apply() on columns in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the apply() function do when used on DataFrame columns in pandas?

It applies a given function to each column (Series) of the DataFrame, allowing you to transform or analyze each column separately.

beginner

How do you apply a function to each column of a DataFrame using apply()?

Use df.apply(your_function). By default, apply() works column-wise, passing each column as a Series to the function.

intermediate

What is the difference between apply() and applymap() in pandas?

apply() works on rows or columns (Series), while applymap() works element-wise on every single value in the DataFrame.

beginner

Example: How to calculate the range (max - min) of each column using apply()?

Use df.apply(lambda col: col.max() - col.min()). This applies the lambda function to each column and returns a Series of ranges.

intermediate

Can apply() on columns handle functions that return multiple values?

Yes, if the function returns a Series or list, apply() will combine these into a DataFrame with new columns.

What is the default axis when using apply() on a pandas DataFrame?

A Columns (axis=0)

B Rows (axis=1)

C Elements (each cell)

D No default axis

By default, apply() works on columns, which is axis=0 in pandas.

Which pandas function applies a function element-wise to every cell in a DataFrame?

A apply()

B applymap()

C map()

D transform()

applymap() applies a function to each element in the DataFrame.

What will df.apply(lambda col: col.mean()) return?

A Mean of each row

B Sum of each row

C Mean of each column

D Sum of each column

The lambda function calculates the mean of each column because apply() works column-wise by default.

If a function passed to apply() returns a Series, what is the output?

A A Series

B A scalar value

C An error

D A DataFrame

When the function returns a Series for each column, apply() combines these into a DataFrame.

How can you apply a function to each row instead of each column using apply()?

A Set <code>axis=1</code>

B Set <code>axis=0</code>

C Use <code>applymap()</code>

D Use <code>map()</code>

Setting axis=1 makes apply() work row-wise.

Explain how the apply() function works on DataFrame columns in pandas and give a simple example.

Think about how you can transform or summarize each column separately.

Describe the difference between apply() and applymap() in pandas.

Consider the level at which the function is applied: whole columns vs individual cells.

Practice

1. What does the apply() function do when used on a pandas DataFrame column?

easy

A. It applies a function to each value in the column.

B. It deletes the column from the DataFrame.

C. It sorts the column values in ascending order.

D. It merges the column with another DataFrame.

Solution

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

   The apply() method runs a function on each element of the column, transforming or analyzing the data.

2. Step 2: Compare with other options

   Deleting, sorting, or merging are not actions performed by apply() on a column.

3. Final Answer:

   It applies a function to each value in the column. -> Option A

4. Quick Check:

   apply() runs function on each column value [OK]

Hint: Remember: apply() runs a function on each column value [OK]

Common Mistakes:

• Thinking apply() deletes or sorts data
• Confusing apply() with merge or drop functions
• Assuming apply() works on entire DataFrame without axis

2. Which of the following is the correct syntax to apply a lambda function that doubles each value in the 'age' column of a DataFrame df?

easy

A. df['age'].apply = lambda x: x * 2

B. df.apply['age'](lambda x: x * 2)

C. df['age'].apply(lambda x: x * 2)

D. apply(df['age'], lambda x: x * 2)

Solution

1. Step 1: Identify correct method call syntax

   The correct way to use apply on a column is df['column'].apply(function). df['age'].apply(lambda x: x * 2) matches this.

2. Step 2: Check lambda function usage

   df['age'].apply(lambda x: x * 2) correctly passes a lambda function that doubles the value. Other options misuse syntax or assignment.

3. Final Answer:

   df['age'].apply(lambda x: x * 2) -> Option C

4. Quick Check:

   Use df['col'].apply(lambda x: ...) [OK]

Hint: Use df['col'].apply(lambda x: ...) for column functions [OK]

Common Mistakes:

• Using square brackets with apply like a list
• Assigning apply instead of calling it
• Calling apply as a standalone function

3. Given the DataFrame df below:

   score
0     10
1     20
2     30
3     40

What is the output of df['score'].apply(lambda x: x // 15)?

medium

A. 0 10
1 20
2 30
3 40
Name: score, dtype: int64

B. 0 0
1 1
2 2
3 3
Name: score, dtype: int64

C. 0 0
1 0
2 1
3 2
Name: score, dtype: int64

D. 0 0
1 1
2 2
3 2
Name: score, dtype: int64

Solution

1. Step 1: Apply the lambda function to each value

   Divide each score by 15 using floor division:
   10 // 15 = 0
   20 // 15 = 1
   30 // 15 = 2
   40 // 15 = 2

2. Step 2: Write the resulting Series

   The output Series is:
   0 0
   1 1
   2 2
   3 2

3. Final Answer:

   0 0 1 1 2 2 3 2 Name: score, dtype: int64 -> Option D

4. Quick Check:

   Floor division on each value = output [OK]

Hint: Apply lambda to each value and calculate carefully [OK]

Common Mistakes:

• Using normal division instead of floor division
• Misreading floor division results
• Confusing output format with DataFrame instead of Series

4. What is wrong with this code snippet?

df['new_col'] = df['value'].apply(x: x + 5)

How can it be fixed?

medium

A. Missing lambda keyword; fix: df['value'].apply(lambda x: x + 5)

B. apply() cannot add numbers; use map() instead

C. Should use square brackets around x; fix: apply([x]: x + 5)

D. Function must be defined outside apply; fix: def f(x): return x+5

Solution

1. Step 1: Identify syntax error in lambda function

   The code uses x: x + 5 without the lambda keyword, which is required to define an anonymous function.

2. Step 2: Correct the syntax

   Add lambda before the function: df['value'].apply(lambda x: x + 5).

3. Final Answer:

   Missing lambda keyword; fix: df['value'].apply(lambda x: x + 5) -> Option A

4. Quick Check:

   Lambda keyword needed for inline functions [OK]

Hint: Always use 'lambda' before inline functions in apply() [OK]

Common Mistakes:

• Omitting the lambda keyword
• Using incorrect brackets or syntax for lambda
• Thinking apply() can't use inline functions

5. You have a DataFrame df with a column 'temperature' in Celsius. You want to create a new column 'temp_f' with Fahrenheit values using apply(). Which code correctly does this?

hard

A. df['temp_f'] = df.apply(lambda c: (c * 9/5) + 32)

B. df['temp_f'] = df['temperature'].apply(lambda c: (c * 9/5) + 32)

C. df['temp_f'] = df['temperature'].apply(lambda c: c + 32 * 9/5)

D. df['temp_f'] = df['temperature'].apply(lambda c: c * (9/5 + 32))

Solution

1. Step 1: Recall the formula for Celsius to Fahrenheit

   The correct formula is Fahrenheit = (Celsius * 9/5) + 32.

2. Step 2: Check each option's lambda function

   df['temp_f'] = df['temperature'].apply(lambda c: (c * 9/5) + 32) correctly applies the formula to each value in 'temperature'. df['temp_f'] = df.apply(lambda c: (c * 9/5) + 32) applies to whole DataFrame, C and D have incorrect formulas.

3. Final Answer:

   df['temp_f'] = df['temperature'].apply(lambda c: (c * 9/5) + 32) -> Option B

4. Quick Check:

   Use correct formula inside apply() on the column [OK]

Hint: Use correct formula inside lambda for each column value [OK]

Common Mistakes:

• Applying function to whole DataFrame instead of column
• Mixing order of operations in formula
• Forgetting parentheses in formula