Liam Brannigan @braaannigan
Polars course discount: https://t.co/XhxIjPe989 rhosignal.com Ireland Joined October 2014-
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While dt.truncate in Polars maps datetimes to the start of a window we can use dt.round to map a datetime to the nearest invterval. Say we want 30 minute intervals then dt.round maps 00:14 to 00:00 and 00:16 to 00:30 Much more on time series in my course: udemy.com/course/data-an…
You can map datetimes into windows in Polars with dt.truncate. Here we take 10-minute data and map i to 15 minute windows using the start datetime of the window. Handy for manual binning of datetimes! Much more on timeseries in my course: udemy.com/course/data-an…
You can create a Polars dataframe from its printed representation. Handy for copying from examples or sharing a dataframe in a chat. Note: it doesn't work for nested dtypes such as Struct or List so don't start replacing .parquet with .txt just yet
You can do temporal aggs with multiple time series in Polars using group_by_dynamic. Here we want 3-hourly averages by vendor so we pass this columns to group_by argument. This means Polars first does a group_by then does group_by_dynamic on each group docs.pola.rs/py-polars/html…
There is a difference between Polars and Pandas when writing to JSON - in Polars the JSON includes the dtypes of each column as well as the values. So if you rountrip via JSON, you conserve dtypes in Polars but not in Pandas. Not an accident - Polars devs focus on type security
Converting a dataframe to JSON for transfer can be failure-prone as dtypes are not guaranteed to be preserved. Instead we can write the file to Parquet in bytes and transfer the bytes. This guarantees the dtypes and we keep Parquet advantages such as a compressed packet to send
It's really striking how much faster Polars has gotten at writing Parquet files with row group statistics. If you've got: - large datasets where - you normally filter by certain columns this can really speed your file I/O up including from cloud storage
After my O'Reilly workshop in early May I'll have a slot free for Polars training/consulting - DM me if you'd like to arrange something for your company. Potential topics include Pandas-to-Polars, time series for quants, large datasets with Polars...
Polars has more support for working with Hive partitioned data in lazy mode. Here for example we make a simple structure like - dataset - id=1 - one.parquet - id=2 - two.parquet We can then filter on the partition in a lazy query More here: udemy.com/course/data-an…
What's your most boomer habit for writing code? Mine seems to be working in a stand-alone terminal rather than doing everything through VSCode
Can't wait til we get quantum computing and most people use it to parse CSVs
You can write lazy queries to parquet with sink_parquet in Polars. This works in streaming mode to process large datasets. The method has seen some improvements in recent versions of Polars - I've just converted a 15 Gb CSV to parquet in minutes Course: udemy.com/course/data-an…
You can still have nice plots, even if you work in the command line with Polars and hvPlot. You simply create the plot object with .plot on a df then call hvplot.show to create a temporary server to render the plot with Bokeh For much more: udemy.com/course/data-an…
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155 Followers 376 Following PhD Student in #DeepLearning for #TimeSeriesAnalysis at IRIMAS @UHA68 in Mulhouse France 🇫🇷, core developer in @aeon_toolkit #comics a proud 🇱🇧🇵🇸@braaannigan Other frameworks with a different execution flow does not have this challenge. With @Panel_org you seldom need caching per session. And for caching globally we use memory by default and you can opt in on diskcache.
@mr_le_fox @braaannigan @solara_dev Yes, that would be way more natural in @solara_dev where your script is executed once, but your component per request.
@braaannigan I think the issue is that Streamlit uses caching as a control flow tool which is pretty awkward. For example Shiny doesn't need caching like this because it minimally re-renders the application. youtu.be/vanSXTOe7ag?si…
@braaannigan @solara_dev I mean: you shouldn’t reload a dataframe from disk on every request but keep it in memory.
😲
You can create a Polars dataframe from its printed representation. Handy for copying from examples or sharing a dataframe in a chat. Note: it doesn't work for nested dtypes such as Struct or List so don't start replacing .parquet with .txt just yet
@braaannigan using Microsoft-owned products (GitHub mostly, VSCode to edit) may be my most boomer habit 😉
OK, so here is the thing. Everyone is dunking on this survey since it purportedly shows how the West is becoming soft and decadent, and Chinese kids have their priorities straight. With all due respect, that’s total midwit analysis. I am one of those kids who grew up in a…
@braaannigan I looooove vscode. my boomer habit at this rate is focusing on stack overflow answers rather than copilots/chatgpt
@braaannigan The rest will just use "Quantum Computing for Python in Excel"
@braaannigan It’s still funky to see filter use keyword arguments instead of a boolean expression, especially when the name of the kwarg is a builtin function! Otherwise very cool!
@braaannigan I won't replace jetbrains products. I know that you kids like vscode, but I like things that just work...
@braaannigan So actually you can do it with (almost) any Python object. import panel as pn pn.extension() pn.panel(some_python_object).show()
@braaannigan Better to optimise them whilst still settling in, compare to predict_proba that is misleading functionality in scikit learn and has been around for over decade.
.collect(gpu=True)
We're very happy to announce that @nvidia and Polars will work together to bring GPU acceleration to Polars DataFrames! 🚀💯 pola.rs/posts/polars-o…
We're very happy to announce that @nvidia and Polars will work together to bring GPU acceleration to Polars DataFrames! 🚀💯 pola.rs/posts/polars-o…
Scikit-learn is old and slow, it is good to see alternatives such as “Polars least squares extension” appearing. Imagine speeding up your regression by up to x270 times! What we really need is scikit replacement for ML, speeding things up similar to what Nixtla did for…
Seeing a bunch of ML/AI éminences grises trying to ratio each other on a slow Monday morning is why I am never ever leaving this hellsite.
@braaannigan I used tweepy for the COVID bot, worked nicely for me to attach images, do threads