applymap() for DataFrame-wide operations in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of applymap() with a simple function

What is the output of this code when applying applymap() to double each element in the DataFrame?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
result = df.applymap(lambda x: x * 2)
print(result)

A

   A  B
0  2  6
1  4  8

B

   A  B
0  1  3
1  2  4

C

   A  B
0  1  6
1  4  8

D

   A  B
0  2  3
1  4  8

💡 Hint

applymap applies the function to every element individually.

The lambda function doubles each element. So 1 becomes 2, 2 becomes 4, 3 becomes 6, and 4 becomes 8.

❓ data_output

intermediate

Result of applymap() with a conditional function

Given this DataFrame, what is the result of applying applymap() with a function that replaces odd numbers with 0 and keeps even numbers unchanged?

Pandas

import pandas as pd

df = pd.DataFrame({'X': [1, 4], 'Y': [3, 6]})
result = df.applymap(lambda x: 0 if x % 2 != 0 else x)
print(result)

A

   X  Y
0  0  0
1  4  6

B

   X  Y
0  1  3
1  4  6

C

   X  Y
0  0  3
1  4  0

D

   X  Y
0  1  0
1  0  6

💡 Hint

Check which numbers are odd and replace them with zero.

1 and 3 are odd, so they become 0. 4 and 6 are even, so they stay the same.

🔧 Debug

advanced

Identify the error in applymap() usage

What error will this code raise when trying to use applymap() with a function that returns a list?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
result = df.applymap(lambda x: [x, x*2])
print(result)

A ValueError: Must produce scalar value

B No error, prints DataFrame with lists as elements

C AttributeError: 'list' object has no attribute 'applymap'

D TypeError: unhashable type: 'list'

💡 Hint

applymap applies the function to each element and can return any object.

applymap applies the function to each element and stores the returned object. Returning a list is allowed, so no error occurs.

🚀 Application

advanced

Using applymap() to format DataFrame values

You want to format all float numbers in a DataFrame to show only two decimal places as strings. Which applymap() function achieves this?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1.2345, 2.3456], 'B': [3.4567, 4.5678]})
result = df.applymap(??? )
print(result)

A lambda x: str(round(x, 2))

B lambda x: f"{x:.2f}"

C lambda x: format(x, '.2f') if isinstance(x, float) else x

D lambda x: round(x, 2)

💡 Hint

Only floats should be formatted as strings, other types remain unchanged.

Option C formats only floats to strings with two decimals, leaving other types untouched.

🧠 Conceptual

expert

Understanding applymap() vs apply() on DataFrames

Which statement correctly describes the difference between applymap() and apply() when used on a pandas DataFrame?

A applymap() applies a function along rows; apply() applies a function element-wise to each cell.

B applymap() and apply() are interchangeable and produce the same results.

C applymap() works only on Series; apply() works only on DataFrames.

D applymap() applies a function element-wise to each cell; apply() applies a function along rows or columns.

💡 Hint

Think about whether the function is applied to each cell or to rows/columns.

applymap() works element-wise on each cell of a DataFrame. apply() works on rows or columns (Series) and can aggregate or transform.