str.contains() for pattern matching in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of str.contains() with case sensitivity

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({'text': ['Apple', 'banana', 'Cherry', 'date']})
result = df['text'].str.contains('a')
print(result.tolist())

A [false, true, false, true]

B [true, true, true, true]

C [true, true, false, true]

D [true, false, false, true]

💡 Hint

Remember that str.contains() is case sensitive by default.

The pattern 'a' matches only lowercase 'a'. 'Apple' has uppercase 'A' so false, 'banana' true, 'Cherry' false, 'date' true. So the result is [false, true, false, true].

❓ data_output

intermediate

Filter rows using str.contains() with regex

Given the DataFrame below, which rows remain after filtering with str.contains('^b.*a$', regex=true)?

Pandas

import pandas as pd

df = pd.DataFrame({'words': ['banana', 'beta', 'bat', 'boa', 'baba', 'cab']})
filtered = df[df['words'].str.contains('^b.*a$', regex=True)]
print(filtered['words'].tolist())

A ['banana', 'beta', 'baba']

B ['banana', 'beta', 'bat', 'baba']

C ['banana', 'beta', 'baba', 'bat', 'boa']

D ['banana', 'beta', 'boa', 'baba']

💡 Hint

The regex '^b.*a$' means strings starting with 'b' and ending with 'a'.

'banana', 'beta', 'boa', 'baba' start with 'b' and end with 'a'. 'bat' ends with 't', 'cab' starts with 'c'. The output preserves order: ['banana', 'beta', 'boa', 'baba'].

🔧 Debug

advanced

Identify the error in str.contains() usage

What error does the following code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'col': ['abc', 'def', 'ghi']})
result = df['col'].str.contains(123)
print(result)

A TypeError: expected string or bytes-like object

B ValueError: invalid regex pattern

C AttributeError: 'int' object has no attribute 'contains'

D No error, outputs a boolean Series

💡 Hint

Check the type of the pattern argument passed to str.contains().

str.contains() expects a string or regex pattern. Passing an integer causes a TypeError.

🚀 Application

advanced

Use str.contains() to find rows with multiple patterns

You want to filter rows where the 'text' column contains either 'cat' or 'dog'. Which code correctly does this?

Pandas

import pandas as pd

df = pd.DataFrame({'text': ['catfish', 'dogma', 'bird', 'catalog', 'frog']})

A df[df['text'].str.contains('cat&dog')]

B df[df['text'].str.contains('cat|dog')]

C df[df['text'].str.contains(['cat', 'dog'])]

D df[df['text'].str.contains('cat,dog')]

💡 Hint

Use regex OR operator '|' inside the pattern string.

Option B uses regex 'cat|dog' which matches strings containing 'cat' or 'dog'. Other options are invalid or do not work as intended.

🧠 Conceptual

expert

Understanding na parameter in str.contains()

What is the output of the following code?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({'text': ['apple', np.nan, 'banana', np.nan]})
result = df['text'].str.contains('a', na=False)
print(result.tolist())

A [true, true, true, true]

B [true, true, false, false]

C [true, false, true, false]

D [true, false, true, true]

💡 Hint

The na parameter controls the output for missing values.

With na=False, missing values (NaN) are treated as false in the output boolean Series.

Practice

1. What does the str.contains() method do in pandas?

easy

A. It checks if a string pattern exists in each element of a Series.

B. It replaces strings matching a pattern with another string.

C. It counts the number of times a pattern appears in a Series.

D. It sorts the Series based on string length.

Solution

1. Step 1: Understand the purpose of str.contains()

   This method is used to check if each string element in a pandas Series contains a specific pattern.

2. Step 2: Compare with other string methods

   Replacing strings or counting occurrences are done by other methods like str.replace() or str.count(). Sorting is unrelated.

3. Final Answer:

   It checks if a string pattern exists in each element of a Series. -> Option A

4. Quick Check:

   str.contains() = check pattern presence [OK]

Hint: Remember: contains means 'is pattern inside?' [OK]

Common Mistakes:

• Confusing contains with replace
• Thinking it counts occurrences
• Assuming it sorts data

2. Which of the following is the correct syntax to find rows where column 'Name' contains the substring 'John' (case insensitive) in a DataFrame df?

easy

A. df[df['Name'].str.contains('John', ignore_case=True)]

B. df[df['Name'].contains('John', case=False)]

C. df[df['Name'].str.contains('John', case=True)]

D. df[df['Name'].str.contains('John', case=False)]

Solution

1. Step 1: Identify correct method and parameters

   The method is str.contains() and to ignore case, use case=False.

