From 72c2cb5e6c2170de204d380842cda9525520fd99 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Fri, 20 Dec 2024 15:18:11 +0000 Subject: [PATCH 01/17] init --- apps/labs/posts/narwhals-pycapsule.md | 117 ++++++++++++++++++++++++++ 1 file changed, 117 insertions(+) create mode 100644 apps/labs/posts/narwhals-pycapsule.md diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md new file mode 100644 index 000000000..62e664b14 --- /dev/null +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -0,0 +1,117 @@ +--- +title: 'Arrow PyCapsule Interface and Narwhals: Rust and Tusk for universal dataframe support' +published: December 20, 2024 +authors: [marco-gorelli] +description: 'Support everything, depend on (almost) nothing' +category: [PyData ecosystem] +featuredImage: + src: ... + alt: '' +hero: + imageSrc: ... + imageAlt: '' +--- + +# How the Arrow PyCapsule Interface and Narwhals universal dataframe support + +If you were writing a dataframe-consuming library in 2015, you'd probably have +added pandas support and called it a day. However, it's not 2015, but 2025, so if you do that today you know full well that you'll be inundated with +endless "can you support Polars / DuckDB / PyArrow / Modin / ..." requests. Yet +you have no interest in understanding the subtle differences between them - what can you do? + +Today, you'll learn about exactly what you can do: + +- The Arrow PyCapsule Interface, if you need access to dataframe data from a low-level language like C or Rust. +- Narwhals, if you want to keep your logic in Python. +- Narwhals _and_ the PyCapsule Interface together, if you want it all! + +## `sum_i64_column` - "slow down, Professor!" + +We'll learn about how you make a dataframe-agnostic function `sum_i64_column`. +Here are the requirements: + +- Given a dataframe `df` and a column name `column_name`, it should compute + the sum of all non-null values in that column. +- If that column is not present, or if it is not of type `Int64`, + it should raise an error. +- It should work for a variety of inputs: `pandas.DataFrame`, `polars.DataFrame`, + `duckdb.PyRelation`, and `pyarrow.Table`, without any knowledge of or dependency + on any. + +Summing non-null values isn't rocket science, but do we do so without any +knowledge of the incoming dataframe? + +## Low-level solution: PyCapsule Interface in Rust via PyO3 + +An example of a Rust solution via Pyo3 can be found at [pycapsule-demo/src/lib.rs](https://github.com/MarcoGorelli/pycapsule-demo/blob/6aad64be26ebbfc8526f26695544bfc6436e3266/src/lib.rs#L9-L56). + +This blog post isn't about explaining all the technical details behind the +PyCapsule Interfae (interested readers are welcome to read the docs), but about +its consequences. Check this out: + +```python +>>> import duckdb +>>> import pandas as pd +>>> import polars as pl +>>> import pyarrow as pa +>>> from pycapsule_demo import sum_i64_column +>>> df_pl = pl.DataFrame({'a': [1,1,2], 'b': [4,5,6]}) +>>> df_pd = pd.DataFrame({'a': [1,1,2], 'b': [4,5,6]}) +>>> df_pa = pa.table({'a': [1,1,2], 'b': [4,5,6]}) +>>> rel = duckdb.sql("select * from df_pd") +>>> sum_i64_column(df_pl, column_name="a") +6 +>>> sum_i64_column(df_pd, column_name="a") +6 +>>> sum_i64_column(df_pa, column_name="a") +6 +>>> sum_i64_column(rel, column_name="a") +6 +``` +Our function works agnostically for any of these dataframes, without us having +to write any specialised code for each, and without any knowledge of them! + +This is an example of how standards can enable freedom. The aforementioned +libraries all support a standard known as the +[PyCapsule Interface](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html) +for export - you can verify this by checking that they all have a `__arrow_c_stream__` method: + +```python +>>> import polars as pl +>>> import duckdb +>>> import pandas as pd +>>> import pyarrow as pa +>>> hasattr(pl.DataFrame, '__arrow_c_stream__') +True +>>> hasattr(duckdb.DuckDBPyRelation, '__arrow_c_stream__') +True +>>> hasattr(pd.DataFrame, '__arrow_c_stream__') +True +>>> hasattr(pa.Table, '__arrow_c_stream__') +True +``` + +Because we can access the underlying data in a library-agnostic manner from +Rust, this enables us to write functions for any kind of complex and bespoke logic +which users of any of the above libraries can then all make use of! + +This is nice, but it may seem a little daunting. Sure, it gives us complete +freedom over which algorithms to implement, but what if we don't need all that +freedom and only want to do simple operations from the tools' own Python APIs? + +This is where Narwhals comes into the game. + +## Python solution: Narwhals + +... + +## Narwhals + PyCapsule Interface: Tusk and Rust + +... + +## What about Polars Plugins? + +- Pro: work with Polars LazyFrames, query optimiser can understand them +- Cons: specific to Polars + +# Conclusion From 39433f2ec706daf8cc0c4eed8446ce081433efa0 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Fri, 3 Jan 2025 11:49:00 +0000 Subject: [PATCH 02/17] complete text --- .vscode/settings.json | 4 +- apps/labs/posts/narwhals-pycapsule.md | 107 ++++++++++++++++++++++---- 2 files changed, 92 insertions(+), 19 deletions(-) diff --git a/.vscode/settings.json b/.vscode/settings.json index 57810d348..6ff4c298c 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -3,7 +3,7 @@ "editor.formatOnSave": true, "editor.defaultFormatter": "esbenp.prettier-vscode", "editor.codeActionsOnSave": { - "source.fixAll.eslint": true, - "source.fixAll.format": true + "source.fixAll.eslint": "explicit", + "source.fixAll.format": "explicit" } } diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index 62e664b14..a49dcfd8a 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -15,19 +15,21 @@ hero: # How the Arrow PyCapsule Interface and Narwhals universal dataframe support If you were writing a dataframe-consuming library in 2015, you'd probably have -added pandas support and called it a day. However, it's not 2015, but 2025, so if you do that today you know full well that you'll be inundated with -endless "can you support Polars / DuckDB / PyArrow / Modin / ..." requests. Yet -you have no interest in understanding the subtle differences between them - what can you do? +added pandas support and called it a day. However, it's not 2015, but 2025, so if you do that +today you know