#polars — Public Fediverse posts

Vous cherchez des idées de lecture pour l'été? Jetez un œil au 24 Heures du jour (avec ma pomme et en exergue "Sophocle, c'est du thriller") :beetjoy: #polars #thriller #samedilecture #suisseromande

Vous cherchez des idées de lecture pour l'été? Jetez un œil au 24 Heures du jour (avec ma pomme et en exergue "Sophocle, c'est du thriller") :beetjoy: #polars #thriller #samedilecture #suisseromande

Vous cherchez des idées de lecture pour l'été? Jetez un œil au 24 Heures du jour (avec ma pomme et en exergue "Sophocle, c'est du thriller") :beetjoy: #polars #thriller #samedilecture #suisseromande

#NVIDIA published a blog post where they present GQE, a GPU-based query engine. Querying data from databases with GPU accelleration is beyond cool, and will certainly optimize the storage requirements for #bigdata due to enabling for more efficient compression algorithms. Here is the blog post:

Designing GPU-Accelerated Query Engines with NVIDIA GQE

#bigdata #databases #polars #pandas #datascience

#NVIDIA published a blog post where they present GQE, a GPU-based query engine. Querying data from databases with GPU accelleration is beyond cool, and will certainly optimize the storage requirements for #bigdata due to enabling for more efficient compression algorithms. Here is the blog post:

Designing GPU-Accelerated Query Engines with NVIDIA GQE

#bigdata #databases #polars #pandas #datascience

#NVIDIA published a blog post where they present GQE, a GPU-based query engine. Querying data from databases with GPU accelleration is beyond cool, and will certainly optimize the storage requirements for #bigdata due to enabling for more efficient compression algorithms. Here is the blog post:

Designing GPU-Accelerated Query Engines with NVIDIA GQE

#bigdata #databases #polars #pandas #datascience

#NVIDIA published a blog post where they present GQE, a GPU-based query engine. Querying data from databases with GPU accelleration is beyond cool, and will certainly optimize the storage requirements for #bigdata due to enabling for more efficient compression algorithms. Here is the blog post:

Designing GPU-Accelerated Query Engines with NVIDIA GQE

#bigdata #databases #polars #pandas #datascience

#NVIDIA published a blog post where they present GQE, a GPU-based query engine. Querying data from databases with GPU accelleration is beyond cool, and will certainly optimize the storage requirements for #bigdata due to enabling for more efficient compression algorithms. Here is the blog post:

Designing GPU-Accelerated Query Engines with NVIDIA GQE

#bigdata #databases #polars #pandas #datascience

Polars is a lightning fast DataFrame library/in-memory query engine with parallel execution and cache efficiency. And now you can use is with the tidyverse syntax: https://www.tidypolars.etiennebacher.com/ #rstats #polars #optimisation

Polars is a lightning fast DataFrame library/in-memory query engine with parallel execution and cache efficiency. And now you can use is with the tidyverse syntax: https://www.tidypolars.etiennebacher.com/ #rstats #polars #optimisation

Polars is a lightning fast DataFrame library/in-memory query engine with parallel execution and cache efficiency. And now you can use is with the tidyverse syntax: https://www.tidypolars.etiennebacher.com/ #rstats #polars #optimisation

Polars is a lightning fast DataFrame library/in-memory query engine with parallel execution and cache efficiency. And now you can use is with the tidyverse syntax: https://www.tidypolars.etiennebacher.com/ #rstats #polars #optimisation

Polars is a lightning fast DataFrame library/in-memory query engine with parallel execution and cache efficiency. And now you can use is with the tidyverse syntax: https://www.tidypolars.etiennebacher.com/ #rstats #polars #optimisation

As a big fan of #Polars when doing #ETL pipelines, and processing #data, I am happy to see them having their distributed engine available for #Kubernetes deployments.

Read the blog post here:
https://pola.rs/posts/polars-distributed-available-on-kubernetes/

#datascience #python #rust

As a big fan of #Polars when doing #ETL pipelines, and processing #data, I am happy to see them having their distributed engine available for #Kubernetes deployments.

