Extract Structured Features with .str.extract() in Pandas

Question

Given a column of product titles like `'Samsung Galaxy S24 - 256GB - Phantom Black'`, extract `storage_gb` (e.g. 256) and `color` into separate columns using regex capture groups.

Accepted Answer

Optimal Solution: import pandas as pd # Named regex capture groups (?Ppattern) pattern = r'(?P\d+)GB\s*-\s*(?P[\w\s]+)$' extracted_df = df['title'].str.extract(pattern) Technical Explanation: `.str.extract(r'(?P...)(?P...)')` evaluates regex capture groups and returns a DataFrame with column headers matching the group names.

title
Samsung Galaxy S24 - 256GB - Phantom Black
Apple iPhone 15 - 128GB - Blue

storage_gb	color
256	Phantom Black
128	Blue