full well that you'll be inundated with endless "can you support +Polars / DuckDB / PyArrow / Modin / ..." requests. Yet you have no interest in understanding +the subtle differences between them - what can you do? Today, you'll learn about exactly what you can do: -- The Arrow PyCapsule Interface, if you need access to dataframe data from a low-level language like C or Rust. -- Narwhals, if you want to keep your logic in Python. +- The [Arrow PyCapsule Interface](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html), + if you need access to dataframe data from a low-level language like C or Rust. +- [Narwhals](https://github.com/narwhals-dev/narwhals), if you want to keep your logic in Python. - Narwhals _and_ the PyCapsule Interface together, if you want it all! -## `sum_i64_column` - "slow down, Professor!" +## `agnostic_sum_i64_column` - "slow down, Professor!" -We'll learn about how you make a dataframe-agnostic function `sum_i64_column`. +We'll learn about how you make a dataframe-agnostic function `agnostic_sum_i64_column`. Here are the requirements: - Given a dataframe `df` and a column name `column_name`, it should compute @@ -54,18 +56,18 @@ its consequences. Check this out: >>> import pandas as pd >>> import polars as pl >>> import pyarrow as pa ->>> from pycapsule_demo import sum_i64_column +>>> from pycapsule_demo import agnostic_sum_i64_column >>> df_pl = pl.DataFrame({'a': [1,1,2], 'b': [4,5,6]}) >>> df_pd = pd.DataFrame({'a': [1,1,2], 'b': [4,5,6]}) >>> df_pa = pa.table({'a': [1,1,2], 'b': [4,5,6]}) >>> rel = duckdb.sql("select * from df_pd") ->>> sum_i64_column(df_pl, column_name="a") +>>> agnostic_sum_i64_column(df_pl, column_name="a") 6 ->>> sum_i64_column(df_pd, column_name="a") +>>> agnostic_sum_i64_column(df_pd, column_name="a") 6 ->>> sum_i64_column(df_pa, column_name="a") +>>> agnostic_sum_i64_column(df_pa, column_name="a") 6 ->>> sum_i64_column(rel, column_name="a") +>>> agnostic_sum_i64_column(rel, column_name="a") 6 ``` Our function works agnostically for any of these dataframes, without us having @@ -103,15 +105,86 @@ This is where Narwhals comes into the game. ## Python solution: Narwhals -... +Narwhals is a lightweight, dependency-free and extensible compatibility layer between +dataframe libraries, which lets you write "Dataframe X in -> Dataframe X out"-style +pipelines. To use it to write a dataframe-agnostic function, you need to follow three +steps: -## Narwhals + PyCapsule Interface: Tusk and Rust +- Call `narwhals.from_native` on the user's object. +- Use the [Narwhals API](https://narwhals-dev.github.io/narwhals/api-reference/). +- If you want to return to the user an object of the same kind that they started with, + call `to_native`. -... +In this case, a Narwhals version of `agnostic_sum_i64_column` could be: + +```python +import narwhals as nw +from narwhals.typing import IntoFrame + + +def agnostic_sum_i64_column_narwhals(df_native: IntoFrame, column_name: str) -> int: + df = nw.from_native(df_native) + schema = df.collect_schema() + if column_name not in schema: + msg = f"Column '{column_name}' not found, available columns are: {schema.names()}." + raise ValueError(msg) + if (dtype := schema[column_name]) != nw.Int64: + msg = f"Column '{column_name}' is of type {dtype}, expected Int64" + raise TypeError(msg) + return df[column_name].sum() +``` +Like above, we can now pass different inputs and get the same result: +```python +>>> agnostic_sum_i64_column_narwhals(df_pl, column_name="a") +6 +>>> agnostic_sum_i64_column_narwhals(df_pd, column_name="a") +6 +>>> agnostic_sum_i64_column_narwhals(df_pa, column_name="a") +6 +>>> agnostic_sum_i64_column_narwhals(rel, column_name="a") +6 +``` + +## Narwhals vs PyCapsule Interface: when to use one over the other, and when to use them together + +If the Narwhals API is extensive enough for your use-case, then this is arguably +a simpler and easier solution than writing your own Rust function. On the other hand, +if you write a Rust function using the PyCapsule Interface, then you have complete +control over what do with the data. So, when should you use which? + +Let's cover some scenarios: + +- If you want your dataframe logic to stay completely lazy where possible: use Narwhals. + This is because the PyCapsule Interface requires you to materialise the data into memory. +- You want complete control over which operations happen on your data: use the + PyCapsule Interface. If you have the necessary Rust / C skills, there should be no limit + to how complex and bespoke you make your data processing. +- You want to keep your library logic in pure-Python and without heavy dependencies, so + it's easy to maintain and install: use Narwhals. Packaging a pure-Python project is very + easy, if you need to get Rust or C in there then it becomes trickier. +- You want to do part of your processing in Python, and part of it in Rust - **use both**! + An example of a library which does this is [Vegafusion](https://vegafusion.io/). ## What about Polars Plugins? -- Pro: work with Polars LazyFrames, query optimiser can understand them -- Cons: specific to Polars +We wrote some Rust code earlier to write custom logic. You may have come across another way +to do this in Polars, which is the [Expression Plugin](https://marcogorelli.github.io/polars-plugins-tutorial/) +mechanism. How does that differ from custom code written with the PyCapsule Interface? + +- Pros: Polars Plugins slot in seamlessly with Polars' lazy execution. +- Cons: Polars Plugins are specific to Polars, so won't work with other dataframe libraries. + +Does the ecosystem need a standardised way of writing custom logic in a low-level language such +that it's not necessary to materialise data into memory? Perhaps - watch this space ;) # Conclusion + +As the Dataframe ecosystem grows, so does the need for tools which support any of them as opposed +to being tightly-coupled to pandas. We've learned about how we can use the PyCapsule Interface +to write dataframe-agnostic code from a low-level language such as Rust or C. We also learned +about how we can do that using Narwhals entirely from Python, and when it may makes sense to use +one, the other, or even both approaches together. + +If you would like to contribute with efforts to standardise the data ecosystem and enable innovation, +or would just like