循环 dataframe 并在每次迭代中提取不同的列

Question

I am trying to loop over a dataframe df and I would like to extract different columns at each iteration.

say I have Columns: ['A', 'B', 'C', 'D', 'E', 'F'] in my df

column_names=[['A','B'],['A','C','D']]

    for index,row in df:  //lets assume index starts with 0
       row[column_names[index]] // However you can not apply this syntax for rows like you could for a df to get a sub dataframe. 

What are my options? I have tried itertuples and iterrows but you can not select different columns by passing a list of column names

Thanks

Answer 1

The easiest way to loop over columns and retrieving a dataframe would be to invert your loops:

for col in column_names:
    for ix in df.index:
        print(df.loc[ix, col])

Answer 2

With iterrows() you get a tuple with index at 0th position and row at the 1st. You might want to use iterrows() as:

column_names=[['A',"B"],['A','C','D']]
for row in df.iterrows():
    print(row[1][column_names[row[0]]].to_frame())

For a df of ones ie:

    A   B   C   D   E   F
0   1.0 1.0 1.0 1.0 1.0 1.0
1   1.0 1.0 1.0 1.0 1.0 1.0

You get:

A    1.0
B    1.0
Name: 0, dtype: float64
A    1.0
C    1.0
D    1.0
Name: 1, dtype: float64