c6d   False    True    True   \n",
       "8  t_312b3509-48c1-4928-a6e5-d3e7a51c216b    True    True    True   \n",
       "9  t_77b02acb-1b1f-4b36-b8fc-2c3e01892b9a   False   False   False   \n",
       "\n",
       "   not_skipped  context_switch  \n",
       "0        False               0  \n",
       "1        False               1  \n",
       "2         True               0  \n",
       "3        False               0  \n",
       "4        False               0  \n",
       "5        False               0  \n",
       "6        False               0  \n",
       "7        False               0  \n",
       "8        False               0  \n",
       "9         True               0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  del sys.path[0]\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "skip_1 = df['skip_1'].replace({False: 0,True: -1})\n",
    "skip_1 = pd.DataFrame(skip_1)\n",
    "skip_2 = df['skip_2'].replace({False: 0,True: -0.5})\n",
    "skip_2 = pd.DataFrame(skip_2)\n",
    "skip_3 = df['skip_3'].replace({False: 0,True: 0.2})\n",
    "skip_3 = pd.DataFrame(skip_3)\n",
    "not_skipped = df['not_skipped'].replace({False: 0,True: 1})\n",
    "not_skipped = pd.DataFrame(not_skipped)\n",
    "\n",
    "### replace columns###\n",
    "\n",
    "df['skip_1']=skip_1\n",
    "df['skip_2']=skip_2\n",
    "df['skip_3']=skip_3\n",
    "df['not_skipped']=not_skipped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                session_id  \\\n",
       "0   0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "1   0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "2   0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "3   0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "4   0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "5   0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "6   0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
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       "14  0_00006529-2d8d-41f8-9f13-7ee65e53ae9a   \n",
       "\n",
       "                            track_id_clean  skip_1  skip_2  skip_3  \\\n",
       "0   t_a0b2a533-caad-4a72-af14-e647bd7e9c1f       0    -0.5     0.2   \n",
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       "2   t_977b3ee2-e7a8-4357-b164-f663446c1a25       0     0.0     0.0   \n",
       "3   t_e2dda38d-f826-481b-b49a-a1d9051f5c6d       0    -0.5     0.2   \n",
       "4   t_b10f5a2a-ee7b-47c3-9789-4ce0f96c6989      -1    -0.5     0.2   \n",
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       "8   t_312b3509-48c1-4928-a6e5-d3e7a51c216b      -1    -0.5     0.2   \n",
       "9   t_77b02acb-1b1f-4b36-b8fc-2c3e01892b9a       0     0.0     0.0   \n",
       "10  t_97f5ce68-328d-4690-891e-55ed364dbd02       0     0.0     0.2   \n",
       "11  t_c0de25db-8c01-4122-9991-27132e852a18       0     0.0     0.2   \n",
       "12  t_1dba18ad-1ddb-4e97-983b-4af57e18d84a      -1    -0.5     0.2   \n",
       "13  t_7e616ed3-115f-4a97-b90e-d128c392fae0       0    -0.5     0.2   \n",
       "14  t_f74acbb5-05a3-4588-ba33-23899276d9d2       0     0.0     0.0   \n",
       "\n",
       "    not_skipped  context_switch  \n",
       "0             0               0  \n",
       "1             0               1  \n",
       "2             1               0  \n",
       "3             0               0  \n",
       "4             0               0  \n",
       "5             0               0  \n",
       "6             0               0  \n",
       "7             0               0  \n",
       "8             0               0  \n",
       "9             1               0  \n",
       "10            0               0  \n",
       "11            0               0  \n",
       "12            0               0  \n",
       "13            0               0  \n",
       "14            1               0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "session_id         object\n",
       "track_id_clean     object\n",
       "skip_1              int64\n",
       "skip_2            float64\n",
       "skip_3            float64\n",
       "not_skipped         int64\n",
       "context_switch      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
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   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_score = df['skip_1'] + df['skip_2']+ df['skip_3']+ df['not_skipped']\n",
    "skip_score = pd.DataFrame(skip_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     0\n",
       "0 -0.3\n",
       "1  0.2\n",
       "2  1.0\n",
       "3 -0.3\n",
       "4 -1.3\n",
       "5 -0.3\n",
       "6  0.2\n",
       "7 -0.3\n",
       "8 -1.3\n",
       "9  1.0"
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     "metadata": {},
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   "source": [
    "skip_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "behavior = dat[['hist_user_behavior_reason_start','hist_user_behavior_reason_end','hist_user_behavior_is_shuffle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hist_user_behavior_reason_start hist_user_behavior_reason_end  \\\n",
       "0                          appload                       endplay   \n",
       "1                         clickrow                       endplay   \n",
       "2                         clickrow                     trackdone   \n",
       "3                        trackdone                        fwdbtn   \n",
       "4                           fwdbtn                        fwdbtn   \n",
       "5                           fwdbtn                       endplay   \n",
       "6                         clickrow                        fwdbtn   \n",
       "7                           fwdbtn                       endplay   \n",
       "8                           fwdbtn                       endplay   \n",
       "9                          appload                     trackdone   \n",
       "10                       trackdone                        fwdbtn   \n",
       "11                          fwdbtn                        fwdbtn   \n",
       "12                          fwdbtn                        fwdbtn   \n",
       "13                          fwdbtn                        fwdbtn   \n",
       "14                          fwdbtn                     trackdone   \n",
       "\n",
       "    hist_user_behavior_is_shuffle  \n",
       "0                           False  \n",
       "1                           False  \n",
       "2                           False  \n",
       "3                           False  \n",
       "4                           False  \n",
       "5                           False  \n",
       "6                           False  \n",
       "7                           False  \n",
       "8                            True  \n",
       "9                            True  \n",
       "10                           True  \n",
       "11                           True  \n",
       "12                           True  \n",
       "13                           True  \n",
       "14                           True  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['appload', 'clickrow', 'trackdone', 'fwdbtn', 'backbtn', 'playbtn',\n",
       "       'trackerror', 'remote', 'endplay', 'popup', 'uriopen'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.hist_user_behavior_reason_start.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['endplay', 'trackdone', 'fwdbtn', 'backbtn', 'logout', 'remote',\n",
       "       'clickrow', 'popup', 'uriopen', 'appload'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.hist_user_behavior_reason_end.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "start = behavior['hist_user_behavior_reason_start'].replace({'trackdone': 1,'remote': 0,'backbtn':0.9,'clickrow':0.2,\n",
    "                                                                'appload':0.2,'endplay':0.4,'playbtn':0.5,'trackerror':0,\n",
    "                                                                'fwdbtn':0.3})\n",
    "start = pd.DataFrame(start)\n",
    "\n",
    "\n",
    "\n",
    "end = behavior['hist_user_behavior_reason_end'].replace({'trackdone': 1,'endplay':0.4,'fwdbtn':0.3,'backbtn':0.9,'remote':0,\n",
    "                                                            'logout':0,'clickrow':0.2})\n",
