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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Polars Masterclass — The Rust DataFrame Engine</title>
<meta property="og:title" content="Polars Masterclass — The Rust DataFrame Engine">
<meta property="og:description" content="Deep dive into the Polars DataFrame engine — lazy evaluation, multi-threaded analytics in Rust.">
<meta property="og:image" content="../mathsGraph.jpg">
<meta property="og:type" content="article">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link href="https://fonts.googleapis.com/css2?family=Anton&family=Courier+Prime:ital,wght@0,400;0,700;1,400&family=DM+Mono:ital,wght@0,400;0,500;1,400&family=Barlow:wght@300;400;500;600&display=swap" rel="stylesheet">
<style>
:root {
--paper: #f2ece0;
--paper2: #ebe4d5;
--paper3: #e0d8c8;
--ink: #1c1a16;
--ink2: #4a4640;
--ink3: #8a8278;
--ink4: #b8b0a4;
--rust: #cc4400;
--rust2: #e05a1a;
--rust-bg: #fdf0e8;
--rust-bd: #f0c4a8;
--blue: #1a5fa8;
--blue-bg: #e8f0f8;
--blue-bd: #a8c4e0;
--green: #2a7a48;
--green-bg: #e8f4ec;
--green-bd: #a0d0b4;
--violet: #6b3fa0;
--violet-bg:#f0ebf8;
--violet-bd:#c8aadc;
--red: #a02020;
--red-bg: #faeaea;
--border: #c8bfb0;
--border2: #d8d0c4;
--code-bg: #1c1a16;
--mono: 'DM Mono', monospace;
--serif: 'Courier Prime', monospace;
--sans: 'Barlow', sans-serif;
--display: 'Anton', sans-serif;
--grid-color: rgba(180,160,130,0.25);
}
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
html { scroll-behavior: smooth; }
body {
background-color: var(--paper);
background-image:
linear-gradient(var(--grid-color) 1px, transparent 1px),
linear-gradient(90deg, var(--grid-color) 1px, transparent 1px);
background-size: 28px 28px;
color: var(--ink);
font-family: var(--sans);
font-size: 14.5px;
line-height: 1.7;
min-height: 100vh;
overflow-x: hidden;
}
/* ── TITLE BLOCK HEADER (engineering drawing style) ── */
header {
position: sticky;
top: 0;
z-index: 200;
background: var(--ink);
border-bottom: 3px solid var(--rust);
}
.header-inner {
max-width: 1440px;
margin: 0 auto;
padding: 0 1.5rem;
display: flex;
align-items: stretch;
height: 52px;
}
.logo-block {
display: flex;
align-items: center;
gap: 12px;
padding-right: 1.5rem;
border-right: 1px solid rgba(255,255,255,0.1);
flex-shrink: 0;
}
.logo-symbol {
font-family: var(--display);
font-size: 2rem;
color: var(--rust2);
line-height: 1;
letter-spacing: -0.02em;
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.logo-sub {
font-family: var(--mono);
font-size: 0.6rem;
color: rgba(255,255,255,0.4);
letter-spacing: 0.12em;
text-transform: uppercase;
line-height: 1.4;
}
.logo-ver {
font-family: var(--mono);
font-size: 0.65rem;
color: var(--rust2);
letter-spacing: 0.06em;
}
nav {
display: flex;
align-items: stretch;
gap: 0;
overflow-x: auto;
scrollbar-width: none;
flex: 1;
padding-left: 1.5rem;
}
nav::-webkit-scrollbar { display: none; }
.nav-btn {
background: none;
border: none;
border-right: 1px solid rgba(255,255,255,0.06);
color: rgba(255,255,255,0.45);
font-family: var(--mono);
font-size: 0.68rem;
letter-spacing: 0.1em;
padding: 0 1.1rem;
cursor: pointer;
white-space: nowrap;
transition: all 0.12s;
text-transform: uppercase;
position: relative;
}
.nav-btn::after {
content: '';
position: absolute;
bottom: 0; left: 0; right: 0;
height: 2px;
background: var(--rust2);
transform: scaleX(0);
transition: transform 0.15s;
}
.nav-btn:hover { color: rgba(255,255,255,0.85); }
.nav-btn:hover::after { transform: scaleX(1); }
.nav-btn.active { color: var(--rust2); }
.nav-btn.active::after { transform: scaleX(1); }
/* ── LAYOUT ── */
.wrapper {
max-width: 1440px;
margin: 0 auto;
padding: 2rem;
position: relative;
}
section { display: none; animation: fadeIn 0.2s ease; }
section.active { display: block; }
@keyframes fadeIn {
from { opacity: 0; transform: translateY(6px); }
to { opacity: 1; transform: translateY(0); }
}
/* ── ENGINEERING DRAWING HEADER ── */
.section-header {
display: grid;
grid-template-columns: 1fr auto;
align-items: stretch;
border: 2px solid var(--ink);
margin-bottom: 2rem;
background: var(--paper2);
}
.sh-main {
padding: 1.25rem 1.5rem;
border-right: 2px solid var(--ink);
}
.sh-meta {
padding: 0.75rem 1.25rem;
min-width: 180px;
display: flex;
flex-direction: column;
justify-content: space-between;
gap: 0.5rem;
}
.sh-meta-row {
display: flex;
flex-direction: column;
}
.sh-meta-label {
font-family: var(--mono);
font-size: 0.58rem;
letter-spacing: 0.12em;
text-transform: uppercase;
color: var(--ink3);
border-bottom: 1px solid var(--border);
padding-bottom: 2px;
margin-bottom: 2px;
}
.sh-meta-val {
font-family: var(--mono);
font-size: 0.72rem;
color: var(--ink);
font-weight: 500;
}
.section-title {
font-family: var(--display);
font-size: 2.8rem;
letter-spacing: 0.04em;
line-height: 1;
color: var(--ink);
margin-bottom: 0.5rem;
}
.section-title em { color: var(--rust); font-style: normal; }
.section-sub {
font-size: 0.88rem;
color: var(--ink2);
font-weight: 300;
max-width: 680px;
line-height: 1.6;
}
/* ── GRIDS ── */
.g2 { display: grid; grid-template-columns: 1fr 1fr; gap: 1.5rem; }
.g3 { display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 1.25rem; }
.g12 { display: grid; grid-template-columns: 1fr 2fr; gap: 1.5rem; }
.g21 { display: grid; grid-template-columns: 2fr 1fr; gap: 1.5rem; }
@media (max-width: 960px) { .g2,.g3,.g12,.g21 { grid-template-columns: 1fr; } }
/* ── CARDS ── */
.card {
background: var(--paper2);
border: 1.5px solid var(--border);
padding: 1.25rem 1.5rem;
position: relative;
}
.card::before {
content: attr(data-label);
position: absolute;
top: -1px; left: 1rem;
font-family: var(--mono);
font-size: 0.6rem;
letter-spacing: 0.1em;
text-transform: uppercase;
background: var(--rust);
color: white;
padding: 1px 8px;
display: block;
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.card-title {
font-family: var(--mono);
font-size: 0.72rem;
letter-spacing: 0.1em;
text-transform: uppercase;
color: var(--ink3);
margin-bottom: 0.75rem;
margin-top: 0.25rem;
display: flex;
align-items: center;
gap: 0.5rem;
}
/* ── CODE BLOCKS ── */
.code-wrap {
background: var(--code-bg);
border: none;
margin: 1rem 0;
position: relative;
font-size: 0;
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.code-header {
display: flex;
align-items: center;
justify-content: space-between;
background: #2a2820;
padding: 0.4rem 1rem;
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}
.code-fname {
font-family: var(--mono);
font-size: 0.68rem;
color: #8a8278;
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}
.code-lang {
font-family: var(--mono);
font-size: 0.6rem;
color: var(--rust2);
letter-spacing: 0.1em;
text-transform: uppercase;
}
pre {
padding: 1.1rem 1.25rem;
overflow-x: auto;
font-family: var(--mono);
font-size: 0.8rem;
line-height: 1.8;
color: #d4cec4;
tab-size: 2;
scrollbar-width: thin;
scrollbar-color: #3a3830 transparent;
}
pre::-webkit-scrollbar { height: 4px; }
pre::-webkit-scrollbar-thumb { background: #3a3830; }
/* Syntax */
.kw { color: #f07060; }
.fn { color: #72c0e8; }
.st { color: #b0d870; }
.cm { color: #5a5650; font-style: italic; }
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.nm { color: #c090e0; }
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.nu { color: #f0a060; }
.dc { color: #80e060; }
.py { color: var(--rust2); font-weight: 500; }
.ty { color: #90e090; }
.ch { color: #f0d080; }
/* ── CALLOUTS ── */
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border: 1.5px solid;
padding: 0.8rem 1rem;
margin: 1rem 0;
font-size: 0.87rem;
line-height: 1.6;
position: relative;
}
.callout strong {
display: block;
font-family: var(--mono);
font-size: 0.65rem;
letter-spacing: 0.12em;
text-transform: uppercase;
margin-bottom: 0.35rem;
}
.c-rust { border-color: var(--rust-bd); background: var(--rust-bg); color: var(--rust); }
.c-blue { border-color: var(--blue-bd); background: var(--blue-bg); color: var(--blue); }
.c-green { border-color: var(--green-bd); background: var(--green-bg); color: var(--green); }
.c-violet { border-color: var(--violet-bd); background: var(--violet-bg); color: var(--violet); }
.c-red { border-color: #e0c0c0; background: var(--red-bg); color: var(--red); }
/* ── BADGES ── */
.badge {
display: inline-flex;
align-items: center;
font-family: var(--mono);
font-size: 0.6rem;
padding: 2px 7px;
letter-spacing: 0.06em;
font-weight: 500;
border: 1px solid;
}
.b-rust { background: var(--rust-bg); color: var(--rust); border-color: var(--rust-bd); }
.b-blue { background: var(--blue-bg); color: var(--blue); border-color: var(--blue-bd); }
.b-green { background: var(--green-bg); color: var(--green); border-color: var(--green-bd); }
.b-violet { background: var(--violet-bg); color: var(--violet); border-color: var(--violet-bd); }
/* ── TABLES ── */
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th {
font-family: var(--mono);
font-size: 0.63rem;
letter-spacing: 0.1em;
text-transform: uppercase;
color: var(--ink3);
border-bottom: 2px solid var(--ink);
border-top: 1.5px solid var(--ink);
padding: 0.5rem 0.85rem;
text-align: left;
background: var(--paper3);
}
td {
padding: 0.55rem 0.85rem;
border-bottom: 1px solid var(--border2);
vertical-align: top;
color: var(--ink2);
}
tr:last-child td { border-bottom: 1.5px solid var(--ink); }
