NaN and None in Pandas - Mini Project: Build & Apply

Handling NaN and None in Pandas

📋 What You'll Learn

💡 Why This Matters

🌍 Real World

Handling missing data is common in real-world datasets like sales, weather, or survey data where some entries might be missing or not recorded.

💼 Career

Data scientists and analysts must know how to detect and handle missing values to prepare clean data for analysis and modeling.

1

Create a DataFrame with missing values

Import pandas as pd and numpy as np. Create a DataFrame called sales_data with a column 'Day' containing 'Monday', 'Tuesday', 'Wednesday', 'Thursday', and 'Friday'. Add a column 'Sales' with values 200, None, 150, np.nan, and 300 respectively.

Pandas

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

Need a hint?

Use pd.DataFrame to create the table. Use None and np.nan to represent missing values.

2

Count missing values in the DataFrame

Create a variable called missing_count that stores the total number of missing values in the sales_data DataFrame using the isna() and sum() methods.

Pandas

import pandas as pd
import numpy as np

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Sales': [200, None, 150, np.nan, 300]
})

# Count missing values in sales_data
# Your code here

Need a hint?

Use sales_data.isna() to find missing values, then sum twice to get total count.

3

Remove rows with missing values

Create a new DataFrame called clean_sales by removing all rows with missing values from sales_data using the dropna() method.

Pandas

import pandas as pd
import numpy as np

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Sales': [200, None, 150, np.nan, 300]
})

missing_count = sales_data.isna().sum().sum()

# Remove rows with missing values and save to clean_sales
# Your code here

Need a hint?

Use dropna() on the DataFrame to remove rows with missing values.

4

Print the cleaned DataFrame

Print the clean_sales DataFrame to show the sales data without missing values.

Pandas

import pandas as pd
import numpy as np

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Sales': [200, None, 150, np.nan, 300]
})

missing_count = sales_data.isna().sum().sum()

clean_sales = sales_data.dropna()

# Print the clean_sales DataFrame
# Your code here

Need a hint?

Use print(clean_sales) to display the DataFrame without missing values.