In [19]:
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import pandas as pd
import csv
import pickle
import numpy as np
#------------Item----------
item = pd.read_csv('d:\GrEx2\item.csv', sep=',', encoding='utf-8')
item.shape
print (item[0:5])
#------------Mail----------
mail = pd.read_csv('d:\GrEx2\mail.csv', sep=',', encoding='utf-8')
mail.shape
#create a column with the total number of emails sent over the 16 campaigns
mail2 = (mail[['mail_1','mail_2', 'mail_3', 'mail_4', 'mail_5','mail_6','mail_7', 'mail_8', 'mail_9',
 'mail_10', 'mail_11', 'mail_12', 'mail_13', 'mail_14','mail_15', 'mail_16']]).sum(axis=1)
#combine the two files into an updated mail version
mail = pd.concat([mail, mail2], axis=1)
#rename the new column of the total number of emails sent
mail.rename(columns={0: 'total_emails'},inplace=True)
print (mail[0:5])
mail.to_csv ('d:\GrEx2\mail.csv', encoding='utf-8')
#------------Customer----------
customer = pd.read_csv('d:\GrEx2\customer.csv', sep=',', encoding='utf-8', low_memory=False)
customer.shape
print (customer[0:5])
#item 1: change NaN
customer.dropna(how='any').shape
customer['zip4'].fillna(value='NO ZIP4',inplace=True)
customer.shape

   acctno  qty    trandate tran_channel   price  totamt       orderno  \
0   GGGSD    1  2009-11-18           RT   93.60   93.60  CCXUKCIXXXKI   
1   GGGSD    1  2009-11-19           RT  213.60  213.60  CCXURVCXXXKI   
2   GGGSD    1  2009-11-19           RT   93.60   93.60  CCXURVCXXXKI   
3  WGDQLA    1  2009-06-09           RT  599.85  599.85  CCXXNNXXXXUX   
4  WGDQLA    1  2009-06-09           RT   39.00   39.00  CCXXNNXXXXUX   

              deptdescr  
0  Portable Electronics  
1  Portable Electronics  
2  Portable Electronics  
3            Home Audio  
4      Small Appliances  
   Unnamed: 0  Unnamed: 0.1  acctno  mail_1  mail_2  mail_3  mail_4  mail_5  \
0           0             0  WLPAQS       0       0       0       1       0   
1           1             1  WGDQLA       0       0       0       0       0   
2           2             2  WDPSHS       1       0       0       0       0   
3           3             3  APSYYW       1       1       1       1       1   
4           4             4  SDHLPH       1       1       1       1       1   

   mail_6  mail_7      ...       mail_10  mail_11  mail_12  mail_13  mail_14  \
0       1       1      ...             1        1        0        1        0   
1       0       0      ...             0        0        1        1        0   
2       0       1      ...             1        0        0        1        0   
3       1       1      ...             1        1        1        1        1   
4       1       1      ...             1        1        1        1        1   

   mail_15  mail_16  total_emails  total_emails.1  total_emails  
0        1        0             9               9             9  
