PyPI 官网下载 | codaio-0.4.6.tar.gz

## Python wrapper for [Coda.io](https://coda.io) API [](https://coda.io/developers/apis/v1beta1)  [](https://github.com/psf/black) [](https://codaio.readthedocs.io/en/latest/?badge=latest)  [](https://pypi.org/project/codaio/)  `codaio` is in active development stage. Issues and PRs very welcome! ### Installation ```shell script pip install codaio ``` ### Config via environment variables The following variables will be called from environment where applicable: * `CODA_API_ENDPOINT` (default value `https://coda.io/apis/v1beta1`) * `CODA_API_KEY` - your API key to use when initializing document from environment ### Quickstart using raw API Coda class provides a wrapper for all API methods. If API response included a JSON it will be returned as a dictionary from all methods. If it didn't a dictionary `{"status": response.status_code}` will be returned. If request wasn't successful a `CodaError` will be raised with details of the API error. ```python from codaio import Coda coda = Coda('YOUR_API_KEY') >>> coda.create_doc('My document') {'id': 'NEW_DOC_ID', 'type': 'doc', 'href': 'https://coda.io/apis/v1beta1/docs/LINK', 'browserLink': 'https://coda.io/d/LINK', 'name': 'My Document', 'owner': 'your@email', 'createdAt': '2019-08-29T11:36:45.120Z', 'updatedAt': '2019-08-29T11:36:45.272Z'} ``` For full API reference for Coda class see [documentation](https://codaio.readthedocs.io/en/latest/index.html#codaio.Coda) ### Quickstart using codaio objects `codaio` implements convenient classes to work with Coda documents: `Document`, `Table`, `Row`, `Column` and `Cell`. ```python from codaio import Coda, Document # Initialize by providing a coda object directly coda = Coda('YOUR_API_KEY') doc = Document('YOUR_DOC_ID', coda=coda) # Or initialiaze from environment by storing your API key in environment variable `CODA_API_KEY` doc = Document.from_environment('YOUR_DOC_ID') doc.list_tables() table = doc.get_table('TABLE_ID') ``` #### Fetching a Row ```python # You can fetch a row by ID row = table['ROW_ID'] ``` #### Fetching a Cell ```python # Or fetch a cell by ROW_ID and COLUMN_ID cell = table['ROW_ID']['COLUMN_ID'] # This is equivalent to getting item from a row cell = row['COLUMN_ID'] # or cell = row['COLUMN_NAME'] # This should work fine if COLUMN_NAME is unique, otherwise it will raise AmbiguousColumn error # or use a Column instance cell = row[column] ``` #### Changing Cell value ```python row['COLUMN_ID'] = 'foo' # or row['Column Name'] = 'foo' ``` #### Iterating over rows ``` # Iterate over rows using IDs -> delete rows that match a condition for row in table.rows(): if row['COLUMN_ID'] == 'foo': row.delete() # Iterate over rows using names -> edit cells in rows that match a condition for row in table.rows(): if row['Name'] == 'bar': row['Value'] = 'spam' ``` #### Upserting new row To upsert a new row you can pass a list of cells to `table.upsert_row()` ```python name_cell = Cell(column='COLUMN_ID', value_storage='new_name') value_cell = Cell(column='COLUMN_ID', value_storage='new_value') table.upsert_row([name_cell, value_cell]) ``` #### Upserting multiple new rows Works like upserting one row, except you pass a list of lists to `table.upsert_rows()` (rows, not row) ```python name_cell_a = Cell(column='COLUMN_ID', value_storage='new_name') value_cell_a = Cell(column='COLUMN_ID', value_storage='new_value') name_cell_b = Cell(column='COLUMN_ID', value_storage='new_name') value_cell_b = Cell(column='COLUMN_ID', value_storage='new_value') table.upsert_rows([[name_cell_a, value_cell_a], [name_cell_b, value_cell_b]]) ``` #### Updating a row To update a row use `table.update_row(row, cells)` ```python row = table['ROW_ID'] name_cell_a = Cell(column='COLUMN_ID', value_storage='new_name') value_cell_a = Cell(column='COLUMN_ID', value_storage='new_value') table.update_row(row, [name_cell_a, value_cell_a]) ``` #### Documentation `codaio` documentation lives at [readthedocs.io](https://codaio.readthedocs.io/en/latest/index.html) #### Testing All tests are in the `/tests` folder. It's a little bit problematic to test against the live API since some responses may take a bit longer, so test results are not reliable enough to use a CI system. Check out the fixtures if you want to improve the testing process.