Pandera: Statistical Data Validation for Pandas Dataframes

A talk by Niels Bantilan introduces pandera, an open source Python package for validating pandas dataframes. The presentation covers data validation theory and practice, using a case study analysis of the Fatal Encounters dataset to demonstrate how the tool can improve reproducibility and reliability in data analysis and machine learning.

Use Cases

• Validate dataframe schemas and data types based on the package's validation features.
• Improve reproducibility in data analysis pipelines based on the described validation framework.
• Conduct robust data quality checks for machine learning projects based on the case study methodology.
• Implement statistical validation rules for tabular data based on the package's functionality.

Strengths

• Focuses on a specific, practical tool for a common data science library (pandas).
• Includes a real-world case study (Fatal Encounters dataset) to demonstrate application.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count, column details, and file formats are unknown, which may limit suitability assessment.

Provenance

Source

Niels Bantilan

Collection Method

Presented as a talk introducing an open source software package.

Time Range

null

Freshness

Last update date is unknown; freshness unverified.

Geography

null