    "end = pd.DataFrame(end)\n",
    "\n",
    "\n",
    "shuffle = behavior['hist_user_behavior_is_shuffle'].replace({True:1,False:0})\n",
    "shuffle = pd.DataFrame(shuffle)\n",
    "\n",
    "behavior['hist_user_behavior_reason_start'] = start\n",
    "behavior['hist_user_behavior_reason_end'] = end\n",
    "behavior['hist_user_behavior_is_shuffle'] = shuffle\n",
    "\n",
    "behavior['hist_user_behavior_reason_start'] = pd.to_numeric(behavior.hist_user_behavior_reason_start, errors='coerce')\n",
    "behavior['hist_user_behavior_reason_end'] = pd.to_numeric(behavior.hist_user_behavior_reason_end, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>hist_user_behavior_reason_end</th>\n",
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       "    </tr>\n",
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       "      <td>10</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hist_user_behavior_reason_start  hist_user_behavior_reason_end  \\\n",
       "0                               0.2                            0.4   \n",
       "1                               0.2                            0.4   \n",
       "2                               0.2                            1.0   \n",
       "3                               1.0                            0.3   \n",
       "4                               0.3                            0.3   \n",
       "5                               0.3                            0.4   \n",
       "6                               0.2                            0.3   \n",
       "7                               0.3                            0.4   \n",
       "8                               0.3                            0.4   \n",
       "9                               0.2                            1.0   \n",
       "10                              1.0                            0.3   \n",
       "11                              0.3                            0.3   \n",
       "12                              0.3                            0.3   \n",
       "13                              0.3                            0.3   \n",
       "14                              0.3                            1.0   \n",
       "\n",
       "    hist_user_behavior_is_shuffle  \n",
       "0                               0  \n",
       "1                               0  \n",
       "2                               0  \n",
       "3                               0  \n",
       "4                               0  \n",
       "5                               0  \n",
       "6                               0  \n",
       "7                               0  \n",
       "8                               1  \n",
       "9                               1  \n",
       "10                              1  \n",
       "11                              1  \n",
       "12                              1  \n",
       "13                              1  \n",
       "14                              1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hist_user_behavior_reason_start    float64\n",
       "hist_user_behavior_reason_end      float64\n",
       "hist_user_behavior_is_shuffle        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "liking1 = ((behavior['hist_user_behavior_reason_start'] * behavior['hist_user_behavior_reason_end']) + \n",
    "          behavior['hist_user_behavior_is_shuffle'])\n",
    "liking1 = pd.DataFrame(liking1)\n",
    "\n",
    "liking2 = ((behavior['hist_user_behavior_reason_start'] + behavior['hist_user_behavior_reason_end']) + \n",
    "          behavior['hist_user_behavior_is_shuffle'])\n",
    "liking2 = pd.DataFrame(liking2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>0.08</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.09</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.12</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.06</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.12</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.12</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.30</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.09</td>\n",
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       "      <td>12</td>\n",
       "      <td>1.09</td>\n",
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       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0\n",
       "0   0.08\n",
       "1   0.08\n",
       "2   0.20\n",
       "3   0.30\n",
       "4   0.09\n",
       "5   0.12\n",
       "6   0.06\n",
       "7   0.12\n",
       "8   1.12\n",
       "9   1.20\n",
       "10  1.30\n",
       "11  1.09\n",
       "12  1.09\n",
       "13  1.09\n",
       "14  1.30"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking1.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:447: RuntimeWarning: invalid value encountered in greater\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:447: RuntimeWarning: invalid value encountered in less\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZiyaYCcWXXT84E8bjdCy6vqdFM3RVXQ0d0EOFZOh6alFdsxYELQ3oQNaJ0aHpMH3tXlJbHaX0tGqGrqqroQN6MSUXDej1aXIuFdA7l0yKQvbFRYPTYTa0+Rfd1t3iZTIYIxrXeRZVHQ0e0FN/SvsLydC15FKXJtIBfWkNHbIvLrKW/WeyFheNz2mWrqqjwQN6Kjg3F1JD1wy9Ls0H9JaFgL6u2YPH5VhWcjHGMDQTXjQhCtCd/rdjAV0tqqpDAzp5Zuja5VLXJoJRPE4HzRmvBYdD2Ni+vHXRKquslKGPzurJRao6Gjqgh6IJHLJwCPRqFkouWh+tR6kedPeiSU7IvrhoMEvLImSsFtUMXVVJQwf0uWicJo9r2S9xNtrlUt8m5mJ0NXuX3b6xw7+s5GJtqWttzGVZyNC1hq6qo6EDeiiayKvDBcDnST1V5Qjo1yeCvHZx3PbHVfmbmIvQ1exedvvWriaGZyLMRRZ60eeX/bcv7nLxuZ20el3ai66qpqEDerCAgG5l6OEydLl84YVz/G9fO8jxgWnbH1vlZzIYozOjw8Wyq68VgIujs/O3DU2HcTpkPiPP1NPq1QxdVU3DB3R/Hh0uUN4ul8vjcySShj/+9lGddK2SzJ0WM+3qawHg/PBCQB+cDtPb6sXpWF6q69b9XFQV5QzoIvLXIjIiIidWuP9hEZnOOED60/YPszyC0XjeGbrb6cDtlLKVXHb2tnB+ZJYvvHjO9sdXq4snkkyHYlkD+tauJjxOB+dGAvO3DU6HltXPLT26n4uqonwy9K8Dj+a45lVjzD3pj8+VPqzKKKTkAuk90W0uucxF4ozNRvn1ff38xv1bePonlzg3HMj9D5VtJoMxgKwB3eV0sL2nmQvpDD2ZNJy8OcPu9a1ZH6u7xcOYZuiqSnIGdGPMT4CJCoyl4gqZFIXy7Il+PX3S/JauJj760C0YAydvai29kibT+7hkq6ED7Oxtmc/QL43NMh2KsW9rZ9Zr2/1uApE4yeTyHRqVKje7auhvFZFjIvK8iNy50kUi8pSIHBKRQ6OjozZ96+IFY/G8dlq0+D32n1p0dXwhoFt/xo/MaIZXSdYq0XVZMnSAW/taGZgMEYzGOXx1EoB9W7IH9Da/G2NS2/EqVWl2BPQjwFZjzN3AF4F/XOlCY8zTxpj9xpj9PT09Nnzr0gQjibxWiVr8ZSi5XJ9YCOgtXhfNHifDGtArKtvGXJl29bZgDFwanePI1Sk6mtxs727Oem2bP9X6OBOKlWewSq2i5IBujJkxxsymP38OcItId8kjq4BgNLFoqXcu5ThX9NpEkFafi/Z0IOhr8zEc0KXjlTQ+t3zr3ExWp8u54QCHr02yb0snjiwdLgBtvtTPcVoDuqqCkgO6iKyX9FJLEbk//ZhrfpVMMmkIxfJvW4Ty1NCvTQTZ0tU0v1q1t83LyIwG9EqyMvSOpuULiwC2rmvG7RRevzLJhZFZ9m3pWPGxrDfmmbAGdFV5OaOZiPw98DDQLSIDwGcAN4Ax5ivA48DHRCQOhIAnTLYzu9aYcDz/vdAtfo+TkYC9v6jXJoLc1rfQMdHb6uPYwJSt30OtbiIYpdXrwuvK/lpwOx3c0t3M94/dBFhxQhSgzZ/6lcp2ypFS5ZYzoBtjfiPH/V8CvmTbiCpkLlJEQLe5hp5MGgYmQrz3jr752/ravAzPhDHG5LXHjCrd0sOhs9nV28q54VmcDuHuTXlk6FpyUVXQsCtFF46fy7/k4nM7Ccfs221xaCZMNJFkS1fT/G19bT7CsSQzYc3wKmV8LkrnCuUWi1VH372+lWbvyq+ZNi25qCpq2IAejFkHRBdScnHYOil6LaPDxdI737qodfRKmQ7F8srQAe5bpdwC0OJx4RCdFFXV0bgBvYDDLSx2l1yyBvT0hk/aulg5U8HYfKlkJXv623EIvG3H6g1cDofQ6nNryUVVRf71hjoTjOR//JzF73ERiiVsq29fnwjikIWzK2Fhj+0RbV2smOlQjI4cAX3Luib+5VOPsH6FPVwytfvdmqGrqmjgDL2Ikkt6x8WITae6X5sIsrHDj9u58GPQDL2ykknDTDh3hg6p/c/zeSNv87t0DkRVRcMGdKsWXljJJX3IhU1ll6vjwUXlFoBmr4sWr4thraFXRCAcxxhoX2Efl2K0aclFVUnDBvRgtLg+dICgTROj1yeCbF3XtOz23javllwqxCqN5JOh50tLLqpaGjagW0eKFdq2CPZk6KFogvG5KJs6lwf0vlafbtBVIVOh1CpROwN6m8+tbYuqKho2oIeKydCtY+hsyNCtkkq2gxL62ry6n0uFWJn0Ssv+i9HepBm6qo6GDejBWAK3UxZNSOZilVzs6EVfCOjLz6XsbfMxPBOhBnZQqHlTQftLLm0+F+FYkkhcjxNUldWwAT0UTcxn3Pny21hyGU6fapMtQ+9t9RKNJzXLq4D5DN3mGjrofi6q8ho2oM9F4qsu4c7GqqEHbQjoI6uWXFK3aeti+VkBvc3ODF2X/6sqaZiFRX938Nqir88MBYglzLLbV2OdbPPCqaH5z4v18tlR3E7h+8duLuttvjw2B8C3Xr82v+Q8l998YEtJ42lU06EYPrdj/s3aDnrIhaqWhs3Qo/EkHldhqz2tCVQ7MvSZcIxWnzvrQpU2X+p9NqB/spfddB7L/gulh1yoamncgJ5I4ilgQhTA63LgEJsCeig+H7iXavXpn+yVMhWK0uG3b1ERQLu1J7quFlUV1rgBPZ7E4yrsf19EaPa45nvYSxEIx1as23pcDnxuhwaECpgOlSFD92uGrqqjcQN6ERk6pFoXS83QjUntH2L9aZ5Nq9dNQDP0spsKxmi3sQcdFkouWkNXldawAT1WRIYOqZWlpQb0cCxJLGFoXaHkAtDiczGrGXrZzZQhQ/e5nXhdDi2ZqYpr2IAeTSQLWlRkafY653dqLJb1i75aht7idTFrQ2lHrW6qDAEdUmUXzdBVpeWMaCLy1yIyIiInVrhfROQvReSCiLwpIvvsH6b9ovEk3qIy9NJLLoF05r1a77MG9PKLxpMEowlbFxVZ2v1uXVikKi6fiPZ14NFV7n8fsCv98RTwV6UPq7ySxhBPGtxFl1ziJS3LX8jQVy+5ROJJYgn7zjBVi83vtGhzDR1SP1udFFWVljOiGWN+AkyscskHgW+alJ8DHSKywa4BlkM0fUBFMZOiTR4nSVPaIRfWn+KtOUougGbpZVSOrXMt7X7dcVFVnh019H7gesbXA+nblhGRp0TkkIgcGh0dteFbFyeaznqLnRSF0nrRZ8JxfG7Hqt9/PqDrxGjZlDOgt+me6KoK7Ajo2ZZbZq1HGGOeNsbsN8bs7+npseFbF6fUDB0oqRc9kKNlETRDr4Tp9F7oHTaeVmTRU4tUNdgR0AeAzRlfbwJu2vC4ZTMf0IvI0JttWP4/E8ojoPs0oJdb+Usupc21KFUoOzbnOgB8QkS+BTwATBtjBm143LKxJhqLy9CtkkvxgXYmHGd79/J90DNZGbodq1LrQSGbqOXrZxfHAPjRqWGaCtx5M5eLo7Mkkoav/8sVvHls/KWbqyk75HwVi8jfAw8D3SIyAHwGcAMYY74CPAe8H7gABIF/U67B2qWUDL3JW1qGnjRm1WX/FrfTgdflIKABvWysfe19BZxala/5vfNjibwCulJ2yBnQjTG/keN+A/xb20ZUAZESArrP7UQoPkMPRhMkzeoti5YWr64WLadgLIHP7cCRZcfLUvkyAnqH7Y+uVHYNuVK0lJKLQ6Sk/VzyaVm06OKi8goXcWpVvqzjCsMxXUegKqchA7rVtljMwiJI1dHnig3o4fxPyGnxaUAvp2A0MR947eaz8bhCpfLVmAE9XXLxFpGhg7X8v7hAO3/kWR4ll2avPVv1quxCsfJl6FY31FyJ+/6sxBjD4asTJJPaRaMWNHRALz5DdxadeQ1Nh/G6HPll6N7Uzo4J/aUti3IG9HKvI3ju+BAf+qvX+MHJobI8vqpNjRnQE0lcDil6MqyUQy5uToXY2OHP63tr62J5haIJ/J7yHKvrcqYOKSnHpHYyafjiS+cBeOVs9VZcq7WnIQN6qMTJMGvHxUIXjSSShqGZMBvbfXldr6tFy8cYU9YMHco3qf3i6WHODAXoaHLz0wtjunhJzWvIgD4XidNcwkKSJo+TeNIQSxT2izQ2GyGWMGzs8Od1fauuFi2bWMKQSJr5rRzKoRwB3RjDF1+6wNZ1TfzRe27lxlSIS2Nztn4PVZjZSJz4GtkVtTEDejQxn/0Ww1pVWOjE6M2pEEDeAV036CqfUCw1B1L2DN3mn93L50Y5fmOajz+8g4dvS+2H9NPzY7Z+D5W/cCzBL/3Xl/l/Xzxf7aEADRrQZyNxmr2llVxg5dWiiaTJOml6cyqEyyF0t6y+7N/SrCWXsrHOa23Jo9uoWOVoO/3HN27Q3eLh1+7dxNZ1zWzpauJVDehV8/yJQUYDEQ4cu7kmSl8NGdDnIvHSMvT0RFq2lrRE0vD//ewyn3/h7LIM/uZ0mA3tPpyO/CZjvS4HLodoQC+DyWAqoHeU4XALS4vXRSiWIJ6078/xM4MB7t7UMb/K+R27uvn5pXE9CKVKvvWL1M7h1yaCnBuerfJoGjCgxxJJIvFkyTV0yJ6hv3h6mEujcwSjCX50ZmT+dmMMg9OhvMstACKii4vKZCqY3jrXb//WuZYWb+rNYi5iz+KiaDzJxdFZblvfOn/bQzu7mY3EOXZ9ypbvofJ3aXSWg5cnePJt2wB44VT1W0gbLqBbLYDlCOinbs7wyrlR7t/Wxf23dHHw0jgjM2EglRGGY0k2tucf0EGX/5fLVCiG15VqLSwXu+dALo3NEk+aRQH9bTu6cQhadqmCZw5dx+kQPv7wDu7d0sEPTw1Xe0iNGNBTQdiOkkswI9DGEkmePTJAf4efD+zdwLtv78PjcvD8idS79o0CJ0QtukFXeUwFY3Q0uZEybMxlWdjT3p6DLs4MBgDYvb5t/rb2Jjd3bGzj8NVJW76Hyk8skeTZwwM8sruX3jYf772jjzcHphmaDld1XA0X0GdtyNCdDsHndizK0M8OBQjFEvzynX24nQ5avC4eua2Xs8MBnj08wNmhAA6Bvrb8JkQtmqGXx3QwWtZyC9i/juDMUAC3U9je07zo9l29rVwarX79tpG8en6UsdkoT7wldbbPL9/RB8ALp6ubpTdcQLdKLqVk6JDK0jMnPY/fmKbJ42R7d8v8bW/d0c07dnZzdGCKI9cm6Wvz4Spw/5iW9H4uyTUwg15PJtMZejnZXXI5OzTDjp4W3EteQ9u7m7k5HS7p0BVVmOMDM4ikSl4AO3pa2N7dzA+rvBVDwwX0hQy9tP7jVq+LwekwSWOIxpOcGZrhro3tizpYnA7h/Xs28CfvvZUHt6/jHTu7C/4+LT4XBl3+b6dIPJHap7wMR89l8rgceJwO2zL0s0OBRfVzy/aeVBJxWRcYVcy5kQCbO5vmd+sUER6+rZeDlyequsio4QL6XCSO2ylF7YWe6cEd6xgJRHjj2iRnhwPEEoY9m9qzXtvR5OGxuzdy75bOgr+P1bM+NhstabxqwdR8y2J5Sy6QekO249Sp6WCMm9PhRfVzi1WCuTiqAb1Szg8HuLWvZdFtuze0Eo0nuT4ZqtKoGjCgz0biNHtcJU+G7e1vZ3Onnx+eHObI1UmavS62rWvO/Q8LNB/QAxHbH7tRTVWgB91i1xzI2WFrQnR5hn5LdzMiaB29QmKJJJfH5tjVt/hnsas3FeAvjFTv59BwAX0uWto+LhYR4QN7NxKIxDk7HOCujW15LxgqREeTG5dDGJ3VgG6XqVC6B70SGbpNe9qfHZoByFpy8bmd9Hf4uaQZekVcGZsjljDLMvQd6YB+fiRQjWEBeQZ0EXlURM6KyAUR+VSW+58UkVEROZr++Kj9Q7XHXKS0fVwybelqYm+6zLKnP3u5pVQOSW0VMKoZum2mgjEcsrD5WTnZ1XZ6ZihAq8/FhhV26tze08KlMc3QK8FaEbqrd/Gba5vPTV+bt6oZes5XtIg4gS8D7wUGgNdF5IAx5tSSS58xxnyiDGO01WwkTl9bftvX5uNX9m7klu5mtnXbX26xdLd65zf2alTGGBbknd4AABMqSURBVNt6xqeCUdr97rIcDr1Ui2/hkJJS/oI7OxRg9/rWFZ+D7d3NHL4yYevzpLI7N5xqQd7Z27Lsvl29rVxc4yWX+4ELxphLxpgo8C3gg+UdVnkYY9L7uNi3w16L18UDt6wra3DoafEyORddM1t0Vtr/9Z1j/If/dYq/ee0Kh9JBqxRToVhFyi2Qen0YCt+ZM5MxZsUOF8uOnmbmogmGZ/QvuXK7MDLLlq6m+XNjM+3sbeHCyGzVNurKJ6D3A9czvh5I37bUh0TkTRH5rohszvZAIvKUiBwSkUOjo5U/aSUSTxJPGltq6JXU0+rFAONzjdfpMheJc+DYTdr9boYDEf7hjRucGy6tRjkVjJW9ZdFix46Z1yaCBCJx7tiwclnPal28qBOjZXduOLBsQtSyo7eFuWiCwSqtGM0noGdLPZe+/XwP2GaM2Qu8CHwj2wMZY542xuw3xuzv6ekpbKQ2sGMfl2roSXe6NGId/cdnR4jEkzx2dz9/+J5d+N1Ojg1MF/14iaRhJlT+RUUWOxYXvZn+/927QlsspBa2gHa6lFs0nupwWTohatnZU91Ol3wC+gCQmXFvAm5mXmCMGTfGWNHmq8B99gzPXnatEq207tZUeWCsATtdnj8+RHeLl63rmnA5HNy5sY1TgzPzB30XaiYcw1DeXRYztdqQoR+/MY3H6eDWFbJCSG0p0exxai96mV0ZnyOeNCv+LHb1WZ0uazegvw7sEpFbRMQDPAEcyLxARDZkfPkYcNq+IdpnNr0xV61l6F6Xkzafq+Ey9FA0wUtnRnj0rr75OYq7N3cQjSfn+7ILVckedMjcoKuEgD4wze0bWuf3QM9GRLilp1mPoyszq9y3tMPFsq7ZQ0eTe+1m6MaYOPAJ4J9JBepvG2NOisjnROSx9GV/ICInReQY8AfAk+UacClqNUOHVB290XrRXzk3QiiW4P13LeQLt3Q30+pzFb3/9/w+6BWaFJ0/pKTIkksyaThxY5q78miL3d7doiWXMjs3PItDWLZBmkVE2NnTUrVOl7z60I0xzxljbjXG7DDG/Mf0bZ82xhxIf/6nxpg7jTF3G2N+yRhzppyDLtZsutOguYwHA5dLT2uqF30tHHNVKc+fGKKr2cP9t3TN3+YQYU9/O+eGA4RjhR8cMZkO6O0VmhQVkZJWi14ZnyMQia9aP7ds72nmxlSoqOdF5ef8cIBt65qzdrhYdvW1cKFKb6wNtVJ0NhJPZUwl7uNSDd0tXiLxZMNspRuJJ/jR6RH+1Z19y35ed2/qIJ40nLw5U9BjRuNJXr8yycYO36rlC7u1+lxMhYrbE/34jdSE6J7+jpzX7l7fijGpRUiqPE7enGH3hpXnMiA1QT0xF2W8Cn9R115kK0GpZ4lWU09rY3W6vHFtitlInHfv7lt236ZOP+uaPRy6OrHqY0RiCW5Mhub/qnn53AjToRi/smdjWca8ko0dfm5MhYraAvn4wDRel2N+sm01VlnGehNQ9poKRrk2Ecz55rqzinu6NFxAr7UJUYvVujg8U90TUSrlF5cnEIG3ZJRbLCLCg9vXcXU8mHUF7dhshP/5xg3+8w/O8OWXL/D1n13h0tgsr54f457NHWVd1ZvNlq4movFkUT+7N29Mc/uGtmV7oGfT3+Gns8nNiRLaOtXKrDfKXOUvawHY6cHC/oK0Q4MFdPv2cam0Nr+b3lYvr5wbbYiDDA5eHuf29W0r1rr3benE7RR+fml80e1zkThfffUSR69PctfGdn75jj6ujM/xP169jNMhPHrX+koMf5EtXU1AaoFQIZJJw8kb03nVzyH1RndXfztvaoZeFtZ6gLs2rv7zWN/mo7vFy/EbGtDLaraGM3SHCB/ev5nZ9MpJSC0Jn43E626iNBpPcvjq5KLJ0KX8Hif3bu7k6PWp+Tc4YwzPHhkgGE3wf7xzB4/ft4mHb+vl9x/Zxe71rTx290bafJWZDM3U1eyh2evi2nhhAf3S2Bxz0URBG7/t6W/nfJETxmp1xwem2bauifYcLa8iwp7+Nk5U4Y21NqNbEYLROLOROJ0V6j8uh/4OP+++vY8XTg3jdd3g6vgcI4EIO3qauXdLJ3dsXH74QS06fmOacCzJA6sEdIAHt6/jF1cmOHx1kod29XDw8gRnhgJ8YM+GRYdxd7d4+e23bivzqFcmImzpaio4Qz+cniNY6eCUbPZuaieeNJwZCnDP5twTqSp/x29Ms29rfofU7NnUwSvnzhOMxucPla+EhsnQrexoy7qmKo+kNO/c1cOWriZevzKBz+3knbt6uDkV5gNffJX//srFag/PFr+4nApkq2XoAOvbfdzS3cwLp4b57PdOcuDYTW7ta+FtO9ZVYpgF2drVxPhcNO8uJWMMX//ZVXb2tnDrCotYstGJ0fIYm41wYyrE3jz/Wtrb307SwKkCO7FK1TAZ+tWJIA6BTR21HdCdDuHJt20jGE3Q1ZxaHPOuW3v4l4tjfP6Fc/zre/tt3R64Gg5eHmdnbwvr0hPBq3nfXes5eHkCn8tBc3rny7W4fezmdB39+kSQ2zfk/kvqlXOjnB6c4b9++G4cBWy7qxOj5THfPprnX0vWdW8OTLN/2+qJiZ0aJkO/Oj7Hxg5/RfuPy8Xnds4Hc0jVkz/zq3eQSBq+/OMLVRxZ6RJJw6ErkznLLZZNnU18aN8mPrB3Iw/f1jt/aO9as6nTj0Pynxj9by9fZGO7j8fuLqzF0poY1QzdXscHphGBO/Msa/a1+ehr81b851D70S0P0XiSgckQW7tqOztfzdZ1zXx4/2b+/hfXGJgsrFa7lpy6OcNsJJ6z3FJr3E4HGzv8XM1jYvTw1Ql+cXmCjz60vagEpJSVtCq7Nwem2d7dTGsBk+p7+jsqHtAbouRy4uY08aRhaxkOcV5Lfv+RnTx7eIAvvXSBP//Q3moPpyhWG+IDt6y9OniprLmP4ZkwfW0+poJRfnJ+lOsTIX5wcoh2v5upYJSzQwE6mtw8cX/WYwVy2tOvE6N2O35jirft6C7o3+zpb+dHZ4aZreCCxobI0A9fmQRga41PiOayscPPbz6whe8cHuB6gR0Va8VzJwa5fUMb61c4O7OW7elvJ5mEv/jReb740nn+nxfO8frlSXxuB9PBKMcHppgJx7l7cwdf+Mg9RXdHWBOjR69N2jn8hjUwGWR4JpLXBmmZ9m5qxxg4WcEsvSEy9ENXJ+hq9hT051Kt+r137eBvD17lq69e4nMfvKvawynItfEgb1yb4lPv213toZTF1nXN/Lv37ebotclUC9yWTh6+rYfOLDs/Dk6F+buD14r6PsYY1rf5+PLLF3E5HRU5OzWX33xgS7WHULSv/8sVHALvvX35NhSryew4emB7Zf7irPuAbozh8NXJuq6fZ1rf7uPX793EM69f5/cf2TW/B0wtOHDsBgC/WuBEYC1p8bp4x64e3rGrfCd2iQjvuq2HZ16/zqmbMwVnluVQ7JtTtQWjcb752lX29Lfz0wtjsELPQbY3rJ5WLxvbfRwtcqvnYtR9yeXqeJCx2WjN958X4ql3bSeaSPL1n12u9lDyZozhn47e5C3bOunPWBSkirOnv511zR5ePjtSdyuJK+m1S+NEE0nedWtvUf/+7Tu7eenMCDPh4nbbLFTdB/TvHE6db72tzidEM+3oaeHRO9fzzdeuEqjQC6lUZ4YCnB+Z5bF7sp0/rgrlEOFdt/ZwczrMuWE99KIY0XiS1y6Os3t9a9FzOr/91tSake8cGrB5dNnVdUA/fHWSv3r5Io/ft6nmF9sU6mMP7yAQjvPlH9fG6tF/OnoTl0P4wJ4NuS9WeblnSwftfjcHjt3g1fOjjAUihGMJYomkZu1LzEbinB6c4fxIgGvjc/z80jjffO0KwWiCd91afHlsz6Z29m3p4G9eu0IyWf7nvG5r6HOROH/87aNsaPfzmV+9g+8dG6z2kCpq76YOPrJ/M//9Jxd5+851PFTGmm2pxmcjPHtkgHfs6l60YEqVxuVw8Ov7+vnBiSGeT39Y1jV7uG9rJ/du6azY6U2VZIwhGk/iXeVkoVgiyZFrkxy7PsXV8SBLw21nk5v33N5bcrvz77xtG5/81lFeOTfKL+0urnSTr7oM6FPBKP/3d9/k2kSQb/3ugw3R3ZLNv3/sTo5cm+SPnjnKc598iN7WtfdXSjyR5BN/9wbToRh/8t7bqj2curOrt5Vdj7QyMRfl4sgskUSSWCLJhZFZfnhqmBdPD/O2Hd08srt31WPVasH4bITXr0xwaWyO0UCESDxJu99Nf4efTZ1+NnU20eZzMTEX5cZUiIOXJ5iNxOlr8/JLu3vZ1duCManTsrqavXS3eGzZRuJ9d23gP7ae5us/u1L2gC75/OklIo8CfwE4gf9hjPnzJfd7gW8C9wHjwEeMMVdWe8z9+/ebQ4cOFTns7OKJJP/r+CD/4funmAzG+NSju/ndd24HaneWvVTDM2H+28sX6Gn18q/v6WdT59qaHH7u+CA/vTDG4/dtYt+W/HayU/YYn43wyrlRDl+dpMnj5KFdPezf2klTDW0xnTSGs0MBDl4e5/zwLCKp9tC+Ni+tPjfDM2FuTIYYn4su+7e7elt45609bO9uLjlw52rL/IsXz/OFF8/xf/7yrXzs4Z04C9ifZykROWyM2Z/1vlwBXUScwDngvcAA8DrwG8aYUxnXfBzYa4z5PRF5Avg1Y8xHVnvcYgN6MmmIxJPEkkmi8SSDU2Eujc3y+pUJnj8+xPhclL2b2vnzX9+7aDvZRg3okFpO/z/fGGAumuCODW1s72lmXbOXZq8Tp0NwOVIn06c+T/3X6RScIrZudGWMIZpIEowmuDQ6x5Frk1wem+PB7esK3rNE2efGVIgfnBjk4ugcLoewe0MbG9t99Lb68LodOEVwOdOvCxFcTkfq8/TrxSGCQ1LtktZ/7WLFJ0MqeIeiCWYjcYamw1yfDHJmKMBUMEarz8VbtnXxlm1dWUtIoWiCG1MhAuEY65o9dLd4bX3jyhXQQ9EE/+7ZNzlw7CYPbu/iCx+5hw3txXVzlRrQ3wr8e2PMv0p//acAxpj/nHHNP6eveU1EXMAQ0GNWefBiA/r337zJJ/7ujWW3+9wO3n17H7+6dyPvub132cHCjRzQIXW+5qsXxvjZxTHCsWTe/87pEHL9eubz+2tM6hcyc16oq9nD/q2dPLSrp6SMRdljaDrMa5fGOT+SCpLFElKvCYfIoteGFQ3mXwIGTEbletn9OXicDraua2L/ti7u2NBW1ddQPgunUgew3ODT/3SCD9+3ic8WufCv1ID+OPCoMeaj6a9/C3jAGPOJjGtOpK8ZSH99MX3N2JLHegp4Kv3lbcDZov6PitMNjOW8qvHo85KdPi/Z6fOSXSWfl63GmKxdDvn8zZHtbW/pu0A+12CMeRp4Oo/vaTsRObTSu1oj0+clO31estPnJbu18rzk04c+AGRu+7YJuLnSNemSSzswYccAlVJK5SefgP46sEtEbhERD/AEcGDJNQeA30l//jjw0mr1c6WUUvbLWXIxxsRF5BPAP5NqW/xrY