Why handling missing data matters in Pandas - See It in Action

Why handling missing data matters

📖 Scenario: Imagine you work in a health clinic. You have a small table of patient data with their ages and blood pressure readings. Some readings are missing. You want to understand why missing data can cause problems before fixing it.

🎯 Goal: You will create a small patient data table with missing values, count how many values are missing, and see how missing data affects calculations like average blood pressure.

📋 What You'll Learn

Create a pandas DataFrame with patient names, ages, and blood pressure readings including missing values

Create a variable to count missing blood pressure values

Calculate the average blood pressure without handling missing data

Print the count of missing values and the average blood pressure

💡 Why This Matters

🌍 Real World

In real health data, missing measurements happen often. Knowing how to find and handle them helps keep analysis accurate.

💼 Career

Data scientists must detect and manage missing data to build reliable models and reports.

1

Create patient data with missing values

Create a pandas DataFrame called patients with these exact columns and values:
'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva']
'Age': [25, 30, 35, 40, 45]
'BloodPressure': [120, None, 130, None, 110]
Use import pandas as pd before creating the DataFrame.

Pandas

import pandas as pd
# Create the patients DataFrame with missing blood pressure values
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary of lists. Use None for missing values.

2

Count missing blood pressure values

Create a variable called missing_count that counts how many missing values are in the 'BloodPressure' column of patients. Use the isna() method and sum().

Pandas

import pandas as pd
patients = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [25, 30, 35, 40, 45],
    'BloodPressure': [120, None, 130, None, 110]
})
# Count missing values in the 'BloodPressure' column
# Your code here

Need a hint?

Use patients['BloodPressure'].isna() to get True for missing values, then sum them.

3

Calculate average blood pressure without handling missing data

Create a variable called average_bp that calculates the mean of the 'BloodPressure' column in patients without removing missing values explicitly. Use the mean() method directly.

Pandas

import pandas as pd
patients = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [25, 30, 35, 40, 45],
    'BloodPressure': [120, None, 130, None, 110]
})
missing_count = patients['BloodPressure'].isna().sum()
# Calculate the average blood pressure without handling missing data
# Your code here

Need a hint?

Use patients['BloodPressure'].mean() directly. It ignores missing values automatically.

4

Print missing count and average blood pressure

Print the text Missing blood pressure values: followed by the value of missing_count. Then print Average blood pressure: followed by the value of average_bp. Use two separate print() statements.

Pandas

import pandas as pd
patients = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [25, 30, 35, 40, 45],
    'BloodPressure': [120, None, 130, None, 110]
})
missing_count = patients['BloodPressure'].isna().sum()
average_bp = patients['BloodPressure'].mean()
# Print the missing count and average blood pressure
# Your code here

Need a hint?

Use print("Missing blood pressure values:", missing_count) and print("Average blood pressure:", average_bp).