query() for fast filtering in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the query() method do in pandas?

The query() method filters rows in a DataFrame using a string expression, making it easier and faster to select data based on conditions.

beginner

How do you write a condition inside query() to select rows where column 'age' is greater than 30?

You write: df.query('age > 30') to select rows where the 'age' column has values greater than 30.

intermediate

Can you use variables inside query()? How?

Yes, use the @ symbol before the variable name inside the query string. For example: df.query('age > @min_age') where min_age is a Python variable.

intermediate

What is one advantage of using query() over traditional boolean indexing?

query() is often faster and the code looks cleaner and easier to read, especially for complex conditions.

advanced

Which characters should you avoid using directly in query() expressions?

Avoid using spaces in column names or special characters like dots. If needed, use backticks around column names with spaces or special characters, e.g., df.query('`my column` > 5').

What does df.query('score >= 80') do?

A Selects rows where 'score' is less than 80

B Updates 'score' to 80

C Selects rows where 'score' is 80 or more

D Deletes rows where 'score' is 80

The query selects rows where the 'score' column has values greater than or equal to 80.

How do you include a Python variable threshold inside a query()?

A df.query('value > #threshold')

B df.query('value > threshold')

C df.query('value > $threshold')

D df.query('value > @threshold')

Use the '@' symbol before the variable name to include Python variables inside query strings.

Which of these is a valid query() expression to filter rows where 'city' is 'Paris'?

A df.query('city == Paris')

B df.query('city == "Paris"')

C df.query('city = Paris')

D df.query('city equals Paris')

Use double equals and quotes around string values inside the query string.

If a column name has a space, how do you reference it in query()?

A Put the column name in backticks, e.g., `my column`

B Replace space with underscore

C Use single quotes around the column name

D Use the column name directly

Backticks allow referencing column names with spaces or special characters inside query strings.

Which is a benefit of using query() over boolean indexing?

A It can be faster and cleaner for complex filters

B It makes code harder to read

C It always runs slower

D It does not support multiple conditions

query() often improves readability and performance for complex filtering.

Explain how to use the query() method to filter a DataFrame for rows where a column meets a condition.

Think about how you write conditions in plain English but inside quotes.

Describe how to include a Python variable inside a query() expression and why this is useful.

Remember the special character before the variable name.