Read the blog post here:
https://pola.rs/posts/polars-distributed-available-on-kubernetes/

#datascience #python #rust

As a big fan of #Polars when doing #ETL pipelines, and processing #data, I am happy to see them having their distributed engine available for #Kubernetes deployments.

Read the blog post here:
https://pola.rs/posts/polars-distributed-available-on-kubernetes/

#datascience #python #rust

As a big fan of #Polars when doing #ETL pipelines, and processing #data, I am happy to see them having their distributed engine available for #Kubernetes deployments.

Read the blog post here:
https://pola.rs/posts/polars-distributed-available-on-kubernetes/

#datascience #python #rust

As a big fan of #Polars when doing #ETL pipelines, and processing #data, I am happy to see them having their distributed engine available for #Kubernetes deployments.

Read the blog post here:
https://pola.rs/posts/polars-distributed-available-on-kubernetes/

#datascience #python #rust

So I moved into industry 1.5 months ago, which has meant a proper switch from R :rstats: to Python :python: (I love both). Here are a few observations for statistics-related stuff in this switch (mainly GLMs, statistical inference, contrasts)

- #polars is really great, I love LazyFrames & streaming millions of rows of parquet files, categorical data, missing data.
- I don't really like using #statsmodels, the interface is clunky and the formula API is unfinished

1/n

#DataScience #Statistics

So I moved into industry 1.5 months ago, which has meant a proper switch from R :rstats: to Python :python: (I love both). Here are a few observations for statistics-related stuff in this switch (mainly GLMs, statistical inference, contrasts)

- #polars is really great, I love LazyFrames & streaming millions of rows of parquet files, categorical data, missing data.
- I don't really like using #statsmodels, the interface is clunky and the formula API is unfinished

1/n

#DataScience #Statistics

So I moved into industry 1.5 months ago, which has meant a proper switch from R :rstats: to Python :python: (I love both). Here are a few observations for statistics-related stuff in this switch (mainly GLMs, statistical inference, contrasts)

- #polars is really great, I love LazyFrames & streaming millions of rows of parquet files, categorical data, missing data.
- I don't really like using #statsmodels, the interface is clunky and the formula API is unfinished

1/n

#DataScience #Statistics

So I moved into industry 1.5 months ago, which has meant a proper switch from R :rstats: to Python :python: (I love both). Here are a few observations for statistics-related stuff in this switch (mainly GLMs, statistical inference, contrasts)

- #polars is really great, I love LazyFrames & streaming millions of rows of parquet files, categorical data, missing data.
- I don't really like using #statsmodels, the interface is clunky and the formula API is unfinished

1/n

#DataScience #Statistics

So I moved into industry 1.5 months ago, which has meant a proper switch from R :rstats: to Python :python: (I love both). Here are a few observations for statistics-related stuff in this switch (mainly GLMs, statistical inference, contrasts)

- #polars is really great, I love LazyFrames & streaming millions of rows of parquet files, categorical data, missing data.
- I don't really like using #statsmodels, the interface is clunky and the formula API is unfinished

1/n

#DataScience #Statistics

Alltså... Polars LazyFrames är ju min nya bästa kompis. Har haft "lära sig polars" på att-göra-listan i tusen år... får igen gräma sig över att det tagit så länge att sätta tänderna i det.

#python #polars #datascience

Alltså... Polars LazyFrames är ju min nya bästa kompis. Har haft "lära sig polars" på att-göra-listan i tusen år... får igen gräma sig över att det tagit så länge att sätta tänderna i det.

#python #polars #datascience

Alltså... Polars LazyFrames är ju min nya bästa kompis. Har haft "lära sig polars" på att-göra-listan i tusen år... får igen gräma sig över att det tagit så länge att sätta tänderna i det.

#python #polars #datascience

Alltså... Polars LazyFrames är ju min nya bästa kompis. Har haft "lära sig polars" på att-göra-listan i tusen år... får igen gräma sig över att det tagit så länge att sätta tänderna i det.

