Skip to main content

birdy is a super awesome Twitter API client for Python.

Project description


``birdy`` is a super awesome Twitter API client for Python in just a little under 400 LOC.



* `Future proof dynamic API with full REST and Streaming API coverage <#ok-im-sold-but-how-do-i-use-it-how-does-this-dynamic-api-construction-work>`_
* `OAuth1 (user) and OAuth2 (app) authentication workflows <#great-what-about-authorization-how-do-i-get-my-access-tokens>`_
* `Automatic JSON decoding <#automatic-json-decoding>`_, `JSONObject <#jsonobject>`_
* `ApiResponse <#apiresponse>`_, `StreamResponse objects <#streamresponse>`_
* `Informative exceptions <#informative-exceptions>`_
* `Easily customizable trough subclassing <#customize-and-extend-trough-subclassing>`_
* `Built on top of the excellent requests and requests-ouathlib libraries <#credits>`_


Import client and initialize it:

.. code-block:: python

from birdy.twitter import UserClient
client = UserClient(CONSUMER_KEY,

GET example (**GET users/show**):

.. code-block:: python

response ='twitter')

POST example (**POST statuses/update**):

.. code-block:: pyhton

response ='Hello @pybirdy!')

Dynamic URL example (**POST statuses/destroy/:id**):

.. code-block:: python

response = client.api.statuses.destroy['240854986559455234'].post()

Streaming API example (**Public Stream POST statuses/filter**):

.. code-block:: python

response ='twitter')

for data in
print data

Why another Python Twitter API client? Aren't there enough?

The concept behind ``birdy`` is so simple and awesome that it just had to be done, and the result is a super light weight and easy to use API client, that covers the whole Twitter REST API in just a little under 400 lines of code.

To achieve this, ``birdy`` relies on established, battle tested python libraries like ``requests`` and ``requests-ouathlib`` to do the heavy lifting, but more importantly it relies on Python's dynamic nature to automatically construct API calls (no individual wrapper functions for API resources needed). This allows ``birdy`` to cover all existing Twitter API resources and any future additions, without the need to update ``birdy`` itself.

Includes full support for both **OAuth1** (user) and **OAuth2** (application) authentication workflows.

Finally, ``birdy`` is simple and explicit by design, besides error handling and JSON decoding it doesn't process the returned data in any way, that is left for you to handle (who'd know better what to do with it).

.. _api-label:

OK, I'm sold, but how do I use it? How does this dynamic API construction work?

The easiest way to show you is by example. Lets say you want to query Twitter for @twitter user information. The Twitter API resource for this is **GET users/show** (`Twitter docs <>`_).

First you will need to import a client, here we import UserClient (OAuth1) and than initialize it.

.. code-block:: python

from birdy.twitter import UserClient
client = UserClient(CONSUMER_KEY,

To query the **GET /users/show** API resource and pass in the parameter screen_name='twitter' you do this.

.. code-block:: python

resource =
response = resource.get(screen_name='twitter')

What happens here is very simple, ``birdy`` translates the ```` part after ``client.api`` into the appropriate API resource path (**'users/show'**). Then when you call get() on the resource, ``birdy`` constructs a full resource URL, appends any parameters passed to get() to it and makes a GET request to that URL and returns the result.

Usually the above example would be shortened to just one line like this.

.. code-block:: python

response ='twitter')

Making a post request is similar, if for example, you would like to post a status update, this is how to do it. The API resource is **POST statuses/update** (`Twitter docs <>`_).

.. code-block:: python

response ='Hello @pybirdy!')

Like before the part after ``client.api`` gets converted to the correct path, only this time post() is called instead of get(), so ``birdy`` makes a POST request and pass parameters (and files) as part of the request body.

For cases when dynamic values are part of the API resource URL, like when deleting a tweet at **POST statuses/destroy/:id** (`Twitter docs <>`_), ``birdy`` supports an alternative, dictionary lookup like, syntax. For example, deleting a tweet with id '240854986559455234' looks like this.

.. code-block:: python

response = client.api.statuses.destroy['240854986559455234'].post()

By now it should be clear what happens above, ``birdy`` builds the API resource path and than makes a POST request, the only difference is that part of the API path is provided like a dictionary key lookup.

Actually any call can be written in this alternative syntax, use whichever you prefer. Both syntax forms can be freely combined as in the example above. Some more examples:

.. code-block:: python

response = client.api['users/show'].get(screen_name='twitter')

response = client.api['users']['show'].get(screen_name='twitter')

response = client.api['statuses/destroy']['240854986559455234'].post()

Is Streaming API supported as well?

