使用 pandas 按日期计数值的频率 [英] Counting frequency of values by date using pandas

问题描述

假设我有以下时间序列:

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    Facebook
2014-10-16 14:15:32    Facebook
2014-10-16 13:41:01    Facebook
2014-10-16 12:50:30    Orkut
2014-10-16 12:28:54    Facebook
2014-10-16 12:26:56    Facebook
2014-10-16 12:25:12    Facebook
...
2014-10-08 15:52:49    Youtube
2014-10-08 15:04:50    Youtube
2014-10-08 15:03:48    Vimeo
2014-10-08 15:02:27    Youtube
2014-10-08 15:01:56    DailyMotion
2014-10-08 13:27:28    Facebook
2014-10-08 13:01:08    Vimeo
2014-10-08 12:52:06    Facebook
2014-10-08 12:43:27    Facebook
Name: summary, Length: 600

我想每周和每年对每个类别(时间序列中的唯一值/因数)进行计数.

I would like to make a count of each category (Unique Value/Factor in the Time Series) per week and year.

Example:

    Week/Year      Category      Count
    1/2014         Facebook      12
    1/2014         Google        5
    1/2014         Youtube       2
...    
    2/2014         Facebook      2
    2/2014         Google        5
    2/2014         Youtube       20
...

如何使用Python熊猫来实现?

How can this be achieved using Python pandas?

推荐答案

将Series转换为DataFrame并使用Pandas的groupby功能(如果您已经拥有DataFrame，然后直接跳过即可添加另一列)可能是最简单的以下).

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).

如果您的Series名为s，则将其转换为DataFrame，如下所示:

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
...

现在为周和年添加另一列(一种方法是使用apply并生成包含周/年数字的字符串):

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
...

最后，将'Week/Year'和'Category'分组，并与size()聚合以获得计数.对于您问题中的数据，将产生以下结果:

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

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