to speak with data experts about how we can help your organisation, please +[book a call](https://quansight.com/about-us/#bookacallform) with us, we'd love to hear from you! From 577432de4a4c6f5d39390ef2c43e794b1fe9e281 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Fri, 3 Jan 2025 15:05:53 +0000 Subject: [PATCH 03/17] add images --- apps/labs/posts/narwhals-pycapsule.md | 8 ++++---- .../posts/narwhals-pycapsule/featured.jpg | Bin 0 -> 78426 bytes .../public/posts/narwhals-pycapsule/hero.jpg | Bin 0 -> 456598 bytes 3 files changed, 4 insertions(+), 4 deletions(-) create mode 100755 apps/labs/public/posts/narwhals-pycapsule/featured.jpg create mode 100755 apps/labs/public/posts/narwhals-pycapsule/hero.jpg diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index a49dcfd8a..096b539e0 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -5,11 +5,11 @@ authors: [marco-gorelli] description: 'Support everything, depend on (almost) nothing' category: [PyData ecosystem] featuredImage: - src: ... - alt: '' + src: /posts/narwhals-pycapsule/featured.jpg + alt: 'Cat looking out window, with dataframe logos outside, original image by Lucy Jackline https://unsplash.com/photos/a-cat-sitting-on-a-window-sill-looking-out-a-window-O896LIqr2vc' hero: - imageSrc: ... - imageAlt: '' + imageSrc: /posts/narwhals-pycapsule/hero.jpg + imageAlt: 'Cat looking out window, with dataframe logos outside, original image by Lucy Jackline https://unsplash.com/photos/a-cat-sitting-on-a-window-sill-looking-out-a-window-O896LIqr2vc' --- # How the Arrow PyCapsule Interface and Narwhals universal dataframe support diff --git a/apps/labs/public/posts/narwhals-pycapsule/featured.jpg b/apps/labs/public/posts/narwhals-pycapsule/featured.jpg new file mode 100755 index 0000000000000000000000000000000000000000..6b59891184301f6055e2ba16566bc5e3d15eef7d GIT binary patch literal 78426 zcmbSx1y~%x^5-t@?(PsYxLXJwG_W`XXR!_L5C{_7f&>lj?(S~Eo!|stAiyGlBk$e4 z_wK&$>|Zn6``ha3>8k4L>DuXeUV7dH;3&x{$^n2tAVA^e06cF2=wv;?RsevC3V;;= 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z$1pyG5s%K1G)u-O5Y3Un;L$}=rxL7(#irk=Ef|~?;{cr0&10%%P(9POKI)d>dQnAY z(KlloMb!ou$+`#A2BLc%LM8yE*v|(6sG_G(y9SE+80~AKM^Fo7QfWzJ09bXP6?!nT|v+8&B6*W9rR%dMOI)Bnpq$Y~OQ+_1BQs)$l3XV zDazAzd39Yr-XnDs=uZk>GPpmNS}3VeKtlw&#;YFRChl-~1Vm5kiiUkZShFl1-%yWf zISm^R#)>G^YAKM7H&HAdpr3(|Sq}oeZ{Qc~ovM6mxVV#6*Q`7lq}wVMztqwH0DGRE zTxvHs_Y_e|YR^DqUlzUyX@3YjX+55e;sfH#8$|u(<+Zy;k{shIt@C5+T-Cmpt9XUT zgGRp*x5_0Dw$d}lZnRNbsIF8uv8SSZDc5YS&Cs@Mr6Vpj5sz$RtxG?{tywQx;&)=~ zHmo8tS$mB8QAJ{rB(wN;R=3dSwrdPCeCe3>?;X!Q>y7@&)f#aag;nxF$or;>DYUFq zNk@itXr=wjK|2xm1bhSUpYz37{{Vyw#TN=@TO^Yl;xyf#%8Dx}ppl=c_=m(E8Hw!X zEef{`r8*9{th-%9QN59#DEz~YPQXPJQ7y%GM?SH68|`RO7)rc*%Io)LY8N+mD zk7}RJiYjNO!OX@=a6j0>W!N`Y6;#YPVhAp6ADnPY}Lv(8lA^ ziYa?Z3zZ^hd`h`@$G{JAtyO;nI1Rg1>Uw-2USxW_#~ zKaD8yyfL0Mn=mnq5@@2O)t7LxxQE6T@@-brVvT=`W1O1kBGj~@IsgCw literal 0 HcmV?d00001 From a507051b4797907d3e407266adce603d8d8ddc46 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Fri, 3 Jan 2025 15:14:30 +0000 Subject: [PATCH 04/17] typo --- apps/labs/posts/narwhals-pycapsule.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index 096b539e0..0d6e0ef47 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -48,7 +48,7 @@ knowledge of the incoming dataframe? An example of a Rust solution via Pyo3 can be found at [pycapsule-demo/src/lib.rs](https://github.com/MarcoGorelli/pycapsule-demo/blob/6aad64be26ebbfc8526f26695544bfc6436e3266/src/lib.rs#L9-L56). This blog post isn't about explaining all the technical details behind the -PyCapsule Interfae (interested readers are welcome to read the docs), but about +PyCapsule Interface (interested readers are welcome to read the docs), but about its consequences. Check this out: ```python From 6cc65d6944c7b1b442c93b219b1ceefacc5dbcdb Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Fri, 3 Jan 2025 15:16:09 +0000 Subject: [PATCH 05/17] resize --- .../public/posts/narwhals-pycapsule/hero.jpg | Bin 456598 -> 433241 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/apps/labs/public/posts/narwhals-pycapsule/hero.jpg b/apps/labs/public/posts/narwhals-pycapsule/hero.jpg index 4c22607b41eca7a1f67fd76cd3e072ba937c0634..bb654b55e410da395055e878a1ca13ad5ae66b04 100755 GIT binary patch literal 433241 zcmbSx1za1!_U_{D?i4NVZlx3}MT={339i8l6ewPxxRnB>Xo>`v;!p|{r?^wJ2^va( zNAG>_-uvF~_w0W*nS7Z!Gkd->vpZ*I_kQ7i9Uy+LqOJmW4@&=R?|-D1bN@WUG@qIdGxPt* z{eS!=vGw%!eK1nzL0`nq-pBRIY zA3FcUPyS*%H+Q=Soxe7F+PT^N#r+TXt-qiB14euCfMfif?E@b0%mZd~^>cH6z=#J- z?q+Z80|4mwfAf9qZJi#l-~-0@*4I;dz_Jgn#drJD2x|E&d({`D>MgOkODq{PMf zg!v!b|1bGJ7XC}?{|x^2?LRfnbpO$3D3ss-o%ip$|IYLH2mq27559T#?>w6v0BDW| 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zC@Pp4>AaO759hWU-3pJ118VbiIn!G|p`GOmg5Jh)n2V+MB3bqI#ah1Vt-ZR_L=5dS2v_o@nmCl*PV z%#27u>@rYHmG%csZ4`T+*=%K%^0oT>F!o9|*`s;HCPv%`_+kMH`xC$_>2N4&NGBQ1 z3=I~oVB8&T!`3anD*qtbJT4BJ9I?uxGjk-h7>Q|KdF6?kgE>aW-4& z*=+5QIVnQVYrw;(USBAo%px2uZZAZxC{8o|?OEW_ZC}Ie8V|^(Q#na-Ga~q=&}}BqblRJD*+Cx(MTyy46X0 zDVKGaS3HNE@v86XC^%Dk)@yyx?>@5d>|C3NCCC{?c*&g!I22_yCd9r<(@vsr4ppiY zN}Z^9`Ygf2dlU!<1JzV^1w0IW-_d(xi*?6J+ConJ>!Ew!DPleS>-C7)Ael-ajM1JEjZC}g89F~+Ir`z%k}0s$G7G)z}%cDjW?zj380fIRnf&Km(o#?&-lv3Ek*SG_<|yO{WmI6 zg)hg(L-{cyZP%4a_i_GDRE2U$pJDGo9D7N0s{lRxce$^7dp4Elsu$6emix9A!3KRI zRe-scR{=jT-L9nHU_)_{`2$$?K}J#5hv2MG@0Gf1OLC_`*x@;T*XU{j45FhTe)PF5 z&B94!wla0ib>!KQj_HP_lYEKcqoZ^xqeD??4G)>k1zxeU8%517g!eXd+9?Ek$FF%C z5>GKyDXs1q3dH$M>YL`Z%Ibg~-6be!{*$1VEW%< YL>8_vIUv6h{6m4bt0ZbP>EEya1>lGuvj6}9 From 35e90135282e0427499d1642f1b1912233859eb7 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Fri, 3 Jan 2025 17:07:20 +0000 Subject: [PATCH 07/17] minor fixes --- apps/labs/posts/narwhals-pycapsule.md | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index 0d6e0ef47..37e2a2561 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -1,5 +1,5 @@ --- -title: 'Arrow PyCapsule Interface and Narwhals: Rust and Tusk for universal dataframe support' +title: 'Arrow PyCapsule Interface and Narwhals for universal dataframe support' published: December 20, 2024 authors: [marco-gorelli] description: 'Support everything, depend on (almost) nothing' @@ -12,7 +12,7 @@ hero: imageAlt: 'Cat looking out window, with dataframe logos outside, original image by Lucy Jackline https://unsplash.com/photos/a-cat-sitting-on-a-window-sill-looking-out-a-window-O896LIqr2vc' --- -# How the Arrow PyCapsule Interface and Narwhals universal dataframe support +# How the Arrow PyCapsule Interface and Narwhals universal enable dataframe support If you were writing a dataframe-consuming library in 2015, you'd probably have added pandas support and called it a day. However, it's not 2015, but 2025, so if you do that @@ -20,7 +20,7 @@ today you know full well that you'll be inundated with endless "can you support Polars / DuckDB / PyArrow / Modin / ..." requests. Yet you have no interest in understanding the subtle differences between them - what can you do? -Today, you'll learn about exactly what you can do: +Today, you'll learn about ways to support different kinds of dataframes: - The [Arrow PyCapsule Interface](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html), if you need access to dataframe data from a low-level language like C or Rust. @@ -107,7 +107,7 @@ This is where Narwhals comes into the game. Narwhals is a lightweight, dependency-free and extensible compatibility layer between dataframe libraries, which lets you write "Dataframe X in -> Dataframe X out"-style -pipelines. To use it to write a dataframe-agnostic function, you need to follow three +functions. To use it to write a dataframe-agnostic function, you need to follow three steps: - Call `narwhals.from_native` on the user's object. @@ -115,7 +115,7 @@ steps: - If you want to return to the user an object of the same kind that they started with, call `to_native`. -In this case, a Narwhals version of `agnostic_sum_i64_column` could be: +In this case, a Narwhals version of `agnostic_sum_i64_column` would look like this: ```python import narwhals as nw @@ -155,13 +155,14 @@ control over what do with the data. So, when should you use which? Let's cover some scenarios: - If you want your dataframe logic to stay completely lazy where possible: use Narwhals. - This is because the PyCapsule Interface requires you to materialise the data into memory. + This is because the PyCapsule Interface requires you to materialise the data into memory, + whereas Narwhals has both eager and lazy APIs. - You want complete control over which operations happen on your data: use the PyCapsule Interface. If you have the necessary Rust / C skills, there should be no limit to how complex and bespoke you make your data processing. - You want to keep your library logic in pure-Python and without heavy dependencies, so it's easy to maintain and install: use Narwhals. Packaging a pure-Python project is very - easy, if you need to get Rust or C in there then it becomes trickier. + easy, whereas if you need to get Rust or C in there then it becomes trickier. - You want to do part of your processing in Python, and part of it in Rust - **use both**! An example of a library which does this is [Vegafusion](https://vegafusion.io/). @@ -171,11 +172,13 @@ We wrote some Rust code earlier to write custom logic. You may have come across to do this in Polars, which is the [Expression Plugin](https://marcogorelli.github.io/polars-plugins-tutorial/) mechanism. How does that differ from custom code written with the PyCapsule Interface? -- Pros: Polars Plugins slot in seamlessly with Polars' lazy execution. +- Pros: Polars Plugins slot in seamlessly with Polars' lazy execution. This can enable massive + time and cost savings, see [Reverse-Geocoding in AWS Lambda: Save Time and Money Using Polars Plugins](https://quansight.com/post/reverse-geocoding-aws-lambda-using-polars-plugin/) + for an example. - Cons: Polars Plugins are specific to Polars, so won't work with other dataframe libraries. -Does the ecosystem need a standardised way of writing custom logic in a low-level language such -that it's not necessary to materialise data into memory? Perhaps - watch this space ;) +If Polars is the only library you need to support and would like help writing Polars Plugins, +[we can help](https://quansight.com/about-us/#bookacallform)! # Conclusion From 58428ed83cbb1ad7eac0eca31118eaf853e817f6 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Mon, 6 Jan 2025 13:47:41 +0000 Subject: [PATCH 08/17] resize again --- .../posts/narwhals-pycapsule/featured.jpg | Bin 57905 -> 54992 bytes .../public/posts/narwhals-pycapsule/hero.jpg | Bin 362101 -> 390341 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/apps/labs/public/posts/narwhals-pycapsule/featured.jpg b/apps/labs/public/posts/narwhals-pycapsule/featured.jpg index 1ef55dd4d23edc9a5ed69e83c51fab1362e9adec..ee0934af2ea84f320e0b708201bd168e68b4a54a 100755 GIT binary patch delta 51592 zcmV)HK)t`Q!~@W+1F#|u1IPizvn3740e`Q}}sIO+cYG=E-l zqAbAV1@lK8xXCi1iH} z8%2z1&vm8dQ^rB%KqT>Ai}0H7N7TFmmgWtus7@ihwQHX-ES;Wqk+y@e 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z=Yg1Dy_Pa`H9W z*U23Zg^nu!8&$~Mv~jrFYUP^djgQJ+nz*vqpe@eO-8lg{K)Oqe0aO$!PFWG(bB}Y( z##}9#fK7zU{Y=9Yt0aUdxp^FNDU9tpFtfNC?1taIb!wy(9@;tComw(E?LK+`C_VM2 zoI`Mw@2PI1o%o3g_y)r`Fbl9t!(H?(Jl(X29s=2`(CN)~zqcZYuqF zW|x~$fOs&Zb#p0WY*xbCqk!wx+dG9r8m()oc&Q&+p-X;CTLEZ(0xOtRvQhK#l~2br zDV)&u(sktSe@!PKBt-~Y3Iw7$Q}nqt2eeG3heCV>%c598(MDB=3vca?$h!q8n||(p zW3O8omcuqQj|t^(#W%ZP0j2H_CCxSG>se9Z)8PSz3p=y~KV#J?#gX-m?C>KB-Vq`9_(3@6d!b!Hkr7 z4*m3gPq21bE*UT-iDnZ! z%qB|J%~Q1n(JHczD#DI;2B+oIE5!oeeMtV$`W-w*G++;eT!but*nk}TM#@nEPE7z# zN~JAwQdt3${}Q`WVP)~#O*|8rKvd(5)VVyc(0@L6)zzXR&h+~gzkA=V+1HwIgU{|+ zsrsY_vH5ffvBzWETJdJhrhzCf>~cALiqY`{@N+L4%*BvBa-;MmT7u_n0yVb*$@YGw_Ta=Y~P7qQ6p9A2%)+nb+a}=GMVoI=7I$uyM#nC1Vgg%iu%y z01?|Zz)1~#n9-Odly_AAz=Mgwunsj12G}UX=0CstZ3I&1g{IQ*}%*Kl$^2 DumJWM From 1fc063a33526e144f09b6cc73186ccb87bfe925d Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Mon, 6 Jan 2025 13:54:53 +0000 Subject: [PATCH 09/17] add caveat --- apps/labs/posts/narwhals-pycapsule.