On 03/04/2022 02.28, anthony.flury wrote:
> On 27/03/2022 15:59, dn wrote:
>
>> What is code coverage?
>> In the simplest words, code coverage is a measure of exhaustiveness of a
>> test suite. 100% code coverage means that a system is fully tested.
>
> Sorry, but that is a gross over-simplificat
On 2022-04-01, Christian Gollwitzer wrote:
> Am 01.04.22 um 01:26 schrieb Grant Edwards:
>> On 2022-03-31, Christian Gollwitzer wrote:
>>> Davmail is written in Java, not Python, but basically this should
>>> not matter if you only use it.
>>
>> Have you used it with OWA as the protocol?
>
> At
On 27/03/2022 15:59, dn wrote:
What is code coverage?
In the simplest words, code coverage is a measure of exhaustiveness of a
test suite. 100% code coverage means that a system is fully tested.
Sorry, but that is a gross over-simplification.
100% coverage means that you have tested all of th
i would like to convert in my flask app an SQL query to an plotly pie chart
using pandas. this is my code :
def query_tickets_status() :
query_result = pd.read_sql ("""
SELECT COUNT(*)count_status, status
FROM tickets
GROUP BY status""", con = mydc_db)
Abdellah ALAOUI ISMAILI wrote:
def query_tickets_status() :
query_result = pd.read_sql ("""
SELECT COUNT(*)count_status, status
FROM tickets
GROUP BY status""", con = mydc_db)
return query_result
labels_statut = query_tickets_status['status']
la
A proposal. Very often dict are used as a deeply nested carrier of
data, usually decoded from JSON. Sometimes I needed to get some of
this data, something like this:
data["users"][0]["address"]["street"]
What about something like this instead?
data.get_deep("users", 0, "address", "street")
and
