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Vikrant79/Coupon Purchase Prediction - BTB Script.ipynb

Created September 4, 2015 20:29
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Coupon Purchase Prediction - BTB Script.ipynb
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Coupon Purchase Prediction - First Script.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn import ensemble, preprocessing, cross_validation\n",
"from sklearn.metrics import roc_auc_score as auc\n",
"from time import time\n",
"from sklearn import svm\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.ensemble import ExtraTreesClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn import neighbors, datasets\n",
"from sklearn import tree\n",
"from sklearn.datasets import make_hastie_10_2\n",
"from sklearn.ensemble import GradientBoostingClassifier"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train = pd.read_csv('C:\\\\Users\\\\Vikrant\\\\Coupon\\\\coupon_list_train.csv')\n",
"test = pd.read_csv('C:\\\\Users\\\\Vikrant\\\\Coupon\\\\coupon_list_test.csv')\n",
"train_visit = pd.read_csv('C:\\\\Users\\\\Vikrant\\\\Coupon\\\\coupon_visit_train.csv')\n",
"user_list = pd.read_csv('C:\\\\Users\\\\Vikrant\\\\Coupon\\\\user_list.csv')\n",
"coupon_area_train = pd.read_csv('C:\\\\Users\\\\Vikrant\\\\Coupon\\\\coupon_area_train.csv')\n",
"coupon_area_test = pd.read_csv('C:\\\\Users\\\\Vikrant\\\\Coupon\\\\coupon_area_test.csv')\n",
"Sample_Submission = pd.read_csv('C:\\\\Users\\\\Vikrant\\\\Coupon\\\\sample_submission.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#train_set = pd.merge(user_list,train_visit,on='USER_ID_hash',how='inner')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#train_set.columns.values['VIEW_COUPON_ID_hash'] = 'Coupon_ID_hash'\n",
"#train_set.rename(columns={'VIEW_COUPON_ID_hash':'Coupon_ID_hash'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#train_set.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#train_set_final = pd.merge(coupon_area_train, train_set, on='Coupon_ID_hash',how='inner')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Y_Coupon_Value = test.COUPON_ID_hash "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Y = np.array(Y_Coupon_Value)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#X_Sample = Sample_Submission.USER_ID_hash"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#for x in xrange(1,5):\n",
" ### Inner for loop\t ###\n",
"test[\"cross\"] = 1"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>USER_ID_hash</th>\n",
" <th>PURCHASED_COUPONS</th>\n",
" <th>cross</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22868</th>\n",
" <td>fff1a623187cefd7a594e338709b0f40</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22869</th>\n",
" <td>fff4a076cfda6ff9dbe85e1cb678791b</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22870</th>\n",
" <td>fff970d2014c3e10a77e38d540239017</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22871</th>\n",
" <td>fffafc024e264d5d539813444cf61199</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22872</th>\n",
" <td>ffff56dbf3c782c3532f88c6c79817ba</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" USER_ID_hash PURCHASED_COUPONS cross\n",
"22868 fff1a623187cefd7a594e338709b0f40 NaN 1\n",
"22869 fff4a076cfda6ff9dbe85e1cb678791b NaN 1\n",
"22870 fff970d2014c3e10a77e38d540239017 NaN 1\n",
"22871 fffafc024e264d5d539813444cf61199 NaN 1\n",
"22872 ffff56dbf3c782c3532f88c6c79817ba NaN 1"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Sample_Submission.tail()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test[\"cross\"] = np.arange(310)\n",
"#np.arange(309)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = 1\n",
"for y in range(0, 22872):\n",
" for x in range(0, 309):\n",
" Sample_Submission[\"cross(y)\"] = x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>USER_ID_hash</th>\n",
" <th>PURCHASED_COUPONS</th>\n",
" <th>cross</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0000b53e182165208887ba65c079fc21</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>00035b86e6884589ec8d28fbf2fe7757</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0005b1068d5f2b8f2a7c978fcfe1ca06</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000cc06982785a19e2a2fdb40b1c9d59</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0013518e41c416cd6a181d277dd8ca0b</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" USER_ID_hash PURCHASED_COUPONS cross\n",
"0 0000b53e182165208887ba65c079fc21 NaN 4\n",
"1 00035b86e6884589ec8d28fbf2fe7757 NaN 4\n",
"2 0005b1068d5f2b8f2a7c978fcfe1ca06 NaN 4\n",
"3 000cc06982785a19e2a2fdb40b1c9d59 NaN 4\n",
