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.