将不同行和列中的值求和

Question

My Data Frame

A B C D  
2 3 4 5  
1 4 5 6  
5 6 7 8  

How do I add values of different rows and different columns

• Column A Row 2 with Column B row 1
• Column A Row 3 with Column B row 2

Similarly for all rows

Answer 1

If you only need do this with two columns (and I understand your question well), I think you can use the shift function.

Your data frame (pandas?) is something like:

d = {'A': [2, 1, 5], 'B': [3, 4, 6], 'C': [4, 5, 7], 'D':[5, 6, 8]}
df = pd.DataFrame(data=d)

So, it's possible to create a new data frame with B column shifted:

df2 = df['B'].shift(1)

which gives:

0    NaN
1    3.0
2    4.0
Name: B, dtype: float64

and then, merge this new data with the previous df and, for example, sum the values:

df = df.join(df2, rsuffix='shift')
df['out'] = df['A'] + df['Bshift']

The final output is in out column:

    A   B   C   D   Bshift  out
0   2   3   4   5   NaN     NaN
1   1   4   5   6   3.0     4.0
2   5   6   7   8   4.0     9.0

But it's only an intuition, I'm not sure about your question!