https://github.com/zazza123/hamana
A python library for seamless data extraction, storage, and SQL-based analysis using pandas and SQLite.
https://github.com/zazza123/hamana
analysis data python
Last synced: 5 months ago
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A python library for seamless data extraction, storage, and SQL-based analysis using pandas and SQLite.
- Host: GitHub
- URL: https://github.com/zazza123/hamana
- Owner: zazza123
- License: bsd-3-clause
- Created: 2024-11-08T08:23:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-09-09T16:50:12.000Z (10 months ago)
- Last Synced: 2025-09-25T06:54:58.398Z (9 months ago)
- Topics: analysis, data, python
- Language: Python
- Homepage:
- Size: 3.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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---
Documentation: https://zazza123.github.io/hamana
**hamana** (*Hamster Analysis*) is a Python library designed to simplify data analysis by combining the practicality of **pandas** and **SQL** in an open-source environment. This library was born from the experience of working in a large company where tools like `SAS` were often used as "shortcuts" to perform SQL queries across different data sources, without fully leveraging their potential. With the goal of providing a free and accessible alternative, `hamana` replicates these functionalities in an open-source context.
## Why Choose `hamana`?
- **Support for Multiple Data Sources**: Connect to various data sources such as relational databases, CSV files, mainframes, and more.
- **SQLite Integration**: Save data locally in an SQLite database, either as a file or in memory.
- **SQL + pandas**: Combine the power of `SQL` with the flexibility of `pandas` for advanced analysis.
- **Open Source**: Available to everyone without licensing costs.
- **Why "Hamster"?**: Because hamsters are awesome!
## Key Features
### 1. Data Extraction
Hamana allows you to extract data from a variety of sources:
- Relational databases (SQLite, Oracle, etc.)
- CSV, Excel, JSON, and other common file formats
- Legacy sources like mainframes
Extractions are automatically saved as `pandas` **DataFrames**, making data manipulation simple and intuitive.
### 2. SQLite Storage
Each extraction can be saved in an **SQLite** database, enabling you to:
- Store data locally for future use
- Perform `SQL` queries to combine extractions from different sources
### 3. Data Analysis
With Hamana, you can:
- Use `pandas` to quickly and flexibly manipulate data
- Write `SQL` queries directly on datasets stored in SQLite
- Integrate `SQL` and `pandas` into a single workflow for advanced analysis
## Installation
Hamana is available on [PyPI](https://pypi.org/project/hamana/), and you can install it easily with pip:
```bash
pip install hamana
```
## Usage Example
Here is an example of how to use Hamana to connect to a data source, extract information, and combine it with another table:
```python
import hamana as hm
# connect hamana database
hm.connect()
# connect to Oracle database
oracle_db = hm.connector.db.Oracle.new(
host = "localhost",
port = 1521,
user = "user",
password = "password"
)
# define, execute and store a query
orders = hm.Query("SELECT * FROM orders")
oracle_db.to_sqlite(orders, table_name = "orders")
# load a CSV file and store it in SQLite
customers = hm.connector.file.CSV("customers.csv")
customers.to_sqlite(table_name = "customers")
# combine the two tables using SQL
customers_orders = hm.execute("""
SELECT
c.customer_name
, o.order_date
, o.total
FROM customers c
JOIN orders o ON
c.customer_id = o.customer_id"""
)
# use `pandas` for further analysis
print(customers_orders.result.head())
# close connection
hm.disconnect()
```
## How to Contribute
If you want to contribute to Hamana:
1. Fork the repository.
2. Create a branch for your changes:
```bash
git checkout -b feature/your-feature-name
```
3. Submit a pull request describing the changes.
All contributions are welcome!
## License
This project is distributed under the **BSD 3-Clause "New" or "Revised"** license.
## Contact
For questions or suggestions, you can open an **Issue** on GitHub or contact me directly.
---
Thank you for choosing Hamana!