isin() for value matching in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the isin() function do in pandas?

The isin() function checks if each element in a pandas Series or DataFrame is contained in a list or set of values. It returns a boolean Series or DataFrame showing True where the element matches any value in the list, and False otherwise.

beginner

How do you use isin() to filter rows in a DataFrame?

You use isin() on a DataFrame column to get a boolean Series, then use that Series to select rows. For example, df[df['column'].isin([value1, value2])] returns rows where the column's value is either value1 or value2.

intermediate

Can isin() be used with multiple columns in a DataFrame?

Yes, you can apply isin() to multiple columns separately or use it on the entire DataFrame to check if each element is in a list of values. It returns a DataFrame of booleans with the same shape as the original.

beginner

What type of object does isin() return when used on a Series?

isin() returns a pandas Series of boolean values. Each boolean indicates whether the corresponding element in the original Series is in the list of values provided.

beginner

Why is isin() useful in data analysis?

isin() helps quickly find and filter data points that match a set of values. This is useful for selecting specific categories, cleaning data, or comparing data against a list of known values.

What does df['A'].isin([1, 2, 3]) return?

A A DataFrame filtered to only rows where 'A' is 1, 2, or 3

B A list of values 1, 2, and 3

C A boolean Series showing True where values in column 'A' are 1, 2, or 3

D An error because <code>isin()</code> cannot take a list

isin() returns a boolean Series indicating which values in the column match any value in the list.

How can you use isin() to select rows where column 'color' is either 'red' or 'blue'?

A df[df['color'].isin(['red', 'blue'])]

B df.isin(['red', 'blue'])

C df['color'] == ['red', 'blue']

D df['color'].contains(['red', 'blue'])

Using isin() on the 'color' column with the list ['red', 'blue'] returns a boolean Series to filter rows.

What type of object can you pass as an argument to isin()?

A Only a DataFrame

B Only a single value

C Only a dictionary

D List, set, or Series of values

isin() accepts any iterable like list, set, or Series to check membership.

If you use df.isin([1, 2]) on a DataFrame, what is the output?

A A DataFrame of booleans showing True where elements are 1 or 2

B A filtered DataFrame with only rows containing 1 or 2

C A Series of booleans for the first column

D An error because <code>isin()</code> only works on Series

When used on a DataFrame, isin() returns a DataFrame of the same shape with True/False values.

Which of these is NOT a correct use of isin()?

A df.isin({10, 20})

B df['col'].isin(10)

C df['col'].isin([10, 20])

D df.isin(pd.Series([10, 20]))

Passing a single value like 10 directly is invalid; isin() expects an iterable like list or set.

Explain how you would use isin() to filter a DataFrame for rows where a column matches any value from a list.

Think about how to get True/False for each row and then select rows.

Describe the difference in output when using isin() on a pandas Series versus on a DataFrame.

Consider the shape and type of the output.

Practice

1. What does the isin() function do in pandas?

easy

A. Checks if each value in a Series or DataFrame is in a given list and returns True or False.

B. Sorts the values in a DataFrame or Series.

C. Calculates the sum of values in a DataFrame column.

D. Removes duplicate values from a DataFrame.

Solution

1. Step 1: Understand the purpose of isin()

   isin() checks if each element in a pandas Series or DataFrame exists in a list or another iterable.

2. Step 2: Identify the output type

   The function returns a boolean Series or DataFrame with True where the value is found and False otherwise.

3. Final Answer:

   Checks if each value in a Series or DataFrame is in a given list and returns True or False. -> Option A

4. Quick Check:

   isin() = membership check [OK]

Hint: Remember: isin() returns True/False for membership [OK]

Common Mistakes:

• Thinking isin() sorts data
• Confusing isin() with aggregation functions
• Assuming it removes duplicates

2. Which of the following is the correct syntax to filter rows in a DataFrame df where column col has values in the list [1, 2, 3]?

easy

A. df.isin(col, [1, 2, 3])

B. df.col.isin([1, 2, 3]) = True

C. df[df.isin(col, [1, 2, 3])]

D. df[df.col.isin([1, 2, 3])]

Solution

1. Step 1: Use isin() on the column

   The correct way is to call isin() on the Series: df.col.isin([1, 2, 3]).

