Slicing a data frame and adding valued based on different slice

June 21, 2023

I have a data frame with a column representing phases (‘A’, ‘B’, and ‘C’). I need to slice the data frame so I have only column ‘A’, divide it by 2 and add the value to the other column.

Here is an example:

import pandas as pd

data = {'time': [1,2,1,2,1,2,],
        'phase': ['A', 'A', 'B', 'B', 'C', 'C' ],
        'value': [2, 3, 4, 5, 6, 7]}
df = pd.DataFrame(data)
print(df)

   time phase  value
0     1     A      2
1     2     A      3
2     1     B      4
3     2     B      5
4     1     C      6
5     2     C      7


slice_A = df.loc[df['phase']== 'A', 'value'] /2 
print(slice_A)

0    1.0
1    1.5
Name: value, dtype: float64

df.loc[df['phase']=='B', 'value'] += slice_A

df
time    phase   value
0   1   A   2.0
1   2   A   3.0
2   1   B   NaN
3   2   B   NaN
4   1   C   6.0
5   2   C   7.0

I understand this is because the index of slice_A is not the same as:

df.loc[df['phase']=='B', 'value']

I tried to reset the series with the index of the sliced data frame. I also tried to work with data frames instead of series, but I couldn’t get it to work. But, I am still getting Nan values.

>Solution :

Convert your series into numpy array to avoid index alignment:

df.loc[df['phase']=='B', 'value'] += slice_A.to_numpy()
print(df)

# Output
   time phase  value
0     1     A    2.0
1     2     A    3.0
2     1     B    5.0
3     2     B    6.5
4     1     C    6.0
5     2     C    7.0

Obviously it works because your have as many A as B. You can add to B: