Open Issues Need Help
View All on GitHubAI Summary: This issue proposes adding a progress tracking feature for long-running distribution fits. Currently, users lack visibility into the progress when fitting many distributions to large datasets. The proposed solution involves implementing a progress callback or logging mechanism to show which distributions have been fitted and provide an estimated time remaining.
Efficiently fit ~100 scipy.stats distributions to your data using Spark's parallel processing with optimized Pandas UDFs and broadcast variables.
AI Summary: This issue proposes adding Probability-Probability (P-P) plots to the project as a new feature. P-P plots are a visualization tool for goodness-of-fit, complementing existing Q-Q plots by focusing on the center of the distribution. The implementation should follow a similar API pattern to the existing `plot_qq()` function.
Efficiently fit ~100 scipy.stats distributions to your data using Spark's parallel processing with optimized Pandas UDFs and broadcast variables.
AI Summary: This issue proposes adding new methods to directly export results to JSON or CSV formats. Currently, users need to convert results to a pandas DataFrame first, which this change aims to simplify by providing `results.to_csv(path)` and `results.to_json(path)`.
Efficiently fit ~100 scipy.stats distributions to your data using Spark's parallel processing with optimized Pandas UDFs and broadcast variables.