notebook.community

Edit and run

EDA





In [44]:



    


from pprint import pprint
import pandas as pd
import numpy as np
import re

LOAD DATA





In [43]:



    


df = pd.read_csv('data/train.tsv',sep='\t')
# keep = set(['item_id','Item Class ID','Product Long Description','Product Name',
#             'Product Short Description','Short Description','tag'])
keep = set(['Product Long Description','Product Name','tag'])
drop = set(df.columns) - keep
df.drop(drop,axis=1,inplace=True)
df.dropna(inplace=True)
df.head(10)



    


















    
Out[43]:










  
    

      
      Product Long Description
      Product Name
      tag
    

  
  
    

      0
      <b>Interfaces/Ports</b><li>Network (RJ-45):   ...
      Cyberpower Metered ATS PDU 200-240V 20A 1U 10-...
      [4483]
    

    

      1
      General InformationThe SmartMount&reg; Univers...
      Peerless SmartMount SF650P Universal Flat Wall...
      [581514]
    

    

      2
      Control Transformer, VA Rating 50VA, Input Vol...
      DAYTON 31EH66 Control Transformer Fused,4.02H ...
      [4483]
    

    

      3
      <b>General Information</b><li>Product Type:   ...
      STEREN-CUSTOM INSTALL 255-047 STEREN MINI TO R...
      [4537]
    

    

      4
      <br><b>LG 47LN5700 47" 1080p 120Hz LED HDTV:</...
      LG 47LN5700 47" 1080p 120Hz LED (3.17" ultra-s...
      [1229817, 1229821]
    

    

      5
      Features- Transformer Ground Connectors are us...
      Morris 91902 Transformer Ground Connector
      [4537]
    

    

      6
      Take a look at billionaire adventurer Reese Ba...
      Newsreaders: Billionaire Goes to Hell / Sitcom...
      [4483]
    

    

      7
      Power Grip RV Power Cord Adapter - These elect...
      Camco Mfg. 15m-30f Electric Adapter 55223
      [4483]
    

    

      8
      TV should be two things: smart and beautiful. ...
      SAMSUNG UN48H6400 48in 1080p 120Hz Apps QuadCo...
      [3304195]
    

    

      9
      <P><FONT size=2 face=Verdana>Prepare yourself ...
      LG 55UF7600 - 55-inch 2160p 120Hz 4K UHD LED T...
      [1180168]

ORIGINAL TAG COUNTS/FREQUENCY





In [66]:



    


df.tag.value_counts()



    


















    
Out[66]:








[4537]                                                   2667
[4483]                                                   2002
[581514]                                                  730
[529295]                                                  513
[95987]                                                   401
[106546]                                                  388
[447913]                                                  379
[4538]                                                    244
[127175]                                                  241
[4536]                                                    220
[1229821]                                                 201
[650659]                                                  189
[1180168]                                                 175
[522484]                                                  169
[1229817]                                                 154
[1070524]                                                 146
[3304195, 1229821]                                         97
[522484, 106546]                                           83
[5065]                                                     80
[447913, 1229821]                                          56
[522484, 127175]                                           55
[106546, 127175]                                           53
[522484, 106546, 127175]                                   49
[1071165]                                                  49
[1225174]                                                  47
[1085065]                                                  47
[106546, 522484]                                           45
[1229817, 1229821]                                         39
[1229821, 447913]                                          37
[3304195, 1229820]                                         36
                                                         ... 
[447913, 4537, 581514]                                      1
[4537, 4536, 581514]                                        1
[581514, 4537, 106546]                                      1
[4483, 581514]                                              1
[1180168, 1229821, 1229818, 1229817]                        1
[95987, 106546, 4483]                                       1
[1229817, 1229821, 1229820, 1229819]                        1
[447913, 1229817, 1229820, 3304195]                         1
[648819, 4483]                                              1
[3304195, 1229821, 1229819, 1229817]                        1
[1229821, 447913, 1229820]                                  1
[1071165, 106546]                                           1
[4536, 4483]                                                1
[522484, 4537, 127175]                                      1
[95987, 127175]                                             1
[447913, 3304195, 1229821, 1180168]                         1
[95987, 4537, 1070524]                                      1
[3304195, 1229818, 447913, 1229817, 1180168, 1229821]       1
[1229817, 1229820, 1180168, 1229821, 447913]                1
[3304195, 4537, 95987]                                      1
[1229821, 1229819, 1229817, 447913]                         1
[1180168, 447913, 1229821]                                  1
[447913, 95987]                                             1
[127175, 106546, 522484]                                    1
[106546, 127175, 95987]                                     1
[4483, 4538]                                                1
[106546, 4483, 522484]                                      1
[1229817, 1229823, 1229821]                                 1
[4483, 522484, 127175, 106546]                              1
[447913, 127175, 106546, 1071165]                           1
Name: tag, dtype: int64

