Filtering with Boolean Conditions

In data analysis, you often need to focus on rows that meet certain criteria — for example, selecting entries where sales exceed $100 or where users are based in the U.S.

Pandas makes this process straightforward through the use of boolean conditions.

Write a condition that evaluates whether each row meets your criteria. The result is a Series of True or False values that Pandas uses to filter the DataFrame.

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

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df[df["Score"] > 80]

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

Filtering allows you to:

• 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). Always wrap each condition in parentheses:

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

This selects rows where both conditions are true.

• Boolean filtering is a powerful way to isolate rows of interest.
• Use comparison operators like >, <, ==, and != for conditions.
• Combine multiple conditions with & and |, wrapping each condition in parentheses.