tr:hover td { background: var(--rust-bg); }
td:first-child { color: var(--ink); font-family: var(--mono); font-size: 0.78rem; }
code, td code, p code, li code, span code {
font-family: var(--mono);
font-size: 0.8em;
background: var(--paper3);
border: 1px solid var(--border);
padding: 0 4px;
color: var(--rust);
}
/* ── PILLS / TABS ── */
.pills { display: flex; gap: 0; margin-bottom: 1.5rem; border: 1.5px solid var(--border); }
.pill {
font-family: var(--mono);
font-size: 0.67rem;
padding: 0.4rem 1rem;
cursor: pointer;
border: none;
border-right: 1px solid var(--border);
background: var(--paper2);
color: var(--ink3);
transition: all 0.12s;
letter-spacing: 0.06em;
text-transform: uppercase;
white-space: nowrap;
}
.pill:last-child { border-right: none; }
.pill:hover { background: var(--rust-bg); color: var(--rust); }
.pill.active { background: var(--rust); color: white; }
.tab-pane { display: none; }
.tab-pane.active { display: block; }
/* ── STAT GRID ── */
.stat-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(120px, 1fr));
border: 1.5px solid var(--ink);
margin-bottom: 2rem;
}
.stat {
padding: 1.1rem 1rem;
text-align: center;
border-right: 1px solid var(--border);
background: var(--paper2);
transition: background 0.12s;
}
.stat:last-child { border-right: none; }
.stat:hover { background: var(--rust-bg); }
.stat-num {
font-family: var(--display);
font-size: 2rem;
letter-spacing: 0.02em;
line-height: 1;
margin-bottom: 0.2rem;
}
.stat-label {
font-family: var(--mono);
font-size: 0.6rem;
color: var(--ink3);
letter-spacing: 0.1em;
text-transform: uppercase;
}
/* ── COMPARISON BRIDGE ── */
.bridge {
display: grid;
grid-template-columns: 1fr 48px 1fr;
gap: 0;
align-items: center;
border: 1.5px solid var(--border);
margin: 0.5rem 0;
background: var(--paper2);
transition: background 0.12s;
}
.bridge:hover { background: var(--rust-bg); }
.bridge-left, .bridge-right {
padding: 0.5rem 1rem;
font-family: var(--mono);
font-size: 0.78rem;
line-height: 1.4;
}
.bridge-left { color: var(--ink3); }
.bridge-right { color: var(--rust); border-left: 1px solid var(--border); }
.bridge-mid {
text-align: center;
color: var(--ink4);
font-size: 0.9rem;
border-left: 1px solid var(--border);
border-right: 1px solid var(--border);
padding: 0.5rem 0;
background: var(--paper3);
}
/* ── PERF BAR ── */
.perf-row {
display: grid;
grid-template-columns: 160px 1fr 80px;
gap: 1rem;
align-items: center;
padding: 0.55rem 0;
border-bottom: 1px solid var(--border2);
}
.perf-label { font-family: var(--mono); font-size: 0.73rem; color: var(--ink2); }
.perf-track { height: 8px; background: var(--paper3); border: 1px solid var(--border); }
.perf-fill { height: 100%; transition: width 1.2s cubic-bezier(0.4,0,0.2,1); }
.perf-val { font-family: var(--mono); font-size: 0.7rem; color: var(--ink3); text-align: right; }
/* ── METHOD CHAIN VISUAL ── */
.chain {
display: flex;
flex-direction: column;
gap: 0;
border: 1.5px solid var(--border);
margin: 1rem 0;
overflow: hidden;
}
.chain-step {
display: grid;
grid-template-columns: 28px 1fr;
border-bottom: 1px solid var(--border2);
transition: background 0.1s;
}
.chain-step:last-child { border-bottom: none; }
.chain-step:hover { background: var(--rust-bg); }
.chain-num {
background: var(--paper3);
border-right: 1px solid var(--border);
display: flex;
align-items: center;
justify-content: center;
font-family: var(--mono);
font-size: 0.6rem;
color: var(--ink4);
padding: 0.5rem 0;
}
.chain-content {
padding: 0.5rem 1rem;
font-family: var(--mono);
font-size: 0.77rem;
line-height: 1.5;
}
.chain-method { color: var(--rust); font-weight: 500; }
.chain-type { color: var(--blue); }
.chain-note { color: var(--ink3); font-style: italic; font-size: 0.7rem; }
/* ── LAZY PLAN DIAGRAM ── */
.plan-box {
border: 1.5px solid var(--border);
padding: 0.65rem 1rem;
margin: 2px 0;
background: var(--paper2);
display: flex;
align-items: center;
gap: 0.75rem;
font-family: var(--mono);
font-size: 0.75rem;
transition: background 0.1s;
cursor: default;
}
.plan-box:hover { background: var(--rust-bg); }
.plan-icon { width: 18px; height: 18px; flex-shrink: 0; border: 1.5px solid; display: flex; align-items: center; justify-content: center; font-size: 0.55rem; font-weight: 700; }
.plan-name { color: var(--ink); font-weight: 500; flex: 1; }
.plan-detail { color: var(--ink3); font-size: 0.68rem; }
.plan-arrow { text-align: center; color: var(--ink4); font-family: var(--mono); font-size: 0.8rem; padding: 1px 0; }
.pi-scan { border-color: var(--blue); color: var(--blue); background: var(--blue-bg); }
