#  Introduction   Working with time series data involves a consistent set of tasks. Raw data arrives at irregular intervals and needs resampling. Anomalous spikes need to be identified before they distort any downstream analysis. Trends and seasonal patterns need separating from noise. And when you have multiple series, understanding how they relate to each other takes more than a quick visual scan. These five Python scripts handle these common time series tasks. They are designed to work with standard CSV or Excel inputs, produce clean outputs, and be straightforward to configure for different datasets. You can get all the scripts on GitHub . #  1. Resampling and Aggregating Irregular Time Series //  The Pain Point Real-world time series data rarely arrives at uniform intervals. Sensor readings, transaction logs, and event streams have gaps, duplicates, and inconsistent timestamps. Before any meaningful analysis, the data needs to be aligned to a consistent frequency.…