This last is for discussion of changes to the Python language itself, in
particular the CPython reference implementation. Python-list or a Pandas
forum are appropriate for this question.
That said, it sounds like you want df.value_counts(). But if not, follow-up
in a more relevant place.
On Thu, Feb 20, 2020, 7:39 AM James Lu <jam...@gmail.com> wrote:
>
>
> On Wed, Feb 19, 2020 at 5:23 PM FilippoM <privacy_ple...@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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