"4 0013518e41c416cd6a181d277dd8ca0b NaN 4"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Sample_Submission.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "'cross'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-21-f13bc72d990e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mSubmission\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSample_Submission\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"cross\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\tools\\merge.pyc\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy)\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[0mright_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mright_on\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mleft_index\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[0mright_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mright_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m copy=copy)\n\u001b[0m\u001b[0;32m 38\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__debug__\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\tools\\merge.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy)\u001b[0m\n\u001b[0;32m 181\u001b[0m (self.left_join_keys,\n\u001b[0;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mright_join_keys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 183\u001b[1;33m self.join_names) = self._get_merge_keys()\n\u001b[0m\u001b[0;32m 184\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\tools\\merge.pyc\u001b[0m in \u001b[0;36m_get_merge_keys\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 341\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 342\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_rkey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 343\u001b[1;33m \u001b[0mright_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mright\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 344\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlk\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mrk\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 345\u001b[0m \u001b[1;31m# avoid key upcast in corner case (length-0)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1795\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1796\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1797\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1798\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1799\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1802\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1803\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1804\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1805\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1806\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1082\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1083\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1084\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1085\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1086\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m 2849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2850\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2851\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2852\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2853\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method)\u001b[0m\n\u001b[0;32m 1570\u001b[0m \"\"\"\n\u001b[0;32m 1571\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1572\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1574\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3824)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3704)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12280)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12231)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 'cross'"
]
}
],
"source": [
"Submission = pd.merge(Sample_Submission, test, on=\"cross\")"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Final = Submission.drop_duplicates(cols='USER_ID_hash')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>2012-06-30 12:00:00</td>\n",
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" <td>1</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3100</th>\n",
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" <tr>\n",
" <th>3410</th>\n",
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" <tr>\n",
" <th>3720</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>4030</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>4340</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>4650</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>4960</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>5270</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>5580</th>\n",
" <td>00454d0ad87f2e423cc3cc201fde8c8c</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5890</th>\n",
" <td>0047658498c5026a2bfa33b6f0573766</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>6200</th>\n",
" <td>0047a9d43268c7cddf4a874c3dbb6f9f</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>6510</th>\n",
" <td>004c5867575223ca7d7d3de4e8e1e23a</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>6820</th>\n",
" <td>004d75de304e67612ec60e675af66839</td>\n",
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" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7130</th>\n",
" <td>004e1508270f3c9a0eb3cdfe89470c12</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>7440</th>\n",
" <td>004e3f8a3635f9a0144771feaf8a1e32</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>7750</th>\n",
" <td>004f67d329e3e56adef98c9aa8074e97</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>8060</th>\n",
" <td>004f6fe912f2fa81036a47204eb69451</td>\n",
