Copying value from one column to another after filtering Dataframe - Simpler and shorter solution

Update

I recently found half a solution to this and wanted to put up that for everyone else who is trying to figure this out.

I solved it for instances where you copy to nan values.

In the example below I have column 'Name', which is missing some names.
Then in column 'Correction' I have values that I have picked up from somewhere else and want to fill into the 'Name' column if there are no names.

    df = pd.DataFrame({'Name': ['A', None, 'C'],               
                  'Correction': [None, 'B', 'Q']})

out:
   Name Correction
0     A  None
1  None     B
2     C     Q

    # Copy values from column 'Correction' to 'Name' for rows where 'Name' is None
    df.loc[:, 'Name'].fillna(df[(df['Name'].isna()==True) & (df['Correction'].isna() == False)]['Correction'], 
                              inplace=True)

out:
  Name Correction
0    A  None
1    B     B
2    C     Q

For non nan cases I have found an improvement as seen in the link below.

But as per a comment it might have issues with nan values which would pose problems for me.

https://datascience.stackexchange.com/questions/23264/improve-pandas-dataframe-filtering-speed

Problem

I am doing a fair bit of filtering of a pandas dataframe and then copying from one column to another on that filter while still using the entire data frame.

So on a sub selection on rows I copy values from one column to another. Then I use the full dataframe (including the now corrected rows) for something.

In the example below I have column 'Name', which is missing some names.
Then in column 'Correction' I have values that I have picked up from somewhere else and want to fill into the 'Name' column if there are no names.

    df = pd.DataFrame({'Name': ['A', None, 'C'],               
                  'Correction': [None, 'B', 'Q']})

out:
   Name Correction
0     A  None
1  None     B
2     C     Q

    # Copy values from column 'Correction' to 'Name' for rows where 'Name' is None
    df.loc[(df['Name'].isna()==True) & (df['Correction'].isna() == False), 'Name'] = \
          df[(df['Name'].isna()==True) & (df['Correction'].isna() == False)]['Correction']

out:
  Name Correction
0    A  None
1    B     B
2    C     Q

That does work but the readability is horrible.

Is there a more "elegant" solution where you don't have to repeat the filter twice in a row like this.

Partial answer, will work for instances where you copy to nan values

I recently found half a solution to this and wanted to put up that for everyone else who is trying to figure this out.

I solved it for instances where you copy to nan values.

In the example below I have column 'Name', which is missing some names. Then in column 'Correction' I have values that I have picked up from somewhere else and want to fill into the 'Name' column if there are no names.

    df = pd.DataFrame({'Name': ['A', None, 'C'],               
                  'Correction': [None, 'B', 'Q']})

out:
   Name Correction
0     A  None
1  None     B
2     C     Q

    # Copy values from column 'Correction' to 'Name' for rows where 'Name' is None
    df.loc[:, 'Name'].fillna(df[(df['Name'].isna()==True) & (df['Correction'].isna() == False)]['Correction'], 
                              inplace=True)

out:
  Name Correction
0    A  None
1    B     B
2    C     Q

For non nan values, a improvement but not a solution

For non nan cases I have found an improvement as seen in the link below.

But as per a comment in the that threat it might have issues with nan values which would pose a problem.

https://datascience.stackexchange.com/questions/23264/improve-pandas-dataframe-filtering-speed