1        0        1             3               3             3  
2        1        0             5               5             5  
3        1        1            16              16            16  
4        1        1            16              16            16  

[5 rows x 22 columns]
      acctno      zip    zip4  ltd_sales  ltd_transactions  ytd_sales_2009  \
0  SSGDWDYYW  60076.0  2463.0      354.0                 1               0   
1  WSWYQHPAA  60076.0  2858.0      159.0                 3              69   
2  PWLYYASDY  60076.0  1902.0       78.0                 1              78   
3  AAGQDSGYQ  60076.0  1901.0      405.0                 1               0   
4  LWGPLALYY  60076.0     NaN      159.0                 1               0   

   ytd_transactions_2009 channel_acquisition buyer_status  zip9_supercode  \
0                      0                  IB       LAPSED       600762463   
1                      1                  RT       ACTIVE       600762858   
2                      1                  RT       ACTIVE       600761902   
3                      0                  RT     INACTIVE       600761901   
4                      0                  RT     INACTIVE       600763419   

     ...     zprchonp  ztennis ztennisp ztravdom ztravdop ztravfor ztravfop  \
0    ...          0.0        U      4.0        U      4.0        U      4.0   
1    ...          0.0        U      4.0        Y      0.0        Y      0.0   
2    ...          0.0        U      2.0        Y      0.0        U      2.0   
3    ...          0.0        U      1.0        U      1.0        U      3.0   
4    ...          0.0        U      2.0        U      1.0        U      4.0   

  zvoluntr zvoluntp  endofline  
0        U      5.0  ENDOFLINE  
1        U      6.0  ENDOFLINE  
2        U      4.0  ENDOFLINE  
3        U      3.0  ENDOFLINE  
4        U      7.0  ENDOFLINE  

[5 rows x 451 columns]
Out[19]:
(50000, 451)
In [20]:
#Item 2: Check for duplicates
dup_customer = customer.duplicated(subset=['zip','zip4', 'zip9_supercode']).sum()
print ("The number of duplicates are:", dup_customer,)
print ('Duplicates are where zip, zip4 and zip9_supercode all match')
#looking at duplicates
customer.drop_duplicates(['zip','zip4', 'zip9_supercode'], inplace=True, keep=False)
customer=customer.drop_duplicates(['zip','zip4', 'zip9_supercode'])
#for reference print to csv file
customer.to_csv('d:\customer2_output.csv', encoding='utf-8')

The number of duplicates are: 22061
Duplicates are where zip, zip4 and zip9_supercode all match
In [21]:
#item 3 check Item acctno against the Customers file and determine the number of records not matching
item.to_csv('d:\item_output.csv', encoding='utf-8')
ind = (~item.acctno.isin(customer.acctno)).sum()
item_missing = (ind==True).sum()
#check if working by printing the list to a csv file
print ("The number of items missing vs customers are:", item_missing,)
