Filling missing values (fillna) in Data Analysis Python - Mini Project: Build & Apply

Filling Missing Values (fillna)

📖 Scenario: Imagine you work for a small online store. You have a table of sales data, but some sales amounts are missing. You want to fill these missing values with zero so you can calculate total sales without errors.

🎯 Goal: You will create a small sales data table with missing values, set a fill value, fill the missing values using fillna, and then print the cleaned data.

📋 What You'll Learn

Create a pandas DataFrame with missing values

Create a variable to hold the fill value

Use the fillna method to fill missing values

Print the cleaned DataFrame

💡 Why This Matters

🌍 Real World

Cleaning missing data is a common task in data analysis to prepare data for calculations and visualizations.

💼 Career

Data analysts and scientists often need to handle missing data to ensure accurate reports and models.

1

Create sales data with missing values

Create a pandas DataFrame called sales_data with one column named 'sales' and these values: 100, None, 250, None, 400.

Data Analysis Python

import pandas as pd
# Create the sales_data DataFrame with missing values
# Your code here

Hint

Use pd.DataFrame with a dictionary where the key is 'sales' and the value is the list of numbers including None for missing values.

2

Set the fill value

Create a variable called fill_value and set it to 0 to use for filling missing sales.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({'sales': [100, None, 250, None, 400]})
# Create fill_value variable and set it to 0
# Your code here

Hint

Just create a variable named fill_value and assign 0 to it.

3

Fill missing values using fillna

Create a new DataFrame called cleaned_sales by filling missing values in sales_data using the fillna method with the variable fill_value.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({'sales': [100, None, 250, None, 400]})
fill_value = 0
# Use fillna to create cleaned_sales DataFrame
# Your code here

Hint

Use sales_data.fillna(fill_value) and assign it to cleaned_sales.

4

Print the cleaned sales data

Print the cleaned_sales DataFrame to see the missing values replaced by zero.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({'sales': [100, None, 250, None, 400]})
fill_value = 0
cleaned_sales = sales_data.fillna(fill_value)
# Print cleaned_sales DataFrame
# Your code here

Hint

Use print(cleaned_sales) to display the DataFrame.