applymap() for DataFrame-wide operations in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply a function to each element of the DataFrame.

Pandas

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

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

Aapply

Bapplymap

Cmap

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply() instead of applymap() which applies function to rows or columns, not elements.

Using map() which is for Series, not DataFrames.

The applymap() method applies a function to every element in the DataFrame.

2fill in blank

medium

Complete the code to convert all DataFrame elements to strings using applymap().

Pandas

df = df.[1](str)

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

Amap

Bapply

Capplymap

Dastype

Attempts:

3 left

💡 Hint

Common Mistakes

Using astype('str') which works but is different from applymap.

Using apply() which applies function to rows or columns.

applymap(str) converts each element to a string by applying str to every element.

3fill in blank

hard

Fix the error in the code to apply a function element-wise to the DataFrame.

Pandas

df = df.[1](lambda x: x + 1)

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

Aapplymap

Btransform

Capply

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Using map() which is for Series, not DataFrames.

Using apply() which applies function to rows or columns.

applymap() is the correct method to apply a function to each element of a DataFrame.

4fill in blank

hard

Fill both blanks to create a DataFrame with doubled values using applymap().

Pandas

df = df.[1](lambda x: x [2] 2)

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

Aapplymap

B*

C+

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply() instead of applymap() for element-wise operation.

Using + instead of * which adds 2 instead of doubling.

Use applymap to apply the lambda function element-wise, and * to multiply each element by 2.

5fill in blank

hard

Fill all three blanks to create a new DataFrame with squared values plus one using applymap().

Pandas

new_df = df.[1](lambda x: (x [2] 2) [3] 1)

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

Aapplymap

B**

C+

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply() instead of applymap().

Using * instead of ** for squaring.

Using - instead of + for adding one.

applymap applies the lambda function element-wise; ** squares the element; + adds one.