query() for fast filtering in Pandas - Time & Space Complexity

Time Complexity: query() for fast filtering

O(n)

Understanding Time Complexity

We want to understand how the time to filter data using pandas' query() grows as the data size increases.

How does filtering with query() scale when we have more rows?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

df = pd.DataFrame({
    'age': range(1000),
    'score': range(1000, 2000)
})

filtered = df.query('age > 500 and score < 1600')

This code creates a DataFrame with 1000 rows and filters rows where age is over 500 and score is less than 1600.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: pandas checks each row to see if it meets the filter condition.
• How many times: Once for every row in the DataFrame.

How Execution Grows With Input

As the number of rows grows, pandas must check each row once to apply the filter.

Input Size (n)	Approx. Operations
10	About 10 checks
100	About 100 checks
1000	About 1000 checks

Pattern observation: The number of operations grows directly with the number of rows.

Final Time Complexity

Time Complexity: O(n)

This means the time to filter grows in a straight line as the data size grows.

Common Mistake

[X] Wrong: "Using query() filters data instantly no matter how big the data is."

[OK] Correct: Even though query() is fast, it still checks each row once, so bigger data takes more time.

Interview Connect

Understanding how filtering scales helps you explain your choices when working with large data in real projects.

Self-Check

"What if we used multiple query() calls chained together? How would the time complexity change?"

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