In [1]:

    

import pandas as pd
import numpy as np
import glob
import time
import os
import pymarc
import marcx
import codecs
import string
os.chdir('/Volumes/jwa_drive1/git/pardee')


    

In [2]:

    

df_contents = pd.read_csv('contents.csv',sep=',')
df_contents = df_contents.dropna()
df_sources = pd.read_csv('source.csv',sep=',')
df_surveys = pd.read_csv('survey.csv', sep=',',dtype={'naics':str})
df_crosswalk = pd.read_excel('SIC_NASIC_Crosswalk.xlsx')
df_crosswalk.rename(columns={'NAICS Industry\xa0':'NAICS Industry'}, inplace=True)
df_crosswalk['NAICS'] = df_crosswalk['NAICS'].astype(str)
df_crosswalk['SIC'] = df_crosswalk['SIC'].astype(str)
df_surveys['naics'] = df_surveys['naics'].astype(str)
df_surveys['sic'] = df_surveys['sic'].astype(str)
header = '<?xml version="1.0" encoding="UTF-8" ?><collection xmlns:marc="http://www.loc.gov/MARC21/slim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">'
footer = '</collection>'
print(df_surveys.columns)
print(df_contents.columns)
print(df_sources.columns)
#df_sources.set_index('c_so_id')


    

    




Index(['sur_id', 'title', 'sic', 'keyword', 'naics'], dtype='object')
Index(['c_id', 'c_sur_id', 'c_so_id', 'c_url'], dtype='object')
Index(['so_id', 's_title', 'location', 'type', 'u_type', 's_url', 'g_url',
       'ezpx'],
      dtype='object')

In [3]:

    

def norm_str(s):
    exclude = set(string.punctuation)
    exclude.add(' ')
    if not type(s) == str:
        s = str(s)
    s = ''.join(ch for ch in s if ch not in exclude)
    #s=s.replace(' ','')
    return(s.lower())

def get_sic(s,df):
    #print(type(df.ntitle))
    #print(type(s.ntitle))
    mask = df.ntitle == s.ntitle
    d = df[mask]
    #print(type(d))
    return(d)


    

In [4]:

    

out = codecs.open('PardeeIS.xml','w', 'utf-8')
out.write(header)
counter = 0
no_856 = 0
proxy_list = ['galegroup.com/','lexisnexis.com/','www.mergentonline.com/','standardandpoors.com/','valueline.com/','proquest.com/','ibisworld.com/','ebscohost.com/']
for index,row in df_surveys.iterrows():
    ## default leader and 008 values
    leader_value = '#####naa#a22#####3##4500'
    _008_value = '######s2015####xxu####eote###000#0#eng#d'
    ## grab the values for sic, naics, keywords from the dataframe
    sic = row['sic']
    naics = row['naics']
    keywords = row['keyword']
    title = row['title']
    ## create empty lists for sics, urls
    sics = []
    urls = []
    ## if no value for sic, lookup in df_crosswalk dataframe
    if sic == 'nan':
        mask = df_crosswalk['NAICS'] == naics
        for index,r in df_crosswalk[mask].iterrows():
            sics.append(r['SIC'])
    else:
        sics.append(sic)
    try:
        sic = sics[0]
    except:
        sic = ''
    ## check for multiple values in the naics field. If so, split them into a list
    naics = naics.replace(' ','')
    if naics.find(',') > 0:
        naics = naics.split(',')
    else:
        naics = [naics]
    ##
    ## get the url
    ##
    mask = df_contents['c_sur_id'] == row['sur_id']
    for index,r in df_contents[mask].iterrows():
        smask = df_sources['so_id'] == r['c_so_id']
        for s_i,s_r in df_sources[smask].iterrows():
            s_title = s_r['s_title']
            s_location = s_r['location']
        urls.append([r['c_url'],s_title,s_location])
    ## create a list of naics
    mask = df_crosswalk['SIC'] == row['sic']
    naics_rows = df_crosswalk[mask]
    ## create a marc record and add fields
    record = marcx.FatRecord()
    record.leader = leader_value
    record.add('008',data = _008_value)
    for n in sics:
        if n != 'nan':
            record.add('024', a=str(n),_2='SICS',indicators=['7',' '])
    for n in naics:
        if n != 'nan':
            record.add('024', a=str(n),_2='NAICS',indicators=['7',' '])
    record.add('041',a='eng')
    record.add('245',a = str(title),indicators=['0','0'])
    ## lookup the NAICS Industry description and add it to a note field
    for index,row in df_crosswalk[mask].iterrows():
        try:
            record.add('500',a = row['NAICS Industry'])
        except:
            pass
    ## add the keywords to a 650 field. Is this the best field?
    record.add('650',a = str(keywords),indicators=['0','4'])
    ## iterate through the urls and add to an 856
    ## need to add a subfield z and subfield 3 with the title and source from the df_sources dataframe
    ##
    record.add('902',a='PardeeIndustrySurvey')
    for n in urls:
        record.remove('856')
        url = str(n[0])
        s_tit = str(n[1])
        s_loc = str(n[2])
        s_link_text = ''
        if s_tit == 'Encyclopedia of American Industries' and len(sic) > 0:
            s_link_text = 'Search SIC [' + sic +'] in Quick Search Box'
        for px in proxy_list:
            if url.find(px) > 0:
                url = url.replace(px,px[:-1]+ '.ezproxy.bu.edu/')
        if s_tit.find('Encyclopedia') == 0:
            record.add('856',u=url,z=s_tit,n=s_loc,y=s_link_text,indicators=['4','0'])
        else:
            record.add('856',u=url,z=s_tit,n=s_loc,indicators=['4','0'])

