str.lower() and str.upper() in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of converting pandas Series to lowercase

What is the output of this code snippet?

Pandas

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

A

0     apple
1    banana
2    cherry
dtype: object

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

C

0     APPLE
1    BANANA
2    CHERRY
dtype: object

D ['APPLE', 'BANANA', 'CHERRY']

💡 Hint

Use str.lower() to convert all strings in the Series to lowercase.

The str.lower() method converts each string in the pandas Series to lowercase, preserving the Series structure and index.

❓ data_output

intermediate

Count uppercase strings in a pandas Series

Given the Series below, how many strings are fully uppercase?

Pandas

import pandas as pd
s = pd.Series(['dog', 'CAT', 'Bird', 'FISH'])
upper_s = s.str.upper()
count_upper = sum(upper_s == s)

A 3

B 2

C 1

D 4

💡 Hint

Compare the original Series with the uppercase version to find matches.

Only 'CAT' and 'FISH' are already uppercase, so after str.upper(), they remain the same. The count is 2.

🔧 Debug

advanced

Identify the error in using str.lower() on a DataFrame column

What error does this code raise?

Pandas

import pandas as pd
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'CHARLIE']})
df['Name_lower'] = df['Name'].lower()

A TypeError: lower() takes no arguments (1 given)

B No error, works fine

C AttributeError: 'Series' object has no attribute 'lower'

D KeyError: 'lower'

💡 Hint

Check if the pandas Series supports the lower() method directly.

The lower() method is a string method, not a pandas Series method. Use str.lower() instead.

🚀 Application

advanced

Filter DataFrame rows with uppercase names

Which code correctly filters rows where the 'Name' column is fully uppercase?

Pandas

import pandas as pd
df = pd.DataFrame({'Name': ['ALICE', 'Bob', 'CHARLIE', 'dave']})

A df[df['Name'].str.lower() == df['Name']]

B df[df['Name'] == df['Name'].str.upper()]

C df[df['Name'].str.upper() == 'NAME']

D df[df['Name'].str.isupper()]

💡 Hint

Use a string method that checks if all characters are uppercase.

str.isupper() returns True for strings that are fully uppercase, so option D filters correctly.

🧠 Conceptual

expert

Understanding behavior of str.upper() with missing values

What is the output of this code?

Pandas

import pandas as pd
s = pd.Series(['hello', None, 'WORLD'])
result = s.str.upper()
print(result)

A

0    HELLO
1     NaN
2    WORLD
dtype: object

B Raises TypeError due to None value

C

0    HELLO
1    NAN
2    WORLD
dtype: object

D

0    HELLO
1     None
2    WORLD
dtype: object

💡 Hint

Check how pandas handles None values in string operations.

pandas converts None to NaN in object dtype Series and str.upper() returns NaN for missing values.