How do you perform string operations on Pandas DataFrame columns?

Pandas exposes string methods through the .str accessor on object-dtype Series. These operations are vectorised over the whole column — no explicit loop needed — and handle NaN values gracefully (they propagate as NaN rather than raising an error).

import pandas as pd

df = pd.DataFrame({'name': ['  Alice Smith  ', 'bob jones', 'CAROL LEE', None],
                   'email': ['alice@corp.com', 'BOB@CORP.COM', 'carol@other.org', None]})

# Case normalisation
df['name'].str.strip().str.title()     # 'Alice Smith', 'Bob Jones', 'Carol Lee', NaN

# Split into multiple columns
df[['first', 'last']] = df['name'].str.strip().str.split(' ', expand=True)

# Contains / startswith / endswith
df[df['email'].str.endswith('@corp.com', na=False)]

# Extract patterns with regex
df['domain'] = df['email'].str.extract(r'@(.+)$')  # captures text after @

# Replace with regex
df['email'].str.lower().str.replace(r'[^a-z0-9@._]', '', regex=True)

# Count occurrences
df['name'].str.count('l')   # 1, 0, 1, NaN

# Length
df['name'].str.len()

# Padding / justification
df['id'].str.zfill(6)       # zero-pad to width 6
df['name'].str.ljust(20, '-')  # left-justify, pad with dashes

The na=False argument in methods like str.contains and str.startswith is important — without it, NaN values produce NaN in the boolean mask, which causes issues in filtering. Passing na=False returns False for NaN rows, keeping them out of the filtered result cleanly.

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