Why DataFrame creation matters in Pandas - See It in Action

Why DataFrame creation matters

📖 Scenario: Imagine you work in a small store. You want to keep track of daily sales data like product names, quantities sold, and prices. Organizing this data well helps you understand your business better.

🎯 Goal: You will create a pandas DataFrame from scratch with sales data. Then, you will set a threshold to filter products sold in large quantities. Finally, you will display the filtered data to see which products sold the most.

📋 What You'll Learn

Create a pandas DataFrame with exact sales data

Create a variable for quantity threshold

Filter the DataFrame for products with quantity sold above the threshold

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Organizing sales data in a DataFrame helps store owners quickly analyze which products sell well and make better decisions.

💼 Career

Data scientists and analysts often create and filter DataFrames to prepare data for reports and insights.

1

Create the sales DataFrame

Import pandas as pd. Create a DataFrame called sales_data with these exact columns and values: 'Product': ['Apples', 'Bananas', 'Cherries', 'Dates'], 'Quantity': [10, 25, 7, 30], 'Price': [0.5, 0.3, 0.2, 1.0].

Pandas

# Your code here

Need a hint?

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

2

Set the quantity threshold

Create a variable called quantity_threshold and set it to 15. This will help us find products sold in quantities greater than 15.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates'],
    'Quantity': [10, 25, 7, 30],
    'Price': [0.5, 0.3, 0.2, 1.0]
})
# Your code here

Need a hint?

Just create a variable and assign the number 15 to it.

3

Filter products by quantity

Create a new DataFrame called high_sales by filtering sales_data to include only rows where Quantity is greater than quantity_threshold.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates'],
    'Quantity': [10, 25, 7, 30],
    'Price': [0.5, 0.3, 0.2, 1.0]
})
quantity_threshold = 15
# Your code here

Need a hint?

Use boolean indexing with sales_data['Quantity'] > quantity_threshold inside the square brackets.

4

Display the filtered DataFrame

Print the high_sales DataFrame to see the products sold in quantities greater than 15.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates'],
    'Quantity': [10, 25, 7, 30],
    'Price': [0.5, 0.3, 0.2, 1.0]
})
quantity_threshold = 15
high_sales = sales_data[sales_data['Quantity'] > quantity_threshold]
# Your code here

Need a hint?

Use print(high_sales) to show the filtered data.