How to get value counts for multiple columns at once in Pandas DataFrame?

Given a Pandas DataFrame that has multiple columns with categorical values (0 or 1), is it possible to conveniently get the value_counts for every column at the same time?

For example, suppose I generate a DataFrame as follows:

import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame(np.random.randint(0, 2, (10, 4)), columns=list('abcd'))

I can get a DataFrame like this:

   a  b  c  d
0  0  1  1  0
1  1  1  1  1
2  1  1  1  0
3  0  1  0  0
4  0  0  0  1
5  0  1  1  0
6  0  1  1  1
7  1  0  1  0
8  1  0  1  1
9  0  1  1  0

How do I conveniently get the value counts for every column and obtain the following conveniently?

   a  b  c  d
0  6  3  2  6
1  4  7  8  4

My current solution is:

pieces = []
for col in df.columns:
    tmp_series = df[col].value_counts()
    tmp_series.name = col
    pieces.append(tmp_series)
df_value_counts = pd.concat(pieces, axis=1)

But there must be a simpler way, like stacking, pivoting, or groupby?

Answer

Just call apply and pass pd.Series.value_counts:

In [212]:
df = pd.DataFrame(np.random.randint(0, 2, (10, 4)), columns=list('abcd'))
df.apply(pd.Series.value_counts)
Out[212]:
   a  b  c  d
0  4  6  4  3
1  6  4  6  7