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Released:

Python client library for AirTable

Project description

Python interface to the Airtable's REST API - https://airtable.com -

For javascript enthusiasts: https://github.com/Airtable/airtable.js

Installation

Airtable Python uses Requests.py:make sure you have it installed by running

Getting started

Once you have created a newbaseand a new table through the Web interface, you're ready to start usingAirtable Python.

Here's an example of response from the Restaurant's example base

API Reference

The available methods closely mimick the RESTAPI:

Get

Given a table name, fetched one or multiple records.

where

Iterate

Given a table name, fetched all records.

where

Note: this returns a generator instead, which you can use to loopeach record:

Create

Creates a new entry in a table, and returns the newly created entry withits new ID.

where

Update

Updates some fields in a specific entry in the table. Fields which arenot explicitely included will not get updated

where

Update All

Like the previous method, but updates all fields, clearing the ones thatare not included in the request.

Delete

Delete a specific record from the table

where

Release

To release, tag the Git repo with a new version number, push that tag to GitHub then Travis CI willdo the rest.

Release historyRelease notifications | RSS feed

0.4.8

0.4.4

0.4.3

0.4.2

0.4.1

0.4.0

0.3.1

0.3.0

0.2.0

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Hashes for airtable-0.4.8-py2.py3-none-any.whl

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Released:

Export Airtable data to files on disk

Project description

Export Airtable data to files on disk

Installation

Install this tool using pip:

Usage

You will need to know the following information:

• Your Airtable base ID - this is a string starting with app...
• Your Airtable API key - this is a string starting with key...
• The names of each of the tables that you wish to export

You can export all of your data to a folder called export/ by running the following:

This example would create two files: export/table1.yml and export/table2.yml.

Rather than passing the API key using the --key option you can set it as an environment variable called AIRTABLE_KEY.

Export options

By default the tool exports your data as YAML.

You can also export as JSON or as newline delimited JSON using the --json or --ndjson options:

You can pass multiple format options at once. This command will create a .json, .yml and .ndjson file for each exported table:

SQLite database export

You can export tables to a SQLite database file using the --sqlite database.db option:

This can be combined with other format options. If you only specify --sqlite the export directory argument will be ignored.

The SQLite database will have a table created for each table you export. Those tables will have a primary key column called airtable_id.

If you run this command against an existing SQLite database records with matching primary keys will be over-written by new records from the export.

Request options

By default the tool uses python-httpx's default configurations.

Github Air Tablet

You can override the user-agent using the --user-agent option:

You can override the timeout during a network read operation using the --http-read-timeout option. If not set, this defaults to 5s.

Running this using GitHub Actions

GitHub Actions is GitHub's workflow automation product. You can use it to run airtable-export in order to back up your Airtable data to a GitHub repository. Doing this gives you a visible commit history of changes you make to your Airtable data - like this one.

To run this for your own Airtable database you'll first need to add the following secrets to your GitHub repository:

AIRTABLE_BASE_ID

The base ID, a string beginning `app...`

AIRTABLE_KEY

Your Airtable API key

AIRTABLE_TABLES

A space separated list of the Airtable tables that you want to backup. If any of these contain spaces you will need to enclose them in single quotes, e.g. 'My table with spaces in the name' OtherTableWithNoSpaces

Once you have set those secrets, add the following as a file called .github/workflows/backup-airtable.yml:

This will run once a day (at 32 minutes past midnight UTC) and will also run if you manually click the 'Run workflow' button, see GitHub Actions: Manual triggers with workflow_dispatch.

Development

To contribute to this tool, first checkout the code. Then create a new virtual environment:

Or if you are using pipenv:

Now install the dependencies and tests:

To run the tests:

Release historyRelease notifications | RSS feed

0.7.1

0.7

Github Airtable Integration

0.6

0.5

0.4

0.3.1

0.3

0.2

Airtable Sdk

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Hashes for airtable_export-0.7.1-py3-none-any.whl

Github Airtable Tutorial

Hashes for airtable-export-0.7.1.tar.gz

Github Airtable Extension