INDIVIDUAL ONLY TAG COUNTS/FREQUENCY





In [25]:



    


df.tag.value_counts()



    


















    
Out[25]:








[4537]                                  2667
[4483]                                  2002
[581514]                                 730
[529295]                                 513
[95987]                                  401
[106546]                                 388
[447913]                                 379
[4538]                                   244
[127175]                                 241
[4536]                                   220
[1229821]                                201
[650659]                                 189
[1180168]                                175
[522484]                                 169
[1229817]                                154
[1070524]                                146
[5065]                                    80
[1071165]                                 49
[1085065]                                 47
[1225174]                                 47
[1229820]                                 23
[62056]                                   22
[3304195]                                 21
[1084835]                                 21
[4457]                                    19
[1229819]                                 18
[1229825]                                 13
[648819]                                   6
[1229818]                                  4
[133270]                                   3
[645319]                                   3
[1229821, 1229823, 1180168, 1229818]       1
[1229821, 1229823, 1229818]                1
[1229817, 1229823, 1229821]                1
Name: tag, dtype: int64

Find the 32 unique shelve tags





In [182]:



    


tags = []
for tag in df.tag:
    t = tag.replace('[','')
    t = t.replace(']','')
    t = t.replace(' ','',10)
    t = t.split(',')
    if len(t) == 1:
        tags.extend(t)
tags.extend(['1229823']) #tag that is never by itself...
unique_tags = ['['+tag+']' for tag in set(tags)]
print(unique_tags)
print ''
print 'total shelves found ==>', len(unique_tags)



    


















    






['[1071165]', '[127175]', '[529295]', '[106546]', '[3304195]', '[4538]', '[645319]', '[4536]', '[4537]', '[1084835]', '[1229818]', '[1229819]', '[1229817]', '[5065]', '[95987]', '[1085065]', '[1229823]', '[1070524]', '[1229820]', '[447913]', '[650659]', '[522484]', '[648819]', '[1225174]', '[62056]', '[1180168]', '[1229825]', '[133270]', '[1229821]', '[4483]', '[4457]', '[581514]']

total shelves found ==> 32

Unique count per tag





In [183]:



    


unique_tag_count = {key:0 for key in unique_tags}
count = 0
for tag in df.tag:
    if tag in unique_tag_count.keys():
        unique_tag_count[tag] += 1
        count += 1
print unique_tag_count
print count



    


















    






{'[4483]': 2002, '[1229818]': 4, '[1225174]': 47, '[650659]': 189, '[1071165]': 49, '[4537]': 2667, '[5065]': 80, '[1229823]': 0, '[62056]': 22, '[1085065]': 47, '[529295]': 513, '[1229825]': 13, '[1229821]': 201, '[4457]': 19, '[648819]': 6, '[3304195]': 21, '[581514]': 730, '[106546]': 388, '[1229819]': 18, '[1070524]': 146, '[127175]': 241, '[4536]': 220, '[447913]': 379, '[1229817]': 154, '[645319]': 3, '[95987]': 401, '[1180168]': 175, '[133270]': 3, '[1084835]': 21, '[522484]': 169, '[4538]': 244, '[1229820]': 23}
9195

