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.

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:

Copy
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:

Copy
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.