Pattern matching with str.contains in Data Analysis Python - Step-by-Step Execution

Concept Flow - Pattern matching with str.contains

Start with DataFrame

↓

Call str.contains(pattern)

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Check each string for pattern

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Return Boolean Series

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Use Boolean Series to filter DataFrame

We start with a DataFrame, use str.contains to check each string for a pattern, get a True/False result, then filter rows based on that.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'David']})
mask = df['Name'].str.contains('a')
result = df[mask]

This code finds rows where the 'Name' column contains the letter 'a' (case-sensitive) and filters the DataFrame.

Execution Table

Step	Row Index	Name Value	Check 'a' in Name	Boolean Result	Filtered Rows
1	0	Alice	No (uppercase 'A' but no lowercase 'a')	False	Exclude
2	1	Bob	No	False	Exclude
3	2	Charlie	Yes ('a' in 'Charlie')	True	Include
4	3	David	Yes ('a' in 'David')	True	Include
5	-	-	-	-	Filter applied: rows 2,3 included; rows 0,1 excluded

💡 All rows checked; filtering done based on Boolean mask.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
mask	None	False	False	True	True	[False, False, True, True]
result	None	Row 0 excluded	Row 1 excluded	Row 2 included	Row 3 included	Filtered DataFrame with rows 2,3

Key Moments - 3 Insights

Why does 'Alice' return False even though it starts with uppercase 'A'?

str.contains is case-sensitive by default, and 'Alice' has no lowercase 'a' (only uppercase 'A'), so it does not match. See execution_table step 1.

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

The Boolean result is False, so that row is excluded from the filtered DataFrame. See execution_table step 2 for 'Bob'.

How does the Boolean mask help filter the DataFrame?

The mask is a list of True/False values for each row. True means include the row, False means exclude. See execution_table final step.

Visual Quiz - 3 Questions

Look at the execution_table, what is the Boolean result for 'Charlie' at step 3?

A True

B Error

C False

D None

💡 Hint

Check the 'Boolean Result' column at step 3 in execution_table.

At which step does the condition 'a' not found in the Name?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look for 'False' in the 'Boolean Result' column in execution_table.

If we add case=False to str.contains, what would happen to 'Alice'?

A It would be False

B It would be True

C It would cause an error

D It would be None

💡 Hint

Case-insensitive matching includes uppercase and lowercase letters; see variable_tracker mask values.

Concept Snapshot

Use str.contains('pattern') on a string column to get a Boolean mask.
This mask shows True where the pattern is found, False otherwise.
By default, matching is case-sensitive.
Use the mask to filter rows in a DataFrame.
Add case=False for case-insensitive matching.

Full Transcript

We start with a DataFrame containing names. We use str.contains to check if each name has the letter 'a'. This returns a list of True or False values, one for each row. True means the pattern is found, False means it is not. We then use this list to keep only the rows where the pattern is found. For example, 'Alice' has no lowercase 'a' (only uppercase 'A'), so it returns False. 'Bob' does not have 'a', so it returns False and is excluded. 'Charlie' and 'David' have lowercase 'a', so they return True and are included. This way, we filter the DataFrame to only include names with lowercase 'a'.

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