Regex operations in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this regex filter in Pandas?

Given the DataFrame df below, what will be the output of df[df['Name'].str.contains('^A.*n$', regex=True)]?

Pandas

import pandas as pd

data = {'Name': ['Alan', 'Ann', 'Aarons', 'Ben', 'Ao']}
df = pd.DataFrame(data)

result = df[df['Name'].str.contains('^A.*n$', regex=True)]
print(result)

A

   Name
0  Alan
1   Ann

B

   Name
0  Alan
1   Ann
2  Aaron

C

   Name
1   Ann
4    An

D

   Name
1   Ann

💡 Hint

The regex '^A.*n$' means the string starts with 'A' and ends with 'n'.

The regex '^A.*n$' matches strings that start with 'A' and end with 'n'. 'Alan' and 'Ann' match this pattern, but 'Aarons' has extra characters after 'n', so it does not match. 'Ao' is too short and does not have characters between 'A' and 'n'.

❓ data_output

intermediate

How many rows match this regex pattern?

Using the DataFrame df below, how many rows will match df['Email'].str.match(r'^[\w.-]+@example\.com$')?

Pandas

import pandas as pd

data = {'Email': ['[e-mail]', '[e-mail]', '[e-mail]', '[e-mail]']}
df = pd.DataFrame(data)

matches = df['Email'].str.match(r'^[\w.-]+@example\.com$')
print(matches.sum())

A 4

B 3

C 1

D 2

💡 Hint

Look for emails ending exactly with '@example.com'.

The regex matches emails that start with word characters, dots or hyphens, followed by '@example.com'. Only '[e-mail]' and '[e-mail]' match.

🔧 Debug

advanced

Why does this regex filter raise an error?

Consider this code snippet:

df['Phone'].str.contains('^\d{3}-\d{3}-\d{4}$')

Why does it raise a ValueError?

Pandas

import pandas as pd

data = {'Phone': ['123-456-7890', '987-654-3210', None]}
df = pd.DataFrame(data)

result = df['Phone'].str.contains('^\d{3}-\d{3}-\d{4}$')

A Because the column contains None values and str.contains does not handle NaNs by default.

B Because the regex pattern needs raw string notation (r'...').

C Because the regex pattern is invalid syntax.

D Because the DataFrame is empty.

💡 Hint

Check how pandas handles missing values in string operations.

The None value in the 'Phone' column causes str.contains to raise a ValueError unless you specify na=False or na=True to handle missing values.

❓ visualization

advanced

Which plot shows the count of rows matching a regex pattern?

Given this DataFrame df with a 'Category' column, which code snippet produces a bar plot showing counts of rows where 'Category' matches regex '^A.*'?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Category': ['Apple', 'Banana', 'Apricot', 'Berry', 'Avocado']}
df = pd.DataFrame(data)

A

df['Category'].value_counts().plot(kind='bar')
plt.show()

B

df['Category'].str.contains('^A.*').plot(kind='bar')
plt.show()

C

df[df['Category'].str.match('^A.*')]['Category'].value_counts().plot(kind='bar')
plt.show()

D

df[df['Category'].str.contains('^A.*')].plot(kind='bar')
plt.show()

💡 Hint

You want counts of matching categories, not just True/False bars.

Option C filters rows matching regex, counts occurrences of each category, then plots a bar chart. Option C tries to plot a boolean Series directly, which is not meaningful. Option C plots counts of all categories, ignoring regex. Option C tries to plot the filtered DataFrame directly, which is not a count plot.

🚀 Application

expert

Extract area codes from phone numbers using regex in Pandas

Given a DataFrame df with a 'Phone' column containing phone numbers like '123-456-7890', which code extracts the area code (first three digits) into a new column 'AreaCode'?

Pandas

import pandas as pd

data = {'Phone': ['123-456-7890', '987-654-3210', '555-123-4567']}
df = pd.DataFrame(data)

A df['AreaCode'] = df['Phone'].str.extract(r'\d{3}-(\d{3})-\d{4}')

B df['AreaCode'] = df['Phone'].str.extract(r'(\d{3})-\d{3}-\d{4}')

C df['AreaCode'] = df['Phone'].str.slice(0, 3)

D df['AreaCode'] = df['Phone'].str.findall(r'\d{3}').str[1]

💡 Hint

Use capturing groups in regex to extract parts of strings.

Option B correctly captures the first three digits (area code) using parentheses in the regex. Option B captures the second group of digits (middle three). Option B slices the string but does not guarantee digits only. Option B finds all groups of three digits and picks the second, which is the middle part, not area code.

Practice

1. What does the str.contains method do in Pandas when used with a regex pattern?

easy

A. It extracts the first match of the regex pattern from each string.

B. It replaces all matches of the regex pattern with a new string.

C. It sorts the DataFrame rows based on regex matches.

D. It checks if each string in a column contains the regex pattern and returns true or false.

Solution

1. Step 1: Understand str.contains usage

   str.contains checks if the regex pattern appears anywhere in each string of the column and returns a boolean Series.

2. Step 2: Differentiate from other methods

   str.replace changes text, str.extract pulls out matches, and sorting is unrelated.

