SQLAlchemy Core Analysis of Metaverse Financial Transactions Dataset

Introduction

This repository contains a Jupyter notebook designed to practice basic functions of SQLAlchemy Core using the Metaverse Financial Transactions dataset from Kaggle. SQLAlchemy Core provides a way to interact directly with SQL expressions and database structures, allowing for fine-grained control over database queries and operations without the abstraction layer provided by the ORM.

The core focus of this notebook is to demonstrate how to convert a CSV file into a SQLite database and leverage SQLAlchemy Core functions for various database operations and analyses.

Contents

• Financial_Transactions_SQLAlchemy_Core.ipynb: Jupyter notebook with SQLAlchemy Core analysis.
• data/: Directory containing sample CSV data files (if applicable).

Prerequisites

To run the notebook, you need to have the following software installed:

• Python 3.11
• Jupyter Notebook
• Required Python packages (Pandas:2.1.4, sqlalchemy:2.0.25)

Examples

Here are some examples of the analyses performed in the notebook:

• Counting the number of transactions: Counts the total number of rows in the dataset.
• Finding unique transaction types: Identifies and counts distinct transaction types.
• Calculating average amount per transaction type: Computes the average transaction amount for each type.
• Top addresses with the most transactions: Lists the top 10 addresses with the highest transaction counts