        if record.has('856'):
            out.write(pymarc.record_to_xml(record ).decode("utf-8"))
            counter += 1
            try:
                print('Record: ',str(counter))
                #print(record)
            except Exception as e:
                print(e)
        else:
            no_856 += 1
            print()
            print("no 856",str(no_856))
            print(record)
out.write(footer)
out.close()


    

    




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Record:  3335
Record:  3336
Record:  3337
Record:  3338
Record:  3339
Record:  3340
Record:  3341
Record:  3342
Record:  3343
Record:  3344
Record:  3345
Record:  3346
Record:  3347
Record:  3348
Record:  3349
Record:  3350
Record:  3351
Record:  3352
Record:  3353
Record:  3354
Record:  3355
Record:  3356
Record:  3357
Record:  3358
Record:  3359
Record:  3360
Record:  3361
Record:  3362
Record:  3363
Record:  3364
Record:  3365
Record:  3366
Record:  3367
Record:  3368
Record:  3369
Record:  3370
Record:  3371
Record:  3372
Record:  3373
Record:  3374
Record:  3375
Record:  3376
Record:  3377
Record:  3378
Record:  3379
Record:  3380
Record:  3381
Record:  3382
Record:  3383
Record:  3384
Record:  3385
Record:  3386
Record:  3387
Record:  3388
Record:  3389
Record:  3390
Record:  3391
Record:  3392
Record:  3393
Record:  3394
Record:  3395
Record:  3396
Record:  3397
Record:  3398
Record:  3399
Record:  3400
Record:  3401
Record:  3402
Record:  3403
Record:  3404
Record:  3405
Record:  3406
Record:  3407
Record:  3408
Record:  3409
Record:  3410
Record:  3411
Record:  3412
Record:  3413
Record:  3414
Record:  3415
Record:  3416
Record:  3417
Record:  3418
Record:  3419
Record:  3420
Record:  3421
Record:  3422
Record:  3423
Record:  3424
Record:  3425
Record:  3426
Record:  3427
Record:  3428
Record:  3429
Record:  3430
Record:  3431
Record:  3432
Record:  3433
Record:  3434
Record:  3435
Record:  3436
Record:  3437
Record:  3438
Record:  3439
Record:  3440
Record:  3441
Record:  3442
Record:  3443
Record:  3444
Record:  3445
Record:  3446
Record:  3447
Record:  3448
Record:  3449
Record:  3450
Record:  3451
Record:  3452
Record:  3453
Record:  3454
Record:  3455
Record:  3456
Record:  3457
Record:  3458
Record:  3459
Record:  3460
Record:  3461
Record:  3462
Record:  3463
Record:  3464
Record:  3465
Record:  3466
Record:  3467
Record:  3468
Record:  3469
Record:  3470
Record:  3471
Record:  3472
Record:  3473
Record:  3474
Record:  3475
Record:  3476
Record:  3477
Record:  3478
Record:  3479
Record:  3480
Record:  3481
Record:  3482
Record:  3483
Record:  3484
Record:  3485
Record:  3486
Record:  3487
Record:  3488
Record:  3489
Record:  3490
Record:  3491
Record:  3492
Record:  3493
Record:  3494
Record:  3495
Record:  3496
Record:  3497
Record:  3498
Record:  3499
Record:  3500
Record:  3501
Record:  3502
Record:  3503
Record:  3504
Record:  3505