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" <td>52</td>\n",
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" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8370</th>\n",
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" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8680</th>\n",
" <td>00563f5737892fc2f75f70bf794f4b43</td>\n",
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" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8990</th>\n",
" <td>0057e72575079113710648ae0f726d97</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7081330</th>\n",
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" <td>1</td>\n",
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" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7081640</th>\n",
" <td>ffb5c3ae7d5725689c74caebd44a8a62</td>\n",
" <td>NaN</td>\n",
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" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7081950</th>\n",
" <td>ffb688037381369f52268077d3941123</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7082260</th>\n",
" <td>ffb98d47f0b6bbf06cb1331e90a55179</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7082570</th>\n",
" <td>ffba61b78f71f7071830adba316031c2</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
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" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7082880</th>\n",
" <td>ffbd0b34af16015bebe4dfa99ab66621</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
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" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7083190</th>\n",
" <td>ffbd648c50a96cc493e960805b3bd30f</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
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" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7083500</th>\n",
" <td>ffbe59c00a8949be5ecc5f1ed871eae0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
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" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7083810</th>\n",
" <td>ffc1ebba501d00aca267ce914e9e3110</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7084120</th>\n",
" <td>ffc76df6fa752ce7b49bb0ea4c97396d</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7084430</th>\n",
" <td>ffc7d9a1cf343d1246c047b6c1984454</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7084740</th>\n",
" <td>ffc9da5c3d1f082c50a120fe2be90913</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7085050</th>\n",
" <td>ffca89ec6ac84029fe7448e4e1c1adf6</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7085360</th>\n",
" <td>ffcd4d5b3682b27845c80ffc16c8c08b</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7085670</th>\n",
" <td>ffd5c4f2a415910b2bb96c0c68a1fcdd</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7085980</th>\n",
" <td>ffd6f660f3688968efa198ee888b1443</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7086290</th>\n",
" <td>ffd96bf2eb13fb10a2a5b3ed09f05f7b</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7086600</th>\n",
" <td>ffda28af997f586147eb23d612894e56</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7086910</th>\n",
" <td>ffdf3421c09fe158686523cea5e8ac39</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7087220</th>\n",
" <td>ffe3e44dab6fd47bd9c02782640c4d4a</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7087530</th>\n",
" <td>ffe65332382f1f264d84fa85a44f564c</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7087840</th>\n",
" <td>ffe7362865b91c287affaf8912ef4750</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7088150</th>\n",
" <td>ffecfec48a5778f3e470d8a4fb804723</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7088460</th>\n",
" <td>ffed1129803b26815be16d5531a27250</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
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" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7088770</th>\n",
" <td>fff193f946ae515d405376cdf7895f78</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7089080</th>\n",
" <td>fff1a623187cefd7a594e338709b0f40</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7089390</th>\n",
" <td>fff4a076cfda6ff9dbe85e1cb678791b</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7089700</th>\n",
" <td>fff970d2014c3e10a77e38d540239017</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7090010</th>\n",
" <td>fffafc024e264d5d539813444cf61199</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7090320</th>\n",
" <td>ffff56dbf3c782c3532f88c6c79817ba</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>グルメ</td>\n",
" <td>グルメ</td>\n",
" <td>52</td>\n",
" <td>5659</td>\n",
" <td>2690</td>\n",
" <td>2012-06-26 12:00:00</td>\n",
" <td>2012-06-30 12:00:00</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>関西</td>\n",
" <td>大阪府</td>\n",
" <td>ミナミ他</td>\n",
" <td>c76ea297ebd3a5a4d3bf9f75269f66fa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>22873 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" USER_ID_hash PURCHASED_COUPONS cross \\\n",
"0 0000b53e182165208887ba65c079fc21 NaN 1 \n",
"310 00035b86e6884589ec8d28fbf2fe7757 NaN 1 \n",
"620 0005b1068d5f2b8f2a7c978fcfe1ca06 NaN 1 \n",
"930 000cc06982785a19e2a2fdb40b1c9d59 NaN 1 \n",
