IT 140 Project Two: Diving into Data Analysis with Python
Project Overview
IT 140 Project Two challenges students to analyze a dataset using Python and Pandas. The dataset, provided by the course, contains information about a company's sales data. The task is to explore the data, perform calculations, and answer specific questions about the company's performance.
The Original Code
Here's an example of the code students might encounter:
import pandas as pd
# Load the sales data into a DataFrame
sales_data = pd.read_csv("sales_data.csv")
# Calculate the total revenue for all products
total_revenue = sales_data["Sales"].sum()
# Print the total revenue
print("Total Revenue:", total_revenue)
Understanding the Problem
The original code snippet illustrates a simple data analysis task. However, the code needs to be enhanced to address the specific requirements outlined in IT 140 Project Two. This includes:
- Data Exploration: Before diving into calculations, it's crucial to understand the data structure, identify any missing values, and explore the distribution of key variables like "Sales" and "Quantity".
- Specific Questions: The project requires answering specific questions about the data, such as:
- What is the average sale value per product?
- Which product has the highest total revenue?
- What is the percentage of sales made in each region?
- Data Visualization: To effectively communicate findings, students should use libraries like Matplotlib or Seaborn to create insightful charts and graphs.
Analysis and Practical Examples
Let's break down the code and add elements to effectively tackle IT 140 Project Two:
-
Loading and Exploring the Data:
import pandas as pd # Load the sales data into a DataFrame sales_data = pd.read_csv("sales_data.csv") # Explore the first few rows print(sales_data.head()) # Get basic information about the DataFrame print(sales_data.info()) # Check for missing values print(sales_data.isnull().sum()) -
Addressing Missing Values:
# Replace missing values with a suitable strategy (e.g., mean, median, or mode) sales_data["Sales"].fillna(sales_data["Sales"].mean(), inplace=True) -
Calculating Key Metrics:
# Calculate average sale value per product average_sale_per_product = sales_data.groupby("Product")["Sales"].mean() # Find the product with the highest total revenue highest_revenue_product = sales_data.groupby("Product")["Sales"].sum().idxmax() # Calculate percentage of sales in each region regional_sales_percentage = ( sales_data.groupby("Region")["Sales"].sum() / sales_data["Sales"].sum() * 100 ) -
Visualizing the Data:
import matplotlib.pyplot as plt # Create a bar chart for regional sales percentage plt.figure(figsize=(10, 6)) plt.bar(regional_sales_percentage.index, regional_sales_percentage.values) plt.title("Percentage of Sales by Region") plt.xlabel("Region") plt.ylabel("Percentage of Sales") plt.show()
Additional Tips and Resources
- Pandas Documentation: https://pandas.pydata.org/pandas-docs/stable/ - This is an excellent resource for learning all about Pandas and its powerful data manipulation capabilities.
- Matplotlib Documentation: https://matplotlib.org/ - This website provides comprehensive documentation on creating various types of plots with Matplotlib.
- Seaborn Documentation: https://seaborn.pydata.org/ - Seaborn simplifies the creation of aesthetically pleasing and informative statistical graphics on top of Matplotlib.
Conclusion
IT 140 Project Two is a valuable opportunity to solidify your understanding of Python's data analysis capabilities. By applying the principles of data exploration, calculation, and visualization, you can unlock insights from datasets and present your findings effectively. Remember to leverage the available resources and practice your coding skills to excel in this project.
