Pattern matching with str.contains in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of str.contains with case sensitivity

What is the output of this code snippet?

Data Analysis Python

import pandas as pd

data = pd.Series(['Apple', 'banana', 'Cherry', 'date'])
result = data.str.contains('a')
print(result.tolist())

A [True, False, False, True]

B [True, True, False, True]

C [True, True, True, True]

D [False, True, False, True]

💡 Hint

Remember that str.contains is case sensitive by default.

The method str.contains('a') checks if the lowercase letter 'a' is in each string. 'Apple' has uppercase 'A' so it returns False, but since 'Apple' starts with uppercase 'A', it does not match lowercase 'a'. 'banana' and 'date' contain lowercase 'a', so True. 'Cherry' does not contain 'a'.

❓ data_output

intermediate

Filtering DataFrame rows with str.contains and regex

Given the DataFrame below, which rows are selected by the filter df[df['Name'].str.contains('^J.*n$', regex=True)]?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'Name': ['John', 'Jason', 'Jordan', 'Joan', 'Jan', 'Jim']})
filtered = df[df['Name'].str.contains('^J.*n$', regex=True)]
print(filtered['Name'].tolist())

A ['John', 'Jordan', 'Jan']

B ['John', 'Jason', 'Jordan', 'Joan', 'Jan']

C ['John', 'Jordan', 'Joan', 'Jan']

D ['John', 'Jan']

💡 Hint

The regex '^J.*n$' means strings starting with 'J' and ending with 'n'.

The regex ^J.*n$ matches strings that start with 'J' and end with 'n'. 'John', 'Jordan', 'Jan' match. 'Jason' and 'Joan' do not end with 'n'. 'Jim' does not.

🔧 Debug

advanced

Identify the error in str.contains usage

What error does this code raise?

Data Analysis Python

import pandas as pd

data = pd.Series(['cat', 'dog', 'bird'])
result = data.str.contains('[a-z')
print(result)

A ValueError: Unbalanced bracket expression

B SyntaxError

C TypeError

D No error, outputs a boolean Series

💡 Hint

Check the regex pattern for correctness.

The regex pattern [a-z is missing a closing bracket, causing an unbalanced bracket error.

❓ visualization

advanced

Visualizing pattern match counts in a DataFrame

Which code snippet correctly creates a bar chart showing counts of rows where 'City' contains 'York' (case insensitive)?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'City': ['New York', 'Yorkshire', 'Los Angeles', 'york', 'Boston']})

# Which code below produces the correct bar chart?

A

counts = df['City'].str.contains('York', case=True).value_counts()
counts.plot(kind='bar')
plt.show()

B

counts = df['City'].str.contains('York').value_counts()
counts.plot(kind='bar')
plt.show()

C

counts = df['City'].str.contains('York', case=False).value_counts()
counts.plot(kind='bar')
plt.show()

D

counts = df['City'].str.contains('york', case=True).value_counts()
counts.plot(kind='bar')
plt.show()

💡 Hint

Look for case insensitive matching.

Option C uses case=False to match 'York' regardless of case, counting all rows containing 'York' or 'york'. Others are case sensitive and miss some matches.

🚀 Application

expert

Extracting and counting patterns with str.contains and groupby

You have a DataFrame with a 'Text' column containing sentences. You want to count how many rows mention either 'cat' or 'dog' (case insensitive). Which code snippet correctly produces a DataFrame with counts for each animal?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'Text': ['I love my Cat', 'Dog is friendly', 'Cats and dogs', 'No pets here', 'dog and cat']})

# Which code below produces a DataFrame with counts of rows mentioning 'cat' and 'dog'?

A

counts = df['Text'].str.contains('cat|dog', case=False).value_counts()
print(counts)

B

counts = pd.Series({
    'cat': df['Text'].str.contains('cat', case=False).sum(),
    'dog': df['Text'].str.contains('dog', case=False).sum()
}).to_frame('Count')
print(counts)

C

counts = df['Text'].apply(lambda x: 'cat' if 'cat' in x else ('dog' if 'dog' in x else None)).value_counts()
print(counts)

D

counts = df['Text'].str.extractall('(cat|dog)', flags=re.IGNORECASE).groupby(0).size().to_frame('Count')
print(counts)

💡 Hint

Use str.contains for each animal separately and sum the booleans.

Option B creates a Series with sums (counts of rows containing each animal ignoring case) for 'cat' and 'dog', then converts to a DataFrame with column 'Count'.