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'.

Practice

1. What is the main purpose of using regular expressions in text cleaning for NLP?

easy

A. To find and remove unwanted patterns or characters in text

B. To train machine learning models directly

C. To store large datasets efficiently

D. To visualize text data with graphs

Solution

1. Step 1: Understand the role of regular expressions

   Regular expressions are used to identify patterns in text, such as unwanted characters or specific sequences.

2. Step 2: Connect to text cleaning

   Text cleaning involves removing or replacing unwanted parts of text to prepare it for analysis or modeling.

3. Final Answer:

   To find and remove unwanted patterns or characters in text -> Option A

4. Quick Check:

   Regular expressions clean text by pattern matching [OK]

Hint: Regular expressions = pattern search and replace in text [OK]

Common Mistakes:

• Confusing regex with model training
• Thinking regex stores data
• Assuming regex creates visualizations

2. Which of the following is the correct Python syntax to import the regular expression module?

easy

A. from regex import *

B. import regex

C. import re

D. import regular_expression

Solution

1. Step 1: Recall Python's regex module name

   Python's built-in module for regular expressions is named 're'.

2. Step 2: Check syntax correctness

   The correct import statement is 'import re' to use regex functions.

3. Final Answer:

   import re -> Option C

4. Quick Check:

   Python regex module = re [OK]

Hint: Remember: Python regex module is 're' not 'regex' [OK]

Common Mistakes:

• Using 'import regex' which is not standard
• Trying to import non-existent modules
• Confusing module names with function names

3. What will be the output of this Python code snippet?

import re
text = "Hello, World! 123"
cleaned = re.sub(r'[^a-zA-Z ]', '', text)
print(cleaned)

medium

A. Hello World

B. Hello World 123

C. Hello, World!

D. HelloWorld123

Solution

1. Step 1: Understand the regex pattern used

   The pattern '[^a-zA-Z ]' means any character NOT a letter (a-z or A-Z) or space.

2. Step 2: Apply re.sub to remove unwanted characters

   All characters except letters and spaces are removed, so commas, exclamation marks, and digits are deleted.

3. Final Answer:

   Hello World -> Option A

4. Quick Check:

   Regex removes non-letters/spaces = 'Hello World ' [OK]

Hint: [^...] means NOT those characters, so it removes digits and punctuation [OK]

Common Mistakes:

• Thinking digits remain after substitution
• Confusing character classes with ranges
• Ignoring spaces in the pattern

4. Identify the error in this regex code snippet for removing digits from text:

import re
text = "Price: 100 dollars"
cleaned = re.sub(r'\d', '', text)
print(cleaned)

medium

A. The pattern '\d' should be '\D' to remove digits

B. The backslash in '\d' is not escaped properly

C. The re.sub function is used incorrectly

D. The code will run correctly and remove digits

Solution

1. Step 1: Check regex pattern correctness

   The pattern r'\d' correctly matches digits (0-9).

2. Step 2: Verify code syntax and function usage

   The code uses raw string r'\d' which properly escapes the backslash, so digits are removed as intended.

3. Final Answer:

   The code will run correctly and remove digits -> Option D

4. Quick Check:

   r'\d' matches digits; re.sub removes them correctly [OK]

Hint: In raw strings, r'\d' matches digits; no extra escaping needed [OK]

Common Mistakes:

• Thinking '\d' needs double escaping outside raw strings
• Confusing '\d' with '\D' (non-digit)
• Assuming re.sub syntax is wrong

5. You want to clean a text dataset by removing all URLs and extra spaces. Which regex pattern and code snippet correctly achieves this in Python?

import re
text = "Visit https://example.com now!  Enjoy!"
cleaned = re.sub(_____, ' ', text)
cleaned = re.sub(r'\s+', ' ', cleaned).strip()
print(cleaned)

hard

A. r'http://[a-z]+'

B. r'https?://\S+'

C. r'www\.[a-z]+\.com'

D. r'https?://[a-z]+'

Solution

1. Step 1: Identify a regex pattern that matches URLs

   The pattern 'https?://' matches 'http://' or 'https://', and '\S+' matches non-space characters following it, capturing full URLs.

2. Step 2: Understand the code's cleaning steps

   First, URLs are replaced by a space, then multiple spaces are reduced to one, and leading/trailing spaces removed.

3. Final Answer:

   r'https?://\S+' -> Option B

4. Quick Check:

   Use 'https?://\S+' to remove URLs effectively [OK]

Hint: Use 'https?://' plus '\S+' to match full URLs [OK]

Common Mistakes:

• Using too narrow patterns missing https or full URL
• Not removing extra spaces after substitution
• Using patterns that match only partial URLs