Interacting with REST APIs

In this lecture we will use the public Twitter APIs to interact with Twitter.

Obtaining my Twitter Keys

To interact with Twitter we need to deal with OAuth security authentication. (This is complicated!!) Fortunately, Twitter offers a simple way to obtain a secure Token for single user applications. You will learn how to create a Twitter developer account and access token in Project 1.

https://twitter.com/data_100

I have setup a developer account and created an application. In the process Twitter generates a set of keys for me which I have stored in my key file:

In [1]:
keyfile = '/Users/jegonzal/Documents/keys/data100_twitter_api.json'

Would you like to see it's contents? (Do I want students tweeting as me?)

Here is what a fake file looks like (these keys are not real...):

In [2]:
with open(keyfile + ".fake", "r") as f:
    print(f.read())

{
   "consumer_key": "asdfasdfasdfasdfasdf",
   "consumer_secret":  "asfasdfasdfasdfasdfasdfasdf",
   "access_token": "5313184768-asfasdfasdfasdfasdf",
   "access_token_secret": "asdfasdfasdfasdfasdfasdf"
}

Naturally the values are all secret. Now let's load the real deal.

Json loader

The following reads the JSON file into a python dictionary

In [3]:
import json
key_file = '/Users/jegonzal/Documents/keys/data100_twitter_api.json'
with open(key_file) as f:
    auth_keys = json.load(f)

I can examine my keys:

In [4]:
auth_keys.keys()
Out[4]:
dict_keys(['consumer_key', 'consumer_secret', 'access_token', 'access_token_secret'])

I won't examine the values ... (Why?)

Twitter Requests Session

I will use the request oauth support to start an Authenticated session:

In [5]:
from requests_oauthlib import OAuth1Session
session = OAuth1Session(auth_keys["consumer_key"],
                        client_secret=auth_keys["consumer_secret"],
                        resource_owner_key=auth_keys["access_token"],
                        resource_owner_secret=auth_keys["access_token_secret"])

The Twitter REST APIs

We want to get all the Tweets from a user. To do this we will use the timeline API:

https://developer.twitter.com/en/docs/tweets/timelines/api-reference/get-statuses-user_timeline.html

Skim the above web page to see how we call this API. What do we need to know:

  1. What kind of request (GET, POST, ...)?
  2. What are the parameters or data that we should send?
  3. What are the returned fields?
  4. Rate limits ...

Getting the Timeline

The following will get the Timeline for "UCBIDS"

In [6]:
url = "https://api.twitter.com/1.1/statuses/user_timeline.json"
resp = session.get(url, params={"screen_name": "UCBIDS"})
resp
Out[6]:
<Response [200]>

Examining the Response

  1. What is it's format?
In [7]:
dict(resp.headers)
Out[7]:
{'cache-control': 'no-cache, no-store, must-revalidate, pre-check=0, post-check=0',
 'content-disposition': 'attachment; filename=json.json',
 'content-encoding': 'gzip',
 'content-length': '3262',
 'content-type': 'application/json;charset=utf-8',
 'date': 'Tue, 13 Feb 2018 08:44:53 GMT',
 'expires': 'Tue, 31 Mar 1981 05:00:00 GMT',
 'last-modified': 'Tue, 13 Feb 2018 08:44:53 GMT',
 'pragma': 'no-cache',
 'server': 'tsa_a',
 'set-cookie': 'personalization_id="v1_VKUx7faEcYKONGtddix5XA=="; Expires=Thu, 13 Feb 2020 08:44:53 UTC; Path=/; Domain=.twitter.com, lang=en; Path=/, guest_id=v1%3A151851149387439453; Expires=Thu, 13 Feb 2020 08:44:53 UTC; Path=/; Domain=.twitter.com',
 'status': '200 OK',
 'strict-transport-security': 'max-age=631138519',
 'x-access-level': 'read-write',
 'x-connection-hash': 'abce66c26ec95cd0d809941aaf6aea35',
 'x-content-type-options': 'nosniff',
 'x-frame-options': 'SAMEORIGIN',
 'x-rate-limit-limit': '900',
 'x-rate-limit-remaining': '899',
 'x-rate-limit-reset': '1518512393',
 'x-response-time': '76',
 'x-transaction': '00e7bb57009e9ed9',
 'x-twitter-response-tags': 'BouncerCompliant',
 'x-xss-protection': '1; mode=block; report=https://twitter.com/i/xss_report'}
In [8]:
resp.content
Out[8]:
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Parsing the JSON Content