Total count/mentions of all 32 shelve tags





In [102]:



    


keys = ['1071165', '127175', '529295', '106546', '3304195', '4538', '645319', '4536',
        '4537', '1084835', '1229818', '1229819', '1229817', '5065', '95987', '1085065', 
        '1229823', '1070524', '1229820', '447913', '650659', '522484', '648819', '1225174', 
        '62056', '1180168', '1229825', '133270', '1229821', '4483', '4457', '581514']
vals = [0]*32
d_total = dict(zip(keys,vals))
total_count = 0
for tag in df.tag:
    for key in keys:
        if re.search(key,tag):
            d_total[key] += 1
            total_count += 1
print d_total
print ''
print 'total tag mentions (including duplicates) ==> ', total_count
print 'num samples ==> ', df.shape[0]



    


















    






{'1071165': 52, '127175': 510, '529295': 520, '106546': 825, '3304195': 345, '4538': 250, '645319': 3, '4536': 229, '4537': 2801, '1084835': 21, '1229818': 16, '1229819': 60, '1229817': 389, '5065': 326, '95987': 619, '1085065': 51, '1070524': 169, '4483': 2110, '447913': 608, '650659': 195, '522484': 603, '648819': 11, '1225174': 53, '62056': 29, '1180168': 271, '1229825': 50, '1229823': 3, '133270': 3, '1229821': 763, '1229820': 175, '4457': 22, '581514': 771}

total tag mentions (including duplicates) ==>  12853
num samples ==>  10584

Create dictionary w/ respect to each unique shelf tag

  • total count (all mentions)
  • frequency of other tags
  • frequency of tag combos




In [178]:



    


d_tag_info = defaultdict(dict)
for key in keys:
    temp_list1 = []
    temp_list2 = []
    temp_dict1 = {}
    temp_dict2 = {}
    temp_dict3 = {}
    
    # list of all tag/tag combos
    for tag in df.tag:
        if re.search(key,tag):
            temp_list1.append(tag)

    # freq of all tag combos        
    for tag in temp_list1:
        if tag in temp_dict1.keys():
            temp_dict1[tag] += 1
        else:
            temp_dict1[tag] = 1
            
    # unique tags        
    for tag in temp_dict1.keys():
        t = tag.replace('[','')
        t = t.replace(']','')
        t = t.replace(' ','',10)
        t = t.split(',')
        temp_list2.extend(t)
    unique_tags = list(set(temp_list2))
    
    # freq of unique tags
    for unique_tag in unique_tags:
        for all_tag in temp_list1:
            try:
                if re.search(unique_tag,all_tag):
                    temp_dict2[unique_tag] += 1
            except:
                temp_dict2[unique_tag] = 1

    # tag info            
    temp_dict3['total'] = d_total[key]
    temp_dict3['tag_freq'] = temp_dict2
    temp_dict3['combo_freq'] = temp_dict1
    d_tag_info[key] = temp_dict3

Review tag freqency and combinations





In [179]:



    


for key,val in d_tag_info.items():
    print ''
    print ''
    print 'tag: {}'.format(key)
    print 'total: {}'.format(val['total'])
    print 'tag freq ==> {}'.format(sorted(val['tag_freq'].items(),key=lambda x: x[1],reverse=True))
    print 'combo freq ==> {}'.format(sorted(val['combo_freq'].items(),key=lambda x: x[1],reverse=True))



    


