#item 3 check Mail acctno against the Customers file and determine the number of records not matching
mail.to_csv('d:\mail_output.csv', encoding='utf-8')
ind = (~customer.acctno.isin(mail.acctno)).sum()
#ind = customer['acctno'].isin(mail['acctno']) & mail['acctno'].isin(customer['acctno'])
mail_missing = (ind==True).sum()
print ("The number of mails missing vs customers are:", mail_missing,)

The number of items missing vs customers are: 0
The number of mails missing vs customers are: 0
In [22]:
# Item #4
import sqlite3
conn = sqlite3.connect('d:\database.db')
print ("Database created and opened succesfully")
#import pandas as pd
myfulldata = pd.merge(item, customer, on=['acctno'], how='outer')
myfulldata2 = pd.merge(myfulldata, mail, on=['acctno'], how='outer')
#Using sqlalchemy create a database
from sqlalchemy.engine import *
from sqlalchemy import create_engine, Table, Column, String, MetaData, select
print ('create db')
#create the database and echo which means show the results to the screen
engine = create_engine('sqlite:///d:/my_database1.sqlite', echo=True)
metadata = MetaData(engine)
myfulldata2.to_sql('Resolved', engine, if_exists='replace')
index_start = myfulldata2.index[-1] +1
print ('database created and data loaded and table created')

Database created and opened succesfully
create db
2017-05-09 11:25:11,358 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2017-05-09 11:25:11,358 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,359 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2017-05-09 11:25:11,360 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,361 INFO sqlalchemy.engine.base.Engine PRAGMA table_info("Resolved")
2017-05-09 11:25:11,362 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,365 INFO sqlalchemy.engine.base.Engine PRAGMA table_info("Resolved")
2017-05-09 11:25:11,366 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,370 INFO sqlalchemy.engine.base.Engine SELECT name FROM sqlite_master WHERE type='table' ORDER BY name
2017-05-09 11:25:11,370 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,372 INFO sqlalchemy.engine.base.Engine PRAGMA table_info("Resolved")
2017-05-09 11:25:11,373 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,406 INFO sqlalchemy.engine.base.Engine PRAGMA foreign_key_list("Resolved")
2017-05-09 11:25:11,407 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,408 INFO sqlalchemy.engine.base.Engine SELECT sql FROM  (SELECT * FROM sqlite_master UNION ALL   SELECT * FROM sqlite_temp_master) WHERE name = 'Resolved' AND type = 'table'
2017-05-09 11:25:11,409 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,411 INFO sqlalchemy.engine.base.Engine PRAGMA index_list("Resolved")
2017-05-09 11:25:11,412 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,413 INFO sqlalchemy.engine.base.Engine PRAGMA index_info("ix_Resolved_index")
