apply() function for custom logic in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply a function that doubles each value in the 'score' column.

Data Analysis Python

df['double_score'] = df['score'].[1](lambda x: x * 2)

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

Agroupby

Bmap

Cfilter

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using map instead of apply, which works differently on Series.

Trying to use filter which is for filtering rows, not transforming values.

The apply() function lets you run a custom function on each element of the column. Here, it doubles each score.

2fill in blank

medium

Complete the code to apply a function that returns 'Pass' if score is 50 or more, else 'Fail'.

Data Analysis Python

df['result'] = df['score'].[1](lambda x: 'Pass' if x >= 50 else 'Fail')

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

Aapply

Bsort_values

Cgroupby

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using filter which is for filtering rows, not transforming values.

Trying to use groupby which is for grouping data, not element-wise operations.

Using apply() lets us run a custom function that checks each score and returns 'Pass' or 'Fail'.

3fill in blank

hard

Fix the error in applying a function that adds 10 to each value in the 'score' column.

Data Analysis Python

df['new_score'] = df['score'].[1](lambda x: x + 10)

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

Amap

Bfilter

Capply

Dreduce

Attempts:

3 left

💡 Hint

Common Mistakes

Using filter which filters rows instead of transforming values.

Using reduce which aggregates values instead of element-wise operation.

apply() is the correct method to apply a function to each element. map() works differently and filter() is for filtering rows.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > in the condition.

Using word instead of len(word) for the value.

The dictionary comprehension uses len(word) to get length and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values only if value is positive.

Data Analysis Python

result = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original key instead of uppercase key.

Using <= or < instead of > for filtering positive values.

The dictionary comprehension uses k.upper() for keys, v for values, and filters values greater than 0 with >.

Practice

1. What does the apply() function do in a pandas DataFrame?

easy

A. It merges two DataFrames together.

B. It sorts the DataFrame by a column.

C. It filters rows based on a condition.

D. It applies a custom function to each row or column.

Solution

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

   The apply() function is used to run a custom function on each row or column of a DataFrame.

2. Step 2: Compare with other DataFrame methods

   Sorting, filtering, and merging are different operations and not done by apply().

3. Final Answer:

   It applies a custom function to each row or column. -> Option D

4. Quick Check:

   apply() = custom function application [OK]

Hint: Remember: apply() runs your function on rows or columns [OK]

Common Mistakes:

• Confusing apply() with sort or filter methods
• Thinking apply() merges DataFrames
• Assuming apply() only works on columns

2. Which of the following is the correct syntax to apply a function f row-wise on a DataFrame df?

easy

A. df.apply(f, axis=0)

B. df.apply(f)

C. df.apply(f, axis=1)

D. df.apply(f, axis=2)

Solution

1. Step 1: Recall axis parameter meaning

   In pandas, axis=1 means apply function row-wise, while axis=0 means column-wise.

2. Step 2: Identify correct axis for row-wise

   Since the question asks for row-wise application, axis=1 is correct.

3. Final Answer:

   df.apply(f, axis=1) -> Option C

4. Quick Check:

   Row-wise apply = axis=1 [OK]

Hint: Use axis=1 for rows, axis=0 for columns [OK]

Common Mistakes:

• Using axis=0 for row-wise apply
• Omitting axis parameter and expecting row-wise
• Using invalid axis values like 2

3. Given the DataFrame:

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

What is the output of:

df['Sum'] = df.apply(lambda row: row['A'] + row['B'], axis=1)
print(df)

medium

A. A B Sum
0 1 3 3
1 2 4 4

B. A B Sum
0 1 3 4
1 2 4 6

C. A B Sum
0 1 3 1
1 2 4 2

D. Error: axis parameter missing

Solution

1. Step 1: Understand the lambda function

   The lambda adds values from columns 'A' and 'B' for each row.

2. Step 2: Apply row-wise addition

   Row 0: 1 + 3 = 4; Row 1: 2 + 4 = 6.

3. Final Answer:

   A B Sum 0 1 3 4 1 2 4 6 -> Option B

4. Quick Check:

   Sum column = A + B per row [OK]

Hint: Sum columns row-wise with apply and axis=1 [OK]

Common Mistakes:

• Forgetting axis=1 causes wrong operation
• Adding columns incorrectly (e.g., column-wise)
• Expecting apply to work without lambda

4. What is wrong with this code snippet?

df = pd.DataFrame({'X': [10, 20], 'Y': [30, 40]})
df['Result'] = df.apply(lambda x: x['X'] * 2 if x['Y'] > 35 else x['X'], axis=0)

medium

A. Using axis=0 applies function column-wise, causing a KeyError.

B. Lambda function syntax is incorrect.

C. DataFrame is missing required columns.

D. No error; code runs correctly.

Solution

1. Step 1: Check axis parameter usage

   axis=0 applies function to each column (Series), not rows.

2. Step 2: Understand lambda function context

   Lambda expects row with keys 'X' and 'Y', but column Series only has index labels, causing KeyError.

3. Final Answer:

   Using axis=0 applies function column-wise, causing a KeyError. -> Option A

4. Quick Check:

   axis=0 = columns, lambda expects rows [OK]

Hint: Use axis=1 for row-wise logic to avoid key errors [OK]

Common Mistakes:

• Confusing axis=0 and axis=1
• Expecting row keys when applying column-wise
• Ignoring error messages about missing keys

5. You have a DataFrame df with columns "score" and "passed" (boolean). You want to create a new column "grade" where:
- If passed is True and score >= 90, grade is 'A'
- If passed is True and score >= 75 but < 90, grade is 'B'
- Otherwise, grade is 'F'
Which apply() function correctly implements this logic?

hard

A. df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] >= 90 else ('B' if r['passed'] and r['score'] >= 75 else 'F'), axis=1)

B. df['grade'] = df.apply(lambda r: 'A' if r['score'] >= 90 else 'B' if r['score'] >= 75 else 'F', axis=0)

C. df['grade'] = df.apply(lambda r: 'A' if r['passed'] or r['score'] >= 90 else 'F', axis=1)

D. df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] > 90 else 'B', axis=1)

Solution

1. Step 1: Check axis parameter for row-wise logic

   axis=1 is needed to apply function on each row.

2. Step 2: Verify conditional logic matches requirements

   df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] >= 90 else ('B' if r['passed'] and r['score'] >= 75 else 'F'), axis=1) correctly checks if passed is True and score thresholds for 'A' and 'B', else 'F'.

3. Step 3: Identify errors in other options

   df['grade'] = df.apply(lambda r: 'A' if r['score'] >= 90 else 'B' if r['score'] >= 75 else 'F', axis=0) uses axis=0 (wrong axis). df['grade'] = df.apply(lambda r: 'A' if r['passed'] or r['score'] >= 90 else 'F', axis=1) uses 'or' instead of 'and'. df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] > 90 else 'B', axis=1) misses 'F' grade and uses wrong comparison for 90.

4. Final Answer:

   df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] >= 90 else ('B' if r['passed'] and r['score'] >= 75 else 'F'), axis=1) -> Option A

5. Quick Check:

   Correct logic and axis=1 for row-wise apply [OK]

Hint: Use axis=1 and nested if-else for multi-condition grading [OK]

Common Mistakes:

• Using axis=0 instead of axis=1
• Using 'or' instead of 'and' in conditions
• Missing else condition for failing grade