hamana 1.0.1

Illustrations by @gaiaparte

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, and you can install it easily with pip:

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:

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:

   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!