str.split() for splitting in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the str.split() method do in pandas?

It splits each string in a pandas Series or DataFrame column into parts based on a separator, returning a list or expanding into multiple columns.

beginner

How do you split a pandas Series column into multiple columns using str.split()?

Use str.split(pat, expand=True). The expand=True makes the split parts become separate columns.

beginner

What is the default separator for str.split() in pandas?

The default separator is any whitespace (spaces, tabs, newlines).

intermediate

How can you limit the number of splits when using str.split()?

Use the n parameter to specify the maximum number of splits to do from the left.

beginner

What happens if you use str.split() without expand=True?

The result is a Series of lists, where each list contains the split parts of the original string.

What does expand=True do in str.split()?

A Removes whitespace

B Limits the number of splits

C Returns split parts as separate columns

D Changes the separator

expand=True makes the split parts become separate columns in the DataFrame.

What is the default separator used by str.split() in pandas?

A Whitespace

B Comma (",")

C Semicolon (";")

D No separator

The default separator is any whitespace like spaces or tabs.

If you want to split a string only once from the left, which parameter do you use?

A count=1

B maxsplit=1

C limit=1

D n=1

The n parameter limits the number of splits from the left.

What type of object does str.split() return by default?

A Series of lists

B DataFrame

C List

D Dictionary

By default, it returns a Series where each element is a list of split parts.

Which of these is a correct way to split a pandas Series column named 'Name' by comma and expand into columns?

A df.str.split('Name', ',', expand=True)

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

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

D df['Name'].split(',')

The correct syntax is df['Name'].str.split(',', expand=True).

Explain how to split a pandas Series column into multiple columns using str.split().

Think about how to turn one column with strings into several columns.

Describe the difference in output when using str.split() with and without expand=True.

Consider the shape and type of the output.

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()