String type (object, string) in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this pandas string operation?

Given the following DataFrame, what will be the output of df['name'].str.upper()?

Pandas

import pandas as pd
df = pd.DataFrame({'name': ['alice', 'Bob', 'CHARLIE']})
result = df['name'].str.upper()
print(result)

A

0      alice
1        bob
2    charlie
Name: name, dtype: object

B

0      ALICE
1        BOB
2    CHARLIE
Name: name, dtype: object

C

0      ALICE
1        bob
2    CHARLIE
Name: name, dtype: object

D

0      Alice
1        Bob
2    Charlie
Name: name, dtype: object

💡 Hint

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

The str.upper() method changes all letters to uppercase regardless of their original case.

❓ data_output

intermediate

How many entries contain the substring 'cat'?

Using the DataFrame below, how many rows have the substring 'cat' in the animal column?

Pandas

import pandas as pd
df = pd.DataFrame({'animal': ['cat', 'dog', 'caterpillar', 'bird', 'concatenate']})
count = df['animal'].str.contains('cat').sum()
print(count)

A 2

B 1

C 4

D 3

💡 Hint

Check which strings include the exact substring 'cat' anywhere inside.

The strings 'cat', 'caterpillar', and 'concatenate' all contain 'cat', so the count is 3.

🔧 Debug

advanced

What error does this code raise?

Consider this code snippet:

import pandas as pd
df = pd.DataFrame({'text': ['apple', 'banana', None]})
df['text'].str.len()

What error or output occurs?

Pandas

import pandas as pd
df = pd.DataFrame({'text': ['apple', 'banana', None]})
result = df['text'].str.len()
print(result)

A

0    5
1    6
2    0
dtype: int64

B TypeError: object of type 'NoneType' has no len()

C

0    5
1    6
2    NaN
dtype: float64

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

💡 Hint

pandas string methods handle None values gracefully by returning NaN.

The str.len() method returns the length of each string. For None values, it returns NaN instead of raising an error.

❓ visualization

advanced

Which plot shows the correct count of string lengths?

Given this DataFrame, which plot correctly shows the count of each string length in the words column?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'words': ['cat', 'dog', 'bird', 'fish', 'elephant']})
lengths = df['words'].str.len()
counts = lengths.value_counts().sort_index()
counts.plot(kind='bar')
plt.show()

A Bar chart with x-axis: [3,4,8], y-axis: [2,2,1]

B Bar chart with x-axis: [3,4,5], y-axis: [1,3,1]

C Bar chart with x-axis: [3,4,5], y-axis: [2,2,1]

D Bar chart with x-axis: [3,4,8], y-axis: [1,3,1]

💡 Hint

Count how many words have length 3, 4, and 8.

Words 'cat' and 'dog' have length 3 (2 counts), 'bird' and 'fish' length 4 (2 counts), 'elephant' length 8 (1 count).

🧠 Conceptual

expert

Why prefer pandas string dtype over object dtype for text data?

Which reason best explains why using pandas string dtype is better than object dtype for text columns?

A String dtype supports missing values natively and has optimized string methods.

B Object dtype uses less memory and is faster for string operations.

C Object dtype allows vectorized string operations which string dtype does not.

D String dtype automatically converts all text to lowercase.

💡 Hint

Think about missing data handling and performance.

The pandas string dtype is designed for text data with native missing value support and optimized methods, unlike object dtype which is a generic container.

Practice

1. What is the main advantage of using pandas string dtype over the traditional object dtype for text data?

easy

A. It provides better support for string methods and handles missing values consistently.

B. It uses less memory than object dtype for numeric data.

C. It automatically converts numbers to strings.

D. It disables all string operations to improve speed.

Solution

1. Step 1: Understand pandas string dtype purpose

   The pandas string dtype is designed to store text data efficiently and provide consistent behavior for missing values.

2. Step 2: Compare with object dtype

   The object dtype can hold any Python object, but string operations are less consistent and missing values are handled differently.

3. Final Answer:

   It provides better support for string methods and handles missing values consistently. -> Option A

4. Quick Check:

   pandas string dtype = better string methods + consistent missing values [OK]

Hint: String dtype improves text handling and missing value support [OK]

Common Mistakes:

• Thinking string dtype reduces memory for numbers
• Believing string dtype disables string operations
• Confusing string dtype with automatic type conversion

2. Which of the following is the correct way to convert a pandas DataFrame column named 'name' to the string dtype?

easy

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

B. df['name'] = df['name'].to_string()

C. df['name'] = df['name'].astype('string')

D. df['name'] = df['name'].astype(str)

Solution

1. Step 1: Identify pandas method for dtype conversion

   To convert a column to pandas string dtype, use astype('string').

