Combining multiple cleaning steps in Pandas - Mini Project: Build & Apply

Combining multiple cleaning steps

📖 Scenario: You have a small sales dataset with some missing values and inconsistent text. You want to clean it so it is easier to analyze.

🎯 Goal: Build a pandas DataFrame with sales data, then clean it by filling missing values and fixing text formatting in one combined step.

📋 What You'll Learn

Create a pandas DataFrame with given sales data

Create a variable for the fill value for missing sales

Use a single chained pandas command to fill missing sales and fix product names

Print the cleaned DataFrame

💡 Why This Matters

🌍 Real World

Cleaning data is a common first step in data science to prepare messy real-world data for analysis.

💼 Career

Data scientists and analysts often combine multiple cleaning steps to make data consistent and ready for insights.

1

Create the sales DataFrame

Create a pandas DataFrame called sales with these exact columns and values:
{'Product': ['apple', 'Banana', 'orange', 'banana', 'Apple'], 'Sales': [10, 15, None, 20, None]}

Pandas

import pandas as pd

# Create the sales DataFrame with the given data
# Your code here

Need a hint?

Use pd.DataFrame() with a dictionary for columns.

2

Set the fill value for missing sales

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

Pandas

import pandas as pd

sales = pd.DataFrame({'Product': ['apple', 'Banana', 'orange', 'banana', 'Apple'], 'Sales': [10, 15, None, 20, None]})

# Create fill_value variable to fill missing sales
# Your code here

Need a hint?

Just assign 0 to fill_value.

3

Clean the sales DataFrame

Create a new DataFrame called cleaned_sales by filling missing values in sales['Sales'] with fill_value and making all sales['Product'] names lowercase in one combined command using method chaining.

Pandas

import pandas as pd

sales = pd.DataFrame({'Product': ['apple', 'Banana', 'orange', 'banana', 'Apple'], 'Sales': [10, 15, None, 20, None]})

fill_value = 0

# Create cleaned_sales by filling missing Sales and lowercasing Product names in one command
# Your code here

Need a hint?

Use assign() to change Product to lowercase and fillna() to fill missing Sales, chaining them together.

4

Print the cleaned DataFrame

Print the cleaned_sales DataFrame to see the cleaned data.

Pandas

import pandas as pd

sales = pd.DataFrame({'Product': ['apple', 'Banana', 'orange', 'banana', 'Apple'], 'Sales': [10, 15, None, 20, None]})

fill_value = 0

cleaned_sales = sales.assign(Product = sales['Product'].str.lower()).fillna({'Sales': fill_value})

# Print cleaned_sales
# Your code here

Need a hint?

Use print(cleaned_sales) to show the cleaned DataFrame.