    







tag: 1071165
total: 52
tag freq ==> [('1071165', 52), ('447913', 2), ('106546', 2), ('127175', 1)]
combo freq ==> [('[1071165]', 49), ('[1071165, 106546]', 1), ('[447913, 127175, 106546, 1071165]', 1), ('[1071165, 447913]', 1)]


tag: 127175
total: 510
tag freq ==> [('127175', 510), ('522484', 185), ('106546', 184), ('95987', 41), ('4483', 14), ('529295', 4), ('4537', 3), ('447913', 2), ('3304195', 2), ('1071165', 1), ('648819', 1), ('1225174', 1), ('1070524', 1), ('1229825', 1), ('4457', 1)]
combo freq ==> [('[127175]', 241), ('[522484, 127175]', 55), ('[106546, 127175]', 53), ('[522484, 106546, 127175]', 49), ('[106546, 522484, 127175]', 18), ('[95987, 522484, 106546, 127175]', 13), ('[127175, 106546]', 12), ('[95987, 522484, 127175]', 10), ('[522484, 127175, 106546]', 7), ('[4483, 522484, 106546, 127175]', 4), ('[95987, 106546, 522484, 127175]', 4), ('[106546, 4483, 522484, 127175]', 3), ('[127175, 522484, 106546]', 3), ('[95987, 522484, 127175, 106546]', 3), ('[127175, 522484]', 3), ('[106546, 127175, 522484]', 2), ('[522484, 95987, 127175]', 2), ('[127175, 529295]', 2), ('[127175, 4483]', 2), ('[3304195, 106546, 522484, 127175]', 1), ('[3304195, 106546, 95987, 127175]', 1), ('[447913, 127175, 106546, 1071165]', 1), ('[127175, 106546, 522484]', 1), ('[95987, 127175, 522484, 106546]', 1), ('[4483, 127175]', 1), ('[127175, 522484, 95987]', 1), ('[529295, 127175]', 1), ('[522484, 4537, 127175]', 1), ('[4537, 127175]', 1), ('[106546, 95987, 127175]', 1), ('[1070524, 127175, 106546]', 1), ('[95987, 447913, 106546, 522484, 127175]', 1), ('[95987, 127175, 106546]', 1), ('[4483, 127175, 529295]', 1), ('[95987, 127175]', 1), ('[127175, 4457, 648819]', 1), ('[522484, 106546, 4483, 127175]', 1), ('[1225174, 127175]', 1), ('[95987, 127175, 522484]', 1), ('[106546, 127175, 95987]', 1), ('[106546, 127175, 4483]', 1), ('[4483, 522484, 127175, 106546]', 1), ('[1229825, 127175, 4537]', 1)]


tag: 529295
total: 520
tag freq ==> [('529295', 520), ('127175', 4), ('4483', 2), ('522484', 1), ('106546', 1)]
combo freq ==> [('[529295]', 513), ('[127175, 529295]', 2), ('[529295, 106546]', 1), ('[4483, 127175, 529295]', 1), ('[4483, 529295]', 1), ('[529295, 522484]', 1), ('[529295, 127175]', 1)]


