isin() for value matching in Pandas - Mini Project: Build & Apply

Using isin() for Value Matching in pandas

📖 Scenario: You work in a store and have a list of products with their categories. You want to find which products belong to certain categories you are interested in.

🎯 Goal: Build a pandas DataFrame with product data, create a list of categories to filter, use isin() to find matching products, and display the filtered results.

📋 What You'll Learn

Create a pandas DataFrame called products with columns 'Product' and 'Category' using exact data.

Create a list called selected_categories with exact category names.

Use isin() on the 'Category' column of products to filter rows.

Print the filtered DataFrame.

💡 Why This Matters

🌍 Real World

Stores and businesses often need to filter product lists by categories to analyze or display specific groups.

💼 Career

Data analysts and scientists use <code>isin()</code> to quickly filter data based on lists of values, which is common in data cleaning and exploration.

1

Create the products DataFrame

Create a pandas DataFrame called products with two columns: 'Product' and 'Category'. Use these exact entries: 'Apple' in 'Fruit', 'Carrot' in 'Vegetable', 'Banana' in 'Fruit', 'Broccoli' in 'Vegetable', and 'Chicken' in 'Meat'.

Pandas

import pandas as pd
# Create the products DataFrame with the exact data
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of entries.

2

Create the list of selected categories

Create a list called selected_categories with these exact values: 'Fruit' and 'Meat'.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Carrot', 'Banana', 'Broccoli', 'Chicken'],
    'Category': ['Fruit', 'Vegetable', 'Fruit', 'Vegetable', 'Meat']
})
# Create the list selected_categories with 'Fruit' and 'Meat'
# Your code here

Hint

Use square brackets to create a list with the exact category names.

3

Filter products using isin()

Create a new DataFrame called filtered_products by selecting rows from products where the 'Category' column values are in the list selected_categories. Use the isin() method on the 'Category' column.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Carrot', 'Banana', 'Broccoli', 'Chicken'],
    'Category': ['Fruit', 'Vegetable', 'Fruit', 'Vegetable', 'Meat']
})
selected_categories = ['Fruit', 'Meat']
# Use isin() on 'Category' to filter products into filtered_products
# Your code here

Hint

Use products['Category'].isin(selected_categories) inside the square brackets to filter rows.

4

Print the filtered products

Print the DataFrame filtered_products to display the products that belong to the selected categories.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Carrot', 'Banana', 'Broccoli', 'Chicken'],
    'Category': ['Fruit', 'Vegetable', 'Fruit', 'Vegetable', 'Meat']
})
selected_categories = ['Fruit', 'Meat']
filtered_products = products[products['Category'].isin(selected_categories)]
# Print the filtered_products DataFrame
# Your code here

Hint

Use print(filtered_products) to show the filtered rows.

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