10_CouponPrediction_Explore_2.ipynb

{"nbformat_minor": 0, "cells": [{"execution_count": 11, "cell_type": "code", "source": "sc", "outputs": [{"execution_count": 11, "output_type": "execute_result", "data": {"text/plain": "<pyspark.context.SparkContext at 0x7fc9c1aeec50>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 12, "cell_type": "code", "source": "sc.version", "outputs": [{"execution_count": 12, "output_type": "execute_result", "data": {"text/plain": "u'1.4.1'"}, "metadata": {}}], "metadata": {"scrolled": false, "collapsed": false, "trusted": true}}, {"execution_count": 13, "cell_type": "code", "source": "#!unzip /resources/coupon_visit_train.csv.zip", "outputs": [], "metadata": {"scrolled": false, "collapsed": false, "trusted": true}}, {"execution_count": 14, "cell_type": "code", "source": "import pandas as pd", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 15, "cell_type": "code", "source": "#returns dataframe with parsed csv \ndef read_csv(textfile, nSampl = 1000):\n    splitfile =  sc.textFile(textfile).map(lambda x: x.split(\",\"))\n    header = splitfile.take(1)[0]\n    body = splitfile.filter(lambda x: x <> header)\n    sampl = body.take(nSampl)\n    sampl_df = pd.DataFrame(sampl, columns=header)\n    schm =  sqlContext.createDataFrame(sampl_df).schema\n    return sqlContext.createDataFrame(body, schm)    ", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 16, "cell_type": "code", "source": "#returns dataframe with parsed csv \ndef read_csv2(textfile, sep=\",\", nSampl = 1000):\n    pd_df =  pd.read_csv(textfile, nrows=nSampl, infer_datetime_format=True)\n    schm =  sqlContext.createDataFrame(pd_df).schema\n    body = sc.textFile(textfile).map(lambda x: x.split(sep)).zipWithIndex().filter(lambda (r,i): i>0).keys()\n    return sqlContext.createDataFrame(body, schm)    ", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 17, "cell_type": "code", "source": "#visits = read_csv(\"/resources/coupon_visit_train.csv\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "#coupons = read_csv2(\"/resources/coupon_list_train_en.csv\")", "outputs": [], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 19, "cell_type": "code", "source": "visits.schema", "outputs": [{"execution_count": 19, "output_type": "execute_result", "data": {"text/plain": "StructType(List(StructField(PURCHASE_FLG,StringType,true),StructField(I_DATE,StringType,true),StructField(PAGE_SERIAL,StringType,true),StructField(REFERRER_hash,StringType,true),StructField(VIEW_COUPON_ID_hash,StringType,true),StructField(USER_ID_hash,StringType,true),StructField(SESSION_ID_hash,StringType,true),StructField(PURCHASEID_hash,StringType,true)))"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 20, "cell_type": "code", "source": "visits.limit(5).toPandas()", "outputs": [{"execution_count": 20, "output_type": "execute_result", "data": {"text/plain": "  PURCHASE_FLG               I_DATE PAGE_SERIAL  \\\n0            0  2012-03-28 14:15:00           7   \n1            0  2012-03-28 14:17:28           9   \n2            0  2012-03-28 14:20:05          16   \n3            0  2012-03-28 14:23:16          18   \n4            0  2012-03-28 14:26:25          20   \n\n                      REFERRER_hash               VIEW_COUPON_ID_hash  \\\n0  7d3892e54acb559ae36c459978489330  34c48f84026e08355dc3bd19b427f09a   \n1  7d3892e54acb559ae36c459978489330  34c48f84026e08355dc3bd19b427f09a   \n2  7d3892e54acb559ae36c459978489330  17c450c3b470c045d35ec22b02daa690   \n3  7d3892e54acb559ae36c459978489330  91a15e6a95d09e5e01b50747833b317d   \n4  7d3892e54acb559ae36c459978489330  96fcbc8f6e45d5a2de1661eb140c6e82   \n\n                       USER_ID_hash                   SESSION_ID_hash  \\\n0  d9dca3cb44bab12ba313eaa681f663eb  673af822615593249e7c6a9a1a6bbb1a   \n1  d9dca3cb44bab12ba313eaa681f663eb  673af822615593249e7c6a9a1a6bbb1a   \n2  d9dca3cb44bab12ba313eaa681f663eb  673af822615593249e7c6a9a1a6bbb1a   \n3  d9dca3cb44bab12ba313eaa681f663eb  673af822615593249e7c6a9a1a6bbb1a   \n4  d9dca3cb44bab12ba313eaa681f663eb  673af822615593249e7c6a9a1a6bbb1a   \n\n  PURCHASEID_hash  \n0                  \n1                  \n2                  \n3                  \n4                  \n\n[5 rows x 8 columns]", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PURCHASE_FLG</th>\n      <th>I_DATE</th>\n      <th>PAGE_SERIAL</th>\n      <th>REFERRER_hash</th>\n      <th>VIEW_COUPON_ID_hash</th>\n      <th>USER_ID_hash</th>\n      <th>SESSION_ID_hash</th>\n      <th>PURCHASEID_hash</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 0</td>\n      <td> 2012-03-28 14:15:00</td>\n      <td>  7</td>\n      <td> 7d3892e54acb559ae36c459978489330</td>\n      <td> 34c48f84026e08355dc3bd19b427f09a</td>\n      <td> d9dca3cb44bab12ba313eaa681f663eb</td>\n      <td> 673af822615593249e7c6a9a1a6bbb1a</td>\n      <td> </td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 0</td>\n      <td> 2012-03-28 14:17:28</td>\n      <td>  9</td>\n      <td> 7d3892e54acb559ae36c459978489330</td>\n      <td> 34c48f84026e08355dc3bd19b427f09a</td>\n      <td> d9dca3cb44bab12ba313eaa681f663eb</td>\n      <td> 673af822615593249e7c6a9a1a6bbb1a</td>\n      <td> </td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 0</td>\n      <td> 2012-03-28 14:20:05</td>\n      <td> 16</td>\n      <td> 7d3892e54acb559ae36c459978489330</td>\n      <td> 17c450c3b470c045d35ec22b02daa690</td>\n      <td> d9dca3cb44bab12ba313eaa681f663eb</td>\n      <td> 673af822615593249e7c6a9a1a6bbb1a</td>\n      <td> </td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 0</td>\n      <td> 2012-03-28 14:23:16</td>\n      <td> 18</td>\n      <td> 7d3892e54acb559ae36c459978489330</td>\n      <td> 91a15e6a95d09e5e01b50747833b317d</td>\n      <td> d9dca3cb44bab12ba313eaa681f663eb</td>\n      <td> 673af822615593249e7c6a9a1a6bbb1a</td>\n      <td> </td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 0</td>\n      <td> 2012-03-28 14:26:25</td>\n      <td> 20</td>\n      <td> 7d3892e54acb559ae36c459978489330</td>\n      <td> 96fcbc8f6e45d5a2de1661eb140c6e82</td>\n      <td> d9dca3cb44bab12ba313eaa681f663eb</td>\n      <td> 673af822615593249e7c6a9a1a6bbb1a</td>\n      <td> </td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 8 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 31, "cell_type": "code", "source": "!rm pyspark_csv.py", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 32, "cell_type": "code", "source": "!wget https://raw.githubusercontent.com/vykhand/pyspark-csv/70413bb9c1345de3d01456f5ea9d58cadab8ccf5/pyspark_csv.py", "outputs": [{"output_type": "stream", "name": "stdout", "text": "--2015-09-26 11:07:12--  https://raw.githubusercontent.com/vykhand/pyspark-csv/70413bb9c1345de3d01456f5ea9d58cadab8ccf5/pyspark_csv.py\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 23.235.46.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|23.235.46.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 5250 (5.1K) [text/plain]\nSaving to: \u2018pyspark_csv.py\u2019\n\n100%[======================================>] 5,250       --.-K/s   in 0s      \n\n2015-09-26 11:07:12 (585 MB/s) - \u2018pyspark_csv.py\u2019 saved [5250/5250]\n\n"}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 1, "cell_type": "code", "source": "import pyspark_csv as pycsv\n#sc.addPyFile('pyspark_csv.py')", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 2, "cell_type": "code", "source": "visits_text =  sc.textFile(\"/resources/coupon_visit_train.csv\")", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 3, "cell_type": "code", "source": "visits_df = pycsv.csvToDataFrame(sqlCtx, visits_text)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 5, "cell_type": "code", "source": "visits_df.schema", "outputs": [{"execution_count": 5, "output_type": "execute_result", "data": {"text/plain": "StructType(List(StructField(PURCHASE_FLG,IntegerType,true),StructField(I_DATE,TimestampType,true),StructField(PAGE_SERIAL,IntegerType,true),StructField(REFERRER_hash,StringType,true),StructField(VIEW_COUPON_ID_hash,StringType,true),StructField(USER_ID_hash,StringType,true),StructField(SESSION_ID_hash,StringType,true),StructField(PURCHASEID_hash,StringType,true)))"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 11, "cell_type": "code", "source": "visits_df.limit(5).toPandas()", "outputs": [{"execution_count": 11, "output_type": "execute_result", "data": {"text/plain": "   PURCHASE_FLG              I_DATE  PAGE_SERIAL  \\\n0             0 2012-03-28 14:15:00            7   \n1             0 2012-03-28 14:17:28            9   \n2             0 2012-03-28 14:20:05           16   \n3             0 2012-03-28 14:23:16           18   \n4             0 2012-03-28 14:26:25           20   \n\n                      REFERRER_hash               VIEW_COUPON_ID_hash  \\\n0  7d3892e54acb559ae36c459978489330  34c48f84026e08355dc3bd19b427f09a   \n1  7d3892e54acb559ae36c459978489330  34c48f84026e08355dc3bd19b427f09a   \n2  7d3892e54acb559ae36c459978489330  17c450c3b470c045d35ec22b02daa690   \n3  7d3892e54acb559ae36c459978489330  91a15e6a95d09e5e01b50747833b317d   \n4  7d3892e54acb559ae36c459978489330  96fcbc8f6e45d5a2de1661eb140c6e82   \n\n                       USER_ID_hash                   SESSION_ID_hash  \\\n0  d9dca3cb44bab12ba313eaa681f663eb  673af822615593249e7c6a9a1a6bbb1a   \n1  d9dca3cb44bab12ba313eaa681f663eb  673af822615593249e7c6a9a1a6bbb1a   \n2  d9dca3cb44bab12ba313eaa681f663eb  673af822615593249e7c6a9a1a6bbb1a   \n3  d9dca3cb44bab12ba313eaa681f663eb  673af822615593249e7c6a9a1a6bbb1a   \n4  d9dca3cb44bab12ba313eaa681f663eb  673af822615593249e7c6a9a1a6bbb1a   \n\n  PURCHASEID_hash  \n0            None  \n1            None  \n2            None  \n3            None  \n4            None  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PURCHASE_FLG</th>\n      <th>I_DATE</th>\n      <th>PAGE_SERIAL</th>\n      <th>REFERRER_hash</th>\n      <th>VIEW_COUPON_ID_hash</th>\n      <th>USER_ID_hash</th>\n      <th>SESSION_ID_hash</th>\n      <th>PURCHASEID_hash</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2012-03-28 14:15:00</td>\n      <td>7</td>\n      <td>7d3892e54acb559ae36c459978489330</td>\n      <td>34c48f84026e08355dc3bd19b427f09a</td>\n      <td>d9dca3cb44bab12ba313eaa681f663eb</td>\n      <td>673af822615593249e7c6a9a1a6bbb1a</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>2012-03-28 14:17:28</td>\n      <td>9</td>\n      <td>7d3892e54acb559ae36c459978489330</td>\n      <td>34c48f84026e08355dc3bd19b427f09a</td>\n      <td>d9dca3cb44bab12ba313eaa681f663eb</td>\n      <td>673af822615593249e7c6a9a1a6bbb1a</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>2012-03-28 14:20:05</td>\n      <td>16</td>\n      <td>7d3892e54acb559ae36c459978489330</td>\n      <td>17c450c3b470c045d35ec22b02daa690</td>\n      <td>d9dca3cb44bab12ba313eaa681f663eb</td>\n      <td>673af822615593249e7c6a9a1a6bbb1a</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>2012-03-28 14:23:16</td>\n      <td>18</td>\n      <td>7d3892e54acb559ae36c459978489330</td>\n      <td>91a15e6a95d09e5e01b50747833b317d</td>\n      <td>d9dca3cb44bab12ba313eaa681f663eb</td>\n      <td>673af822615593249e7c6a9a1a6bbb1a</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>2012-03-28 14:26:25</td>\n      <td>20</td>\n      <td>7d3892e54acb559ae36c459978489330</td>\n      <td>96fcbc8f6e45d5a2de1661eb140c6e82</td>\n      <td>d9dca3cb44bab12ba313eaa681f663eb</td>\n      <td>673af822615593249e7c6a9a1a6bbb1a</td>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"scrolled": false, "collapsed": false, "trusted": true}}, {"execution_count": 6, "cell_type": "code", "source": "coupons_text = sc.textFile(\"/resources/coupon_list_train_en.csv\", use_unicode=True)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 7, "cell_type": "code", "source": "coupons_df = pycsv.csvToDataFrame(sqlContext,coupons_text)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 10, "cell_type": "code", "source": "coupons_df.first()", "outputs": [{"execution_count": 10, "output_type": "execute_result", "data": {"text/plain": "Row(large_area_name=u'\\xe4\\xb9\\x9d\\xe5\\xb7\\x9e\\xe3\\x83\\xbb\\xe6\\xb2\\x96\\xe7\\xb8\\x84', ken_name=u'\\xe7\\xa6\\x8f\\xe5\\xb2\\xa1\\xe7\\x9c\\x8c', small_area_name=u'\\xe7\\xa6\\x8f\\xe5\\xb2\\xa1', GENRE_NAME=u'\\xe3\\x82\\xb0\\xe3\\x83\\xab\\xe3\\x83\\xa1', CAPSULE_TEXT=u'\\xe3\\x82\\xb0\\xe3\\x83\\xab\\xe3\\x83\\xa1', PRICE_RATE=51, CATALOG_PRICE=3100, DISCOUNT_PRICE=1500, DISPFROM=datetime.datetime(2012, 6, 19, 12, 0), DISPEND=datetime.datetime(2012, 6, 23, 12, 0), DISPPERIOD=4, VALIDFROM=datetime.datetime(2012, 7, 2, 0, 0), VALIDEND=datetime.datetime(2012, 10, 21, 0, 0), VALIDPERIOD=111, USABLE_DATE_MON=1, USABLE_DATE_TUE=1, USABLE_DATE_WED=1, USABLE_DATE_THU=1, USABLE_DATE_FRI=1, USABLE_DATE_SAT=1, USABLE_DATE_SUN=1, USABLE_DATE_HOLIDAY=1, USABLE_DATE_BEFORE_HOLIDAY=1, COUPON_ID_hash=u'dfac78c08662d02b2669313e785a8034', en_CAPSULE_TEXT=u'Food', en_GENRE_NAME=u'Food', en_small_area_name=u'Fukuoka', en_ken_name=u'Fukuoka Prefecture', en_large_area_name=u'Kyushu-Okinawa')"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 8, "cell_type": "code", "source": "users_df = pycsv.csvToDataFrame( sqlContext, sc.textFile(\"/resources/user_list_en.csv\"), nSampl = 10000)", "outputs": [], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 21, "cell_type": "code", "source": "coupons_df.count()", "outputs": [{"execution_count": 21, "output_type": "execute_result", "data": {"text/plain": "19413"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 22, "cell_type": "code", "source": "users_df.count()", "outputs": [{"execution_count": 22, "output_type": "execute_result", "data": {"text/plain": "22873"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 11, "cell_type": "code", "source": "users_df.take(1)", "outputs": [{"execution_count": 11, "output_type": "execute_result", "data": {"text/plain": "[Row(PREF_NAME=None, REG_DATE=datetime.datetime(2012, 3, 28, 14, 14, 18), SEX_ID=u'f', AGE=25, WITHDRAW_DATE=None, USER_ID_hash=u'd9dca3cb44bab12ba313eaa681f663eb', en_PREF_NAME=None)]"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 9, "cell_type": "code", "source": "visits_df.registerTempTable(\"visits\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 10, "cell_type": "code", "source": "coupons_df.registerTempTable(\"coupons\")", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 11, "cell_type": "code", "source": "users_df.registerTempTable(\"users\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 27, "cell_type": "code", "source": "visits_agg = '''\\\nselect USER_ID_hash as user_id, VIEW_COUPON_ID_hash as coup_id, sum(PURCHASE_FLG) as  num_purchases\nFROM visits where PURCHASE_FLG=1\ngroup by USER_ID_hash, VIEW_COUPON_ID_hash\\\n'''\nvisits_agg =  sqlContext.sql(visits_agg)\nvisits_agg.registerTempTable(\"visits_agg\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 20, "cell_type": "code", "source": "from pyspark.sql.functions import *", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 28, "cell_type": "code", "source": "visits_agg.cache()\nvisits_agg.orderBy(desc(\"num_purchases\")).limit(10).toPandas()", "outputs": [{"execution_count": 28, "output_type": "execute_result", "data": {"text/plain": "                            user_id                           coup_id  \\\n0  9e3868ace7572f1c30859260272836e1  e56a866487df621415a59241af970ada   \n1  776ba7c7c6f37dabb5f75aabbff57b38  d37d3d0b8c034f6b29b66889fff83b7f   \n2  51c4f3aef6c7a0574001baa717751b0f  626769570ddeae80222a5a0ba5d998df   \n3  4951b85ba60b883a8aad009d802304a5  7edc5efeefb47228e363f043d5cbad88   \n4  1be6c7ecd35523258c4b592613d144e5  40d3cc4b08a1877f2cb580059f4438b3   \n5  9e3868ace7572f1c30859260272836e1  7edc5efeefb47228e363f043d5cbad88   \n6  b978dd16a4595b52b04a26453861fe7d  d37a0f4175e02b85d1d91529e6f106d0   \n7  776ba7c7c6f37dabb5f75aabbff57b38  05b89fdebdfc23854c5d739f69122e0d   \n8  332842367488a861f1d53d928393143a  90923b28b74e086e4b9aec2da404f40c   \n9  ca2be9d36e1594d9d8ed53eb98bc47f0  40d3cc4b08a1877f2cb580059f4438b3   \n\n   num_purchases  \n0             30  \n1             29  \n2             24  \n3             19  \n4             18  \n5             15  \n6             14  \n7             14  \n8             13  \n9             12  ", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>coup_id</th>\n      <th>num_purchases</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>9e3868ace7572f1c30859260272836e1</td>\n      <td>e56a866487df621415a59241af970ada</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>776ba7c7c6f37dabb5f75aabbff57b38</td>\n      <td>d37d3d0b8c034f6b29b66889fff83b7f</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>51c4f3aef6c7a0574001baa717751b0f</td>\n      <td>626769570ddeae80222a5a0ba5d998df</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4951b85ba60b883a8aad009d802304a5</td>\n      <td>7edc5efeefb47228e363f043d5cbad88</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1be6c7ecd35523258c4b592613d144e5</td>\n      <td>40d3cc4b08a1877f2cb580059f4438b3</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>9e3868ace7572f1c30859260272836e1</td>\n      <td>7edc5efeefb47228e363f043d5cbad88</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>b978dd16a4595b52b04a26453861fe7d</td>\n      <td>d37a0f4175e02b85d1d91529e6f106d0</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>776ba7c7c6f37dabb5f75aabbff57b38</td>\n      <td>05b89fdebdfc23854c5d739f69122e0d</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>332842367488a861f1d53d928393143a</td>\n      <td>90923b28b74e086e4b9aec2da404f40c</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>ca2be9d36e1594d9d8ed53eb98bc47f0</td>\n      <td>40d3cc4b08a1877f2cb580059f4438b3</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"scrolled": false, "collapsed": false, "trusted": true}}, {"execution_count": 29, "cell_type": "code", "source": "visits_agg.count()", "outputs": [{"execution_count": 29, "output_type": "execute_result", "data": {"text/plain": "107753"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 30, "cell_type": "code", "source": "!rm -rf visits_agg", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 31, "cell_type": "code", "source": "visits_agg.write.save(\"visits_agg\")", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 42, "cell_type": "code", "source": "visits_agg = sqlContext.read.load(\"visits_agg\")\nvisits_agg.registerTempTable(\"visits_agg\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 43, "cell_type": "code", "source": "query_agg = '''\\\nSELECT u.USER_ID_hash, \n       c.en_GENRE_NAME,\n       case when c.USABLE_DATE_BEFORE_HOLIDAY = 1 then 1 else 0 end as usable_before_holiday,\n       case when c.USABLE_DATE_HOLIDAY = 1 then 1 else 0 end as usable_holiday,\n       c.en_small_area_name as small_area,\n       c.en_ken_name as ken, \n       c.en_large_area_name as large_area,\n       u.en_PREF_NAME as pref_name,\n       u.SEX_ID as sex,\n       u.AGE as age,\n       avg(c.PRICE_RATE) as avg_rate,\n       avg(c.CATALOG_PRICE) as avg_price,\n       avg(c.DISCOUNT_PRICE) as avg_discount_price,\n       sum(c.DISCOUNT_PRICE) as sum_disc_price,\n       avg(c.VALIDPERIOD) as avg_valid_period,\n       avg(c.DISPPERIOD) as avg_disp_period,\n       sum(v.num_purchases) as  num_purchases\n  FROM users u LEFT JOIN visits_agg v ON (u.USER_ID_hash = v.user_id)\n  LEFT JOIN  coupons c ON (c.COUPON_ID_hash = v.coup_id)\nGROUP BY u.USER_ID_hash,\n       c.en_GENRE_NAME,\n       c.USABLE_DATE_BEFORE_HOLIDAY,\n       c.USABLE_DATE_HOLIDAY,\n       c.en_small_area_name,\n       c.en_ken_name, \n       c.en_large_area_name,\n       u.en_PREF_NAME,\n       u.SEX_ID,\n       u.AGE\\\n       '''\n\nuser_purchases_agg = sqlContext.sql(query_agg)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 44, "cell_type": "code", "source": "user_purchases_agg.cache()\nuser_purchases_agg.orderBy(desc(\"num_purchases\")).limit(10).toPandas()", "outputs": [{"execution_count": 44, "output_type": "execute_result", "data": {"text/plain": "                       USER_ID_hash     en_GENRE_NAME  usable_before_holiday  \\\n0  9e3868ace7572f1c30859260272836e1              Food                      1   \n1  8b4acd809e6714ac4ad267d0d2dd86c6  Delivery service                      0   \n2  4c20177df5716ba57105ba5d9ed3c460              Food                      1   \n3  4951b85ba60b883a8aad009d802304a5              Food                      1   \n4  776ba7c7c6f37dabb5f75aabbff57b38  Delivery service                      0   \n5  181d381fe0ff496d5bf6f3479073c180              Food                      1   \n6  acd5e20541a5177e039835cef949d38d              Food                      1   \n7  9e0f0f96e48213a377ad89603cbe8e91              Food                      1   \n8  51c4f3aef6c7a0574001baa717751b0f              Food                      1   \n9  4c36f07d9745c8924a0d0c8d44f2abbe              Food                      1   \n\n   usable_holiday                    small_area                 ken  \\\n0               1                  Minami other    Osaka prefecture   \n1               0  Ginza Shinbashi, Tokyo, Ueno               Tokyo   \n2               1                       