[Doc]: Enhance Narwhals Tutorials with Backend-Agnostic Patterns

[philip-ndikum]

What type of report is this?

Correction

Please describe the issue.

Description

Narwhals tutorials could be significantly enhanced by consolidating backend-agnostic patterns into a single, robust tutorial tailored for enterprise-grade machine learning (ML) and artificial intelligence (AI) workflows. This tutorial would focus on practical, production-ready patterns that simplify common tasks, ensure backend consistency, and align with scalable development workflows.

Key Focus Areas:

1. Data Validation Patterns:

   • Eager validation for immediate feedback (e.g., numeric and categorical feature validation).
   • Lazy validation for optimized workflows across larger datasets.

2. Time Series Operations:

   • Group-level metrics such as temporal aggregations (mean, null counts) for time-indexed datasets.
   • Temporal validation for uniqueness, consistency, and handling mixed frequencies.

3. Feature Engineering:

   • Backend-agnostic numeric and categorical transformations.
   • Patterns for missing value imputation, standardization, and case consistency.

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In our package TemporalScope, which leverages Narwhals for model-agnostic explainability in AI/ML workflows, these patterns would be immensely valuable for ensuring robust data preparation and validation. Specifically:

• Use Case:
  • Validating and transforming features across Pandas, Polars, and Dask backends for explainable ML workflows.
  • Handling time series data in both single-step and multi-step forecasting pipelines.
• Development Workflow:
  • Lean Main Environment:
    A hatch environment limited to Narwhals, without heavy dependencies like Pandas or Dask.
  • Comprehensive Test Environment:
    A hatch environment including all relevant libraries (Pandas, Polars, Dask) to validate runtime behavior and backend-agnostic patterns.

By integrating these patterns into a single, enterprise-grade tutorial, Narwhals would provide developers with clear, actionable guidance for robust AI/ML workflows [CC @kanenorman].

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Suggestion

Create a condensed tutorial notebook that demonstrates these patterns, building directly on the feedback shared:

• Universal Backend Support:
  Showcase compatibility with Pandas, Polars, and Dask.
• Core Narwhals Patterns:
  Focus on the use of @nw.narwhalify, lazy/eager evaluation strategies, and backend-agnostic transformations.
• Production-Ready Use Cases:
  Condense practical examples that are directly applicable to AI/ML pipelines, following @FBruzzesi recommendations (e.g., using pass_through=True or strict=False where necessary).

If you have a suggestion on how it should be, add it below.

No response