2. Step 2: Use boolean indexing to filter rows

   Wrap the boolean Series inside df[ ... ] to select rows where condition is True.

3. Final Answer:

   df[df.col.isin([1, 2, 3])] -> Option D

4. Quick Check:

   Filter rows with boolean mask = df[df.col.isin([1, 2, 3])] [OK]

Hint: Call isin() on column, then filter with df[...] [OK]

Common Mistakes:

• Calling isin() on DataFrame with wrong arguments
• Using assignment instead of filtering
• Incorrect method call syntax

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'A': [10, 20, 30, 40], 'B': ['x', 'y', 'z', 'x']})

What is the output of df['B'].isin(['x', 'z'])?

medium

A. [True, False, True, True]

B. [False, True, False, False]

C. [True, True, True, True]

D. [False, False, False, False]

Solution

1. Step 1: Check each value in column 'B'

   Values are ['x', 'y', 'z', 'x']. We check if each is in ['x', 'z'].

2. Step 2: Mark True if value is in list, else False

   'x' is in list -> True, 'y' not in list -> False, 'z' in list -> True, 'x' in list -> True.

3. Final Answer:

   [True, False, True, True] -> Option A

4. Quick Check:

   Membership check matches [True, False, True, True] [OK]

Hint: Check each value against list, True if found [OK]

Common Mistakes:

• Mixing True/False order
• Confusing column values
• Assuming all True or all False

4. What is wrong with this code snippet?

df = pd.DataFrame({'num': [1, 2, 3, 4]})
mask = df.isin([1, 3])
filtered = df[mask]

medium

A. The DataFrame df must have a column named 'mask' to filter.

B. The isin() method is called on the whole DataFrame, resulting in a DataFrame of booleans, which cannot be used directly for filtering.

C. The variable mask should be converted to a list before filtering.

D. The list passed to isin() must be a string, not integers.

Solution

1. Step 1: Understand df.isin([1, 3]) output

   This returns a DataFrame of booleans with the same shape as df, not a single boolean Series.

2. Step 2: Why filtering with DataFrame mask fails

   Using a DataFrame of booleans as an index mask causes an error because pandas expects a boolean Series or array matching the index length.

3. Final Answer:

   The isin() method is called on the whole DataFrame, resulting in a DataFrame of booleans, which cannot be used directly for filtering. -> Option B

4. Quick Check:

   Mask must be Series, not DataFrame [OK]

Hint: Use isin() on a column, not whole DataFrame for filtering [OK]

Common Mistakes:

• Using DataFrame mask instead of Series mask for filtering
• Passing wrong data types to isin()
• Trying to filter with a DataFrame of booleans

5. You have a DataFrame df with a column status containing values like 'new', 'pending', 'done', and 'cancelled'. You want to create a new column is_active that is True only if status is 'new' or 'pending'. Which code correctly does this using isin()?

hard

A. df['is_active'] = df['status'] == ['new', 'pending']

B. df['is_active'] = df.isin(['new', 'pending'])

C. df['is_active'] = df['status'].isin(['new', 'pending'])

D. df['is_active'] = df['status'].isin('new', 'pending')

Solution

1. Step 1: Use isin() on the 'status' column with a list of target values

   Correct syntax is df['status'].isin(['new', 'pending']) which returns a boolean Series.

2. Step 2: Assign the boolean Series to new column 'is_active'

   This creates a new column with True where status is 'new' or 'pending', False otherwise.

3. Final Answer:

   df['is_active'] = df['status'].isin(['new', 'pending']) -> Option C

4. Quick Check:

   Use isin() on column with list [OK]

Hint: Assign isin() result directly to new column [OK]

Common Mistakes:

• Calling isin() on whole DataFrame
• Passing multiple arguments instead of a list
• Using equality operator with list