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CSV Data Cleaning & Python Automation
I developed a reusable Python/Pandas workflow to transform a messy sales CSV into a clean, analysis-ready dataset.
The workflow automatically:
Standardizes inconsistent whitespace in text fields
Validates and standardizes dates
Validates numeric fields such as quantity and unit price
Detects duplicate records
Identifies missing or invalid values
Calculates transaction revenue
Generates an exceptions report
Compares before/after record counts
Produces a reusable Python script and cleaned CSV output
Results
20 records processed with 0 missing values, 0 duplicate records removed, and 0 exceptions identified.
The cleaned dataset contained 8 columns, including a calculated Revenue field. Total revenue was 10,157, with an average transaction revenue of 507.85.
Tools
Python | Pandas | Jupyter Notebook | CSV
Deliverables
Cleaned CSV, exceptions report, reusable Python script, and data-quality summary.
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Related posts
Built an end-to-end pipeline against the UKHSA public health API and an interactive dashboard on top of it. Handles paginated fetching and nested JSON parsing, with a caching fallback so the dashboard keeps serving data through upstream outages (the reliability work turned out to be larger than the fetching). The interface uses async refresh and dynamic controls, updating without page reloads.
This project is an end-to-end Sales Analytics solution built using Python, SQL, and Power BI.
The workflow includes:
Data Cleaning using Python (Pandas)
Data Storage using SQLite
Business Analysis using SQL Queries
Interactive Dashboard using Power BI
Automated Business Report Generation using Python
The goal of this project is to analyze sales performance, identify profitable products, monitor regional performance, and generate business recommendations.
Dataset
Dataset: Sample Superstore Dataset
Records: 9,994
Features:
Order Details
Customer Information
Product Information
Sales
Profit
Discount
Region
Category
Sub-Category
Tools & Technologies
Python
Pandas
SQLite
SQL
Power BI
Data Visualization
Business Intelligence
Project Workflow
CSV Dataset
↓
Data Cleaning (Python)
↓
SQLite Database
↓
SQL Analysis
↓
Power BI Dashboard
↓
AI Business Report
Dashboard Pages
Executive Overview Total Sales Total Profit Total Orders Profit Margin Regional Sales Analysis Category Performance Analysis
Product Performance Analysis Top Profitable Products Loss-Making Products Profit by Category Sales by Sub-Category
Sales Trends Analysis Sales Trends Over Time Profit Trends Seasonal Performance Insights Key Business Insights West Region generated the highest sales. Technology is the most profitable category. Several products generate significant losses and require pricing review. Sales performance varies significantly across regions. Automated AI Report The project includes a Python-based reporting system that automatically generates:
Author
Gourav Jangid
Aspiring Data Analyst
Open to Data Analytics Opportunities
Retail Inventory and Optimization Tool
Retailers often collect large amounts of sales and inventory data but lack
a system to turn it into clear, actionable decisions, making it hard to
know which products to reorder, which are slow-moving, and which risk
stockouts or overstocking.
I built an end-to-end analytics pipeline to solve this. Using Python and
Pandas, I cleaned a raw retail dataset, handling missing values based on
what each one actually meant in context (e.g. a missing discount treated
as "none," while a missing inventory value was filled with the average
rather than falsely assumed to be zero). The cleaned data was loaded into
MySQL for exploratory analysis, then connected to an interactive Power BI
dashboard summarizing revenue, stock levels, demand fulfillment, and sales
trends by category, store, and season.
Key findings included a 95.71% demand fulfillment rate (revealing real,
recoverable lost sales), evenly balanced sales across all products
categories and seasons, and one store carrying noticeably higher inventory
than others despite similar sales flagging a possible overstocking risk.