"1240 0013518e41c416cd6a181d277dd8ca0b NaN 1 \n",
"1550 001acdee812a18acfd7509172bed5700 NaN 1 \n",
"1860 001fd7876e3aa29393537c6baf308e43 NaN 1 \n",
"2170 002383753c1e5d6305c8aff6f89e26d6 NaN 1 \n",
"2480 0025cae7997d25ea5cf8851bb099c798 NaN 1 \n",
"2790 002822059a01d895fad84f2f2ff5c1f1 NaN 1 \n",
"3100 002ae30377cd30f65652e52618e8b2d6 NaN 1 \n",
"3410 002b08971471e6083dd716f6c3bb6572 NaN 1 \n",
"3720 002bbdd51b2a042c051c66c43b55439a NaN 1 \n",
"4030 002f5c29fdf99b4115bf4a93c9241d19 NaN 1 \n",
"4340 003a7b4941222b7e507fdc9e95de2cc1 NaN 1 \n",
"4650 003e02424d0a6ec3ee80d231939fac7c NaN 1 \n",
"4960 0042fc64a751d0b98e9148e3c366d319 NaN 1 \n",
"5270 00441c9b51cfe60b82bdf7a20ad79fc8 NaN 1 \n",
"5580 00454d0ad87f2e423cc3cc201fde8c8c NaN 1 \n",
"5890 0047658498c5026a2bfa33b6f0573766 NaN 1 \n",
"6200 0047a9d43268c7cddf4a874c3dbb6f9f NaN 1 \n",
"6510 004c5867575223ca7d7d3de4e8e1e23a NaN 1 \n",
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"7130 004e1508270f3c9a0eb3cdfe89470c12 NaN 1 \n",
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"8370 0051705e1aedf39d6c3019ef7a71d480 NaN 1 \n",
"8680 00563f5737892fc2f75f70bf794f4b43 NaN 1 \n",
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"... ... ... ... \n",
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"7082880 ffbd0b34af16015bebe4dfa99ab66621 NaN 1 \n",
"7083190 ffbd648c50a96cc493e960805b3bd30f NaN 1 \n",
"7083500 ffbe59c00a8949be5ecc5f1ed871eae0 NaN 1 \n",
"7083810 ffc1ebba501d00aca267ce914e9e3110 NaN 1 \n",
"7084120 ffc76df6fa752ce7b49bb0ea4c97396d NaN 1 \n",
"7084430 ffc7d9a1cf343d1246c047b6c1984454 NaN 1 \n",
"7084740 ffc9da5c3d1f082c50a120fe2be90913 NaN 1 \n",
"7085050 ffca89ec6ac84029fe7448e4e1c1adf6 NaN 1 \n",
"7085360 ffcd4d5b3682b27845c80ffc16c8c08b NaN 1 \n",
"7085670 ffd5c4f2a415910b2bb96c0c68a1fcdd NaN 1 \n",
"7085980 ffd6f660f3688968efa198ee888b1443 NaN 1 \n",
"7086290 ffd96bf2eb13fb10a2a5b3ed09f05f7b NaN 1 \n",
"7086600 ffda28af997f586147eb23d612894e56 NaN 1 \n",
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"7087220 ffe3e44dab6fd47bd9c02782640c4d4a NaN 1 \n",
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"7090320 ffff56dbf3c782c3532f88c6c79817ba NaN 1 \n",
"\n",
" CAPSULE_TEXT GENRE_NAME PRICE_RATE CATALOG_PRICE DISCOUNT_PRICE \\\n",
"0 グルメ グルメ 52 5659 2690 \n",
"310 グルメ グルメ 52 5659 2690 \n",
"620 グルメ グルメ 52 5659 2690 \n",
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"6510 グルメ グルメ 52 5659 2690 \n",
"6820 グルメ グルメ 52 5659 2690 \n",
"7130 グルメ グルメ 52 5659 2690 \n",
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"7750 グルメ グルメ 52 5659 2690 \n",
"8060 グルメ グルメ 52 5659 2690 \n",
"8370 グルメ グルメ 52 5659 2690 \n",
"8680 グルメ グルメ 52 5659 2690 \n",
"8990 グルメ グルメ 52 5659 2690 \n",
"... ... ... ... ... ... \n",
"7081330 グルメ グルメ 52 5659 2690 \n",
"7081640 グルメ グルメ 52 5659 2690 \n",
"7081950 グルメ グルメ 52 5659 2690 \n",
"7082260 グルメ グルメ 52 5659 2690 \n",
"7082570 グルメ グルメ 52 5659 2690 \n",
"7082880 グルメ グルメ 52 5659 2690 \n",
"7083190 グルメ グルメ 52 5659 2690 \n",
"7083500 グルメ グルメ 52 5659 2690 \n",
"7083810 グルメ グルメ 52 5659 2690 \n",
"7084120 グルメ グルメ 52 5659 2690 \n",
"7084430 グルメ グルメ 52 5659 2690 \n",
"7084740 グルメ グルメ 52 5659 2690 \n",
"7085050 グルメ グルメ 52 5659 2690 \n",
"7085360 グルメ グルメ 52 5659 2690 \n",
"7085670 グルメ グルメ 52 5659 2690 \n",
"7085980 グルメ グルメ 52 5659 2690 \n",
"7086290 グルメ グルメ 52 5659 2690 \n",
"7086600 グルメ グルメ 52 5659 2690 \n",
"7086910 グルメ グルメ 52 5659 2690 \n",
"7087220 グルメ グルメ 52 5659 2690 \n",
"7087530 グルメ グルメ 52 5659 2690 \n",
"7087840 グルメ グルメ 52 5659 2690 \n",
"7088150 グルメ グルメ 52 5659 2690 \n",
"7088460 グルメ グルメ 52 5659 2690 \n",
"7088770 グルメ グルメ 52 5659 2690 \n",
"7089080 グルメ グルメ 52 5659 2690 \n",
"7089390 グルメ グルメ 52 5659 2690 \n",
"7089700 グルメ グルメ 52 5659 2690 \n",
"7090010 グルメ グルメ 52 5659 2690 \n",
"7090320 グルメ グルメ 52 5659 2690 \n",
"\n",
" DISPFROM DISPEND \\\n",
"0 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"310 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"620 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"930 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"1240 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"1550 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"1860 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"2170 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"2480 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"2790 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"3100 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"3410 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"3720 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"4030 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"4340 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"4650 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"4960 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"5270 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"5580 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"5890 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"6200 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"6510 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"6820 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7130 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7440 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7750 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"8060 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"8370 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"8680 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"8990 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"... ... ... \n",
