Question

I have a Receipt Journal DataFrame that contains about 400,000 rows of the following: Snippet1

The row which contains a value for the "Tender" column (e.g., Cash, CrCd) is the receipt total. The rows that follow are the items in that transaction. I effectively want to match each of these items to the number from the receipt total row in a new column, yielding the following: Snippet2

I was able to achieve this in Excel by setting cell O2 to =IF(N2="",O1,C2) and dragging. Ideally I would like to avoid any use of Excel to manipulate the data.

Is there a way to do this in Pandas without using iterrows() or itertuples()? Both of these took exponential time to complete.

UPDATE: Here is the comma-delimited text of the dataframe for testing:

Company Name,Str,Rcpt#,Rcpt Date,Time,Ext O P$,Disc %,Ext D$,Ext P$,Rcpt T$,Shipping w/T,Fee $ w/T,Rcpt Total,Tender
,2,32381,4/5/2015,5:51p,1.96,0,0,1.96,0.04,0,0,2,Cash
,2683,18924,VC,,Item_Desc,1,1.5,0,0.25,,,,
,2713,505101,VC1,C12A,Item_desc,1,0.46,0,0.12,,,,
,,32382,4/5/2015,6:01p,18.3,0,0,18.3,1.7,0,0,20,CrCd
,3034,502201,AC,,Item_desc,1,9.15,0,3.36,,,,
,3034,502201,AC5,,Item_desc,1,9.15,0,3.36,,,,
,,32383,4/5/2015,6:08p,9.15,0,0,9.15,0.85,0,0,10,Cash
,3034,502201,AC5,,Item_Desc,1,9.15,0,3.36,,,,
,,32384,4/5/2015,6:13p,18.3,0,0,18.3,1.7,0,0,20,CrCd
,2212,505201,GV,J25A,Item_desc,1,9.15,0,1.56,,,,
,2212,505201,GV,J25A,Item_desc,1,9.15,0,1.56,,,,
,,32385,4/5/2015,6:15p,4.5,0,0,4.5,0,0,0,4.5,Cash
,4619,18924,VC,,Item_desc,1,4.5,0,0.5,,,,
,,32386,4/5/2015,6:15p,4.5,0,0,4.5,0,0,0,4.5,Cash
,4619,18924,VC,,Item_desc,1,4.5,0,0.5,,,,

Answer 1

UPDATE:

In [11]: df['ReceiptNumber'] = (df.assign(ReceiptNumber=np.where(pd.notnull(df.Tender),
   ....:                                                         df['Rcpt#'],
   ....:                                                         np.nan))['ReceiptNumber']
   ....:                          .fillna(method='pad')
   ....:                          .astype(int))

In [12]: df[['Rcpt#','Tender','ReceiptNumber']]
Out[12]:
     Rcpt# Tender  ReceiptNumber
0    32381   Cash          32381
1    18924    NaN          32381
2   505101    NaN          32381
3    32382   CrCd          32382
4   502201    NaN          32382
5   502201    NaN          32382
6    32383   Cash          32383
7   502201    NaN          32383
8    32384   CrCd          32384
9   505201    NaN          32384
10  505201    NaN          32384
11   32385   Cash          32385
12   18924    NaN          32385
13   32386   Cash          32386
14   18924    NaN          32386

OLD answer:

df.assign(ReceiptNumber=np.where(pd.notnull(df.Tender),
                                 df['Rcpt#'],
                                 np.nan))['ReceiptNumber']
  .fillna(method='pad')

PS this snippet wasn't tested as you didn't provide your data set in the text form, so i couldn't copy & paste it

Pandas Create Column that Copies Another Cell Value

1 个答案: