datasets/pr_8137/en/_app/immutable/nodes/53.c5dfd4e8.js

import{s as Bs,n as Ys,o as Qs}from"../chunks/scheduler.d75c11ed.js";import{S as Zs,i as Es,e as p,s as e,c as r,h as vs,a as o,d as t,b as n,f as xs,g as i,j as M,k as Gs,l as Xs,m as l,n as c,t as m,o as h,p as u}from"../chunks/index.4ec9dfe9.js";import{C as Hs,H as S,E as zs}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.1fd9202e.js";import{C as H}from"../chunks/CodeBlock.9181a37c.js";function Ls(bs){let j,D,z,F,d,P,f,V,y,gs=`This document is a quick introduction to using <code>datasets</code> with Polars, with a particular focus on how to process
datasets using Polars functions, and how to convert a dataset to Polars or from Polars.`,W,b,Ts="This is particularly useful as it allows fast zero-copy operations, since both <code>datasets</code> and Polars use Arrow under the hood.",A,g,N,T,Js="By default, datasets return regular Python objects: integers, floats, strings, lists, etc.",K,J,ws='To get Polars DataFrames or Series instead, you can set the format of the dataset to <code>polars</code> using <a href="/docs/datasets/pr_8137/en/package_reference/main_classes#datasets.Dataset.with_format">Dataset.with_format()</a>:',O,w,ss,U,Us="This also works for <code>IterableDataset</code> objects obtained e.g. using <code>load_dataset(..., streaming=True)</code>:",as,$,ts,_,ls,I,$s='Polars functions are generally faster than regular hand-written python functions, and therefore they are a good option to optimize data processing. You can use Polars functions to process a dataset in <a href="/docs/datasets/pr_8137/en/package_reference/main_classes#datasets.Dataset.map">Dataset.map()</a> or <a href="/docs/datasets/pr_8137/en/package_reference/main_classes#datasets.Dataset.filter">Dataset.filter()</a>:',es,C,ns,k,_s="We use <code>batched=True</code> because it is faster to process batches of data in Polars rather than row by row. It’s also possible to use <code>batch_size=</code> in <code>map()</code> to set the size of each <code>df</code>.",ps,q,Is='This also works for <a href="/docs/datasets/pr_8137/en/package_reference/main_classes#datasets.IterableDataset.map">IterableDataset.map()</a> and <a href="/docs/datasets/pr_8137/en/package_reference/main_classes#datasets.IterableDataset.filter">IterableDataset.filter()</a>.',os,R,rs,x,Cs='Many functions are available in Polars and for any data type: string, floats, integers, etc. You can find the full list <a href="https://docs.pola.rs/api/python/stable/reference/expressions/functions.html" rel="nofollow">here</a>. Those functions are written in Rust and run on batches of data which enables fast data processing.',is,G,ks="Here is an example that shows a 5x speed boost using Polars instead of a regular python function to extract solutions from a LLM reasoning dataset:",cs,B,ms,Y,hs,Q,qs="To import data from Polars, you can use <code>Dataset.from_polars()</code>:",us,Z,Ms,E,Rs="And you can use <code>Dataset.to_polars()</code> to export a Dataset to a Polars DataFrame:",js,v,ds,X,fs,L,ys;return d=new Hs({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),f=new S({props:{title:"Use with Polars",local:"use-with-polars",headingTag:"h1"}}),g=new S({props:{title:"Dataset format",local:"dataset-format",headingTag:"h2"}}),w=new H({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset
<span class="hljs-meta">&gt;&gt;&gt; </span>data = {<span class="hljs-string">&quot;col_0&quot;</span>: [<span class="hljs-string">&quot;a&quot;</span>, <span class="hljs-string">&quot;b&quot;</span>, <span class="hljs-string">&quot;c&quot;</span>, <span class="hljs-string">&quot;d&quot;</span>], <span class="hljs-string">&quot;col_1&quot;</span>: [<span class="hljs-number">0.</span>, <span class="hljs-number">0.</span>, <span class="hljs-number">1.</span>, <span class="hljs-number">1.</span>]}
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = Dataset.from_dict(data)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = ds.with_format(<span class="hljs-string">&quot;polars&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds[<span class="hljs-number">0</span>]       <span class="hljs-comment"># pl.DataFrame</span>
shape: (<span class="hljs-number">1</span>, <span class="hljs-number">2</span>)
┌───────┬───────┐
│ col_0 ┆ col_1 │
│ ---   ┆ ---   │
│ <span class="hljs-built_in">str</span>   ┆ f64   │
╞═══════╪═══════╡
│ a     ┆ <span class="hljs-number">0.0</span>   │
└───────┴───────┘
<span class="hljs-meta">&gt;&gt;&gt; </span>ds[:<span class="hljs-number">2</span>]      <span class="hljs-comment"># pl.DataFrame</span>
shape: (<span class="hljs-number">2</span>, <span class="hljs-number">2</span>)
┌───────┬───────┐
│ col_0 ┆ col_1 │
│ ---   ┆ ---   │
│ <span class="hljs-built_in">str</span>   ┆ f64   │
╞═══════╪═══════╡
│ a     ┆ <span class="hljs-number">0.0</span>   │
│ b     ┆ <span class="hljs-number">0.0</span>   │
└───────┴───────┘
<span class="hljs-meta">&gt;&gt;&gt; </span>ds[<span class="hljs-string">&quot;data&quot;</span>]  <span class="hljs-comment"># pl.Series</span>
shape: (<span class="hljs-number">4</span>,)
Series: <span class="hljs-string">&#x27;col_0&#x27;</span> [<span class="hljs-built_in">str</span>]
[
        <span class="hljs-string">&quot;a&quot;</span>
        <span class="hljs-string">&quot;b&quot;</span>
        <span class="hljs-string">&quot;c&quot;</span>
        <span class="hljs-string">&quot;d&quot;</span>
]`,wrap:!1}}),$=new H({props:{code:"ZHMlMjAlM0QlMjBkcy53aXRoX2Zvcm1hdCglMjJwb2xhcnMlMjIpJTBBZm9yJTIwZGYlMjBpbiUyMGRzLml0ZXIoYmF0Y2hfc2l6ZSUzRDIpJTNBJTBBJTIwJTIwJTIwJTIwcHJpbnQoZGYpJTBBJTIwJTIwJTIwJTIwYnJlYWs=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>ds = ds.with_format(<span class="hljs-string">&quot;polars&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> df <span class="hljs-keyword">in</span> ds.<span class="hljs-built_in">iter</span>(batch_size=<span class="hljs-number">2</span>):
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(df)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">break</span>
shape: (<span class="hljs-number">2</span>, <span class="hljs-number">2</span>)
┌───────┬───────┐
│ col_0 ┆ col_1 │
│ ---   ┆ ---   │
│ <span class="hljs-built_in">str</span>   ┆ f64   │
╞═══════╪═══════╡
│ a     ┆ <span class="hljs-number">0.0</span>   │
│ b     ┆ <span class="hljs-number">0.0</span>   │
└───────┴───────┘`,wrap:!1}}),_=new S({props:{title:"Process data",local:"process-data",headingTag:"h2"}}),C=new H({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> polars <span class="hljs-keyword">as</span> pl
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset
<span class="hljs-meta">&gt;&gt;&gt; </span>data = {<span class="hljs-string">&quot;col_0&quot;</span>: [<span class="hljs-string">&quot;a&quot;</span>, <span class="hljs-string">&quot;b&quot;</span>, <span class="hljs-string">&quot;c&quot;</span>, <span class="hljs-string">&quot;d&quot;</span>], <span class="hljs-string">&quot;col_1&quot;</span>: [<span class="hljs-number">0.</span>, <span class="hljs-number">0.</span>, <span class="hljs-number">1.</span>, <span class="hljs-number">1.</span>]}
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = Dataset.from_dict(data)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = ds.with_format(<span class="hljs-string">&quot;polars&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = ds.<span class="hljs-built_in">map</span>(<span class="hljs-keyword">lambda</span> df: df.with_columns(pl.col(<span class="hljs-string">&quot;col_1&quot;</span>).add(<span class="hljs-number">1</span>).alias(<span class="hljs-string">&quot;col_2&quot;</span>)), batched=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds[:<span class="hljs-number">2</span>]
shape: (<span class="hljs-number">2</span>, <span class="hljs-number">3</span>)
┌───────┬───────┬───────┐
│ col_0 ┆ col_1 ┆ col_2 │
│ ---   ┆ ---   ┆ ---   │
│ <span class="hljs-built_in">str</span>   ┆ f64   ┆ f64   │
╞═══════╪═══════╪═══════╡
│ a     ┆ <span class="hljs-number">0.0</span>   ┆ <span class="hljs-number">1.0</span>   │
│ b     ┆ <span class="hljs-number">0.0</span>   ┆ <span class="hljs-number">1.0</span>   │
└───────┴───────┴───────┘
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = ds.<span class="hljs-built_in">filter</span>(<span class="hljs-keyword">lambda</span> df: df[<span class="hljs-string">&quot;col_0&quot;</span>] == <span class="hljs-string">&quot;b&quot;</span>, batched=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds[<span class="hljs-number">0</span>]
shape: (<span class="hljs-number">1</span>, <span class="hljs-number">3</span>)
┌───────┬───────┬───────┐
│ col_0 ┆ col_1 ┆ col_2 │
│ ---   ┆ ---   ┆ ---   │
│ <span class="hljs-built_in">str</span>   ┆ f64   ┆ f64   │
╞═══════╪═══════╪═══════╡
│ b     ┆ <span class="hljs-number">0.0</span>   ┆ <span class="hljs-number">1.0</span>   │
└───────┴───────┴───────┘`,wrap:!1}}),R=new S({props:{title:"Example: data extraction",local:"example-data-extraction",headingTag:"h3"}}),B=new H({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

ds = load_dataset(<span class="hljs-string">&quot;ServiceNow-AI/R1-Distill-SFT&quot;</span>, <span class="hljs-string">&quot;v0&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)

<span class="hljs-comment"># Using a regular python function</span>
pattern = re.<span class="hljs-built_in">compile</span>(<span class="hljs-string">&quot;boxed\\\\{(.*)\\\\}&quot;</span>)
result_ds = ds.<span class="hljs-built_in">map</span>(<span class="hljs-keyword">lambda</span> x: {<span class="hljs-string">&quot;value_solution&quot;</span>: m.group(<span class="hljs-number">1</span>) <span class="hljs-keyword">if</span> (m:=pattern.search(x[<span class="hljs-string">&quot;solution&quot;</span>])) <span class="hljs-keyword">else</span> <span class="hljs-literal">None</span>})
<span class="hljs-comment"># Time: 10s</span>

<span class="hljs-comment"># Using a Polars function</span>
expr = pl.col(<span class="hljs-string">&quot;solution&quot;</span>).<span class="hljs-built_in">str</span>.extract(<span class="hljs-string">&quot;boxed\\\\{(.*)\\\\}&quot;</span>).alias(<span class="hljs-string">&quot;value_solution&quot;</span>)
result_ds = ds.with_format(<span class="hljs-string">&quot;polars&quot;</span>).<span class="hljs-built_in">map</span>(<span class="hljs-keyword">lambda</span> df: df.with_columns(expr), batched=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Time: 2s</span>`,wrap:!1}}),Y=new S({props:{title:"Import or Export from Polars",local:"import-or-export-from-polars",headingTag:"h2"}}),Z=new H({props:{code:"ZHMlMjAlM0QlMjBEYXRhc2V0LmZyb21fcG9sYXJzKGRmKQ==",highlighted:"ds = Dataset.from_polars(df)",wrap:!1}}),v=new H({props:{code:"ZGYlMjAlM0QlMjBEYXRhc2V0LnRvX3BvbGFycyhkcyk=",highlighted:"df = Dataset.to_polars(ds)",wrap:!1}}),X=new zs({props:{source:"https://github.com/huggingface/datasets/blob/main/docs/source/use_with_polars.mdx"}}),{c(){j=p("meta"),D=e(),z=p("p"),F=e(),r(d.$$.fragment),P=e(),r(f.$$.fragment),V=e(),y=p("p"),y.innerHTML=gs,W=e(),b=p("p"),b.innerHTML=Ts,A=e(),r(g.$$.fragment),N=e(),T=p("p"),T.textContent=Js,K=e(),J=p("p"),J.innerHTML=ws,O=e(),r(w.$$.fragment),ss=e(),U=p("p"),U.innerHTML=Us,as=e(),r($.$$.fragment),ts=e(),r(_.$$.fragment),ls=e(),I=p("p"),I.innerHTML=$s,es=e(),r(C.$$.fragment),ns=e(),k=p("p"),k.innerHTML=_s,ps=e(),q=p("p"),q.innerHTML=Is,os=e(),r(R.$$.fragment),rs=e(),x=p("p"),x.innerHTML=Cs,is=e(),G=p("p"),G.textContent=ks,cs=e(),r(B.$$.fragment),ms=e(),r(Y.$$.fragment),hs=e(),Q=p("p"),Q.innerHTML=qs,us=e(),r(Z.$$.fragment),Ms=e(),E=p("p"),E.innerHTML=Rs,js=e(),r(v.$$.fragment),ds=e(),r(X.$$.fragment),fs=e(),L=p("p"),this.h()},l(s){const a=vs("svelte-u9bgzb",document.head);j=o(a,"META",{name:!0,content:!0}),a.forEach(t),D=n(s),z=o(s,"P",{}),xs(z).forEach(t),F=n(s),i(d.$$.fragment,s),P=n(s),i(f.$$.fragment,s),V=n(s),y=o(s,"P",{"data-svelte-h":!0}),M(y)!=="svelte-16svw65"&&(y.innerHTML=gs),W=n(s),b=o(s,"P",{"data-svelte-h":!0}),M(b)!=="svelte-ypkj15"&&(b.innerHTML=Ts),A=n(s),i(g.$$.fragment,s),N=n(s),T=o(s,"P",{"data-svelte-h":!0}),M(T)!=="svelte-ej8pz8"&&(T.textContent=Js),K=n(s),J=o(s,"P",{"data-svelte-h":!0}),M(J)!