"7081330 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7081640 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7081950 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7082260 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7082570 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7082880 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7083190 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
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"7084120 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
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"7084740 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7085050 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7085360 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7085670 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7085980 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7086290 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7086600 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7086910 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7087220 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7087530 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7087840 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7088150 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7088460 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7088770 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7089080 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7089390 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7089700 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7090010 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"7090320 2012-06-26 12:00:00 2012-06-30 12:00:00 \n",
"\n",
" ... USABLE_DATE_THU USABLE_DATE_FRI \\\n",
"0 ... 1 1 \n",
"310 ... 1 1 \n",
"620 ... 1 1 \n",
"930 ... 1 1 \n",
"1240 ... 1 1 \n",
"1550 ... 1 1 \n",
"1860 ... 1 1 \n",
"2170 ... 1 1 \n",
"2480 ... 1 1 \n",
"2790 ... 1 1 \n",
"3100 ... 1 1 \n",
"3410 ... 1 1 \n",
"3720 ... 1 1 \n",
"4030 ... 1 1 \n",
"4340 ... 1 1 \n",
"4650 ... 1 1 \n",
"4960 ... 1 1 \n",
"5270 ... 1 1 \n",
"5580 ... 1 1 \n",
"5890 ... 1 1 \n",
"6200 ... 1 1 \n",
"6510 ... 1 1 \n",
"6820 ... 1 1 \n",
"7130 ... 1 1 \n",
"7440 ... 1 1 \n",
"7750 ... 1 1 \n",
"8060 ... 1 1 \n",
"8370 ... 1 1 \n",
"8680 ... 1 1 \n",
"8990 ... 1 1 \n",
"... ... ... ... \n",
"7081330 ... 1 1 \n",
"7081640 ... 1 1 \n",
"7081950 ... 1 1 \n",
"7082260 ... 1 1 \n",
"7082570 ... 1 1 \n",
"7082880 ... 1 1 \n",
"7083190 ... 1 1 \n",
"7083500 ... 1 1 \n",
"7083810 ... 1 1 \n",
"7084120 ... 1 1 \n",
"7084430 ... 1 1 \n",
"7084740 ... 1 1 \n",
"7085050 ... 1 1 \n",
"7085360 ... 1 1 \n",
"7085670 ... 1 1 \n",
"7085980 ... 1 1 \n",
"7086290 ... 1 1 \n",
"7086600 ... 1 1 \n",
"7086910 ... 1 1 \n",
"7087220 ... 1 1 \n",
"7087530 ... 1 1 \n",
"7087840 ... 1 1 \n",
"7088150 ... 1 1 \n",
"7088460 ... 1 1 \n",
"7088770 ... 1 1 \n",
"7089080 ... 1 1 \n",
"7089390 ... 1 1 \n",
"7089700 ... 1 1 \n",
"7090010 ... 1 1 \n",
"7090320 ... 1 1 \n",
"\n",
" USABLE_DATE_SAT USABLE_DATE_SUN USABLE_DATE_HOLIDAY \\\n",
"0 1 1 1 \n",
"310 1 1 1 \n",
"620 1 1 1 \n",
"930 1 1 1 \n",
"1240 1 1 1 \n",
"1550 1 1 1 \n",
"1860 1 1 1 \n",
"2170 1 1 1 \n",
"2480 1 1 1 \n",
"2790 1 1 1 \n",
"3100 1 1 1 \n",
"3410 1 1 1 \n",
"3720 1 1 1 \n",
"4030 1 1 1 \n",
"4340 1 1 1 \n",
"4650 1 1 1 \n",
"4960 1 1 1 \n",
"5270 1 1 1 \n",
"5580 1 1 1 \n",
"5890 1 1 1 \n",
"6200 1 1 1 \n",
"6510 1 1 1 \n",
"6820 1 1 1 \n",
"7130 1 1 1 \n",
"7440 1 1 1 \n",
"7750 1 1 1 \n",
"8060 1 1 1 \n",
"8370 1 1 1 \n",
"8680 1 1 1 \n",
"8990 1 1 1 \n",
"... ... ... ... \n",
"7081330 1 1 1 \n",
"7081640 1 1 1 \n",
"7081950 1 1 1 \n",
"7082260 1 1 1 \n",
"7082570 1 1 1 \n",
"7082880 1 1 1 \n",
"7083190 1 1 1 \n",
"7083500 1 1 1 \n",
"7083810 1 1 1 \n",
"7084120 1 1 1 \n",
"7084430 1 1 1 \n",
"7084740 1 1 1 \n",
"7085050 1 1 1 \n",
"7085360 1 1 1 \n",
"7085670 1 1 1 \n",
"7085980 1 1 1 \n",
"7086290 1 1 1 \n",
"7086600 1 1 1 \n",
"7086910 1 1 1 \n",
"7087220 1 1 1 \n",
"7087530 1 1 1 \n",
"7087840 1 1 1 \n",
"7088150 1 1 1 \n",
"7088460 1 1 1 \n",
"7088770 1 1 1 \n",
"7089080 1 1 1 \n",
"7089390 1 1 1 \n",
"7089700 1 1 1 \n",
"7090010 1 1 1 \n",
"7090320 1 1 1 \n",
"\n",
" USABLE_DATE_BEFORE_HOLIDAY large_area_name ken_name \\\n",
"0 1 関西 大阪府 \n",
"310 1 関西 大阪府 \n",
"620 1 関西 大阪府 \n",
"930 1 関西 大阪府 \n",
"1240 1 関西 大阪府 \n",
"1550 1 関西 大阪府 \n",
"1860 1 関西 大阪府 \n",
"2170 1 関西 大阪府 \n",
"2480 1 関西 大阪府 \n",
"2790 1 関西 大阪府 \n",
"3100 1 関西 大阪府 \n",
"3410 1 関西 大阪府 \n",
"3720 1 関西 大阪府 \n",
"4030 1 関西 大阪府 \n",
"4340 1 関西 大阪府 \n",
"4650 1 関西 大阪府 \n",
"4960 1 関西 大阪府 \n",
"5270 1 関西 大阪府 \n",
"5580 1 関西 大阪府 \n",
"5890 1 関西 大阪府 \n",
"6200 1 関西 大阪府 \n",
"6510 1 関西 大阪府 \n",
"6820 1 関西 大阪府 \n",
"7130 1 関西 大阪府 \n",
"7440 1 関西 大阪府 \n",
"7750 1 関西 大阪府 \n",
"8060 1 関西 大阪府 \n",
"8370 1 関西 大阪府 \n",
"8680 1 関西 大阪府 \n",
"8990 1 関西 大阪府 \n",
"... ... ... ... \n",
"7081330 1 関西 大阪府 \n",
"7081640 1 関西 大阪府 \n",
"7081950 1 関西 大阪府 \n",
"7082260 1 関西 大阪府 \n",
"7082570 1 関西 大阪府 \n",
"7082880 1 関西 大阪府 \n",
"7083190 1 関西 大阪府 \n",
"7083500 1 関西 大阪府 \n",
"7083810 1 関西 大阪府 \n",
"7084120 1 関西 大阪府 \n",
"7084430 1 関西 大阪府 \n",
"7084740 1 関西 大阪府 \n",
"7085050 1 関西 大阪府 \n",
"7085360 1 関西 大阪府 \n",
"7085670 1 関西 大阪府 \n",
"7085980 1 関西 大阪府 \n",
"7086290 1 関西 大阪府 \n",
"7086600 1 関西 大阪府 \n",
"7086910 1 関西 大阪府 \n",
"7087220 1 関西 大阪府 \n",
"7087530 1 関西 大阪府 \n",
"7087840 1 関西 大阪府 \n",
"7088150 1 関西 大阪府 \n",
"7088460 1 関西 大阪府 \n",
"7088770 1 関西 大阪府 \n",
"7089080 1 関西 大阪府 \n",
"7089390 1 関西 大阪府 \n",
"7089700 1 関西 大阪府 \n",
"7090010 1 関西 大阪府 \n",
"7090320 1 関西 大阪府 \n",
"\n",
" small_area_name COUPON_ID_hash \n",
"0 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"310 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"620 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"930 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"1240 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"1550 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"1860 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"2170 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"2480 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"2790 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"3100 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"3410 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"3720 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"4030 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"4340 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"4650 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"4960 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"5270 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"5580 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"5890 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"6200 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"6510 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"6820 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7130 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
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"7750 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
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"8680 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"8990 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"... ... ... \n",
"7081330 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7081640 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7081950 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7082260 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7082570 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7082880 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7083190 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
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"7084120 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7084430 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7084740 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7085050 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7085360 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7085670 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7085980 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7086290 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7086600 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7086910 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7087220 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7087530 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7087840 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7088150 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7088460 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7088770 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7089080 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7089390 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7089700 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7090010 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"7090320 ミナミ他 c76ea297ebd3a5a4d3bf9f75269f66fa \n",
"\n",
"[22873 rows x 27 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Final"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Submission = pd.merge(Sample_Submission,train_visit,on='USER_ID_hash',how='inner')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Submission.rename(columns={'VIEW_COUPON_ID_hash':'Coupon_ID_hash'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Values = test.COUPON_ID_hash.values"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Values"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Submission = Submission[Submission.column('Coupon_ID_hash').isin(Values)]\n",
"Final = Submission[Submission.Coupon_ID_hash == \"Values\"]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>USER_ID_hash</th>\n",
" <th>PURCHASED_COUPONS</th>\n",
" <th>PURCHASE_FLG</th>\n",
" <th>I_DATE</th>\n",
" <th>PAGE_SERIAL</th>\n",
