When /r/games disabled the downvoting button via CSS

Subreddit disabled downvote button on 2013-01-16, thread here

Data processing

Google BigQuery

SELECT author, num_comments, score, ups, downs, gilded, created_utc FROM [fh-bigquery:reddit_posts.full_corpus_201509] 
where subreddit = 'Games' 
AND created_utc BETWEEN 1356998400 AND 1359676800



In [12]:

    
!ls









    



games-analysis.ipynb  reddit_games_comments_notext_201301.csv
readme.md	      reddit_posts_games_201301.csv



In [13]:

    
!pip install bokeh
import pandas as pd
import seaborn as sns
from bokeh.charts import TimeSeries, output_file, show

%matplotlib inline









    



Requirement already satisfied: bokeh in /home/stuart/anaconda3/lib/python3.5/site-packages



In [14]:

    
posts_df = pd.DataFrame.from_csv("reddit_posts_games_201301.csv")



In [15]:

    
posts_df[0:5]









    Out[15]:






  
    
      
      num_comments
      score
      ups
      downs
      gilded
      created_utc
    
    
      author
      
      
      
      
      
      
    
  
  
    
      Antipode2
      0
      0
      0
      0
      0
      1359644323
    
    
      [deleted]
      160
      26
      26
      0
      0
      1358042458
    
    
      [deleted]
      1
      0
      0
      0
      0
      1358262178
    
    
      Placenta_Claus
      6
      8
      8
      0
      0
      1357698707
    
    
      translucent
      356
      86
      86
      0
      0
      1357999989



In [16]:

    
posts_df['created'] = pd.to_datetime(posts_df.created_utc, unit='s')
posts_df['created_date'] = posts_df.created.dt.date



In [17]:

    
posts_df['downs'] = posts_df.score - posts_df.ups



In [18]:

    
posts_time_ups = posts_df.set_index('created_date').ups.sort_index()
posts_time_ups[0:5]









    Out[18]:





created_date
2013-01-01     0
2013-01-01    10
2013-01-01     1
2013-01-01    58
2013-01-01     3
Name: ups, dtype: int64



In [19]:

    
posts_date_df = posts_df.set_index('created').sort_index()

posts_date_df[0:5]









    Out[19]:






  
    
      
      num_comments
      score
      ups
      downs
      gilded
      created_utc
      created_date
    
    
      created
      
      
      
      
      
      
      
    
  
  
    
      2013-01-01 00:16:34
      3
      1
      1
      0
      0
      1356999394
      2013-01-01
    
    
      2013-01-01 00:26:50
      0
      0
      0
      0
      0
      1357000010
      2013-01-01
    
    
      2013-01-01 01:16:01
      3
      0
      0
      0
      0
      1357002961
      2013-01-01
    
    
      2013-01-01 01:17:08
      1
      4
      4
      0
      0
      1357003028
      2013-01-01
    
    
      2013-01-01 02:06:07
      3
      0
      0
      0
      0
      1357005967
      2013-01-01



In [20]:

    
posts_groupby = posts_date_df.groupby([pd.TimeGrouper('1D', closed='left')])

Visualizations

Daily average of number of comments per post



In [21]:

    
posts_groupby.mean().num_comments.plot(kind='barh', figsize=[8,8])









    Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f216cb2f860>

Daily average of number of upvotes per post



In [22]:

    
posts_groupby.mean().ups.plot(kind='barh', figsize=[8,8])









    Out[22]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f216c9fb828>



In [ ]:



In [ ]:



In [ ]:

	num_comments	score	ups	downs	gilded	created_utc
author
Antipode2	0	0	0	0	0	1359644323
[deleted]	160	26	26	0	0	1358042458
[deleted]	1	0	0	0	0	1358262178
Placenta_Claus	6	8	8	0	0	1357698707
translucent	356	86	86	0	0	1357999989

	num_comments	score	ups	downs	gilded	created_utc	created_date
created
2013-01-01 00:16:34	3	1	1	0	0	1356999394	2013-01-01
2013-01-01 00:26:50	0	0	0	0	0	1357000010	2013-01-01
2013-01-01 01:16:01	3	0	0	0	0	1357002961	2013-01-01
2013-01-01 01:17:08	1	4	4	0	0	1357003028	2013-01-01
2013-01-01 02:06:07	3	0	0	0	0	1357005967	2013-01-01