Python Tools for Time Series Data and Statistical Models

Summary

The document recommends Python tools for work commonly taught through time series software. It presents pandas as the core library for representing and managing dated observations, with Series and DataFrame structures. The capabilities highlighted include combining time series, plotting with a companion plotting library, handling missing observations, changing data frequency, and calculating rolling or expanding statistics. These functions support common preparation and exploratory analysis tasks in quantitative research.

For statistical modeling, the response points to statsmodels, describing its range from basic regression to more advanced dynamic factor models. The evidence is a practical overview of library roles rather than a benchmark, tutorial, or detailed comparison with the software in the original question. It does not cover installation, model diagnostics, forecasting workflows, or the many other specialized Python packages available, so readers may need additional guidance for a particular time series method or course syllabus.

Key ideas

• Pandas provides data structures for organizing time series observations.
• Its utilities support merging series, handling missing values, resampling frequencies, and rolling calculations.
• Plotting can be combined with pandas workflows through a plotting library.
• Statsmodels supplies statistical models ranging from regression to dynamic factor analysis.

Tags

# Which python packages would you recommend for time series analysis?


# Which python packages would you recommend for time series analysis?












I am taking a time series analysis class that uses EViews. What packages in python do equivalent work?

## Answer by Helin (score 4, accepted)

https://quant.stackexchange.com/a/31789

The gold standard for time series analysis in Python is pandas. Pandas was originally developed at AQR to support their in-house research and has since been open-sourced. It has very high-performance implementations for data structures such as `Series`, `DataFrame`, and `Panel`.

Pandas itself is very rich. Not only does it let you create time series representations effortless, it has built-in utilities for merging time series, plotting data (requires matplotlib), handling missing data, resampling time series into different frequencies, calculating rolling/expanding-window statistics, etc.

Because it's the gold standard, many Python numerical packages work seamlessly with pandas to provide even more capabilities. For example, statsmodels is a statistical package that lets you run everything from a simple linear regression to a complicated dynamic factor model.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.