【博文】A Beginner’s Guide to Tweet Analytics with Pandas

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1. def load_tweets(tweet_file):
   
2. 
   
3.     """ Load and process a Twitter analytics data file """
   
4. 
   
5.     # Read tweet data (obtained from Twitter Analytics)
   
6.     tweet_df = pd.read_csv(tweet_file)
   
7. 
   
8.     # Drop irrelevant columns
   
9.     tweet_df = tweet_df.drop(tweet_df.columns[[13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]], axis=1)
   
10. 
    
11.     return tweet_df

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http://www.kdnuggets.com/2017/03/beginners-guide-tweet-analytics-pandas.html

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关键词：Analytics Analytic beginner beginn pandas

1. Basic Tweet Stats
   
2. 
   
3. So, given what data is shown in the output of running head() on the dataset above, and having a rough intuition of what tweet metrics would be useful, we will grab the following stats:
   
4. 
   
5. Retweets - Mean RTs per tweet & top 5 RTed tweets
   
6. Likes - Mean likes per tweet & top 5 liked tweets
   
7. Impressions - Mean impressions per tweet & top 5 tweets with most impressions
   
8. # Total tweets
   
9. print 'Total tweets this period:', len(tweet_df.index), '\n'
   
10. 
    
11. # Retweets
    
12. tweet_df = tweet_df.sort_values(by='retweets', ascending=False)
    
13. tweet_df = tweet_df.reset_index(drop=True)
    
14. print 'Mean retweets:', round(tweet_df['retweets'].mean(),2), '\n'
    
15. print 'Top 5 RTed tweets:'
    
16. print '------------------'
    
17. for i in range(5):
    
18.     print tweet_df['Tweet text'].ix[i], '-', tweet_df['retweets'].ix[i]
    
19. print '\n'
    
20.    
    
21. # Likes
    
22. tweet_df = tweet_df.sort_values(by='likes', ascending=False)
    
23. tweet_df = tweet_df.reset_index(drop=True)
    
24. print 'Mean likes:', round(tweet_df['likes'].mean(),2), '\n'
    
25. print 'Top 5 liked tweets:'
    
26. print '-------------------'
    
27. for i in range(5):
    
28.     print tweet_df['Tweet text'].ix[i], '-', tweet_df['likes'].ix[i]
    
29. print '\n'
    
30. 
    
31. # Impressions
    
32. tweet_df = tweet_df.sort_values(by='impressions', ascending=False)
    
33. tweet_df = tweet_df.reset_index(drop=True)
    
34. print 'Mean impressions:', round(tweet_df['impressions'].mean(),2), '\n'
    
35. print 'Top 5 tweets with most impressions:'
    
36. print '-----------------------------------'
    
37. for i in range(5):
    
38.     print tweet_df['Tweet text'].ix[i], '-', tweet_df['impressions'].ix[i]

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1. Top #Hashtags and @Mentions
   
2. 
   
3. It's no secret that hashtags play an important role in Twitter, and mentions can also help grow your network and influence. Together they help put the 'social' in social networking, transforming platforms like Twitter from passive experiences to very active ones. With that, getting a handle on the most social aspect of this social network can be a helpful endeavour.
   
4. 
   
5. # Hashtags & mentions
   
6. tag_dict = {}
   
7. mention_dict = {}
   
8. 
   
9. for i in tweet_df.index:
   
10.     tweet_text = tweet_df.ix[i]['Tweet text']
    
11.     tweet = tweet_text.lower()
    
12.     tweet_tokenized = tweet.split()
    
13. 
    
14.     for word in tweet_tokenized:
    
15.         # Hashtags - tokenize and build dict of tag counts
    
16.         if (word[0:1] == '#' and len(word) > 1):
    
17.             key = word.translate(string.maketrans("",""), string.punctuation)
    
18.             if key in tag_dict:
    
19.                 tag_dict[key] += 1
    
20.             else:
    
21.                 tag_dict[key] = 1
    
22. 
    
23.         # Mentions - tokenize and build dict of mention counts
    
24.         if (word[0:1] == '@' and len(word) > 1):
    
25.             key = word.translate(string.maketrans("",""), string.punctuation)
    
26.             if key in mention_dict:
    
27.                 mention_dict[key] += 1
    
28.             else:
    
29.                 mention_dict[key] = 1
    
30. 
    
31. # The 10 most popular tags and counts
    
32. top_tags = dict(sorted(tag_dict.iteritems(), key=operator.itemgetter(1), reverse=True)[:10])
    
33. top_tags_sorted = sorted(top_tags.items(), key=lambda x: x[1])[::-1]
    
34. print 'Top 10 hashtags:'
    
35. print '----------------'
    
36. for tag in top_tags_sorted:
    
37.     print tag[0], '-', str(tag[1])
    
38.    
    
39. # The 10 most popular mentions and counts
    
40. top_mentions = dict(sorted(mention_dict.iteritems(), key=operator.itemgetter(1), reverse=True)[:10])
    
41. top_mentions_sorted = sorted(top_mentions.items(), key=lambda x: x[1])[::-1]
    
42. print '\nTop 10 mentions:'
    
43. print '----------------'
    
44. for mention in top_mentions_sorted:
    
45.     print mention[0], '-', str(mention[1])

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1. Time-series Analysis
   
2. 
   
3. Finally, let's have a look at some very basic temporal data. We will check mean impressions for tweets based -- independently -- on both the hour of day and day of week that they are tweeted. I caution (once gain) that this is based on very little data, and so nothing useful will likely be gleaned. However, given much larger amounts of tweet data, entire social media campaigns are planned.
   
4. 
   
5. While this is based on impressions, it could just as reasonably (and easily changed to) be based on engagements, or RTs, or whatever else you pleased. Working in advertising, and promoting tweets? Maybe you are more interested in some of those promotion* metrics we hacked off the dataset at the start.
   
6. 
   
7. We have to convert the Twitter supplied date field to a legitimate Python datetime object, bin the data based on which hourly slot it falls into, identify days of week, and then capture this data in a couple of additional columns in the DataFrame, which we will pillage for stats afterward.
   
8. 
   
9. # Time-series impressions (DOW, HOD, etc) (0 = Sunday... 6 = Saturday)
   
10. gmt_offset = -4
    
11. 
    
12. # Create proper datetime column, apply local GMT offset
    
13. tweet_df['ts'] = pd.to_datetime(tweet_df['time'])
    
14. tweet_df['ts'] = tweet_df.ts + pd.to_timedelta(gmt_offset, unit='h')
    
15. 
    
16. # Add hour of day and day of week columns
    
17. tweet_df['hod'] = [t.hour for t in tweet_df.ts]
    
18. tweet_df['dow'] = [t.dayofweek for t in tweet_df.ts]
    
19. 
    
20. hod_dict = {}
    
21. hod_count = {}
    
22. dow_dict = {}
    
23. dow_count = {}
    
24. weekday_dict = {0: 'Mon', 1: 'Tue', 2: 'Wed', 3: 'Thu', 4: 'Fri', 5: 'Sat', 6: 'Sun'}
    
25. 
    
26. # Process tweets, collect stats
    
27. for i in tweet_df.index:
    
28.     hod = tweet_df.ix[i]['hod']
    
29.     dow = tweet_df.ix[i]['dow']
    
30.     imp = tweet_df.ix[i]['impressions']
    
31. 
    
32.     if hod in hod_dict:
    
33.         hod_dict[hod] += int(imp)
    
34.         hod_count[hod] += 1
    
35.     else:
    
36.         hod_dict[hod] = int(imp)
    
37.         hod_count[hod] = 1
    
38. 
    
39.     if dow in dow_dict:
    
40.         dow_dict[dow] += int(imp)
    
41.         dow_count[dow] += 1
    
42.     else:
    
43.         dow_dict[dow] = int(imp)
    
44.         dow_count[dow] = 1
    
45. 
    
46. print 'Average impressions per tweet by hour tweeted:'
    
47. print '----------------------------------------------'
    
48. for hod in hod_dict:
    
49.     print hod, '-', hod+1, ':', hod_dict[hod]/hod_count[hod], '=>', hod_count[hod], 'tweets'
    
50. 
    
51. print '\nAverage impressions per tweet by day of week tweeted:'
    
52. print '-----------------------------------------------------'
    
53. for dow in dow_dict:
    
54.     print weekday_dict[dow], ':', dow_dict[dow]/dow_count[dow], '=>', dow_count[dow], ' tweets'

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