Record:  3506
Record:  3507
Record:  3508
Record:  3509
Record:  3510
Record:  3511
Record:  3512
Record:  3513
Record:  3514
Record:  3515
Record:  3516
Record:  3517
Record:  3518
Record:  3519
Record:  3520
Record:  3521
Record:  3522
Record:  3523
Record:  3524
Record:  3525
Record:  3526
Record:  3527
Record:  3528
Record:  3529
Record:  3530
Record:  3531
Record:  3532
Record:  3533
Record:  3534
Record:  3535
Record:  3536
Record:  3537
Record:  3538
Record:  3539
Record:  3540
Record:  3541
Record:  3542
Record:  3543
Record:  3544
Record:  3545
Record:  3546
Record:  3547
Record:  3548
Record:  3549
Record:  3550
Record:  3551
Record:  3552
Record:  3553
Record:  3554
Record:  3555
Record:  3556
Record:  3557
Record:  3558
Record:  3559
Record:  3560
Record:  3561
Record:  3562
Record:  3563
Record:  3564
Record:  3565
Record:  3566
Record:  3567
Record:  3568
Record:  3569
Record:  3570
Record:  3571
Record:  3572
Record:  3573
Record:  3574
Record:  3575
Record:  3576
Record:  3577
Record:  3578
Record:  3579
Record:  3580
Record:  3581
Record:  3582
Record:  3583
Record:  3584
Record:  3585
Record:  3586
Record:  3587
Record:  3588
Record:  3589
Record:  3590
Record:  3591
Record:  3592
Record:  3593
Record:  3594
Record:  3595
Record:  3596
Record:  3597
Record:  3598
Record:  3599
Record:  3600
Record:  3601
Record:  3602
Record:  3603
Record:  3604
Record:  3605
Record:  3606
Record:  3607
Record:  3608
Record:  3609
Record:  3610
Record:  3611
Record:  3612
Record:  3613
Record:  3614
Record:  3615
Record:  3616
Record:  3617
Record:  3618
Record:  3619
Record:  3620
Record:  3621
Record:  3622
Record:  3623
Record:  3624
Record:  3625
Record:  3626
Record:  3627
Record:  3628
Record:  3629
Record:  3630
Record:  3631
Record:  3632
Record:  3633
Record:  3634
Record:  3635
Record:  3636
Record:  3637
Record:  3638
Record:  3639
Record:  3640
Record:  3641
Record:  3642
Record:  3643
Record:  3644
Record:  3645
Record:  3646
Record:  3647
Record:  3648
Record:  3649
Record:  3650
Record:  3651
Record:  3652
Record:  3653
Record:  3654
Record:  3655
Record:  3656
Record:  3657
Record:  3658
Record:  3659
Record:  3660
Record:  3661
Record:  3662
Record:  3663
Record:  3664
Record:  3665
Record:  3666
Record:  3667

In [5]:

    

ser = df_surveys['title']
ser.name = 'ntitle'
df_surveys = pd.concat([df_surveys,ser],axis=1)
for index,row in df_surveys.iterrows():
    ntitle = norm_str(row['title'])
    #print(ntitle)
    df_surveys.loc[df_surveys['title'] == row['title'], 'ntitle'] = ntitle

#df_crosswalk.head()

ser = df_crosswalk['SIC Industry']
ser.name = 'ntitle'
df_crosswalk = pd.concat([df_crosswalk,ser],axis=1)
for index,row in df_crosswalk.iterrows():
    ntitle = norm_str(row['SIC Industry'])
    #print(ntitle)
    df_crosswalk.loc[df_crosswalk['SIC Industry'] == row['SIC Industry'], 'ntitle'] = ntitle
#df_crosswalk.head()


