Project 1 - Sales Data Analysis

Sales analysis is a useful beginner-friendly project because it combines data cleaning, aggregation, and visualization.

Imagine a company provides a CSV file containing order dates, products, quantities, and prices. The objective is to identify revenue trends and the best-performing products.

Step 1: Load and Inspect the Dataset

import pandas as pd

df = pd.read_csv("sales.csv")

print(df.head())

print(df.info())

print(df.isnull().sum())

The dataset might contain columns such as OrderDate, Product, Quantity, and UnitPrice.

Step 2: Clean and Transform the Data

df["OrderDate"] = pd.to_datetime(

df["OrderDate"],

errors="coerce"

)

df["Quantity"] = pd.to_numeric(

df["Quantity"],

errors="coerce"

)

df["UnitPrice"] = pd.to_numeric(

df["UnitPrice"],

errors="coerce"

)

df = df.dropna(

subset=["OrderDate", "Product", "Quantity", "UnitPrice"]

)

df["Revenue"] = df["Quantity"] * df["UnitPrice"]

This converts columns to suitable data types, removes records with missing values in essential fields, and calculates revenue. In a real project, investigate why records are missing before deciding whether to remove them.

Step 3: Analyze Product Performance

product_sales = (

df.groupby("Product")["Revenue"]

.sum()

.sort_values(ascending=False)

)

print(product_sales.head(10))

This identifies the products with the highest total revenue.

Step 4: Visualize the Results

import matplotlib.pyplot as plt

product_sales.head(10).sort_values().plot(

kind="barh",

figsize=(9, 5)

)

plt.title("Top 10 Products by Revenue")

plt.xlabel("Revenue")

plt.ylabel("Product")

plt.tight_layout()

plt.show()

Possible project outcomes:

• Identify products generating the most revenue.
• Compare monthly sales performance.
• Find periods with declining sales.
• Help business teams plan inventory and promotions.

For a portfolio project, include the business question, data-cleaning decisions, visualizations, and actionable findings rather than submitting code alone.