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.