Towards Data Science 8:31 pm on May 27, 2024
This article introduces a flexible time series filtering processor for Polars in Python Data Science using Pandas and Machine Learning principles, extending the functionality of ad-hoc analysis. It's envisioned as an essential part of data processing pipelines and can integrate with other operations like resampling or feature engineering.
- Flexible Time Series Filtering: A tool that enables easy, concise filtering of time-based data series using Polars.
- Integration Potential: Seamless integration with resampling and feature engineering processes for comprehensive analysis.
- Ad-Hoc Analysis Enhancement: Provides a more intuitive method to filter time-series data using custom duration expressions.
- Customizable Filtering Expressions: The ability to define new, simple expression shortcuts for common time durations like "we" or "eve".
- Processor Development Stage: Currently in early development stages with plans for expanding functionality and customizability.
https://towardsdatascience.com/intuitive-temporal-dataframe-filtration-fa9d5da734b3
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