2017-05-09 11:25:11,414 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,415 INFO sqlalchemy.engine.base.Engine PRAGMA index_list("Resolved")
2017-05-09 11:25:11,416 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,418 INFO sqlalchemy.engine.base.Engine PRAGMA index_info("ix_Resolved_index")
2017-05-09 11:25:11,419 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,420 INFO sqlalchemy.engine.base.Engine SELECT sql FROM  (SELECT * FROM sqlite_master UNION ALL   SELECT * FROM sqlite_temp_master) WHERE name = 'Resolved' AND type = 'table'
2017-05-09 11:25:11,421 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:11,434 INFO sqlalchemy.engine.base.Engine 
DROP TABLE "Resolved"
2017-05-09 11:25:11,434 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:12,620 INFO sqlalchemy.engine.base.Engine COMMIT
2017-05-09 11:25:12,669 INFO sqlalchemy.engine.base.Engine 
CREATE TABLE "Resolved" (
	"index" BIGINT, 
	acctno TEXT, 
	qty FLOAT, 
	trandate TEXT, 
	tran_channel TEXT, 
	price FLOAT, 
	totamt FLOAT, 
	orderno TEXT, 
	deptdescr TEXT, 
	zip FLOAT, 
	zip4 TEXT, 
	ltd_sales FLOAT, 
	ltd_transactions FLOAT, 
	ytd_sales_2009 FLOAT, 
	ytd_transactions_2009 FLOAT, 
	channel_acquisition TEXT, 
	buyer_status TEXT, 
	zip9_supercode FLOAT, 
	crrt TEXT, 
	dpbc FLOAT, 
	filler TEXT, 
	read_all_types TEXT, 
	book_club TEXT, 
	video_club TEXT, 
	amex_prem TEXT, 
	amex_reg TEXT, 
	debit_cc TEXT, 
	disc_prem TEXT, 
	disc_reg TEXT, 
	other_prem_cc TEXT, 
	other_reg_cc TEXT, 
	store_reg_cc TEXT, 
	visa_prem TEXT, 
	visa_reg TEXT, 
	gas_cc TEXT, 
	mc_prem TEXT, 
	mc_reg TEXT, 
	major_cc TEXT, 
	animal_welfare TEXT, 
	child_welfare TEXT, 
	culture_active TEXT, 
	environmental TEXT, 
	health_related TEXT, 
	political TEXT, 
	religious_contrib TEXT, 
	social_service TEXT, 
	cd_mm_current TEXT, 
	ira_current TEXT, 
	re_current TEXT, 
	stock_bond_current TEXT, 
	veteran TEXT, 
	computer_electronic TEXT, 
	home_garden TEXT, 
	sports_related TEXT, 
	hh_contact_lenses TEXT, 
	inc_scs_amt_v4 FLOAT, 
	inc_wioutscs_v4 TEXT, 
	inc_withscs_v4 TEXT, 
	inc_woutscs_amt_4 FLOAT, 
	fipsstcd FLOAT, 
	fipscnty FLOAT, 
	tract FLOAT, 
	block FLOAT, 
	block_id FLOAT, 
	mcd_ccd FLOAT, 
	cbsa_cd FLOAT, 
	lat TEXT, 
	long TEXT, 
	level_lat_long TEXT, 
	popund10 FLOAT, 
	pop_un18 FLOAT, 
	popund25 FLOAT, 
	pop25_34 FLOAT, 
	pop35_44 FLOAT, 
	pop45_54 FLOAT, 
	pop55_64 FLOAT, 
	pop65_p FLOAT, 
	pop75_up FLOAT, 
	pop18_65 FLOAT, 
	medianage FLOAT, 
	phhwhite FLOAT, 
	hh_black FLOAT, 
	p_asian FLOAT, 
	hh_hispa FLOAT, 
	p_nonhisp FLOAT, 
	hh_wh_hi FLOAT, 
	hh_bk_hi FLOAT, 
	p_hhfami FLOAT, 
	pop_sposhhs FLOAT, 
	pop_chldhhs FLOAT, 
	pop_adpchldfam FLOAT, 
	pop_nofamhhs FLOAT, 
	p_hhnohiswht FLOAT, 
	p_famhhchild FLOAT, 
	p_female FLOAT, 
	p_famhhnochd FLOAT, 
	p_marry FLOAT, 
	p_du_occu FLOAT, 
	p_ownocc FLOAT, 
	p_renter FLOAT, 
	pop_urbarea FLOAT, 
	pop_urbclus FLOAT, 
	pop_rurfarm FLOAT, 
	pop_rurnofarm FLOAT, 
