On Wed, Feb 19, 2020 at 5:23 PM FilippoM <[email protected]> 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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