3. Final Answer:

   It checks if each string in a column contains the regex pattern and returns true or false. -> Option D

4. Quick Check:

   str.contains = Boolean check [OK]

Hint: Contains returns true/false if pattern found in each string [OK]

Common Mistakes:

• Confusing contains with replace or extract
• Thinking contains modifies the data
• Assuming contains returns matched text

2. Which of the following is the correct syntax to replace every sequence of consecutive digits in a Pandas column df['col'] with the character 'X' using regex?

easy

A. df['col'].str.replace('\d+', 'X')

B. df['col'].str.replace('[0-9]', 'X')

C. df['col'].replace('[0-9]', 'X')

D. df['col'].str.extract('[0-9]', 'X')

Solution

1. Step 1: Identify correct method for regex replacement

   Use str.replace on the Series to replace regex matches. df['col'].replace('[0-9]', 'X') is wrong because replace without str is for exact values, not regex.

2. Step 2: Check regex pattern and syntax

   df['col'].str.replace('[0-9]', 'X') matches single digits only ('123' -> 'XXX'). df['col'].str.replace('\d+', 'X') matches sequences of one or more digits ('123' -> 'X').

3. Final Answer:

   df['col'].str.replace('\d+', 'X') -> Option A

4. Quick Check:

   Use str.replace with regex pattern [OK]

Hint: Use str.replace with regex pattern and replacement string [OK]

Common Mistakes:

• Using DataFrame replace instead of Series.str.replace
• Wrong regex pattern syntax
• Using str.extract instead of replace

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'text': ['apple123', 'banana', 'cherry456', 'date789']})
df['numbers'] = df['text'].str.extract('(\d+)')

What will be the content of the new column numbers?

medium

A. ['123', 'banana', '456', '789']

B. ['apple', NaN, 'cherry', 'date']

C. ['123', NaN, '456', '789']

D. [NaN, NaN, NaN, NaN]

Solution

1. Step 1: Understand str.extract with regex

   The regex (\d+) captures one or more digits. str.extract returns the first match or NaN if none.

2. Step 2: Apply regex to each string

   'apple123' matches '123', 'banana' has no digits so NaN, 'cherry456' matches '456', 'date789' matches '789'.

3. Final Answer:

   ['123', NaN, '456', '789'] -> Option C

4. Quick Check:

   Extract digits or NaN if none [OK]

Hint: Extract returns first regex group or NaN if no match [OK]

Common Mistakes:

• Expecting extract to return full string
• Confusing extract with contains or replace
• Not handling NaN for no matches

4. Consider this code snippet:

import pandas as pd
df = pd.DataFrame({'col': ['abc123', 'def456', 'ghi789']})
df['col'].str.match('[a-z]+\d')

What is the error or issue in this code?

medium

A. The regex pattern misses the last digit, so match returns false for all rows.

B. The code raises a SyntaxError due to incorrect regex syntax.

C. The method str.match does not accept regex patterns.

D. The code runs correctly and returns true for all rows.

Solution

1. Step 1: Analyze the regex pattern

   The pattern [a-z]+\d matches one or more lowercase letters followed by exactly one digit from the start of the string.

2. Step 2: Compare pattern with data strings

   The strings 'abc123', 'def456', 'ghi789' have multiple digits at the end, but the pattern only expects one digit. Since str.match matches from the start and the pattern does not cover the full digit sequence, it returns False for all rows.

3. Final Answer:

   The regex pattern misses the last digit, so match returns false for all rows. -> Option A

4. Quick Check:

   str.match matches from start; pattern must cover full prefix for True [OK]

Hint: str.match anchors at start (prefix match), pattern must cover full prefix [OK]

Common Mistakes:

• Thinking str.match requires full string match
• Confusing str.match with str.fullmatch
• Thinking regex syntax is invalid here

5. You have a DataFrame df with a column 'emails' containing email addresses mixed with invalid entries. How would you create a new column 'valid' that is true only for rows where 'emails' contains a valid email pattern using regex in Pandas?

hard

A. df['valid'] = df['emails'].str.extract(r'^[\w\.-]+@[\w\.-]+\.\w+$')

B. df['valid'] = df['emails'].str.contains(r'^[\w\.-]+@[\w\.-]+\.\w+$')

C. df['valid'] = df['emails'].str.replace(r'^[\w\.-]+@[\w\.-]+\.\w+$', 'true')

D. df['valid'] = df['emails'].str.match(r'\w+@\w+')

Solution

1. Step 1: Choose method to check pattern presence

   str.contains returns true/false if regex matches anywhere in string, suitable for validation.

2. Step 2: Use correct regex for full email validation

   The regex ^[\w\.-]+@[\w\.-]+\.\w+$ matches a full email pattern from start to end. df['valid'] = df['emails'].str.extract(r'^[\w\.-]+@[\w\.-]+\.\w+$') extracts matched text, not boolean. df['valid'] = df['emails'].str.replace(r'^[\w\.-]+@[\w\.-]+\.\w+$', 'true') replaces text, not boolean. df['valid'] = df['emails'].str.match(r'\w+@\w+') uses str.match but regex is incomplete and may match partial emails.

3. Final Answer:

   df['valid'] = df['emails'].str.contains(r'^[\w\.-]+@[\w\.-]+\.\w+$') -> Option B

4. Quick Check:

   Use str.contains with full regex for validation [OK]

Hint: Use str.contains with full regex pattern for true/false validation [OK]

Common Mistakes:

• Using str.extract which returns strings, not booleans
• Using str.replace which changes data instead of checking
• Using incomplete regex that matches partial emails