Sales Data Analysis

Python tutorial · PySpark.in

Build a complete data analysis project that loads real data from CSV, cleans the data, analyzes sales, and generates insights.

👉 This is portfolio-level work (very important for data analyst roles)

Basic Structure
- Pandas → data handling
- Data Cleaning → fix missing/duplicate data
- Aggregation → total, average, grouping
- Filtering & Sorting → insights

Sample Dataset (sales_data.csv)
Product,Category,Sales
Laptop,Electronics,50000
Phone,Electronics,30000
Shirt,Clothing,2000
Laptop,Electronics,45000
Shoes,Clothing,3000
Phone,Electronics,35000

```

import pandas as pd

# Step 1: Create sample sales data
data = {
"Product": ["Laptop", "Mobile", "Laptop", "Tablet", "Mobile", "Headphones", "Tablet"],
"Category": ["Electronics", "Electronics", "Electronics", "Electronics", "Electronics", "Accessories", "Electronics"],
"Sales": [50000, 30000, 50000, None, 45000, 12000, 25000]
}

sample_df = pd.DataFrame(data)

# Step 2: Save sample data to CSV
sample_df.to_csv("sales_data.csv", index=False)

# Step 3: Load data
df = pd.read_csv("sales_data.csv")

# Step 4: Data cleaning
df = df.drop_duplicates()
df["Sales"] = df["Sales"].fillna(df["Sales"].mean())

# Step 5: Basic analysis
print("Total Sales:", df["Sales"].sum())
print("Average Sales:", df["Sales"].mean())
print("Max Sale:", df["Sales"].max())

# Step 6: Sales by category
category_sales = df.groupby("Category")["Sales"].sum()
print("\nSales by Category:")
print(category_sales)

# Step 7: Best selling product
product_sales = df.groupby("Product")["Sales"].sum()
print("\nBest Product:", product_sales.idxmax())

# Step 8: Filter high sales
high_sales = df[df["Sales"] > 30000]
print("\nHigh Sales Records:")
print(high_sales)

# Step 9: Sort data
sorted_data = df.sort_values(by="Sales", ascending=False)
print("\nSorted Data:")
print(sorted_data)

```

🧠 Explanation

1. Data Loading: CSV file is loaded into a DataFrame
2. Data Cleaning: Removes duplicate rows, fills missing values with average
3. Basic Metrics: Total, average, and max sales calculated
4. Grouping (Core Skill ): Sales aggregated by category and product
5. Insights Extraction: Best-selling product identified, high-value sales filtered
6. Sorting: Data arranged to highlight top records

📊 Key Insights You Can Extract
- Which category generates the most revenue
- Which product performs best
- High-value transactions
- Overall sales trends

Sales Data Analysis

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