Exploratory Data Analysis (EDA) template in Data Analysis Python - Mini Project: Build & Apply

Exploratory Data Analysis (EDA) template

📖 Scenario: You have a small dataset about daily sales in a store. You want to understand the data better by exploring it step-by-step.

🎯 Goal: Build a simple Exploratory Data Analysis (EDA) template in Python that loads data, sets a configuration, applies core analysis, and prints the results.

📋 What You'll Learn

Create a dictionary with sales data for 5 days

Add a threshold variable to filter days with sales above it

Use a loop to select days with sales above the threshold

Print the filtered days and their sales

💡 Why This Matters

🌍 Real World

Exploratory Data Analysis helps you understand data before making decisions. For example, a store manager can see which days have high sales.

💼 Career

Data analysts and scientists use EDA to clean and understand data before building models or reports.

1

DATA SETUP: Create the sales data dictionary

Create a dictionary called daily_sales with these exact entries: 'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100

Data Analysis Python

# Create the daily_sales dictionary with exact day and sales values
# Your code here

Hint

Use curly braces to create a dictionary with day names as keys and sales numbers as values.

2

CONFIGURATION: Set the sales threshold

Create a variable called sales_threshold and set it to 100

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
# Set the sales_threshold variable to 100
# Your code here

Hint

Just assign the number 100 to the variable sales_threshold.

3

CORE LOGIC: Filter days with sales above the threshold

Create a new dictionary called filtered_sales using a for loop with variables day and sales to iterate over daily_sales.items(). Add entries to filtered_sales only if sales is greater than sales_threshold

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
sales_threshold = 100
# Create filtered_sales dictionary with days having sales above sales_threshold
# Your code here

Hint

Start with an empty dictionary. Use a for loop to check each day's sales. Add to filtered_sales only if sales are above the threshold.

4

OUTPUT: Print the filtered sales dictionary

Write a print statement to display the filtered_sales dictionary

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
sales_threshold = 100
filtered_sales = {}
for day, sales in daily_sales.items():
    if sales > sales_threshold:
        filtered_sales[day] = sales
# Print the filtered_sales dictionary
# Your code here

Hint

Use print(filtered_sales) to show the filtered dictionary.