Python is widely used for analyzing time-series data due to its versatility and extensive library ecosystem. Python's popularity stems from its simplicity, flexibility, and the availability of numerous tools and modules that make it an ideal choice for businesses looking to analyze data and make informed decisions. NumPy provides advanced numerical processing capabilities, pandas offers practical data analysis and manipulation toolkit, and Matplotlib is a popular plotting library that allows users to create visually appealing visualizations. Additionally, Python's extensive library ecosystem includes time-series specific libraries such as Tsfresh, Sktime, AutoTS, and Prophet, which offer various features for extracting insights from time-series data. These tools enable data scientists and analysts to work with time-series data efficiently and effectively. By connecting Python with Timescale, users can leverage the efficiency of a relational database designed specifically for time-series analysis, allowing them to perform complex analytics like machine learning, identifying anomalies, and projections while keeping their data in the database.