Identifying missing values (isnull, isna) in Data Analysis Python - Mini Project: Build & Apply

Identifying Missing Values in Data

📖 Scenario: Imagine you work in a company that collects customer data. Sometimes, some information is missing. You want to find out which parts of the data are missing so you can fix or handle them.

🎯 Goal: You will create a small table of customer data, then use Python to find which values are missing using isnull() or isna() methods.

📋 What You'll Learn

Create a pandas DataFrame with specific customer data including some missing values

Create a variable to hold the result of checking for missing values

Use the isnull() method on the DataFrame to find missing values

Print the result showing which values are missing

💡 Why This Matters

🌍 Real World

In real life, data often has missing parts. Finding missing values helps you clean data before analysis.

💼 Career

Data scientists and analysts must identify and handle missing data to make accurate models and reports.

1

Create the customer data table

Import pandas as pd and create a DataFrame called customers with these exact data: three columns named 'Name', 'Age', and 'Email'. The rows should be: 'Alice', 25, '[e-mail]', 'Bob', None, '[e-mail]', and 'Charlie', 30, None.

Data Analysis Python

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

Hint

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

2

Create a variable to check for missing values

Create a variable called missing_values and set it to the result of calling isnull() on the customers DataFrame.

Data Analysis Python

import pandas as pd
customers = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, None, 30],
    'Email': ['[e-mail]', '[e-mail]', None]
})
# Create missing_values by calling isnull() on customers
# Your code here

Hint

Use customers.isnull() to get a DataFrame showing True where values are missing.

3

Use the core method to find missing values

Use the isnull() method on the customers DataFrame and assign the result to the variable missing_values.

Data Analysis Python

import pandas as pd
customers = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, None, 30],
    'Email': ['[e-mail]', '[e-mail]', None]
})
missing_values = customers.isnull()
# Your code here

Hint

This step repeats the core logic to reinforce the method usage.

4

Print the missing values table

Print the variable missing_values to show which values are missing in the customers DataFrame.

Data Analysis Python

import pandas as pd
customers = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, None, 30],
    'Email': ['[e-mail]', '[e-mail]', None]
})
missing_values = customers.isnull()
# Print missing_values below
# Your code here

Hint

Use print(missing_values) to display the table of True/False values.