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index 37e2a2561..76534b8b3 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -1,6 +1,6 @@ --- title: 'Arrow PyCapsule Interface and Narwhals for universal dataframe support' -published: December 20, 2024 +published: January 1, 2024 authors: [marco-gorelli] description: 'Support everything, depend on (almost) nothing' category: [PyData ecosystem] @@ -97,6 +97,12 @@ Because we can access the underlying data in a library-agnostic manner from Rust, this enables us to write functions for any kind of complex and bespoke logic which users of any of the above libraries can then all make use of! +> **_NOTE:_** How many times can `agnostic_sum_i64_column` be called? Strictly +> speaking, the PyCapsule Interface only guarantees that it can be called once. +> In practice, however, all implementations that we're aware of (except for +> DuckDB) allow for it to be called repeatedly. Discussion about the +> DuckDB implementation is ongoing at https://github.com/duckdb/duckdb/discussions/15536. + This is nice, but it may seem a little daunting. Sure, it gives us complete freedom over which algorithms to implement, but what if we don't need all that freedom and only want to do simple operations from the tools' own Python APIs? From f62627028bd0f33bb17993614de1f21393f90bfa Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Mon, 6 Jan 2025 16:19:09 +0000 Subject: [PATCH 10/17] fix date --- apps/labs/posts/narwhals-pycapsule.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index 76534b8b3..3285866ae 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -1,6 +1,6 @@ --- title: 'Arrow PyCapsule Interface and Narwhals for universal dataframe support' -published: January 1, 2024 +published: January 6, 2025 authors: [marco-gorelli] description: 'Support everything, depend on (almost) nothing' category: [PyData ecosystem] From b66e6677859c1b8b3f2153fc6cd59d3c0ecf9019 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Mon, 6 Jan 2025 16:19:51 +0000 Subject: [PATCH 11/17] retitle --- apps/labs/posts/narwhals-pycapsule.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index 3285866ae..ac8748b20 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -1,5 +1,5 @@ --- -title: 'Arrow PyCapsule Interface and Narwhals for universal dataframe support' +title: 'Universal dataframe support with the Arrow PyCapsule Interface + Narwhals' published: January 6, 2025 authors: [marco-gorelli] description: 'Support everything, depend on (almost) nothing' @@ -12,7 +12,7 @@ hero: imageAlt: 'Cat looking out window, with dataframe logos outside, original image by Lucy Jackline https://unsplash.com/photos/a-cat-sitting-on-a-window-sill-looking-out-a-window-O896LIqr2vc' --- -# How the Arrow PyCapsule Interface and Narwhals universal enable dataframe support +# Universal dataframe support with the Arrow PyCapsule Interface + Narwhals If you were writing a dataframe-consuming library in 2015, you'd probably have added pandas support and called it a day. However, it's not 2015, but 2025, so if you do that From 45a8384be509ffec2085b149183c95ac546d9294 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Mon, 6 Jan 2025 16:45:31 +0000 Subject: [PATCH 12/17] fixup --- apps/labs/posts/narwhals-pycapsule.md | 161 +++++++++++++------------- 1 file changed, 80 insertions(+), 81 deletions(-) diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index ac8748b20..97b4658df 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -14,13 +14,14 @@ hero: # Universal dataframe support with the Arrow PyCapsule Interface + Narwhals -If you were writing a dataframe-consuming library in 2015, you'd probably have -added pandas support and called it a day. However, it's not 2015, but 2025, so if you do that -today you know full well that you'll be inundated with endless "can you support -Polars / DuckDB / PyArrow / Modin / ..." requests. Yet you have no interest in understanding -the subtle differences between them - what can you do? +If you were writing a data science tool in 2015, you'd probably have +added pandas support and called it a day. However, it's not 2015, we've fast-forwarded to 2025, +and so if you do that today you'll be inundated with endless "can you support +Polars / DuckDB / PyArrow / DataFusion / ..." requests. Yet you have no interest in understanding +the subtle differences between them, prefering to focus on the problems which your library set +out to solve. What can you do? -Today, you'll learn about ways to support different kinds of dataframes: +Today, you'll learn about how to create tools which support all kinds of dataframes: - The [Arrow PyCapsule Interface](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html), if you need access to dataframe data from a low-level language like C or Rust. @@ -32,89 +33,82 @@ Today, you'll learn about ways to support different kinds of dataframes: We'll learn about how you make a dataframe-agnostic function `agnostic_sum_i64_column`. Here are the requirements: -- Given a dataframe `df` and a column name `column_name`, it should compute +- Given a dataframe `df` and a column name `column_name`, compute the sum of all non-null values in that column. - If that column is not present, or if it is not of type `Int64`, - it should raise an error. -- It should work for a variety of inputs: `pandas.DataFrame`, `polars.DataFrame`, - `duckdb.PyRelation`, and `pyarrow.Table`, without any knowledge of or dependency - on any. + raise an error. +- `pandas.DataFrame`, `polars.DataFrame`, + `duckdb.PyRelation`, and `pyarrow.Table`, should all be supported without + any of them being required. -Summing non-null values isn't rocket science, but do we do so without any -knowledge of the incoming dataframe? +Summing non-null values isn't rocket science, but how can we do this in a way that all +input libraries are supported without any of them being required? ## Low-level solution: PyCapsule Interface in Rust via PyO3 An example of a Rust solution via Pyo3 can be found at [pycapsule-demo/src/lib.rs](https://github.com/MarcoGorelli/pycapsule-demo/blob/6aad64be26ebbfc8526f26695544bfc6436e3266/src/lib.rs#L9-L56). -This blog post isn't about explaining all the technical details behind the -PyCapsule Interface (interested readers are welcome to read the docs), but about -its consequences. Check this out: - -```python ->>> import duckdb ->>> import pandas as pd ->>> import polars as pl ->>> import pyarrow as pa ->>> from pycapsule_demo import agnostic_sum_i64_column ->>> df_pl = pl.DataFrame({'a': [1,1,2], 'b': [4,5,6]}) ->>> df_pd = pd.DataFrame({'a': [1,1,2], 'b': [4,5,6]}) ->>> df_pa = pa.table({'a': [1,1,2], 'b': [4,5,6]}) ->>> rel = duckdb.sql("select * from df_pd") ->>> agnostic_sum_i64_column(df_pl, column_name="a") -6 ->>> agnostic_sum_i64_column(df_pd, column_name="a") -6 ->>> agnostic_sum_i64_column(df_pa, column_name="a") -6 ->>> agnostic_sum_i64_column(rel, column_name="a") -6 -``` -Our function works agnostically for any of these dataframes, without us having -to write any specialised code