String accessor (.str) methods in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of .str.upper() on a Series

What is the output of this code snippet?

Data Analysis Python

import pandas as pd
s = pd.Series(['apple', 'Banana', 'Cherry'])
result = s.str.upper()
print(result)

A

0    APPLE
1    Banana
2    CHERRY
dtype: object

B

0    apple
1    Banana
2    Cherry
dtype: object

C

0     APPLE
1    BANANA
2    CHERRY
dtype: object

D

0    apple
1    BANANA
2    cherry
dtype: object

💡 Hint

The .str.upper() method converts all characters in each string to uppercase.

The .str.upper() method changes all letters in each string of the Series to uppercase. So all words become fully uppercase.

❓ data_output

intermediate

Count of strings containing 'a' using .str.contains()

Given the Series below, what is the count of strings that contain the letter 'a' (case insensitive)?

Data Analysis Python

import pandas as pd
s = pd.Series(['Cat', 'dog', 'parrot', 'fish', 'ant'])
count = s.str.contains('a', case=False).sum()
print(count)

A 1

B 3

C 4

D 2

💡 Hint

Use .str.contains() with case=False to ignore letter case.

The strings containing 'a' or 'A' are 'Cat', 'parrot', and 'ant', so count is 3.

🔧 Debug

advanced

Identify the error in .str.replace() usage

What error does this code raise?

Data Analysis Python

import pandas as pd
s = pd.Series(['apple', 'banana', 'cherry'])
result = s.str.replace('a', 1)
print(result)

A TypeError: expected string or bytes-like object

B ValueError: invalid replacement

C

No error, output is:
0    1pple
1    b1n1n1
2    cherry
dtype: object

D AttributeError: 'Series' object has no attribute 'str'

💡 Hint

Check the type of the replacement argument in .str.replace().

The .str.replace() method expects the replacement to be a string, but 1 is an integer, causing a TypeError.

🧠 Conceptual

advanced

Behavior of .str.split() with n parameter

What is the output of this code?

Data Analysis Python

import pandas as pd
s = pd.Series(['a,b,c', 'd,e,f', 'g,h,i'])
result = s.str.split(',', n=1)
print(result)

A

0    [a, b, c]
1    [d, e, f]
2    [g, h, i]
dtype: object

B

0    [a]
1    [d]
2    [g]
dtype: object

C

0    [a,b,c]
1    [d,e,f]
2    [g,h,i]
dtype: object

D

0    [a, b,c]
1    [d, e,f]
2    [g, h,i]
dtype: object

💡 Hint

The n parameter limits the number of splits from the left.

With n=1, only the first comma is used to split each string, so the result is a list with two elements: before and after the first comma.

🚀 Application

expert

Extract domain names from email addresses

Given a Series of email addresses, which option correctly extracts the domain names (part after '@')?

Data Analysis Python

import pandas as pd
emails = pd.Series(['[e-mail]', '[e-mail]', '[e-mail]'])
domains = ???
print(domains)

A emails.str.partition('@')[2]

B emails.str.extract(r'@(\w+\.\w+)')[0]

C emails.str.split('@').str[1]

D emails.str.replace(r'.*@', '')

💡 Hint

Use a method that splits the string at '@' and returns the part after it.

The .str.partition('@')[2] returns the part after '@' correctly. Option A returns a Series of lists, not strings. Option A's regex misses dots in domain names. Option A uses replace with regex but without regex=True, so it fails.

Practice

1. What does the .str accessor do in pandas when used on a text column?

easy

A. It allows applying string methods to each element in the column.

B. It converts the column to numeric values.

C. It sorts the column alphabetically.

D. It removes missing values from the column.

Solution

1. Step 1: Understand the purpose of .str in pandas

   The .str accessor is used to apply string methods element-wise on a pandas Series containing text data.

2. Step 2: Compare options with the known function

   Only It allows applying string methods to each element in the column. correctly describes this behavior. Options A, B, and D describe other unrelated operations.