Sure, since version 0.2, ``birdy`` comes with full support for Streaming API out of the box. Access to the Streaming API is provided by a special ``StreamClient``.

``StreamClient`` can't be used to obtain access tokens, but you can use ``UserClient`` to get them.

To work with the Streaming API, first import the client and initialize it.

.. code-block:: python

from birdy.twitter import StreamClient
client = StreamClient(CONSUMER_KEY,

To access resources on the **Public** stream, like **POST statuses/filter** (`Twitter docs <>`_)

.. code-block:: python

resource ='twitter')

For **User** stream resource **GET user** (`Twitter docs <>`_)

.. code-block:: python

resource = client.userstream.user.get()

And for **Site** stream resource **GET site** (`Twitter docs <>`_)

.. code-block:: python

resource =

To access the data in the stream you iterate over ```` like this

.. code-block:: python

for data in
print data

Great, what about authorization? How do I get my access tokens?

``birdy`` supports both **OAuth1** and **OAuth2** authentication workflows by providing two different clients, a ``UserClient`` and ``AppClient`` respectively. While requests to API resources, like in above examples are the same in both clients, the workflow for obtaining access tokens is slightly different.

Before you get started, you will need to `register <>`_ your application with Twitter, to obtain your application's ``CONSUMER_KEY`` and ``CONSUMER_SECRET``.

OAuth1 workflow for user authenticated requests (UserClient)

Step 1: Creating a client instance

First you need to import the ``UserClient`` and create an instance with your apps ``CONSUMER_KEY`` and ``CONSUMER_SECRET``.

.. code-block:: python

from birdy.twitter import UserClient



Step 2: Get request token and authorization URL

Pass ``callback_url`` only if you have a Web app, Desktop and Mobile apps **do not** require it.

Next you need to fetch request token from Twitter. If you are building a "Sign-in with Twitter" type application it's done like this.

.. code-block:: python

token = client.get_signin_token(CALLBACK_URL)

Otherwise like this.

.. code-block:: python

token = client.get_authorize_token(CALLBACK_URL)

Save ``token.oauth_token`` and ``token.oauth_token_secret`` for later user, as this are not the final token and secret.

.. code-block:: python

ACCESS_TOKEN = token.oauth_token
ACCESS_TOKEN_SECRET = token.oauth_token_secret

Direct the user to Twitter authorization url obtained from ``token.auth_url``.

Step 3: OAuth verification

If you have a Desktop or Mobile app, ``OAUTH_VERIFIER`` is the PIN code, you can skip the part about extraction.

After authorizing your application on Twitter, the user will be redirected back to the ``callback_url`` provided during client initialization in *Step 1*.

You will need to extract the ``OAUTH_VERIFIER`` from the URL. Most web frameworks provide an easy way of doing this or you can parse the URL yourself using ``urlparse`` module (if that is your thing).

Django and Flask examples:

.. code-block:: python

OAUTH_VERIFIER = request.GET['oauth_verifier']

OAUTH_VERIFIER = request.args.get('oauth_verifier')

Once you have the ``OAUTH_VERIFIER`` you can use it to obtain the final access token and secret. To do that you will need to create a new instance of ``UserClient``, this time also passing in ``ACCESS_TOKEN`` and ``ACCESS_TOKEN_SECRET`` obtained in *Step 2* and then fetch the tokens.

.. code-block:: python


token = client.get_access_token(OAUTH_VERIFIER)

Now that you have the final access token and secret you can save ``token.oauth_token`` and ``token.oauth_token_secret`` to the database for later use, also you can use the client to start making API request immediately. For example, you can retrieve the users home timeline like this.

.. code-block:: python

response = client.api.statuses.home_timeline.get()

That's it you have successfully authorized the user, retrieved the tokens and can now make API calls on their behalf.

OAuth2 workflow for app authenticated requests (AppClient)

Step 1: Creating a client instance

For OAuth2 you will be using the ``AppClient``, so first you need to import it and create an instance with your apps ``CONSUMER_KEY`` and ``CONSUMER_SECRET``.

.. code-block:: python

from birdy.twitter import AppClient



Step 2: Getting the access token

OAuth2 workflow is much simpler compared to OAuth1, to obtain the access token you simply do this.

.. code-block:: python

access_token = client.get_access_token()

That's it, you can start using the client immediately to make API request on behalf of the app. It's recommended you save the ``access_token`` for later use. You initialize the client with a saved token like this.

