Dropping missing values with dropna() in Pandas - Mini Project: Build & Apply

Dropping missing values with dropna()

📖 Scenario: You have a small dataset of sales records. Some records have missing values. You want to clean the data by removing any rows that have missing values.

🎯 Goal: Build a small pandas DataFrame with sales data, then use dropna() to remove rows with missing values, and finally display the cleaned data.

📋 What You'll Learn

Create a pandas DataFrame called sales_data with exact columns and values

Create a variable called cleaned_data that holds the DataFrame after dropping rows with missing values using dropna()

Print the cleaned_data DataFrame

💡 Why This Matters

🌍 Real World

Cleaning data by removing incomplete records is a common step before analysis or machine learning.

💼 Career

Data scientists and analysts often use <code>dropna()</code> to prepare datasets for accurate insights and models.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and values:
'Product': ['Pen', 'Notebook', 'Pencil', 'Eraser', 'Marker'],
'Price': [1.5, 2.0, None, 0.5, 1.0],
'Quantity': [10, None, 15, 20, 10].
Use None for missing values.

Pandas

# Import pandas and create the sales_data DataFrame with missing values
# Your code here

Need a hint?

Use pd.DataFrame() with a dictionary. Use None for missing values.

2

Create a variable for cleaned data

Create a variable called cleaned_data that stores the result of calling dropna() on sales_data to remove rows with any missing values.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Pencil', 'Eraser', 'Marker'],
    'Price': [1.5, 2.0, None, 0.5, 1.0],
    'Quantity': [10, None, 15, 20, 10]
})

# Create cleaned_data by dropping rows with missing values from sales_data
# Your code here

Need a hint?

Use dropna() on sales_data and assign it to cleaned_data.

3

Check the cleaned data

Use print() to display the cleaned_data DataFrame.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Pencil', 'Eraser', 'Marker'],
    'Price': [1.5, 2.0, None, 0.5, 1.0],
    'Quantity': [10, None, 15, 20, 10]
})

cleaned_data = sales_data.dropna()

# Print the cleaned_data DataFrame
# Your code here

Need a hint?

Use print(cleaned_data) to see the cleaned DataFrame.

4

View the final cleaned data output

Run the program to see the printed cleaned_data DataFrame without any missing values.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Pencil', 'Eraser', 'Marker'],
    'Price': [1.5, 2.0, None, 0.5, 1.0],
    'Quantity': [10, None, 15, 20, 10]
})

cleaned_data = sales_data.dropna()

print(cleaned_data)

# Run the code to see the cleaned data output

Need a hint?

The output should show only rows without any missing values.