2. Step 2: Check syntax correctness

   df[df['Name'].str.contains('John', case=False)] uses correct method and parameter. df[df['Name'].contains('John', case=False)] misses str. df[df['Name'].str.contains('John', ignore_case=True)] uses wrong parameter ignore_case. df[df['Name'].str.contains('John', case=True)] sets case=True, which is case sensitive.

3. Final Answer:

   df[df['Name'].str.contains('John', case=False)] -> Option D

4. Quick Check:

   Use str.contains() with case=False [OK]

Hint: Use case=False for ignoring case in str.contains() [OK]

Common Mistakes:

• Forgetting to use str accessor
• Using wrong parameter name like ignore_case
• Setting case=True when case insensitive needed

3. Given the DataFrame df below, what is the output of df['City'].str.contains('York', na=False)?

import pandas as pd
df = pd.DataFrame({'City': ['New York', 'Yorkshire', None, 'Los Angeles']})

medium

A. [False, True, False, False]

B. [True, False, False, False]

C. [True, True, False, False]

D. [True, True, True, False]

Solution

1. Step 1: Check each element for 'York'

   'New York' contains 'York' (True), 'Yorkshire' contains 'York' (True), None is missing so replaced by False due to na=False, 'Los Angeles' does not contain 'York' (False).

2. Step 2: Construct boolean Series output

   The result is [True, True, False, False].

3. Final Answer:

   [True, True, False, False] -> Option C

4. Quick Check:

   Check each string for 'York' with na=False [OK]

Hint: Use na=False to handle missing values safely [OK]

Common Mistakes:

• Ignoring missing values causing errors
• Assuming None matches pattern
• Misreading which strings contain 'York'

4. What is wrong with this code snippet?

df[df['Email'].str.contains('@gmail.com')]

Assume some values in 'Email' column are missing (NaN).

medium

A. It will return all rows because '@gmail.com' matches everything.

B. It will raise an error due to missing values without handling na parameter.

C. It will ignore case sensitivity by default causing wrong results.

D. It will convert all emails to lowercase automatically.

Solution

1. Step 1: Understand missing data impact on str.contains()

   If the column has NaN values, str.contains() raises an error unless na parameter is set.

2. Step 2: Check code for na parameter

   The code does not set na=False, so it will raise an error when encountering NaN.

3. Final Answer:

   It will raise an error due to missing values without handling na parameter. -> Option B

4. Quick Check:

   Missing na causes errors with NaN [OK]

Hint: Always add na=False to avoid errors with missing data [OK]

Common Mistakes:

• Ignoring NaN causes runtime error
• Assuming default ignores case or missing data
• Thinking it auto-lowers case

5. You have a DataFrame df with a column 'Description' containing product details. You want to filter rows where 'Description' contains either 'red' or 'blue' (case insensitive), but some rows have missing values. Which code snippet correctly does this?

hard

A. df[df['Description'].str.contains('red|blue', case=False, na=False)]

B. df[df['Description'].str.contains('red|blue', case=True)]

C. df[df['Description'].str.contains('red|blue', na=True)]

D. df[df['Description'].str.contains('red|blue', case=False)]

Solution

1. Step 1: Use regex pattern for multiple options

   To match 'red' or 'blue', use the regex pattern 'red|blue'.

2. Step 2: Handle case insensitivity and missing values

   Set case=False for ignoring case and na=False to avoid errors on missing data.

3. Step 3: Verify options

   df[df['Description'].str.contains('red|blue', case=False, na=False)] correctly uses both case=False and na=False. df[df['Description'].str.contains('red|blue', case=True)] is case sensitive. df[df['Description'].str.contains('red|blue', na=True)] uses invalid na=True. df[df['Description'].str.contains('red|blue', case=False)] misses na=False.

4. Final Answer:

   df[df['Description'].str.contains('red|blue', case=False, na=False)] -> Option A

5. Quick Check:

   Use regex with case=False and na=False [OK]

Hint: Use regex 'red|blue' with case=False and na=False [OK]

Common Mistakes:

• Forgetting na=False causes errors
• Using case sensitive search unintentionally
• Wrong regex or missing regex for multiple patterns