SQL to Pandas: Practical Data Analysis

This project demonstrates how SQL-style analytical queries can be translated into pandas workflows, applied to a transactional dataset to generate practical insights.

The focus is on translating structured query logic into flexible Python workflows, while maintaining a clear link between data manipulation and business interpretation.

Motivation

Many analysts are fluent in SQL but need to transition to Python-based data workflows for more flexible analysis and modelling.

Overview

The notebook walks through a typical analytical workflow:

• filtering and selecting data
• aggregating metrics (e.g. revenue by customer)
• ranking and sorting results
• analysing distributions and concentration

Each step is framed using SQL-style queries alongside their pandas equivalents.

Example

SQL:
SELECT country, AVG(revenue)
FROM sales
GROUP BY country;

Equivalent pandas:

df.groupby("country")["revenue"].mean()

Key Analyses

Business Problem 1

A Pareto-style analysis is used to examine how revenue is distributed across customers.

The analysis shows that approximately X% of customers generate 80% of total revenue, providing a data-driven view of concentration rather than assuming a standard 80/20 split.

Business Problem 2

Relational databases - combine the datasets to compare the salaries of each department.

Interpretation

This type of analysis is commonly used in:

• customer analytics
• product performance tracking
• financial reporting

and helps inform decisions around targeting, retention and resource allocation.

Tools

• Python (pandas, NumPy, matplotlib)
• SQL-style query logic
• Exploratory data analysis

Features

• SQL-to-pandas translations for common analytical queries
• End-to-end workflow: filtering → aggregation → ranking → interpretation
• Pareto analysis for revenue concentration
• Practical examples for analysts transitioning from SQL to Python

For full interactivity (e.g. navigation and outputs), run the notebook locally.

Summary

This project illustrates how SQL-style queries can be translated into pandas workflows to support practical, real-world data analysis, combining structured querying with flexible exploration and interpretation.

Related Projects

• sql2csv: Convert SQL outputs to CSV via shell scripting