Sales Data Analysis Project | Python & Pandas

1. Data Cleaning & Preparation

I started by inspecting the dataset and checking the data types and missing values:

dd.info()

dd.isnull().sum()

One of the challenges I found was that the Revenue column was not stored as a numeric value. It contained the currency label "EGP", which prevented me from performing numerical calculations directly.

So, I first cleaned the column and converted it into a numeric data type:

dd["Revenue"] = (

dd["Revenue"]

.str.replace("EGP", "", regex=False)

.str.strip()

.astype(float)

)

After converting Revenue into a proper numeric format, I could use it for further calculations and analysis.

I also found missing values in the Quantity column.

Instead of simply removing these records, I used the existing business relationship:

Revenue = Quantity × Unit Price

So I calculated the missing Quantity values using:

dd["Quantity"] = dd["Revenue"] / dd["Unit Price"]

This allowed me to recover the missing values while preserving the available records.

🔹 2. Data Analysis

After cleaning and preparing the data, I analyzed key business questions such as:

- 💰 What is the total Sales?

- 📦 Which Category generates the highest Sales?

- 🌍 Which Region performs best?

🔹 3. Data Visualization

I created multiple visualizations using Matplotlib, including:

📊 Sales by Category

🛍️ Top Products

🌍 Sales by Region