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.

Practice

1. What does the pandas method str.lower() do when applied to a Series of text data?

easy

A. Converts all characters in the text to lowercase.

B. Converts all characters in the text to uppercase.

C. Removes all spaces from the text.

D. Reverses the order of characters in the text.

Solution

1. Step 1: Understand the method purpose

   The str.lower() method changes all letters in text to lowercase.

2. Step 2: Compare with other options

   Other options describe different actions like uppercase, removing spaces, or reversing text, which are not what str.lower() does.

3. Final Answer:

   Converts all characters in the text to lowercase. -> Option A

4. Quick Check:

   str.lower() = lowercase conversion [OK]

Hint: Lowercase means all letters small [OK]

Common Mistakes:

• Confusing str.lower() with str.upper()
• Thinking it removes spaces
• Assuming it reverses text

2. Which of the following is the correct way to convert a pandas Series named names to uppercase?

easy

A. names.str.upper()

B. str.upper(names)

C. names.upper()

D. names.str.upper

Solution

1. Step 1: Recall pandas string method syntax

   To apply string methods on a pandas Series, use Series.str.method() syntax.

2. Step 2: Check each option

   names.str.upper() uses names.str.upper(), which is correct. names.upper() misses .str, str.upper(names) is incorrect function call, names.str.upper misses parentheses.

3. Final Answer:

   names.str.upper() -> Option A

4. Quick Check:

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

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

Common Mistakes:

• Calling upper() directly on Series without .str
• Forgetting parentheses after method
• Using str.upper() as a standalone function

3. Given the pandas Series data = pd.Series(['Apple', 'Banana', 'Cherry']), what is the output of data.str.lower()?

medium

A. ['Apple', 'Banana', 'Cherry']

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

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

D. ['aPPLE', 'bANANA', 'cHERRY']

Solution

1. Step 1: Understand the input Series

   The Series contains capitalized fruit names: 'Apple', 'Banana', 'Cherry'.

2. Step 2: Apply str.lower() method

   This method converts all letters to lowercase, so each word becomes fully lowercase.

3. Final Answer:

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

4. Quick Check:

   str.lower() makes all letters lowercase [OK]

Hint: str.lower() makes all letters small [OK]

Common Mistakes:

• Expecting original case to remain
• Confusing with str.upper() output
• Thinking only first letter changes

4. What is wrong with this code snippet to convert a pandas Series cities to uppercase?

cities = pd.Series(['Paris', 'London', 'Berlin'])
cities_upper = cities.upper()

medium

A. Missing import statement for pandas.

B. The code should use cities.upper without parentheses.

C. Series cannot be converted to uppercase.

D. Method upper() should be called as cities.str.upper().

Solution

1. Step 1: Identify method usage on Series

   String methods like upper() must be called with .str accessor on pandas Series.

2. Step 2: Check the code

   The code uses cities.upper() which is invalid; correct is cities.str.upper().

3. Final Answer:

   Method upper() should be called as cities.str.upper(). -> Option D

4. Quick Check:

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

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

Common Mistakes:

• Calling upper() directly on Series
• Forgetting .str accessor
• Removing parentheses from method call

5. You have a pandas DataFrame df with a column 'Product' containing mixed case product names. You want to create a new column 'Product_Upper' with all names in uppercase. Which code correctly does this?

hard

A. df['Product_Upper'] = df['Product'].upper()

B. df['Product_Upper'] = df['Product'].str.upper()

C. df['Product_Upper'] = str.upper(df['Product'])

D. df['Product_Upper'] = df['Product'].str.lower()

Solution

1. Step 1: Understand the DataFrame and goal

   The goal is to create a new column with uppercase product names from an existing column.

2. Step 2: Apply correct method for uppercase conversion

   Use .str.upper() on the Series to convert text to uppercase correctly.

3. Step 3: Check options

   df['Product_Upper'] = df['Product'].str.upper() uses correct syntax. df['Product_Upper'] = df['Product'].upper() misses .str, df['Product_Upper'] = str.upper(df['Product']) uses wrong function call, df['Product_Upper'] = df['Product'].str.lower() converts to lowercase, not uppercase.

4. Final Answer:

   df['Product_Upper'] = df['Product'].str.upper() -> Option B

5. Quick Check:

   Use .str.upper() to uppercase Series text [OK]

Hint: Use .str.upper() to convert Series text uppercase [OK]

Common Mistakes:

• Calling upper() without .str
• Using str.upper() as a function on Series
• Confusing upper() with lower()