Pandas cut() function is used to separate the array elements into different bins . The cut function is mainly used to perform statistical analysis on scalar data.
Syntax: cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates=”raise”,)
Parameters:
x: The input array to be binned. Must be 1-dimensional.
bins: defines the bin edges for the segmentation.
right : (bool, default True ) Indicates whether bins includes the rightmost edge or not. If right == True (the default), then the bins [1, 2, 3, 4] indicate (1,2], (2,3], (3,4].
labels : (array or bool, optional) Specifies the labels for the returned bins. Must be the same length as the resulting bins. If False, returns only integer indicators of the bins.
retbins : (bool, default False) Whether to return the bins or not. Useful when bins is provided as a scalar.
Example 1: Let’s say we have an array of 10 random numbers from 1 to 100 and we wish to separate data into 5 bins of (1,20] , (20,40] , (40,60] , (60,80] , (80,100] .
Python3
import pandas as pdimport numpy as npdf= pd.DataFrame({'number': np.random.randint(1, 100, 10)})df['bins'] = pd.cut(x=df['number'], bins=[1, 20, 40, 60, 80, 100])print(df)# We can check the frequency of each binprint(df['bins'].unique()) |
Output:
Example 2: We can also add labels to our bins, for example let’s look at the previous example and add some labels to it
Python3
import pandas as pdimport numpy as npdf = pd.DataFrame({'number': np.random.randint(1, 100, 10)})df['bins'] = pd.cut(x=df['number'], bins=[1, 20, 40, 60, 80, 100], labels=['1 to 20', '21 to 40', '41 to 60', '61 to 80', '81 to 100'])print(df)# We can check the frequency of each binprint(df['bins'].unique()) |
Output:
