query() for fast filtering in Pandas - Mini Project: Build & Apply

Filter Sales Data Using pandas query()

📖 Scenario: You work in a store and have a table of sales data. You want to quickly find sales that meet certain conditions, like sales above a certain amount or sales from a specific product.

🎯 Goal: Build a small program that uses pandas query() to filter sales data based on a condition.

📋 What You'll Learn

Create a pandas DataFrame with sales data

Create a variable for the sales amount threshold

Use query() to filter rows where sales are above the threshold

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Filtering data quickly helps businesses analyze sales trends and make decisions fast.

💼 Career

Data analysts and scientists often use pandas <code>query()</code> to filter large datasets efficiently.

1

Create the sales DataFrame

Import pandas as pd and create a DataFrame called sales with these exact columns and rows:
Product: 'Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'
Amount: 50, 30, 20, 40, 10

Pandas

import pandas as pd
# Create the sales DataFrame with Product and Amount columns
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Set the sales amount threshold

Create a variable called threshold and set it to 25 to filter sales above this amount.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
    'Amount': [50, 30, 20, 40, 10]
})
# Create threshold variable and set to 25
# Your code here

Hint

Just assign the number 25 to a variable named threshold.

3

Filter sales using query()

Use query() on the sales DataFrame to create a new DataFrame called filtered_sales that only has rows where Amount is greater than threshold.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
    'Amount': [50, 30, 20, 40, 10]
})
threshold = 25
# Use query() to filter sales where Amount > threshold
# Your code here

Hint

Use sales.query('Amount > @threshold') to filter rows where Amount is greater than threshold. The @ symbol lets you use the variable threshold inside the query string.

4

Print the filtered sales

Print the filtered_sales DataFrame to see the rows where sales amount is greater than 25.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
    'Amount': [50, 30, 20, 40, 10]
})
threshold = 25
filtered_sales = sales.query('Amount > @threshold')
# Print filtered_sales
# Your code here

Hint

Use print(filtered_sales) to show the filtered rows.

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