Pattern matching with str.contains in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

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

It checks if each string in a pandas Series contains a specified pattern or substring, returning a Series of True or False values.

beginner

How can you make str.contains() case insensitive?

By setting the parameter case=False, the method ignores case when searching for the pattern.

intermediate

What happens if str.contains() encounters a missing value (NaN) in the Series?

By default, it returns NaN for that position, but you can set na=False to treat missing values as not matching the pattern.

beginner

How do you use str.contains() to filter rows in a DataFrame where a column contains a specific word?

Use it inside boolean indexing like df[df['column'].str.contains('word', na=False)] to keep rows where 'word' appears in 'column'.

intermediate

Can str.contains() use regular expressions for pattern matching?

Yes, by default it treats the pattern as a regular expression, allowing complex pattern matching. You can disable this with regex=False.

What does str.contains('cat') return when applied to a pandas Series?

A A Series of True/False indicating if 'cat' is in each string

B A list of strings containing 'cat'

C The count of 'cat' occurrences

D The original Series unchanged

It returns a Series of True or False values showing if 'cat' is found in each string.

How do you ignore case when using str.contains()?

A Set <code>case=False</code>

B Set <code>ignore_case=True</code>

C Set <code>case=True</code>

D Use <code>lower()</code> on the Series first

Setting case=False makes the search case insensitive.

What parameter do you use to treat NaN values as False in str.contains()?

A ignore_na=True

B na=True

C fillna=False

D na=False

The na=False parameter treats missing values as not matching the pattern.

By default, does str.contains() treat the pattern as a regular expression?

A Only if the pattern is a string

B No

C Yes

D Only if <code>regex=True</code> is set

By default, str.contains() uses regex for pattern matching.

Which of these filters a DataFrame to rows where 'name' column contains 'John'?

A df[df['name'].contains('John')]

B df[df['name'].str.contains('John', na=False)]

C df[df['name'].str.match('John')]

D df[df['name'].str.find('John')]

Use str.contains() with na=False inside boolean indexing to filter rows.

Explain how to use str.contains() to find rows in a DataFrame where a column includes a certain substring, including how to handle missing values and case sensitivity.

Think about filtering with True/False and controlling how missing data and letter case affect the search.

Describe the role of regular expressions in str.contains() and how to disable them if you want a simple substring search.

Consider how regex allows patterns and how to turn it off for plain text matching.

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