tag: 106546
total: 825
tag freq ==> [('106546', 825), ('522484', 313), ('127175', 184), ('95987', 119), ('4483', 36), ('3304195', 5), ('447913', 4), ('4537', 3), ('1071165', 2), ('581514', 2), ('1070524', 1), ('529295', 1)]
combo freq ==> [('[106546]', 388), ('[522484, 106546]', 83), ('[106546, 127175]', 53), ('[522484, 106546, 127175]', 49), ('[106546, 522484]', 45), ('[95987, 106546, 522484]', 19), ('[106546, 522484, 127175]', 18), ('[95987, 522484, 106546]', 17), ('[95987, 106546]', 15), ('[106546, 95987]', 14), ('[522484, 106546, 95987]', 13), ('[95987, 522484, 106546, 127175]', 13), ('[106546, 4483]', 13), ('[127175, 106546]', 12), ('[522484, 127175, 106546]', 7), ('[522484, 95987, 106546]', 5), ('[4483, 522484, 106546, 127175]', 4), ('[522484, 4483, 106546]', 4), ('[95987, 106546, 522484, 127175]', 4), ('[106546, 522484, 95987]', 4), ('[106546, 4483, 522484, 127175]', 3), ('[4483, 106546]', 3), ('[127175, 522484, 106546]', 3), ('[95987, 522484, 127175, 106546]', 3), ('[3304195, 522484, 106546]', 2), ('[95987, 447913, 106546]', 2), ('[522484, 106546, 4483]', 2), ('[106546, 127175, 522484]', 2), ('[3304195, 106546, 522484, 127175]', 1), ('[3304195, 106546, 95987, 127175]', 1), ('[447913, 127175, 106546, 1071165]', 1), ('[127175, 106546, 522484]', 1), ('[581514, 4537, 106546]', 1), ('[95987, 127175, 522484, 106546]', 1), ('[95987, 106546, 4483]', 1), ('[1071165, 106546]', 1), ('[581514, 106546, 522484]', 1), ('[4537, 106546]', 1), ('[106546, 95987, 127175]', 1), ('[4537, 95987, 106546, 522484]', 1), ('[4483, 106546, 522484]', 1), ('[3304195, 95987, 522484, 106546]', 1), ('[1070524, 127175, 106546]', 1), ('[106546, 95987, 522484]', 1), ('[95987, 447913, 106546, 522484, 127175]', 1), ('[95987, 127175, 106546]', 1), ('[529295, 106546]', 1), ('[522484, 106546, 4483, 127175]', 1), ('[106546, 4483, 522484]', 1), ('[4483, 522484, 106546]', 1), ('[106546, 127175, 95987]', 1), ('[106546, 127175, 4483]', 1), ('[4483, 522484, 127175, 106546]', 1)]


tag: 3304195
total: 345
tag freq ==> [('3304195', 345), ('1229821', 264), ('1229817', 104), ('1229820', 82), ('447913', 37), ('1180168', 25), ('1229819', 24), ('522484', 9), ('95987', 6), ('106546', 5), ('1229818', 5), ('4537', 3), ('127175', 2), ('1229825', 2), ('650659', 2)]
combo freq ==> [('[3304195, 1229821]', 97), ('[3304195, 1229820]', 36), ('[3304195, 1229821, 1229817]', 34), ('[1229817, 3304195, 1229821]', 28), ('[3304195, 1229821, 1229820]', 26), ('[3304195]', 21), ('[3304195, 447913, 1229821]', 19), ('[1180168, 3304195, 1229821, 1229817]', 9), ('[447913, 1229817, 1229821, 3304195]', 8), ('[3304195, 1229821, 1229820, 1229819]', 7), ('[3304195, 1229821, 1229819]', 6), ('[3304195, 1229821, 1180168, 1229817]', 5), ('[3304195, 522484]', 4), ('[1229817, 3304195, 1229821, 1229820]', 4), ('[3304195, 1229820, 1229819]', 3), ('[1180168, 3304195, 1229821]', 3), ('[1180168, 3304195, 1229821, 1229818, 1229817]', 3), ('[447913, 3304195, 1229819, 1229821]', 3), ('[3304195, 522484, 106546]', 2), ('[3304195, 1229817]', 2), ('[447913, 3304195]', 2), ('[3304195, 447913, 1229820]', 2), ('[3304195, 1229821, 1229820, 1229817]', 2), ('[3304195, 1229821, 1229825]', 2), ('[1229817, 3304195, 1229821, 1229819]', 2), ('[447913, 3304195, 1229821, 1180168]', 1), ('[3304195, 106546, 522484, 127175]', 1), ('[3304195, 106546, 95987, 127175]', 1), ('[447913, 1229817, 1229820, 3304195]', 1), ('[3304195, 650659, 95987, 1229817]', 1), ('[3304195, 4537, 95987, 522484]', 1), ('[3304195, 1229821, 1229819, 1229817]', 1), ('[1180168, 3304195, 1229817]', 1), ('[3304195, 95987, 522484, 106546]', 1), ('[3304195, 650659, 95987, 4537]', 1), ('[3304195, 1229821, 1180168]', 1), ('[3304195, 1229818, 447913, 1229817, 1180168, 1229821]', 1), ('[3304195, 1229821, 1229820, 1229819, 1229817]', 1), ('[1180168, 3304195, 1229821, 1229818, 1229819, 1229817]', 1), ('[3304195, 4537, 95987]', 1)]


