Selecting rows by condition in Pandas - Mini Project: Build & Apply

Selecting rows by condition

📖 Scenario: You work in a small bookstore. You have a list of books with their prices and want to find which books cost more than $20.

🎯 Goal: Build a program that selects and shows only the books priced above $20 from the list.

📋 What You'll Learn

Create a pandas DataFrame with book titles and prices

Create a variable to hold the price threshold

Use a condition to select rows where price is greater than the threshold

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Filtering data by conditions is common in business to find important items, like expensive products or customers with high spending.

💼 Career

Data analysts and scientists often filter datasets to focus on relevant information for reports and decisions.

1

Create the books DataFrame

Import pandas as pd. Create a DataFrame called books with these exact entries: titles 'Book A', 'Book B', 'Book C', and prices 15, 25, 30 respectively.

Pandas

import pandas as pd
# Create the DataFrame called books with titles and prices
# Your code here

Hint

Use pd.DataFrame with a dictionary containing lists for titles and prices.

2

Set the price threshold

Create a variable called price_threshold and set it to 20.

Pandas

import pandas as pd
books = pd.DataFrame({
    'title': ['Book A', 'Book B', 'Book C'],
    'price': [15, 25, 30]
})
# Create price_threshold variable and set it to 20
# Your code here

Hint

Just assign the number 20 to the variable price_threshold.

3

Select books priced above the threshold

Create a new DataFrame called expensive_books by selecting rows from books where the price column is greater than price_threshold.

Pandas

import pandas as pd
books = pd.DataFrame({
    'title': ['Book A', 'Book B', 'Book C'],
    'price': [15, 25, 30]
})
price_threshold = 20
# Select rows where price > price_threshold into expensive_books
# Your code here

Hint

Use books[books['price'] > price_threshold] to filter rows.

4

Print the filtered books

Print the expensive_books DataFrame to show the books priced above $20.

Pandas

import pandas as pd
books = pd.DataFrame({
    'title': ['Book A', 'Book B', 'Book C'],
    'price': [15, 25, 30]
})
price_threshold = 20
expensive_books = books[books['price'] > price_threshold]
# Print expensive_books
# Your code here

Hint

Use print(expensive_books) to display the filtered DataFrame.

Practice

1. What does the expression df[df['age'] > 30] do in pandas?

easy

A. Deletes rows where the 'age' column is greater than 30

B. Selects columns where the 'age' value is greater than 30

C. Selects rows where the 'age' column is greater than 30

D. Sorts the DataFrame by the 'age' column

Solution

1. Step 1: Understand the condition inside the brackets

   The condition df['age'] > 30 creates a True/False mask for each row based on the 'age' value.

2. Step 2: Apply the condition to select rows

   Using df[condition] returns only rows where the condition is True, i.e., 'age' > 30.

3. Final Answer:

   Selects rows where the 'age' column is greater than 30 -> Option C

4. Quick Check:

   Condition filters rows [OK]

Hint: Use df[condition] to filter rows by True/False mask [OK]

Common Mistakes:

• Thinking it selects columns instead of rows
• Confusing filtering with sorting
• Using parentheses incorrectly around conditions

2. Which of the following is the correct syntax to select rows where column 'score' equals 100?

easy

A. df[df.score = 100]

B. df[df['score'] = 100]

C. df[df['score'] === 100]

D. df[df['score'] == 100]

Solution

1. Step 1: Identify correct equality operator in pandas

   In pandas, to compare values, use double equals ==, not single = or triple ===.

2. Step 2: Check correct column access syntax

   Using df['score'] is correct and safe for all column names.

3. Final Answer:

   df[df['score'] == 100] -> Option D

4. Quick Check:

   Use == for comparison and brackets for column [OK]

Hint: Use df[df['col'] == value] with double equals for condition [OK]

Common Mistakes:

• Using single = instead of == for comparison
• Using triple === which is invalid in Python
• Accessing columns without quotes when name has special chars

3. Given the DataFrame:

import pandas as pd
data = {'name': ['Ann', 'Bob', 'Cara', 'Dan'], 'age': [25, 35, 30, 40]}
df = pd.DataFrame(data)

What is the output of df[df['age'] >= 30]?

medium

A. Rows with names Bob, Cara, and Dan

B. Rows with names Ann and Bob

C. Rows with names Ann and Cara

D. Rows with names Dan only

Solution

1. Step 1: Evaluate the condition df['age'] >= 30

   This condition is True for ages 35 (Bob), 30 (Cara), and 40 (Dan).

2. Step 2: Select rows where condition is True

   Rows with names Bob, Cara, and Dan are returned.

3. Final Answer:

   Rows with names Bob, Cara, and Dan -> Option A

4. Quick Check:

   age >= 30 selects Bob, Cara, Dan [OK]

Hint: Check condition True rows, then list their names [OK]

Common Mistakes:

• Including Ann who is 25 (less than 30)
• Confusing >= with >
• Misreading the DataFrame data

4. What is wrong with this code to select rows where 'score' is above 50?

df[df['score'] > 50 && df['age'] < 30]

medium

A. Using && instead of & for combining conditions

B. Missing parentheses around each condition

C. Using single & instead of &&

D. Using | instead of & for and condition

Solution

1. Step 1: Check operator for combining conditions in pandas

   In pandas, use single ampersand & for 'and', not double ampersand &&.

2. Step 2: Confirm correct syntax

   Correct syntax is df[(df['score'] > 50) & (df['age'] < 30)] with parentheses and single &.

3. Final Answer:

   Using && instead of & for combining conditions -> Option A

4. Quick Check:

   Use & not && for and in pandas [OK]

Hint: Use & for and, | for or, with parentheses [OK]

Common Mistakes:

• Using && which causes syntax error
• Not adding parentheses around each condition
• Using | instead of & for and

5. You have a DataFrame df with columns 'city', 'temperature', and 'humidity'. How do you select rows where the city is 'Paris' and temperature is above 20 or humidity is below 30?

hard

A. df[(df['city'] == 'Paris') & (df['temperature'] > 20) | (df['humidity'] < 30)]

B. df[(df['city'] == 'Paris') & ((df['temperature'] > 20) | (df['humidity'] < 30))]

C. df[df['city'] == 'Paris' & df['temperature'] > 20 | df['humidity'] < 30]

D. df[df['city'] == 'Paris' and df['temperature'] > 20 or df['humidity'] < 30]

Solution

1. Step 1: Understand operator precedence and grouping

   Logical AND (&) has higher precedence than OR (|), so parentheses are needed to group OR conditions correctly.

2. Step 2: Write correct condition with parentheses

   We want city == 'Paris' AND (temperature > 20 OR humidity < 30), so use:
   df[(df['city'] == 'Paris') & ((df['temperature'] > 20) | (df['humidity'] < 30))]

3. Final Answer:

   df[(df['city'] == 'Paris') & ((df['temperature'] > 20) | (df['humidity'] < 30))] -> Option B

4. Quick Check:

   Group OR inside AND with parentheses [OK]

Hint: Use parentheses to group OR inside AND conditions [OK]

Common Mistakes:

• Missing parentheses causing wrong logic
• Using Python 'and'/'or' instead of & and |
• Not using parentheses around each condition