Map numeric data into categories/bins in pandas Data Frame
by shivanisanju03 Updated: Jan 24, 2023
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
Instructions
Follow the steps carefully to get the output easily.
- Install pandas on your IDE(Any of your favorite IDE).
- Copy the snippet using the 'copy' and paste it in your IDE.
- Add print statement at end of the code(refer preview of the output).
- 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.
- The solution is created in Jupyter notebook 6.5.2.
- The solution is tested on Python 3.11.1.
- 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
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pandasby pandas-dev
Python
37363
Version:v2.0.0rc1
License: Permissive (BSD-3-Clause)
You can also search for any dependent libraries on kandi like 'pandas'.
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