Counting missing values in Pandas - Mini Project: Build & Apply

Counting missing values

📖 Scenario: You work as a data analyst. You have a small table of sales data. Some values are missing because the data was not recorded properly.You want to find out how many missing values are in the data to understand its quality.

🎯 Goal: Create a pandas DataFrame with sales data including some missing values. Then count how many missing values are in each column.

📋 What You'll Learn

Use pandas to create a DataFrame

Include missing values using None or numpy.nan

Count missing values per column using pandas methods

Print the count of missing values

💡 Why This Matters

🌍 Real World

Data scientists often check for missing data to decide how to clean or fill it before analysis.

💼 Career

Counting missing values is a basic but essential skill for data cleaning in data analyst and data scientist roles.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and values:
'Product': ['Apple', 'Banana', 'Orange', 'Grapes'],
'Price': [1.2, None, 0.8, 2.5],
'Quantity': [10, 15, None, 8].

Pandas

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

Need a hint?

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

2

Create a variable to hold missing values count

Create a variable called missing_counts and set it to None. This will hold the count of missing values later.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Orange', 'Grapes'],
    'Price': [1.2, None, 0.8, 2.5],
    'Quantity': [10, 15, None, 8]
})

# Create a variable missing_counts and set it to None
# Your code here

Need a hint?

Just write missing_counts = None to prepare the variable.

3

Count missing values in each column

Set the variable missing_counts to the result of calling sales_data.isnull().sum(). This counts missing values per column.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Orange', 'Grapes'],
    'Price': [1.2, None, 0.8, 2.5],
    'Quantity': [10, 15, None, 8]
})

missing_counts = None

# Count missing values per column and assign to missing_counts
# Your code here

Need a hint?

Use sales_data.isnull() to find missing values, then .sum() to count them per column.

4

Print the missing values count

Print the variable missing_counts to show the count of missing values in each column.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Orange', 'Grapes'],
    'Price': [1.2, None, 0.8, 2.5],
    'Quantity': [10, 15, None, 8]
})

missing_counts = sales_data.isnull().sum()

# Print the missing_counts variable
# Your code here

Need a hint?

Use print(missing_counts) to display the counts.