	p_hhincoles10m FLOAT, 
	p_hhincom10_14 FLOAT, 
	p_hhincom15_19 FLOAT, 
	p_hhincom20_24 FLOAT, 
	p_hhincom25_29 FLOAT, 
	p_hhincom30_34 FLOAT, 
	p_hhincom35_39 FLOAT, 
	p_hhincom40_44 FLOAT, 
	p_hhincom45_49 FLOAT, 
	p_hhincom50_59 FLOAT, 
	p_hhincom60_74 FLOAT, 
	p_hhincom75_99 FLOAT, 
	p_hhincom100_124 FLOAT, 
	p_hhincom125_149 FLOAT, 
	p_hhincom150_199 FLOAT, 
	p_hhincom20_up FLOAT, 
	med_inc FLOAT, 
	med_famincom FLOAT, 
	med_nofamincom FLOAT, 
	p_capita_incom FLOAT, 
	female_labor_for FLOAT, 
	p_ind_agricultur FLOAT, 
	p_ind_mining FLOAT, 
	p_ind_construct FLOAT, 
	p_ind_manufact FLOAT, 
	p_ind_wholesale FLOAT, 
	p_ind_retailtrd FLOAT, 
	p_ind_transport FLOAT, 
	p_ind_utilitie FLOAT, 
	p_ind_informat FLOAT, 
	p_ind_finalnce FLOAT, 
	p_ind_realestat FLOAT, 
	p_ind_proffes FLOAT, 
	p_ind_manage FLOAT, 
	p_ind_adm_suppot FLOAT, 
	p_ind_edalservic FLOAT, 
	p_ind_healthcare FLOAT, 
	p_ind_arts FLOAT, 
	p_ind_accommod FLOAT, 
	p_ind_otherserv FLOAT, 
	p_ind_pubadmis FLOAT, 
	pcarpool FLOAT, 
	med_trav_towrk FLOAT, 
	avg_commutetim FLOAT, 
	wrk_tra_90_up FLOAT, 
	wrk_tra_60_89 FLOAT, 
	med_home FLOAT, 
	unit_mortg_1st FLOAT, 
	med_rent FLOAT, 
	med_dwell_age FLOAT, 
	med_lor FLOAT, 
	house_stability FLOAT, 
	p_hh_span_speak FLOAT, 
	cur_est_med_inc FLOAT, 
	state_inc_index FLOAT, 
	state_inc_deciles FLOAT, 
	cur_st_est_fam_inc FLOAT, 
	rural_urban_code FLOAT, 
	census_fact1 FLOAT, 
	census_fact2 FLOAT, 
	census_fact3 FLOAT, 
	census_fact4 FLOAT, 
	census_seg1 FLOAT, 
	census_seg2 FLOAT, 
	census_seg3 FLOAT, 
	census_seg4 FLOAT, 
	geopixelcode TEXT, 
	nsm TEXT, 
	pixel FLOAT, 
	pixelgeo FLOAT, 
	exage TEXT, 
	estage FLOAT, 
	adult1_g TEXT, 
	married TEXT, 
	ethnic_match TEXT, 
	ethnic_detail TEXT, 
	language TEXT, 
	religion TEXT, 
	ethnic_group FLOAT, 
	e_tech TEXT, 
	ethnic_country FLOAT, 
	rrc FLOAT, 
	adult1_r TEXT, 
	deceased_indicator TEXT, 
	homeownr TEXT, 
	phomownr FLOAT, 
	renter TEXT, 
	prenter TEXT, 
	add_type TEXT, 
	lor1 FLOAT, 
	dus TEXT, 
	num_child FLOAT, 
	c_00_03 TEXT, 
	c_04_06 TEXT, 
	c_07_09 TEXT, 
	c_10_12 TEXT, 
	c_13_18 TEXT, 
	adult2_g TEXT, 
	ad2_estage FLOAT, 
	ad2age TEXT, 
	adult2_r TEXT, 
	adult3_g TEXT, 
	ad3_estage FLOAT, 
	ad3age TEXT, 
	adult3_r TEXT, 
	adult4_g TEXT, 
	ad4_estage FLOAT, 
	ad4age TEXT, 
	adult4_r TEXT, 
	adult5_g TEXT, 
	ad5_estage FLOAT, 
	ad5age TEXT, 
	adult5_r TEXT, 
	adult6_g TEXT, 
	ad6_estage FLOAT, 
	ad6age TEXT, 
	adult6_r TEXT, 
	adult7_g TEXT, 
	ad7_estage FLOAT, 
	ad7age TEXT, 
	adult7_r TEXT, 
	adult8_g TEXT, 
	ad8_estage FLOAT, 
	ad8age TEXT, 
	adult8_r TEXT, 
	numbadlt FLOAT, 
	ind_dmr TEXT, 
	hh_dmr TEXT, 
	hh_multi TEXT, 
	dm_gifts FLOAT, 
	dm_foods FLOAT, 
	dm_books FLOAT, 
	dm_gardn FLOAT, 
	dm_craft FLOAT, 
	dm_fem FLOAT, 
	dm_male FLOAT, 
	dm_up FLOAT, 
	dm_gen FLOAT, 
	mgz_hlth FLOAT, 
	mgz_food FLOAT, 
	mgz_grdn FLOAT, 
	mgz_relg FLOAT, 
	mgz_male FLOAT, 
	mgz_fem FLOAT, 