" <th>REFERRER_hash</th>\n",
" <th>Coupon_ID_hash</th>\n",
" <th>SESSION_ID_hash</th>\n",
" <th>PURCHASEID_hash</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [USER_ID_hash, PURCHASED_COUPONS, PURCHASE_FLG, I_DATE, PAGE_SERIAL, REFERRER_hash, Coupon_ID_hash, SESSION_ID_hash, PURCHASEID_hash]\n",
"Index: []"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Final.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "'COUPON_ID_hash'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-36-93b3dceebc31>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mSubmission_Final\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSubmission\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'COUPON_ID_hash'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'inner'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\tools\\merge.pyc\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy)\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[0mright_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mright_on\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mleft_index\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[0mright_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mright_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m copy=copy)\n\u001b[0m\u001b[0;32m 38\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__debug__\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\tools\\merge.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy)\u001b[0m\n\u001b[0;32m 181\u001b[0m (self.left_join_keys,\n\u001b[0;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mright_join_keys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 183\u001b[1;33m self.join_names) = self._get_merge_keys()\n\u001b[0m\u001b[0;32m 184\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\tools\\merge.pyc\u001b[0m in \u001b[0;36m_get_merge_keys\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 350\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 351\u001b[0m \u001b[0mright_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 352\u001b[1;33m \u001b[0mleft_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 353\u001b[0m \u001b[0mjoin_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 354\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0m_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mleft_on\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1795\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1796\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1797\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1798\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1799\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1802\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1803\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1804\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1805\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1806\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1082\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1083\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1084\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1085\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1086\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m 2849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2850\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2851\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2852\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2853\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Vikrant\\Anaconda\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method)\u001b[0m\n\u001b[0;32m 1570\u001b[0m \"\"\"\n\u001b[0;32m 1571\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1572\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1574\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3824)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3704)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12280)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12231)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 'COUPON_ID_hash'"
]
}
],
"source": [
"Submission_Final = pd.merge(Submission,test,on='COUPON_ID_hash',how='inner')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Final = Submission.drop_duplicates(cols='USER_ID_hash', take_last=True)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th>1515</th>\n",
" <td>0042fc64a751d0b98e9148e3c366d319</td>\n",
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" <th>2152</th>\n",
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" <td>004d75de304e67612ec60e675af66839</td>\n",
" <td>0a5237f5cd81aeab2cc635167b9cc793</td>\n",
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" <td>004e1508270f3c9a0eb3cdfe89470c12</td>\n",
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" <th>3639</th>\n",
" <td>004e3f8a3635f9a0144771feaf8a1e32</td>\n",
" <td>f0c9b3690c85188f1f3c76f293c1604a</td>\n",
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" <td>004f67d329e3e56adef98c9aa8074e97</td>\n",
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" <td>d4a4ba60e43198b0ec4b1a15f7659128</td>\n",