2. Step 2: Check each option

   df['name'] = df['name'].astype('string') uses astype('string'), which is correct. df['name'] = df['name'].to_string() calls a method that converts to a single string, not dtype. df['name'] = str(df['name']) converts the whole Series to a Python string, which is incorrect. df['name'] = df['name'].astype(str) uses Python str type, not pandas string dtype.

3. Final Answer:

   df['name'] = df['name'].astype('string') -> Option C

4. Quick Check:

   Use astype('string') to convert column dtype [OK]

Hint: Use astype('string') to convert column to pandas string dtype [OK]

Common Mistakes:

• Using Python str() instead of astype('string')
• Calling to_string() which returns a single string
• Using astype(str) which gives object dtype

3. Given the following code, what will be the output of print(df['city'].str.upper())?

import pandas as pd
data = {'city': ['Paris', 'london', None, 'New York']}
df = pd.DataFrame(data)
df['city'] = df['city'].astype('string')

medium

A. 0 PARIS
1 LONDON
2 None
3 NEW YORK
Name: city, dtype: object

B. 0 PARIS
1 LONDON
2 <NA>
3 NEW YORK
Name: city, dtype: string

C. Error because None cannot be converted to uppercase

D. 0 Paris
1 london
2 <NA>
3 New York
Name: city, dtype: string

Solution

1. Step 1: Convert column to pandas string dtype

   The column city is converted to pandas string dtype, which supports missing values as <NA>.

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

   The str.upper() method converts all strings to uppercase and leaves <NA> as is without error.

3. Final Answer:

   0 PARIS 1 LONDON 2 <NA> 3 NEW YORK Name: city, dtype: string -> Option B

4. Quick Check:

   str.upper() converts strings, keeps <NA> unchanged [OK]

Hint: pandas string methods handle <NA> safely without errors [OK]

Common Mistakes:

• Expecting None instead of <NA> after conversion
• Thinking str.upper() raises error on missing values
• Confusing dtype object with string dtype output

4. What is wrong with this code snippet that tries to convert a column to string dtype?

df['col'] = df['col'].astype(str).astype('string')

medium

A. It first converts to Python string objects, losing pandas string dtype benefits.

B. It will raise a TypeError because of double astype calls.

C. It correctly converts to pandas string dtype with no issues.

D. It converts numeric values to integers instead of strings.

Solution

1. Step 1: Analyze the first conversion to str

   The first astype(str) converts the column to Python string objects (dtype object), losing pandas string dtype features.

2. Step 2: Analyze the second conversion to 'string'

   The second astype('string') converts the object dtype to pandas string dtype, but the initial conversion may cause unexpected behavior or inefficiency.

3. Final Answer:

   It first converts to Python string objects, losing pandas string dtype benefits. -> Option A

4. Quick Check:

   Avoid double conversion; use astype('string') directly [OK]

Hint: Convert directly with astype('string'), avoid intermediate astype(str) [OK]

Common Mistakes:

• Thinking double astype calls cause errors
• Believing intermediate conversion improves performance
• Ignoring pandas string dtype advantages

5. You have a DataFrame with a column 'comments' containing text and some missing values. You want to replace all missing values with the string 'No comment' and then convert the column to pandas string dtype. Which code snippet correctly does this?

hard

A. df['comments'] = df['comments'].replace(None, 'No comment').astype(str)

B. df['comments'] = df['comments'].astype('string').replace(None, 'No comment')

C. df['comments'] = df['comments'].fillna(pd.NA).astype('string')

D. df['comments'] = df['comments'].fillna('No comment').astype('string')

Solution

1. Step 1: Replace missing values before conversion

   Use fillna('No comment') to replace missing values with the desired string before converting dtype.

2. Step 2: Convert column to pandas string dtype

   Apply astype('string') after filling missing values to ensure the column uses pandas string dtype.

3. Final Answer:

   df['comments'] = df['comments'].fillna('No comment').astype('string') -> Option D

4. Quick Check:

   Fill missing first, then convert to string dtype [OK]

Hint: Fill missing values before astype('string') conversion [OK]

Common Mistakes:

• Filling missing after conversion causing dtype issues
• Using astype(str) instead of astype('string')
• Replacing None without handling pandas <NA> properly