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
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() = col
df_value_counts = pd.concat(pieces, axis=1)

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

Just call apply and pass pd.Series.value_counts:

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

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']})


       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")

   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
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:


To get the counts only for specific columns:

df[['a', 'b']].apply(pd.Series.value_counts)

where df is the name of your dataframe and ‘a’ and ‘b’ are the columns for which you want to count the values.

The solution that selects all categorical columns and makes a dataframe with all value counts at once:

df = pd.DataFrame({
'fruits': ['apple', 'mango', 'apple', 'mango', 'mango', 'pear', 'mango'],
'vegetables': ['cucumber', 'eggplant', 'tomato', 'tomato', 'tomato', 'tomato', 'pumpkin'],
'sauces': ['chili', 'chili', 'ketchup', 'ketchup', 'chili', '1000 islands', 'chili']})

cat_cols = df.select_dtypes(include=object).columns.tolist()
    .melt(var_name="column", value_name="value")
.rename(columns={0: 'counts'})
.sort_values(by=['column', 'counts']))

column      value   
fruits      pear            1
            apple           2
            mango           4
sauces      1000 islands    1
            ketchup         2
            chili           4
vegetables  pumpkin         1
            eggplant        1
            cucumber        1
            tomato          4

You can also try this code:

for i in heart.columns:
    x = heart[i].value_counts()
    print("Column name is:",i,"and it value is:",x)

Your solution wrapped in one line looks even simpler than using groupby, stacking etc:

pd.concat([df[column].value_counts() for column in df], axis = 1)

This is what worked for me:

for column in df.columns:
     print("\n" + column)

link to source

You can use a lambda function:

df.apply(lambda x: x.value_counts())

Ran into this to see if there was a better way of doing what I was doing. Turns out calling df.apply(pd.value_counts) on a DataFrame whose columns each have their own many distinct values will result in a pretty substantial performance hit.

In this case, it is better to simply iterate over the non-numeric columns in a dictionary comprehension, and leave it as a dictionary:

types_to_count = {"object", "category", "string"}
result = {
    col: df[col].value_counts()
    for col in df.columns[df.dtypes.isin(types_to_count)]

The filtering by types_to_count helps to ensure you don’t try to take the value_counts of continuous data.

Another solution which can be done:

df = pd.DataFrame(np.random.randint(0, 2, (10, 4)), columns=list('abcd'))
l1 = pd.Series()
for var in df.columns:
    l2 = df[var].value_counts()
    l1 = pd.concat([l1, l2], axis = 1)

Sometimes some columns are subsequent in hierarchy, in that case I recommend to “group” them and then make counts:

# note: "_id" is whatever column you have to make the counts with len()
cat_cols = ['column_1', 'column_2']
df.groupby(cat_cols).agg(count=('_id', lambda x: len(x)))

<table border="1" class="dataframe">
    <tr style="text-align: right;">
      <th rowspan="3" valign="top">category_1</th>
      <th rowspan="5" valign="top">category_2</th>
      <th>Good mood</th>

Bonus: you can change len(x) to x.nunique() or other lambda functions you want.