    

In [6]:

    

#df_surveys.head()
mask = ((df_surveys['sic'] == 'nan') & (df_surveys['naics'] == 'nan'))
df_surveys[mask]
nosic_nonaics = df_surveys[mask]
print(nosic_nonaics.shape)
nosic_nonaics.head()
matches = 0
for index,row in nosic_nonaics.iterrows():
    #nosic_nonaics.loc[nosic_nonaics['ntitle'] == row['ntitle'],'sic'] = get_sic(row,df_crosswalk)
    df_ret = get_sic(row,df_crosswalk)
    if df_ret.shape[0] > 0:
        sic = str(df_ret['SIC'])
        sic= sic[:sic.index('\n')]
        sic = sic[sic.rindex(' '):].strip()
        print(row.title,sic)
        nosic_nonaics.loc[nosic_nonaics['ntitle'] == row['ntitle'],'sic'] = sic


    

    




(1821, 6)
Books 2730
Footwear 5139
Book Publishing 2731
Car Washes 7542
Bowling Centers 7933
Bookstores 5942
Boat Dealers 5551
Engineering Services 8711
Department Stores 5311
Footwear 5139
Life Insurance 6311
Natural Gas: Distribution 4924
Colleges and Universities 8221
Florists 5992
Furniture Stores 5712
Title Insurance 6361
Highway and Street Construction 1611
Home Healthcare Services 8082
Kidney Dialysis Centers 8092
Legal Services 8111
Marinas 4493
Musical Instrument Stores 5736
Petroleum Refining 2910
Recreational Vehicle Dealers 5560
Shoe Stores 5660
Testing Laboratories 8734
Used Merchandise Stores 5932
Veterinary Services 740
Medical Equipment Rental 7352
Dental Laboratories 8072






    




/Users/jammerman/anaconda/envs/py3k/lib/python3.3/site-packages/pandas/core/indexing.py:415: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self.obj[item] = s

In [7]:

    

mask = ((df_surveys['sic'] == 'nan') & (df_surveys['naics'] == 'nan'))
df_surveys[mask]
nosic_nonaics = df_surveys[mask]
print(nosic_nonaics.shape)
nosic_nonaics.head()


    

    




(1821, 6)






    
Out[7]:






  

    

      

      
sur_id
      
title
      
sic
      
keyword
      
naics
      
ntitle
    
  
  

    

      
16
      
17
      
Adult Entertainment
      
nan
      
pornographic porn
      
nan
      
adultentertainment
    
    

      
17
      
18
      
Adult Obesity
      
nan
      
overweight over weight obese healthcare health...
      
nan
      
adultobesity
    
    

      
18
      
19
      
Advertising
      
nan
      
ads direct-mail marketing agencies  services
      
nan
      
advertising
    
    

      
21
      
24
      
Advertising and Marketing
      
nan
      
advertising
      
nan
      
advertisingandmarketing
    
    

      
26
      
32
      
Aerospace/Defense
      
nan
      
defense aircraft military weapons missiles air...
      
nan
      
aerospacedefense
    
  

In [8]:

    

df_crosswalk.head()


    

    
Out[8]:






  

    

      

      
SIC
      
SIC Industry
      
NAICS
      
NAICS Industry
      
ntitle
    
  
  

    

      
0
      
3291
      
Abrasive products
      
327910
      
Abrasive Product Manufacturing
      
abrasiveproducts
    
    

      
1
      
2891
      
Adhesives and sealants
      
325520
      
Adhesive Manufacturing
      
adhesivesandsealants
    
    

      
2
      
9511
      
Air, water, & solid waste management
      
924110
      
Administration of Air and Water Resource and S...
      
airwatersolidwastemanagement
    
    

      
3
      
9512
      
Land, mineral, wildlife conservation
      
924120
      
Administration of Conservation Programs
      
landmineralwildlifeconservation
    
    

      
4
      
9410
      
Admin. of Educational Programs
      
923110
      
Administration of Education Programs
      
adminofeducationalprograms
    
  

In [9]:

    

sics


    

    
Out[9]:





[]

In [10]:

    

df_surveys


    

    
Out[10]:






  

    

      

      
sur_id
      
title
      
sic
      
keyword
      
naics
      
ntitle
    
  
  

    

      
0
      
1
      
Abrasive Products
      
3291
      
fabricated metals  grinding industrial equipme...
      
