spotify_multivariate_label_creation-2nd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "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": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dat = pd.read_csv(r'F:\\AppliedDataScience\\data\\dat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "   Unnamed: 0                              session_id  session_position  \\\n",
       "0           0  0_00006f66-33e5-4de7-a324-2d18e439fc1e                 1   \n",
       "1           1  0_00079a23-1600-486a-91bd-5208be0c745a                 7   \n",
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       "3           3  0_013cc010-c476-4ad2-8972-73449e0b2ef4                 9   \n",
       "4           4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290                 7   \n",
       "\n",
       "   session_length                                track_id  skip_1  skip_2  \\\n",
       "0              20  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   \n",
       "1              12  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   \n",
       "2              20  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   \n",
       "3              13  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   \n",
       "4              12  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   \n",
       "\n",
       "   skip_3  not_skipped  context_switch       ...          valence  \\\n",
       "0   False         True               0       ...         0.152255   \n",
       "1    True        False               0       ...         0.152255   \n",
       "2   False         True               0       ...         0.152255   \n",
       "3   False         True               0       ...         0.152255   \n",
       "4    True        False               0       ...         0.152255   \n",
       "\n",
       "   acoustic_vector_0  acoustic_vector_1  acoustic_vector_2  acoustic_vector_3  \\\n",
       "0          -0.815775           0.386409            0.23016           0.028028   \n",
       "1          -0.815775           0.386409            0.23016           0.028028   \n",
       "2          -0.815775           0.386409            0.23016           0.028028   \n",
       "3          -0.815775           0.386409            0.23016           0.028028   \n",
       "4          -0.815775           0.386409            0.23016           0.028028   \n",
       "\n",
       "   acoustic_vector_4  acoustic_vector_5 acoustic_vector_6  acoustic_vector_7  \\\n",
       "0          -0.333373           0.015452          -0.35359           0.205826   \n",
       "1          -0.333373           0.015452          -0.35359           0.205826   \n",
       "2          -0.333373           0.015452          -0.35359           0.205826   \n",
       "3          -0.333373           0.015452          -0.35359           0.205826   \n",
       "4          -0.333373           0.015452          -0.35359           0.205826   \n",
       "\n",
       "  predicted_output  \n",
       "0                1  \n",
       "1                0  \n",
       "2                1  \n",
       "3                0  \n",
       "4                0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'session_id', 'session_position', 'session_length',\n",
       "       'track_id', 'skip_1', 'skip_2', 'skip_3', 'not_skipped',\n",
       "       'context_switch', '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",
       "       'label', 'duration', 'release_year', 'us_popularity_estimate',\n",
       "       'acousticness', 'beat_strength', 'bounciness', 'danceability',\n",
       "       'dyn_range_mean', 'energy', 'flatness', 'instrumentalness', 'key',\n",
       "       'liveness', 'loudness', 'mechanism', 'mode', 'organism', 'speechiness',\n",
       "       'tempo', 'time_signature', 'valence', 'acoustic_vector_0',\n",
       "       'acoustic_vector_1', 'acoustic_vector_2', 'acoustic_vector_3',\n",
       "       'acoustic_vector_4', 'acoustic_vector_5', 'acoustic_vector_6',\n",
       "       'acoustic_vector_7', 'predicted_output'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "dat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = dat[['session_id','track_id', 'skip_1', 'skip_2', 'skip_3', 'not_skipped','context_switch']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                               session_id  \\\n",
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       "1  0_00079a23-1600-486a-91bd-5208be0c745a   \n",
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       "4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
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       "9  0_03373b7c-3c30-4bac-a909-1a4ecc310797   \n",
       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
       "0  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   False   \n",
       "1  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False    True   \n",
       "2  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   False   \n",
       "3  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   False   \n",
       "4  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False    True   \n",
       "5  t_0479f24c-27d2-46d6-a00c-7ec928f2b539    True    True    True   \n",
       "6  t_0479f24c-27d2-46d6-a00c-7ec928f2b539    True    True    True   \n",
       "7  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   False   \n",
       "8  t_0479f24c-27d2-46d6-a00c-7ec928f2b539    True    True    True   \n",
       "9  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   False   \n",
       "\n",
       "   not_skipped  context_switch  \n",
       "0         True               0  \n",
       "1        False               0  \n",
       "2         True               0  \n",
       "3         True               0  \n",
       "4        False               0  \n",
       "5        False               0  \n",
       "6        False               0  \n",
       "7        False               0  \n",
       "8        False               0  \n",
       "9        False               1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\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/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\Ted\\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/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n",
      "C:\\Users\\Ted\\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/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Ted\\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/indexing.html#indexing-view-versus-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": 8,
   "metadata": {},
   "outputs": [
    {
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       "                                session_id  \\\n",
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       "\n",
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       "14  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "\n",
       "    not_skipped  context_switch  \n",
       "0             1               0  \n",
       "1             0               0  \n",
       "2             1               0  \n",
       "3             1               0  \n",
       "4             0               0  \n",
       "5             0               0  \n",
       "6             0               0  \n",
       "7             0               0  \n",
       "8             0               0  \n",
       "9             0               1  \n",
       "10            0               0  \n",
       "11            0               1  \n",
       "12            1               0  \n",
       "13            0               0  \n",
