README.md
# gspread-models
[](https://codeclimate.com/github/s2t2/gspread-models-py/maintainability)  [](https://opensource.org/licenses/MIT)
The [`gspread-models`](https://github.com/s2t2/gspread-models-py) package is an Object Relational Mapper (ORM) for the Google Sheets API. It provides a straightforward and intuitive model-based query interface, making it easy to interact with Google Sheets as if it were more like a database. This package offers a fast and flexible way to get up and running with a Google Sheets database, for rapid prototyping and development in Python.
Key Features:
+ **Read and Write Data:** Seamlessly read and write data to and from Google Sheets.
+ **Easy Setup:** Minimal schema requirements make it simple to get started.
+ **Intuitive Query Interface:** Familiar object-oriented query methods inspired by ActiveRecord (Ruby) and SQLAlchemy (Python).
+ **Auto-incrementing ID**: Automatically manages a primary key "id" column.
+ **Timestamps**: Automatically manages a "created_at" timestamp column.
+ **Datetime Handling**: Converts datetime columns to Python datetime objects for easier manipulation.
+ **Flexible Migrations**: Easily update the schema by modifying your Google Sheet and updating the corresponding list of columns.
## Installation
Install the package from PyPI:
```sh
pip install gspread_models
```
## Quick Start
### Setup
**Step 1:** Bind the base model to your Google Sheets document and your credentials (see [Authentication](./docs/authentication.md) for more details):
```py
from gspread_models.base import BaseModel
BaseModel.bind(
document_id="your-document-id",
credentials_filepath="/path/to/google-credentials.json"
)
```
**Step 2:** Define your own light-weight class that inherits from the base model:
```python
class Book(BaseModel):
SHEET_NAME = "books"
COLUMNS = ["title", "author", "year"]
```
When defining your class, specify a `SHEET_NAME` as well as a list of sheet-specific `COLUMNS`.
**Step 3:** Setup a corresponding sheet for this model.
To support the example above, create a sheet called "books", and specify an initial row of column headers: "id", "title", "author", "year", and "created_at".
> NOTE: In addition to the sheet-specific attributes ("title", "author", and "year"), the base model will manage metadata columns, including a unique identifier ("id") as well as a timestamp ("created_at").
### Usage
Once you have your model class setup, you can utilize the [Query Interface](./queries.md), to read and write data to the sheet.
Writing / appending records to the sheet:
```py
Book.create_all([
{"title": "To Kill a Mockingbird", "author": "Harper Lee", "year": 1960},
{"title": "1984", "author": "George Orwell", "year": 1949},
{"title": "The Great Gatsby", "author": "F. Scott Fitzgerald", "year": 1925},
{"title": "The Catcher in the Rye", "author": "J.D. Salinger", "year": 1951},
{"title": "Pride and Prejudice", "author": "Jane Austen", "year": 1813},
])
```
Fetching all records from the sheet:
```py
books = Book.all()
for book in books:
print(book.id, "|", book.title, "|", book.author)
#> 1 | To Kill a Mockingbird | Harper Lee
#> 2 | 1984 | George Orwell
#> 3 | The Great Gatsby | F. Scott Fitzgerald
#> 4 | The Catcher in the Rye | J.D. Salinger
#> 5 | Pride and Prejudice | Jane Austen
```
It is easy to create a pandas DataFrame from the returned objects by converting each to a dictionary:
```py
from pandas import DataFrame
books_df = DataFrame([dict(book) for book in books])
books_df.head()
#> id title author year created_at
#> 1 To Kill a Mockingbird Harper Lee 1960 2024-05-22 21:36:25.582605+00:00
#> 2 1984 George Orwell 1949 2024-05-22 21:36:25.582738+00:00
#> 3 The Great Gatsby F. Scott Fitzgerald 1925 2024-05-22 21:36:25.582778+00:00
#> 4 The Catcher in the Rye J.D. Salinger 1951 2024-05-22 21:36:25.582813+00:00
#> 5 Pride and Prejudice Jane Austen 1813 2024-05-22 21:36:25.582846+00:00
```
For more details, see the usage documentation below:
+ [Query Interface](./docs/queries.md)
+ [Authentication](./docs/authentication.md)
+ [Project File Organization](./docs/organization.md)
+ [Pandas Support](./docs/pandas_support.md)
## Examples
Here are some examples that demonstrate the usage of `gspread-models` within a variety of contexts:
+ [Demo Notebook](./docs/notebooks/demo_v1_0_7.ipynb)
+ [Flask Sheets Web Application Template](https://github.com/prof-rossetti/flask-sheets-template-2024)
If you use the `gspread-models` package, you are encouraged to add your project to this list, by submitting a pull request or opening an issue.
## Contributing
Contributions welcome! Here are some reference guides to help you get started as a contributor or maintainer of this package:
+ [Contributor's Guide](./docs/CONTRIBUTING.md)
+ [Google Cloud Setup Guide](./docs/setup/google-cloud.md)
+ [Google Sheets Setup Guide](./docs/setup/google-sheets.md)
+ [GitHub Actions Setup Guide](./docs/setup/github-actions.md)
## Acknowlegements
This package is built on top of the awesome [`gspread`](https://github.com/burnash/gspread) package.
## [License](/LICENSE)