Fukuoka  Fukuoka Prefecture   \n3               1                  Minami other    Osaka prefecture   \n4               0                         Akita               Akita   \n5               1                       Fukuoka  Fukuoka Prefecture   \n6               1                  Minami other    Osaka prefecture   \n7               1                      Northern    Osaka prefecture   \n8               1      Akasaka, Roppongi, Azabu               Tokyo   \n9               1                       Okinawa             Okinawa   \n\n       large_area           pref_name sex  age   avg_rate     avg_price  \\\n0          Kansai                None   f   51  50.000000   1260.000000   \n1           Kanto                None   m   51  79.090909  14668.181818   \n2  Kyushu-Okinawa     Saga Prefecture   m   61  52.600000   2929.750000   \n3          Kansai    Osaka prefecture   f   51  53.333333   1446.666667   \n4       Northeast  Niigata Prefecture   m   57  52.000000   2100.000000   \n5  Kyushu-Okinawa  Fukuoka Prefecture   m   53  53.714286   3475.333333   \n6          Kansai                None   f   39  55.190476   3644.428571   \n7          Kansai    Osaka prefecture   m   45  50.272727   4954.363636   \n8           Kanto               Tokyo   m   28  52.666667   5193.333333   \n9  Kyushu-Okinawa             Okinawa   m   37  54.000000   3470.000000   \n\n   avg_discount_price  sum_disc_price  avg_valid_period  avg_disp_period  \\\n0          630.000000            1260         89.000000         3.500000   \n1         2648.181818           58260               NaN         2.227273   \n2         1338.000000           26760        138.100000         3.900000   \n3          609.166667            3655        148.833333         3.333333   \n4         1000.000000            1000               NaN         4.000000   \n5         1540.476190           32350        139.095238         4.476190   \n6         1598.095238           33560        129.190476         3.619048   \n7         2445.363636           26899        149.818182         5.181818   \n8         2353.333333            7060        156.333333         3.333333   \n9         1588.571429           33360        157.142857         3.952381   \n\n   num_purchases  \n0             45  \n1             35  \n2             32  \n3             30  \n4             29  \n5             28  \n6             28  \n7             27  \n8             27  \n9             26  ", "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>en_GENRE_NAME</th>\n      <th>usable_before_holiday</th>\n      <th>usable_holiday</th>\n      <th>small_area</th>\n      <th>ken</th>\n      <th>large_area</th>\n      <th>pref_name</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>avg_rate</th>\n      <th>avg_price</th>\n      <th>avg_discount_price</th>\n      <th>sum_disc_price</th>\n      <th>avg_valid_period</th>\n      <th>avg_disp_period</th>\n      <th>num_purchases</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>9e3868ace7572f1c30859260272836e1</td>\n      <td>Food</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Minami other</td>\n      <td>Osaka prefecture</td>\n      <td>Kansai</td>\n      <td>None</td>\n      <td>f</td>\n      <td>51</td>\n      <td>50.000000</td>\n      <td>1260.000000</td>\n      <td>630.000000</td>\n      <td>1260</td>\n      <td>89.000000</td>\n      <td>3.500000</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8b4acd809e6714ac4ad267d0d2dd86c6</td>\n      <td>Delivery service</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Ginza Shinbashi, Tokyo, Ueno</td>\n      <td>Tokyo</td>\n      <td>Kanto</td>\n      <td>None</td>\n      <td>m</td>\n      <td>51</td>\n      <td>79.090909</td>\n      <td>14668.181818</td>\n      <td>2648.181818</td>\n      <td>58260</td>\n      <td>NaN</td>\n      <td>2.227273</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4c20177df5716ba57105ba5d9ed3c460</td>\n      <td>Food</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Fukuoka</td>\n      <td>Fukuoka Prefecture</td>\n      <td>Kyushu-Okinawa</td>\n      <td>Saga Prefecture</td>\n      <td>m</td>\n      <td>61</td>\n      <td>52.600000</td>\n      <td>2929.750000</td>\n      <td>1338.000000</td>\n      <td>26760</td>\n      <td>138.100000</td>\n      <td>3.900000</td>\n      <td>32</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4951b85ba60b883a8aad009d802304a5</td>\n      <td>Food</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Minami other</td>\n      <td>Osaka prefecture</td>\n      <td>Kansai</td>\n      <td>Osaka prefecture</td>\n      <td>f</td>\n      <td>51</td>\n      <td>53.333333</td>\n      <td>1446.666667</td>\n      <td>609.166667</td>\n      <td>3655</td>\n      <td>148.833333</td>\n      <td>3.333333</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>776ba7c7c6f37dabb5f75aabbff57b38</td>\n      <td>Delivery service</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Akita</td>\n      <td>Akita</td>\n      <td>Northeast</td>\n      <td>Niigata Prefecture</td>\n      <td>m</td>\n      <td>57</td>\n      <td>52.000000</td>\n      <td>2100.000000</td>\n      <td>1000.000000</td>\n      <td>1000</td>\n      <td>NaN</td>\n      <td>4.000000</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>181d381fe0ff496d5bf6f3479073c180</td>\n      <td>Food</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Fukuoka</td>\n      <td>Fukuoka Prefecture</td>\n      <td>Kyushu-Okinawa</td>\n      <td>Fukuoka Prefecture</td>\n      <td>m</td>\n      <td>53</td>\n      <td>53.714286</td>\n      <td>3475.333333</td>\n      <td>1540.476190</td>\n      <td>32350</td>\n      <td>139.095238</td>\n      <td>4.476190</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>acd5e20541a5177e039835cef949d38d</td>\n      <td>Food</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Minami other</td>\n      <td>Osaka prefecture</td>\n      <td>Kansai</td>\n      <td>None</td>\n      <td>f</td>\n      <td>39</td>\n      <td>55.190476</td>\n      <td>3644.428571</td>\n      <td>1598.095238</td>\n      <td>33560</td>\n      <td>129.190476</td>\n      <td>3.619048</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>9e0f0f96e48213a377ad89603cbe8e91</td>\n      <td>Food</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Northern</td>\n      <td>Osaka prefecture</td>\n      <td>Kansai</td>\n      <td>Osaka prefecture</td>\n      <td>m</td>\n      <td>45</td>\n      <td>50.272727</td>\n      <td>4954.363636</td>\n      <td>2445.363636</td>\n      <td>26899</td>\n      <td>149.818182</td>\n      <td>5.181818</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>51c4f3aef6c7a0574001baa717751b0f</td>\n      <td>Food</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Akasaka, Roppongi, Azabu</td>\n      <td>Tokyo</td>\n      <td>Kanto</td>\n      <td>Tokyo</td>\n      <td>m</td>\n      <td>28</td>\n      <td>52.666667</td>\n      <td>5193.333333</td>\n      <td>2353.333333</td>\n      <td>7060</td>\n      <td>156.333333</td>\n      <td>3.333333</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>4c36f07d9745c8924a0d0c8d44f2abbe</td>\n      <td>Food</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Okinawa</td>\n      <td>Okinawa</td>\n      <td>Kyushu-Okinawa</td>\n      <td>Okinawa</td>\n      <td>m</td>\n      <td>37</td>\n      <td>54.000000</td>\n      <td>3470.000000</td>\n      <td>1588.571429</td>\n      <td>33360</td>\n      <td>157.142857</td>\n      <td>3.952381</td>\n      <td>26</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 87, "cell_type": "code", "source": "user_purchases_agg.count()", "outputs": [{"execution_count": 87, "output_type": "execute_result", "data": {"text/plain": "86665"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 45, "cell_type": "code", "source": "user_purchases_agg.write.save(\"user_purchases\")", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 88, "cell_type": "code", "source": "user_purchases_agg.registerTempTable(\"user_purch\")", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 89, "cell_type": "code", "source": "dt = sqlContext.sql(\"select ken, sum(num_purchases) sm from user_purch group by ken order by sm desc\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 90, "cell_type": "code", "source": "df = dt.toPandas()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 24, "cell_type": "code", "source": "import matplotlib.pyplot as plt\n%matplotlib inline\nimport seaborn as sns", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 91, "cell_type": "code", "source": "df.head()", "outputs": [{"execution_count": 91, "output_type": "execute_result", "data": {"text/plain": "                   ken     sm\n0                Tokyo  55959\n1     Osaka prefecture  15061\n2  Kanagawa Prefecture   8604\n3             Hokkaido   4693\n4     Hyogo Prefecture   4519", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ken</th>\n      <th>sm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Tokyo</td>\n      <td>55959</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Osaka prefecture</td>\n      <td>15061</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Kanagawa Prefecture</td>\n      <td>8604</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Hokkaido</td>\n      <td>4693</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Hyogo Prefecture</td>\n      <td>4519</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 92, "cell_type": "code", "source": "sns.set_style(\"whitegrid\")\nsns.set_color_codes(\"pastel\")\nf, ax = plt.subplots(figsize=(10,15))\nsns.barplot(y=\"ken\", x=\"sm\", orient = \"h\", data = df)\nsns.despine(left=True, bottom=True)", "outputs": [{"output_type": "display_data", "data": {"image/png": 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KlClVuj4gIMD6JgoRERERkcrU2eRZbr2T2YU1HcIvOpldSLOaDkJERETqLCXP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\n4MG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cUz766CP++te/3rbxXnvtNT766KPbNp6IVG4O5R2AiIjcW5o2bcobb7zBxo0bOXfu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\nAAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f63de04a350>"}, "metadata": {}}], "metadata": {"scrolled": false, "collapsed": false, "trusted": true}}, {"execution_count": 93, "cell_type": "code", "source": "dt = sqlContext.sql(\"select en_GENRE_NAME genre, sex,  sum(num_purchases) sm from user_purch group by en_GENRE_NAME, sex order by sm desc\")\n\ndf = dt.toPandas()", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 71, "cell_type": "code", "source": "df[df['sex']==\"m\"].head()", "outputs": [{"execution_count": 