String accessor (.str) methods in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the .str accessor in pandas?

The .str accessor allows you to apply string methods to each element in a pandas Series that contains text data, making it easy to manipulate text in columns.

beginner

How do you convert all text in a pandas Series to lowercase using .str methods?

Use series.str.lower() to convert all characters in each string of the Series to lowercase.

beginner

What does series.str.contains('abc') do?

It checks each string in the Series to see if it contains the substring 'abc' and returns a Series of True or False values.

beginner

How can you extract the first 3 characters from each string in a pandas Series?

Use series.str[:3] to slice the first three characters from each string in the Series.

beginner

What will series.str.replace('a', 'x') do?

It replaces every occurrence of the letter 'a' with 'x' in each string of the Series.

Which pandas method would you use to check if strings in a Series start with 'Hello'?

A series.str.contains('Hello')

B series.str.match('Hello')

C series.str.find('Hello')

D series.str.startswith('Hello')

The startswith() method checks if each string starts exactly with the given substring.

What does series.str.len() return?

A The length of each string in the Series

B The total number of strings in the Series

C The number of unique strings

D The number of missing values

str.len() returns a Series with the length of each string element.

How do you remove whitespace from the start and end of strings in a Series?

A series.str.remove()

B series.str.trim()

C series.str.strip()

D series.str.clean()

str.strip() removes whitespace from both ends of each string.

Which method splits strings in a Series by a delimiter?

A series.str.split(',')

B series.str.divide(',')

C series.str.cut(',')

D series.str.partition(',')

str.split() splits each string into a list using the delimiter.

What type of object is returned by series.str.extract(r'(\d+)')?

A A Series of booleans

B A DataFrame with extracted groups

C A list of strings

D An integer count

str.extract() returns a DataFrame with the matched groups from the regex.

Explain how the .str accessor helps when working with text data in pandas Series.

Think about how you would handle text in many rows at once.

Describe three common string operations you can perform using pandas .str methods.

Consider operations useful for cleaning or analyzing text.

Practice

1. What does the .str accessor do in pandas when used on a text column?

easy

A. It allows applying string methods to each element in the column.

B. It converts the column to numeric values.

C. It sorts the column alphabetically.

D. It removes missing values from the column.

Solution

1. Step 1: Understand the purpose of .str in pandas

   The .str accessor is used to apply string methods element-wise on a pandas Series containing text data.

2. Step 2: Compare options with the known function

   Only It allows applying string methods to each element in the column. correctly describes this behavior. Options A, B, and D describe other unrelated operations.

3. Final Answer:

   It allows applying string methods to each element in the column. -> Option A

4. Quick Check:

   .str applies string methods [OK]

Hint: Remember: .str works like string methods on each item [OK]

Common Mistakes:

• Thinking .str changes data type
• Confusing .str with sorting or filtering
• Assuming it removes missing values automatically

2. Which of the following is the correct syntax to convert all strings in a pandas Series df['name'] to lowercase using .str?

easy

A. df['name'].str.toLower()

B. df['name'].lower()

C. df.str['name'].lower()

D. df['name'].str.lower()

Solution

1. Step 1: Recall correct .str usage

   To apply string methods on a Series, use Series.str.method(). Here, lower() is the method to convert to lowercase.

2. Step 2: Check each option's syntax

   df['name'].str.lower() uses df['name'].str.lower(), which is correct. df['name'].lower() misses .str. df.str['name'].lower() has wrong order. df['name'].str.toLower() uses incorrect method name toLower() (JavaScript style).

3. Final Answer:

   df['name'].str.lower() -> Option D

4. Quick Check:

   Use .str.lower() on Series [OK]

Hint: Always use .str before string methods on Series [OK]

Common Mistakes:

• Omitting .str and calling string method directly
• Using JavaScript method names like toLower()
• Incorrect order like df.str['name']

3. Given the pandas Series:

import pandas as pd
s = pd.Series(['Apple', 'banana', 'Cherry'])
result = s.str.upper()

What is the value of result?

medium

A. ['APPLE', 'BANANA', 'CHERRY']

B. ['apple', 'banana', 'cherry']

C. ['Apple', 'banana', 'Cherry']

D. Error: str.upper() not found

Solution

1. Step 1: Understand what .str.upper() does

   This method converts each string in the Series to uppercase letters.

2. Step 2: Apply .str.upper() to each element

   'Apple' becomes 'APPLE', 'banana' becomes 'BANANA', and 'Cherry' becomes 'CHERRY'.

3. Final Answer:

   ['APPLE', 'BANANA', 'CHERRY'] -> Option A

4. Quick Check:

   .str.upper() converts all to uppercase [OK]

Hint: Uppercase all strings with .str.upper() [OK]

Common Mistakes:

• Expecting lowercase output
• Thinking original case remains unchanged
• Assuming method causes error

4. What is wrong with this code snippet?

import pandas as pd
s = pd.Series(['cat', 'dog', 'bird'])
result = s.str.capitalize

medium

A. Series does not support .str accessor

B. Wrong method name, should be capital

C. Missing parentheses to call the method

D. No error, code runs fine

Solution

1. Step 1: Identify the method call syntax

   String methods must be called with parentheses, e.g., capitalize(), not just referenced.

2. Step 2: Check the code snippet

   The code uses s.str.capitalize without parentheses, so it returns a method object, not the transformed Series.

3. Final Answer:

   Missing parentheses to call the method -> Option C

4. Quick Check:

   Always add () to call string methods [OK]

Hint: Add () to call string methods, not just reference [OK]

Common Mistakes:

• Forgetting parentheses after method name
• Using wrong method names
• Thinking Series lacks .str accessor

5. You have a pandas Series s with email addresses. You want to extract the domain part (after '@') for each email. Which code correctly uses .str methods to do this?

hard

A. s.str.replace('@', '')

B. s.str.split('@').str[1]

C. s.str.find('@')

D. s.str.extract('@')

Solution

1. Step 1: Understand the goal

   We want the part after '@' in each email string.

2. Step 2: Analyze each option

   s.str.split('@').str[1] splits each string at '@' and selects the second part (index 1), which is the domain. s.str.extract('@') extracts only '@' character, not the domain. s.str.find('@') returns the position of '@', not the domain. s.str.replace('@', '') removes '@' but keeps the rest combined.

3. Final Answer:

   s.str.split('@').str[1] -> Option B

4. Quick Check:

   Split by '@' and take second part [OK]

Hint: Split by '@' and select index 1 for domain [OK]

Common Mistakes:

• Using extract('@') which only matches '@'
• Using find('@') which returns position, not substring
• Replacing '@' removes it but does not isolate domain