Why string operations matter in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'name' column to lowercase.

Pandas

df['name'] = df['name'].[1]()

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

Aupper

Blower

Ccapitalize

Dtitle

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'upper()' which makes letters uppercase instead.

Using 'capitalize()' which only changes the first letter.

Using lower() converts all characters in the string to lowercase.

2fill in blank

medium

Complete the code to check if the 'email' column contains '@' symbol.

Pandas

df['email'].str.[1]('@')

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

Astartswith

Bendswith

Ccontains

Dmatch

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'startswith()' which only checks the beginning of the string.

Using 'endswith()' which only checks the end of the string.

The contains() method checks if the string has the given substring anywhere.

3fill in blank

hard

Fix the error in the code to replace spaces with underscores in the 'city' column.

Pandas

df['city'] = df['city'].str.[1](' ', '_')

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

Areplace

Bswap

Cchange

Dsubstitute

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'swap' which is not a pandas string method.

Using 'change' or 'substitute' which do not exist as string methods.

The replace() method replaces specified characters in strings.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 4 letters.

Pandas

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

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

Alen(word)

Blen(word) > 4

Cword.startswith('a')

Dword.isupper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using conditions unrelated to length like 'startswith' or 'isupper'.

Using the word itself instead of its length in the dictionary.

The dictionary comprehension maps each word to its length if the word length is greater than 4.

5fill in blank

hard

Fill all three blanks to filter a DataFrame for rows where 'name' starts with 'J' and create a list of uppercase names.

Pandas

filtered = df[df['name'].str.[1]('J')]
names = [n.[2]() for n in filtered['name'] if n.[3]()]

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

Astartswith

Bupper

Cisalpha

Dendswith

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'endswith' instead of 'startswith' for filtering.

Using 'lower' instead of 'upper' for conversion.

Using 'isdigit' or other checks instead of 'isalpha'.

The code filters names starting with 'J', then creates a list of uppercase names that contain only letters.