Statistical Types for Pandas DataFrames and Friends: Parse, Validate, and Synthesize DataFrames with Generative Schemas

This webinar comes from Machine Learning Engineer Niels Bantilan. As well as working as an ML Engineer at Union.ai, Niels is also creator of UnionML, creator of Pandera, open Source Maintainer of Flyte.

Synopsis

DataFrames are one of the key data structures that data practitioners use to manipulate tabular data. The pandas library, for example, is flexible and powerful, but working with DataFrames for complex use cases often leads to unexpected data types, invalid values, and overall opacity in the contents of a particular DataFrame as it’s transformed from its raw form to one that’s ready for analysis.

In this workshop, you will learn how to ensure data quality with Pandera, a statistical data testing tool for pandas-like DataFrames, so that you can be more confident in the correctness of your code.

Covering:

• Why should I validate data?

• What’s data testing, and how can I put it into practice?

• Pandera quickstart: create statistical types for your DataFrames

• Example 1: Validate your Data analysis

• Example 2: Validate your Machine Learning Pipeline

• Conclusion: How can I start using Pandera in my work?

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