332999
      
abrasiveproducts
    
    

      
1
      
2
      
AC Drives
      
nan
      
electronics industrial equipment  electric pow...
      
221122
      
acdrives
    
    

      
2
      
3
      
AC Drives: Global
      
nan
      
electronics industrial equipment  electric pow...
      
221122
      
acdrivesglobal
    
    

      
3
      
4
      
Accident and Health Insurance
      
6321
      
Health Medical Insurance Carriers carrier Emp...
      
524114, 525190, 524130
      
accidentandhealthinsurance
    
    

      
4
      
5
      
Accountancy
      
nan
      
accounting tax services accountants designing ...
      
541211
      
accountancy
    
    

      
5
      
6
      
Accountancy: Global
      
nan
      
accounting tax services accountants designing ...
      
541211
      
accountancyglobal
    
    

      
6
      
7
      
Accounting, Auditing, and Bookkeeping Services
      
8721
      
accountants cpas payroll service accounting bo...
      
541211, 541214, 541219
      
accountingauditingandbookkeepingservices
    
    

      
7
      
8
      
Adhesives and Sealants
      
2891
      
manufacturing  industrial household glues caul...
      
325520
      
adhesivesandsealants
    
    

      
8
      
9
      
Adjustment and Collection Services
      
7322
      
collection agencies repossession services clai...
      
561440, 561491
      
adjustmentandcollectionservices
    
    

      
9
      
10
      
Administration of Educational Programs
      
9411
      
educational scholarships education central coo...
      
923110
      
administrationofeducationalprograms
    
    

      
10
      
11
      
Administration of General Economic Programs
      
9611
      
tourism  data analysis analyses develop develo...
      
926110
      
administrationofgeneraleconomicprograms
    
    

      
11
      
12
      
Administration of Housing Programs
      
9531
      
authorities governments  building standards  d...
      
925110
      
administrationofhousingprograms
    
    

      
12
      
13
      
Administration of Public Health Programs
      
9431
      
governments mental cancer disease control mate...
      
923120
      
administrationofpublichealthprograms
    
    

      
13
      
14
      
Administration of Social, Human Resource, and ...
      
9441
      
elderly senior citizens child welfare children...
      
923130
      
administrationofsocialhumanresourceandincomema...
    
    

      
14
      
15
      
Administration of Urban Planning and Community...
      
9532
      
slum clearance development land renewal federa...
      
92512
      
administrationofurbanplanningandcommunityandru...
    
    

      
15
      
16
      
Administration of Veterans Affairs, Except Hea...
      
9451
      
services training counseling governments feder...
      
923140
      
administrationofveteransaffairsexcepthealthand...
    
    

      
16
      
17
      
Adult Entertainment
      
nan
      
pornographic porn
      
nan
      
adultentertainment
    
    

      
17
      
18
      
Adult Obesity
      
nan
      
overweight over weight obese healthcare health...
      
nan
      
adultobesity
    
    

      
18
      
19
      
Advertising
      
nan
      
ads direct-mail marketing agencies  services
      
nan
      
advertising
    
    

      
19
      
21
      
Advertising Agencies
      
7311
      
writing copy artwork graphics creative work pe...
      
541810
      
advertisingagencies
    
    

      
20
      
441365
      
Photographic Equipment and Supplies
      
5043
      
wholesalers wholesaling  distribution cameras ...
      
421410
      
photographicequipmentandsupplies
    
    

      
21
      
24
      
Advertising and Marketing
      
nan
      
advertising
      
nan
      
advertisingandmarketing
    
    

      
22
      
26
      
Advertising, Not Elsewhere Classified
      
7319
      
agency aerial circular handbill distribution d...
      
