How to Perform Data Binning in Python (With Examples)

You can use the following basic syntax to perform data binning on a pandas DataFrame:

import pandas as pd

#perform binning with 3 bins
df['new_bin'] = pd.qcut(df['variable_name'], q=3)

The following examples show how to use this syntax in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'points': [4, 4, 7, 8, 12, 13, 15, 18, 22, 23, 23, 25],
                   'assists': [2, 5, 4, 7, 7, 8, 5, 4, 5, 11, 13, 8],
                   'rebounds': [7, 7, 4, 6, 3, 8, 9, 9, 12, 11, 8, 9]})

#view DataFrame
print(df)

    points  assists  rebounds
0        4        2         7
1        4        5         7
2        7        4         4
3        8        7         6
4       12        7         3
5       13        8         8
6       15        5         9
7       18        4         9
8       22        5        12
9       23       11        11
10      23       13         8
11      25        8         9

Example 1: Perform Basic Data Binning

The following code shows how to perform data binning on the points variable using the qcut() function with specific break marks:

#perform data binning on points variable
df['points_bin'] = pd.qcut(df['points'], q=3)

#view updated DataFrame
print(df)

    points  assists  rebounds        points_bin
0        4        2         7   (3.999, 10.667]
1        4        5         7   (3.999, 10.667]
2        7        4         4   (3.999, 10.667]
3        8        7         6   (3.999, 10.667]
4       12        7         3  (10.667, 19.333]
5       13        8         8  (10.667, 19.333]
6       15        5         9  (10.667, 19.333]
7       18        4         9  (10.667, 19.333]
8       22        5        12    (19.333, 25.0]
9       23       11        11    (19.333, 25.0]
10      23       13         8    (19.333, 25.0]
11      25        8         9    (19.333, 25.0]

Notice that each row of the data frame has been placed in one of three bins based on the value in the points column.

We can use the value_counts() function to find how many rows have been placed in each bin:

#count frequency of each bin
df['points_bin'].value_counts()

(3.999, 10.667]     4
(10.667, 19.333]    4
(19.333, 25.0]      4
Name: points_bin, dtype: int64

We can see that each bin contains 4 observations.

Example 2: Perform Data Binning with Specific Quantiles

We can also perform data binning by using specific quantiles:

#perform data binning on points variable with specific quantiles
df['points_bin'] = pd.qcut(df['points'], q=[0, .2, .4, .6, .8, 1])

#view updated DataFrame
print(df)

    points  assists  rebounds    points_bin
0        4        2         7  (3.999, 7.2]
1        4        5         7  (3.999, 7.2]
2        7        4         4  (3.999, 7.2]
3        8        7         6   (7.2, 12.4]
4       12        7         3   (7.2, 12.4]
5       13        8         8  (12.4, 16.8]
6       15        5         9  (12.4, 16.8]
7       18        4         9  (16.8, 22.8]
8       22        5        12  (16.8, 22.8]
9       23       11        11  (22.8, 25.0]
10      23       13         8  (22.8, 25.0]
11      25        8         9  (22.8, 25.0]

Example 3: Perform Data Binning with Labels

We can also perform data binning by using specific quantiles and specific labels:

#perform data binning on points variable with specific quantiles and labels
df['points_bin'] = pd.qcut(df['points'],
                           q=[0, .2, .4, .6, .8, 1],
                           labels=['A', 'B', 'C', 'D', 'E'])

#view updated DataFrame
print(df)

    points  assists  rebounds points_bin
0        4        2         7          A
1        4        5         7          A
2        7        4         4          A
3        8        7         6          B
4       12        7         3          B
5       13        8         8          C
6       15        5         9          C
7       18        4         9          D
8       22        5        12          D
9       23       11        11          E
10      23       13         8          E
11      25        8         9          E