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

Concept Flow - str.contains() for pattern matching

Start with DataFrame

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Select column with strings

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Apply str.contains(pattern)

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Check each string for pattern match

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Return Boolean Series

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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.