       "14            0               0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "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": 10,
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "behavior = dat[['hist_user_behavior_reason_start','hist_user_behavior_reason_end','hist_user_behavior_is_shuffle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>appload</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>playbtn</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>trackdone</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>trackdone</td>\n",
       "      <td>endplay</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>trackdone</td>\n",
       "      <td>fwdbtn</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hist_user_behavior_reason_start hist_user_behavior_reason_end  \\\n",
       "0                        trackdone                     trackdone   \n",
       "1                           fwdbtn                       endplay   \n",
       "2                        trackdone                     trackdone   \n",
       "3                           fwdbtn                     trackdone   \n",
       "4                        trackdone                        fwdbtn   \n",
       "5                           fwdbtn                        fwdbtn   \n",
       "6                           fwdbtn                        fwdbtn   \n",
       "7                        trackdone                        fwdbtn   \n",
       "8                           fwdbtn                        fwdbtn   \n",
       "9                          appload                        fwdbtn   \n",
       "10                          fwdbtn                        fwdbtn   \n",
       "11                         playbtn                        fwdbtn   \n",
       "12                       trackdone                     trackdone   \n",
       "13                       trackdone                       endplay   \n",
       "14                       trackdone                        fwdbtn   \n",
       "\n",
       "    hist_user_behavior_is_shuffle  \n",
       "0                            True  \n",
       "1                           False  \n",
       "2                           False  \n",
       "3                           False  \n",
       "4                            True  \n",
       "5                           False  \n",
       "6                           False  \n",
       "7                           False  \n",
       "8                           False  \n",
       "9                            True  \n",
       "10                          False  \n",
       "11                          False  \n",
       "12                          False  \n",
       "13                          False  \n",
       "14                          False  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['trackdone', 'fwdbtn', 'appload', 'playbtn', 'clickrow', 'backbtn',\n",
       "       'remote', 'endplay', 'trackerror'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.hist_user_behavior_reason_start.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['trackdone', 'endplay', 'fwdbtn', 'backbtn', 'remote', 'logout',\n",
       "       'clickrow'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.hist_user_behavior_reason_end.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\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/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\Users\\Ted\\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/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Ted\\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/indexing.html#indexing-view-versus-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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.3</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hist_user_behavior_reason_start  hist_user_behavior_reason_end  \\\n",
       "0                               1.0                            1.0   \n",
       "1                               0.3                            0.4   \n",
       "2                               1.0                            1.0   \n",
       "3                               0.3                            1.0   \n",
       "4                               1.0                            0.3   \n",
       "5                               0.3                            0.3   \n",
       "6                               0.3                            0.3   \n",
       "7                               1.0                            0.3   \n",
       "8                               0.3                            0.3   \n",
       "9                               0.2                            0.3   \n",
       "10                              0.3                            0.3   \n",
       "11                              0.5                            0.3   \n",
       "12                              1.0                            1.0   \n",
       "13                              1.0                            0.4   \n",
       "14                              1.0                            0.3   \n",
       "\n",
       "    hist_user_behavior_is_shuffle  \n",
       "0                               1  \n",
       "1                               0  \n",
       "2                               0  \n",
       "3                               0  \n",
       "4                               1  \n",
       "5                               0  \n",
       "6                               0  \n",
       "7                               0  \n",
       "8                               0  \n",
       "9                               1  \n",
       "10                              0  \n",
       "11                              0  \n",
       "12                              0  \n",
       "13                              0  \n",
       "14                              0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>0</th>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.30</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0\n",
       "0   2.00\n",
       "1   0.12\n",
       "2   1.00\n",
       "3   0.30\n",
       "4   1.30\n",
       "5   0.09\n",
       "6   0.09\n",
       "7   0.30\n",
       "8   0.09\n",
       "9   1.06\n",
       "10  0.09\n",
       "11  0.15\n",
       "12  1.00\n",
       "13  0.40\n",
       "14  0.30"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking1.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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1Lcb2d3E7rNz7sT2EY0k+9Q8vG9byQxWyqquUOgD8P8AK/J3W+n8s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\nfxEmXEZntkMXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(liking1,bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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cy9VR4q7RcCxF1pS/YczjdtES8DAXKT+gjuR70fd3ll8GaalGG2LlfEUFdxE5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\n1BakwV0ppbag/x+XgExBEbipEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(liking2,bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skip_score['liking'] = liking2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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j4sAXSQXsY8AjxpgjInKfiNyaPu0/icgREXkV+E/AZ9P3HQS+TuqPxz7gvvQx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\nDfxKKbXCaOBXSqkV5v8HpZUSUymlLOIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(skip_score[0],bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "liking_array = liking2.values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167880, 1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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-27-0040b6b601e3>\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[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   1410\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1411\u001b[0m     X = check_array(X, sparse_format, copy=copy,\n\u001b[1;32m-> 1412\u001b[1;33m                     estimator='the normalize function', dtype=FLOAT_DTYPES)\n\u001b[0m\u001b[0;32m   1413\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[0m\n\u001b[0;32m   1414\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[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, 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    449\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[0m\n\u001b[0;32m    450\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n\u001b[1;32m--> 451\u001b[1;33m                              % (array.ndim, estimator_name))\n\u001b[0m\u001b[0;32m    452\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m             \u001b[0m_assert_all_finite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167880, 1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3. ],\n",
