Python Pandas-将功能应用于分组的数据框

Question

I have a dataframe as follows:

    A       B         C     
0  foo  1.496337 -0.604264  
1  bar -0.025106  0.257354 
2  foo  0.958001  0.933328 
3  foo -1.126581  0.570908
4  bar -0.428304  0.881995 
5  foo -0.955252  1.408930 
6  bar  0.504582  0.455287 
7  bar -1.076096  0.536741 
8  bar  0.351544 -1.146554 
9  foo  0.430260 -0.348472 

I would like to get the max of column B of each group (when grouped by A ) and add it the the column C . So here is what I tried:

Group by A :

df = df.groupby(by='A')

Get the maximum of column B and then tried to apply it to column 'C':

for name in ['foo','bar']:
    maxi = df.get_group(name)['B'].max()
    df.get_group(name)['C'] = df.get_group(name)['C']+maxi

At this point pandas suggests Try using .loc[row_indexer,col_indexer] = value instead . Does this mean I have to use for loops on rows with a if on the column A value and modify the C data one by one? I mean that does not seem to be pandas-ish and I feel that I am missing something. How could I better work around this grouped dataframe?

Answer 1

Such operations are done using transforms or aggregations. In your case you need transform

# groupby 'A'
grouped = df.groupby('A')

# transform B so every row becomes the maximum along the group:
max_B = grouped['B'].transform('max')

# add the new column to the old df
df['D'] = df['A'] + max_B

Or in one line:

In [2]: df['D'] = df.groupby('A')['B'].transform('max') + df['C']

In [3]: df
Out[3]: 
     A         B         C         D
0  foo  1.496337 -0.604264  0.892073
1  bar -0.025106  0.257354  0.761936
2  foo  0.958001  0.933328  2.429665
3  foo -1.126581  0.570908  2.067245
4  bar -0.428304  0.881995  1.386577
5  foo -0.955252  1.408930  2.905267
6  bar  0.504582  0.455287  0.959869
7  bar -1.076096  0.536741  1.041323
8  bar  0.351544 -1.146554 -0.641972
9  foo  0.430260 -0.348472  1.147865

For more info, see http://pandas.pydata.org/pandas-docs/stable/groupby.html