#python #polars #datascience

Alltså... Polars LazyFrames är ju min nya bästa kompis. Har haft "lära sig polars" på att-göra-listan i tusen år... får igen gräma sig över att det tagit så länge att sätta tänderna i det.

#python #polars #datascience

@thealexmerced thanks! Added to wish list in manning. Better 2buy there vs Amazon to get the ai features?

I guess Manning got rid of old option to buy coins 2 read individual pages? was a cool feature 2 bad.

Thanks for reminder about #datafusion i guess it & #polars have excellent #iceberg support & can be used from #rust

I was thinking about replacing a #pyspark glue job with a rust #lambda on #aws

Just found your excellent medium account. Best of luck at your upcoming talk!

@thealexmerced thanks! Added to wish list in manning. Better 2buy there vs Amazon to get the ai features?

I guess Manning got rid of old option to buy coins 2 read individual pages? was a cool feature 2 bad.

Thanks for reminder about #datafusion i guess it & #polars have excellent #iceberg support & can be used from #rust

I was thinking about replacing a #pyspark glue job with a rust #lambda on #aws

Just found your excellent medium account. Best of luck at your upcoming talk!

@thealexmerced congrats on new #iceberg book! Looks good & good timing with all the new features coming out lately. Do you cover much re: cloud architecture or is that too specific (& which cloud would you choose anyway)?

saw you wrote #polars book as well. What do you think about using polars from #rust ? should work even better than from #python i would think but wasn’t sure. For career security in #ai age i was thinking about switching my #dataengineering focus to rust — thoughts?

@thealexmerced congrats on new #iceberg book! Looks good & good timing with all the new features coming out lately. Do you cover much re: cloud architecture or is that too specific (& which cloud would you choose anyway)?

saw you wrote #polars book as well. What do you think about using polars from #rust ? should work even better than from #python i would think but wasn’t sure. For career security in #ai age i was thinking about switching my #dataengineering focus to rust — thoughts?

Just updated my small :python: package polarsgrid for tidyverse-style expand_grid functionality in python polars 🐻‍❄️

https://pypi.org/project/polarsgrid/

Version 0.4.0 has unit tests, more efficient row-index computation, better CI.

Major new user-facing improvement is that you can now enter any iterable as the input, not just lists. So create your grid with range(10) instead of list(range(10)).

See this preprint for use-case
https://arxiv.org/abs/2509.11741

#python #polars #rstats

Just updated my small :python: package polarsgrid for tidyverse-style expand_grid functionality in python polars 🐻‍❄️

https://pypi.org/project/polarsgrid/

Version 0.4.0 has unit tests, more efficient row-index computation, better CI.

Major new user-facing improvement is that you can now enter any iterable as the input, not just lists. So create your grid with range(10) instead of list(range(10)).

See this preprint for use-case
https://arxiv.org/abs/2509.11741

#python #polars #rstats

Just updated my small :python: package polarsgrid for tidyverse-style expand_grid functionality in python polars 🐻‍❄️

https://pypi.org/project/polarsgrid/

Version 0.4.0 has unit tests, more efficient row-index computation, better CI.

Major new user-facing improvement is that you can now enter any iterable as the input, not just lists. So create your grid with range(10) instead of list(range(10)).

See this preprint for use-case
https://arxiv.org/abs/2509.11741

#python #polars #rstats

Just updated my small :python: package polarsgrid for tidyverse-style expand_grid functionality in python polars 🐻‍❄️

https://pypi.org/project/polarsgrid/

Version 0.4.0 has unit tests, more efficient row-index computation, better CI.

Major new user-facing improvement is that you can now enter any iterable as the input, not just lists. So create your grid with range(10) instead of list(range(10)).

See this preprint for use-case
https://arxiv.org/abs/2509.11741

#python #polars #rstats

Just updated my small :python: package polarsgrid for tidyverse-style expand_grid functionality in python polars 🐻‍❄️

https://pypi.org/project/polarsgrid/

Version 0.4.0 has unit tests, more efficient row-index computation, better CI.

Major new user-facing improvement is that you can now enter any iterable as the input, not just lists. So create your grid with range(10) instead of list(range(10)).

