str accessor for string methods in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'name' column to lowercase using pandas str accessor.

Pandas

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

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

Aupper

Btitle

Ccapitalize

Dlower

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 '@' using pandas str accessor.

Pandas

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

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

Astartswith

Bendswith

Ccontains

Dmatch

Attempts:

3 left

💡 Hint

Common Mistakes

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

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

Using 'match' is for regex matching, not simple substring check.

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

3fill in blank

hard

Fix the error in the code to remove whitespace from the 'city' column using pandas str accessor.

Pandas

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

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

Astrip

Btrim

Cclean

Dremove

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'trim' is not a pandas str method.

Using 'clean' or 'remove' are not valid string methods.

The str.strip() method removes whitespace from both ends of the string.

4fill in blank

hard

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

Pandas

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

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' selects words shorter than 4.

Using 'word' as value stores the word itself, not its length.

The dictionary comprehension uses len(word) as value and filters words with length greater than 4 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys, values from data, only if value is positive.

Pandas

result = [1]: [2] for k, v in data.items() if v [3] 0}

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k' instead of 'k.upper()' keeps keys lowercase.

Using '<' or other operators changes the filter condition incorrectly.

The dictionary comprehension uses uppercase keys with k.upper(), values as v, and filters for positive values with >.

Practice

1. What does the str accessor in pandas allow you to do with a DataFrame column?

easy

A. Perform mathematical operations on numeric data

B. Sort the DataFrame rows by index

C. Apply string methods to each element in the column

D. Merge two DataFrames based on a key column

Solution

1. Step 1: Understand the purpose of the str accessor

   The str accessor is designed to apply string methods element-wise on pandas Series or DataFrame columns containing text data.

2. Step 2: Compare options with the str accessor's function

   Performing mathematical operations on numeric data, sorting the DataFrame rows by index, and merging two DataFrames based on a key column are unrelated to string methods. Only applying string methods to each element in the column correctly describes the str accessor's function.

3. Final Answer:

   Apply string methods to each element in the column -> Option C

4. Quick Check:

   str accessor = apply string methods [OK]

Hint: Remember: str accessor works only on text columns [OK]

Common Mistakes:

• Thinking str accessor works on numbers
• Confusing str accessor with DataFrame merge
• Assuming str accessor sorts data

2. Which of the following is the correct syntax to convert all strings in a pandas Series names to lowercase using the str accessor?

easy

A. names.str.lower()

B. names.lower()

C. str.lower(names)

D. names.to_lower()

Solution

1. Step 1: Recall the correct usage of str accessor

   To apply string methods on a pandas Series, use Series.str.method(). Here, lower() converts strings to lowercase.

2. Step 2: Evaluate each option

   names.str.lower() uses names.str.lower(), which is correct. names.lower() misses str. str.lower(names) incorrectly calls str.lower as a function with Series argument. names.to_lower() uses a non-existent method to_lower().

3. Final Answer:

   names.str.lower() -> Option A

4. Quick Check:

   Use Series.str.method() for string methods [OK]

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

Common Mistakes:

• Calling string methods directly on Series without .str
• Using wrong method names like to_lower()
• Passing Series as argument to str functions

3. Given the pandas Series data = pd.Series(['Apple', 'Banana', 'Cherry']), what is the output of data.str[0]?

medium

A. ['Apple', 'Banana', 'Cherry']

B. ['A', 'B', 'C']

C. ['pple', 'anana', 'herry']

D. Error: cannot index strings this way

Solution

1. Step 1: Understand what data.str[0] does

   The str accessor allows indexing each string element. str[0] extracts the first character of each string in the Series.

2. Step 2: Apply indexing to each element

   For 'Apple', first character is 'A'; for 'Banana', 'B'; for 'Cherry', 'C'. So the result is ['A', 'B', 'C'].

3. Final Answer:

   ['A', 'B', 'C'] -> Option B

4. Quick Check:

   str[0] = first char of each string [OK]

Hint: str[index] extracts character at position index [OK]

Common Mistakes:

• Expecting full strings instead of first chars
• Thinking it slices off first char
• Assuming error occurs on indexing

4. What is wrong with this code snippet?

df = pd.DataFrame({'col': ['a', 'b', 'c']})
df['col'] = df['col'].str.upper

medium

A. Missing parentheses to call the method

B. Using .str.upper on numeric data

C. DataFrame column name is invalid

D. str accessor cannot be used on DataFrame columns

Solution

1. Step 1: Identify the method call syntax error

   The code assigns df['col'].str.upper without parentheses, so it assigns the method itself, not the result of calling it.

2. Step 2: Correct the code

   Adding parentheses df['col'].str.upper() calls the method and returns the uppercase strings.

3. Final Answer:

   Missing parentheses to call the method -> Option A

4. Quick Check:

   Call string methods with () [OK]

Hint: Always add () to call string methods [OK]

Common Mistakes:

• Forgetting parentheses after string methods
• Trying to use str methods on numbers
• Confusing Series and DataFrame usage

5. You have a DataFrame df with a column 'email' containing email addresses. How would you extract the domain part (after '@') for each email using the str accessor?

hard

A. df['email'].str.find('@')

B. df['email'].str.replace('@', '')

C. df['email'].str.upper()

D. df['email'].str.split('@').str[1]

Solution

1. Step 1: Understand the goal to extract domain after '@'

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

2. Step 2: Use str.split('@') to split each string into two parts

   Splitting by '@' creates a list like ['user', 'domain.com']. Using str[1] selects the domain part.

3. Step 3: Check other options

   df['email'].str.replace('@', '') removes '@' but does not extract domain. df['email'].str.find('@') returns index of '@', not domain. df['email'].str.upper() converts to uppercase, unrelated.

4. Final Answer:

   df['email'].str.split('@').str[1] -> Option D

5. Quick Check:

   Split by '@' then select index 1 [OK]

Hint: Split by '@' then pick second part with str[1] [OK]

Common Mistakes:

• Using replace instead of split
• Trying to find index instead of extracting substring
• Ignoring method chaining with str accessor