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Map numeric data into categories/bins in pandas Data Frame

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by shivanisanju03 dot icon Updated: Apr 6, 2023

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In this Kit, we will see how to map numeric data into categories/bins in pandas Data Frame with simple steps. Binning data is also often referred to under several other terms, such as discrete binning, quantization, and discretization. In this tutorial, you will learn about two different Pandas methods, .cut () and .qcut (), for binning your data. These methods allow you to bin data into custom-sized and equally-sized bins, respectively. np.digitize provides another clean solution. The idea is to define your boundaries and names, create a dictionary, then apply np.digitize to your Age column. Finally, use your dictionary to map your category names. Note that the lower bound is used for mapping to a bin for boundary cases.

 

Please check the below code to know how to map numeric data into categories/bins in pandas DataFrame.

Fig: Preview of the output that you will get on running this code from your IDE

Code

In this solution we're using Pandas Library

How to map numeric data into categories / bins in Pandas dataframe

PythondotLines of Code : 31dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
bins = [0, 2, 18, 35, 65, np.inf]
names = ['<2', '2-18', '18-35', '35-65', '65+']

df['AgeRange'] = pd.cut(df['Age'], bins, labels=names)

print(df.dtypes)

# Age             int64
# Age_units      object
# AgeRange     category
# dtype: object

import pandas as pd, numpy as np

df = pd.DataFrame({'Age': [99, 53, 71, 84, 84],
                   'Age_units': ['Y', 'Y', 'Y', 'Y', 'Y']})

bins = [0, 2, 18, 35, 65]
names = ['<2', '2-18', '18-35', '35-65', '65+']

d = dict(enumerate(names, 1))

df['AgeRange'] = np.vectorize(d.get)(np.digitize(df['Age'], bins))

   Age Age_units AgeRange
0   99         Y      65+
1   53         Y    35-65
2   71         Y      65+
3   84         Y      65+
4   84         Y      65+

Instructions

Follow the steps carefully to get the output easily.

  1. Install pandas on your IDE(Any of your favorite IDE).
  2. Copy the snippet using the 'copy' and paste it in your IDE.
  3. Add print statement at end of the code(refer preview of the output).
  4. Run the file to generate the output.


I hope you found this useful. I have added the link to dependent library, version information in the following sections.


I found this code snippet by searching for 'how to map numeric data into categories/bin in pandas dataframe' in kandi. You can try any such use case!

Dependent Library

pandasby pandas-dev

Python doticonstar image 38689 doticonVersion:v2.0.2doticon
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Support
    Quality
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            pandasby pandas-dev

            Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

            Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
            Support
              Quality
                Security
                  License
                    Reuse

                      You can also search for any dependent libraries on kandi like 'pandas'.

                      Environment tested

                      I tested this solution in the following versions. Be mindful of changes when working with other versions.

                      1. The solution is created in Jupyter notebook 6.5.2.
                      2. The solution is tested on Python 3.11.1.
                      3. Pandas version-v1.5.3.



                      Using this solution, we are able to map numeric data into categories/bin in pandas dataframe with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to map numeric data into categories/bin in pandas dataframe.

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