=="svelte-1xcsdw3"&&(J.innerHTML=ws),O=n(s),i(w.$$.fragment,s),ss=n(s),U=o(s,"P",{"data-svelte-h":!0}),M(U)!=="svelte-2qljfh"&&(U.innerHTML=Us),as=n(s),i($.$$.fragment,s),ts=n(s),i(_.$$.fragment,s),ls=n(s),I=o(s,"P",{"data-svelte-h":!0}),M(I)!=="svelte-k1ojsd"&&(I.innerHTML=$s),es=n(s),i(C.$$.fragment,s),ns=n(s),k=o(s,"P",{"data-svelte-h":!0}),M(k)!=="svelte-gjuiu1"&&(k.innerHTML=_s),ps=n(s),q=o(s,"P",{"data-svelte-h":!0}),M(q)!=="svelte-29os9h"&&(q.innerHTML=Is),os=n(s),i(R.$$.fragment,s),rs=n(s),x=o(s,"P",{"data-svelte-h":!0}),M(x)!=="svelte-88snhv"&&(x.innerHTML=Cs),is=n(s),G=o(s,"P",{"data-svelte-h":!0}),M(G)!=="svelte-1seljjf"&&(G.textContent=ks),cs=n(s),i(B.$$.fragment,s),ms=n(s),i(Y.$$.fragment,s),hs=n(s),Q=o(s,"P",{"data-svelte-h":!0}),M(Q)!=="svelte-1djltp1"&&(Q.innerHTML=qs),us=n(s),i(Z.$$.fragment,s),Ms=n(s),E=o(s,"P",{"data-svelte-h":!0}),M(E)!=="svelte-1odzkea"&&(E.innerHTML=Rs),js=n(s),i(v.$$.fragment,s),ds=n(s),i(X.$$.fragment,s),fs=n(s),L=o(s,"P",{}),xs(L).forEach(t),this.h()},h(){Gs(j,"name","hf:doc:metadata"),Gs(j,"content",Ss)},m(s,a){Xs(document.head,j),l(s,D,a),l(s,z,a),l(s,F,a),c(d,s,a),l(s,P,a),c(f,s,a),l(s,V,a),l(s,y,a),l(s,W,a),l(s,b,a),l(s,A,a),c(g,s,a),l(s,N,a),l(s,T,a),l(s,K,a),l(s,J,a),l(s,O,a),c(w,s,a),l(s,ss,a),l(s,U,a),l(s,as,a),c($,s,a),l(s,ts,a),c(_,s,a),l(s,ls,a),l(s,I,a),l(s,es,a),c(C,s,a),l(s,ns,a),l(s,k,a),l(s,ps,a),l(s,q,a),l(s,os,a),c(R,s,a),l(s,rs,a),l(s,x,a),l(s,is,a),l(s,G,a),l(s,cs,a),c(B,s,a),l(s,ms,a),c(Y,s,a),l(s,hs,a),l(s,Q,a),l(s,us,a),c(Z,s,a),l(s,Ms,a),l(s,E,a),l(s,js,a),c(v,s,a),l(s,ds,a),c(X,s,a),l(s,fs,a),l(s,L,a),ys=!0},p:Ys,i(s){ys||(m(d.$$.fragment,s),m(f.$$.fragment,s),m(g.$$.fragment,s),m(w.$$.fragment,s),m($.$$.fragment,s),m(_.$$.fragment,s),m(C.$$.fragment,s),m(R.$$.fragment,s),m(B.$$.fragment,s),m(Y.$$.fragment,s),m(Z.$$.fragment,s),m(v.$$.fragment,s),m(X.$$.fragment,s),ys=!0)},o(s){h(d.$$.fragment,s),h(f.$$.fragment,s),h(g.$$.fragment,s),h(w.$$.fragment,s),h($.$$.fragment,s),h(_.$$.fragment,s),h(C.$$.fragment,s),h(R.$$.fragment,s),h(B.$$.fragment,s),h(Y.$$.fragment,s),h(Z.$$.fragment,s),h(v.$$.fragment,s),h(X.$$.fragment,s),ys=!1},d(s){s&&(t(D),t(z),t(F),t(P),t(V),t(y),t(W),t(b),t(A),t(N),t(T),t(K),t(J),t(O),t(ss),t(U),t(as),t(ts),t(ls),t(I),t(es),t(ns),t(k),t(ps),t(q),t(os),t(rs),t(x),t(is),t(G),t(cs),t(ms),t(hs),t(Q),t(us),t(Ms),t(E),t(js),t(ds),t(fs),t(L)),t(j),u(d,s),u(f,s),u(g,s),u(w,s),u($,s),u(_,s),u(C,s),u(R,s),u(B,s),u(Y,s),u(Z,s),u(v,s),u(X,s)}}}const Ss='{"title":"Use with Polars","local":"use-with-polars","sections":[{"title":"Dataset format","local":"dataset-format","sections":[],"depth":2},{"title":"Process data","local":"process-data","sections":[{"title":"Example: data extraction","local":"example-data-extraction","sections":[],"depth":3}],"depth":2},{"title":"Import or Export from Polars","local":"import-or-export-from-polars","sections":[],"depth":2}],"depth":1}';function Ds(bs){return Qs(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class As extends Zs{constructor(j){super(),Es(this,j,Ds,Ls,Bs,{})}}export{As as component};