如何在熊猫中将一列的值与另一列的值相除/相乘？

Question

My data frame looks liks this

Minutes Played, Points, Assists

      MP   PTS   TRB   AST    FG%  BLK    3P%
0   2810   793   678   117  0.485   74  0.315
1    263   101    30    19  0.402    7  0.385
2   4241  1170  1178   144  0.548  201  0.000

I want to convert that data frame to data-frame with these columns

Points/Minutes, Assists/Minutes

Basically first column is total-minutes played, I want to covert all of the remaining stats to per minute basis.

Right I am doing

 input_data['PTS']/input_data['MP']

and then I am concatenating all of the series, what is the pythonic way of doing this? How can I do this using Map/lambda operation?

Answer 1

IIUC you can use:

print input_data
     MP   PTS   TRB  AST    FG%  BLK    3P%
0  2810   793   678  117  0.485   74  0.315
1   263   101    30   19  0.402    7  0.385
2  4241  1170  1178  144  0.548  201  0.000

input_data['A'] = input_data['PTS']/input_data['MP']
input_data['B'] = input_data['AST']/input_data['MP']
print input_data
     MP   PTS   TRB  AST    FG%  BLK    3P%         A         B
0  2810   793   678  117  0.485   74  0.315  0.282206  0.041637
1   263   101    30   19  0.402    7  0.385  0.384030  0.072243
2  4241  1170  1178  144  0.548  201  0.000  0.275878  0.033954

print pd.DataFrame({'A': input_data['A'],'B': input_data['B']}, index=input_data.index)
          A         B
0  0.282206  0.041637
1  0.384030  0.072243
2  0.275878  0.033954

Answer 2

Divide all columns in the dataframe except the first by the first column.

df.iloc[:, ].apply(lambda s: s / df.iloc[:, 0])
        PTS       TRB       AST       FG%       BLK       3P%
0  0.282206  0.241281  0.041637  0.000173  0.026335  0.000112
1  0.384030  0.114068  0.072243  0.001529  0.026616  0.001464
2  0.275878  0.277765  0.033954  0.000129  0.047394  0.000000

This also works:

df.iloc[:, 1:].div(df.iloc[:, 0].values, axis=0)

I believe you will then need to recalculate your FG% and 3P% columns. This will do the division on your original dataframe, leaving MP, FG% and 3P% untouched.

df.iloc[:, [1, 2, 3, 5]] = df.iloc[:, [1, 2, 3, 5]].div(df.iloc[:, 0].values, axis=0)

>>> df
     MP       PTS       TRB       AST    FG%       BLK    3P%
0  2810  0.282206  0.241281  0.041637  0.485  0.026335  0.315
1   263  0.384030  0.114068  0.072243  0.402  0.026616  0.385
2  4241  0.275878  0.277765  0.033954  0.548  0.047394  0.000

ps Go Warriors!!!

Answer 3

No, concatenating the new series is plenty idiomatic.

You can also use df['newcol'] = ... to build up what you want.