How do I create a new dataframe based on row values of multiple columns in python?

Question

I have multiple columns that contained only 0s or 1s.

Apple	Orange	Pear
1	0	1
0	0	1
1	1	0

I would like to count and input the number of 0s (in "Wrong" column) and 1s (in "Correct" column) of each column in the new dataframe, and total them up into a table that looks like the following.

Fruit	Correct	Wrong
Apple	2	1
Orange	1	2
Pear	2	1

I tried a blend of value_counts(), groupby(), and pandas.pivot_table, but got stuck with the manipulation of the table.

Answer 1

Try this:

df.apply(pd.Series.value_counts).rename(index={0:'Wrong', 1:'Correct'}).T

Use pd.DataFrame.apply to "apply" pd.Series.value_counts to each column of the dataframe, then rename the index values using a dictionary for 0 and 1 to Wrong and Correct. Lastly, use T to transpose the dataframe.

Output:

        Wrong  Correct
Apple       1        2
Orange      2        1
Pear        1        2

And, you can add .rename_axis('Fruit').reset_index() to get:

    Fruit  Wrong  Correct
0   Apple      1        2
1  Orange      2        1
2    Pear      1        2