Regex operations in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the str.contains() method do in Pandas when used with regex?

It checks each string in a Series to see if it contains a pattern defined by a regular expression, returning a Series of True or False values.

beginner

How do you use regex to extract a pattern from a Pandas Series?

Use the str.extract() method with a regex pattern. It returns a DataFrame with the matched groups.

beginner

What is the purpose of the str.replace() method with regex in Pandas?

It replaces parts of strings that match a regex pattern with a specified replacement string.

intermediate

How can you filter rows in a DataFrame where a column matches a regex pattern?

Use df[df['column'].str.contains('pattern', regex=True)] to keep rows where the pattern is found.

intermediate

What does setting na=False do in str.contains()?

It treats missing values (NaN) as False, so they don't cause errors and are excluded from matches.

Which Pandas method extracts regex groups from a Series?

A str.extract()

B str.contains()

C str.replace()

D str.match()

str.extract() pulls out matching groups from strings using regex.

What does str.contains('abc', regex=True) return?

A Replaces 'abc' with True

B A Series of strings containing 'abc'

C A DataFrame with extracted 'abc' matches

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

It returns True or False for each string depending on if 'abc' is found.

How do you replace all digits in a Series with '#' using regex?

A str.extract('\d')

B str.replace('#', '\d', regex=True)

C str.replace('\d', '#', regex=True)

D str.contains('\d')

Use str.replace() with regex pattern '\d' to replace digits.

What happens if you use str.contains() on a Series with NaN values without na=False?

A Raises an error

B Returns False for NaN values

C Ignores NaN values

D Returns True for NaN values

By default, it returns NaN for NaN values, not an error. Use na=False to treat NaN as False.

Which method would you use to check if a string starts with a pattern using regex?

A str.contains()

B str.match()

C str.extract()

D str.replace()

str.match() checks if the entire string matches a regex pattern, often used for start matches.

Explain how to filter a Pandas DataFrame column to keep only rows where the text matches a regex pattern.

Think about using a method that returns True/False for each row and then selecting rows.

Describe how to extract parts of strings from a Pandas Series using regex groups.

Focus on the method that pulls out matching parts instead of just checking presence.

Practice

1. What does the str.contains method do in Pandas when used with a regex pattern?

easy

A. It extracts the first match of the regex pattern from each string.

B. It replaces all matches of the regex pattern with a new string.

C. It sorts the DataFrame rows based on regex matches.

D. It checks if each string in a column contains the regex pattern and returns true or false.

Solution

1. Step 1: Understand str.contains usage

   str.contains checks if the regex pattern appears anywhere in each string of the column and returns a boolean Series.

2. Step 2: Differentiate from other methods

   str.replace changes text, str.extract pulls out matches, and sorting is unrelated.

3. Final Answer:

   It checks if each string in a column contains the regex pattern and returns true or false. -> Option D

4. Quick Check:

   str.contains = Boolean check [OK]

Hint: Contains returns true/false if pattern found in each string [OK]

Common Mistakes:

• Confusing contains with replace or extract
• Thinking contains modifies the data
• Assuming contains returns matched text

2. Which of the following is the correct syntax to replace every sequence of consecutive digits in a Pandas column df['col'] with the character 'X' using regex?

easy

A. df['col'].str.replace('\d+', 'X')

B. df['col'].str.replace('[0-9]', 'X')

C. df['col'].replace('[0-9]', 'X')

D. df['col'].str.extract('[0-9]', 'X')

Solution

1. Step 1: Identify correct method for regex replacement

   Use str.replace on the Series to replace regex matches. df['col'].replace('[0-9]', 'X') is wrong because replace without str is for exact values, not regex.

2. Step 2: Check regex pattern and syntax

   df['col'].str.replace('[0-9]', 'X') matches single digits only ('123' -> 'XXX'). df['col'].str.replace('\d+', 'X') matches sequences of one or more digits ('123' -> 'X').

