On Wed, Feb 19, 2020 at 5:23 PM FilippoM <privacy_please@filippo.it> wrote:

Hi, I've got a Pandas data frame that looks like this

In [69]: data.head
Out[69]:
<bound method NDFrame.head of      OS and Version         Status
0          Android        VIDEO_OK
1     Android 4.2.2       VIDEO_OK
2         Android 9       VIDEO_OK
3          iOS 13.3       VIDEO_OK
4        Windows 10       VIDEO_OK
5         Android 9       VIDEO_OK
             ...            ...
24       Windows 10       VIDEO_OK
25        Android 9       VIDEO_OK
26    Android 6.0.1       VIDEO_OK
27       Windows XP       VIDEO_OK
28    Android 8.0.0  VIDEO_FAILURE
29      Android 6.0       VIDEO_OK
             ...            ...
2994        iOS 9.1       VIDEO_OK
2995      Android 9       VIDEO_OK
2996     Windows 10       VIDEO_OK
2997      Android 9       VIDEO_OK
2998     Windows 10       VIDEO_OK
2999       iOS 13.3       VIDEO_OK


with 109 possible values of the OS columns and just two possible values
()VIDEO_OK and VIDEO_FAILURE) in the status column.

How can I use Pandas' dataframe magic to calculate, for each of the
possible 109 values, how many have VIDEO_OK, and how many have
VIDEO_FAILURE I have respectively?

I would like to end up with something like

In[]: num_of_oks{"iOS 13.3"}
Out:  15

In[]: num_of_not_oks{"iOS 13.3"}
Out:  3

I am trying to do some matplotlib scatter plotting

Thanks


--
https://mail.python.org/mailman/listinfo/python-list

Have you considered using traditional unix tools, like cut and count? Or traditional SQL. 

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