	mgz_fam FLOAT, 
	con_gen FLOAT, 
	con_hlth FLOAT, 
	con_polt FLOAT, 
	con_relg FLOAT, 
	sweeps FLOAT, 
	doitself FLOAT, 
	news FLOAT, 
	photo FLOAT, 
	respodds FLOAT, 
	respmisc FLOAT, 
	occupation FLOAT, 
	occupation_group TEXT, 
	ind_ed FLOAT, 
	preschld TEXT, 
	childprob TEXT, 
	ca00_03k TEXT, 
	ca00_03i TEXT, 
	ca04_06k TEXT, 
	ca04_06i TEXT, 
	ca07_09k TEXT, 
	ca07_09i TEXT, 
	ca10_12k TEXT, 
	ca10_12i TEXT, 
	ca13_18k TEXT, 
	ca13_18i TEXT, 
	ssn TEXT, 
	phonematch TEXT, 
	timezone FLOAT, 
	cnty_inc FLOAT, 
	nat_inc FLOAT, 
	mailpref TEXT, 
	home_bus TEXT, 
	bus_ownr TEXT, 
	itmm FLOAT, 
	itmm_scs FLOAT, 
	newcar FLOAT, 
	usedcar FLOAT, 
	impact FLOAT, 
	buy_american FLOAT, 
	show_me_money FLOAT, 
	go_with_flow FLOAT, 
	no_time_present FLOAT, 
	never_empty_handed FLOAT, 
	on_the_road FLOAT, 
	look_atme_now FLOAT, 
	stop_smell_roses FLOAT, 
	work_play_hard FLOAT, 
	penny_saved_earned FLOAT, 
	all_inthe_name FLOAT, 
	email_receptive FLOAT, 
	centsch TEXT, 
	channel_dominance TEXT, 
	ad_web FLOAT, 
	ad_magazine FLOAT, 
	ad_newspaper FLOAT, 
	ad_radio FLOAT, 
	ad_tv FLOAT, 
	stincind FLOAT, 
	ctincind FLOAT, 
	esthmvl FLOAT, 
	echvinx FLOAT, 
	echvpct FLOAT, 
	datedeed FLOAT, 
	sales TEXT, 
	transtyp TEXT, 
	loan_amt TEXT, 
	loan_typ TEXT, 
	loan_trm FLOAT, 
	loan_knd TEXT, 
	year_blt FLOAT, 
	estaerng TEXT, 
	estloantovalrng FLOAT, 
	estmortpayrng TEXT, 
	estmortamtrng TEXT, 
	m_hh_level TEXT, 
	m_grptype_median TEXT, 
	m_global_z4 TEXT, 
	zcredit TEXT, 
	zcrafts TEXT, 
	zgourmet TEXT, 
	zcomputr TEXT, 
	zhitech TEXT, 
	zonline TEXT, 
	zspender TEXT, 
	presence_of_smoker TEXT, 
	zgolfers TEXT, 
	zgolferp FLOAT, 
	zdonors TEXT, 
	zdonorsp FLOAT, 
	zpets TEXT, 
	zpetsp FLOAT, 
	zarts TEXT, 
	zartsp FLOAT, 
	zmob TEXT, 
	zmobp FLOAT, 
	zfitness TEXT, 
	zfitnesp FLOAT, 
	zoutdoor TEXT, 
	zoutdoop FLOAT, 
	ztravany TEXT, 
	ztravanp FLOAT, 
	zinvestr TEXT, 
	zinvestp FLOAT, 
	zautoown TEXT, 
	zautoowp FLOAT, 
	zgarden TEXT, 
	zgardenp FLOAT, 
	zcollect TEXT, 
	zcollecp FLOAT, 
	zcruise TEXT, 
	zcruisep FLOAT, 
	zsports TEXT, 
	zsportsp FLOAT, 
	zsweeps TEXT, 
	zsweepsp FLOAT, 
	zpolitic TEXT, 
	zpolitip FLOAT, 
	zmusic TEXT, 
	zmusicp FLOAT, 
	zread TEXT, 
	zreadp FLOAT, 
	zchldprd TEXT, 
	zchldprp FLOAT, 
	zdiy TEXT, 
	zdiyp FLOAT, 
	zselfimp TEXT, 
	zselfipp FLOAT, 
	zreligon TEXT, 
	zreligop FLOAT, 
	zgrandpr TEXT, 
	zgrandpp FLOAT, 
	zclothng TEXT, 
	zclothnp FLOAT, 
	zdonenvr TEXT, 
	zdonenvp FLOAT, 
	zmutual TEXT, 
	zmutualp FLOAT, 
	zwghtcon TEXT, 
	zwghtcop FLOAT, 
	zprchphn TEXT, 
	zprchphp FLOAT, 
	zprchtv TEXT, 
	zprchtvp FLOAT, 
	zmobmult TEXT, 
	zmobmulp FLOAT, 
	zcredplp FLOAT, 
	zcredplt TEXT, 
	zdogs TEXT, 
	zdogsp FLOAT, 
	zcats TEXT, 
	zcatsp FLOAT, 
	zhealth TEXT, 
	zhealthp FLOAT, 
	zautoint TEXT, 
	zautoinp FLOAT, 
	zskiing TEXT, 
	zskiingp FLOAT, 
	zastrlgy TEXT, 
	zastrlgp FLOAT, 
	zboats TEXT, 
	zboatsp FLOAT, 
	zcell TEXT, 
