Python Dataframe map indexes to a column of lists for extracting elements-CodePudding

I have a data frame consisting of lists as elements. Also, I have a list of known indexes. Now I want to extract the elements present in those indexes in each row. My code:

df = pd.DataFrame({'A':[[7,8],[4,5,NaN],[NaN,1,9]],'match_idx':[1,0,NaN]})
df

      A             match_idx
0   [7, 8]          1
1   [4, 5, nan]     0
2   [nan, 1, 9]     NaN

# in each row, let's find the values located in the match_idx position

Present solution:

df['A_element'] = df.apply(lambda x: x['A'][x['match_idx']] if ~x['match_idx'].isnan() else np.nan,axis=1)

AttributeError: 'float' object has no attribute 'isnan'

Expected solution:

df = 
      A             match_idx   A_element
0   [7, 8]          1           8
1   [4, 5, nan]     0           4
2   [nan, 1, 9]     NaN         NaN

CodePudding user response：

For tet non missing values use notna with convert indices to integer:

df['A_element'] = [a[int(i)] if pd.notna(i) else np.nan
                               for a, i in zip(df['A'], df['match_idx'])]

Or:

df['A_element'] = df.apply(lambda x: x['A'][int(x['match_idx'])] 
                           if pd.notna(x['match_idx']) else np.nan,axis=1)

print (df)
             A  match_idx  A_element
0       [7, 8]        1.0        8.0
1  [4, 5, nan]        0.0        4.0
2  [nan, 1, 9]        NaN        NaN