tag: 4538
total: 250
tag freq ==> [('4538', 250), ('95987', 3), ('4537', 2), ('4483', 2)]
combo freq ==> [('[4538]', 244), ('[95987, 4538]', 3), ('[4538, 4537]', 1), ('[4483, 4538]', 1), ('[4483, 4537, 4538]', 1)]


tag: 645319
total: 3
tag freq ==> [('645319', 3)]
combo freq ==> [('[645319]', 3)]


tag: 4536
total: 229
tag freq ==> [('4536', 229), ('4537', 5), ('4483', 3), ('581514', 1), ('447913', 1)]
combo freq ==> [('[4536]', 220), ('[4536, 4537]', 3), ('[4483, 4536]', 2), ('[4537, 4536, 581514]', 1), ('[447913, 4536]', 1), ('[4536, 4483]', 1), ('[4537, 4536]', 1)]


tag: 4537
total: 2801
tag freq ==> [('4537', 2801), ('4483', 42), ('581514', 36), ('95987', 30), ('1070524', 12), ('447913', 8), ('522484', 7), ('4536', 5), ('127175', 3), ('106546', 3), ('3304195', 3), ('4538', 2), ('1180168', 2), ('1085065', 1), ('1229825', 1), ('650659', 1)]
combo freq ==> [('[4537]', 2667), ('[581514, 4537]', 25), ('[4483, 4537]', 24), ('[95987, 4537]', 16), ('[4537, 4483]', 14), ('[1070524, 4537]', 8), ('[4537, 581514]', 7), ('[447913, 4537]', 5), ('[4537, 95987]', 5), ('[4537, 1070524]', 3), ('[4536, 4537]', 3), ('[4483, 581514, 4537]', 1), ('[4538, 4537]', 1), ('[447913, 4537, 581514]', 1), ('[581514, 4537, 106546]', 1), ('[3304195, 4537, 95987]', 1), ('[3304195, 650659, 95987, 4537]', 1), ('[3304195, 4537, 95987, 522484]', 1), ('[4537, 95987, 522484]', 1), ('[4537, 1180168, 1085065]', 1), ('[95987, 4537, 522484]', 1), ('[4537, 127175]', 1), ('[4537, 106546]', 1), ('[522484, 4537]', 1), ('[4537, 95987, 106546, 522484]', 1), ('[4537, 95987, 1180168]', 1), ('[4537, 4483, 447913]', 1), ('[4483, 4537, 447913]', 1), ('[522484, 4537, 127175]', 1), ('[522484, 95987, 4537]', 1), ('[4537, 4536, 581514]', 1), ('[4483, 4537, 4538]', 1), ('[1229825, 127175, 4537]', 1), ('[95987, 4537, 1070524]', 1), ('[4537, 4536]', 1)]


tag: 1084835
total: 21
tag freq ==> [('1084835', 21)]
combo freq ==> [('[1084835]', 21)]


tag: 1229818
total: 16
tag freq ==> [('1229818', 16), ('1180168', 9), ('1229821', 9), ('1229817', 9), ('3304195', 5), ('447913', 2), ('1229823', 2), ('1229819', 1)]
combo freq ==> [('[1229818]', 4), ('[1180168, 3304195, 1229821, 1229818, 1229817]', 3), ('[1229817, 1229818]', 2), ('[1180168, 1229818]', 1), ('[3304195, 1229818, 447913, 1229817, 1180168, 1229821]', 1), ('[447913, 1229821, 1229818, 1229817, 1180168]', 1), ('[1180168, 1229821, 1229818, 1229817]', 1), ('[1180168, 3304195, 1229821, 1229818, 1229819, 1229817]', 1), ('[1229821, 1229823, 1180168, 1229818]', 1), ('[1229821, 1229823, 1229818]', 1)]


