Working with Strings

Text Processing Patterns

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LearnStep 1 of 3

Idiomatic Text Processing in Python

Learning Objectives

•Parse structured text data like CSVs and logs using standard library tools
•Normalize text for consistent comparison and storage
•Format and wrap text output for readability
•Use string templates for safe and flexible text generation

Lesson Outline

LearnStep 1/3

Idiomatic Text Processing in Python

Python is renowned for its text processing capabilities. While basic string methods are powerful, standard library modules like csv, textwrap, and string offer robust solutions for common patterns.

1. Parsing CSV and Logs

Don't split CSV lines manually. Use the csv module to handle quoting and delimiters correctly. For complex logs, re (regular expressions) is the standard tool.

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2. Text Normalization

To compare strings reliably, normalize them. This often involves case folding (more aggressive than lowercasing) and Unicode normalization.

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3. Formatting with textwrap

The textwrap module is perfect for CLI output or cleaning up multi-line strings used in code.

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4. String Templates

While f-strings are great for known data, string.Template is safer for user-provided formats because it doesn't allow arbitrary code execution.

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