Counting frequency of values by date using pandas

Question

Let\'s suppose I have following Time Series:

Timestamp              Category
2014-10-16 15:05:17    Facebook
2014-10-16 14:56:37    Vimeo
2014-10-16 14:25:16         


        

Answer 1

It might be easiest to turn your Series into a DataFrame and use Pandas' groupby functionality (if you already have a DataFrame then skip straight to adding another column below).

If your Series is called s, then turn it into a DataFrame like so:

>>> df = pd.DataFrame({'Timestamp': s.index, 'Category': s.values})
>>> df
       Category           Timestamp
0      Facebook 2014-10-16 15:05:17
1         Vimeo 2014-10-16 14:56:37
2      Facebook 2014-10-16 14:25:16
...

Now add another column for the week and year (one way is to use apply and generate a string of the week/year numbers):

>>> df['Week/Year'] = df['Timestamp'].apply(lambda x: "%d/%d" % (x.week, x.year))
>>> df
             Timestamp     Category Week/Year
0  2014-10-16 15:05:17     Facebook   42/2014
1  2014-10-16 14:56:37        Vimeo   42/2014
2  2014-10-16 14:25:16     Facebook   42/2014
...

Finally, group by 'Week/Year' and 'Category' and aggregate with size() to get the counts. For the data in your question this produces the following:

>>> df.groupby(['Week/Year', 'Category']).size()
Week/Year  Category   
41/2014    DailyMotion    1
           Facebook       3
           Vimeo          2
           Youtube        3
42/2014    Facebook       7
           Orkut          1
           Vimeo          1