Python Data Analysis Capstones

Practical data workflows with pandas, Jupyter, Excel, JSON, and SQLite

A collection of focused projects for loading, exploring, transforming, filtering, sorting, and exporting structured data in Python.

Overview

This repository brings together four practical capstone exercises that use common business data formats. Each notebook follows a clear workflow: load source data, inspect its structure, answer questions with Python, and save useful results for further use.

The projects demonstrate how the same analytical mindset transfers across flat files, spreadsheets, semi-structured JSON, and relational databases.

Project Map

Capstone	Data source	What it demonstrates	Key files
Student Marks	CSV	DataFrame exploration, filtering, sorting, and exporting transformed results	Notebook · Source data
Product Analysis	Excel	Reading workbooks, inspecting product data, selecting categories, and exporting expensive products	Notebook · Source data
Employee Records	JSON	Loading nested records, transforming JSON data into tabular form, and querying employee information	Notebook · Source data
Organisation Database	SQLite	Connecting to a database, inspecting tables, and using SQL queries to retrieve organisation data	Notebook · Database

Skills Practised

• Reading and writing CSV, Excel, JSON, and SQLite data
• Exploring datasets with pandas
• Selecting rows and columns with expressive conditions
• Sorting and grouping tabular data
• Working with DataFrames and database results
• Exporting analysis outputs to reusable files
• Building repeatable notebook-based data workflows

Repository Structure

.
├── csv_capstone/
│   ├── students.ipynb
│   ├── student_marks.csv
│   ├── student_sorted_df.csv
│   └── students_sorted_csv.csv
├── excel_capstone/
│   ├── product.ipynb
│   ├── product_catalog.xlsx
│   └── expensive_prod.xlsx
├── json_capstone/
│   ├── employee.ipynb
│   ├── employees.json
│   └── finance_json.json
├── sqlite_capstone/
│   ├── organisation.ipynb
│   └── organisation.db
├── requirements.txt
└── readme.md

Getting Started

1. Clone the repository

git clone <repository-url>
cd python_tdd

2. Create and activate a virtual environment

python3 -m venv .venv
source .venv/bin/activate

On Windows, activate the environment with:

.venv\Scripts\Activate.ps1

3. Install dependencies

python -m pip install --upgrade pip
python -m pip install -r requirements.txt

4. Open the notebooks

jupyter notebook

Open any capstone notebook and run the cells from top to bottom. VS Code users can open the repository directly and run the notebooks with the Jupyter extension.

Outputs

The analysis produces reusable files alongside the source data, including sorted student datasets and filtered Excel results. Keeping these outputs in each capstone folder makes the transformation from raw input to processed data easy to follow.

Tools Used

Tool	Purpose
Python	Core programming language
Jupyter Notebook	Interactive analysis and documentation
pandas	Data loading, transformation, and analysis
openpyxl	Excel workbook support
SQLite	Relational data storage and querying

Learning Focus

The project is designed as a practical foundation for data analysis. It focuses on writing readable steps, understanding how data is shaped in different storage formats, and producing results that can be inspected or reused outside the notebook.

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Built as a practical Python data analysis portfolio.