3. Final Answer:

   It allows applying string methods to each element in the column. -> Option A

4. Quick Check:

   .str applies string methods [OK]

Hint: Remember: .str works like string methods on each item [OK]

Common Mistakes:

• Thinking .str changes data type
• Confusing .str with sorting or filtering
• Assuming it removes missing values automatically

2. Which of the following is the correct syntax to convert all strings in a pandas Series df['name'] to lowercase using .str?

easy

A. df['name'].str.toLower()

B. df['name'].lower()

C. df.str['name'].lower()

D. df['name'].str.lower()

Solution

1. Step 1: Recall correct .str usage

   To apply string methods on a Series, use Series.str.method(). Here, lower() is the method to convert to lowercase.

2. Step 2: Check each option's syntax

   df['name'].str.lower() uses df['name'].str.lower(), which is correct. df['name'].lower() misses .str. df.str['name'].lower() has wrong order. df['name'].str.toLower() uses incorrect method name toLower() (JavaScript style).

3. Final Answer:

   df['name'].str.lower() -> Option D

4. Quick Check:

   Use .str.lower() on Series [OK]

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

Common Mistakes:

• Omitting .str and calling string method directly
• Using JavaScript method names like toLower()
• Incorrect order like df.str['name']

3. Given the pandas Series:

import pandas as pd
s = pd.Series(['Apple', 'banana', 'Cherry'])
result = s.str.upper()

What is the value of result?

medium

A. ['APPLE', 'BANANA', 'CHERRY']

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

C. ['Apple', 'banana', 'Cherry']

D. Error: str.upper() not found

Solution

1. Step 1: Understand what .str.upper() does

   This method converts each string in the Series to uppercase letters.

2. Step 2: Apply .str.upper() to each element

   'Apple' becomes 'APPLE', 'banana' becomes 'BANANA', and 'Cherry' becomes 'CHERRY'.

3. Final Answer:

   ['APPLE', 'BANANA', 'CHERRY'] -> Option A

4. Quick Check:

   .str.upper() converts all to uppercase [OK]

Hint: Uppercase all strings with .str.upper() [OK]

Common Mistakes:

• Expecting lowercase output
• Thinking original case remains unchanged
• Assuming method causes error

4. What is wrong with this code snippet?

import pandas as pd
s = pd.Series(['cat', 'dog', 'bird'])
result = s.str.capitalize

medium

A. Series does not support .str accessor

B. Wrong method name, should be capital

C. Missing parentheses to call the method

D. No error, code runs fine

Solution

1. Step 1: Identify the method call syntax

   String methods must be called with parentheses, e.g., capitalize(), not just referenced.

2. Step 2: Check the code snippet

   The code uses s.str.capitalize without parentheses, so it returns a method object, not the transformed Series.

3. Final Answer:

   Missing parentheses to call the method -> Option C

4. Quick Check:

   Always add () to call string methods [OK]

Hint: Add () to call string methods, not just reference [OK]

Common Mistakes:

• Forgetting parentheses after method name
• Using wrong method names
• Thinking Series lacks .str accessor

5. You have a pandas Series s with email addresses. You want to extract the domain part (after '@') for each email. Which code correctly uses .str methods to do this?

hard

A. s.str.replace('@', '')

B. s.str.split('@').str[1]

C. s.str.find('@')

D. s.str.extract('@')

Solution

1. Step 1: Understand the goal

   We want the part after '@' in each email string.

2. Step 2: Analyze each option

   s.str.split('@').str[1] splits each string at '@' and selects the second part (index 1), which is the domain. s.str.extract('@') extracts only '@' character, not the domain. s.str.find('@') returns the position of '@', not the domain. s.str.replace('@', '') removes '@' but keeps the rest combined.

3. Final Answer:

   s.str.split('@').str[1] -> Option B

4. Quick Check:

   Split by '@' and take second part [OK]

Hint: Split by '@' and select index 1 for domain [OK]

Common Mistakes:

• Using extract('@') which only matches '@'
• Using find('@') which returns position, not substring
• Replacing '@' removes it but does not isolate domain