Stata 16 first introduced the ability to embed and execute Python code directly from a running Stata session (in do-files, ado-files, or interactively). This allows users to leverage Python‘s extensive libraries—for web scraping, natural language processing, or advanced machine learning—without leaving the Stata environment.
Stata/MP leverages multi-core processing to run analyses significantly faster than other editions. For a logistic regression with 10 million observations, Stata/MP2 runs nearly twice as fast as Stata/SE, while Stata/MP4 is almost four times faster. Stata 18
The minor update StataMP 18.5 further strengthened Python integration with features including auto-completion, the %help magic command, and improved output control, facilitating seamless Stata-Python collaboration. Stata 16 first introduced the ability to embed
The (Stata Function Interface) Python module provides classes for accessing Stata‘s core features—including datasets, frames, macros, scalars, matrices, value labels, and Mata matrices—from Python. For a logistic regression with 10 million observations,
Stata 18 builds upon features introduced in Stata 17 while adding many new ones. Key differences include:
While Stata‘s basic syntax is straightforward, advanced features—especially the new Bayesian tools and Python integration—require dedicated study. The extensive documentation and community resources help, but proficiency demands time investment.
The PyStata environment is further matured in this release. Users can interactively call Stata from Python or execute Python code directly inside Stata scripts ( do-files ). Data frames transfer instantly between the two environments in memory, eliminating the need to export and import intermediate files. Similar zero-latency data sharing is supported for R integration. Summary of Key Commands Introduced or Enhanced Primary Function Target Field didregress Estimates heterogeneous difference-in-differences Econometrics / Policy Evaluation lpproject Computes local projections for impulse responses Macroeconomics / Finance gs Manages group sequential designs for interim analysis Biostatistics / Clinical Trials collect Aggregates and styles multi-model statistical output Reporting / Academic Publishing pystata Manages dual Stata-Python scripting environments Data Science / Machine Learning