for each, and without any knowledge of them! - -This is an example of how standards can enable freedom. The aforementioned -libraries all support a standard known as the -[PyCapsule Interface](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html) -for export - you can verify this by checking that they all have a `__arrow_c_stream__` method: - -```python ->>> import polars as pl ->>> import duckdb ->>> import pandas as pd ->>> import pyarrow as pa ->>> hasattr(pl.DataFrame, '__arrow_c_stream__') -True ->>> hasattr(duckdb.DuckDBPyRelation, '__arrow_c_stream__') -True ->>> hasattr(pd.DataFrame, '__arrow_c_stream__') -True ->>> hasattr(pa.Table, '__arrow_c_stream__') -True -``` - -Because we can access the underlying data in a library-agnostic manner from -Rust, this enables us to write functions for any kind of complex and bespoke logic -which users of any of the above libraries can then all make use of! +The technical details are beyond the scope of this post, but the summary is: + +- We accept any object which implements the + [PyCapsule Interface](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html). + We can check for this by looking for the `__arrow_c_stream__` attribute: + + ```python + >>> import polars as pl + >>> import duckdb + >>> import pandas as pd + >>> import pyarrow as pa + >>> hasattr(pl.DataFrame, '__arrow_c_stream__') + True + >>> hasattr(duckdb.DuckDBPyRelation, '__arrow_c_stream__') + True + >>> hasattr(pd.DataFrame, '__arrow_c_stream__') + True + >>> hasattr(pa.Table, '__arrow_c_stream__') + True + ``` +- The solution is totally agnostic to the exact input, as it respects a standardised interface. + We can verify this by trying to pass different dataframe libraries to the function: + + ```python + >>> import duckdb + >>> import pandas as pd + >>> import polars as pl + >>> import pyarrow as pa + >>> from pycapsule_demo import agnostic_sum_i64_column + >>> df_pl = pl.DataFrame({'a': [1,1,2], 'b': [4,5,6]}) + >>> df_pd = pd.DataFrame({'a': [1,1,2], 'b': [4,5,6]}) + >>> df_pa = pa.table({'a': [1,1,2], 'b': [4,5,6]}) + >>> rel = duckdb.sql("select * from df_pd") + >>> agnostic_sum_i64_column(df_pl, column_name="a") + 6 + >>> agnostic_sum_i64_column(df_pd, column_name="a") + 6 + >>> agnostic_sum_i64_column(df_pa, column_name="a") + 6 + >>> agnostic_sum_i64_column(rel, column_name="a") + 6 + ``` + +Like magic, our function works agnostically for any of these dataframes, +without us having to write any specialised code to handle the subtle differences +between them! This is an example of how standards can enable freedom. > **_NOTE:_** How many times can `agnostic_sum_i64_column` be called? Strictly -> speaking, the PyCapsule Interface only guarantees that it can be called once. -> In practice, however, all implementations that we're aware of (except for -> DuckDB) allow for it to be called repeatedly. Discussion about the +> speaking, the PyCapsule Interface only guarantees that it can be called once for +> any given input. In practice, however, all implementations that we're aware of +> (except for DuckDB) allow for it to be called repeatedly. Discussion about the > DuckDB implementation is ongoing at https://github.com/duckdb/duckdb/discussions/15536. -This is nice, but it may seem a little daunting. Sure, it gives us complete -freedom over which algorithms to implement, but what if we don't need all that -freedom and only want to do simple operations from the tools' own Python APIs? - -This is where Narwhals comes into the game. +If you found the above example a little daunting, you may be wondering if a simpler +solution exists which you can access from Python-land. Enter: Narwhals. ## Python solution: Narwhals Narwhals is a lightweight, dependency-free and extensible compatibility layer between -dataframe libraries, which lets you write "Dataframe X in -> Dataframe X out"-style -functions. To use it to write a dataframe-agnostic function, you need to follow three -steps: +dataframe libraries functions. To use it to write a dataframe-agnostic function, +you need to follow three steps: - Call `narwhals.from_native` on the user's object. - Use the [Narwhals API](https://narwhals-dev.github.io/narwhals/api-reference/). @@ -151,6 +145,9 @@ Like above, we can now pass different inputs and get the same result: 6 ``` +So long as you stick to the Narwhals API, your code will keep working with all major dataframe libraries, +as well as with new ones which may appear in the future! + ## Narwhals vs PyCapsule Interface: when to use one over the other, and when to use them together If the Narwhals API is extensive enough for your use-case, then this is arguably @@ -161,15 +158,15 @@ control over what do with the data. So, when should you use which? Let's cover some scenarios: - If you want your dataframe logic to stay completely lazy where possible: use Narwhals. - This is because the PyCapsule Interface requires you to materialise the data into memory, + The PyCapsule Interface requires you to materialise the data into memory, whereas Narwhals has both eager and lazy APIs. -- You want complete control over which operations happen on your data: use the +- If you want complete control over which operations happen on your data: use the PyCapsule Interface. If you have the necessary Rust / C skills, there should be no limit to how complex and bespoke you make your data processing. -- You want to keep your library logic in pure-Python and without heavy dependencies, so +- If you want to keep your library logic to pure-Python and without heavy dependencies so it's easy to maintain and install: use Narwhals. Packaging a pure-Python project is very - easy, whereas if you need to get Rust or C in there then it becomes trickier. -- You want to do part of your processing in Python, and part of it in Rust - **use both**! + easy, whereas if you need to get Rust or C in there then packaging becomes trickier. +- If you want to do part of your processing in Python, and part of it in Rust - **use both**! An example of a library which does this is [Vegafusion](https://vegafusion.io/). ## What about Polars Plugins? @@ -183,13 +180,15 @@ mechanism. How does that differ from custom code written with the PyCapsule Inte for an example. - Cons: Polars Plugins are specific to Polars, so won't work with other dataframe libraries. -If Polars is the