	zcellp FLOAT, 
	zcommcon TEXT, 
	zcommcop FLOAT, 
	zhmdecor TEXT, 
	zhmdecop FLOAT, 
	zhomeent TEXT, 
	zhomeenp FLOAT, 
	zkitchen TEXT, 
	zkitchep FLOAT, 
	zmobav TEXT, 
	zmobavp FLOAT, 
	zmobbook TEXT, 
	zmobboop FLOAT, 
	zmobclth TEXT, 
	zmobcltp FLOAT, 
	zmobfin TEXT, 
	zmobfinp FLOAT, 
	zmobgift TEXT, 
	zmobgifp FLOAT, 
	zmobgrdn TEXT, 
	zmobgrdp FLOAT, 
	zmobjwl TEXT, 
	zmobjwlp FLOAT, 
	zmusclas TEXT, 
	zmusclap FLOAT, 
	zmuscntr TEXT, 
	zmuscntp FLOAT, 
	zmuscrst TEXT, 
	zmuscrsp FLOAT, 
	zmusoldi TEXT, 
	zmusoldp FLOAT, 
	zmusrock TEXT, 
	zmusrocp FLOAT, 
	zpbcare TEXT, 
	zpbcarep FLOAT, 
	zphoto TEXT, 
	zphotop FLOAT, 
	zprchonl TEXT, 
	zprchonp FLOAT, 
	ztennis TEXT, 
	ztennisp FLOAT, 
	ztravdom TEXT, 
	ztravdop FLOAT, 
	ztravfor TEXT, 
	ztravfop FLOAT, 
	zvoluntr TEXT, 
	zvoluntp FLOAT, 
	endofline TEXT, 
	"Unnamed: 0" FLOAT, 
	"Unnamed: 0.1" FLOAT, 
	mail_1 FLOAT, 
	mail_2 FLOAT, 
	mail_3 FLOAT, 
	mail_4 FLOAT, 
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2017-05-09 11:25:12,671 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:12,813 INFO sqlalchemy.engine.base.Engine COMMIT
2017-05-09 11:25:12,815 INFO sqlalchemy.engine.base.Engine CREATE INDEX "ix_Resolved_index" ON "Resolved" ("index")
2017-05-09 11:25:12,816 INFO sqlalchemy.engine.base.Engine ()
2017-05-09 11:25:12,925 INFO sqlalchemy.engine.base.Engine COMMIT
2017-05-09 11:25:14,763 INFO sqlalchemy.engine.base.Engine BEGIN (implicit)
2017-05-09 11:26:01,043 INFO sqlalchemy.engine.base.Engine INSERT INTO "Resolved" ("index", acctno, qty, trandate, tran_channel, price, totamt, orderno, deptdescr, zip, zip4, ltd_sales, ltd_transactions, ytd_sales_2009, ytd_transactions_2009, channel_acquisition, buyer_status, zip9_supercode, crrt, dpbc, filler, read_all_types, book_club, video_club, amex_prem, amex_reg, debit_cc, disc_prem, disc_reg, other_prem_cc, other_reg_cc, store_reg_cc, visa_prem, visa_reg, gas_cc, mc_prem, mc_reg, major_cc, animal_welfare, child_welfare, culture_active, environmental, health_related, political, religious_contrib, social_service, cd_mm_current, ira_current, re_current, stock_bond_current, veteran, computer_electronic, home_garden, sports_related, hh_contact_lenses, inc_scs_amt_v4, inc_wioutscs_v4, inc_withscs_v4, inc_woutscs_amt_4, fipsstcd, fipscnty, tract, block, block_id, mcd_ccd, cbsa_cd, lat, long, level_lat_long, popund10, pop_un18, popund25, pop25_34, pop35_44, pop45_54, pop55_64, 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3062.0, '3N', '3P', 'A02', 'A02', 'C', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 'U', 6.0, 'U', 7.0, 'U', 7.0, 'U', 1.0, 'U', 8.0, 'U', 4.0, 'U', 6.0, 'U', 1.0, 'U', 5.0, 'U', 8.0, 'U', 7.0, 'U', 7.0, 'U', 1.0, 'U', 5.0, 'U', 6.0, 'U', 8.0, 'U', 4.0, 'U', 8.0, 'U', 5.0, 'U', 8.0, 'U', 4.0, 'U', 9.0, 'U', 9.0, 'U', 2.0, 'U', 3.0, 'U', 4.0, 'U', 5.0, 'U', 3.0, 'U', 2.0, 'U', 8.0, 8.0, 'U', 'U', 7.0, 