Why systematic cleaning matters in Pandas - See It in Action

Why Systematic Cleaning Matters

📖 Scenario: Imagine you work in a small online store. You have a list of customer orders, but the data is messy. Some orders have missing prices, some have wrong product names, and some have extra spaces. To understand your sales, you need to clean this data carefully.

🎯 Goal: You will create a small dataset of orders, set a rule to identify invalid prices, clean the data by fixing or removing bad entries, and finally show the cleaned data. This will teach you why cleaning data step-by-step is important before analysis.

📋 What You'll Learn

Create a pandas DataFrame with given order data

Set a price threshold to identify invalid prices

Use pandas methods to clean the data systematically

Print the cleaned DataFrame as the final output

💡 Why This Matters

🌍 Real World

Cleaning data is a crucial first step in any data analysis or business decision. Messy data can lead to wrong conclusions.

💼 Career

Data scientists and analysts spend a lot of time cleaning data before they can analyze it. This project shows why systematic cleaning is important.

1

Create the initial orders DataFrame

Import pandas as pd and create a DataFrame called orders with these exact columns and rows:
OrderID: [101, 102, 103, 104, 105]
Product: [' apple', 'banana', 'Orange ', 'banana', 'apple']
Price: [1.2, 0, 0.8, -1, 1.5]

Pandas

# Import pandas and create the DataFrame orders with the given data
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary of lists for columns.

2

Set a price threshold for invalid prices

Create a variable called min_price and set it to 0.5. This will help us find prices that are too low or invalid.

Pandas

import pandas as pd
orders = pd.DataFrame({
    'OrderID': [101, 102, 103, 104, 105],
    'Product': ['  apple', 'banana', 'Orange ', 'banana', 'apple'],
    'Price': [1.2, 0, 0.8, -1, 1.5]
})
# Create min_price variable and set it to 0.5
# Your code here

Need a hint?

Just create a variable named min_price and assign 0.5.

3

Clean the data by fixing product names and filtering prices

Create a new DataFrame called clean_orders by doing these steps:
1. Remove extra spaces from the Product column using str.strip().
2. Keep only rows where Price is greater than or equal to min_price.

Pandas

import pandas as pd
orders = pd.DataFrame({
    'OrderID': [101, 102, 103, 104, 105],
    'Product': ['  apple', 'banana', 'Orange ', 'banana', 'apple'],
    'Price': [1.2, 0, 0.8, -1, 1.5]
})
min_price = 0.5
# Create clean_orders by stripping spaces and filtering prices
# Your code here

Need a hint?

Use str.strip() on the Product column and filter rows with orders['Price'] >= min_price.

4

Print the cleaned orders DataFrame

Print the clean_orders DataFrame to see the cleaned data.

Pandas

import pandas as pd
orders = pd.DataFrame({
    'OrderID': [101, 102, 103, 104, 105],
    'Product': ['  apple', 'banana', 'Orange ', 'banana', 'apple'],
    'Price': [1.2, 0, 0.8, -1, 1.5]
})
min_price = 0.5
orders['Product'] = orders['Product'].str.strip()
clean_orders = orders[orders['Price'] >= min_price]
# Print the clean_orders DataFrame
# Your code here

Need a hint?

Use print(clean_orders) to show the cleaned data.