The response is encoded in JSON (see headers or the content). We will use the json parsing library built into python:

In [9]:
bd_tweets = json.loads(resp.content)
bd_tweets
Out[9]:
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  'in_reply_to_user_id_str': '2713589684',
  'is_quote_status': False,
  'lang': 'en',
  'place': None,
  'possibly_sensitive': False,
  'retweet_count': 0,
  'retweeted': False,
  'source': '<a href="http://www.hootsuite.com" rel="nofollow">Hootsuite</a>',
  'text': '@UCBIDS now hiring Open Source Scientific Python Developers | https://t.co/pX4rPv6p1l',
  'truncated': False,
  'user': {'contributors_enabled': False,
   'created_at': 'Thu Aug 07 03:29:04 +0000 2014',
   'default_profile': False,
   'default_profile_image': False,
   'description': 'Berkeley Institute for Data Science',
   'entities': {'description': {'urls': []},
    'url': {'urls': [{'display_url': 'bids.berkeley.edu',
       'expanded_url': 'http://bids.berkeley.edu',
       'indices': [0, 22],
       'url': 'http://t.co/gVz5D7qURY'}]}},
   'favourites_count': 805,
   'follow_request_sent': False,
   'followers_count': 1818,
   'following': False,
   'friends_count': 149,
   'geo_enabled': False,
   'has_extended_profile': False,
   'id': 2713589684,
   'id_str': '2713589684',
   'is_translation_enabled': False,
   'is_translator': False,
   'lang': 'en',
   'listed_count': 110,
   'location': 'Berkeley, CA',
   'name': 'BIDS',
   'notifications': False,
   'profile_background_color': '003262',
   'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',
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   'profile_image_url': 'http://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
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   'protected': False,
   'screen_name': 'UCBIDS',
   'statuses_count': 744,
   'time_zone': 'Pacific Time (US & Canada)',
   'translator_type': 'none',
   'url': 'http://t.co/gVz5D7qURY',
   'utc_offset': -28800,
   'verified': False}},
 {'contributors': None,
  'coordinates': None,
  'created_at': 'Sat Dec 23 03:05:03 +0000 2017',
  'entities': {'hashtags': [],
   'symbols': [],
   'urls': [{'display_url': 'ow.ly/SBLi30hnwK8',
     'expanded_url': 'http://ow.ly/SBLi30hnwK8',
     'indices': [77, 100],
     'url': 'https://t.co/tvlTbn7Rak'}],
   'user_mentions': [{'id': 2713589684,
     'id_str': '2713589684',
     'indices': [0, 7],
     'name': 'BIDS',
     'screen_name': 'UCBIDS'}]},
  'favorite_count': 5,
  'favorited': False,
  'geo': None,
  'id': 944403452477755393,
  'id_str': '944403452477755393',
  'in_reply_to_screen_name': 'UCBIDS',
  'in_reply_to_status_id': None,
  'in_reply_to_status_id_str': None,
  'in_reply_to_user_id': 2713589684,
  'in_reply_to_user_id_str': '2713589684',
  'is_quote_status': False,
  'lang': 'en',
  'place': None,
  'possibly_sensitive': False,
  'retweet_count': 3,
  'retweeted': False,
  'source': '<a href="http://www.hootsuite.com" rel="nofollow">Hootsuite</a>',
  'text': '@UCBIDS receives Sloan Foundation Grant to contribute to NumPy development | https://t.co/tvlTbn7Rak',
  'truncated': False,
  'user': {'contributors_enabled': False,
   'created_at': 'Thu Aug 07 03:29:04 +0000 2014',
   'default_profile': False,
   'default_profile_image': False,
   'description': 'Berkeley Institute for Data Science',
   'entities': {'description': {'urls': []},
    'url': {'urls': [{'display_url': 'bids.berkeley.edu',
       'expanded_url': 'http://bids.berkeley.edu',
       'indices': [0, 22],
       'url': 'http://t.co/gVz5D7qURY'}]}},
   'favourites_count': 805,
   'follow_request_sent': False,
   'followers_count': 1818,
   'following': False,
   'friends_count': 149,
   'geo_enabled': False,
   'has_extended_profile': False,
   'id': 2713589684,
   'id_str': '2713589684',
   'is_translation_enabled': False,
   'is_translator': False,
   'lang': 'en',
   'listed_count': 110,
   'location': 'Berkeley, CA',
   'name': 'BIDS',
   'notifications': False,
   'profile_background_color': '003262',
   'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',
   'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',
   'profile_background_tile': False,
   'profile_image_url': 'http://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
   'profile_image_url_https': 'https://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