.pi-filter { border-color: var(--rust); color: var(--rust); background: var(--rust-bg); }
.pi-select { border-color: var(--violet); color: var(--violet); background: var(--violet-bg); }
.pi-join { border-color: var(--green); color: var(--green); background: var(--green-bg); }
.pi-agg { border-color: var(--rust); color: var(--rust); background: var(--rust-bg); }
.pi-sort { border-color: var(--ink3); color: var(--ink3); background: var(--paper3); }
.pi-sink { border-color: var(--ink); color: white; background: var(--ink); }
/* ── DTYPE TABLE ── */
.dtype-grid {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(200px, 1fr));
gap: 1px;
background: var(--border);
border: 1.5px solid var(--border);
margin: 1rem 0;
}
.dtype-cell {
background: var(--paper2);
padding: 0.55rem 0.85rem;
display: flex;
align-items: baseline;
gap: 0.5rem;
}
.dtype-name { font-family: var(--mono); font-size: 0.75rem; color: var(--rust); font-weight: 500; }
.dtype-sub { font-family: var(--mono); font-size: 0.65rem; color: var(--ink3); }
/* ── SEPARATOR ── */
.sep { border: none; border-top: 1.5px solid var(--border); margin: 2rem 0; }
.sep-heavy { border-top: 2px solid var(--ink); }
/* ── TEXT ── */
p { margin-bottom: 0.9rem; color: var(--ink2); font-weight: 300; }
p:last-child { margin-bottom: 0; }
h3 { font-family: var(--display); font-size: 1.5rem; letter-spacing: 0.04em; color: var(--ink); margin: 1.75rem 0 0.6rem; }
h3:first-child { margin-top: 0; }
h4 { font-family: var(--mono); font-size: 0.72rem; color: var(--ink3); letter-spacing: 0.1em; text-transform: uppercase; margin: 1.2rem 0 0.5rem; }
ul, ol { padding-left: 1.5rem; color: var(--ink2); font-weight: 300; }
ul li, ol li { margin-bottom: 0.3rem; }
strong { color: var(--ink); font-weight: 600; }
::-webkit-scrollbar { width: 5px; height: 5px; }
::-webkit-scrollbar-track { background: var(--paper3); }
::-webkit-scrollbar-thumb { background: var(--border); }
</style>
</head>
<body>
<nav style="font-family:'DM Mono',monospace;font-size:0.7rem;padding:8px 16px;background:#1c1a16;color:#b8b0a4;border-bottom:1px solid #333;letter-spacing:0.03em;position:sticky;top:0;z-index:9999;">
<a href="../index.html" style="color:#cc4400;text-decoration:none;">KeGG</a>
<span style="color:#555;margin:0 6px;">/</span>
<span style="color:#b8b0a4;">Masterclasses</span>
<span style="color:#555;margin:0 6px;">/</span>
<span style="color:#f2ece0;">Polars Masterclass — The Rust DataFrame Engine</span>
</nav>
<header>
<div class="header-inner">
<div class="logo-block">
<div class="logo-symbol">PL</div>
<div>
<div style="font-family:var(--mono);font-size:0.72rem;color:rgba(255,255,255,0.7);letter-spacing:0.08em">POLARS</div>
<div class="logo-sub">Rust DataFrame Engine</div>
<div class="logo-ver">v1.x STABLE</div>
</div>
</div>
<nav>
<button class="nav-btn active" onclick="show('orient',this)">ORIENT</button>
<button class="nav-btn" onclick="show('lazy',this)">LAZY API</button>
<button class="nav-btn" onclick="show('exprs',this)">EXPRESSIONS</button>
<button class="nav-btn" onclick="show('dtypes',this)">DTYPES</button>
<button class="nav-btn" onclick="show('io',this)">I/O</button>
<button class="nav-btn" onclick="show('groupby',this)">GROUP BY</button>
<button class="nav-btn" onclick="show('join',this)">JOINS</button>
<button class="nav-btn" onclick="show('plugins',this)">ECOSYSTEM</button>
</nav>
</div>
</header>
<div class="wrapper">
<!-- ══════════════════════════════════════════════
SECTION 1: ORIENTATION
══════════════════════════════════════════════ -->
<section id="orient" class="active">
<div class="section-header">
<div class="sh-main">
<div class="section-title">THE THIRD <em>PILLAR</em></div>
<div class="section-sub">Polars is a Rust-native DataFrame library with a Python API. It is not a faster pandas — it is a different paradigm. Lazy evaluation, expression trees, query optimization, zero-copy Apache Arrow, and a type system that prevents entire categories of bugs at construction time.</div>
</div>
<div class="sh-meta">
<div class="sh-meta-row"><div class="sh-meta-label">Engine</div><div class="sh-meta-val">Rust + SIMD</div></div>
<div class="sh-meta-row"><div class="sh-meta-label">Memory Model</div><div class="sh-meta-val">Apache Arrow</div></div>
<div class="sh-meta-row"><div class="sh-meta-label">Evaluation</div><div class="sh-meta-val">Lazy / Eager</div></div>
<div class="sh-meta-row"><div class="sh-meta-label">Null Handling</div><div class="sh-meta-val">Explicit — no NaN</div></div>
</div>
</div>
<div class="stat-grid">
<div class="stat"><div class="stat-num" style="color:var(--rust)">0</div><div class="stat-label">GIL Dependency</div></div>
