How to Split a Pandas DataFrame into Multiple DataFrames by DateTime Ranges

Learn how to efficiently split a pandas DataFrame into multiple DataFrames based on specific DateTime ranges. Perfect for data analysis and manipulation using Python.
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How to Split a Pandas DataFrame into Multiple DataFrames by DateTime Ranges

When working with time series data in Python, it's often necessary to perform analyses on specific time intervals within a larger dataset. The Pandas library provides efficient tools to handle such tasks. In this post, we'll explore how to split a pandas DataFrame into multiple DataFrames by specific DateTime ranges.

Prerequisites

To follow along with the examples below, ensure you have the following installed:

Python (preferably version 3.x)

Pandas library

You can install Pandas using pip if you haven't already:

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Creating a Sample DataFrame

We'll start by creating a sample DataFrame that includes a DateTime column. This will serve as our dataset for splitting.

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Splitting the DataFrame by DateTime Ranges

To split the DataFrame by specific DateTime ranges, we will define the ranges and use Boolean indexing.

Define DateTime Ranges

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Splitting the DataFrame

Using the predefined ranges, we can create separate DataFrames for each range using Boolean indexing.

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Verify the Split DataFrames

Printing the DataFrames will confirm the splitting process.

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If executed correctly, you should now have three separate DataFrames, each containing data for the specified DateTime ranges.

Conclusion

Splitting a pandas DataFrame by DateTime ranges is a common requirement in time-series data analysis. With Pandas, this can be done efficiently using Boolean indexing. Understanding how to subset your data based on DateTime ranges will enable you to perform more granular analyses and manipulations in your data science projects.

Whether you're working on financial data, stock market analysis, or any other time-bound dataset, being able to split your DataFrame effectively will save you time and enhance your workflow.

Happy coding!