I found my idea implementation for Django :)
https://github.com/coady/django-model-values
On Sun, May 6, 2018 at 6:14 AM Massimo Di Pierro
wrote:
> I would very muck have a db().select_as_pandas() that avoids parsing
> the database response a puts the tuples representing the rows directly in
What I did was to convert the rows to html with SQLTABLE and then use the
pandas function df = pd.read_html()
Works well.
On Saturday, May 5, 2018 at 8:14:46 PM UTC-7, Massimo Di Pierro wrote:
>
> I would very muck have a db().select_as_pandas() that avoids parsing
> the database response a
I would very muck have a db().select_as_pandas() that avoids parsing
the database response a puts the tuples representing the rows directly in
DB. If nobody beats me on the time, I may get it done next week.
Massimo
On Friday, 4 May 2018 08:26:35 UTC-5, Richard wrote:
>
> By include in pand
By include in pandas I mean add support to pydal in pandas so you can do
something like :
df = pd.DataFrame.from_pydal(db(...).select(...))
Richard
On Fri, May 4, 2018 at 1:10 AM, Jurgis Pralgauskis <
jurgis.pralgaus...@gmail.com> wrote:
> I'd like not to include sth into Pandas, but to adapt P
I'd like not to include sth into Pandas, but to adapt Pandas syntax for DAL
(SELECT part mostly, in my case) :)
--
Jurgis Pralgauskis
tel: 8-616 77613
2018-05-03 04:05 popiet "Richard Vézina" rašė:
I use this to merge join dal query :
# merge_dicts is from here :
#
http://stackoverflow.com/que
I use this to merge join dal query :
# merge_dicts is from here :
#
http://stackoverflow.com/questions/38987/how-can-i-merge-two-python-dictionaries-in-a-single-expression
def merge_dicts(*dict_args):
"""
Given any number of dicts, shallow copy and merge into a new dict,
precedence goe
Bt if I want select cols/filter rows/aggregate/ join tables
- with Pandas syntax directly from DB (for it to work as DAL, not with
another DAL syntax) ?
--
Jurgis Pralgauskis
tel: 8-616 77613
2018-05-02, tr 22:22, Richard Vézina rašė:
> I am not sure I understand what you want...
>
> It a nice
I am not sure I understand what you want...
It a nice thing if Pandas use RAM it means that it use the fastest
component of you computer... It should make Pandas fast...
I am not sure what would involve overloading operators to use pyDAL...
Pandas is used to transform data... If what you do on yo
Hi,
Pandas syntax seems very nice (short), and popular ...
But Pandas eat RAM..., and well, most of data is in DB...
so I wonder, how hard would it be to overload operators to use pyDAL (or
other DAL/ORM)?
what are the main challanges?
--
Resources:
- http://web2py.com
- http://web2py.com/bo
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