Sample DataFrame context
A notes column containing codes mixed with other text.
Syntax or pattern
df["code"].str.extract(r"([A-Z]{3}-\d{4})")5 practical examples
Find missing values
Count blanks in every column.
missing = df.isna().sum()The output shows which fields need attention.
Fill missing categories
Replace blank regions with Unknown.
df["region"] = df["region"].fillna("Unknown")The category can now be grouped without losing rows.
Remove duplicate emails
Keep one row per email address.
customers = customers.drop_duplicates(subset=["email"])The customer list becomes cleaner for reporting.
Standardize text
Remove spaces and fix casing.
df["customer_name"] = df["customer_name"].str.strip().str.title()Names become more consistent.
Convert messy numbers
Turn text revenue into numeric values.
df["revenue"] = pd.to_numeric(df["revenue"], errors="coerce")Invalid values become missing values that can be reviewed.
Common mistakes to avoid
- Changing the original DataFrame before checking the result.
- Forgetting to inspect row counts before and after the operation.
- Using a pattern without confirming column names and data types.
FAQ
What is the main use of extract Regex Examples?
Extract IDs, dates, codes and parts of text into new columns.
Should I use this in a notebook or a script?
Both work. Use a notebook while exploring the data, then move the final workflow into a repeatable script when it is stable.
How do I avoid breaking my source data?
Create a copy of the DataFrame before transforming it and check row counts, missing values and sample rows after each important step.