str.contains() for pattern matching in Pandas

Introduction

We use str.contains() to find if text data has a certain pattern or word. It helps us quickly check or filter data based on text.

You want to find all rows in a table where a column has a specific word.

You need to filter emails that contain '@gmail.com'.

You want to check if product descriptions mention 'organic'.

You want to find phone numbers that start with a certain area code.

You want to detect if a comment contains a certain keyword.

Syntax

Pandas

DataFrame['column_name'].str.contains(pattern, case=True, na=False, regex=True)

pattern is the text or regular expression you want to find.

case=True means matching is case sensitive. Use case=False to ignore case.

Examples

Find rows where 'Name' has 'John' exactly (case sensitive).

Pandas

df['Name'].str.contains('John')

Find emails containing '@gmail.com' ignoring case differences.

Pandas

df['Email'].str.contains('@gmail.com', case=False)

Find descriptions containing either 'organic' or 'natural' using regex.

Pandas

df['Description'].str.contains('organic|natural', regex=True)

Find phone numbers starting with any 3 digits (area code) using regex.

Pandas

df['Phone'].str.contains('^\d{3}', regex=True)

Sample Program

This program creates a table of products with descriptions. It then finds all products whose description mentions 'organic' or 'natural', ignoring case. It prints the original and filtered tables.

Pandas

import pandas as pd

# Create a sample DataFrame
products = pd.DataFrame({
    'Product': ['Apple Juice', 'Orange Juice', 'Organic Milk', 'Natural Honey', 'Regular Milk'],
    'Description': ['Fresh apple juice', 'Sweet orange juice', '100% organic milk', 'Pure natural honey', None]
})

print('Original DataFrame:')
print(products)

# Find rows where Description contains 'organic' or 'natural' ignoring case
pattern = 'organic|natural'
filtered = products[products['Description'].str.contains(pattern, case=False, na=False, regex=True)]

print('\nFiltered DataFrame (contains "organic" or "natural"):')
print(filtered)

Important Notes

Time complexity: O(n) where n is number of rows, because it checks each row's text.

Space complexity: O(n) for the boolean mask created during filtering.

Common mistake: Forgetting na=False causes errors if there are missing values.

Use str.contains() when you want to check if text includes a pattern. Use other methods like str.startswith() if you only want to check the start.

Summary

str.contains() helps find text patterns in columns.

It supports case sensitivity and regular expressions.

Always handle missing data with na=False to avoid errors.

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