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'.

Practice

1. What does the pandas str.strip() method do when applied to a Series of strings?

easy

A. Removes spaces from the start and end of each string

B. Removes all spaces inside each string

C. Converts all characters to uppercase

D. Splits strings into lists of words

Solution

1. Step 1: Understand the purpose of str.strip()

   This method removes whitespace characters only from the beginning and end of each string in the Series.

2. Step 2: Differentiate from other string methods

   Unlike str.replace() or str.upper(), it does not change spaces inside the string or letter cases.

3. Final Answer:

   Removes spaces from the start and end of each string -> Option A

4. Quick Check:

   str.strip() removes edge spaces [OK]

Hint: Remember: strip trims only start/end spaces, not inside [OK]

Common Mistakes:

• Thinking it removes spaces inside the string
• Confusing with uppercasing or splitting
• Assuming it removes all whitespace characters everywhere

2. Which of the following is the correct syntax to remove leading and trailing spaces from a pandas Series named data?

easy

A. data.strip()

B. data.strip.str()

C. data.str.strip()

D. strip(data)

Solution

1. Step 1: Recall pandas string accessor usage

   To use string methods on a Series, you must use the .str accessor before the method.

2. Step 2: Identify correct method call

   The correct syntax is data.str.strip(). Other options either miss the accessor or have wrong order.

3. Final Answer:

   data.str.strip() -> Option C

4. Quick Check:

   Use .str before string methods [OK]

Hint: Use .str before string methods on Series [OK]

Common Mistakes:

• Calling strip() directly on Series without .str
• Using wrong order like .strip.str()
• Trying to call strip as a standalone function

3. Given the pandas Series:

import pandas as pd
data = pd.Series(['  apple ', 'banana  ', '  cherry'])
cleaned = data.str.strip()

What is the output of print(cleaned.tolist())?

medium

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

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

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

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

Solution

1. Step 1: Understand what str.strip() does on each string

   It removes spaces from the start and end of each string, so ' apple ' becomes 'apple', 'banana ' becomes 'banana', and ' cherry' becomes 'cherry'.

2. Step 2: Convert the cleaned Series to a list

   Using tolist() shows the cleaned strings without leading or trailing spaces.

3. Final Answer:

   ['apple', 'banana', 'cherry'] -> Option B

4. Quick Check:

   strip removes edge spaces, output clean list [OK]

Hint: strip removes spaces only at string edges [OK]

Common Mistakes:

• Expecting spaces inside strings to be removed
• Confusing strip with replace or split
• Not converting Series to list before printing

4. You run this code:

import pandas as pd
s = pd.Series(['  dog', 'cat  ', '  bird  '])
s_clean = s.strip()

But get an error. What is the problem?

medium

A. You should use s.str.strip() instead of s.strip()

B. You forgot to import pandas

C. The Series contains numbers, so strip won't work

D. strip() does not remove spaces

Solution

1. Step 1: Identify the error cause

   Calling strip() directly on a pandas Series causes an AttributeError because Series objects do not have a direct strip() method.

2. Step 2: Correct method usage

   Use the string accessor .str to apply string methods on Series: s.str.strip().

3. Final Answer:

   You should use s.str.strip() instead of s.strip() -> Option A

4. Quick Check:

   Use .str before string methods on Series [OK]

Hint: Always use .str before string methods on Series [OK]

Common Mistakes:

• Calling strip() directly on Series
• Assuming strip works on non-string data
• Ignoring error messages about missing method

5. You have a DataFrame df with a column 'Name' containing names with extra spaces:

import pandas as pd
df = pd.DataFrame({'Name': ['  Mary Ann  ', 'Bob', '  Charlie  ', 'David ']})

How do you clean the 'Name' column to remove spaces only at the start and end, keeping spaces inside names intact?

hard

A. df['Name'] = df['Name'].lstrip().str.rstrip()

B. df['Name'] = df['Name'].str.replace(' ', '')

C. df['Name'] = df['Name'].strip(' ')

D. df['Name'] = df['Name'].str.strip()

Solution

1. Step 1: Understand the goal

   We want to remove only spaces at the start and end of each string, not spaces inside the names (e.g., keep 'Mary Ann' intact).

2. Step 2: Choose the correct method

   str.strip() removes leading and trailing whitespace. df['Name'] = df['Name'].str.strip() uses it correctly. df['Name'] = df['Name'].lstrip().str.rstrip() fails with AttributeError (no .str before lstrip()). df['Name'] = df['Name'].str.replace(' ', '') removes all spaces (e.g., 'MaryAnn'). df['Name'] = df['Name'].strip(' ') fails (no .str accessor).

3. Final Answer:

   df['Name'] = df['Name'].str.strip() -> Option D

4. Quick Check:

   Use str.strip() to clean edges only [OK]

Hint: Use str.strip() to clean edges, not inside spaces [OK]

Common Mistakes:

• Using str.replace(' ', '') removes all spaces, including inside names
• Forgetting .str accessor before lstrip() when chaining methods
• Calling strip() directly on Series without .str accessor