Choose polars when: (+6)

Choose polars when:

• Working with datasets too large for pandas
• Need maximum performance for data transformations
• Processing data in memory-constrained environments
• Lazy evaluation and query optimization are valuable

Choose streamlit when:

• Rapid prototyping of data applications
• Internal tools and demos
• Data scientists building apps (minimal frontend knowledge)
• Need quick iteration on interactive visualizations

Choose dash when:

• Building production-grade dashboards
• Enterprise features required (authentication, scaling)
• Complex callback interactions between components
• Plotly ecosystem integration is desired

Choose autoviz when:

• Quick initial data exploration
• Need automated chart type selection
• Time is limited for manual visualization
• Working with unfamiliar datasets

Choose ydata-profiling when:

• Comprehensive data quality assessment needed
• Generating shareable HTML reports
• Identifying data issues (missing values, duplicates)
• Need correlation analysis and distribution insights

Choose great-tables when:

• Creating publication-quality table output
• Need fine-grained control over table styling
• Generating tables for reports or presentations
• Export to multiple formats (HTML, LaTeX, PNG)

Choose sweetviz when:

• Comparing two datasets (train/test, before/after)
• Target variable analysis for ML projects
• Visual EDA with minimal code
• Need side-by-side feature comparisons