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.