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Data binning is a process of transforming continuous data into discrete data by grouping values into bins or intervals. This technique can be used to reduce the data set to make it easier to analyze. In Python, this process is performed using the pandas.cut() function. This function takes the data set, the number of bins, and the intervals for each bin as parameters and returns an array of the binned data. Examples of data binning in Python include creating a histogram, creating a bar chart, and creating a box plot.
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 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