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.

Practice

1. What is the main purpose of the query() method in pandas?

easy

A. To filter rows of a DataFrame using a readable string expression

B. To sort the DataFrame by column values

C. To merge two DataFrames based on a key column

D. To create a new DataFrame from scratch

Solution

1. Step 1: Understand the purpose of query()

   The query() method is designed to filter rows in a DataFrame by using a string expression that specifies conditions on columns.

2. Step 2: Compare with other DataFrame methods

   Sorting is done by sort_values(), merging by merge(), and creating new DataFrames is done by constructors, not query().

3. Final Answer:

   To filter rows of a DataFrame using a readable string expression -> Option A

4. Quick Check:

   query() filters rows [OK]

Hint: Remember: query() filters rows with string conditions [OK]

Common Mistakes:

• Confusing query() with sorting or merging methods
• Thinking query() creates new DataFrames
• Assuming query() modifies columns

2. Which of the following is the correct syntax to filter rows where column age is greater than 30 using query()?

easy

A. df.query('age > "30"')

B. df.query(age > 30)

C. df.query('age > 30')

D. df.query('age => 30')

Solution

1. Step 1: Check the correct string format for query()

   The condition must be passed as a string, so it should be inside quotes: 'age > 30'.

2. Step 2: Validate the comparison operator and quotes

   Operator must be >, not =>. Also, 30 should be a number, not a string with quotes.

3. Final Answer:

   df.query('age > 30') -> Option C

4. Quick Check:

   Use string with correct operator [OK]

Hint: Put condition inside quotes with correct operators [OK]

Common Mistakes:

• Omitting quotes around the condition string
• Using wrong comparison operator => instead of >
• Putting numbers inside quotes making them strings

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'name': ['Anna', 'Bob', 'Cara'], 'score': [85, 92, 78]})

What is the output of df.query('score >= 80')?

medium

A. Rows with names Anna and Bob only

B. Rows with names Bob and Cara only

C. Rows with names Anna and Cara only

D. All rows

Solution

1. Step 1: Identify rows where score >= 80

   Scores are Anna:85, Bob:92, Cara:78. Only Anna and Bob have scores 80 or above.

2. Step 2: Understand query() filters these rows

   The query returns rows where the condition is true, so rows for Anna and Bob are returned.

3. Final Answer:

   Rows with names Anna and Bob only -> Option A

4. Quick Check:

   score >= 80 filters Anna and Bob [OK]

Hint: Check numeric condition matches rows carefully [OK]

Common Mistakes:

• Including rows that don't meet the condition
• Confusing >= with > or <= operators
• Assuming query returns all rows

4. What is wrong with this code snippet?

df.query('age > 25 and city == New York')

medium

A. The column names must be in quotes

B. The operator 'and' is not allowed in query()

C. The query() method cannot filter by multiple conditions

D. The string New York should be quoted inside the query string

Solution

1. Step 1: Check string literals inside query()

   String values like city names must be quoted inside the query string, e.g., city == "New York".

2. Step 2: Verify use of 'and' and column names

   The 'and' operator is allowed, and column names do not need quotes inside the query string.

3. Final Answer:

   The string New York should be quoted inside the query string -> Option D

4. Quick Check:

   Quote string values inside query() [OK]

Hint: Quote string values inside query() conditions [OK]

Common Mistakes:

• Not quoting string literals in conditions
• Thinking 'and' is invalid in query()
• Quoting column names unnecessarily

5. You have a DataFrame df with columns temperature and humidity. You want to filter rows where temperature is above 20 and humidity is below 50. Which query() call correctly does this?

hard

A. df.query('temperature > 20 or humidity < 50')

B. df.query('temperature > 20 and humidity < 50')

C. df.query('temperature > 20 & humidity < 50')

D. df.query('temperature > 20 && humidity < 50')

Solution

1. Step 1: Understand logical operators in query()

   Within query strings, use and and or for combining conditions, not & or &&.

2. Step 2: Match the condition requirements

   The problem wants temperature > 20 and humidity < 50, so the correct operator is and.

3. Final Answer:

   df.query('temperature > 20 and humidity < 50') -> Option B

4. Quick Check:

   Use 'and' for multiple conditions in query() [OK]

Hint: Use 'and' not '&' to combine conditions in query() [OK]

Common Mistakes:

• Using bitwise operators (&, &&) instead of 'and'
• Using 'or' instead of 'and' when both conditions needed
• Mixing Python operators outside query() syntax