Regular expressions for text cleaning in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the regular expressions module.

NLP

import [1]

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

Are

Bregexp

Cregex

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'regex' or 'regexp' which are not standard Python modules.

Trying to import 'text' which is unrelated.

The Python module for regular expressions is called 're'.

2fill in blank

medium

Complete the code to remove all digits from the text using regex substitution.

NLP

clean_text = re.sub(r'[1]', '', text)

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

A\D

B\d

C\s

D\w

Attempts:

3 left

💡 Hint

Common Mistakes

Using '\w' which removes letters and digits, not just digits.

Using '\s' which matches whitespace, not digits.

The pattern '\d' matches any digit character, so replacing it removes digits.

3fill in blank

hard

Fix the error in the regex pattern to remove punctuation marks from the text.

NLP

clean_text = re.sub(r'[[1]]', '', text)

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

A.,!?

B\.,!\?

C\.,!?

D\.,!\?\-

Attempts:

3 left

💡 Hint

Common Mistakes

Using patterns that do not include the dash or fail to handle special characters like '-' properly.

Missing some punctuation marks like dash '-' in the pattern.

Option D provides the pattern that safely matches common punctuation (period, comma, exclamation, question mark, dash) inside the character class, using escapes where appropriate (e.g., for dash).

4fill in blank

hard

Complete the code to create a regex that removes all whitespace characters including tabs and newlines.

NLP

clean_text = re.sub(r'[1]', '', text)

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

A\s

C\S

Attempts:

3 left

💡 Hint

Common Mistakes

Replacing with a space ' ' instead of empty string, which does not remove whitespace.

Using '\S' which matches non-whitespace characters.

The pattern '\s' matches any whitespace character, and replacing it with an empty string '' removes all whitespace.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps words to their lengths, but only for words longer than 3 characters.

NLP

lengths = { [1]: [2] for [3] in words if len([3]) > 3 }

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

Aword

Blen(word)

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Mapping keys or values incorrectly.

The comprehension uses 'word' as the variable, maps 'word' to its length using len(word), and iterates over 'words'.