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In [42]:

    
%pylab inline
import pandas as pd









    



Populating the interactive namespace from numpy and matplotlib



In [43]:

    
train_set = pd.read_csv('./competition_data/train_set.csv', parse_dates=['quote_date'])



In [44]:

    
print train_set.shape
print train_set.dtypes
print train_set.sample(10, random_state=0)









    



(30213, 8)
tube_assembly_id              object
supplier                      object
quote_date            datetime64[ns]
annual_usage                   int64
min_order_quantity             int64
bracket_pricing               object
quantity                       int64
cost                         float64
dtype: object
      tube_assembly_id supplier quote_date  annual_usage  min_order_quantity  \
9730          TA-05679   S-0066 2013-08-04             0                   0   
7442          TA-04183   S-0066 2013-10-01             0                   0   
19179         TA-13388   S-0054 2009-05-26             1                   0   
9565          TA-05576   S-0072 2002-08-30            88                   1   
718           TA-00338   S-0066 2013-07-01             0                   0   
14127         TA-08815   S-0058 2008-12-01           129                  35   
252           TA-00108   S-0066 2013-10-01             0                   0   
990           TA-00497   S-0066 2013-09-01             0                   0   
19822         TA-14032   S-0041 2013-08-05             1                  20   
26509         TA-19805   S-0066 2013-08-01             0                   0   

      bracket_pricing  quantity       cost  
9730              Yes         1  28.616259  
7442              Yes       100   6.279107  
19179             Yes         1  20.078120  
9565               No         1   4.434602  
718               Yes        10   4.609872  
14127              No        35   6.167825  
252               Yes       100  11.178313  
990               Yes         5   9.178013  
19822              No        20   6.732583  
26509             Yes         1  21.513663



In [45]:

    
train_set.groupby('bracket_pricing').count()









    Out[45]:






  
    
      
      tube_assembly_id
      supplier
      quote_date
      annual_usage
      min_order_quantity
      quantity
      cost
    
    
      bracket_pricing
      
      
      
      
      
      
      
    
  
  
    
      No
      3930
      3930
      3930
      3930
      3930
      3930
      3930
    
    
      Yes
      26283
      26283
      26283
      26283
      26283
      26283
      26283



In [65]:

    
train_set['year'] = train_set.quote_date.apply(lambda x : x.year)
train_set['month'] = train_set.quote_date.apply(lambda x : x.month)
train_set.drop(['quote_date'], axis=1, inplace=True)
# train_set['quantity_rep'] = train_set.quantity.apply(lambda x : 1.0 / x)



In [66]:

    
train_set.head()









    Out[66]:






  
    
      
      tube_assembly_id
      supplier
      annual_usage
      min_order_quantity
      bracket_pricing
      quantity
      cost
      year
      month
    
  
  
    
      0
      TA-00002
      S-0066
      0
      0
      Yes
      1
      21.905933
      2013
      7
    
    
      1
      TA-00002
      S-0066
      0
      0
      Yes
      2
      12.341214
      2013
      7
    
    
      2
      TA-00002
      S-0066
      0
      0
      Yes
      5
      6.601826
      2013
      7
    
    
      3
      TA-00002
      S-0066
      0
      0
      Yes
      10
      4.687770
      2013
      7
    
    
      4
      TA-00002
      S-0066
      0
      0
      Yes
      25
      3.541561
      2013
      7

Output file



In [68]:

    
train_set.to_csv('train_set_ym.csv', index=False)

-----



In [7]:

    
train_set_dummies = pd.get_dummies(train_set, columns=['supplier', 'bracket_pricing'])
train_set_dummies = train_set_dummies.drop(['bracket_pricing_No'], axis=1)



In [8]:

    
train_set_dummies.head()









    Out[8]:






  
    
      
      tube_assembly_id
      quote_date
      annual_usage
      min_order_quantity
      quantity
      cost
      year
      quantity_rep
      supplier_S-0003
      supplier_S-0004
      ...
      supplier_S-0096
      supplier_S-0097
      supplier_S-0104
      supplier_S-0105
      supplier_S-0106
      supplier_S-0107
      supplier_S-0108
      supplier_S-0109
      supplier_S-0111
      bracket_pricing_Yes
    
  
  
    
      0
      TA-00002
      2013-07-07
      0
      0
      1
      21.905933
      2013
      1.00
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      1
      TA-00002
      2013-07-07
      0
      0
      2
      12.341214
      2013
      0.50
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      2
      TA-00002
      2013-07-07
      0
      0
      5
      6.601826
      2013
      0.20
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      3
      TA-00002
      2013-07-07
      0
      0
      10
      4.687770
      2013
      0.10
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      4
      TA-00002
      2013-07-07
      0
      0
      25
      3.541561
      2013
      0.04
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
  

5 rows × 66 columns

Output file



In [9]:

    
# train_set_dummies.to_csv('train_set_adjusted.csv', index=False)

-----



In [10]:

    
# train_set_no_date = train_set.drop(['quote_date'], axis=1)



In [11]:

    
# train_set_no_date_dummies = pd.get_dummies(train_set_no_date, columns=['supplier', 'bracket_pricing'])



In [12]:

    
# train_set_no_date_dummies_drop_bracket_n = \
# train_set_no_date_dummies.drop(['bracket_pricing_No'], axis=1)



In [13]:

