设置DataFrame的列为熊猫中另一个列的总和

Question

I have a DataFrame that looks like the below, call this "values":

I would like to create another, call it "sums" that contains the sum of the DataFrame "values" from the column in "sums" to the end. It would look like the below:

I would like to create this without looking through the entire DataFrame, data point by data point. I have been trying with .apply() as seen below, but I keep getting the error: unsupported operand type(s) for +: 'int' and 'datetime.date'

In [26]: values = pandas.DataFrame({0:[96,54,27,28],
              1:[55,75,32,37],2:[54,99,36,46],3:[35,77,0,10],4:[62,25,0,25],
              5:[0,66,0,89],6:[0,66,0,89],7:[0,0,0,0],8:[0,0,0,0]})

In [28]: sums = values.copy()

In [29]: sums.iloc[:,:] = ''         

In [31]: for column in sums:
    ...:     sums[column].apply(sum(values.loc[:,column:]))
    ...:     
Traceback (most recent call last):

  File "<ipython-input-31-030442e5005e>", line 2, in <module>
    sums[column].apply(sum(values.loc[:,column:]))
  File "C:\WinPython64bit\python-3.5.2.amd64\lib\site-packages\pandas\core\series.py", line 2220, in apply
    mapped = lib.map_infer(values, f, convert=convert_dtype)
  File "pandas\src\inference.pyx", line 1088, in pandas.lib.map_infer (pandas\lib.c:63043)

TypeError: 'numpy.int64' object is not callable


In [32]: for column in sums:
    ...:     sums[column] = sum(values.loc[:,column:])

In [33]: sums
Out[33]: 
    0   1   2   3   4   5   6   7  8
0  36  36  35  33  30  26  21  15  8
1  36  36  35  33  30  26  21  15  8
2  36  36  35  33  30  26  21  15  8
3  36  36  35  33  30  26  21  15  8

Is there a way to do this without looping each point individually?

Answer 1

Without looping, you can reverse your dataframe, cumsum per line and then re-reverse it:

>>> values.iloc[:,::-1].cumsum(axis=1).iloc[:,::-1]
     0    1    2    3    4    5   6  7  8
0  302  206  151   97   62    0   0  0  0
1  462  408  333  234  157  132  66  0  0
2   95   68   36    0    0    0   0  0  0
3  324  296  259  213  203  178  89  0  0

Answer 2

You can use the .cumsum() method to get the cumulative sum. The problem is that is operates from left to right, where you need it from right to left.

So we will reverse you data frame, use cumsum() , then set the axes back into the proper order.

import pandas as pd

values = pd.DataFrame({0:[96,54,27,28],
          1:[55,75,32,37],2:[54,99,36,46],3:[35,77,0,10],4:[62,25,0,25],
          5:[0,66,0,89],6:[0,66,0,89],7:[0,0,0,0],8:[0,0,0,0]})

values[values.columns[::-1]].cumsum(axis=1).reindex_axis(values.columns, axis=1)

# returns:
     0    1    2    3    4    5   6  7  8
0  302  206  151   97   62    0   0  0  0
1  462  408  333  234  157  132  66  0  0
2   95   68   36    0    0    0   0  0  0
3  324  296  259  213  203  178  89  0  0