String cleaning (strip, lower, replace) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this string cleaning code?

Consider the following Python code that cleans a string by stripping spaces, converting to lowercase, and replacing characters. What is the output?

Data Analysis Python

text = '  Hello World!  '
cleaned = text.strip().lower().replace('o', '0')
print(cleaned)

A 'hell0 w0rld!'

B ' hello world! '

C 'Hello World!'

D 'hello world!'

💡 Hint

Remember that strip() removes spaces only at the start and end, lower() changes all letters to lowercase, and replace() swaps specified characters.

The strip() removes spaces at both ends, lower() converts all letters to lowercase, and replace('o', '0') changes all 'o' letters to zero '0'. So ' Hello World! ' becomes 'hell0 w0rld!'.

❓ data_output

intermediate

How many unique cleaned strings are in this list?

Given a list of strings, we clean each by stripping spaces and converting to lowercase. How many unique cleaned strings remain?

Data Analysis Python

texts = ['  Apple', 'apple ', 'APPLE', 'Banana', ' banana ', 'BANANA  ']
cleaned_texts = [t.strip().lower() for t in texts]
unique_count = len(set(cleaned_texts))
print(unique_count)

A 3

B 2

C 6

D 1

💡 Hint

Think about how strip() and lower() normalize the strings so that different cases and spaces become the same.

After cleaning, all 'Apple' variants become 'apple' and all 'Banana' variants become 'banana'. So only two unique strings remain.

🔧 Debug

advanced

What error does this string cleaning code raise?

This code tries to clean a list of strings but raises an error. What is the error?

Data Analysis Python

texts = ['Data ', None, ' Science']
cleaned = [t.strip().lower() for t in texts]
print(cleaned)

A AttributeError: 'NoneType' object has no attribute 'strip'

B TypeError: 'list' object is not callable

C IndexError: list index out of range

D ValueError: invalid literal for int() with base 10

💡 Hint

Check what happens when the code tries to call strip() on None.

None is not a string and does not have the strip() method, so calling t.strip() on None raises AttributeError.

🚀 Application

advanced

Which option produces the cleaned DataFrame with replaced characters?

You have a DataFrame with a column 'Name' containing messy strings. You want to strip spaces, convert to lowercase, and replace 'a' with '@'. Which code produces the correct cleaned DataFrame?

Data Analysis Python

import pandas as pd
df = pd.DataFrame({'Name': [' Anna ', 'Mark', '  Sara', 'paul ']})

A df['Cleaned'] = df['Name'].str.strip().lower().replace('a', '@')

B df['Cleaned'] = df['Name'].strip().lower().replace('a', '@')

C df['Cleaned'] = df['Name'].apply(lambda x: x.strip().lower().replace('a', '@'))

D df['Cleaned'] = df['Name'].str.strip().str.lower().str.replace('a', '@')

💡 Hint

Remember to use pandas string methods with .str for vectorized operations.

Option D correctly chains pandas string methods with .str accessor. Option D fails because strip() and lower() are not methods on Series directly. Option D works but is slower and less idiomatic. Option D is invalid syntax.

🧠 Conceptual

expert

Why is chaining string methods with .str important in pandas?

In pandas, why do we use .str before string methods like strip(), lower(), and replace() when cleaning a Series of strings?

A Because .str is required to convert strings to uppercase only.

B Because .str converts the Series into a list of strings before applying methods.

C Because .str allows vectorized string operations on each element of the Series efficiently.

D Because .str automatically removes missing values before applying string methods.

💡 Hint

Think about how pandas handles operations on columns with many values.

The .str accessor enables vectorized string operations on each element of a pandas Series, making the operations efficient and concise.