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Practice

Filtering with Boolean Conditions

In data analysis, you often need to focus on rows that meet specific criteria, such as selecting only rows where sales exceed $100 or users are located in the US.

Pandas makes this simple using boolean conditions.


How It Works​

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

For example, to filter rows where the value in the "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 the condition is True.


Why It's Useful​

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:

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

This selects rows where both conditions are true.


Summary​

  • 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.

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