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

Question

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 1

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 

Answer 2

There is actually a fairly interesting and advanced way of doing this problem with crosstab and melt

df = pd.DataFrame({'a': ['table', 'chair', 'chair', 'lamp', 'bed'],                    'b': ['lamp', 'candle', 'chair', 'lamp', 'bed'],                    'c': ['mirror', 'mirror', 'mirror', 'mirror', 'mirror']})  df         a       b       c 0  table    lamp  mirror 1  chair  candle  mirror 2  chair   chair  mirror 3   lamp    lamp  mirror 4    bed     bed  mirror 

We can first melt the DataFrame

df1 = df.melt(var_name='columns', value_name='index') df1     columns   index 0        a   table 1        a   chair 2        a   chair 3        a    lamp 4        a     bed 5        b    lamp 6        b  candle 7        b   chair 8        b    lamp 9        b     bed 10       c  mirror 11       c  mirror 12       c  mirror 13       c  mirror 14       c  mirror 

And then use the crosstab function to count the values for each column. This preserves the data type as ints which wouldn't be the case for the currently selected answer:

pd.crosstab(index=df1['index'], columns=df1['columns'])  columns  a  b  c index            bed      1  1  0 candle   0  1  0 chair    2  1  0 lamp     1  2  0 mirror   0  0  5 table    1  0  0 

Or in one line, which expands the column names to parameter names with ** (this is advanced)

pd.crosstab(**df.melt(var_name='columns', value_name='index')) 

Also, value_counts is now a top-level function. So you can simplify the currently selected answer to the following:

df.apply(pd.value_counts)