Can I set dataframe values without using iterrows()?

Question

Original DataSet

In [2]: import pandas as pd
   ...: 
   ...: # Original DataSet
   ...: d = {'A': [1,1,1,1,2,2,2,2,3],
   ...:      'B': ['a','a','a','x','b','b','b','x','c'],
   ...:      'C': [11,22,33,44,55,66,77,88,99],}
   ...: 
   ...: df = pd.DataFrame(d)
   ...: df

Out[2]: 
   A  B   C
0  1  a  11
1  1  a  22
2  1  a  33
3  1  x  44
4  2  b  55
5  2  b  66
6  2  b  77
7  2  x  88
8  3  c  99

Given a dataframe, I would like a flexible, efficient way to reset specific values based on certain conditions in two columns.

Conditions:

• in Column B: for any row with value 'x',
• in Column C: set the value of these row-elements to the value of the next row.

Desired Outcome

Out[3]: 
   A  B   C
0  1  a  11
1  1  a  22
2  1  a  33
3  1  x  55
4  2  b  55
5  2  b  66
6  2  b  77
7  2  x  99
8  3  c  99

I learned I can accomplish this using iterrows() (see below),

# Code that produces the above outcome
for idx, x_row in df[df['B'] == 'x'].iterrows():
    df.loc[idx, 'C'] = df.loc[idx+1, 'C']
df

but I need to do this many times, and I understand iterrows() is slow. Are there better pandas-y, broadcasting-like ways of getting the desired outcome more efficiently?

Answer 1

This should do what you want:

df.C[df.B == 'x'] = df.C.shift(-1)