481219, 541830 , 541850 , 541870, 541890
      
advertisingnotelsewhereclassified
    
    

      
23
      
27
      
Advertising: Global
      
nan
      
agencies display services  marketing
      
541850, 541891
      
advertisingglobal
    
    

      
24
      
28
      
Aerospace and Defense
      
nan
      
military airplanes aeronautics aerospace plane...
      
481211
      
aerospaceanddefense
    
    

      
25
      
31
      
Aerospace and Defense: Global
      
nan
      
military airplanes aeronautics aerospace plane...
      
481211
      
aerospaceanddefenseglobal
    
    

      
26
      
32
      
Aerospace/Defense
      
nan
      
defense aircraft military weapons missiles air...
      
nan
      
aerospacedefense
    
    

      
27
      
33
      
Agribusiness
      
nan
      
farms agriculture farming crops dairy organics...
      
nan
      
agribusiness
    
    

      
28
      
34
      
Agricultural Products
      
nan
      
farms agriculture farming  cereals roots tuber...
      
424590, 493130
      
agriculturalproducts
    
    

      
29
      
35
      
Agricultural Products: Global
      
nan
      
farms agriculture farming  cereals roots tuber...
      
nan
      
agriculturalproductsglobal
    
    

      
...
      
...
      
...
      
...
      
...
      
...
      
...
    
    

      
3764
      
443192
      
Private Firefighting Services
      
nan
      
firefighters
      
nan
      
privatefirefightingservices
    
    

      
3765
      
443193
      
Retail Store Fixture Dealers
      
nan
      
fixtures Storage racks retail stores
      
nan
      
retailstorefixturedealers
    
    

      
3766
      
443194
      
Tower Crane Manufacturing
      
nan
      
cranes towers high rises buildings
      
nan
      
towercranemanufacturing
    
    

      
3767
      
443195
      
Scaffolding Contractors
      
nan
      
construction remodeling painting
      
nan
      
scaffoldingcontractors
    
    

      
3768
      
443196
      
Dump Truck Services
      
nan
      
dump trucks
      
nan
      
dumptruckservices
    
    

      
3769
      
443197
      
Ornamental & Architectural Metalwork Services
      
nan
      
fences, handrails, balcony rails, screen doors...
      
nan
      
ornamentalarchitecturalmetalworkservices
    
    

      
3770
      
443198
      
Metal Door Manufacturing
      
nan
      
windows doors manufacturing screens moldings t...
      
nan
      
metaldoormanufacturing
    
    

      
3771
      
443199
      
Bridge & Structure Painting Contractors
      
nan
      
bridges coatings cables
      
nan
      
bridgestructurepaintingcontractors
    
    

      
3772
      
443200
      
Firestop Contractors
      
nan
      
firestops
      
nan
      
firestopcontractors
    
    

      
3773
      
443201
      
Concrete Reinforcing Bar Manufacturing
      
nan
      
rebar construction bridges buildings highways ...
      
nan
      
concretereinforcingbarmanufacturing
    
    

      
3774
      
443202
      
Reconstituted Wood Product Manufacturing
      
nan
      
NaN
      
nan
      
reconstitutedwoodproductmanufacturing
    
    

      
3775
      
443203
      
Wood Flooring Manufacturing
      
nan
      
homes floors coverings textiles household viny...
      
nan
      
woodflooringmanufacturing
    
    

      
3776
      
443204
      
Window Coverings Wholesaling
      
nan
      
curtains, blinds and draperies
      
nan
      
windowcoveringswholesaling
    
    

      
3777
      
443205
      
Corrosion Protection Engineering Services
      
nan
      
coatings
      
nan
      
corrosionprotectionengineeringservices
    
    

      
3778
      
443206
      
Cementing Oil & Gas Well Services
      
nan
      
wells
      
nan
      
cementingoilgaswellservices
    
    

      
3779
      
443207
      
Soundproofing Contractors
      
nan
      
NaN
      
nan
      
soundproofingcontractors
    
    

      
3780
      
443208
      
Water & Air Quality Testing Services
      
nan
      
Asbestos mold bacteria
      
nan
      
waterairqualitytestingservices
    
    

      
3781
      
443209
      
Commercial Property Remodeling
      
nan
      
remodels buildings
      
nan
      
commercialpropertyremodeling
    
    

