String accessor (.str) methods in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'name' column in the DataFrame to lowercase using the string accessor.

Data Analysis Python

df['name_lower'] = df['name'].str.[1]()

Drag options to blanks, or click blank then click option'

Acapitalize

Bupper

Clower

Dtitle

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'upper' instead of 'lower' changes letters to uppercase.

Using 'capitalize' only changes the first letter to uppercase.

Using 'title' capitalizes the first letter of each word.

The str.lower() method converts all characters in the string to lowercase.

2fill in blank

medium

Complete the code to check if the 'email' column contains the '@' symbol using the string accessor.

Data Analysis Python

df['has_at'] = df['email'].str.[1]('@')

Drag options to blanks, or click blank then click option'

Acontains

Bmatch

Cendswith

Dstartswith

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'startswith' checks only the start of the string.

Using 'endswith' checks only the end of the string.

Using 'match' is for regular expressions and may not work as expected here.

The str.contains() method checks if the string contains a specific substring.

3fill in blank

hard

Fix the error in the code to extract the first 3 characters from the 'code' column using the string accessor.

Data Analysis Python

df['code_start'] = df['code'].str.[1](0, 3)

Drag options to blanks, or click blank then click option'

Asubstr

Bget

Cextract

Dslice

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'substr' is not a valid pandas string accessor method.

Using 'get' is for getting element by index, not substring.

Using 'extract' is for regex extraction, not simple slicing.

The str.slice(0, 3) method extracts a substring from index 0 up to but not including index 3.

4fill in blank

hard

Fill both blanks to create a new column with the 'city' names trimmed of whitespace and converted to uppercase.

Data Analysis Python

df['city_clean'] = df['city'].str.[1]().str.[2]()

Drag options to blanks, or click blank then click option'

Astrip

Blower

Cupper

Dcapitalize

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lower' instead of 'upper' changes letters to lowercase.

Using 'capitalize' only changes the first letter.

Not stripping whitespace before changing case.

First, str.strip() removes whitespace from both ends. Then, str.upper() converts text to uppercase.

5fill in blank

hard

Fill all three blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 4 characters.

Data Analysis Python

lengths = { [1]: [2] for [3] in words if len([3]) > 4 }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Using 'item' instead of 'word' causes confusion.

Not using len(word) for values.

The dictionary comprehension uses word as key and len(word) as value, iterating over word in words.

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