str.strip() for whitespace in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of str.strip() on pandas Series with whitespace

What is the output of the following code?

import pandas as pd
s = pd.Series(['  apple ', ' banana', 'cherry  ', '  date  '])
result = s.str.strip()
print(result.tolist())

Pandas

import pandas as pd
s = pd.Series(['  apple ', ' banana', 'cherry  ', '  date  '])
result = s.str.strip()
print(result.tolist())

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

B ['apple', 'banana', 'cherry', 'date']

C ['apple ', ' banana', 'cherry', 'date ']

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

💡 Hint

str.strip() removes spaces from both ends of each string in the Series.

The str.strip() method removes leading and trailing whitespace from each string in the pandas Series. So all spaces before and after the words are removed.

❓ data_output

intermediate

Count of strings after stripping whitespace

After stripping whitespace from the Series below, how many strings start with the letter 'b'?

import pandas as pd
s = pd.Series(['  apple ', ' banana', 'blueberry  ', '  date  ', '  berry'])
stripped = s.str.strip()
count_b = stripped.str.startswith('b').sum()
print(count_b)

Pandas

import pandas as pd
s = pd.Series(['  apple ', ' banana', 'blueberry  ', '  date  ', '  berry'])
stripped = s.str.strip()
count_b = stripped.str.startswith('b').sum()
print(count_b)

A 3

B 2

C 1

D 4

💡 Hint

Check which strings start with 'b' after removing spaces.

After stripping, the strings starting with 'b' are 'banana', 'blueberry', and 'berry', so count is 3.

🔧 Debug

advanced

Identify the error in using str.strip() on a non-string column

What error will this code raise?

import pandas as pd
df = pd.DataFrame({'numbers': [1, 2, 3, 4]})
df['clean'] = df['numbers'].str.strip()

Pandas

import pandas as pd
df = pd.DataFrame({'numbers': [1, 2, 3, 4]})
df['clean'] = df['numbers'].str.strip()

A KeyError: 'str'

B No error, returns original numbers

C TypeError: strip() argument must be str, not int

D AttributeError: Can only use .str accessor with string values!

💡 Hint

The .str accessor only works on string data.

The .str accessor raises AttributeError if the Series contains non-string values like integers.

🚀 Application

advanced

Using str.strip() to clean a DataFrame column before analysis

You have a DataFrame with a column 'city' containing city names with extra spaces. Which code snippet correctly cleans the 'city' column by removing leading and trailing spaces?

import pandas as pd
df = pd.DataFrame({'city': [' New York ', 'Los Angeles ', ' Chicago', 'Houston ']})

Pandas

import pandas as pd
df = pd.DataFrame({'city': [' New York ', 'Los Angeles ', ' Chicago', 'Houston ']})

A df['city'] = df['city'].apply(strip())

B df['city'] = df['city'].strip()

C df['city'] = df['city'].str.strip()

D df['city'] = df['city'].map(str.strip)

💡 Hint

Use the pandas string accessor for element-wise string operations.

Option C uses the correct pandas .str accessor to apply strip to each string in the Series. Option C tries to call strip on the whole Series, which is invalid. Option C calls strip() without a function reference. Option C passes str.strip but map expects a function without parentheses, so it would work but is less idiomatic.

🧠 Conceptual

expert

Understanding str.strip() behavior with custom characters

What will be the output of this code?

import pandas as pd
s = pd.Series(['xxapplexx', 'xbanana', 'cherryxx', 'xxdate'])
result = s.str.strip('x')
print(result.tolist())

Pandas

import pandas as pd
s = pd.Series(['xxapplexx', 'xbanana', 'cherryxx', 'xxdate'])
result = s.str.strip('x')
print(result.tolist())

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

B ['xxapplexx', 'xbanana', 'cherryxx', 'xxdate']

C ['applexx', 'banana', 'cherry', 'date']

D ['apple', 'banana', 'cherryxx', 'date']

💡 Hint

str.strip(chars) removes all characters in 'chars' from both ends until a different character is found.

The str.strip('x') removes all 'x' characters from the start and end of each string. So 'xxapplexx' becomes 'apple', 'xbanana' becomes 'banana', 'cherryxx' becomes 'cherry', and 'xxdate' becomes 'date'.