The gspread-models 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.
Install the package from PyPI:
pip install gspread_modelsStep 1: Bind the base model to your Google Sheets document and your credentials (see Authentication for more details):
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:
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").
Once you have your model class setup, you can utilize the Query Interface, to read and write data to the sheet.
Writing / appending records to the sheet:
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:
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 AustenIt is easy to create a pandas DataFrame from the returned objects by converting each to a dictionary:
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+00:00For more details, see the usage documentation below:
Here are some examples that demonstrate the usage of gspread-models within a variety of contexts:
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.
Contributions welcome! Here are some reference guides to help you get started as a contributor or maintainer of this package:
This package is built on top of the awesome gspread package.