      
3782
      
443210
      
Precision Agriculture Systems & Services
      
nan
      
agricultural harvesting crops farms farming
      
nan
      
precisionagriculturesystemsservices
    
    

      
3783
      
443211
      
3D Printer Manufacturing
      
nan
      
printing manufacturing three-dimensional  prin...
      
nan
      
3dprintermanufacturing
    
    

      
3784
      
443212
      
Night Vision Equipment Manufacturing
      
nan
      
NVD's binoculars goggles
      
nan
      
nightvisionequipmentmanufacturing
    
    

      
3785
      
443213
      
Headphone Manufacturing
      
nan
      
headphones earphones, earbuds
      
nan
      
headphonemanufacturing
    
    

      
3786
      
443214
      
LIDAR Device Manufacturing
      
nan
      
ranges ranging distances lasers
      
nan
      
lidardevicemanufacturing
    
    

      
3787
      
443215
      
Security Paper Printing
      
nan
      
watermarks watermarked ink
      
nan
      
securitypaperprinting
    
    

      
3788
      
443216
      
Uninterruptible Power Supply System Manufacturing
      
nan
      
blackouts
      
nan
      
uninterruptiblepowersupplysystemmanufacturing
    
    

      
3789
      
443217
      
Variable Message Signage Manufacturing
      
nan
      
signs
      
nan
      
variablemessagesignagemanufacturing
    
    

      
3790
      
443218
      
Carbon Monoxide Alarm Manufacturing
      
nan
      
alarms CO2
      
nan
      
carbonmonoxidealarmmanufacturing
    
    

      
3791
      
443219
      
Fire & Smoke Alarm Manufacturing
      
nan
      
alarms
      
nan
      
firesmokealarmmanufacturing
    
    

      
3792
      
443220
      
Electronic Access Control System Manufacturing
      
nan
      
smart cards security door locks biometrics
      
nan
      
electronicaccesscontrolsystemmanufacturing
    
    

      
3793
      
443221
      
Activated Carbon Manufacturing
      
nan
      
purification
      
nan
      
activatedcarbonmanufacturing
    
  

3794 rows × 6 columns

In [11]:

    

records = pymarc.parse_xml_to_array(codecs.open('PardeeIS.xml','rb','utf-8'))


    

In [12]:

    

url_dict = {}
counter = 0
clientsibisworld = [] 
wwwibisworld = [] 
umi = []
lexisnexis = []
mintel = [] 
galegroup = []
ebscohost = [] 
mergentonline = [] 
doi = []
for rec in records:
    rec = marcx.FatRecord.from_record(rec)

