Access previous value in same DataFrame column

Question

Here is my code:

a = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]], columns=[\'A\', \'B\'])

print(a)

a[\'C\'] = 1 # or np.nan or is there a way to         


        

Answer 1

From your code:

# Variable a BEFORE apply
   A   B
0  1   2
1  3   4
2  5   6
3  7   8
4  9  10

# Variable a AFTER apply
   A   B   C
0  1   2   4
1  3   4   8
2  5   6  12
3  7   8  16
4  9  10  20

Assuming this output is really what you want, then:

a = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]], columns=['A', 'B'])

a['C'] = a['A'] + a['B'] + 1

I'm a little confused as to why you would want to access a['C'].shift(1) since all the values are the same anyway, and you are trying not to initialize it.

If you want a working example of using df.shift(n), try:

a['Shift'] = a['A'] + a['B'].shift(1)

Which would give you:

   A   B   C  Shift
0  1   2   4    NaN
1  3   4   8    5.0
2  5   6  12    9.0
3  7   8  16   13.0
4  9  10  20   17.0

This would give you A(i) + B(i+1), where i is the row number. Since you shifted column B by 1, the first sum is NaN.

Answer 2

I guess you need this:

a['C'] = a['A'] + a['B']
a['D'] = a['C'].cumsum()

because summing with previous element is a cumulative sum.