   'profile_link_color': '9DAD33',
   'profile_sidebar_border_color': '000000',
   'profile_sidebar_fill_color': '000000',
   'profile_text_color': '000000',
   'profile_use_background_image': False,
   'protected': False,
   'screen_name': 'UCBIDS',
   'statuses_count': 744,
   'time_zone': 'Pacific Time (US & Canada)',
   'translator_type': 'none',
   'url': 'http://t.co/gVz5D7qURY',
   'utc_offset': -28800,
   'verified': False}},
 {'contributors': None,
  'coordinates': None,
  'created_at': 'Thu Dec 21 22:10:06 +0000 2017',
  'entities': {'hashtags': [],
   'symbols': [],
   'urls': [{'display_url': 'ow.ly/tghA30hnwTK',
     'expanded_url': 'http://ow.ly/tghA30hnwTK',
     'indices': [62, 85],
     'url': 'https://t.co/pX4rPv6p1l'}],
   'user_mentions': [{'id': 2713589684,
     'id_str': '2713589684',
     'indices': [0, 7],
     'name': 'BIDS',
     'screen_name': 'UCBIDS'}]},
  'favorite_count': 3,
  'favorited': False,
  'geo': None,
  'id': 943966838094008320,
  'id_str': '943966838094008320',
  'in_reply_to_screen_name': 'UCBIDS',
  'in_reply_to_status_id': None,
  'in_reply_to_status_id_str': None,
  'in_reply_to_user_id': 2713589684,
  'in_reply_to_user_id_str': '2713589684',
  'is_quote_status': False,
  'lang': 'en',
  'place': None,
  'possibly_sensitive': False,
  'retweet_count': 7,
  'retweeted': False,
  'source': '<a href="http://www.hootsuite.com" rel="nofollow">Hootsuite</a>',
  'text': '@UCBIDS now hiring Open Source Scientific Python Developers | https://t.co/pX4rPv6p1l',
  'truncated': False,
  'user': {'contributors_enabled': False,
   'created_at': 'Thu Aug 07 03:29:04 +0000 2014',
   'default_profile': False,
   'default_profile_image': False,
   'description': 'Berkeley Institute for Data Science',
   'entities': {'description': {'urls': []},
    'url': {'urls': [{'display_url': 'bids.berkeley.edu',
       'expanded_url': 'http://bids.berkeley.edu',
       'indices': [0, 22],
       'url': 'http://t.co/gVz5D7qURY'}]}},
   'favourites_count': 805,
   'follow_request_sent': False,
   'followers_count': 1818,
   'following': False,
   'friends_count': 149,
   'geo_enabled': False,
   'has_extended_profile': False,
   'id': 2713589684,
   'id_str': '2713589684',
   'is_translation_enabled': False,
   'is_translator': False,
   'lang': 'en',
   'listed_count': 110,
   'location': 'Berkeley, CA',
   'name': 'BIDS',
   'notifications': False,
   'profile_background_color': '003262',
   'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',
   'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',
   'profile_background_tile': False,
   'profile_image_url': 'http://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
   'profile_image_url_https': 'https://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
   'profile_link_color': '9DAD33',
   'profile_sidebar_border_color': '000000',
   'profile_sidebar_fill_color': '000000',
   'profile_text_color': '000000',
   'profile_use_background_image': False,
   'protected': False,
   'screen_name': 'UCBIDS',
   'statuses_count': 744,
   'time_zone': 'Pacific Time (US & Canada)',
   'translator_type': 'none',
   'url': 'http://t.co/gVz5D7qURY',
   'utc_offset': -28800,
   'verified': False}},
 {'contributors': None,
  'coordinates': None,
  'created_at': 'Thu Dec 21 19:05:09 +0000 2017',
  'entities': {'hashtags': [],
   'symbols': [],
   'urls': [{'display_url': 'ow.ly/SBLi30hnwK8',
     'expanded_url': 'http://ow.ly/SBLi30hnwK8',
     'indices': [77, 100],
     'url': 'https://t.co/tvlTbn7Rak'}],
   'user_mentions': [{'id': 2713589684,
     'id_str': '2713589684',
     'indices': [0, 7],
     'name': 'BIDS',
     'screen_name': 'UCBIDS'}]},
  'favorite_count': 3,
  'favorited': False,
  'geo': None,
  'id': 943920292145090563,
  'id_str': '943920292145090563',
  'in_reply_to_screen_name': 'UCBIDS',
  'in_reply_to_status_id': None,
  'in_reply_to_status_id_str': None,
  'in_reply_to_user_id': 2713589684,
  'in_reply_to_user_id_str': '2713589684',
  'is_quote_status': False,
  'lang': 'en',
  'place': None,
  'possibly_sensitive': False,
  'retweet_count': 1,
  'retweeted': False,
  'source': '<a href="http://www.hootsuite.com" rel="nofollow">Hootsuite</a>',
  'text': '@UCBIDS receives Sloan Foundation Grant to contribute to NumPy development | https://t.co/tvlTbn7Rak',
  'truncated': False,
  'user': {'contributors_enabled': False,