8xJEfkccMgYcwD4GvA3InKBVGb+RDkHXaSqlHpqgD4v2enzkp0+L9mtieclrz50pZRSa19d7+WilFKNRAO6UkrViboK6CLyqIicFZELIvKpLPd7ReSZ9P0HRWRb5UdZeXk8L0+KyKiIHE1/fLQa46w0EflrERlJr6PIdr+IyF+mn7c3RWRfpcdYDXk8Lw+LyHTG6+XTlR5jpYnIZhH5sYicFpGTIvLJLNdU//VijKmLD1ITtheB7YAHOAbcseSajwNfSX/+BPBMtce9Rp6XJ4EvVXusVXhu3gnsA06scP/7gedJrbN4EDhY7TGvkeflYeD71R5nhZ+TDcC+9OetpLZDWfp7VPXXSz1l6PcDF4wxl4wxUeBbwAeXXPNB4Bvpz78LvFvs3HhibcrneWlIxpifsPp6iQ8C3zQpPwc6RKTuN2zP43lpOMaYQWPMkfTnAeA0sPQ0lqq/XuopoPcD1zO+HmD5Ez5/jTEmDkwDlTm9tXryeV4APpT+M/G7IrI5y/2NKN/nrhG9VUSOicjzInJntQdTSelS7b3AwSV3Vf31Uk8B3bYtCupMPv/P3wO2GWP2Ai+y8FdMo2vE10s+jpDaT+Ru4IvAP1Z5PBUjIi3As8AfGmNmlt6d5Z9U9PVSTwFdtyjILufzYowZN8ZE0l9+ldS+9iq/11TDMcbMGGNm058/B7hFpLvKwyo7EXGTCuZ/a4z5hyyXVP31Uk8BXbcoyC7n87KkzvcYqfqgSj1Pv53uXngQmDbGNNZZhlmIyHpr7klE7icVR8arO6rySv//fg04bYz5/AqXVf31UjtHk+Rg6meLAlvl+bz8gYg8BsRJPS9PVm3AFSQif0+qY6NbRAaAzwBuAGPMV4DnSHUuXACCwL+pzkgrK4/n5XHgYyISB0LAEw2QGL0d+C3guIgcTd/2Z8AWWDuvF136r5RSdaKeSi5KKdXQNKArpVSd0ICulFJ1QgO6UkrVCQ3oSilVJzSgK6VUndCArpRSdeL/B2Nzub96s6QAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(liking1,bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(liking2,bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_score['liking'] = liking2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(skip_score[0],bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "liking_array = liking2.values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3673820, 1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found array with dim 3. the normalize function expected <= 2.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-2893efe2f0da>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnormalized_X\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreprocessing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mliking_array\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py\u001b[0m in \u001b[0;36mnormalize\u001b[1;34m(X, norm, axis, copy, return_norm)\u001b[0m\n\u001b[0;32m   1612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1613\u001b[0m     X = check_array(X, sparse_format, copy=copy,\n\u001b[1;32m-> 1614\u001b[1;33m                     estimator='the normalize function', dtype=FLOAT_DTYPES)\n\u001b[0m\u001b[0;32m   1615\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1616\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    537\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    538\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n\u001b[1;32m--> 539\u001b[1;33m                              % (array.ndim, estimator_name))\n\u001b[0m\u001b[0;32m    540\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    541\u001b[0m             _assert_all_finite(array,\n",
      "\u001b[1;31mValueError\u001b[0m: Found array with dim 3. the normalize function expected <= 2."
     ]
    }
   ],
   "source": [
    "normalized_X = preprocessing.normalize([liking_array])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3673820, 1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6],\n",
       "       [0.6],\n",
       "       [1.2],\n",
       "       ...,\n",
       "       [1.6],\n",
       "       [2.2],\n",
       "       [2.2]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "zoopy = np.reshape(liking_array, (2, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6, 0.6, 1.2, ..., 3. , 2.3, 2.3],\n",
       "       [2.3, 1.6, 1.6, ..., 1.6, 2.2, 2.2]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1836910)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "zoopy = zoopy.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6, 0.6, 1.2, ..., 3. , 2.3, 2.3],\n",
       "       [2.3, 1.6, 1.6, ..., 1.6, 2.2, 2.2]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6, 2.3],\n",
       "       [0.6, 1.6],\n",
       "       [1.2, 1.6],\n",
       "       ...,\n",
       "       [3. , 1.6],\n",
       "       [2.3, 2.2],\n",
       "       [2.3, 2.2]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1836910, 2)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dropna(arr, *args, **kwarg):\n",
    "    assert isinstance(arr, np.ndarray)\n",
    "    dropped=pd.DataFrame(arr).dropna(*args, **kwarg).values\n",
    "    if arr.ndim==1:\n",
    "        dropped=dropped.flatten()\n",
    "    return dropped\n",
    "zoopy = dropna(zoopy)\n",
    "#zoopy = zoopy[~np.isnan(zoopy)]\n",
    "#zoopy = zoopy[np.isfinite(zoopy)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1836872, 2)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], shape=(0, 2), dtype=int64)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argwhere(np.isnan(zoopy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_zoopy = preprocessing.normalize(zoopy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.2524219 , 0.96761727],\n",
       "       [0.35112344, 0.93632918],\n",
       "       [0.6       , 0.8       ],\n",
       "       ...,\n",
       "       [0.88235294, 0.47058824],\n",
       "       [0.72264186, 0.69122265],\n",
       "       [0.72264186, 0.69122265]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_zoopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "like = normalized_zoopy.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "like = pd.DataFrame(like)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.252422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.967617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.351123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.936329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.630593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.776114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.447214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.894427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.252422\n",
       "1  0.967617\n",
       "2  0.351123\n",
       "3  0.936329\n",
       "4  0.600000\n",
       "5  0.800000\n",
       "6  0.630593\n",
       "7  0.776114\n",
       "8  0.447214\n",
       "9  0.894427"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "like.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(like,bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>0</th>\n",
       "      <th>liking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  liking\n",
       "0 -0.3     0.6\n",
       "1  0.2     0.6\n",
       "2  1.0     1.2\n",
       "3 -0.3     1.3\n",
       "4 -1.3     0.6\n",
       "5 -0.3     0.7\n",
       "6  0.2     0.5\n",
       "7 -0.3     0.7\n",
       "8 -1.3     1.7\n",
       "9  1.0     2.2"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skip_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.252422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.967617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.351123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.936329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.252422\n",
       "1  0.967617\n",
       "2  0.351123\n",
       "3  0.936329\n",
       "4  0.600000"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "like.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_score['normalized_liking'] = like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "mul_label = skip_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>0</th>\n",
       "      <th>liking</th>\n",
       "      <th>normalized_liking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.252422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.967617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.351123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.936329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  liking  normalized_liking\n",
       "0 -0.3     0.6           0.252422\n",
       "1  0.2     0.6           0.967617\n",
       "2  1.0     1.2           0.351123\n",
       "3 -0.3     1.3           0.936329\n",
       "4 -1.3     0.6           0.600000"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "mul_label = mul_label.rename(columns = {0: \"skip_score\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "   skip_score  liking  normalized_liking\n",
       "0        -0.3     0.6           0.252422\n",
       "1         0.2     0.6           0.967617\n",
       "2         1.0     1.2           0.351123\n",
       "3        -0.3     1.3           0.936329\n",
       "4        -1.3     0.6           0.600000\n",
