Boolean filtering in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to filter rows where the 'age' column is greater than 30.

Data Analysis Python

filtered_data = data[data['age'] [1] 30]

Drag options to blanks, or click blank then click option'

A>

B<

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' will select ages less than 30.

Using '==' will select only ages exactly 30.

The '>' operator filters rows where the 'age' is greater than 30.

2fill in blank

medium

Complete the code to filter rows where the 'score' column is equal to 100.

Data Analysis Python

perfect_scores = data[data['score'] [1] 100]

Drag options to blanks, or click blank then click option'

A==

B>=

C<

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of '==' causes syntax errors.

Using '>=' selects scores 100 or higher, not just 100.

The '==' operator checks for exact equality, selecting rows where 'score' is 100.

3fill in blank

hard

Fix the error in the code to filter rows where 'height' is less than or equal to 170.

Data Analysis Python

short_people = data[data['height'] [1] 170]

Drag options to blanks, or click blank then click option'

A<

B==

C=>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=>' causes syntax errors.

Using '<' excludes rows where height equals 170.

The correct operator for 'less than or equal to' is '<='.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

Data Analysis Python

lengths = {word: [1] for word in words if [2]

Drag options to blanks, or click blank then click option'

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' as the value instead of its length.

Checking 'word > 3' which compares strings to numbers incorrectly.

The dictionary maps each word to its length using len(word). The filter keeps words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their scores only if the score is greater than 0.

Data Analysis Python

result = [1]: [2] for [3], [2] in data.items() if [2] > 0

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

Ck

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper() for keys.

Using k as the value instead of v.

Using v as the loop variable instead of k.

The dictionary keys are uppercase versions of k using k.upper(). The values are v. The loop variable is k from data.items(). The filter keeps items where v is greater than 0.

Practice

1. What does boolean filtering do in data analysis with Python?

easy

A. Changes the data type of columns

B. Removes duplicate rows from the data

C. Sorts the data in ascending order

D. Selects rows where a condition is true

Solution

1. Step 1: Understand boolean filtering purpose

   Boolean filtering is used to pick rows that meet a true condition.

2. Step 2: Compare options to definition

   Only Selects rows where a condition is true describes selecting rows based on true conditions.

3. Final Answer:

   Selects rows where a condition is true -> Option D

4. Quick Check:

   Boolean filtering = Select rows true condition [OK]

Hint: Boolean filtering picks rows where condition is true [OK]

Common Mistakes:

• Confusing filtering with sorting
• Thinking it changes data types
• Assuming it removes duplicates

2. Which of the following is the correct syntax to filter rows where column age is greater than 30 in a DataFrame df?

easy

A. df['age' > 30]

B. df[df['age'] > 30]

C. df.age[df.age > 30]

D. df[df['age'] < 30]

Solution

1. Step 1: Identify correct boolean filtering syntax

   The correct way is to put the condition inside square brackets after the DataFrame.

2. Step 2: Check each option

   df[df['age'] > 30] uses df[df['age'] > 30], which is correct syntax. df['age' > 30] is invalid syntax. df.age[df.age > 30] incorrectly filters only the age column, returning a Series not DataFrame. df[df['age'] < 30] filters less than 30, not greater.

3. Final Answer:

   df[df['age'] > 30] -> Option B

4. Quick Check:

   Correct syntax = df[df['age'] > 30] [OK]

Hint: Use df[condition] with column in quotes for filtering [OK]

Common Mistakes:

• Missing brackets around condition
• Using wrong comparison operator
• Not quoting column name

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({
  'name': ['Anna', 'Bob', 'Cara', 'Dan'],
  'score': [85, 92, 78, 90],
  'passed': [True, True, False, True]
})

What will df[df['score'] > 80 and df['passed']] return?

medium

A. ValueError due to missing parentheses

B. Rows where score > 80 or passed is true

C. Rows where score > 80 and passed is true

D. Empty DataFrame

Solution

1. Step 1: Analyze the boolean expression

   The expression uses and without parentheses around each condition, which causes a ValueError in pandas.

2. Step 2: Understand pandas boolean filtering rules

   Each condition must be in parentheses when combined with & or |. So df['score'] > 80 and df['passed'] raises ValueError.

3. Final Answer:

   ValueError due to missing parentheses -> Option A

4. Quick Check:

   Use parentheses around conditions with & or | [OK]

Hint: Always wrap each condition in parentheses when using & or | [OK]

Common Mistakes:

• Not using parentheses around conditions
• Confusing & with and
• Expecting output without fixing syntax

4. Consider this code snippet:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3, 4]})
filtered = df[df['A'] > 2 or df['A'] < 4]

What is the error and how to fix it?

medium

A. ValueError: Use parentheses around conditions with or

B. SyntaxError: Missing colon after if

C. No error, code runs fine

D. ValueError: Cannot compare integers

Solution

1. Step 1: Identify the error cause

   The expression df['A'] > 2 or df['A'] < 4 lacks parentheses around each condition, causing a ValueError.

2. Step 2: Fix the error by adding parentheses

   Correct syntax is df[(df['A'] > 2) | (df['A'] < 4)] to ensure proper evaluation.

3. Final Answer:

   ValueError: Use parentheses around conditions with or -> Option A

4. Quick Check:

   Parentheses needed around each condition with | or & [OK]

Hint: Wrap each condition in parentheses when using | or & [OK]

Common Mistakes:

• Forgetting parentheses around conditions
• Using Python 'or' instead of |
• Assuming no error with missing parentheses

5. You have a DataFrame df with columns age and income. You want to filter rows where age is between 25 and 40 inclusive, and income is above 50000. Which code correctly applies boolean filtering?

hard

A. df[(df['age'] > 25) | (df['age'] < 40) & (df['income'] > 50000)]

B. df[df['age'] >= 25 & df['age'] <= 40 & df['income'] > 50000]

C. df[(df['age'] >= 25) & (df['age'] <= 40) & (df['income'] > 50000)]

D. df[df['age'] >= 25 and df['age'] <= 40 and df['income'] > 50000]

Solution

1. Step 1: Understand the filtering conditions

   We want age between 25 and 40 inclusive, so age >= 25 and age <= 40, and income > 50000.

2. Step 2: Check correct syntax for combining conditions

   Conditions must be in parentheses and combined with &. df[(df['age'] >= 25) & (df['age'] <= 40) & (df['income'] > 50000)] correctly uses parentheses and & operators.

3. Step 3: Identify errors in other options

   df[df['age'] >= 25 & df['age'] <= 40 & df['income'] > 50000] lacks parentheses around each condition, causing errors. df[(df['age'] > 25) | (df['age'] < 40) & (df['income'] > 50000)] uses | instead of & incorrectly. df[df['age'] >= 25 and df['age'] <= 40 and df['income'] > 50000] uses Python 'and' which does not work with pandas Series.

4. Final Answer:

   df[(df['age'] >= 25) & (df['age'] <= 40) & (df['income'] > 50000)] -> Option C

5. Quick Check:

   Use parentheses and & for multiple conditions [OK]

Hint: Use parentheses and & to combine multiple conditions [OK]

Common Mistakes:

• Using Python 'and' instead of &
• Missing parentheses around conditions
• Mixing & and | incorrectly