str accessor for string methods in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of str.upper() on a pandas Series

What is the output of the following code?

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

Pandas

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 applied to a pandas Series converts every string element to uppercase letters.

❓ 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)?

import pandas as pd
s = pd.Series(['Cat', 'dog', 'Parrot', 'fish', 'ant'])
count = s.str.contains('a', case=False).sum()
print(count)

Pandas

import pandas as pd
s = pd.Series(['Cat', 'dog', 'Parrot', 'fish', 'ant'])
count = s.str.contains('a', case=False).sum()
print(count)

A 3

B 2

C 4

D 1

💡 Hint

Check which strings have 'a' or 'A' in them.

The strings 'Cat', 'Parrot', and 'ant' contain 'a' or 'A', so the count is 3.

🔧 Debug

advanced

Identify the error in using str.replace()

What error does the following code raise?

import pandas as pd
s = pd.Series(['apple', 'banana', 'cherry'])
result = s.str.replace('a', 1)
print(result)

Pandas

import pandas as pd
s = pd.Series(['apple', 'banana', 'cherry'])
result = s.str.replace('a', 1)
print(result)

A AttributeError: 'Series' object has no attribute 'str'

B ValueError: invalid replacement string

C TypeError: expected string or bytes-like object

D No error, output is ['1pple', 'b1n1n1', 'cherry']

💡 Hint

Check the type of the replacement argument in str.replace.

The replacement argument must be a string; passing an integer causes a TypeError.

❓ visualization

advanced

Visualize string length distribution using str.len()

You have a pandas Series of words. Which code snippet correctly creates a histogram of their lengths?

import pandas as pd
import matplotlib.pyplot as plt
s = pd.Series(['dog', 'elephant', 'cat', 'hippopotamus', 'fox'])
# Which code below creates the histogram?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
s = pd.Series(['dog', 'elephant', 'cat', 'hippopotamus', 'fox'])

A

plt.hist(s.str.len())
plt.show()

B

plt.hist(s.len())
plt.show()

C

plt.hist(len(s))
plt.show()

D

plt.hist(s.str.length())
plt.show()

💡 Hint

Use the str accessor to get string lengths.

The str.len() method returns the length of each string element, which can be plotted.

🚀 Application

expert

Filter DataFrame rows where a column's strings start with a vowel

Given the DataFrame below, which code correctly filters rows where the 'Name' column starts with a vowel (a, e, i, o, u), case insensitive?

import pandas as pd
df = pd.DataFrame({'Name': ['Alice', 'bob', 'Eve', 'Oscar', 'Uma', 'Charlie']})

Pandas

import pandas as pd
df = pd.DataFrame({'Name': ['Alice', 'bob', 'Eve', 'Oscar', 'Uma', 'Charlie']})

A

filtered = df[df['Name'].str.match('[aeiou]$', case=False)]
print(filtered)

B

filtered = df[df['Name'].str.startswith('aeiou')]
print(filtered)

C

filtered = df[df['Name'].str.contains('^[aeiou]', case=True)]
print(filtered)

D

filtered = df[df['Name'].str.match('^[aeiou]', case=False)]
print(filtered)

💡 Hint

Use a regex pattern with str.match to check the start of the string.

str.match with regex '^[aeiou]' and case=False matches strings starting with any vowel ignoring case.