Pattern matching with str.contains in Data Analysis Python - Mini Project: Build & Apply

Pattern Matching with str.contains in Data Science

📖 Scenario: You work in a small bookstore. You have a list of book titles and want to find which books mention the word "Python" in their titles. This helps you quickly find all Python-related books.

🎯 Goal: Build a simple program that uses pattern matching with str.contains to find book titles containing the word "Python".

📋 What You'll Learn

Create a pandas DataFrame with a column named title containing the exact book titles given.

Create a variable called pattern that holds the string 'Python'.

Use str.contains with the pattern variable to filter the DataFrame for titles containing "Python".

Print the filtered DataFrame showing only the matching book titles.

💡 Why This Matters

🌍 Real World

Finding specific keywords in text data is common in data science, such as searching product names, customer reviews, or document contents.

💼 Career

Pattern matching with <code>str.contains</code> is a basic skill for data cleaning, filtering, and text analysis used by data analysts and scientists.

1

Create the book titles DataFrame

Import pandas as pd and create a DataFrame called books with a column title containing these exact book titles: 'Learn Python Programming', 'Data Science Handbook', 'Python for Data Analysis', 'Cooking 101', 'Advanced Python Techniques'.

Data Analysis Python

import pandas as pd
# Create the DataFrame 'books' with the exact 'title' column
# Your code here

Hint

Use pd.DataFrame with a dictionary where the key is 'title' and the value is a list of the exact book titles.

2

Create the pattern variable

Create a variable called pattern and set it to the string 'Python'.

Data Analysis Python

import pandas as pd

books = pd.DataFrame({
    'title': [
        'Learn Python Programming',
        'Data Science Handbook',
        'Python for Data Analysis',
        'Cooking 101',
        'Advanced Python Techniques'
    ]
})

# Create the variable 'pattern' with the string 'Python'
# Your code here

Hint

Just assign the string 'Python' to the variable pattern.

3

Filter titles using str.contains

Use str.contains with the variable pattern on the title column of books to create a new DataFrame called python_books that contains only the rows where the title includes "Python".

Data Analysis Python

import pandas as pd

books = pd.DataFrame({
    'title': [
        'Learn Python Programming',
        'Data Science Handbook',
        'Python for Data Analysis',
        'Cooking 101',
        'Advanced Python Techniques'
    ]
})

pattern = 'Python'

# Create 'python_books' by filtering 'books' where 'title' contains 'pattern'
# Your code here

Hint

Use books['title'].str.contains(pattern) inside the brackets to filter rows.

4

Print the filtered book titles

Print the DataFrame python_books to show only the book titles that contain the word "Python".

Data Analysis Python

import pandas as pd

books = pd.DataFrame({
    'title': [
        'Learn Python Programming',
        'Data Science Handbook',
        'Python for Data Analysis',
        'Cooking 101',
        'Advanced Python Techniques'
    ]
})

pattern = 'Python'

python_books = books[books['title'].str.contains(pattern)]

# Print the 'python_books' DataFrame
# Your code here

Hint

Use print(python_books) to display the filtered DataFrame.

Practice

1. What does the str.contains method do when used on a pandas Series?

easy

A. It replaces all occurrences of a pattern with another string.

B. It counts how many times a pattern appears in each string.

C. It splits each string into a list based on a pattern.

D. It checks if each string contains a specified pattern and returns true or false.

Solution

1. Step 1: Understand str.contains purpose

   The method checks each string in a Series for a pattern and returns true or false accordingly.

2. Step 2: Compare with other string methods

   Replacing, splitting, or counting are different methods like replace(), split(), or count(), not contains().

3. Final Answer:

   It checks if each string contains a specified pattern and returns true or false. -> Option D

4. Quick Check:

   str.contains returns boolean mask [OK]

Hint: Remember: contains returns true/false for pattern presence [OK]

Common Mistakes:

• Confusing contains with replace or split
• Thinking it returns counts instead of booleans
• Assuming it modifies the original data

2. Which of the following is the correct syntax to filter rows in a DataFrame df where the column 'name' contains the substring 'cat'?

easy

A. df[df['name'].str.contain('cat')]

B. df[df['name'].contains('cat')]

C. df[df['name'].str.contains('cat')]

D. df[df['name'].str.contains(cat)]

Solution

1. Step 1: Identify correct method usage

   The correct method is str.contains called on the Series with the pattern as a string argument.

