Filling missing values with fillna() in Pandas - Mini Project: Build & Apply

Filling missing values with fillna()

📖 Scenario: You work in a small shop that keeps track of daily sales in a table. Sometimes, the sales data is missing for some days. You want to fill those missing sales numbers with zero so you can calculate total sales easily.

🎯 Goal: Build a small program that creates a sales data table with some missing values, sets a fill value, fills the missing values using fillna(), and then shows the updated table.

📋 What You'll Learn

Create a pandas DataFrame called sales_data with the exact data given.

Create a variable called fill_value and set it to 0.

Use fillna() on sales_data with fill_value to fill missing values and save it as filled_sales.

Print the filled_sales DataFrame.

💡 Why This Matters

🌍 Real World

In real life, sales or sensor data often have missing values. Filling them with a default value helps keep calculations accurate and avoids errors.

💼 Career

Data analysts and scientists frequently clean data by filling missing values before analysis or building models.

1

Create the sales data with missing values

Create a pandas DataFrame called sales_data with these exact values: the column 'Day' with values ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'] and the column 'Sales' with values [200, None, 150, None, 300].

Pandas

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

Need a hint?

Use pd.DataFrame with a dictionary for columns. Use None for missing values.

2

Set the fill value

Create a variable called fill_value and set it to 0.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, None, 150, None, 300]
})

# Create fill_value variable and set it to 0
# Your code here

Need a hint?

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

3

Fill missing values using fillna()

Use fillna() on sales_data with fill_value to fill missing values. Save the result in a new variable called filled_sales.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, None, 150, None, 300]
})
fill_value = 0

# Fill missing values in sales_data using fill_value and save as filled_sales
# Your code here

Need a hint?

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

4

Print the filled sales data

Print the filled_sales DataFrame to see the missing values replaced with zero.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, None, 150, None, 300]
})
fill_value = 0
filled_sales = sales_data.fillna(fill_value)

# Print the filled_sales DataFrame
# Your code here

Need a hint?

Use print(filled_sales) to show the DataFrame.