       "5        -0.3     0.7           0.800000\n",
       "6         0.2     0.5           0.630593\n",
       "7        -0.3     0.7           0.776114\n",
       "8        -1.3     1.7           0.447214\n",
       "9         1.0     2.2           0.894427"
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     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "mul_label.head(10)"
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  {
   "cell_type": "code",
   "execution_count": 141,
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   "outputs": [
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       "                                     session_id  \\\n",
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       "4        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "...                                         ...   \n",
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       "3673819  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "\n",
       "                                 track_id_clean  skip_1  skip_2  skip_3  \\\n",
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       "...                                         ...     ...     ...     ...   \n",
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       "\n",
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     "execution_count": 141,
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "df.rename(columns = {'track_id_clean':'track_id'}, inplace = True)\n",
    "df['hist_user_behavior_reason_start'] = behavior['hist_user_behavior_reason_start']\n",
    "df['hist_user_behavior_reason_end'] = behavior['hist_user_behavior_reason_end']\n",
    "df['hist_user_behavior_is_shuffle'] = behavior['hist_user_behavior_is_shuffle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3673816</td>\n",
       "      <td>0_fffff7ac-faea-4a39-aa66-770be5429a33</td>\n",
       "      <td>t_a66ea088-b357-449a-8a1e-64dd0b8d6cb5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3673817</td>\n",
       "      <td>0_fffff7ac-faea-4a39-aa66-770be5429a33</td>\n",
       "      <td>t_a004df37-97e0-4c20-999e-2844ddf9e733</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3673818</td>\n",
       "      <td>0_fffff7ac-faea-4a39-aa66-770be5429a33</td>\n",
       "      <td>t_508ab67d-dd11-478c-8b45-5c522df5e03d</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3673819</td>\n",
       "      <td>0_fffff7ac-faea-4a39-aa66-770be5429a33</td>\n",
       "      <td>t_a004df37-97e0-4c20-999e-2844ddf9e733</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3673820 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     session_id  \\\n",
       "0        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "1        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "2        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "3        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "4        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "...                                         ...   \n",
       "3673815  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "3673816  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "3673817  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "3673818  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "3673819  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "\n",
       "                                       track_id  skip_1  skip_2  skip_3  \\\n",
       "0        t_a0b2a533-caad-4a72-af14-e647bd7e9c1f       0    -0.5     0.2   \n",
       "1        t_a998907c-e11c-4a4e-b9d2-b34846bfb839       0     0.0     0.2   \n",
       "2        t_977b3ee2-e7a8-4357-b164-f663446c1a25       0     0.0     0.0   \n",
       "3        t_e2dda38d-f826-481b-b49a-a1d9051f5c6d       0    -0.5     0.2   \n",
       "4        t_b10f5a2a-ee7b-47c3-9789-4ce0f96c6989      -1    -0.5     0.2   \n",
       "...                                         ...     ...     ...     ...   \n",
       "3673815  t_a66ea088-b357-449a-8a1e-64dd0b8d6cb5      -1    -0.5     0.2   \n",
       "3673816  t_a66ea088-b357-449a-8a1e-64dd0b8d6cb5      -1    -0.5     0.2   \n",
       "3673817  t_a004df37-97e0-4c20-999e-2844ddf9e733      -1    -0.5     0.2   \n",
       "3673818  t_508ab67d-dd11-478c-8b45-5c522df5e03d      -1    -0.5     0.2   \n",
       "3673819  t_a004df37-97e0-4c20-999e-2844ddf9e733      -1    -0.5     0.2   \n",
       "\n",
       "         not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0                  0               0                              0.2   \n",
       "1                  0               1                              0.2   \n",
       "2                  1               0                              0.2   \n",
       "3                  0               0                              1.0   \n",
       "4                  0               0                              0.3   \n",
       "...              ...             ...                              ...   \n",
       "3673815            0               0                              1.0   \n",
       "3673816            0               0                              0.3   \n",
       "3673817            0               0                              0.3   \n",
       "3673818            0               0                              0.3   \n",
       "3673819            0               0                              0.9   \n",
       "\n",
       "         hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  \n",
       "0                                  0.4                              0  \n",
       "1                                  0.4                              0  \n",
       "2                                  1.0                              0  \n",
       "3                                  0.3                              0  \n",
       "4                                  0.3                              0  \n",
       "...                                ...                            ...  \n",
       "3673815                            0.3                              1  \n",
       "3673816                            0.3                              1  \n",
       "3673817                            0.3                              1  \n",
       "3673818                            0.9                              1  \n",
       "3673819                            0.3                              1  \n",
       "\n",
       "[3673820 rows x 10 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3673820\n"
     ]
    }
   ],
   "source": [
    "print(len(mul_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>session_id</th>\n",
       "      <th>track_id</th>\n",
       "      <th>skip_1</th>\n",
       "      <th>skip_2</th>\n",
       "      <th>skip_3</th>\n",
       "      <th>not_skipped</th>\n",
       "      <th>context_switch</th>\n",
       "      <th>hist_user_behavior_reason_start</th>\n",
       "      <th>hist_user_behavior_reason_end</th>\n",
       "      <th>hist_user_behavior_is_shuffle</th>\n",
       "      <th>skip_score</th>\n",
       "      <th>liking</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0_00001e52-55c0-470c-8d32-85c691c2019a</td>\n",
       "      <td>t_a0b2a533-caad-4a72-af14-e647bd7e9c1f</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0_00001e52-55c0-470c-8d32-85c691c2019a</td>\n",
       "      <td>t_a998907c-e11c-4a4e-b9d2-b34846bfb839</td>\n",
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       "      <td>0.4</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0_00001e52-55c0-470c-8d32-85c691c2019a</td>\n",
       "      <td>t_977b3ee2-e7a8-4357-b164-f663446c1a25</td>\n",
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       "      <td>1.2</td>\n",
       "      <td>2.2</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0_00001e52-55c0-470c-8d32-85c691c2019a</td>\n",
       "      <td>t_e2dda38d-f826-481b-b49a-a1d9051f5c6d</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0_00001e52-55c0-470c-8d32-85c691c2019a</td>\n",
       "      <td>t_b10f5a2a-ee7b-47c3-9789-4ce0f96c6989</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3673815</td>\n",
       "      <td>0_fffff7ac-faea-4a39-aa66-770be5429a33</td>\n",
