str.replace() for substitution in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of str.replace() with regex in pandas

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({'text': ['apple123', 'banana456', 'cherry789']})
df['cleaned'] = df['text'].str.replace(r'\d+', '', regex=True)
print(df['cleaned'].tolist())

A ['apple', 'banana', 'cherry']

B ['apple123', 'banana456', 'cherry789']

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

D ['apple ', 'banana ', 'cherry ']

💡 Hint

The regex '\d+' matches digits and str.replace removes them.

The code removes all digits from each string in the 'text' column using regex '\d+'. The result is the list without numbers.

❓ data_output

intermediate

Resulting DataFrame after str.replace() substitution

Given the DataFrame below, what is the resulting DataFrame after applying the str.replace() method?

Pandas

import pandas as pd

df = pd.DataFrame({'city': ['New York', 'Los Angeles', 'San Francisco']})
df['city'] = df['city'].str.replace(' ', '_')
print(df)

A

          city
0      NewYork
1   LosAngeles
2  SanFrancisco

B

          city
0      New_York
1   Los_Angeles
2  San_Francisco

C

          city
0      New-York
1   Los-Angeles
2  San-Francisco

D

          city
0      New York
1   Los Angeles
2  San Francisco

💡 Hint

str.replace(' ', '_') replaces spaces with underscores.

The spaces in city names are replaced by underscores, so 'New York' becomes 'New_York', etc.

🔧 Debug

advanced

Identify the error in str.replace() usage

What error will this code raise and why?

Pandas

import pandas as pd

df = pd.DataFrame({'code': ['A1', 'B2', 'C3']})
df['code'] = df['code'].str.replace('\d+', '')
print(df)

A TypeError: replace() missing 1 required positional argument: 'repl'

B ValueError: regex pattern not provided

C SyntaxError due to missing raw string prefix

D No error, output is ['A', 'B', 'C']

💡 Hint

Check the parameters passed to str.replace() in pandas.

In pandas, str.replace() requires both 'pat' and 'repl' arguments. Here, repl is missing, causing TypeError.

🚀 Application

advanced

Using str.replace() to clean phone numbers

You have a DataFrame column with phone numbers in various formats. Which code snippet correctly removes all non-digit characters to keep only numbers?

Pandas

import pandas as pd

df = pd.DataFrame({'phone': ['[phone]', '987.654.3210', '[phone]']})

A df['phone_clean'] = df['phone'].str.replace(r'[^0-9]', '')

B df['phone_clean'] = df['phone'].str.replace(r'\d', '', regex=True)

C df['phone_clean'] = df['phone'].str.replace(r'\D', '', regex=True)

D df['phone_clean'] = df['phone'].str.replace(r'\s', '')

💡 Hint

Use regex to remove all non-digit characters.

The pattern '\D' matches any non-digit character. Replacing them with '' keeps only digits.

🧠 Conceptual

expert

Behavior of str.replace() with overlapping patterns

Consider the string 'aaaa'. What is the result of applying str.replace('aa', 'b') once in pandas with regex=False?

Pandas

import pandas as pd

s = pd.Series(['aaaa'])
result = s.str.replace('aa', 'b', regex=False)
print(result[0])

A 'bb'

B 'aaaa'

C 'ab'

D 'ba'

💡 Hint

str.replace replaces non-overlapping occurrences from left to right.

The first 'aa' is replaced by 'b', then the next 'aa' is replaced by 'b', resulting in 'bb'.