<div class="stat"><div class="stat-num" style="color:var(--blue)">Arrow</div><div class="stat-label">Memory Format</div></div>
<div class="stat"><div class="stat-num" style="color:var(--green)">Lazy</div><div class="stat-label">Default Eval Mode</div></div>
<div class="stat"><div class="stat-num" style="color:var(--violet)">SIMD</div><div class="stat-label">CPU Vectorization</div></div>
<div class="stat"><div class="stat-num" style="color:var(--rust)">∞</div><div class="stat-label">Streaming Support</div></div>
<div class="stat"><div class="stat-num" style="color:var(--blue)">Null</div><div class="stat-label">Not NaN</div></div>
</div>
<div class="g2">
<div>
<div class="card" data-label="mental model">
<div class="card-title">pandas → Polars: the paradigm shift</div>
<div class="bridge">
<div class="bridge-left">df['col'].fillna(0)</div>
<div class="bridge-mid">→</div>
<div class="bridge-right">pl.col('col').fill_null(0)</div>
</div>
<div class="bridge">
<div class="bridge-left">df['col'].apply(fn)</div>
<div class="bridge-mid">→</div>
<div class="bridge-right">pl.col('col').map_elements(fn)</div>
</div>
<div class="bridge">
<div class="bridge-left">df[df['x'] > 5]</div>
<div class="bridge-mid">→</div>
<div class="bridge-right">.filter(pl.col('x') > 5)</div>
</div>
<div class="bridge">
<div class="bridge-left">df.groupby('k').agg({'v': 'sum'})</div>
<div class="bridge-mid">→</div>
<div class="bridge-right">.group_by('k').agg(pl.col('v').sum())</div>
</div>
<div class="bridge">
<div class="bridge-left">df.merge(other, on='id')</div>
<div class="bridge-mid">→</div>
<div class="bridge-right">.join(other, on='id', how='left')</div>
</div>
<div class="bridge">
<div class="bridge-left">df['new'] = df['a'] + df['b']</div>
<div class="bridge-mid">→</div>
<div class="bridge-right">.with_columns((pl.col('a') + pl.col('b')).alias('new'))</div>
</div>
<div class="bridge">
<div class="bridge-left">df.sort_values('col')</div>
<div class="bridge-mid">→</div>
<div class="bridge-right">.sort('col', descending=False)</div>
</div>
<div class="bridge">
<div class="bridge-left">pd.read_csv() → eager</div>
<div class="bridge-mid">→</div>
<div class="bridge-right">pl.scan_csv() → lazy LazyFrame</div>
</div>
<div class="bridge">
<div class="bridge-left">np.nan for missing</div>
<div class="bridge-mid">→</div>
<div class="bridge-right">null — explicit, typed</div>
</div>
</div>
</div>
<div>
<div class="card" data-label="quick start">
<div class="card-title">Getting Started — Zero to 100</div>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">install + first queries</span><span class="code-lang">python</span></div>
<pre><span class="py">import</span> polars <span class="py">as</span> pl
<span class="cm"># The two modes — choose your weapon</span>
df <span class="op">=</span> pl.read_parquet(<span class="st">'events.parquet'</span>) <span class="cm"># eager: loads now</span>
lf <span class="op">=</span> pl.scan_parquet(<span class="st">'events.parquet'</span>) <span class="cm"># lazy: builds a plan</span>
<span class="cm"># Core pattern: build expression, call collect()</span>
result <span class="op">=</span> (
pl.scan_parquet(<span class="st">'s3://lake/events/**/*.parquet'</span>)
.filter(pl.col(<span class="st">'event_type'</span>) <span class="op">==</span> <span class="st">'purchase'</span>)
.filter(pl.col(<span class="st">'event_ts'</span>) <span class="op">>=</span> pl.lit(<span class="st">'2024-01-01'</span>).str.to_datetime())
.with_columns(
(pl.col(<span class="st">'amount_cents'</span>) <span class="op">/</span> <span class="nu">100</span>).alias(<span class="st">'amount'</span>),
pl.col(<span class="st">'event_ts'</span>).dt.truncate(<span class="st">'1d'</span>).alias(<span class="st">'day'</span>)
)
.group_by([<span class="st">'day'</span>, <span class="st">'user_id'</span>])
.agg(
pl.len().alias(<span class="st">'purchase_count'</span>),
pl.col(<span class="st">'amount'</span>).sum().alias(<span class="st">'total_spend'</span>),
pl.col(<span class="st">'amount'</span>).mean().alias(<span class="st">'avg_order'</span>)
)
.sort(<span class="st">'total_spend'</span>, descending<span class="op">=True</span>)
.collect() <span class="cm"># ← triggers optimized execution</span>
)
<span class="cm"># Instant profiling — like DuckDB SUMMARIZE:</span>
<span class="py">print</span>(df.describe()) <span class="cm"># count, mean, std, min, max per column</span>
<span class="py">print</span>(df.schema) <span class="cm"># full type information</span>
<span class="py">print</span>(df.estimated_size(<span class="st">'mb'</span>)) <span class="cm"># memory footprint</span></pre>