       "      <td>t_a66ea088-b357-449a-8a1e-64dd0b8d6cb5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3673816</td>\n",
       "      <td>0_fffff7ac-faea-4a39-aa66-770be5429a33</td>\n",
       "      <td>t_a66ea088-b357-449a-8a1e-64dd0b8d6cb5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3673817</td>\n",
       "      <td>0_fffff7ac-faea-4a39-aa66-770be5429a33</td>\n",
       "      <td>t_a004df37-97e0-4c20-999e-2844ddf9e733</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <td>3673818</td>\n",
       "      <td>0_fffff7ac-faea-4a39-aa66-770be5429a33</td>\n",
       "      <td>t_508ab67d-dd11-478c-8b45-5c522df5e03d</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3673819</td>\n",
       "      <td>0_fffff7ac-faea-4a39-aa66-770be5429a33</td>\n",
       "      <td>t_a004df37-97e0-4c20-999e-2844ddf9e733</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3673820 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     session_id  \\\n",
       "0        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "1        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "2        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "3        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "4        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "...                                         ...   \n",
       "3673815  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "3673816  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "3673817  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "3673818  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "3673819  0_fffff7ac-faea-4a39-aa66-770be5429a33   \n",
       "\n",
       "                                       track_id  skip_1  skip_2  skip_3  \\\n",
       "0        t_a0b2a533-caad-4a72-af14-e647bd7e9c1f       0    -0.5     0.2   \n",
       "1        t_a998907c-e11c-4a4e-b9d2-b34846bfb839       0     0.0     0.2   \n",
       "2        t_977b3ee2-e7a8-4357-b164-f663446c1a25       0     0.0     0.0   \n",
       "3        t_e2dda38d-f826-481b-b49a-a1d9051f5c6d       0    -0.5     0.2   \n",
       "4        t_b10f5a2a-ee7b-47c3-9789-4ce0f96c6989      -1    -0.5     0.2   \n",
       "...                                         ...     ...     ...     ...   \n",
       "3673815  t_a66ea088-b357-449a-8a1e-64dd0b8d6cb5      -1    -0.5     0.2   \n",
       "3673816  t_a66ea088-b357-449a-8a1e-64dd0b8d6cb5      -1    -0.5     0.2   \n",
       "3673817  t_a004df37-97e0-4c20-999e-2844ddf9e733      -1    -0.5     0.2   \n",
       "3673818  t_508ab67d-dd11-478c-8b45-5c522df5e03d      -1    -0.5     0.2   \n",
       "3673819  t_a004df37-97e0-4c20-999e-2844ddf9e733      -1    -0.5     0.2   \n",
       "\n",
       "         not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0                  0               0                              0.2   \n",
       "1                  0               1                              0.2   \n",
       "2                  1               0                              0.2   \n",
       "3                  0               0                              1.0   \n",
       "4                  0               0                              0.3   \n",
       "...              ...             ...                              ...   \n",
       "3673815            0               0                              1.0   \n",
       "3673816            0               0                              0.3   \n",
       "3673817            0               0                              0.3   \n",
       "3673818            0               0                              0.3   \n",
       "3673819            0               0                              0.9   \n",
       "\n",
       "         hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  \\\n",
       "0                                  0.4                              0   \n",
       "1                                  0.4                              0   \n",
       "2                                  1.0                              0   \n",
       "3                                  0.3                              0   \n",
       "4                                  0.3                              0   \n",
       "...                                ...                            ...   \n",
       "3673815                            0.3                              1   \n",
       "3673816                            0.3                              1   \n",
       "3673817                            0.3                              1   \n",
       "3673818                            0.9                              1   \n",
       "3673819                            0.3                              1   \n",
       "\n",
       "         skip_score  liking  rating  \n",
       "0              -0.3     0.6     0.3  \n",
       "1               0.2     0.6     0.8  \n",
       "2               1.0     1.2     2.2  \n",
       "3              -0.3     1.3     1.0  \n",
       "4              -1.3     0.6    -0.7  \n",
       "...             ...     ...     ...  \n",
       "3673815        -1.3     2.3     1.0  \n",
       "3673816        -1.3     1.6     0.3  \n",
       "3673817        -1.3     1.6     0.3  \n",
       "3673818        -1.3     2.2     0.9  \n",
       "3673819        -1.3     2.2     0.9  \n",
       "\n",
       "[3673820 rows x 13 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['skip_score'] = mul_label['skip_score']\n",
    "df['liking'] = mul_label['liking']\n",
    "df['rating'] = df['skip_score'] + df['liking']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat1 = dat\n",
    "dat = pd.read_csv(r'C:\\Users\\User\\Downloads\\20181120_track_features\\track_features\\tf_000000000001.csv')\n",
    "df1 = pd.merge(df, dat, on = 'track_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                                     session_id  \\\n",
       "0        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "1        0_00a20441-f168-41c7-9bf6-93ddeb022f00   \n",
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       "...                                         ...   \n",
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       "1870453  0_fffef4da-9663-4898-b689-439966333d9c   \n",
       "1870454  0_ffff3e31-b986-4d87-9754-86919a7ae997   \n",
       "\n",
       "                                       track_id  skip_1  skip_2  skip_3  \\\n",
       "0        t_a0b2a533-caad-4a72-af14-e647bd7e9c1f       0    -0.5     0.2   \n",
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       "...                                         ...     ...     ...     ...   \n",
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       "1870454  t_4500b098-1559-4415-89ec-0377b0717dfe       0     0.0     0.0   \n",
       "\n",
       "         not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0                  0               0                              0.2   \n",
       "1                  0               0                              0.2   \n",
       "2                  0               0                              0.2   \n",
       "3                  1               0                              0.3   \n",
       "4                  1               0                              1.0   \n",
       "...              ...             ...                              ...   \n",
       "1870450            1               1                              0.2   \n",
       "1870451            1               0                              1.0   \n",
       "1870452            0               0                              0.3   \n",
       "1870453            0               0                              0.3   \n",
       "1870454            1               0                              1.0   \n",
       "\n",
       "         hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  ...  \\\n",
       "0                                  0.4                              0  ...   \n",
       "1                                  0.4                              0  ...   \n",
       "2                                  0.4                              0  ...   \n",
       "3                                  1.0                              0  ...   \n",
       "4                                  1.0                              0  ...   \n",