   'created_at': 'Thu Aug 07 03:29:04 +0000 2014',
   'default_profile': False,
   'default_profile_image': False,
   'description': 'Berkeley Institute for Data Science',
   'entities': {'description': {'urls': []},
    'url': {'urls': [{'display_url': 'bids.berkeley.edu',
       'expanded_url': 'http://bids.berkeley.edu',
       'indices': [0, 22],
       'url': 'http://t.co/gVz5D7qURY'}]}},
   'favourites_count': 805,
   'follow_request_sent': False,
   'followers_count': 1818,
   'following': False,
   'friends_count': 149,
   'geo_enabled': False,
   'has_extended_profile': False,
   'id': 2713589684,
   'id_str': '2713589684',
   'is_translation_enabled': False,
   'is_translator': False,
   'lang': 'en',
   'listed_count': 110,
   'location': 'Berkeley, CA',
   'name': 'BIDS',
   'notifications': False,
   'profile_background_color': '003262',
   'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',
   'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',
   'profile_background_tile': False,
   'profile_image_url': 'http://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
   'profile_image_url_https': 'https://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
   'profile_link_color': '9DAD33',
   'profile_sidebar_border_color': '000000',
   'profile_sidebar_fill_color': '000000',
   'profile_text_color': '000000',
   'profile_use_background_image': False,
   'protected': False,
   'screen_name': 'UCBIDS',
   'statuses_count': 744,
   'time_zone': 'Pacific Time (US & Canada)',
   'translator_type': 'none',
   'url': 'http://t.co/gVz5D7qURY',
   'utc_offset': -28800,
   'verified': False}},
 {'contributors': None,
  'coordinates': None,
  'created_at': 'Fri Dec 01 00:18:45 +0000 2017',
  'entities': {'hashtags': [],
   'symbols': [],
   'urls': [{'display_url': 'ow.ly/2cix30gW0NC',
     'expanded_url': 'http://ow.ly/2cix30gW0NC',
     'indices': [55, 78],
     'url': 'https://t.co/E47F3uNI3H'}],
   'user_mentions': [{'id': 2713589684,
     'id_str': '2713589684',
     'indices': [27, 34],
     'name': 'BIDS',
     'screen_name': 'UCBIDS'}]},
  'favorite_count': 2,
  'favorited': False,
  'geo': None,
  'id': 936389065028571136,
  'id_str': '936389065028571136',
  'in_reply_to_screen_name': None,
  'in_reply_to_status_id': None,
  'in_reply_to_status_id_str': None,
  'in_reply_to_user_id': None,
  'in_reply_to_user_id_str': None,
  'is_quote_status': False,
  'lang': 'en',
  'place': None,
  'possibly_sensitive': False,
  'retweet_count': 1,
  'retweeted': False,
  'source': '<a href="http://www.hootsuite.com" rel="nofollow">Hootsuite</a>',
  'text': 'TOMORROW - One more day of @UCBIDS TextXD Conference | https://t.co/E47F3uNI3H',
  'truncated': False,
  'user': {'contributors_enabled': False,
   'created_at': 'Thu Aug 07 03:29:04 +0000 2014',
   'default_profile': False,
   'default_profile_image': False,
   'description': 'Berkeley Institute for Data Science',
   'entities': {'description': {'urls': []},
    'url': {'urls': [{'display_url': 'bids.berkeley.edu',
       'expanded_url': 'http://bids.berkeley.edu',
       'indices': [0, 22],
       'url': 'http://t.co/gVz5D7qURY'}]}},
   'favourites_count': 805,
   'follow_request_sent': False,
   'followers_count': 1818,
   'following': False,
   'friends_count': 149,
   'geo_enabled': False,
   'has_extended_profile': False,
   'id': 2713589684,
   'id_str': '2713589684',
   'is_translation_enabled': False,
   'is_translator': False,
   'lang': 'en',
   'listed_count': 110,
   'location': 'Berkeley, CA',
   'name': 'BIDS',
   'notifications': False,
   'profile_background_color': '003262',
   'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',
   'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',
   'profile_background_tile': False,
   'profile_image_url': 'http://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
   'profile_image_url_https': 'https://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
   'profile_link_color': '9DAD33',
   'profile_sidebar_border_color': '000000',
   'profile_sidebar_fill_color': '000000',
   'profile_text_color': '000000',
   'profile_use_background_image': False,
   'protected': False,
   'screen_name': 'UCBIDS',
   'statuses_count': 744,
   'time_zone': 'Pacific Time (US & Canada)',
   'translator_type': 'none',
   'url': 'http://t.co/gVz5D7qURY',
   'utc_offset': -28800,
   'verified': False}}]

How many tweets did we get:

In [10]:
len(bd_tweets)
Out[10]:
20

Examining a Tweet

  1. What fields do we have?
  2. What is the recursive structure?
In [11]:
bd_tweets[0]
Out[11]:
{'contributors': None,
 'coordinates': None,
 'created_at': 'Mon Feb 12 21:40:04 +0000 2018',
 'entities': {'hashtags': [],
  'symbols': [],
  'urls': [{'display_url': 'twitter.com/i/web/status/9…',
    'expanded_url': 'https://twitter.com/i/web/status/963165833563435009',
    'indices': [117, 140],
    'url': 'https://t.co/CNPbR16Dyh'}],
  'user_mentions': []},
 'favorite_count': 0,
 'favorited': False,
 'geo': None,
 'id': 963165833563435009,
 'id_str': '963165833563435009',
 'in_reply_to_screen_name': None,
 'in_reply_to_status_id': None,
 'in_reply_to_status_id_str': None,
 'in_reply_to_user_id': None,
 'in_reply_to_user_id_str': None,
 'is_quote_status': False,
 'lang': 'en',
 'place': None,
 'possibly_sensitive': False,
 'retweet_count': 1,
 'retweeted': False,
 'source': '<a href="http://www.hootsuite.com" rel="nofollow">Hootsuite</a>',
 'text': 'Joe Hellerstein on "Supporting the Data Science Lifecycle" | Berkeley Distinguished Lectures in Data Science - TUES… https://t.co/CNPbR16Dyh',
 'truncated': True,
 'user': {'contributors_enabled': False,
  'created_at': 'Thu Aug 07 03:29:04 +0000 2014',
  'default_profile': False,
  'default_profile_image': False,
  'description': 'Berkeley Institute for Data Science',
  'entities': {'description': {'urls': []},
   'url': {'urls': [{'display_url': 'bids.berkeley.edu',
      'expanded_url': 'http://bids.berkeley.edu',
      'indices': [0, 22],
      'url': 'http://t.co/gVz5D7qURY'}]}},
  'favourites_count': 805,
  'follow_request_sent': False,
  'followers_count': 1818,
  'following': False,
  'friends_count': 149,
  'geo_enabled': False,
  'has_extended_profile': False,
  'id': 2713589684,
  'id_str': '2713589684',
  'is_translation_enabled': False,
  'is_translator': False,
  'lang': 'en',
  'listed_count': 110,
  'location': 'Berkeley, CA',
  'name': 'BIDS',
  'notifications': False,
  'profile_background_color': '003262',
  'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',
  'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',
  'profile_background_tile': False,
  'profile_image_url': 'http://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
  'profile_image_url_https': 'https://pbs.twimg.com/profile_images/517779187864567808/GDwgyZ3b_normal.png',
  'profile_link_color': '9DAD33',
  'profile_sidebar_border_color': '000000',
  'profile_sidebar_fill_color': '000000',
  'profile_text_color': '000000',
  'profile_use_background_image': False,
  'protected': False,
  'screen_name': 'UCBIDS',
  'statuses_count': 744,
  'time_zone': 'Pacific Time (US & Canada)',
  'translator_type': 'none',
  'url': 'http://t.co/gVz5D7qURY',
  'utc_offset': -28800,
  'verified': False}}

