https://github.com/aniruddha-biswas/load-large-datasets-into-mysql-using-python
Python script to load large Excel/CSV datasets into MySQL using Jupyter Notebook, pandas, and SQLAlchemy.
https://github.com/aniruddha-biswas/load-large-datasets-into-mysql-using-python
database largedata mysql pandas python sql
Last synced: 20 days ago
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Python script to load large Excel/CSV datasets into MySQL using Jupyter Notebook, pandas, and SQLAlchemy.
- Host: GitHub
- URL: https://github.com/aniruddha-biswas/load-large-datasets-into-mysql-using-python
- Owner: ANIRUDDHA-BISWAS
- Created: 2025-03-28T08:25:50.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-28T17:15:51.000Z (about 1 year ago)
- Last Synced: 2025-07-19T18:43:18.841Z (10 months ago)
- Topics: database, largedata, mysql, pandas, python, sql
- Language: Python
- Homepage:
- Size: 7.81 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Load Large Datasets into MySQL using Python
Python script to load large Excel/CSV datasets into MySQL using Jupyter Notebook, pandas, and SQLAlchemy.
# Excel/CSV to MySQL Using Python
This repository contains a step-by-step Jupyter Notebook that demonstrates how to efficiently load large datasets from Excel or CSV files into a MySQL database using Python.
🔧 **Tools Used:**
- Python (Jupyter Notebook)
- pandas
- SQLAlchemy
- MySQL
- Excel/CSV files
### 📌 Key Features:
- Clean and transform data using `pandas`
- Connect Jupyter Notebook to MySQL with `SQLAlchemy` and `PyMySQL`
- Insert data in bulk using `to_sql()` method
- Handle large files with `chunksize` for efficient uploading
### 🚀 Ideal For:
- Data Analysts
- Data Engineers
- Students and beginners working with databases
- Anyone automating data imports into MySQL
---
📂 The notebook is well-commented and beginner-friendly.
Feel free to fork, use, and modify it in your own projects. Contributions and suggestions are welcome!
## 🤝 Connect with Me
- 📫 LinkedIn: [Aniruddha Biswas](https://www.linkedin.com/in/aniruddha-biswas)