.. code-block:: python


Keep in mind that OAuth2 authenticated requests are **read-only** and not all API resources are available. Check `Twitter docs <>`_ for more information.

Any other useful features I should know about?

Of course, ``birdy`` comes with some handy features, to ease your development, right out of the box. Lets take a look at some of the goodies.

Automatic JSON decoding

JSON data returned by the REST and Streaming API is automatically decoded to native Python objects, no extra coding necessary, start using the data right away.


When decoding JSON data, ``objects`` are, instead of a regular Python dictionary, converted to a ``JSONObject``, which is a read-only dictionary subclass with attribute style access in addition to regular dictionary lookup style, for convenience. The following code produces the same result

.. code-block:: python

followers_count =['followers_count']

followers_count =


Don't want to use JSONObject? No problem, this behavior can be changed by means of subclassing.


Calls to REST API resources return a ``ApiResponse``, which in addition to returned data, also gives you access to response headers (useful for checking rate limits) and resource URL.

.. code-block:: python # decoded JSON data
response.resource_url # resource URL
response.headers # dictionary containing response HTTP headers


``StreamResponse`` is returned when calling Streaming API resources and provides the **stream()** method which returns an iterator used to receive JSON decoded streaming data. Like ``ApiResponse`` it also gives you access to response headers and resource URL.

.. code-block:: python # a generator method used to iterate over the stream

for data in
print data

Informative exceptions

There are 4 types of exceptions in ``birdy`` all subclasses of base ``BirdyException`` (which is never directly raised).

* ``TwitterClientError`` raised for connection and access token retrieval errors
* ``TwitterApiError`` raised when Twitter returns an error
* ``TwitterAuthError`` raised when authentication fails, ``TwitterApiError`` subclass
* ``TwitterRateLimitError`` raised when rate limit for resource is reached, ``TwitterApiError`` subclass

``TwitterApiError`` and ``TwitterClientError`` instances (exepct for access token retrieval errors) provide a informative error description which includes the resource URL and request method used (very handy when tracking errors in logs), also available is the following:

.. code-block:: python

exception.request_method # HTTP method used to make the request (GET or POST)
exception.resource_url # URL of the API resource called
exception.status_code # HTTP status code returned by Twitter
exception.error_code # error code returned by Twitter
exception.headers # dictionary containing response HTTP headers

Customize and extend trough subclassing

``birdy`` was built with subclassing in mind, if you wish to change the way it works, all you have to do is subclass one of the clients and override some methods and you are good to go.

Subclassing a client and then using the subclass instance in your code is actually **the recommended way** of using ``birdy``.

For example, if you don't wish to use ``JSONObject`` you have to override **get_json_object_hook()** method.

.. code-block:: python

from birdy.twitter import UserClient

class MyClient(UserClient)
def get_json_object_hook(data):
return data

client = MyClient(...)
response ='twitter')

Or maybe, if you want global error handling for common errors, just override **handle_response()** method.

.. code-block:: python

class MyClient(UserClient)
def handle_response(self, method, response):
response = super(MyClient, self).handle_response(method, response)
except TwitterApiError, e:
# Your error handling code
return response

Another use of subclassing is configuration of ``requests.Session`` instance (`docs <>`_) used to make HTTP requests, to configure it, you override the **configure_oauth_session()** method.

.. code-block:: python

class MyClient(UserClient)
def configure_oauth_session(self, session):
session = super(MyClient, self).configure_oauth_session(session)
session.proxies = {'http': ''}
return session

Do you accept contributions and feature requests?

**Yes**, both contributions (including feedback) and feature requests are welcome, the proper way in both cases is to first open an issue on `GitHub <>`_ and we will take if from there.

Keep in mind that I work on this project on my free time, so I might not be able to respond right way.

What does the future hold? Will there be a 1.0 release?

Next release (0.3) will focus on Python 3 support, the version after that should be 1.0 which will focus on unit tests.

After 1.0 some of the possible features are:

* Cursors for REST API
* Automatic reconnecting for Streaming API


``birdy`` would not exists if not for the excellent `requests <>`_ and `requests-oauthlib <>`_ libraries and the wonderful `Python <>`_ programing language.

Also thanks to `Twython <>`_ for inspiration and `python-twitter <>`_ for motivation.

Question, comments, ...

If you need to contact me, you can follow me on Twitter (`@sect2k <>`_) or drop me an email at ` <>`_.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
birdy-0.2.tar.gz (10.5 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page