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
Notice that each row has been assigned a bin based on the value of the points column and the bins have been labeled using letters.
Additional Resources
The following tutorials explain how to perform other common tasks in pandas:
Pandas: How to Use value_counts() Function
Pandas: How to Create Pivot Table with Count of Values
Pandas: How to Count Occurrences of Specific Value in Column