    domain = rec['856']['u'][:rec['856']['u'].rindex('/')]
#    domain = domain[domain.index('.')+1:domain.index('.',domain.index('.')+1)]
    domain = domain[domain.index(':')+3:domain.index('.',domain.index('.')+1)] 
    #print(domain)
    if domain == 'www.ibisworld':
        rec.remove('856')
        rec.add('856', u='http://clients1.ibisworld.com/reports/us/industry/home.aspx',z='IBISWorld Industry Reports',n='IBISWorld',indicators='40')
        clientsibisworld.append(rec)
        if rec['856']['u'][:rec['856']['u'].rindex('/')] in url_dict:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] += 1
        else:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] = 1
    if domain == 'clients1.ibisworld':
        rec.remove('856')
        rec.add('856', u='http://clients1.ibisworld.com/reports/us/industry/home.aspx',z='IBISWorld Industry Reports',n='IBISWorld',indicators='40')
        clientsibisworld.append(rec)
        if rec['856']['u'][:rec['856']['u'].rindex('/')] in url_dict:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] += 1
        else:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] = 1
    if domain == 'clients.ibisworld':
        rec.remove('856')
        rec.add('856', u='http://clients1.ibisworld.com/reports/us/industry/home.aspx',z='IBISWorld Industry Reports',n='IBISWorld',indicators='40')
        clientsibisworld.append(rec)
        if rec['856']['u'][:rec['856']['u'].rindex('/')] in url_dict:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] += 1
        else:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] = 1
    if domain == 'www.lexisnexis':
        url = rec['856']['u'].replace('.ezproxy.bu.edu','')
        rec.remove('856')
        rec.add('856', u=url,z='Hoovers Industry Snapshots',n='Lexis Nexis',indicators='40')
        lexisnexis.append(rec)
        if rec['856']['u'][:rec['856']['u'].rindex('/')] in url_dict:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] += 1
        else:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] = 1
    if domain == 'proquest.umi':
        rec.remove('856')
        rec.add('856', u='http://search.proquest.com/browseterms/firstResearch_business?accountid=9676',z='First Research Reports',n='ABI-Inform',indicators='40')
        umi.append(rec)
        if rec['856']['u'][:rec['856']['u'].rindex('/')] in url_dict:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] += 1
        else:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] = 1
    if domain == 'academic.mintel':
        mintel.append(rec)
        if rec['856']['u'][:rec['856']['u'].rindex('/')] in url_dict:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] += 1
        else:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] = 1
    if domain == 'find.galegroup':
        url = rec['856']['u'].replace('.ezproxy.bu.edu','')
        _z = rec['856']['z']
        _n = rec['856']['n']
        rec.remove('856')
        rec.add('856', u=url,z=_z,n=_z,indicators='40')
        galegroup.append(rec)
        if rec['856']['u'][:rec['856']['u'].rindex('/')] in url_dict:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] += 1
        else:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] = 1
    if domain == 'search.ebscohost':
        url = rec['856']['u'].replace('.ezproxy.bu.edu','')
        _z = rec['856']['z']
        _n = rec['856']['n']
        rec.remove('856')
        rec.add('856', u=url,z=_z,n=_z,indicators='40')
        ebscohost.append(rec)
        if rec['856']['u'][:rec['856']['u'].rindex('/')] in url_dict:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] += 1
        else:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] = 1
    if domain == 'www.mergentonline':
        url = rec['856']['u'].replace('.ezproxy.bu.edu','')
        _z = rec['856']['z']
        _n = rec['856']['n']
        rec.remove('856')
        rec.add('856', u=url,z=_z,n=_z,indicators='40')
        mergentonline.append(rec)
        if rec['856']['u'][:rec['856']['u'].rindex('/')] in url_dict:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] += 1
        else:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] = 1
    if domain == 'dx.doi':
        url = rec['856']['u'].replace('.ezproxy.bu.edu','')
        _z = rec['856']['z']
        _n = rec['856']['n']
        rec.remove('856')
        rec.add('856', u=url,z=_z,n=_z,indicators='40')
        doi.append(rec)
        if rec['856']['u'][:rec['856']['u'].rindex('/')] in url_dict:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] += 1
        else:
            url_dict[rec['856']['u'][:rec['856']['u'].rindex('/')]] = 1
                    
for k,v in url_dict.items():
    print(str(counter),k,str(v))
    counter += 1


    

    




0 http://www.lexisnexis.com/us/lnacademic/search 1
1 http://search.proquest.com/browseterms 326
2 http://find.galegroup.com/gvrl 996
3 http://www.lexisnexis.com/us/lnacademic/api/version1 127
4 http://clients1.ibisworld.com/reports/us/industry 1213
5 http://dx.doi.org/10.1787 8
6 http://www.mergentonline.com 25
7 http://academic.mintel.com 562
8 http://search.ebscohost.com 409

In [17]:

    

groups = [(clientsibisworld,'ibis.xml'),(umi,'umi.xml'),
          (lexisnexis,'lexisnexis.xml'),(mintel,'mintel.xml'),
          (galegroup,'galegroup.xml'),(ebscohost,'ebscohost.xml'),
          (mergentonline,'mergentonline.xml'),(doi,'doi.xml'),]
for group in groups:
    print()
    file_name = group[1]
    out = codecs.open(file_name,'w','utf-8')
    print(file_name)
    records = group[0]  
    out.write(header)
    for rec in records:
        out.write(pymarc.record_to_xml(rec ).decode("utf-8"))
        #print(rec)
    out.write(footer)
    out.close()


    

    




ibis.xml

umi.xml

lexisnexis.xml

mintel.xml

galegroup.xml

ebscohost.xml

mergentonline.xml

doi.xml

In [ ]: