OK, boring! Where is Python 4?
Not so fast! The next release after 3.9 will be 3.10. It will be an incremental
improvement over 3.9, just as 3.9 was over 3.8, and so on.
In fact, our newest Release Manager, Pablo Galindo Salgado, prepared the first
alpha release of what will become 3.10.0 a
> On 5 Oct 2020, at 20:38, Barry Warsaw wrote:
>
> Larry, from all of us, and from me personally, thank you so much for your
> invaluable contributions to Python.
Yes, definitely! Thank you.
> Enjoy your retirement!
Not so fast! Now you have all that extra free time to return to the Gilecto
On behalf of the Python development community and the Python 3.9 release team,
I’m pleased to announce the availability of Python 3.9.0.
Python 3.9.0 is the newest feature release of the Python language, and it
contains many new features and optimizations. You can find Python 3.9.0 here:
https:
On Mon, Oct 5, 2020 at 9:39 PM Barry Warsaw wrote:
>
> They say being a Python Release Manager is a thankless job, so the Python
> Secret Underground (PSU), which emphatically does not exist, hereby
> officially doesn’t thank Larry for his years of diligent service as the
> Python 3.4 and 3.5 r
Thank you Larry!
On Mon, Oct 5, 2020 at 11:39 AM Barry Warsaw wrote:
> They say being a Python Release Manager is a thankless job, so the Python
> Secret Underground (PSU), which emphatically does not exist, hereby
> officially doesn’t thank Larry for his years of diligent service as the
> Pytho
They say being a Python Release Manager is a thankless job, so the Python
Secret Underground (PSU), which emphatically does not exist, hereby officially
doesn’t thank Larry for his years of diligent service as the Python 3.4 and 3.5
release manager.
On the other hand, the Python Steering Counci
On Mon, Oct 5, 2020 at 6:47 AM Shaozhong SHI wrote:
>
> Hi, I managed to flatten it with json_normalize first.
>
> from pandas.io.json import json_normalize
> atable = json_normalize(d)
> atable
>
> Then, I got this table.
>
> brandId brandName careHome constituency
> currentRatings.overall.keyQu
Hi, I managed to flatten it with json_normalize first.
from pandas.io.json import json_normalize
atable = json_normalize(d)
atable
Then, I got this table.
brandId brandName careHome constituency
currentRatings.overall.keyQuestionRatings currentRatings.overall.rating
currentRatings.overall.report
Another nice debugger feature is to step up with "u", this will take
you to the parent frame where you can again inspect the variables.
I use this when I want to reverse engineer how the interpreter got to
a specific line.
Maybe worth mentioning that Werkzeug provides in-browser interactive
debug
