Why string operations matter in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'name' column to lowercase.

Pandas

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

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

Aupper

Blower

Ccapitalize

Dtitle

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'upper()' which makes letters uppercase instead.

Using 'capitalize()' which only changes the first letter.

Using lower() converts all characters in the string to lowercase.

2fill in blank

medium

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

Pandas

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()' which only checks the beginning of the string.

Using 'endswith()' which only checks the end of the string.

The contains() method checks if the string has the given substring anywhere.

3fill in blank

hard

Fix the error in the code to replace spaces with underscores in the 'city' column.

Pandas

df['city'] = df['city'].str.[1](' ', '_')

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

Areplace

Bswap

Cchange

Dsubstitute

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'swap' which is not a pandas string method.

Using 'change' or 'substitute' which do not exist as string methods.

The replace() method replaces specified characters in strings.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 4 letters.

Pandas

lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 4

Cword.startswith('a')

Dword.isupper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using conditions unrelated to length like 'startswith' or 'isupper'.

Using the word itself instead of its length in the dictionary.

The dictionary comprehension maps each word to its length if the word length is greater than 4.

5fill in blank

hard

Fill all three blanks to filter a DataFrame for rows where 'name' starts with 'J' and create a list of uppercase names.

Pandas

filtered = df[df['name'].str.[1]('J')]
names = [n.[2]() for n in filtered['name'] if n.[3]()]

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

Astartswith

Bupper

Cisalpha

Dendswith

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'endswith' instead of 'startswith' for filtering.

Using 'lower' instead of 'upper' for conversion.

Using 'isdigit' or other checks instead of 'isalpha'.

The code filters names starting with 'J', then creates a list of uppercase names that contain only letters.

Practice

1. Why do we use string operations like .str.lower() on pandas columns?

easy

A. To convert numbers into strings

B. To sort the rows in the DataFrame

C. To remove missing values from the column

D. To make text data consistent by changing all letters to lowercase

Solution

1. Step 1: Understand string consistency

   Text data can have mixed uppercase and lowercase letters, which can cause mismatches when comparing or grouping.

2. Step 2: Use .str.lower() to unify text

   Applying .str.lower() converts all letters to lowercase, making data consistent and easier to analyze.

3. Final Answer:

   To make text data consistent by changing all letters to lowercase -> Option D

4. Quick Check:

   String case normalization = consistent text [OK]

Hint: Use .str.lower() to unify text case quickly [OK]

Common Mistakes:

• Thinking .str.lower() changes numbers
• Confusing string operations with sorting
• Assuming it removes missing values

2. Which of the following is the correct syntax to check if a pandas column 'Name' contains the substring 'john' (case insensitive)?

easy

A. df['Name'].str.find('john', case_sensitive=False)

B. df['Name'].contains('john', ignore_case=True)

C. df['Name'].str.contains('john', case=False)

D. df['Name'].str.match('john', ignore_case=True)

Solution

1. Step 1: Identify correct method for substring search

   The pandas string method to check substring presence is .str.contains().

2. Step 2: Use correct parameters for case insensitivity

   The parameter to ignore case is case=False inside .str.contains().

3. Final Answer:

   df['Name'].str.contains('john', case=False) -> Option C

4. Quick Check:

   Substring search with case=False = correct syntax [OK]

Hint: Use .str.contains('text', case=False) for case-insensitive search [OK]

Common Mistakes:

• Using .contains() instead of .str.contains()
• Wrong parameter names like ignore_case
• Using .str.match() which matches whole string

3. What will be the output of this code?

import pandas as pd
df = pd.DataFrame({'City': ['New York', 'los angeles', 'Chicago', 'new orleans']})
df['City_lower'] = df['City'].str.lower()
print(df['City_lower'])

medium

A. 0 new york
1 los angeles
2 chicago
3 new orleans
Name: City_lower, dtype: object

B. 0 New York
1 Los Angeles
2 Chicago
3 New Orleans
Name: City_lower, dtype: object

C. 0 NEW YORK
1 LOS ANGELES
2 CHICAGO
3 NEW ORLEANS
Name: City_lower, dtype: object

D. Error because .str.lower() cannot be applied to DataFrame columns

Solution

1. Step 1: Understand .str.lower() effect on strings

   The method converts all characters in each string to lowercase.

2. Step 2: Apply .str.lower() on 'City' column

   Each city name is converted to lowercase, so 'New York' becomes 'new york', etc.

3. Final Answer:

   0 new york 1 los angeles 2 chicago 3 new orleans Name: City_lower, dtype: object -> Option A

4. Quick Check:

   .str.lower() converts text to lowercase [OK]

Hint: Remember .str.lower() changes all letters to lowercase [OK]

Common Mistakes:

• Expecting original case to remain
• Thinking it converts to uppercase
• Assuming .str.lower() causes error on columns

4. The following code throws an error. What is the mistake?

df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie']})
df['Name_lower'] = df['Name'].lower()

medium

A. You must use .str.lower() to apply string method on a pandas Series

B. The column 'Name' does not exist in the DataFrame

C. The method .lower() should be called without parentheses

D. You cannot create new columns in pandas DataFrame

Solution

1. Step 1: Identify method usage on pandas Series

   Directly calling .lower() on a pandas Series causes error because it's a string method for single strings, not Series.

2. Step 2: Use .str accessor for string methods on Series

   To apply string methods on a whole column, use .str.lower() instead of .lower().

3. Final Answer:

   You must use .str.lower() to apply string method on a pandas Series -> Option A

4. Quick Check:

   Use .str.lower() on Series, not .lower() [OK]

Hint: Use .str before string methods on pandas columns [OK]

Common Mistakes:

• Calling .lower() directly on Series
• Forgetting .str accessor
• Thinking .lower() works on whole column

5. You have a DataFrame with a column 'Email' containing email addresses with mixed cases and extra spaces. Which sequence of string operations will clean the data by trimming spaces and converting all emails to lowercase?

hard

A. df['Email'].lower().strip()

B. df['Email'].str.strip().str.lower()

C. df['Email'].strip().lower()

D. df['Email'].str.lower().strip()

Solution

1. Step 1: Remove extra spaces using .str.strip()

   Use .str.strip() to remove spaces from start and end of each string in the column.

2. Step 2: Convert all text to lowercase with .str.lower()

   Apply .str.lower() after stripping to unify case.

3. Step 3: Chain methods correctly

   Chaining .str.strip().str.lower() applies both operations in order on the Series.

4. Final Answer:

   df['Email'].str.strip().str.lower() -> Option B

5. Quick Check:

   Chain .str.strip() then .str.lower() for clean emails [OK]

Hint: Chain .str.strip() then .str.lower() to clean text [OK]

Common Mistakes:

• Calling .lower() or .strip() without .str
• Wrong order of operations
• Using methods directly on Series without .str