</div>
</div>
</div>
</div>
<hr class="sep sep-heavy">
<h3>Why Rust? The Technical Reality</h3>
<div class="g3">
<div class="card" data-label="no GIL">
<div class="card-title">True Parallelism</div>
<p>Python's Global Interpreter Lock prevents true multi-threading. Polars' Rust core releases the GIL immediately. Every operation — sort, group_by, join — uses all CPU cores via Rayon's work-stealing thread pool. A 16-core machine runs 16× faster, not 1×.</p>
</div>
<div class="card" data-label="memory">
<div class="card-title">Zero-Copy Arrow</div>
<p>Polars stores data in Apache Arrow columnar buffers. When you pass a Polars DataFrame to DuckDB or PyArrow, <strong>no data is copied</strong>. The same memory is read by all three engines. The full dbt + DuckDB + Polars stack moves data between tools in nanoseconds.</p>
</div>
<div class="card" data-label="safety">
<div class="card-title">Type System Catches Bugs</div>
<p>Polars' schema is fixed at LazyFrame construction. If you <code>.filter(pl.col('amt') > 'oops')</code> — comparing numeric to string — the error fires at query build time, not hours into a production run. Entire categories of runtime bugs become compile-time errors.</p>
</div>
</div>
</section>
<!-- ══════════════════════════════════════════════
SECTION 2: LAZY API
══════════════════════════════════════════════ -->
<section id="lazy">
<div class="section-header">
<div class="sh-main">
<div class="section-title">THE <em>LAZY</em> API</div>
<div class="section-sub">LazyFrame is the heart of Polars. You describe what you want — not how to compute it. Polars' query optimizer then rewrites, reorders, and parallelizes your plan before executing a single byte of data access.</div>
</div>
<div class="sh-meta">
<div class="sh-meta-row"><div class="sh-meta-label">Evaluation</div><div class="sh-meta-val">Deferred</div></div>
<div class="sh-meta-row"><div class="sh-meta-label">Optimizer</div><div class="sh-meta-val">Predicate / Projection</div></div>
<div class="sh-meta-row"><div class="sh-meta-label">Trigger</div><div class="sh-meta-val">.collect()</div></div>
</div>
</div>
<div class="g2">
<div>
<h3>The Lazy Execution Pipeline</h3>
<p>Every <code>.scan_*()</code> call returns a <code>LazyFrame</code>. Chained operations build a logical plan. <code>.collect()</code> runs the optimizer, produces a physical plan, and executes it.</p>
<div class="chain">
<div class="chain-step">
<div class="chain-num">1</div>
<div class="chain-content"><span class="chain-method">pl.scan_parquet</span>(<span class="st" style="font-size:0.75rem">'events/**/*.parquet'</span>)<br><span class="chain-note">→ LazyFrame. Zero data read. Registers scan source.</span></div>
</div>
<div class="chain-step">
<div class="chain-num">2</div>
<div class="chain-content"><span class="chain-method">.filter</span>(pl.col(<span class="st" style="font-size:0.75rem">'ts'</span>) >= <span class="st" style="font-size:0.75rem">'2024-01-01'</span>)<br><span class="chain-note">→ Adds predicate node to plan. Optimizer will push this to scan.</span></div>
</div>
<div class="chain-step">
<div class="chain-num">3</div>
<div class="chain-content"><span class="chain-method">.select</span>([<span class="st" style="font-size:0.75rem">'user_id'</span>, <span class="st" style="font-size:0.75rem">'amount'</span>, <span class="st" style="font-size:0.75rem">'ts'</span>])<br><span class="chain-note">→ Projection pushdown: only these 3 columns read from disk.</span></div>
</div>
<div class="chain-step">
<div class="chain-num">4</div>
<div class="chain-content"><span class="chain-method">.group_by</span>(<span class="st" style="font-size:0.75rem">'user_id'</span>)<span class="chain-method">.agg</span>(pl.col(<span class="st" style="font-size:0.75rem">'amount'</span>).sum())<br><span class="chain-note">→ Aggregation node. Optimizer may split for streaming.</span></div>
</div>
<div class="chain-step">
<div class="chain-num">5</div>
<div class="chain-content"><span class="chain-method">.collect</span>() → <span class="chain-type">DataFrame</span><br><span class="chain-note">→ Triggers: optimize → physical plan → parallel execution → Arrow buffer.</span></div>
</div>
</div>
<div class="callout c-rust">
<strong>The Optimizer Rewrites Your Code</strong>
You write filters after joins — the optimizer moves them before. You select 3 columns from a 50-column file — the optimizer pushes projection to the file reader. You chain two filters — they're merged into one pass. You never see this happening, but it's why Polars is often 5–50× faster than equivalent pandas code.