3. Final Answer:

   df['col'].str.replace('\d+', 'X') -> Option A

4. Quick Check:

   Use str.replace with regex pattern [OK]

Hint: Use str.replace with regex pattern and replacement string [OK]

Common Mistakes:

• Using DataFrame replace instead of Series.str.replace
• Wrong regex pattern syntax
• Using str.extract instead of replace

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'text': ['apple123', 'banana', 'cherry456', 'date789']})
df['numbers'] = df['text'].str.extract('(\d+)')

What will be the content of the new column numbers?

medium

A. ['123', 'banana', '456', '789']

B. ['apple', NaN, 'cherry', 'date']

C. ['123', NaN, '456', '789']

D. [NaN, NaN, NaN, NaN]

Solution

1. Step 1: Understand str.extract with regex

   The regex (\d+) captures one or more digits. str.extract returns the first match or NaN if none.

2. Step 2: Apply regex to each string

   'apple123' matches '123', 'banana' has no digits so NaN, 'cherry456' matches '456', 'date789' matches '789'.

3. Final Answer:

   ['123', NaN, '456', '789'] -> Option C

4. Quick Check:

   Extract digits or NaN if none [OK]

Hint: Extract returns first regex group or NaN if no match [OK]

Common Mistakes:

• Expecting extract to return full string
• Confusing extract with contains or replace
• Not handling NaN for no matches

4. Consider this code snippet:

import pandas as pd
df = pd.DataFrame({'col': ['abc123', 'def456', 'ghi789']})
df['col'].str.match('[a-z]+\d')

What is the error or issue in this code?

medium

A. The regex pattern misses the last digit, so match returns false for all rows.

B. The code raises a SyntaxError due to incorrect regex syntax.

C. The method str.match does not accept regex patterns.

D. The code runs correctly and returns true for all rows.

Solution

1. Step 1: Analyze the regex pattern

   The pattern [a-z]+\d matches one or more lowercase letters followed by exactly one digit from the start of the string.

2. Step 2: Compare pattern with data strings

   The strings 'abc123', 'def456', 'ghi789' have multiple digits at the end, but the pattern only expects one digit. Since str.match matches from the start and the pattern does not cover the full digit sequence, it returns False for all rows.

3. Final Answer:

   The regex pattern misses the last digit, so match returns false for all rows. -> Option A

4. Quick Check:

   str.match matches from start; pattern must cover full prefix for True [OK]

Hint: str.match anchors at start (prefix match), pattern must cover full prefix [OK]

Common Mistakes:

• Thinking str.match requires full string match
• Confusing str.match with str.fullmatch
• Thinking regex syntax is invalid here

5. You have a DataFrame df with a column 'emails' containing email addresses mixed with invalid entries. How would you create a new column 'valid' that is true only for rows where 'emails' contains a valid email pattern using regex in Pandas?

hard

A. df['valid'] = df['emails'].str.extract(r'^[\w\.-]+@[\w\.-]+\.\w+$')

B. df['valid'] = df['emails'].str.contains(r'^[\w\.-]+@[\w\.-]+\.\w+$')

C. df['valid'] = df['emails'].str.replace(r'^[\w\.-]+@[\w\.-]+\.\w+$', 'true')

D. df['valid'] = df['emails'].str.match(r'\w+@\w+')

Solution

1. Step 1: Choose method to check pattern presence

   str.contains returns true/false if regex matches anywhere in string, suitable for validation.

2. Step 2: Use correct regex for full email validation

   The regex ^[\w\.-]+@[\w\.-]+\.\w+$ matches a full email pattern from start to end. df['valid'] = df['emails'].str.extract(r'^[\w\.-]+@[\w\.-]+\.\w+$') extracts matched text, not boolean. df['valid'] = df['emails'].str.replace(r'^[\w\.-]+@[\w\.-]+\.\w+$', 'true') replaces text, not boolean. df['valid'] = df['emails'].str.match(r'\w+@\w+') uses str.match but regex is incomplete and may match partial emails.

3. Final Answer:

   df['valid'] = df['emails'].str.contains(r'^[\w\.-]+@[\w\.-]+\.\w+$') -> Option B

4. Quick Check:

   Use str.contains with full regex for validation [OK]

Hint: Use str.contains with full regex pattern for true/false validation [OK]

Common Mistakes:

• Using str.extract which returns strings, not booleans
• Using str.replace which changes data instead of checking
• Using incomplete regex that matches partial emails