twitter-api.rtf

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\outl0\strokewidth0 \strokec2 import tweepy\
from tweepy import OAuthHandler\
import json\
from timeit import default_timer as timer\
\
# Query Twitter API for each tweet in the Twitter archive and save JSON in a text file\
# These are hidden to comply with Twitter's API terms and conditions\
consumer_key = 'HIDDEN'\
consumer_secret = 'HIDDEN'\
access_token = 'HIDDEN'\
access_secret = 'HIDDEN'\
\
auth = OAuthHandler(consumer_key, consumer_secret)\
auth.set_access_token(access_token, access_secret)\
\
api = tweepy.API(auth, wait_on_rate_limit=True)\
\
# NOTE TO STUDENT WITH MOBILE VERIFICATION ISSUES:\
# df_1 is a DataFrame with the twitter_archive_enhanced.csv file. You may have to\
# change line 17 to match the name of your DataFrame with twitter_archive_enhanced.csv\
# NOTE TO REVIEWER: this student had mobile verification issues so the following\
# Twitter API code was sent to this student from a Udacity instructor\
# Tweet IDs for which to gather additional data via Twitter's API\
tweet_ids = df_1.tweet_id.values\
len(tweet_ids)\
\
# Query Twitter's API for JSON data for each tweet ID in the Twitter archive\
count = 0\
fails_dict = \{\}\
start = timer()\
# Save each tweet's returned JSON as a new line in a .txt file\
with open('tweet_json.txt', 'w') as outfile:\
    # This loop will likely take 20-30 minutes to run because of Twitter's rate limit\
    for tweet_id in tweet_ids:\
        count += 1\
        print(str(count) + ": " + str(tweet_id))\
        try:\
            tweet = api.get_status(tweet_id, tweet_mode='extended')\
            print("Success")\
            json.dump(tweet._json, outfile)\
            outfile.write('\\n')\
        except tweepy.TweepError as e:\
            print("Fail")\
            fails_dict[tweet_id] = e\
            pass\
end = timer()\
print(end - start)\
print(fails_dict)\
}