    
# train_set_no_date_dummies_drop_bracket_n.to_csv('train_set_no_date_dummies_drop_bracket_n.csv', index=False)



In [14]:

    
train_set_no_bracket = train_set[train_set.bracket_pricing == 'No']



In [23]:

    
train_set_no_bracket.head()









    Out[23]:






  
    
      
      tube_assembly_id
      supplier
      quote_date
      annual_usage
      min_order_quantity
      bracket_pricing
      quantity
      cost
      year
      quantity_rep
    
  
  
    
      32
      TA-00013
      S-0026
      1992-07-21
      50
      1
      No
      1
      10.004284
      1992
      1
    
    
      41
      TA-00021
      S-0030
      2014-02-10
      1
      1
      No
      1
      3.433061
      2014
      1
    
    
      42
      TA-00022
      S-0013
      2011-07-27
      1
      1
      No
      1
      8.563178
      2011
      1
    
    
      59
      TA-00027
      S-0062
      1993-09-24
      30
      1
      No
      1
      2.737546
      1993
      1
    
    
      74
      TA-00037
      S-0062
      1994-09-01
      600
      1
      No
      1
      1.563517
      1994
      1



In [22]:

    
train_set_no_bracket.year.hist(bins=20)
plt.show()



In [18]:

    
train_set_yes_bracket = train_set[train_set.bracket_pricing == 'Yes']



In [21]:

    
train_set_yes_bracket.year.hist(bins=20)
plt.show()



In [41]:

    
pd.pivot_table(train_set, index='bracket_pricing', aggfunc='count')









    Out[41]:






  
    
      
      annual_usage
      cost
      min_order_quantity
      quantity
      quantity_rep
      quote_date
      supplier
      tube_assembly_id
      year
    
    
      bracket_pricing
      
      
      
      
      
      
      
      
      
    
  
  
    
      No
      3930
      3930
      3930
      3930
      3930
      3930
      3930
      3930
      3930
    
    
      Yes
      26283
      26283
      26283
      26283
      26283
      26283
      26283
      26283
      26283



In [40]:

    
pd.value_counts(train_set_no_bracket.supplier)









    Out[40]:





S-0066    790
S-0072    782
S-0041    624
S-0013    505
S-0026    470
S-0058    126
S-0064    106
S-0062    102
S-0104     90
S-0030     87
S-0081     41
S-0105     32
S-0014     23
S-0005     23
S-0031     15
S-0070     14
S-0043     14
S-0027     10
S-0092      8
S-0054      7
S-0015      5
S-0042      5
S-0007      5
S-0011      4
S-0023      4
S-0009      4
S-0025      3
S-0060      3
S-0059      2
S-0061      2
S-0111      2
S-0107      2
S-0078      2
S-0096      2
S-0074      1
S-0087      1
S-0022      1
S-0024      1
S-0029      1
S-0095      1
S-0012      1
S-0051      1
S-0068      1
S-0109      1
S-0108      1
S-0004      1
S-0106      1
S-0006      1
S-0003      1
S-0046      1
dtype: int64



In [39]:

    
pd.value_counts(train_set_yes_bracket.supplier)









    Out[39]:





S-0066    19763
S-0041     2699
S-0072     1535
S-0054      831
S-0058      390
S-0064      340
S-0026      257
S-0062      162
S-0014      116
S-0013       49
S-0030       24
S-0081       23
S-0042       14
S-0018       10
S-0027       10
S-0080       10
S-0056        9
S-0008        7
S-0031        6
S-0050        5
S-0070        4
S-0074        4
S-0090        3
S-0097        3
S-0092        2
S-0059        2
S-0005        1
S-0087        1
S-0105        1
S-0046        1
S-0011        1
dtype: int64



In [ ]:

	tube_assembly_id	supplier	quote_date	annual_usage	min_order_quantity	quantity	cost
bracket_pricing
No	3930	3930	3930	3930	3930	3930	3930
Yes	26283	26283	26283	26283	26283	26283	26283

	tube_assembly_id	supplier	bracket_pricing	quantity	cost	year	month
0	TA-00002	S-0066	Yes	1	21.905933	2013	7
1	TA-00002	S-0066	Yes	2	12.341214	2013	7
2	TA-00002	S-0066	Yes	5	6.601826	2013	7
3	TA-00002	S-0066	Yes	10	4.687770	2013	7
4	TA-00002	S-0066	Yes	25	3.541561	2013	7

	tube_assembly_id	quote_date	quantity	cost	year	quantity_rep	...	bracket_pricing_Yes
0	TA-00002	2013-07-07	1	21.905933	2013	1.00	...	1
1	TA-00002	2013-07-07	2	12.341214	2013	0.50	...	1
2	TA-00002	2013-07-07	5	6.601826	2013	0.20	...	1
3	TA-00002	2013-07-07	10	4.687770	2013	0.10	...	1
4	TA-00002	2013-07-07	25	3.541561	2013	0.04	...	1

	tube_assembly_id	supplier	quote_date	annual_usage	min_order_quantity	bracket_pricing	quantity	cost	year	quantity_rep
32	TA-00013	S-0026	1992-07-21	50	1	No	1	10.004284	1992	1
41	TA-00021	S-0030	2014-02-10	1	1	No	1	3.433061	2014	1
42	TA-00022	S-0013	2011-07-27	1	1	No	1	8.563178	2011	1
59	TA-00027	S-0062	1993-09-24	30	1	No	1	2.737546	1993	1
74	TA-00037	S-0062	1994-09-01	600	1	No	1	1.563517	1994	1