tag: 1229819
total: 60
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total: 763
tag freq ==> [('1229821', 763), ('3304195', 264), ('1229817', 215), ('447913', 178), ('1229820', 89), ('1180168', 81), ('1229819', 38), ('1229825', 29), ('1229818', 9), ('1229823', 3)]
combo freq ==> [('[1229821]', 201), ('[3304195, 1229821]', 97), ('[447913, 1229821]', 56), ('[1229817, 1229821]', 39), ('[1229821, 447913]', 37), ('[3304195, 1229821, 1229817]', 34), ('[1229817, 3304195, 1229821]', 28), ('[3304195, 1229821, 1229820]', 26), ('[1229821, 1229817]', 21), ('[3304195, 447913, 1229821]', 19), ('[1229821, 1229825]', 17), ('[1229821, 1229820]', 16), ('[1180168, 1229821, 1229817]', 10), ('[1180168, 3304195, 1229821, 1229817]', 9), ('[1229821, 1180168, 1229817]', 9), ('[447913, 1229817, 1229821, 3304195]', 8), ('[3304195, 1229821, 1229820, 1229819]', 7), ('[447913, 1229817, 1229821]', 7), ('[447913, 1229820, 1229821]', 7), ('[1180168, 1229821]', 6), ('[3304195, 1229821, 1229819]', 6), ('[3304195, 1229821, 1180168, 1229817]', 5), ('[1229821, 1180168]', 5), ('[1229817, 3304195, 1229821, 1229820]', 4), ('[1229821, 1229820, 1180168]', 4), ('[1180168, 1229821, 1229820, 1229819]', 4), ('[1229821, 1229817, 447913]', 4), ('[1180168, 3304195, 1229821]', 3), ('[447913, 3304195, 1229819, 1229821]', 3), ('[1229821, 1229820, 1229825]', 3), ('[1180168, 3304195, 1229821, 1229818, 1229817]', 3), ('[1229821, 1229819]', 3), ('[447913, 1180168, 1229821, 1229817]', 2), ('[447913, 1229817, 1229820, 1229821]', 2), ('[1229817, 3304195, 1229821, 1229819]', 2), ('[1229817, 1229821, 1229825]', 2), ('[447913, 1229821, 1229817]', 2), ('[1229820, 1229821]', 2), ('[1180168, 1229821, 1229820]', 2), ('[1229821, 447913, 1229817]', 2), ('[1229817, 1229821, 447913]', 2), ('[1229821, 1180168, 447913]', 2), ('[3304195, 1229821, 1229820, 1229817]', 2), ('[3304195, 1229821, 1229825]', 2), ('[1229820, 447913, 1229821]', 2), ('[1180168, 1229821, 447913]', 2), ('[447913, 3304195, 1229821, 1180168]', 1), ('[447913, 1229821, 1229818, 1229817, 1180168]', 1), ('[3304195, 1229821, 1180168]', 1), ('[1229821, 447913, 1180168]', 1), ('[1229821, 1229823, 1180168, 1229818]', 1), ('[447913, 1229817, 1229821, 1229819]', 1), ('[1229817, 447913, 1229820, 1229819, 1180168, 1229821]', 1), ('[1229821, 1229825, 447913]', 1), ('[1180168, 3304195, 1229821, 1229818, 1229819, 1229817]', 1), ('[1180168, 1229821, 1229818, 1229817]', 1), ('[1229817, 1229821, 1229819]', 1), ('[3304195, 1229821, 1229819, 1229817]', 1), ('[1229817, 1229821, 1229820, 1229819]', 1), ('[1229821, 1229820, 1180168, 1229819]', 1), ('[1180168, 1229821, 447913, 1229817]', 1), ('[1229821, 1229819, 1229817, 447913]', 1), ('[447913, 1229821, 1229819, 1229817]', 1), ('[1229825, 447913, 1229821]', 1), ('[1229821, 1229825, 1229819]', 1), ('[447913, 1229825, 1229821]', 1), ('[1180168, 447913, 1229821]', 1), ('[1229821, 1229823, 1229818]', 1), ('[447913, 1229821, 1229819]', 1), ('[1229821, 447913, 1229820]', 1), ('[1229817, 1229823, 1229821]', 1), ('[447913, 1229817, 1229821, 1180168]', 1), ('[1229817, 1229820, 1180168, 1229821, 447913]', 1), ('[3304195, 1229818, 447913, 1229817, 1180168, 1229821]', 1), ('[3304195, 1229821, 1229820, 1229819, 1229817]', 1), ('[1229817, 1229821, 1229820]', 1), ('[1229821, 1229820, 447913]', 1), ('[447913, 1229819, 1229821]', 1), ('[447913, 1229821, 1229825]', 1), ('[1229821, 1180168, 447913, 1229817]', 1)]


