String methods

Text columns use the .str accessor for vectorized string operations: lower/upper, strip, contains, split, and extract—without slow Python loops.

Common .str methods

• s.str.lower(), s.str.upper(), s.str.strip()
• s.str.contains('pattern') — boolean mask
• s.str.replace('old', 'new')
• s.str.split(',', expand=True) — split into columns
• s.str.extract(r'(\d+)') — regex capture groups

Example

import pandas as pd
emails = pd.Series([' Ana@Mail.com ', 'bob@test.org'])
clean = emails.str.strip().str.lower()
domains = emails.str.split('@').str[1]
print(clean, domains, sep='\n')

Nullable string dtype

StringDtype ('string') supports pd.NA missing strings—prefer over object for text columns in new code.

Important interview questions and answers

1. Q: Why .str?
   A: Dispatches vectorized string ops; NaN propagates safely without errors.
2. Q: contains regex?
   A: Pass regex=True and use raw strings for pattern matching.

Self-check

1. Clean emails to lowercase stripped form.
2. Extract domain after @ with str.split.

Tip: Chain .str.strip().str.lower() on messy CSV text columns early in cleaning.