Examining First 5 Tweets:

We can loop over the dictionaries and print fields:

In [12]:
for t in bd_tweets[:5]:
    print(t['created_at'])
    print(t['text'], "\n")

Mon Feb 12 21:40:04 +0000 2018
Joe Hellerstein on "Supporting the Data Science Lifecycle" | Berkeley Distinguished Lectures in Data Science - TUES… https://t.co/CNPbR16Dyh 

Mon Feb 12 19:45:05 +0000 2018
Love Data Week - February 12-16, 2018 - @UCBIDS | https://t.co/ThmnV6vTM8 

Mon Feb 12 19:26:02 +0000 2018
Berkeley Distinguished Lectures in Data Science - Spring 2018 Series begins February 13th @UCBIDS | https://t.co/m4hs4j8p4X 

Wed Feb 07 20:59:51 +0000 2018
The Hacker Within, TONIGHT 5pm @UCBIDS - Intro to Jupyter Notebooks with Stuart Geiger | https://t.co/wIJh5dplN9 

Wed Feb 07 17:25:06 +0000 2018
The Hacker WIthin, TODAY 5pm @UCBIDS | Intro to Jupyter Notebooks with Stuart Geiger | https://t.co/fFGEt9Qkip

Making a Dataframe

Pandas can build a DataFrame from the dictionaries and even the raw JSON

In [13]:
import pandas as pd
df = pd.DataFrame(bd_tweets)
df.head()
Out[13]:
contributors coordinates created_at entities favorite_count favorited geo id id_str in_reply_to_screen_name ... is_quote_status lang place possibly_sensitive retweet_count retweeted source text truncated user
0 None None Mon Feb 12 21:40:04 +0000 2018 {'hashtags': [], 'symbols': [], 'user_mentions... 0 False None 963165833563435009 963165833563435009 None ... False en None False 1 False <a href="http://www.hootsuite.com" rel="nofoll... Joe Hellerstein on "Supporting the Data Scienc... True {'id': 2713589684, 'id_str': '2713589684', 'na...
1 None None Mon Feb 12 19:45:05 +0000 2018 {'hashtags': [], 'symbols': [], 'user_mentions... 3 False None 963136897295048705 963136897295048705 None ... False en None False 2 False <a href="http://www.hootsuite.com" rel="nofoll... Love Data Week - February 12-16, 2018 - @UCBID... False {'id': 2713589684, 'id_str': '2713589684', 'na...
2 None None Mon Feb 12 19:26:02 +0000 2018 {'hashtags': [], 'symbols': [], 'user_mentions... 0 False None 963132106217050113 963132106217050113 None ... False en None False 1 False <a href="http://www.hootsuite.com" rel="nofoll... Berkeley Distinguished Lectures in Data Scienc... False {'id': 2713589684, 'id_str': '2713589684', 'na...
3 None None Wed Feb 07 20:59:51 +0000 2018 {'hashtags': [], 'symbols': [], 'user_mentions... 3 False None 961343775099490310 961343775099490310 None ... False en None False 1 False <a href="http://www.hootsuite.com" rel="nofoll... The Hacker Within, TONIGHT 5pm @UCBIDS - Intro... False {'id': 2713589684, 'id_str': '2713589684', 'na...
4 None None Wed Feb 07 17:25:06 +0000 2018 {'hashtags': [], 'symbols': [], 'user_mentions... 0 False None 961289731316289538 961289731316289538 None ... False en None False 1 False <a href="http://www.hootsuite.com" rel="nofoll... The Hacker WIthin, TODAY 5pm @UCBIDS | Intro t... False {'id': 2713589684, 'id_str': '2713589684', 'na...