tag: 1229820
total: 175
tag freq ==> [('1229820', 175), ('1229821', 89), ('3304195', 82), ('447913', 38), ('1229819', 18), ('1229817', 14), ('1180168', 13), ('1229825', 5), ('95987', 1)]
combo freq ==> [('[3304195, 1229820]', 36), ('[3304195, 1229821, 1229820]', 26), ('[1229820]', 23), ('[1229821, 1229820]', 16), ('[447913, 1229820]', 16), ('[447913, 1229820, 1229821]', 7), ('[3304195, 1229821, 1229820, 1229819]', 7), ('[1229817, 3304195, 1229821, 1229820]', 4), ('[1229821, 1229820, 1180168]', 4), ('[1180168, 1229821, 1229820, 1229819]', 4), ('[3304195, 1229820, 1229819]', 3), ('[1229820, 447913]', 3), ('[1229821, 1229820, 1229825]', 3), ('[447913, 1229817, 1229820, 1229821]', 2), ('[3304195, 1229821, 1229820, 1229817]', 2), ('[1229820, 1229821]', 2), ('[1180168, 1229821, 1229820]', 2), ('[3304195, 447913, 1229820]', 2), ('[1229820, 447913, 1229821]', 2), ('[447913, 1229817, 1229820, 3304195]', 1), ('[1229817, 1229821, 1229820, 1229819]', 1), ('[1229817, 447913, 1229820, 1229819, 1180168, 1229821]', 1), ('[1229821, 1229820, 1180168, 1229819]', 1), ('[95987, 447913, 1229820, 1229825]', 1), ('[1229820, 1229825]', 1), ('[1229817, 1229820, 1180168, 1229821, 447913]', 1), ('[3304195, 1229821, 1229820, 1229819, 1229817]', 1), ('[1229817, 1229821, 1229820]', 1), ('[1229821, 1229820, 447913]', 1), ('[1229821, 447913, 1229820]', 1)]


tag: 4457
total: 22
tag freq ==> [('4457', 22), ('522484', 2), ('127175', 1), ('648819', 1)]
combo freq ==> [('[4457]', 19), ('[522484, 4457]', 2), ('[127175, 4457, 648819]', 1)]


tag: 581514
total: 771
tag freq ==> [('581514', 771), ('4537', 36), ('95987', 3), ('447913', 3), ('1229825', 2), ('106546', 2), ('4483', 2), ('4536', 1), ('522484', 1)]
combo freq ==> [('[581514]', 730), ('[581514, 4537]', 25), ('[4537, 581514]', 7), ('[447913, 95987, 1229825, 581514]', 2), ('[4483, 581514, 4537]', 1), ('[4483, 581514]', 1), ('[95987, 581514]', 1), ('[447913, 4537, 581514]', 1), ('[4537, 4536, 581514]', 1), ('[581514, 4537, 106546]', 1), ('[581514, 106546, 522484]', 1)]

Content source: marwin-ko/projects

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