71, "output_type": "execute_result", "data": {"text/plain": "                      genre sex     sm\n1          Delivery service   m  18242\n2                      Food   m  15237\n5              Other coupon   m   8425\n6  Hotel and Japanese hotel   m   7628\n9                 Gift card   m   3243", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>genre</th>\n      <th>sex</th>\n      <th>sm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Delivery service</td>\n      <td>m</td>\n      <td>18242</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Food</td>\n      <td>m</td>\n      <td>15237</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Other coupon</td>\n      <td>m</td>\n      <td>8425</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Hotel and Japanese hotel</td>\n      <td>m</td>\n      <td>7628</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Gift card</td>\n      <td>m</td>\n      <td>3243</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 94, "cell_type": "code", "source": "df[df['sex']==\"f\"].head()", "outputs": [{"execution_count": 94, "output_type": "execute_result", "data": {"text/plain": "                      genre sex     sm\n0          Delivery service   f  27257\n3                      Food   f  11394\n4              Other coupon   f   9109\n7  Hotel and Japanese hotel   f   4686\n8                 Gift card   f   3250", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>genre</th>\n      <th>sex</th>\n      <th>sm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Delivery service</td>\n      <td>f</td>\n      <td>27257</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Food</td>\n      <td>f</td>\n      <td>11394</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Other coupon</td>\n      <td>f</td>\n      <td>9109</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Hotel and Japanese hotel</td>\n      <td>f</td>\n      <td>4686</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Gift card</td>\n      <td>f</td>\n      <td>3250</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 95, "cell_type": "code", "source": "f,a  = plt.subplots(figsize=(17,10))\nsns.set_color_codes(\"pastel\")\nsns.barplot(x=\"genre\", y=\"sm\", hue=\"sex\", data = df)\n", "outputs": [{"execution_count": 95, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f63de21ea90>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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1r3+dl19+OUlSqVTy3e9+t9cXBwAAAPSsx7ifM2dOTjvttJx++uk56KDf3aL/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\nMcYYY4wx1sP9P6ApnVv1z5dgAAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f63de1bcc90>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 103, "cell_type": "code", "source": "dt  = sqlContext.sql(\"select USER_ID_hash, sum(num_purchases) nm from user_purch group by USER_ID_hash\")", "outputs": [], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 104, "cell_type": "code", "source": "dt.filter(\"nm > 0\").count() ", "outputs": [{"execution_count": 104, "output_type": "execute_result", "data": {"text/plain": "19394"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 105, "cell_type": "code", "source": "dt.filter(\"nm = 0\").count() ", "outputs": [{"execution_count": 105, "output_type": "execute_result", "data": {"text/plain": "3479"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 106, "cell_type": "code", "source": "dt.orderBy(desc(\"nm\")).limit(10).toPandas()", "outputs": [{"execution_count": 106, "output_type": "execute_result", "data": {"text/plain": "                       USER_ID_hash   nm\n0  332842367488a861f1d53d928393143a  104\n1  acd5e20541a5177e039835cef949d38d  100\n2  2f3eee0e00feb2020d5a4e99e31adfba   88\n3  9e3ac24ea566eb7d12a78c7f2cd94f66   87\n4  4c20177df5716ba57105ba5d9ed3c460   85\n5  14f7ddf4be9862cb56998bfc79e34da2   81\n6  322a3a65e6683b6881eeb0b1a889e3f4   81\n7  f40c19b0bbf863e7276041ef04e1b91d   81\n8  4951b85ba60b883a8aad009d802304a5   81\n9  9e0f0f96e48213a377ad89603cbe8e91   80", "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>nm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>332842367488a861f1d53d928393143a</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>acd5e20541a5177e039835cef949d38d</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2f3eee0e00feb2020d5a4e99e31adfba</td>\n      <td>88</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9e3ac24ea566eb7d12a78c7f2cd94f66</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4c20177df5716ba57105ba5d9ed3c460</td>\n      <td>85</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>14f7ddf4be9862cb56998bfc79e34da2</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>322a3a65e6683b6881eeb0b1a889e3f4</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>f40c19b0bbf863e7276041ef04e1b91d</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>4951b85ba60b883a8aad009d802304a5</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>9e0f0f96e48213a377ad89603cbe8e91</td>\n      <td>80</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 15, "cell_type": "code", "source": "dates_agg_query = '''\\\nselect c.en_ken_name, year(I_DATE) yr, month(I_DATE) m, weekofyear(I_DATE) wk, from_unixtime(unix_timestamp(I_DATE),'E') wday, sum(PURCHASE_FLG) as  num_purchases\nFROM visits v, coupons