Back ArrowExplore all topics
technology logo
technology logo

Map numeric data into categories/bins in pandas Data Frame

share link

by shivanisanju03 dot icon Updated: Jan 24, 2023

Solution Kit Solution Kit  

In this Kit ,we are going to 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, youll learn about two different Pandas methods, .cut () and .qcut () for binning your data. These methods will allow you to bin data into custom-sized bins 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 for boundary cases the lower bound is used for mapping to a bin.


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!

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.

Dependent Library

pandasby pandas-dev

Python doticonstar image 37363 doticonVersion:v2.0.0rc1doticon
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

            pandasby pandas-dev

            Python doticon star image 37363 doticonVersion:v2.0.0rc1doticon 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'.

                      Support

                      1. For any support on kandi solution kits, please use the chat
                      2. For further learning resources, visit the Open Weaver Community learning page.


                      See similar Kits and Libraries

                      Open Weaver – Develop Applications Faster with Open Source

                      kandi

                      Community and Support

                      Company

                      Follow

                      • © 2023 Open Weaver Inc.