'U', 6.0, 'U', 9.0, 'U', 8.0, 'U', 1.0, 'U', 4.0, 'U', 6.0, 'U', 1.0, 'U', 1.0, 'U', 1.0, 'U', 3.0, 'U', 6.0, 'U', 4.0, 'U', 7.0, 'U', 4.0, 'U', 5.0, 'U', 7.0, 'U', 8.0, 'U', 6.0, 'U', 3.0, 'U', 8.0, 'U', 9.0, 'U', 4.0, 'U', 3.0, 'U', 4.0, 'U', 5.0, 'U', 8.0, 'U', 1.0, 'U', 1.0, 'U', 1.0, 'U', 1.0, 'ENDOFLINE', 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 3.0, 3.0)  ... displaying 10 of 102103 total bound parameter sets ...  (102101, 'GYLPGDGYG', None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, 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None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, 30942.0, 30942.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0))
2017-05-09 11:26:37,969 INFO sqlalchemy.engine.base.Engine COMMIT
2017-05-09 11:26:39,948 INFO sqlalchemy.engine.base.Engine SELECT name FROM sqlite_master WHERE type='table' ORDER BY name
2017-05-09 11:26:39,949 INFO sqlalchemy.engine.base.Engine ()
database created and data loaded and table created
In [23]:
#create a table based on specific datafields
table4 = pd.read_sql_query('SELECT acctno, total_emails, ytd_transactions_2009, ytd_sales_2009,zhomeent, zmobav, zcredit, zhitech FROM Resolved WHERE total_emails >7', engine)
table4.head()
#Insert a new column based on the data type with 1 being assigned to Y and 0 to all other data types
table4['ZHOMEENT01'] = (table4['zhomeent']=='Y').astype(int)
table4['ZMOBAV01'] = (table4['zmobav']=='Y').astype(int)
table4['ZCREDIT01'] = (table4['zcredit']=='Y').astype(int)
table4['ZHITECH01'] = (table4['zhitech']=='Y').astype(int)
#The file that has all of the data fields needed for the direct mail marketing campaign
table4.to_csv ('d:/GrEx2/5_xyzmrktgcampaign.csv')

2017-05-09 11:26:39,961 INFO sqlalchemy.engine.base.Engine SELECT acctno, total_emails, ytd_transactions_2009, ytd_sales_2009,zhomeent, zmobav, zcredit, zhitech FROM Resolved WHERE total_emails >7
2017-05-09 11:26:39,962 INFO sqlalchemy.engine.base.Engine ()
In [27]:
# Item 6 Cross Tabulations for each Z variables and their numeric version
#Crosstab for zhomeent
ct_zhomeent = pd.crosstab([table4.zhomeent], [table4.ZHOMEENT01], margins=True).fillna(0)
ct_zhomeent.to_csv ('d:\GrEx2/ct_zhomeent.csv')
#Crosstab for zmobav
ct_zmobav = pd.crosstab([table4.zmobav], [table4.ZMOBAV01], margins=True).fillna(0)
ct_zmobav.to_csv ('d:\GrEx2/ct_zmobav.csv')
#Crosstab for zcredit
ct_zcredit = pd.crosstab([table4.zcredit], [table4.ZCREDIT01], margins=True).fillna(0)
ct_zcredit.to_csv ('d:\GrEx2/ct_zcredit.csv')
#Crosstab for zhitech
ct_zhitech = pd.crosstab([table4.zhitech], [table4.ZHITECH01], margins=True).fillna(0)
ct_zhitech.to_csv ('d:\GrEx2/ct_zhitech.csv')
In [28]:
#Item 7 Pickle-It
myfulldata2.to_pickle("grex2_myfulldata.pkl")
myfulldata2 = pd.read_pickle("grex2_myfulldata.pkl")
myfulldata2.to_csv('d:\GrEx2/grex2_myfulldata_pickled.csv')
print ('done')

done