5 rows × 24 columns

In [14]:
import pandas as pd
df = pd.read_json(resp.content)
df.head()
Out[14]:
contributors coordinates created_at entities favorite_count favorited geo id id_str in_reply_to_screen_name ... is_quote_status lang place possibly_sensitive retweet_count retweeted source text truncated user
0 NaN NaN 2018-02-12 21:40:04 {'hashtags': [], 'symbols': [], 'user_mentions... 0 False NaN 963165833563435009 963165833563435008 None ... False en NaN False 1 False <a href="http://www.hootsuite.com" rel="nofoll... Joe Hellerstein on "Supporting the Data Scienc... True {'id': 2713589684, 'id_str': '2713589684', 'na...
1 NaN NaN 2018-02-12 19:45:05 {'hashtags': [], 'symbols': [], 'user_mentions... 3 False NaN 963136897295048705 963136897295048704 None ... False en NaN False 2 False <a href="http://www.hootsuite.com" rel="nofoll... Love Data Week - February 12-16, 2018 - @UCBID... False {'id': 2713589684, 'id_str': '2713589684', 'na...
2 NaN NaN 2018-02-12 19:26:02 {'hashtags': [], 'symbols': [], 'user_mentions... 0 False NaN 963132106217050113 963132106217050112 None ... False en NaN False 1 False <a href="http://www.hootsuite.com" rel="nofoll... Berkeley Distinguished Lectures in Data Scienc... False {'id': 2713589684, 'id_str': '2713589684', 'na...
3 NaN NaN 2018-02-07 20:59:51 {'hashtags': [], 'symbols': [], 'user_mentions... 3 False NaN 961343775099490310 961343775099490304 None ... False en NaN False 1 False <a href="http://www.hootsuite.com" rel="nofoll... The Hacker Within, TONIGHT 5pm @UCBIDS - Intro... False {'id': 2713589684, 'id_str': '2713589684', 'na...
4 NaN NaN 2018-02-07 17:25:06 {'hashtags': [], 'symbols': [], 'user_mentions... 0 False NaN 961289731316289538 961289731316289536 None ... False en NaN False 1 False <a href="http://www.hootsuite.com" rel="nofoll... The Hacker WIthin, TODAY 5pm @UCBIDS | Intro t... False {'id': 2713589684, 'id_str': '2713589684', 'na...

5 rows × 24 columns

Getting Lots of Tweets

The Twitter API limits how many tweets you can obtain in a single request to 200 tweets. Therefore to go back in time you need to repeatedly call requesting earlier tweets than the oldest tweet you have. This will return 200 more tweets that are at least as old (including the oldest tweet again ...).

This is an excellent example of being stateless. The Twitter Server does not need to remember which tweets it sent us. Instead we tell it where to start reading in each request.

The following block of code iterates until no new Tweets are returned.

In [15]:
pd.to_datetime(bd_tweets[0]['created_at'])
Out[15]:
Timestamp('2018-02-12 21:40:04')
In [16]:
def get_timeline(session, screen_name):
    """
    Constructs a dictionary of all available tweets from a given screen name.
    session: a request session that has been auntheticated 
    screen_name: the screen name from which to get the timeline
    
    returns: a list all tweets
    """
    url = "https://api.twitter.com/1.1/statuses/user_timeline.json"
    tweets = {}
    # Make an initial request
    resp = session.get(url, params = {"screen_name": screen_name, "count": 200})
    old_tweetid_len = -1
    # Loop while the response is OK and we are still getting new tweets
    while resp.ok and old_tweetid_len < len(tweets):
        new_tweets = {t['id']: t for t in json.loads(resp.content)}
        old_tweetid_len = len(tweets)
        tweets.update(new_tweets)
        min_id = min(tweets.keys())
        resp = session.get(url, params = {"screen_name": screen_name, "count": 200, "max_id": min_id})
        print("Oldest Tweet:", tweets[min_id]['created_at'], "\"", tweets[min_id]["text"], "\"")
    return list(tweets.values())
In [17]:
all_ds_tweets = get_timeline(session, "UCBIDS")

