Michel Blancard

http://tljh.jupyter.org/en/latest/howto/admin/resource-estimation.html suggests the memory for each user is around 150-180MB (127MB reported by nbresuse with a margin of 20-40%). Why do you take the figure of 200MB? I added a...

Downgrading temporarily npm (to 8.10) fixed this issue for me.

Any update on this issue ? The type is still optional for v11.1.0 : https://github.com/stripe/stripe-node/blob/v11.1.0/types/2022-11-15/Invoices.d.ts#L43

Hi @MarcoGorelli , thanks for you analysis. I was in holidays so I didn't answer in time. However, here are a few arguments you might want to consider. Let's look...

Here is a toy example: ```javascript const Hapi = require('@hapi/hapi'); const Joi = require('joi'); const Blipp = require('blipp'); const Inert = require('@hapi/inert'); const Vision = require('@hapi/vision'); const HapiSwagger = require('hapi-swagger')...

For the moment, I use this temporary fix before the group by: ```python first_timestamp = df["t"][0] floored = floor_timestamp(timestamp=first_timestamp, offset=5) prefix = pl.DataFrame( data={ "t": pl.Series([floored]), "v": [0.0], } )...

Thanks for the solution! It solves my problem when `every` is also daily. But if `every` is monthly, this offsets the label of the windows. I believe that there is...

Of course: ```python from datetime import UTC, datetime, timedelta import polars as pl df = pl.DataFrame( data={ "t": pl.Series( [ datetime(2024, 2, 28, 3, 0, tzinfo=UTC), datetime(2024, 2, 29, 3,...