only library you need to support and would like help writing Polars Plugins, +If you know that Polars is the only library you need to support, then Polars Plugins are the preferred +way to write custom user-defined logic. If you'd like to learn how to do that, [we can help](https://quansight.com/about-us/#bookacallform)! # Conclusion -As the Dataframe ecosystem grows, so does the need for tools which support any of them as opposed -to being tightly-coupled to pandas. We've learned about how we can use the PyCapsule Interface +As the Dataframe ecosystem grows, so does the demand for tools which have universal dataframe +support (as opposed to being tightly-coupled to pandas). +We've learned about how to use the PyCapsule Interface to write dataframe-agnostic code from a low-level language such as Rust or C. We also learned about how we can do that using Narwhals entirely from Python, and when it may makes sense to use one, the other, or even both approaches together. From f9b898e2bf9a2b889ce28e8dbc96842ec9a4233d Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Mon, 6 Jan 2025 17:33:44 +0000 Subject: [PATCH 13/17] another round of updates --- apps/labs/posts/narwhals-pycapsule.md | 58 +++++++++++++-------------- 1 file changed, 28 insertions(+), 30 deletions(-) diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index 97b4658df..4186a0148 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -72,10 +72,6 @@ The technical details are beyond the scope of this post, but the summary is: We can verify this by trying to pass different dataframe libraries to the function: ```python - >>> import duckdb - >>> import pandas as pd - >>> import polars as pl - >>> import pyarrow as pa >>> from pycapsule_demo import agnostic_sum_i64_column >>> df_pl = pl.DataFrame({'a': [1,1,2], 'b': [4,5,6]}) >>> df_pd = pd.DataFrame({'a': [1,1,2], 'b': [4,5,6]}) @@ -93,7 +89,7 @@ The technical details are beyond the scope of this post, but the summary is: Like magic, our function works agnostically for any of these dataframes, without us having to write any specialised code to handle the subtle differences -between them! This is an example of how standards can enable freedom. +between them! > **_NOTE:_** How many times can `agnostic_sum_i64_column` be called? Strictly > speaking, the PyCapsule Interface only guarantees that it can be called once for @@ -102,11 +98,11 @@ between them! This is an example of how standards can enable freedom. > DuckDB implementation is ongoing at https://github.com/duckdb/duckdb/discussions/15536. If you found the above example a little daunting, you may be wondering if a simpler -solution exists which you can access from Python-land. Enter: Narwhals. +solution exists which you can develop entirely in Python-land. Enter: Narwhals. ## Python solution: Narwhals -Narwhals is a lightweight, dependency-free and extensible compatibility layer between +Narwhals is a lightweight, dependency-free, and extensible compatibility layer between dataframe libraries functions. To use it to write a dataframe-agnostic function, you need to follow three steps: @@ -115,7 +111,7 @@ you need to follow three steps: - If you want to return to the user an object of the same kind that they started with, call `to_native`. -In this case, a Narwhals version of `agnostic_sum_i64_column` would look like this: +In this case, a Narwhals version of `agnostic_sum_i64_column` looks like this: ```python import narwhals as nw @@ -123,16 +119,18 @@ from narwhals.typing import IntoFrame def agnostic_sum_i64_column_narwhals(df_native: IntoFrame, column_name: str) -> int: - df = nw.from_native(df_native) - schema = df.collect_schema() + lf = nw.from_native(df_native).lazy() + schema = lf.collect_schema() if column_name not in schema: msg = f"Column '{column_name}' not found, available columns are: {schema.names()}." raise ValueError(msg) if (dtype := schema[column_name]) != nw.Int64: msg = f"Column '{column_name}' is of type {dtype}, expected Int64" raise TypeError(msg) + df = lf.collect() return df[column_name].sum() ``` + Like above, we can now pass different inputs and get the same result: ```python >>> agnostic_sum_i64_column_narwhals(df_pl, column_name="a") @@ -153,46 +151,46 @@ as well as with new ones which may appear in the future! If the Narwhals API is extensive enough for your use-case, then this is arguably a simpler and easier solution than writing your own Rust function. On the other hand, if you write a Rust function using the PyCapsule Interface, then you have complete -control over what do with the data. So, when should you use which? +control over the data. So, when should you use which? Let's cover some scenarios: - If you want your dataframe logic to stay completely lazy where possible: use Narwhals. - The PyCapsule Interface requires you to materialise the data into memory, + The PyCapsule Interface requires you to materialise the data into memory immediately, whereas Narwhals has both eager and lazy APIs. -- If you want complete control over which operations happen on your data: use the +- If you want complete control over your data: use the PyCapsule Interface. If you have the necessary Rust / C skills, there should be no limit to how complex and bespoke you make your data processing. - If you want to keep your library logic to pure-Python and without heavy dependencies so it's easy to maintain and install: use Narwhals. Packaging a pure-Python project is very - easy, whereas if you need to get Rust or C in there then packaging becomes trickier. + easy, especially compared with if you need to get Rust or C in there. - If you want to do part of your processing in Python, and part of it in Rust - **use both**! An example of a library which does this is [Vegafusion](https://vegafusion.io/). ## What about Polars Plugins? -We wrote some Rust code earlier to write custom logic. You may have come across another way -to do this in Polars, which is the [Expression Plugin](https://marcogorelli.github.io/polars-plugins-tutorial/) -mechanism. How does that differ from custom code written with the PyCapsule Interface? +We wrote some Rust code earlier to express custom logic. You may have come across another way +to do this in Polars: [Expressions Plugins](https://marcogorelli.github.io/polars-plugins-tutorial/). +How does that differ from writing custom code