       "       [0.7],\n",
       "       [2. ],\n",
       "       ...,\n",
       "       [3. ],\n",
       "       [2.3],\n",
       "       [1.6]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zoopy = np.reshape(liking_array, (2, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3. , 0.7, 2. , ..., 2. , 2. , 1.9],\n",
       "       [2. , 3. , 0.6, ..., 3. , 2.3, 1.6]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 83940)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zoopy = zoopy.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3. , 0.7, 2. , ..., 2. , 2. , 1.9],\n",
       "       [2. , 3. , 0.6, ..., 3. , 2.3, 1.6]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3. , 2. ],\n",
       "       [0.7, 3. ],\n",
       "       [2. , 0.6],\n",
       "       ...,\n",
       "       [2. , 3. ],\n",
       "       [2. , 2.3],\n",
       "       [1.9, 1.6]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "normalized_zoopy = preprocessing.normalize(zoopy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.83205029, 0.5547002 ],\n",
       "       [0.22722962, 0.97384121],\n",
       "       [0.95782629, 0.28734789],\n",
       "       ...,\n",
       "       [0.5547002 , 0.83205029],\n",
       "       [0.65617871, 0.75460552],\n",
       "       [0.7649112 , 0.64413575]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_zoopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "like = normalized_zoopy.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "like = pd.DataFrame(like)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
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GMv7lN+2XXVg1xvBfnz5MYDzKF393B26HHY/LwWffs4Uj3cP8qLVzhme1tqfe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\nC9LkrpRSFqTJXSmlLOj/B+UjnX3/0aH5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(like,bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  liking\n",
       "0  1.0     3.0\n",
       "1  0.2     0.7\n",
       "2  1.0     2.0\n",
       "3  1.0     1.3\n",
       "4  0.2     2.3\n",
       "5 -1.3     0.6\n",
       "6 -1.3     0.6\n",
       "7  0.0     1.3\n",
       "8 -1.3     0.6\n",
       "9  0.0     1.5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skip_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>0</th>\n",
       "      <td>0.832050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.554700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.227230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.973841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.957826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.832050\n",
       "1  0.554700\n",
       "2  0.227230\n",
       "3  0.973841\n",
       "4  0.957826"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "like.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skip_score['normalized_liking'] = like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mul_label = skip_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.832050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.554700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.973841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.957826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  liking  normalized_liking\n",
       "0  1.0     3.0           0.832050\n",
       "1  0.2     0.7           0.554700\n",
       "2  1.0     2.0           0.227230\n",
       "3  1.0     1.3           0.973841\n",
       "4  0.2     2.3           0.957826"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mul_label = mul_label.rename(columns = {0: \"skip_score\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>skip_score</th>\n",
       "      <th>liking</th>\n",
       "      <th>normalized_liking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.832050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.554700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.227230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.973841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.957826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.287348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.585491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.810679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.854199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.519947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   skip_score  liking  normalized_liking\n",
       "0         1.0     3.0           0.832050\n",
       "1         0.2     0.7           0.554700\n",
       "2         1.0     2.0           0.227230\n",
       "3         1.0     1.3           0.973841\n",
       "4         0.2     2.3           0.957826\n",
       "5        -1.3     0.6           0.287348\n",
       "6        -1.3     0.6           0.585491\n",
       "7         0.0     1.3           0.810679\n",
       "8        -1.3     0.6           0.854199\n",
       "9         0.0     1.5           0.519947"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_label.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frames = [df, behavior, mul_label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = pd.concat(frames,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr 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>normalized_liking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_00006f66-33e5-4de7-a324-2d18e439fc1e</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</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>0.832050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0_00079a23-1600-486a-91bd-5208be0c745a</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</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>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.554700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_012b0fb4-0cc3-429f-9a78-cc6e622153fb</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</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>0.227230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_013cc010-c476-4ad2-8972-73449e0b2ef4</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</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>0.973841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_01a5f0dc-9938-48c9-92f1-c7e51f34d290</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</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>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.957826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               session_id  \\\n",
       "0  0_00006f66-33e5-4de7-a324-2d18e439fc1e   \n",
       "1  0_00079a23-1600-486a-91bd-5208be0c745a   \n",