Oldest Tweet: Tue Sep 06 20:58:21 +0000 2016 " RT @kylebarbary: "Why your field needs a hack week" talk by @Tiana_Athriel at @UCBIDS https://t.co/YAAN6Ll6bj "
Oldest Tweet: Mon Nov 02 17:38:31 +0000 2015 " @tranlib We are having the panel recorded, so you can find it on our video page after it is processed. :) "
Oldest Tweet: Fri Mar 20 16:07:18 +0000 2015 " What happens when #datascience is done well and looks easy? http://t.co/EGA9XdzXhq "
Oldest Tweet: Mon Nov 03 23:54:26 +0000 2014 " We're up and running at BIDS! Come visit us in 190 Doe Library on UC Berkeley's campus: M-F 9:00am-6:00pm. "
Oldest Tweet: Mon Nov 03 23:54:26 +0000 2014 " We're up and running at BIDS! Come visit us in 190 Doe Library on UC Berkeley's campus: M-F 9:00am-6:00pm. "

Construct a DataFrame from the Tweets

In [18]:
df = pd.DataFrame(all_ds_tweets)[['id', 'created_at', 'text']]
df['created_at'] = pd.to_datetime(df['created_at'])
df['len'] = df['text'].str.len()
df = df.sort_values("created_at", ascending=False)
df.head()
Out[18]:
id created_at text len
0 963165833563435009 2018-02-12 21:40:04 Joe Hellerstein on "Supporting the Data Scienc... 140
1 963136897295048705 2018-02-12 19:45:05 Love Data Week - February 12-16, 2018 - @UCBID... 73
2 963132106217050113 2018-02-12 19:26:02 Berkeley Distinguished Lectures in Data Scienc... 123
3 961343775099490310 2018-02-07 20:59:51 The Hacker Within, TONIGHT 5pm @UCBIDS - Intro... 112
4 961289731316289538 2018-02-07 17:25:06 The Hacker WIthin, TODAY 5pm @UCBIDS | Intro t... 110

Post Tweets

We can use the Twitter API to also post tweets. Examine the following page:

https://developer.twitter.com/en/docs/tweets/post-and-engage/overview

Posting "Hello World"

We will post to the class Twitter Account https://twitter.com/data_100

In [19]:
url = "https://api.twitter.com/1.1/statuses/update.json"
resp = session.post(url, data = {"status": "Hello World!"})
resp
Out[19]:
<Response [200]>

Look at the Website

https://twitter.com/data_100

In [20]:
tweet = json.loads(resp.content)

Deleting the Tweet

https://developer.twitter.com/en/docs/tweets/post-and-engage/api-reference/post-statuses-destroy-id

In [21]:
url = "https://api.twitter.com/1.1/statuses/destroy/{old_id}.json".format(old_id = tweet['id'])
resp = session.post(url)
resp
Out[21]:
<Response [200]>

Look at the Website

https://twitter.com/data_100

Liking Everything Culler

https://developer.twitter.com/en/docs/tweets/post-and-engage/api-reference/post-favorites-create

In [22]:
df[df['text'].str.contains("Culler")]
Out[22]:
id created_at text len
55 869628677499138048 2017-05-30 18:56:48 David Culler named interim dean of @UCBerkeley... 99
In [23]:
for tweetid in df[df['text'].str.contains("Culler")]['id']:
    url = "https://api.twitter.com/1.1/favorites/create.json"
    resp = session.post(url, data = {"id": tweetid})
    print(resp)

<Response [200]>

Look at the Website

https://twitter.com/data_100

Disliking Everything Culler

https://developer.twitter.com/en/docs/tweets/post-and-engage/api-reference/post-favorites-destroy

In [24]:
for tweetid in df[df['text'].str.contains("Culler")]['id']:
    url = "https://api.twitter.com/1.1/favorites/destroy.json"
    resp = session.post(url, data = {"id": tweetid})
    print(resp)

<Response [200]>

Look at the Website

https://twitter.com/data_100

Following a Hashtag

In [25]:
url = "https://stream.twitter.com/1.1/statuses/filter.json"
resp = session.post(url, data={"track": "datascience"}, stream=True)
resp
Out[25]:
<Response [200]>
In [26]:
try:
    for line in resp.iter_lines():
        if len(line) > 0:
            try:
                tweet = json.loads(line)
            except:
                print(line)
            print(tweet['text'])
except:
    # It is important to close the connections since there is a limit on the number of active sessions.
    print("Closing Connection")
    resp.close()

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Closing Connection