Why exploratory inspection guides analysis in Data Analysis Python - See It in Action

Why Exploratory Inspection Guides Analysis

📖 Scenario: Imagine you are a data analyst working with sales data from a small store. Before making any decisions or building models, you want to understand the data by looking at it carefully. This helps you find patterns, spot mistakes, and decide what to do next.

🎯 Goal: You will create a small dataset, set a threshold to find high sales, filter the data using that threshold, and then print the filtered results. This shows how exploring data step-by-step helps guide your analysis.

📋 What You'll Learn

Create a dictionary called sales_data with product names as keys and sales numbers as values

Create a variable called high_sales_threshold and set it to 50

Use a dictionary comprehension to create a new dictionary high_sales with only products having sales greater than high_sales_threshold

Print the high_sales dictionary to see the filtered results

💡 Why This Matters

🌍 Real World

Exploratory inspection helps analysts understand data quality and patterns before making decisions or building models.

💼 Career

Data analysts and scientists use exploratory data analysis to guide their work and avoid mistakes.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Apples': 30, 'Bananas': 55, 'Cherries': 45, 'Dates': 70, 'Elderberries': 20

Data Analysis Python

# Create the sales_data dictionary with product names and sales numbers
# Your code here

Need a hint?

Use curly braces {} to create the dictionary with the exact keys and values.

2

Set the high sales threshold

Create a variable called high_sales_threshold and set it to 50

Data Analysis Python

sales_data = {'Apples': 30, 'Bananas': 55, 'Cherries': 45, 'Dates': 70, 'Elderberries': 20}
# Create the high_sales_threshold variable and set it to 50
# Your code here

Need a hint?

Just assign the number 50 to the variable high_sales_threshold.

3

Filter products with high sales

Use a dictionary comprehension to create a new dictionary called high_sales that includes only products from sales_data with sales greater than high_sales_threshold

Data Analysis Python

sales_data = {'Apples': 30, 'Bananas': 55, 'Cherries': 45, 'Dates': 70, 'Elderberries': 20}
high_sales_threshold = 50
# Create high_sales dictionary with products having sales > high_sales_threshold
# Your code here

Need a hint?

Use {product: sales for product, sales in sales_data.items() if sales > high_sales_threshold} to filter the dictionary.

4

Print the filtered high sales products

Write a print statement to display the high_sales dictionary

Data Analysis Python

sales_data = {'Apples': 30, 'Bananas': 55, 'Cherries': 45, 'Dates': 70, 'Elderberries': 20}
high_sales_threshold = 50
high_sales = {product: sales for product, sales in sales_data.items() if sales > high_sales_threshold}
# Print the high_sales dictionary
# Your code here

Need a hint?

Use print(high_sales) to show the filtered dictionary.