       "...                                ...                            ...  ...   \n",
       "1870450                            1.0                              0  ...   \n",
       "1870451                            1.0                              1  ...   \n",
       "1870452                            0.3                              1  ...   \n",
       "1870453                            0.3                              1  ...   \n",
       "1870454                            1.0                              0  ...   \n",
       "\n",
       "         time_signature   valence  acoustic_vector_0  acoustic_vector_1  \\\n",
       "0                     4  0.844416          -0.618357           0.390469   \n",
       "1                     4  0.844416          -0.618357           0.390469   \n",
       "2                     4  0.844416          -0.618357           0.390469   \n",
       "3                     4  0.844416          -0.618357           0.390469   \n",
       "4                     4  0.844416          -0.618357           0.390469   \n",
       "...                 ...       ...                ...                ...   \n",
       "1870450               4  0.290829          -0.812441           0.177448   \n",
       "1870451               4  0.372372          -0.732193           0.249331   \n",
       "1870452               4  0.681922          -0.943088           0.279992   \n",
       "1870453               4  0.903801          -0.221563          -0.052246   \n",
       "1870454               4  0.721773          -0.781271           0.231033   \n",
       "\n",
       "         acoustic_vector_2  acoustic_vector_3  acoustic_vector_4  \\\n",
       "0                 0.448867           0.275075           0.144140   \n",
       "1                 0.448867           0.275075           0.144140   \n",
       "2                 0.448867           0.275075           0.144140   \n",
       "3                 0.448867           0.275075           0.144140   \n",
       "4                 0.448867           0.275075           0.144140   \n",
       "...                    ...                ...                ...   \n",
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       "1870453           0.041409           0.066371          -0.377817   \n",
       "1870454           0.375227           0.458440          -0.060287   \n",
       "\n",
       "         acoustic_vector_5  acoustic_vector_6  acoustic_vector_7  \n",
       "0                 0.088700           0.463565           0.083241  \n",
       "1                 0.088700           0.463565           0.083241  \n",
       "2                 0.088700           0.463565           0.083241  \n",
       "3                 0.088700           0.463565           0.083241  \n",
       "4                 0.088700           0.463565           0.083241  \n",
       "...                    ...                ...                ...  \n",
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       "\n",
       "[1870455 rows x 42 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                     session_id  session_position  \\\n",
       "0        0_00001e52-55c0-470c-8d32-85c691c2019a                 1   \n",
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       "3        0_05c168a6-d905-427e-8a6a-4b12311fcb6d                 7   \n",
       "4        0_05d6ac38-9e98-44de-b097-048f1c0f95db                 6   \n",
       "...                                         ...               ...   \n",
       "1870450  0_fffd79ea-ddae-4200-be2e-8152c85d7f9f                10   \n",
       "1870451  0_fffef4da-9663-4898-b689-439966333d9c                 3   \n",
       "1870452  0_fffef4da-9663-4898-b689-439966333d9c                 6   \n",
       "1870453  0_fffef4da-9663-4898-b689-439966333d9c                 8   \n",
       "1870454  0_ffff3e31-b986-4d87-9754-86919a7ae997                 1   \n",
       "\n",
       "         session_length                                track_id  skip_1  \\\n",
       "0                    16  t_a0b2a533-caad-4a72-af14-e647bd7e9c1f   False   \n",
       "1                    20  t_a0b2a533-caad-4a72-af14-e647bd7e9c1f   False   \n",
       "2                    20  t_a0b2a533-caad-4a72-af14-e647bd7e9c1f   False   \n",
       "3                    20  t_a0b2a533-caad-4a72-af14-e647bd7e9c1f   False   \n",
       "4                    16  t_a0b2a533-caad-4a72-af14-e647bd7e9c1f   False   \n",
       "...                 ...                                     ...     ...   \n",
       "1870450              20  t_02bb5521-e44a-4e37-8da8-34fe29864157   False   \n",
       "1870451              20  t_8cdc2107-a0ff-4cf8-9c7e-f78305183fb1   False   \n",
       "1870452              20  t_197534e3-d3ee-4731-90d1-de65ef06b748    True   \n",
       "1870453              20  t_bfd37181-1b0e-4ab0-b528-1beabca7735b   False   \n",
       "1870454              20  t_4500b098-1559-4415-89ec-0377b0717dfe   False   \n",
       "\n",
       "         skip_2  skip_3  not_skipped  context_switch  no_pause_before_play  \\\n",
       "0          True    True        False               0                     0   \n",
       "1         False    True        False               0                     1   \n",
       "2         False    True        False               0                     1   \n",
       "3         False   False         True               0                     1   \n",
       "4         False   False         True               0                     1   \n",
       "...         ...     ...          ...             ...                   ...   \n",
       "1870450   False   False         True               1                     1   \n",
       "1870451   False   False         True               0                     1   \n",
       "1870452    True    True        False               0                     1   \n",
       "1870453    True    True        False               0                     1   \n",
       "1870454   False   False         True               0                     0   \n",
       "\n",
       "         ...  time_signature   valence  acoustic_vector_0  acoustic_vector_1  \\\n",
       "0        ...               4  0.844416          -0.618357           0.390469   \n",
       "1        ...               4  0.844416          -0.618357           0.390469   \n",
       "2        ...               4  0.844416          -0.618357           0.390469   \n",
       "3        ...               4  0.844416          -0.618357           0.390469   \n",
       "4        ...               4  0.844416          -0.618357           0.390469   \n",
       "...      ...             ...       ...                ...                ...   \n",
       "1870450  ...               4  0.290829          -0.812441           0.177448   \n",
       "1870451  ...               4  0.372372          -0.732193           0.249331   \n",
       "1870452  ...               4  0.681922          -0.943088           0.279992   \n",
       "1870453  ...               4  0.903801          -0.221563          -0.052246   \n",
       "1870454  ...               4  0.721773          -0.781271           0.231033   \n",
       "\n",
       "         acoustic_vector_2  acoustic_vector_3 acoustic_vector_4  \\\n",
       "0                 0.448867           0.275075          0.144140   \n",
       "1                 0.448867           0.275075          0.144140   \n",
       "2                 0.448867           0.275075          0.144140   \n",
       "3                 0.448867           0.275075          0.144140   \n",
       "4                 0.448867           0.275075          0.144140   \n",
       "...                    ...                ...               ...   \n",
       "1870450           0.249105           0.338340         -0.125992   \n",
       "1870451           0.208726           0.116819         -0.224502   \n",
       "1870452           0.188085           0.181983         -0.292232   \n",
       "1870453           0.041409           0.066371         -0.377817   \n",
       "1870454           0.375227           0.458440         -0.060287   \n",
       "\n",
       "         acoustic_vector_5 acoustic_vector_6 acoustic_vector_7  \n",
       "0                 0.088700          0.463565          0.083241  \n",
