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Most real-world data doesn't come to you as neatly structured Python objects. It comes as files — rows of text, values separated by commas — exported from spreadsheets, databases, APIs, and log systems. CSV (Comma-Separated Values) is the simplest and most universal tabular format. If you can parse and process CSV, you can handle data from almost anywhere.

CSV at its simplest

A CSV file looks like this:

name,age,city
Alice,30,NYC
Bob,25,SF
Carol,40,LA

The first line is the header (column names). Each following line is a record. Values are separated by commas.

Parsing CSV by hand

For simple cases, basic string operations work:

zip(fields, values) pairs each header with its value, then dict() builds the lookup. The age stays as a STRING — convert it with int() if you need arithmetic.

When manual parsing fails

Manual .split(",") works for simple CSVs but breaks on:

• Values containing commas: "Smith, John"
• Quoted strings: "Hello, world",42
• Embedded newlines inside quoted values
• Different delimiters (tabs, semicolons)

For real-world CSV, use the csv module from the standard library:

csv.DictReader is even cleaner — uses the header row to give you dicts:

Reading from a file

The with block ensures the file is closed even if an error occurs.

Writing CSV

The newline="" is important on Windows — without it you get extra blank lines.

Type conversion

CSV values are always strings. You decide which to convert:

Common mistakes

• Using .split(",") for production CSV — works for clean data, breaks on quoted commas. Use the csv module.
• Forgetting that values are strings — "30" + 1 is TypeError. Always convert numeric columns.
• Forgetting newline="" when writing on Windows — produces blank lines.
• Ignoring the header — when you .split(",") raw lines, the first row is the header, not data.