</div>
</div>
<div>
<h3>Query Plan Inspection</h3>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">inspect + optimize + stream</span><span class="code-lang">python</span></div>
<pre><span class="py">import</span> polars <span class="py">as</span> pl
lf <span class="op">=</span> (
pl.scan_parquet(<span class="st">'events.parquet'</span>)
.filter(pl.col(<span class="st">'event_type'</span>) <span class="op">==</span> <span class="st">'purchase'</span>)
.select([<span class="st">'user_id'</span>, <span class="st">'amount'</span>, <span class="st">'event_ts'</span>])
.group_by(<span class="st">'user_id'</span>)
.agg(pl.col(<span class="st">'amount'</span>).sum())
)
<span class="cm"># See the LOGICAL plan (your intent):</span>
<span class="py">print</span>(lf.explain(optimized<span class="op">=False</span>))
<span class="cm"># See the OPTIMIZED plan (what actually runs):</span>
<span class="py">print</span>(lf.explain(optimized<span class="op">=True</span>))
<span class="cm"># Notice: filter & select are pushed INTO the ParquetScan.</span>
<span class="cm"># Visualize as a tree (requires graphviz):</span>
lf.show_graph(optimized<span class="op">=True</span>)
<span class="cm"># ─── STREAMING — larger-than-RAM execution ───────────</span>
<span class="cm"># collect() loads everything into RAM.</span>
<span class="cm"># collect(engine='streaming') processes in chunks.</span>
result <span class="op">=</span> (
pl.scan_parquet(<span class="st">'s3://lake/events/**/*.parquet'</span>)
.filter(pl.col(<span class="st">'amount'</span>) <span class="op">></span> <span class="nu">0</span>)
.group_by(<span class="st">'user_id'</span>)
.agg(pl.col(<span class="st">'amount'</span>).sum())
.collect(engine<span class="op">=</span><span class="st">'streaming'</span>) <span class="cm"># process 500MB at a time</span>
)
<span class="cm"># sink_parquet — stream directly to disk, never loads to RAM:</span>
(
pl.scan_parquet(<span class="st">'huge_100gb_file.parquet'</span>)
.filter(pl.col(<span class="st">'status'</span>) <span class="op">==</span> <span class="st">'active'</span>)
.sink_parquet(<span class="st">'output/active_users.parquet'</span>)
<span class="cm"># executes streaming — never materializes full DataFrame</span>
)
<span class="cm"># sink_csv, sink_ipc, sink_ndjson also available</span>
(
pl.scan_parquet(<span class="st">'events.parquet'</span>)
.sink_ipc(<span class="st">'events.arrow'</span>) <span class="cm"># Arrow IPC stream format</span>
)</pre>
</div>
<h3>Logical Plan Nodes</h3>
<div class="plan-box"><div class="plan-icon pi-scan">SC</div><div class="plan-name">ParquetScan / CsvScan / IpcScan</div><div class="plan-detail">source + pushdown</div></div>
<div class="plan-arrow">↓ (predicate + projection pushed here)</div>
<div class="plan-box"><div class="plan-icon pi-filter">FL</div><div class="plan-name">Filter</div><div class="plan-detail">Boolean mask predicate</div></div>
<div class="plan-arrow">↓</div>
<div class="plan-box"><div class="plan-icon pi-select">SL</div><div class="plan-name">Select / WithColumns</div><div class="plan-detail">Expression evaluation</div></div>
<div class="plan-arrow">↓</div>
<div class="plan-box"><div class="plan-icon pi-join">JN</div><div class="plan-name">Join</div><div class="plan-detail">Hash / sort-merge / cross</div></div>
<div class="plan-arrow">↓</div>
<div class="plan-box"><div class="plan-icon pi-agg">AG</div><div class="plan-name">GroupBy / Aggregate</div><div class="plan-detail">Parallel hash aggregation</div></div>
<div class="plan-arrow">↓</div>
<div class="plan-box"><div class="plan-icon pi-sort">SO</div><div class="plan-name">Sort</div><div class="plan-detail">Parallel sort or top-k</div></div>
<div class="plan-arrow">↓</div>
<div class="plan-box"><div class="plan-icon pi-sink">→</div><div class="plan-name">Collect / Sink</div><div class="plan-detail">DataFrame or streaming output</div></div>
</div>
</div>
<hr class="sep">
<h3>Scan Options — The Ingress Interface</h3>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">scan_* — full parameter reference</span><span class="code-lang">python</span></div>
<pre><span class="cm"># scan_parquet — the workhorse</span>
pl.scan_parquet(
source <span class="op">=</span> <span class="st">'s3://bucket/data/**/*.parquet'</span>,
n_rows <span class="op">=</span> <span class="nu">1_000_000</span>, <span class="cm"># early stop (fast LIMIT)</span>
row_index_name <span class="op">=</span> <span class="st">'_row_idx'</span>, <span class="cm"># inject row numbers</span>
row_index_offset <span class="op">=</span> <span class="nu">0</span>,
parallel <span class="op">=</span> <span class="st">'auto'</span>, <span class="cm"># 'columns' | 'row_groups' | 'prefiltered'</span>
use_statistics <span class="op">=</span> <span class="kw">True</span>, <span class="cm"># row group zone map pushdown</span>
hive_partitioning <span class="op">=</span> <span class="kw">True</span>, <span class="cm"># inject partition columns from path</span>
hive_schema <span class="op">=</span> {<span class="st">'year'</span>: pl.Int32, <span class="st">'month'</span>: pl.Int32},
include_file_paths <span class="op">=</span> <span class="st">'_source_file'</span>, <span class="cm"># track which file each row came from</span>
storage_options <span class="op">=</span> {<span class="st">'aws_access_key_id'</span>: <span class="st">'...'</span>, <span class="st">'region'</span>: <span class="st">'us-east-1'</span>}
)
<span class="cm"># scan_csv — streaming CSV with full control</span>
pl.scan_csv(
source <span class="op">=</span> <span class="st">'data/*.csv'</span>,
separator <span class="op">=</span> <span class="st">','</span>,
