尝试使用Python / pandas基于来自另一个数据帧的列的内部总和来创建新的数据帧

Question

Let's assume I have a pandas dataframe df as follow:

df = DataFrame({'Col1':[1,2,3,4], 'Col2':[5,6,7,8]})

    Col1 Col2
0      1      5
1      2      6
2      3      7
3      4      8

Is there a way for me to change a column into the sum of all the following elements in the column?

For example for 'Col1' the result would be:

    Col1   Col2
0     10      5
1      9      6
2      7      7
3      4      8

1 becomes 1 + 2 + 3 + 4 = 10
2 becomes 2 + 3 + 4 = 9
3 becomes 3 + 4 = 7
4 remains 4

If this is possible, is there a way for me to specify a cut off index after which this behavior would take place? For example if the cut off index would be the key 1, the result would be:

    Col1   Col2
0      1      5
1      2      6
2      7      7
3      4      8

I am thinking there is no other way than using loops to do this, but I thought there might be a way using vectorized calculations.

Thanks heaps

Answer 1

Yes, you could use loop but very cheap one:

def sum_col(column,start=0):
    l = len(column)
    return [column.values[i:].sum() for i in range(start,l)]

And usage:

data['Col1'] = sum_col(data['Col1'],0)

Answer 2

Here is one way to avoid loop.

import pandas as pd

your_df = pd.DataFrame({'Col1':[1,2,3,4], 'Col2':[5,6,7,8]})

def your_func(df, column, cutoff):
    # do cumsum and flip over
    x = df[column][::-1].cumsum()[::-1]
    df[column][df.index > cutoff] = x[x.index > cutoff]     
    return df

# to use it
your_func(your_df, column='Col1', cutoff=1)

Out[68]: 
   Col1  Col2
0     1     5
1     2     6
2     7     7
3     4     8