str.split() for splitting in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to split the 'Name' column by spaces.

Pandas

df['Name_split'] = df['Name'].str.[1](' ')

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

Asplit

Bjoin

Creplace

Dstrip

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'join' instead of 'split'.

Forgetting to specify the separator.

Using 'replace' which changes text but does not split.

The str.split() method splits strings in a Series by the specified separator.

2fill in blank

medium

Complete the code to split the 'Address' column by commas.

Pandas

df['Address_parts'] = df['Address'].str.[1](',')

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

Asplit

Bstrip

Cfind

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'replace' which changes commas but does not split.

Using 'find' which only locates text but does not split.

To split text by commas, use str.split(',').

3fill in blank

hard

Fix the error in splitting the 'Data' column by semicolon.

Pandas

df['Data_split'] = df['Data'].str.[1](';')

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

Ajoin

Bstrip

Csplit

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'join' which causes an error here.

Using 'replace' which does not split.

The correct method to split strings is str.split(), not join or others.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

Pandas

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

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' in the condition.

Using 'word' instead of 'len(word)' for the value.

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

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()' for keys.

Using '<' instead of '>' in the condition.

This dictionary comprehension uses uppercase keys, keeps values, and filters values greater than zero.

Practice

1. What does the str.split() method do in pandas when applied to a text column?

easy

A. It breaks the text into parts based on a separator.

B. It merges multiple columns into one string.

C. It converts numbers to strings.

D. It removes all spaces from the text.

Solution

1. Step 1: Understand the purpose of str.split()

   The method splits strings into parts using a separator, like spaces or commas.

2. Step 2: Compare with other options

   Options A, B, and C describe different string operations, not splitting.

3. Final Answer:

   It breaks the text into parts based on a separator. -> Option A

4. Quick Check:

   Split means break text apart [OK]

Hint: Split means break text into pieces by separator [OK]

Common Mistakes:

• Confusing split with join or merge
• Thinking split removes spaces instead of using them as separators
• Assuming split converts data types

2. Which of the following is the correct syntax to split a pandas column 'Name' by spaces into multiple columns?

easy

A. df['Name'].split(expand=True)

B. df['Name'].split(' ')

C. df.str.split(' ', 'Name')

D. df['Name'].str.split(' ', expand=True)

Solution

1. Step 1: Recall pandas string method syntax

   Use str.split() on a Series with expand=True to get multiple columns.

2. Step 2: Check each option

   df['Name'].split(' ') misses str, C has wrong argument order, D misses separator argument.

3. Final Answer:

   df['Name'].str.split(' ', expand=True) -> Option D

4. Quick Check:

   Use str.split with expand=True [OK]

Hint: Use df['col'].str.split(sep, expand=True) for columns [OK]

Common Mistakes:

• Forgetting to use .str before split
• Not setting expand=True to get multiple columns
• Passing wrong arguments or order

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'FullName': ['Alice Smith', 'Bob Jones', 'Carol Lee']})
df[['First', 'Last']] = df['FullName'].str.split(' ', expand=True)
print(df)

What will be the output?

medium

A. A DataFrame with columns FullName, First, Last where First and Last contain split names.

B. An error because split cannot create multiple columns.

C. A DataFrame with only First and Last columns, FullName removed.

D. A DataFrame with FullName column unchanged and First column with full names.

Solution

1. Step 1: Understand the split with expand=True

   Splitting 'FullName' by space creates two columns: 'First' and 'Last'.

2. Step 2: Check DataFrame structure after assignment

   Original 'FullName' stays, new columns 'First' and 'Last' are added with split parts.

3. Final Answer:

   A DataFrame with columns FullName, First, Last where First and Last contain split names. -> Option A

4. Quick Check:

   Split with expand=True adds columns [OK]

Hint: expand=True adds new columns, original stays [OK]

Common Mistakes:

• Expecting original column to be removed
• Thinking split returns a list inside one column
• Confusing expand=True effect

4. What is wrong with this code snippet?

df['City', 'State'] = df['Location'].str.split(',', expand=True)

medium

A. expand=True should be False to assign multiple columns.

B. The split separator ',' is incorrect for splitting locations.

C. You cannot assign multiple columns using a tuple as column names like ('City', 'State').

D. The code is correct and will work without errors.

Solution

1. Step 1: Check assignment syntax for multiple columns

   Assigning multiple columns requires a list of column names, not a tuple.

2. Step 2: Identify correct syntax

   Use df[['City', 'State']] = ... with double brackets for multiple columns.

3. Final Answer:

   You cannot assign multiple columns using a tuple as column names like ('City', 'State'). -> Option C

4. Quick Check:

   Use double brackets for multiple columns [OK]

Hint: Use double brackets [[]] to assign multiple columns [OK]

Common Mistakes:

• Using single brackets with tuple for multiple columns
• Confusing expand=True with assignment syntax
• Assuming tuple works like list for columns

5. You have a DataFrame with a column 'Tags' containing strings like 'red,blue,green'. You want to create a new DataFrame where each tag is a separate row linked to the original row index. Which pandas method combined with str.split() helps achieve this?

hard

A. Use str.split(',', expand=True) to create multiple columns.

B. Use str.split(',').explode() to split and expand tags into rows.

C. Use str.split(',').apply(list) to convert tags to lists.

D. Use str.split(',').join() to merge tags back.

Solution

1. Step 1: Understand the goal to expand tags into rows

   Splitting creates lists; explode() turns list elements into separate rows.

2. Step 2: Evaluate options for correct method

   Use str.split(',').explode() to split and expand tags into rows. correctly chains str.split() with explode() to get one tag per row.

3. Final Answer:

   Use str.split(',').explode() to split and expand tags into rows. -> Option B

4. Quick Check:

   Split then explode = rows per item [OK]

Hint: Split then explode to get rows per list item [OK]

Common Mistakes:

• Using expand=True creates columns, not rows
• Forgetting to use explode() after split
• Confusing join() with explode()