has_header <span class="op">=</span> <span class="kw">True</span>,
skip_rows <span class="op">=</span> <span class="nu">0</span>,
dtypes <span class="op">=</span> {<span class="st">'id'</span>: pl.Int64, <span class="st">'amount'</span>: pl.Float64},
null_values <span class="op">=</span> [<span class="st">''</span>, <span class="st">'N/A'</span>, <span class="st">'null'</span>],
try_parse_dates <span class="op">=</span> <span class="kw">True</span>, <span class="cm"># auto-detect date columns</span>
ignore_errors <span class="op">=</span> <span class="kw">True</span>, <span class="cm"># skip malformed rows</span>
encoding <span class="op">=</span> <span class="st">'utf8-lossy'</span>, <span class="cm"># handle dirty encodings</span>
comment_prefix <span class="op">=</span> <span class="st">'#'</span> <span class="cm"># skip comment lines</span>
)
<span class="cm"># scan_ndjson — Kafka S3 sink output</span>
pl.scan_ndjson(
source <span class="op">=</span> <span class="st">'s3://kafka-sink/topic=events/**/*.json'</span>,
infer_schema_length <span class="op">=</span> <span class="nu">1000</span>, <span class="cm"># sample N rows for schema inference</span>
schema <span class="op">=</span> pl.Schema({ <span class="cm"># or provide explicit schema</span>
<span class="st">'event_id'</span>: pl.String,
<span class="st">'user_id'</span>: pl.Int64,
<span class="st">'event_ts'</span>: pl.Datetime(<span class="st">'us'</span>),
<span class="st">'payload'</span>: pl.String, <span class="cm"># keep JSON as string for later parsing</span>
})
)</pre>
</div>
</section>
<!-- ══════════════════════════════════════════════
SECTION 3: EXPRESSIONS
══════════════════════════════════════════════ -->
<section id="exprs">
<div class="section-header">
<div class="sh-main">
<div class="section-title"><em>EXPRESSIONS</em> — THE ALGEBRA</div>
<div class="section-sub">Polars expressions are composable, lazy computation units. They describe transformations on columns without executing them. The optimizer analyzes expression trees to determine parallelism, eliminate dead computation, and push work down to the I/O layer.</div>
</div>
<div class="sh-meta">
<div class="sh-meta-row"><div class="sh-meta-label">Type</div><div class="sh-meta-val">pl.Expr</div></div>
<div class="sh-meta-row"><div class="sh-meta-label">Entry Point</div><div class="sh-meta-val">pl.col()</div></div>
<div class="sh-meta-row"><div class="sh-meta-label">Compose</div><div class="sh-meta-val">Chain methods</div></div>
</div>
</div>
<div class="pills" id="ex-pills">
<button class="pill active" onclick="showTab('ex','string',this)">String</button>
<button class="pill" onclick="showTab('ex','datetime',this)">Datetime</button>
<button class="pill" onclick="showTab('ex','list',this)">List / Struct</button>
<button class="pill" onclick="showTab('ex','window',this)">Window</button>
<button class="pill" onclick="showTab('ex','conditional',this)">When/Then</button>
<button class="pill" onclick="showTab('ex','meta',this)">Meta Exprs</button>
</div>
<!-- STRING -->
<div id="ex-string" class="tab-pane active">
<div class="g2">
<div class="code-wrap">
<div class="code-header"><span class="code-fname">pl.col().str — string namespace</span><span class="code-lang">python</span></div>
<pre><span class="cm"># The .str namespace wraps every string operation</span>
df.with_columns([
pl.col(<span class="st">'email'</span>)
.str.to_lowercase()
.str.strip_chars()
.str.split(<span class="st">'@'</span>)
.list.first() <span class="cm"># → local part of email</span>
.alias(<span class="st">'email_local'</span>),
pl.col(<span class="st">'phone'</span>)
.str.replace_all(r<span class="st">'[^\d]'</span>, <span class="st">''</span>) <span class="cm"># strip non-digits (regex)</span>
.str.zfill(<span class="nu">10</span>) <span class="cm"># zero-pad to 10 chars</span>
.alias(<span class="st">'phone_clean'</span>),
pl.col(<span class="st">'payload_str'</span>)
.str.json_path_match(<span class="st">'$.user.id'</span>) <span class="cm"># JSONPath on string column</span>
.cast(pl.Int64)
.alias(<span class="st">'user_id_extracted'</span>),
pl.col(<span class="st">'event_type'</span>)
.str.contains(<span class="st">'purchase'</span>) <span class="cm"># boolean mask</span>
.alias(<span class="st">'is_purchase'</span>),
pl.col(<span class="st">'url'</span>)
.str.extract(r<span class="st">'utm_source=([^&]+)'</span>, group_index<span class="op">=</span><span class="nu">1</span>)
.alias(<span class="st">'utm_source'</span>),
pl.col(<span class="st">'tags_csv'</span>)
.str.split(<span class="st">','</span>) <span class="cm"># → List[String]</span>
.list.eval(pl.element().str.strip_chars())
.alias(<span class="st">'tags'</span>),
pl.col(<span class="st">'name'</span>).str.count_matches(r<span class="st">'\s+'</span>).alias(<span class="st">'word_count'</span>),
pl.col(<span class="st">'text'</span>).str.len_chars().alias(<span class="st">'char_count'</span>),
pl.col(<span class="st">'code'</span>).str.starts_with(<span class="st">'ORD-'</span>).alias(<span class="st">'is_order'</span>),
])</pre>
</div>
<div class="callout c-rust">
<strong>Regex is Rust Regex — Fast</strong>
All <code>.str.contains()</code>, <code>.str.extract()</code>, and <code>.str.replace_all()</code> use the Rust <code>regex</code> crate. It's compiled once, executed across all CPU cores on vectorized string data. Regex processing in Polars is orders of magnitude faster than Python's <code>re</code> module in a loop.
</div>
</div>
</div>
<!-- DATETIME -->
<div id="ex-datetime" class="tab-pane">
<div class="g2">
<div class="code-wrap">
<div class="code-header"><span class="code-fname">pl.col().dt — datetime namespace</span><span class="code-lang">python</span></div>