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Practice

Filtering with Boolean Conditions

Often in data analysis, you want to narrow your dataset to rows that meet specific criteria — like selecting only the rows where sales exceeded $100 or users are located in the US.

Pandas makes this easy using boolean conditions.

How It Works​

You write a condition that checks whether each row meets your requirement. The result is a series of True or False values — which Pandas can use to filter the DataFrame.

For example, to filter for rows where the value in a "Score" column is greater than 80:

Filter with a Boolean Condition
df[df["Score"] > 80]

This returns a new DataFrame containing only the rows where that condition is True.

Why It's Useful​

Filtering helps you:

  • Focus on relevant data
  • Explore subsets of your dataset
  • Prepare data for visualization or modeling

You can also combine conditions using logical operators like & (AND) and | (OR), but they require parentheses:

Combine Conditions
df[(df["Age"] > 30) & (df["Country"] == "Canada")]

This selects rows where both conditions are true.

Summary​

  • Boolean filtering is a powerful tool to isolate rows of interest.
  • You can filter using conditions like >, <, ==, !=, etc.
  • Combine multiple conditions with & and |, and wrap each condition in parentheses.

What’s Next?​

Practice filtering in the Jupyter notebook!