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Python wrapper for Coda.io API

Project description

Python wrapper for Coda.io API

CodaAPI PyPI - Python Version Code style: black Documentation Status PyPI PyPI - Downloads

codaio is in active development stage. Issues and PRs very welcome!

Installation

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.

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

Quickstart using codaio objects

codaio implements convenient classes to work with Coda documents: Document, Table, Row, Column and Cell.

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

# You can fetch a row by ID
row  = table['ROW_ID']

Using with Pandas

If you want to load a codaio Table or Row into pandas, you can use the Table.to_dict() or Row.to_dict() methods:

import pandas as pd

df = pd.DataFrame(table.to_dict())

Fetching a Cell

# 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

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()

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)

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)

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

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.

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