String methods on Series in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert all strings in the Series to lowercase.

Data Analysis Python

import pandas as pd

s = pd.Series(['Apple', 'Banana', 'Cherry'])
lower_s = s.str.[1]()

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

Aupper

Blower

Ccapitalize

Dtitle

Attempts:

3 left

💡 Hint

Common Mistakes

Using str.upper() instead of str.lower()

Forgetting to use the str accessor before the method

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

2fill in blank

medium

Complete the code to check if each string in the Series contains the letter 'a'.

Data Analysis Python

import pandas as pd

s = pd.Series(['Apple', 'Banana', 'Cherry'])
contains_a = s.str.[1]('a')

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

Astartswith

Bendswith

Ccontains

Dmatch

Attempts:

3 left

💡 Hint

Common Mistakes

Using str.startswith() or str.endswith() which only check start or end

Using str.match() which expects a regex pattern for full match

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

3fill in blank

hard

Fix the error in the code to replace all occurrences of 'a' with 'o' in the Series.

Data Analysis Python

import pandas as pd

s = pd.Series(['Apple', 'Banana', 'Cherry'])
replaced = s.str.[1]('a', 'o')

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

Achange

Bsubstitute

Cswitch

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like str.substitute or str.switch

Forgetting to use the str accessor

The correct method to replace substrings in Series strings is str.replace().

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 5 characters.

Data Analysis Python

words = ['apple', 'banana', 'cherry', 'date']
lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 5

Cword.startswith('b')

Dword == 'apple'

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.startswith('b') which filters by first letter

Using word == 'apple' which filters only one word

The dictionary comprehension uses len(word) as value and filters words with length greater than 5.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words containing 'e'.

Data Analysis Python

words = ['apple', 'banana', 'cherry', 'date']
result = { [1]: [2] for word in words if word.[3]('e') }

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

Aword.upper()

Blen(word)

Ccontains

Dstartswith

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'startswith' instead of 'contains' for filtering

Not converting words to uppercase for keys

The dictionary comprehension uses word.upper() as key, len(word) as value, and filters words containing 'e' using str.contains.

Practice

1. What does the .str.lower() method do when applied to a pandas Series?

easy

A. Converts all characters in each string of the Series to lowercase.

B. Removes all whitespace from each string in the Series.

C. Counts the number of characters in each string of the Series.

D. Replaces all digits in each string with a special character.

Solution

1. Step 1: Understand the .str accessor

   The .str accessor allows string methods to be applied element-wise on a pandas Series.

2. Step 2: Identify the effect of .lower()

   The .lower() method converts all uppercase letters in each string to lowercase.

3. Final Answer:

   Converts all characters in each string of the Series to lowercase. -> Option A

4. Quick Check:

   .str.lower() = lowercase conversion [OK]

Hint: Remember .str.lower() makes all letters small [OK]

Common Mistakes:

• Confusing .str.lower() with .str.strip()
• Thinking it removes whitespace
• Assuming it counts characters

2. Which of the following is the correct syntax to check if each string in a pandas Series names contains the substring 'data'?

easy

A. names.contains(str('data'))

B. names.contains('data')

C. names.str.contains(data)

D. names.str.contains('data')

Solution

1. Step 1: Recall the correct usage of string methods on Series

   String methods must be accessed via the .str accessor on a pandas Series.

2. Step 2: Check the correct method call syntax

   The method .contains() requires the substring as a string argument inside quotes.

3. Final Answer:

   names.str.contains('data') -> Option D

4. Quick Check:

   Use .str.contains('substring') for substring check [OK]

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

Common Mistakes:

• Omitting .str accessor
• Passing variable without quotes
• Using .contains() directly on Series

3. Given the pandas Series fruits = pd.Series(['Apple', 'banana', 'Cherry', 'date']), what is the output of fruits.str.upper()?

medium

A. ['apple', 'banana', 'cherry', 'date']

B. ['APPLE', 'banana', 'CHERRY', 'DATE']

C. ['APPLE', 'BANANA', 'CHERRY', 'DATE']

D. ['Apple', 'banana', 'Cherry', 'date']

Solution

1. Step 1: Understand .str.upper() effect on Series

   The .str.upper() method converts all characters in each string to uppercase.

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

   Each fruit name is converted fully to uppercase: 'Apple' -> 'APPLE', 'banana' -> 'BANANA', etc.

3. Final Answer:

   ['APPLE', 'BANANA', 'CHERRY', 'DATE'] -> Option C

4. Quick Check:

   .str.upper() = all uppercase strings [OK]

Hint: Uppercase method makes all letters capital [OK]

Common Mistakes:

• Thinking it only affects first letter
• Confusing with .str.capitalize()
• Expecting original case unchanged

4. What is wrong with this code snippet?

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

medium

A. It raises a FutureWarning and requires regex=false to avoid it.

B. It works correctly and removes 'd' from each string.

C. It raises a TypeError because 'replace' is not a string method.

D. It returns null instead of a Series.

Solution

1. Step 1: Understand .str.replace() default behavior

   By default, .str.replace() treats the first argument as a regex pattern.

2. Step 2: Identify warning about regex default change

   Recent pandas versions raise a FutureWarning suggesting to specify regex=false when replacing literal strings.

3. Final Answer:

   It raises a FutureWarning and requires regex=false to avoid it. -> Option A

4. Quick Check:

   .str.replace() warns about regex=true default [OK]

Hint: Add regex=false to .str.replace() for literal replace [OK]

Common Mistakes:

• Ignoring FutureWarning
• Assuming no warning occurs
• Expecting error instead of warning

5. You have a pandas Series emails = pd.Series(['[e-mail]', '[e-mail]', '[e-mail]']). How can you extract the domain names (like 'example.com') from each email using string methods?

hard

A. emails.str.extract('(@.*)')

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

C. emails.str.replace('.*@', '')

D. emails.str.slice(start=emails.str.find('@')+1)

Solution

1. Step 1: Understand the goal to get text after '@'

   We want the part of each string after the '@' symbol, which is the domain.

2. Step 2: Use .str.split('@') and select the second part

   Splitting each string by '@' creates a list; index 1 is the domain part.

3. Final Answer:

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

4. Quick Check:

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

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

Common Mistakes:

• Using incorrect regex in .str.extract()
• Forgetting to select index after split
• Using .str.replace() incorrectly