Regex operations in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select rows where the 'Name' column contains the letter 'a'.

Pandas

filtered = df[df['Name'].str.[1]('a')]

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

Areplace

Bmatch

Cfindall

Dcontains

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'match' instead of 'contains' which only checks the start of the string.

Using 'replace' which changes strings instead of filtering.

The contains method checks if the pattern exists anywhere in the string.

2fill in blank

medium

Complete the code to replace all digits in the 'Code' column with '#' symbol.

Pandas

df['Code'] = df['Code'].str.[1]('\d', '#')

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

Areplace

Bcontains

Cmatch

Dfindall

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'contains' which only checks for presence but does not replace.

Using 'match' which only checks the start of the string.

The replace method replaces all occurrences of the regex pattern with the given string.

3fill in blank

hard

Fix the error in the code to find all words starting with 'a' in the 'Text' column.

Pandas

df['Words'] = df['Text'].str.[1]('\ba\w*')

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

Afindall

Breplace

Ccontains

Dmatch

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'match' which only checks the start of the string and returns a boolean.

Using 'contains' which returns a boolean, not the matched words.

The findall method returns all matches of the regex pattern in each string.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, only for words longer than 3 letters.

Pandas

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 the word itself as the value instead of its length.

Using '<' instead of '>' which selects shorter words.

We want the length of each word as the value, and only include words longer than 3 letters.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys, their counts as values, only for counts greater than 1.

Pandas

result = [1]: [2] for word, count in counts.items() if count [3] 1}

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

Aword.upper()

Bcount

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word instead of uppercase for keys.

Using '<' instead of '>' which filters wrong counts.

The keys are uppercase words, values are counts, and we filter counts greater than 1.