What is correct syntax to swap column values for selected rows in a pandas data frame using just one line?

Question

I am using pandas version 0.14.1 with Python 2.7.5, and I have a data frame with three columns, e.g.:

import pandas as pd

d = {\'L\':  [\'left\', \'right\',         


        

Answer 1

The key thing to note here is that pandas attempts to automatically align rows and columns using the index and column names. Hence, you need to somehow tell pandas to ignore the column names here. One way is as @DSM does, by converting to a numpy array. Another way is to rename the columns:

>>> df.loc[idx] = df.loc[idx].rename(columns={'R':'L','L':'R'})

      L      R  VALUE
0  left  right     -1
1  left  right      1
2  left  right     -1
3  left  right      1
4  left  right     -1
5  left  right      1

Answer 2

You can also do this with np.select and df.where i.e

Option 1: np.select

df[['L','R']] = pd.np.select(df['VALUE'] == 1, df[['R','L']].values, df[['L','R']].values)

Option 2: df.where

df[['L','R']] = df[['R','L']].where(df['VALUE'] == 1, df[['L','R']].values)

Option 3: df.mask

df[['L','R']] = df[['L','R']].mask( df['VALUE'] == 1, df[['R','L']].values)

Output:

    L      R  VALUE
0  left  right     -1
1  left  right      1
2  left  right     -1
3  left  right      1
4  left  right     -1
5  left  right      1

Answer 3

One way you could avoid alignment on column names would be to drop down to the underlying array via .values:

In [33]: df
Out[33]: 
       L      R  VALUE
0   left  right     -1
1  right   left      1
2   left  right     -1
3  right   left      1
4   left  right     -1
5  right   left      1

In [34]: df.loc[idx,['L','R']] = df.loc[idx,['R','L']].values

In [35]: df
Out[35]: 
      L      R  VALUE
0  left  right     -1
1  left  right      1
2  left  right     -1
3  left  right      1
4  left  right     -1
5  left  right      1