c \nwhere v.PURCHASE_FLG=1\nand c.COUPON_ID_hash = v.VIEW_COUPON_ID_hash\ngroup by c.en_ken_name, year(I_DATE), month(I_DATE), weekofyear(I_DATE), from_unixtime(unix_timestamp(I_DATE),'E')\\\n'''\ndates_agg =  sqlContext.sql(dates_agg_query)\ndates_agg.registerTempTable(\"dates_agg\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 17, "cell_type": "code", "source": "dates_agg.cache()\ndates_agg.count()", "outputs": [{"execution_count": 17, "output_type": "execute_result", "data": {"text/plain": "8327"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 18, "cell_type": "code", "source": "dates_agg.registerTempTable(\"dates_agg\")", "outputs": [], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 21, "cell_type": "code", "source": "dates_agg.orderBy(desc(\"num_purchases\")).limit(10).toPandas()", "outputs": [{"execution_count": 21, "output_type": "execute_result", "data": {"text/plain": "  en_ken_name    yr   m  wk wday  num_purchases\n0       Tokyo  2012   6  23  Mon           1662\n1       Tokyo  2012   5  21  Mon            646\n2       Tokyo  2012   6  23  Tue            584\n3       Tokyo  2012   6  23  Wed            514\n4       Tokyo  2012   5  21  Tue            486\n5       Tokyo  2011  10  43  Thu            436\n6       Tokyo  2012   4  14  Tue            424\n7       Tokyo  2011  10  43  Fri            360\n8       Tokyo  2011  12  50  Fri            356\n9       Tokyo  2011  11  44  Sun            353", "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>en_ken_name</th>\n      <th>yr</th>\n      <th>m</th>\n      <th>wk</th>\n      <th>wday</th>\n      <th>num_purchases</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Tokyo</td>\n      <td>2012</td>\n      <td>6</td>\n      <td>23</td>\n      <td>Mon</td>\n      <td>1662</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Tokyo</td>\n      <td>2012</td>\n      <td>5</td>\n      <td>21</td>\n      <td>Mon</td>\n      <td>646</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Tokyo</td>\n      <td>2012</td>\n      <td>6</td>\n      <td>23</td>\n      <td>Tue</td>\n      <td>584</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Tokyo</td>\n      <td>2012</td>\n      <td>6</td>\n      <td>23</td>\n      <td>Wed</td>\n      <td>514</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Tokyo</td>\n      <td>2012</td>\n      <td>5</td>\n      <td>21</td>\n      <td>Tue</td>\n      <td>486</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Tokyo</td>\n      <td>2011</td>\n      <td>10</td>\n      <td>43</td>\n      <td>Thu</td>\n      <td>436</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Tokyo</td>\n      <td>2012</td>\n      <td>4</td>\n      <td>14</td>\n      <td>Tue</td>\n      <td>424</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Tokyo</td>\n      <td>2011</td>\n      <td>10</td>\n      <td>43</td>\n      <td>Fri</td>\n      <td>360</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Tokyo</td>\n      <td>2011</td>\n      <td>12</td>\n      <td>50</td>\n      <td>Fri</td>\n      <td>356</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Tokyo</td>\n      <td>2011</td>\n      <td>11</td>\n      <td>44</td>\n      <td>Sun</td>\n      <td>353</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 38, "cell_type": "code", "source": "dt = sqlContext.sql(\"select yr*100+m m_yr, wday, sum(num_purchases) as nm from dates_agg group by  yr*100+m, wday\")\ndf = dt.toPandas()  ", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 39, "cell_type": "code", "source": "plt.figure(figsize=(12,12))\nsns.heatmap(df.pivot(\"m_yr\", \"wday\", \"nm\"), annot=True, fmt=\"d\", linewidths=.5)", "outputs": [{"execution_count": 39, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f19304e0390>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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ZXSf47pUvDM9XSi5DCtQmTZrc9Ovy5ctGhPyfhYWG4uPpUaStWe06mM2Ft6hu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\nUrbOBwYGMmTIEGJjY41oUkRERESKCEMS1ODgYObOnUtCQoJtW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"text/plain": "<matplotlib.figure.Figure at 0x7f192df35450>"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 40, "cell_type": "code", "source": "dates_agg.write.save(\"dates_agg\")", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 76, "cell_type": "code", "source": "query_details = '''\\\nSELECT v.PURCHASE_FLG as  num_purchases,\n       v.I_DATE as view_date,\n       c.en_GENRE_NAME,\n       c.en_CAPSULE_TEXT,\n       c.PRICE_RATE,\n       c.CATALOG_PRICE,\n       c.DISCOUNT_PRICE,\n       c.DISPFROM,\n       c.DISPEND,\n       c.VALIDFROM,\n       c.VALIDEND,\n       c.VALIDPERIOD,\n       c.USABLE_DATE_BEFORE_HOLIDAY,\n       c.USABLE_DATE_HOLIDAY,\n       c.en_small_area_name as small_area,\n       c.en_ken_name as ken, \n       c.en_large_area_name as large_area,\n       u.en_PREF_NAME as pref_name,\n       u.SEX_ID as sex,\n       u.AGE as age,\n       u.USER_ID_hash as user_id,\n       c.COUPON_ID_hash as coupon_id\n  FROM visits v, users u, coupons c \n WHERE v.VIEW_COUPON_ID_hash = c.COUPON_ID_hash\n   AND v.USER_ID_hash = u.USER_ID_hash\\\n'''\n\ndataset = sqlContext.sql(query)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.10", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}