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.
TL;DR
Features
Installation
The easiest and recommended way to install birdy is from PyPI
pip install birdy
Usage
Import client and initialize it:
from birdy.twitter import UserClient
client = UserClient(CONSUMER_KEY,
CONSUMER_SECRET,
ACCESS_TOKEN,
ACCESS_TOKEN_SECRET)
GET example (GET users/show):
response = client.api.users.show.get(screen_name='twitter')
response.data
POST example (POST statuses/update):
response = client.api.statuses.update.post(status='Hello @pybirdy!')
Dynamic URL example (POST statuses/destroy/:id):
response = client.api.statuses.destroy['240854986559455234'].post()
Streaming API example (Public Stream POST statuses/filter):
response = client.stream.statuses.filter.post(track='twitter')
for data in response.stream():
print data
Supported Python version
birdy works with both python2 (2.7+) and python3 (3.4+).
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).
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.
from birdy.twitter import UserClient
client = UserClient(CONSUMER_KEY,
CONSUMER_SECRET,
ACCESS_TOKEN,
ACCESS_TOKEN_SECRET)
To query the GET /users/show API resource and pass in the parameter screen_name=‘twitter’ you do this.
resource = client.api.users.show
response = resource.get(screen_name='twitter')
What happens here is very simple, birdy translates the users.show 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.
response = client.api.users.show.get(screen_name='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).
response = client.api.statuses.update.post(status='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.
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:
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.
from birdy.twitter import StreamClient
client = StreamClient(CONSUMER_KEY,
CONSUMER_SECRET,
ACCESS_TOKEN,
ACCESS_TOKEN_SECRET)
To access resources on the Public stream, like POST statuses/filter (Twitter docs)
resource = client.stream.statuses.filter.post(track='twitter')
For User stream resource GET user (Twitter docs)
resource = client.userstream.user.get()
And for Site stream resource GET site (Twitter docs)
resource = client.sitestream.site.get()
To access the data in the stream you iterate over resource.stream() like this
for data in resource.stream():
print data
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.
JSONObject
When decoding JSON data, objects are, instead of a regular Python dictionary, converted to a JSONObject, which is dictionary subclass with attribute style access in addition to regular dictionary lookup style, for convenience. The following code produces the same result
followers_count = response.data['followers_count']
followers_count = response.data.followers_count
ApiResponse
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.
response.data # decoded JSON data
response.resource_url # resource URL
response.headers # dictionary containing response HTTP headers
StreamResponse
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.
response.stream() # a generator method used to iterate over the stream
for data in response.stream():
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:
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 through 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 codeis 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.
from birdy.twitter import UserClient
class MyClient(UserClient):
@staticmethod
def get_json_object_hook(data):
return data
client = MyClient(...)
response = client.api.users.show.get(screen_name='twitter')
Or maybe, if you want global error handling for common errors, just override handle_response() method.
class MyClient(UserClient):
def handle_response(self, method, response):
try:
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.
class MyClient(UserClient):
def configure_oauth_session(self, session):
session = super(MyClient, self).configure_oauth_session(session)
session.proxies = {'http': 'foo.bar:3128'}
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.
Credits
birdy would not exists if not for the excellent requests and requests-oauthlib libraries and the wonderful Python programing language.
Question, comments, …
If you need to contact me, you can find me on Twitter ([@sect2k](https://twitter.com/sect2k/)).
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