       "1                 0.088700          0.463565          0.083241  \n",
       "2                 0.088700          0.463565          0.083241  \n",
       "3                 0.088700          0.463565          0.083241  \n",
       "4                 0.088700          0.463565          0.083241  \n",
       "...                    ...               ...               ...  \n",
       "1870450           0.082681         -0.072090          0.248295  \n",
       "1870451          -0.001379         -0.437451          0.123034  \n",
       "1870452          -0.085075         -0.581773          0.157372  \n",
       "1870453           0.001521         -0.205437          0.382329  \n",
       "1870454           0.155044          0.384306          0.141446  \n",
       "\n",
       "[1870455 rows x 50 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat1.rename(columns = {'track_id_clean':'track_id'}, inplace = True)\n",
    "df2 = pd.merge(dat1, dat, on = 'track_id')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv('dat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df1[['session_id','track_id', 'skip_1', 'skip_2', 'skip_3', 'not_skipped','context_switch',\n",
    "          'hist_user_behavior_reason_start','hist_user_behavior_reason_end','hist_user_behavior_is_shuffle',\n",
    "         'skip_score','liking','rating']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>track_id</th>\n",
       "      <th>skip_1</th>\n",
       "      <th>skip_2</th>\n",
       "      <th>skip_3</th>\n",
       "      <th>not_skipped</th>\n",
       "      <th>context_switch</th>\n",
       "      <th>hist_user_behavior_reason_start</th>\n",
       "      <th>hist_user_behavior_reason_end</th>\n",
       "      <th>hist_user_behavior_is_shuffle</th>\n",
       "      <th>skip_score</th>\n",
       "      <th>liking</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0_00001e52-55c0-470c-8d32-85c691c2019a</td>\n",
       "      <td>t_a0b2a533-caad-4a72-af14-e647bd7e9c1f</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0_00a20441-f168-41c7-9bf6-93ddeb022f00</td>\n",
       "      <td>t_a0b2a533-caad-4a72-af14-e647bd7e9c1f</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0_00e25cfa-fb17-4c17-a126-04dc55c66259</td>\n",
       "      <td>t_a0b2a533-caad-4a72-af14-e647bd7e9c1f</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0_05c168a6-d905-427e-8a6a-4b12311fcb6d</td>\n",
       "      <td>t_a0b2a533-caad-4a72-af14-e647bd7e9c1f</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0_05d6ac38-9e98-44de-b097-048f1c0f95db</td>\n",
       "      <td>t_a0b2a533-caad-4a72-af14-e647bd7e9c1f</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1870450</td>\n",
       "      <td>0_fffd79ea-ddae-4200-be2e-8152c85d7f9f</td>\n",
       "      <td>t_02bb5521-e44a-4e37-8da8-34fe29864157</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1870451</td>\n",
       "      <td>0_fffef4da-9663-4898-b689-439966333d9c</td>\n",
       "      <td>t_8cdc2107-a0ff-4cf8-9c7e-f78305183fb1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1870452</td>\n",
       "      <td>0_fffef4da-9663-4898-b689-439966333d9c</td>\n",
       "      <td>t_197534e3-d3ee-4731-90d1-de65ef06b748</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1870453</td>\n",
       "      <td>0_fffef4da-9663-4898-b689-439966333d9c</td>\n",
       "      <td>t_bfd37181-1b0e-4ab0-b528-1beabca7735b</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1870454</td>\n",
       "      <td>0_ffff3e31-b986-4d87-9754-86919a7ae997</td>\n",
       "      <td>t_4500b098-1559-4415-89ec-0377b0717dfe</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1870455 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     session_id  \\\n",
       "0        0_00001e52-55c0-470c-8d32-85c691c2019a   \n",
       "1        0_00a20441-f168-41c7-9bf6-93ddeb022f00   \n",
       "2        0_00e25cfa-fb17-4c17-a126-04dc55c66259   \n",
       "3        0_05c168a6-d905-427e-8a6a-4b12311fcb6d   \n",
       "4        0_05d6ac38-9e98-44de-b097-048f1c0f95db   \n",
       "...                                         ...   \n",
       "1870450  0_fffd79ea-ddae-4200-be2e-8152c85d7f9f   \n",
       "1870451  0_fffef4da-9663-4898-b689-439966333d9c   \n",
       "1870452  0_fffef4da-9663-4898-b689-439966333d9c   \n",
       "1870453  0_fffef4da-9663-4898-b689-439966333d9c   \n",
       "1870454  0_ffff3e31-b986-4d87-9754-86919a7ae997   \n",
       "\n",
       "                                       track_id  skip_1  skip_2  skip_3  \\\n",
       "0        t_a0b2a533-caad-4a72-af14-e647bd7e9c1f       0    -0.5     0.2   \n",
       "1        t_a0b2a533-caad-4a72-af14-e647bd7e9c1f       0     0.0     0.2   \n",
       "2        t_a0b2a533-caad-4a72-af14-e647bd7e9c1f       0     0.0     0.2   \n",
       "3        t_a0b2a533-caad-4a72-af14-e647bd7e9c1f       0     0.0     0.0   \n",
       "4        t_a0b2a533-caad-4a72-af14-e647bd7e9c1f       0     0.0     0.0   \n",
       "...                                         ...     ...     ...     ...   \n",
       "1870450  t_02bb5521-e44a-4e37-8da8-34fe29864157       0     0.0     0.0   \n",
       "1870451  t_8cdc2107-a0ff-4cf8-9c7e-f78305183fb1       0     0.0     0.0   \n",
       "1870452  t_197534e3-d3ee-4731-90d1-de65ef06b748      -1    -0.5     0.2   \n",
       "1870453  t_bfd37181-1b0e-4ab0-b528-1beabca7735b       0    -0.5     0.2   \n",
       "1870454  t_4500b098-1559-4415-89ec-0377b0717dfe       0     0.0     0.0   \n",
       "\n",
       "         not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0                  0               0                              0.2   \n",
       "1                  0               0                              0.2   \n",
       "2                  0               0                              0.2   \n",
       "3                  1               0                              0.3   \n",
       "4                  1               0                              1.0   \n",
       "...              ...             ...                              ...   \n",
       "1870450            1               1                              0.2   \n",
       "1870451            1               0                              1.0   \n",
       "1870452            0               0                              0.3   \n",
       "1870453            0               0                              0.3   \n",
       "1870454            1               0                              1.0   \n",
       "\n",
       "         hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  \\\n",
       "0                                  0.4                              0   \n",
       "1                                  0.4                              0   \n",
       "2                                  0.4                              0   \n",
       "3                                  1.0                              0   \n",
       "4                                  1.0                              0   \n",
       "...                                ...                            ...   \n",
       "1870450                            1.0                              0   \n",
       "1870451                            1.0                              1   \n",
       "1870452                            0.3                              1   \n",
       "1870453                            0.3                              1   \n",
       "1870454                            1.0                              0   \n",
       "\n",
       "         skip_score  liking  rating  \n",
       "0              -0.3     0.6     0.3  \n",
       "1               0.2     0.6     0.8  \n",
       "2               0.2     0.6     0.8  \n",
       "3               1.0     1.3     2.3  \n",
       "4               1.0     2.0     3.0  \n",
       "...             ...     ...     ...  \n",
       "1870450         1.0     1.2     2.2  \n",
       "1870451         1.0     3.0     4.0  \n",
       "1870452        -1.3     1.6     0.3  \n",
       "1870453        -0.3     1.6     1.3  \n",
       "1870454         1.0     2.0     3.0  \n",
       "\n",
       "[1870455 rows x 13 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('merged.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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