See this preprint for use-case
https://arxiv.org/abs/2509.11741

#python #polars #rstats

Pandasはもう古い？2026年のPythonデータ分析ライブラリを整理してみた
https://qiita.com/YASUHARA-Wataru/items/5a8a16a2b7e91c3536a8?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #Python #機械学習 #pandas #データ分析 #Polars

Pandasはもう古い？2026年のPythonデータ分析ライブラリを整理してみた
https://qiita.com/YASUHARA-Wataru/items/5a8a16a2b7e91c3536a8?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #Python #機械学習 #pandas #データ分析 #Polars

RE: https://mastodon.energy/@catalystcoop/116165894396269040

It's easy to query the bulk Parquet outputs with #polars or @duckdb or @pandas_dev

So far we've only applied basic transforms -- real dtypes & NULL values, standardized categorical values, etc. But the upgrade to a modern cloud-native format is a huge improvement over nested zipfiles.

And a big thanks to the folks at the AWS Open Data Registry for giving us a TB of free public storage so we can publish big data like this conveniently.

Let us know what you think, and what additional kinds of data cleaning, entity classification, record linkage, or additional aggregated outputs would be useful.

#OpenData #Energy #FOSS

RE: https://mastodon.energy/@catalystcoop/116165894396269040

It's easy to query the bulk Parquet outputs with #polars or @duckdb or @pandas_dev

So far we've only applied basic transforms -- real dtypes & NULL values, standardized categorical values, etc. But the upgrade to a modern cloud-native format is a huge improvement over nested zipfiles.

And a big thanks to the folks at the AWS Open Data Registry for giving us a TB of free public storage so we can publish big data like this conveniently.

Let us know what you think, and what additional kinds of data cleaning, entity classification, record linkage, or additional aggregated outputs would be useful.

#OpenData #Energy #FOSS

RE: https://mastodon.energy/@catalystcoop/116165894396269040

It's easy to query the bulk Parquet outputs with #polars or @duckdb or @pandas_dev

So far we've only applied basic transforms -- real dtypes & NULL values, standardized categorical values, etc. But the upgrade to a modern cloud-native format is a huge improvement over nested zipfiles.

And a big thanks to the folks at the AWS Open Data Registry for giving us a TB of free public storage so we can publish big data like this conveniently.

Let us know what you think, and what additional kinds of data cleaning, entity classification, record linkage, or additional aggregated outputs would be useful.

#OpenData #Energy #FOSS

RE: https://mastodon.energy/@catalystcoop/116165894396269040

It's easy to query the bulk Parquet outputs with #polars or @duckdb or @pandas_dev

So far we've only applied basic transforms -- real dtypes & NULL values, standardized categorical values, etc. But the upgrade to a modern cloud-native format is a huge improvement over nested zipfiles.

And a big thanks to the folks at the AWS Open Data Registry for giving us a TB of free public storage so we can publish big data like this conveniently.

Let us know what you think, and what additional kinds of data cleaning, entity classification, record linkage, or additional aggregated outputs would be useful.

#OpenData #Energy #FOSS

RE: https://mastodon.energy/@catalystcoop/116165894396269040

It's easy to query the bulk Parquet outputs with #polars or @duckdb or @pandas_dev

So far we've only applied basic transforms -- real dtypes & NULL values, standardized categorical values, etc. But the upgrade to a modern cloud-native format is a huge improvement over nested zipfiles.

And a big thanks to the folks at the AWS Open Data Registry for giving us a TB of free public storage so we can publish big data like this conveniently.

Let us know what you think, and what additional kinds of data cleaning, entity classification, record linkage, or additional aggregated outputs would be useful.

#OpenData #Energy #FOSS

Here is how you can explore massive datasets from the terminal 💯

🌀 **datui** — A high-performance TUI for analyzing tabular data

🔥 Query with SQL, render charts, transform data & stream huge files with Polars

🦀 Written in Rust & built with @ratatui_rs

⭐ GitHub: https://github.com/derekwisong/datui

#rustlang #ratatui #tui #data #analytics #polars #cli #devtools