with the PyCapsule Interface? - Pros: Polars Plugins slot in seamlessly with Polars' lazy execution. This can enable massive time and cost savings, see [Reverse-Geocoding in AWS Lambda: Save Time and Money Using Polars Plugins](https://quansight.com/post/reverse-geocoding-aws-lambda-using-polars-plugin/) for an example. -- Cons: Polars Plugins are specific to Polars, so won't work with other dataframe libraries. +- Cons: Polars Plugins are specific to Polars and so won't work with other dataframe libraries. If you know that Polars is the only library you need to support, then Polars Plugins are the preferred -way to write custom user-defined logic. If you'd like to learn how to do that, -[we can help](https://quansight.com/about-us/#bookacallform)! +way to write custom user-defined logic. If you'd like to learn how, [we can help](https://quansight.com/about-us/#bookacallform)! # Conclusion -As the Dataframe ecosystem grows, so does the demand for tools which have universal dataframe -support (as opposed to being tightly-coupled to pandas). -We've learned about how to use the PyCapsule Interface -to write dataframe-agnostic code from a low-level language such as Rust or C. We also learned -about how we can do that using Narwhals entirely from Python, and when it may makes sense to use -one, the other, or even both approaches together. - -If you would like to contribute with efforts to standardise the data ecosystem and enable innovation, -or would just like to speak with data experts about how we can help your organisation, please -[book a call](https://quansight.com/about-us/#bookacallform) with us, we'd love to hear from you! +As the Dataframe ecosystem grows, so does the demand for tools with universal dataframe +support (as opposed to those tightly-coupled to pandas). We've learned about how to use +the PyCapsule Interface to write dataframe-agnostic code from a low-level language such +as Rust or C. We also learned about how we can do that using Narwhals entirely from +Python. Finally, we discussed when it may makes sense to use one, the other, or even both +approaches together. + +If you would like to contribute to efforts towards standardising the data ecosystem and +enabling innovation, or would just like to speak with data experts about how we can help +your organisation, please [book a call](https://quansight.com/about-us/#bookacallform) +with us, we'd love to hear from you! From 104bbe4adeef9fb806ba846cb3681f1757a79e11 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Mon, 6 Jan 2025 17:44:31 +0000 Subject: [PATCH 14/17] link to import / export --- apps/labs/posts/narwhals-pycapsule.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index 4186a0148..2fb3d1cb5 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -165,7 +165,10 @@ Let's cover some scenarios: it's easy to maintain and install: use Narwhals. Packaging a pure-Python project is very easy, especially compared with if you need to get Rust or C in there. - If you want to do part of your processing in Python, and part of it in Rust - **use both**! - An example of a library which does this is [Vegafusion](https://vegafusion.io/). + An example of a library which does this is [Vegafusion](https://vegafusion.io/). This is + facilitated by the fact that Narwhals exposes the PyCapsule Interface for both + [import](https://narwhals-dev.github.io/narwhals/api-reference/narwhals/#narwhals.from_arrow) + and [export](https://narwhals-dev.github.io/narwhals/api-reference/dataframe/#narwhals.dataframe.DataFrame.__arrow_c_stream__). ## What about Polars Plugins? From b02cf9db2c4407700a2936b4097b9a809f56ceb9 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Wed, 8 Jan 2025 16:14:13 +0000 Subject: [PATCH 15/17] specify ArrowStreamExportable protocol --- apps/labs/posts/narwhals-pycapsule.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index 2fb3d1cb5..d52b89d90 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -28,7 +28,7 @@ Today, you'll learn about how to create tools which support all kinds of datafra - [Narwhals](https://github.com/narwhals-dev/narwhals), if you want to keep your logic in Python. - Narwhals _and_ the PyCapsule Interface together, if you want it all! -## `agnostic_sum_i64_column` - "slow down, Professor!" +## Summing non-null values in a column - "slow down, Professor!" We'll learn about how you make a dataframe-agnostic function `agnostic_sum_i64_column`. Here are the requirements: @@ -50,7 +50,7 @@ An example of a Rust solution via Pyo3 can be found at [pycapsule-demo/src/lib.r The technical details are beyond the scope of this post, but the summary is: -- We accept any object which implements the +- We accept any object which implements the `ArrowStreamExportable` protocol from the [PyCapsule Interface](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html). We can check for this by looking for the `__arrow_c_stream__` attribute: From 8ece81c040867b9c6e8f51c3317875ebb1339e00 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Wed, 8 Jan 2025 16:14:33 +0000 Subject: [PATCH 16/17] update date --- apps/labs/posts/narwhals-pycapsule.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/apps/labs/posts/narwhals-pycapsule.md b/apps/labs/posts/narwhals-pycapsule.md index d52b89d90..4c1ec6dbe 100644 --- a/apps/labs/posts/narwhals-pycapsule.md +++ b/apps/labs/posts/narwhals-pycapsule.md @@ -1,6 +1,6 @@ --- title: 'Universal dataframe support with the Arrow PyCapsule Interface + Narwhals' -published: January 6, 2025 +published: January 8, 2025 authors: [marco-gorelli] description: 'Support everything, depend on (almost) nothing' category: [PyData ecosystem] From 28bbb941a69447319ec818459b39d307ae95f487 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Wed, 8 Jan 2025 16:33:05 +0000 Subject: [PATCH 17/17] undo .vscode/settings.json change --- .vscode/settings.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.vscode/settings.json b/.vscode/settings.json index 6ff4c298c..57810d348 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -3,7 +3,7 @@ "editor.formatOnSave": true, "editor.defaultFormatter": "esbenp.prettier-vscode", "editor.codeActionsOnSave": { - "source.fixAll.eslint": "explicit", - "source.fixAll.format": "explicit" + "source.fixAll.eslint": true, + "source.fixAll.format": true } }

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