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" <tr>\n",
" <th>4204</th>\n",
" <td>00563f5737892fc2f75f70bf794f4b43</td>\n",
" <td>5689b186b402bc92de3cac1537c673f7</td>\n",
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" <th>4285</th>\n",
" <td>0057e72575079113710648ae0f726d97</td>\n",
" <td>38d680729917a4827c0203702d94d5dc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2829709</th>\n",
" <td>ffb4cb9419b6f156a2b26252aa0132ae</td>\n",
" <td>3ebc70d8055d185d1dd1d493d6c0c92c</td>\n",
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" <tr>\n",
" <th>2829907</th>\n",
" <td>ffb5c3ae7d5725689c74caebd44a8a62</td>\n",
" <td>50a2859c5579d9ba02d9e9122b285739</td>\n",
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" <tr>\n",
" <th>2830382</th>\n",
" <td>ffb688037381369f52268077d3941123</td>\n",
" <td>d19ba3c731f88d5dba3c0b8d1258e8ba</td>\n",
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" <tr>\n",
" <th>2830438</th>\n",
" <td>ffb98d47f0b6bbf06cb1331e90a55179</td>\n",
" <td>9a59e0b49c7d24aa4992add0d3214963</td>\n",
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" <tr>\n",
" <th>2830439</th>\n",
" <td>ffba61b78f71f7071830adba316031c2</td>\n",
" <td>e1f4096b770aefb5a3c53bb5beffc5fc</td>\n",
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" <th>2830448</th>\n",
" <td>ffbd0b34af16015bebe4dfa99ab66621</td>\n",
" <td>d5d2c154a6325c45870bb227e789810e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2830476</th>\n",
" <td>ffbd648c50a96cc493e960805b3bd30f</td>\n",
" <td>bd5a37c2ec8ee807a7780257976d94a1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2830602</th>\n",
" <td>ffbe59c00a8949be5ecc5f1ed871eae0</td>\n",
" <td>b78ade1d6575c8eab90545fefe621fc5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2831236</th>\n",
" <td>ffc1ebba501d00aca267ce914e9e3110</td>\n",
" <td>feb5e98d9efaa65759f1aca32b77d3ec</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2831238</th>\n",
" <td>ffc76df6fa752ce7b49bb0ea4c97396d</td>\n",
" <td>59fabe0626b84c86b62f141987344d82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2831266</th>\n",
" <td>ffc7d9a1cf343d1246c047b6c1984454</td>\n",
" <td>5f2dc9b63281f6e43099d37b12efbf27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2831279</th>\n",
" <td>ffc9da5c3d1f082c50a120fe2be90913</td>\n",
" <td>892855ecc87dbb5733a69b529ec4f2bb</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2831283</th>\n",
" <td>ffca89ec6ac84029fe7448e4e1c1adf6</td>\n",
" <td>fff58e3960585ba89a4e649f5c7cc11e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2832139</th>\n",
" <td>ffcd4d5b3682b27845c80ffc16c8c08b</td>\n",
" <td>e788b81fddbef87941e3fa1726ede608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2832140</th>\n",
" <td>ffd5c4f2a415910b2bb96c0c68a1fcdd</td>\n",
" <td>634b06c096f032220506d3e0c9557fe8</td>\n",
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" <td>ffd6f660f3688968efa198ee888b1443</td>\n",
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" <td>ffd96bf2eb13fb10a2a5b3ed09f05f7b</td>\n",
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" <td>c84f18bb6dd5add884c88c3007ecd557</td>\n",
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" <td>76a46d957a033782818b19ead189fc1a</td>\n",
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" <tr>\n",
" <th>2832713</th>\n",
" <td>ffe3e44dab6fd47bd9c02782640c4d4a</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2832762</th>\n",
" <td>ffe65332382f1f264d84fa85a44f564c</td>\n",
" <td>c4199b2629d52eedaf7115ba1db9e18a</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>ffe7362865b91c287affaf8912ef4750</td>\n",
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" <tr>\n",
" <th>2832810</th>\n",
" <td>ffecfec48a5778f3e470d8a4fb804723</td>\n",
" <td>9f13698f7cbb5d57a0982fd02465b0ff</td>\n",
" </tr>\n",
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" <th>2832813</th>\n",
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" <td>a262c7ff56a5cd3de3c5c40443f3018c</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>22805 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" USER_ID_hash VIEW_COUPON_ID_hash\n",
"74 0000b53e182165208887ba65c079fc21 e8a1e8b5719eee0552d0e7ce53f0abd5\n",
"76 00035b86e6884589ec8d28fbf2fe7757 25a27d420caa1c46a8d3c0572d27868a\n",
"82 0005b1068d5f2b8f2a7c978fcfe1ca06 f0f66195d527a5a9509e139ed367b879\n",
"352 000cc06982785a19e2a2fdb40b1c9d59 974e494d6b26ad3b7f7ff324553ef6ea\n",
"381 0013518e41c416cd6a181d277dd8ca0b d4044d9643e305fc4a4c05124059d31a\n",
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"4285 0057e72575079113710648ae0f726d97 38d680729917a4827c0203702d94d5dc\n",
"... ... ...\n",
"2829709 ffb4cb9419b6f156a2b26252aa0132ae 3ebc70d8055d185d1dd1d493d6c0c92c\n",
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"2833177 fffafc024e264d5d539813444cf61199 134e74a0073d63d6344dca458ce0a632\n",
"2833179 ffff56dbf3c782c3532f88c6c79817ba a262c7ff56a5cd3de3c5c40443f3018c\n",
"\n",
"[22805 rows x 2 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Final"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
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"file_extension": ".py",
"mimetype": "text/x-python",
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