2. Step 2: Check syntax details

   df[df['name'].str.contains('cat')] uses str.contains('cat') correctly with quotes around 'cat'. df[df['name'].contains('cat')] misses str, C has a typo, D misses quotes around 'cat'.

3. Final Answer:

   df[df['name'].str.contains('cat')] -> Option C

4. Quick Check:

   Use str.contains with string pattern in quotes [OK]

Hint: Use df['col'].str.contains('pattern') with quotes [OK]

Common Mistakes:

• Forgetting to use .str before contains
• Missing quotes around the pattern string
• Typo in method name like contain instead of contains

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({'text': ['apple pie', 'banana split', 'cherry tart', 'apple turnover']})

What will df[df['text'].str.contains('apple')] return?

medium

A. Rows where 'text' contains 'apple': 'apple pie' and 'apple turnover'.

B. Rows where 'text' contains 'banana' only.

C. All rows because 'apple' is in every string.

D. An empty DataFrame because 'apple' is not found.

Solution

1. Step 1: Check which rows contain 'apple'

   Rows 0 ('apple pie') and 3 ('apple turnover') contain the substring 'apple'.

2. Step 2: Understand filtering result

   The filter returns only these rows, excluding others like 'banana split' and 'cherry tart'.

3. Final Answer:

   Rows where 'text' contains 'apple': 'apple pie' and 'apple turnover'. -> Option A

4. Quick Check:

   Filter keeps rows with 'apple' substring [OK]

Hint: Filter keeps only rows where pattern is found [OK]

Common Mistakes:

• Assuming all rows are returned
• Confusing 'apple' with other fruits
• Expecting a count instead of filtered rows

4. Consider this code snippet:

import pandas as pd
df = pd.DataFrame({'col': ['dog', 'cat', 'bird']})
filtered = df[df['col'].str.contains(cat)]
print(filtered)

What is the error and how to fix it?

medium

A. TypeError because df is not defined; import pandas first.

B. NameError because 'cat' is not quoted; fix by using 'cat' in quotes.

C. AttributeError because str.contains is misspelled; fix spelling.

D. No error; code runs correctly.

Solution

1. Step 1: Identify cause of error

   The pattern 'cat' is used without quotes, so Python treats it as a variable, causing NameError.

2. Step 2: Correct the syntax

   Put 'cat' in quotes: str.contains('cat') to pass a string pattern.

3. Final Answer:

   NameError because 'cat' is not quoted; fix by using 'cat' in quotes. -> Option B

4. Quick Check:

   Pattern must be a string in quotes [OK]

Hint: Always quote string patterns in str.contains [OK]

Common Mistakes:

• Forgetting quotes around pattern
• Assuming pattern can be variable without definition
• Ignoring error messages about undefined names

5. You have a DataFrame df with a column 'comments' containing text. You want to filter rows where 'comments' contain either 'error' or 'fail' (case insensitive). Which code correctly does this?

hard

A. df[df['comments'].str.contains('error|fail', case=false)]

B. df[df['comments'].str.contains('error' or 'fail', case=true)]

C. df[df['comments'].str.contains(['error', 'fail'], case=false)]

D. df[df['comments'].str.contains('error|fail', case=true)]

Solution

1. Step 1: Use regex pattern for multiple words

   To match 'error' or 'fail', use regex 'error|fail' inside str.contains.

2. Step 2: Set case insensitive matching

   Use case=false to ignore case differences.

3. Step 3: Check options

   df[df['comments'].str.contains('error|fail', case=false)] correctly uses regex and case=false. df[df['comments'].str.contains('error' or 'fail', case=true)] misuses 'or' inside string, C passes list instead of string, A uses case=true which is case sensitive.

4. Final Answer:

   df[df['comments'].str.contains('error|fail', case=false)] -> Option A

5. Quick Check:

   Use regex with | and case=false for case-insensitive OR [OK]

Hint: Use regex 'word1|word2' with case=false for OR matching [OK]

Common Mistakes:

• Using Python 'or' inside string instead of regex |
• Passing list instead of string pattern
• Forgetting case=false for case-insensitive search