str.contains() for pattern matching in Pandas - Step-by-Step Execution

Concept Flow - str.contains() for pattern matching

Start with DataFrame

↓

Select column with strings

↓

Apply str.contains(pattern)

↓

Check each string for pattern match

↓

Return Boolean Series

↓

Use Boolean Series to filter or analyze

The flow starts with a DataFrame, selects a string column, applies str.contains() to check each string for a pattern, and returns a Boolean Series for filtering or analysis.

Execution Sample

Pandas

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie', 'David']}
df = pd.DataFrame(data)

mask = df['Name'].str.contains('a')
print(mask)

This code checks which names contain the letter 'a' and prints a Boolean Series showing True or False for each row.

Execution Table

Step	Row Index	String Value	Pattern	Match Result	Boolean Output
1	0	Alice	'a'	Does not contain 'a' (case sensitive)	False
2	1	Bob	'a'	Does not contain 'a'	False
3	2	Charlie	'a'	Contains 'a'	True
4	3	David	'a'	Contains 'a'	True
5	N/A	N/A	N/A	All rows checked	Boolean Series complete

💡 All rows processed, Boolean Series created indicating pattern presence per row.

Variable Tracker

Variable	Start	After Row 1	After Row 2	After Row 3	After Row 4	Final
mask	empty	False	False	True	True	[False, False, True, True]

Key Moments - 3 Insights

Why does 'Alice' return False even though it has 'A'?

By default, str.contains() is case sensitive, so lowercase 'a' does not match uppercase 'A'. If you want case-insensitive matching, you must add the parameter case=False.

What happens if the pattern is not found in any string?

The Boolean Series will have all False values, as shown in the execution_table rows where no match occurs (like for 'Bob').

Can str.contains() handle regular expressions?

Yes, str.contains() treats the pattern as a regular expression by default, so you can use regex patterns for complex matching.

Visual Quiz - 3 Questions

Look at the execution table, what is the Boolean output for 'Bob' at step 2?

A True

B False

C Error

D None

💡 Hint

Check the 'Boolean Output' column for Row Index 1 in the execution_table.

At which step does the pattern 'a' first match a string?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Match Result' column and find the first True match.

If we add case=False to str.contains(), what would be the Boolean output for 'Alice' at step 1?

A True

B False

C Error

D Depends on regex

💡 Hint

Case insensitive matching means uppercase and lowercase letters both match; check variable_tracker for mask values.

Concept Snapshot

str.contains(pattern) checks if each string in a pandas Series contains the pattern.
Returns a Boolean Series with True/False per row.
By default, matching is case sensitive and pattern is a regex.
Use case=False for case-insensitive matching.
Useful for filtering DataFrames by string content.

Full Transcript

We start with a DataFrame containing names. We select the 'Name' column and apply str.contains('a') to check if each name contains the letter 'a'. The method returns a Boolean Series indicating True for names containing 'a' and False otherwise. For example, 'Charlie' returns True because it contains 'a'. This Boolean Series can be used to filter or analyze the DataFrame. By default, matching is case sensitive and uses regex patterns. You can set case=False for case-insensitive matching. The execution table shows each row checked and the Boolean result. The variable tracker shows how the mask variable builds up with True or False values after each row. Common confusions include case sensitivity and regex usage. The visual quiz tests understanding of these steps and outputs.

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