       "2  0_012b0fb4-0cc3-429f-9a78-cc6e622153fb   \n",
       "3  0_013cc010-c476-4ad2-8972-73449e0b2ef4   \n",
       "4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
       "0  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
       "1  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "2  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
       "3  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
       "4  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "\n",
       "   not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0            1               0                              1.0   \n",
       "1            0               0                              0.3   \n",
       "2            1               0                              1.0   \n",
       "3            1               0                              0.3   \n",
       "4            0               0                              1.0   \n",
       "\n",
       "   hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  skip_score  \\\n",
       "0                            1.0                              1         1.0   \n",
       "1                            0.4                              0         0.2   \n",
       "2                            1.0                              0         1.0   \n",
       "3                            1.0                              0         1.0   \n",
       "4                            0.3                              1         0.2   \n",
       "\n",
       "   liking  normalized_liking  \n",
       "0     3.0           0.832050  \n",
       "1     0.7           0.554700  \n",
       "2     2.0           0.227230  \n",
       "3     1.3           0.973841  \n",
       "4     2.3           0.957826  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "X = result[['skip_1','skip_2','skip_3','context_switch','hist_user_behavior_reason_start','hist_user_behavior_reason_end',\n",
    "            \n",
    "            'hist_user_behavior_is_shuffle']]\n",
    "y1 = result[['skip_score']]\n",
    "y2 = result[['normalized_liking']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##### predicting the skip score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_skip_score = sm.OLS(y1, X).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x207d0764cf8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_skip_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_skip_score = model_skip_score.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_skip_score = pd.DataFrame(predictions_skip_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.165435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.908970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.845195</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.973240\n",
       "1  0.165435\n",
       "2  0.908970\n",
       "3  0.845195\n",
       "4  0.211694"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_skip_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### predict the normalized liking ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0.634429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.573398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.691377</td>\n",
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      "text/plain": [
       "          0\n",
       "0  0.685047\n",
       "1  0.634429\n",
       "2  0.634997\n",
       "3  0.573398\n",
       "4  0.691377"
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     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model_normalized_liking = sm.OLS(y2, X).fit()\n",
    "predictions_normalized_liking = model_normalized_liking.predict(X)\n",
    "predictions_normalized_liking = pd.DataFrame(predictions_normalized_liking)\n",
    "predictions_normalized_liking.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pout = predictions_skip_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pout['p_like'] = predictions_normalized_liking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
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       "          0    p_like\n",
       "0  0.973240  0.685047\n",
       "1  0.165435  0.634429\n",
       "2  0.908970  0.634997\n",
       "3  0.845195  0.573398\n",
       "4  0.211694  0.691377"
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     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "pout.head()"
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  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "pout.rename(columns = {0:'p_skip'}, inplace = True) "
   ]
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  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
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       "     p_skip    p_like\n",
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   "cell_type": "code",
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    "result['p_skip'] = pout['p_skip']"
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  {
   "cell_type": "code",
   "execution_count": 78,
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    "result['p_like'] = pout['p_like']"
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  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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       "                               session_id  \\\n",
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       "4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
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       "1  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
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       "\n",
       "   not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0            1               0                              1.0   \n",
       "1            0               0                              0.3   \n",
       "2            1               0                              1.0   \n",
       "3            1               0                              0.3   \n",
       "4            0               0                              1.0   \n",
       "\n",
       "   hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  skip_score  \\\n",
       "0                            1.0                              1         1.0   \n",
       "1                            0.4                              0         0.2   \n",
       "2                            1.0                              0         1.0   \n",
       "3                            1.0                              0         1.0   \n",
       "4                            0.3                              1         0.2   \n",
       "\n",
       "   liking  normalized_liking    p_skip    p_like  \n",
       "0     3.0           0.832050  0.973240  0.685047  \n",
       "1     0.7           0.554700  0.165435  0.634429  \n",
       "2     2.0           0.227230  0.908970  0.634997  \n",
       "3     1.3           0.973841  0.845195  0.573398  \n",
       "4     2.3           0.957826  0.211694  0.691377  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
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yvtJHLGGY0IuqjjMRjrHjyBA3nbv4hdS5bjqnlVNj0xzqD+VgZLkzPhWltgDl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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(result['p_skip'],bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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br2XfkUn+49/vp1yp8u9edyVh1WHM7dd98nyRx45Mcna2TDl0/ODF83zp0aMU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\nAQYy8BwFm7urAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(result['p_like'],bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}