🔥 Single Biggest Idea Behind Polars Isn't Rust — It's LAZY 🔥 Part(2/5)

If you're still processing data in sequential steps (Pandas-style), you're missing out on 90% of Polars' performance gains.

This is the core difference: Eager vs. Lazy. Understanding this makes the Expression API click.

❌ 𝐓𝐇𝐄 𝐄𝐀𝐆𝐄𝐑 (𝐏𝐀𝐍𝐃𝐀𝐒) 𝐖𝐀𝐘: 𝐄𝐱𝐞𝐜𝐮𝐭𝐞 𝐈𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞𝐥𝐲

Every line runs instantly, creating a new DataFrame in memory at each step.

import pandas as pd
df = pd.read_csv("large_file.csv") # 1. Loads ALL columns
df['doubled'] = df['new_val'] * 2 # 2. Creates new copy
df = df.groupby('category').sum() # 3. Final compute

🚨 Result: Huge memory footprint, wasted I/O, no query optimization.

✅ 𝐓𝐇𝐄 𝐋𝐀𝐙𝐘 (𝐏𝐎𝐋𝐀𝐑𝐒) 𝐖𝐀𝐘: 𝐏𝐥𝐚𝐧 𝐅𝐢𝐫𝐒𝐭, 𝐄𝐱𝐞𝐜𝐮𝐭𝐞 𝐎𝐧𝐜𝐄

Polars records all operations, builds an optimized plan, and only runs when you call .collect(). This unlocks QUERY OPTIMIZATION.

🎯 The Two-Step Dance:

Step 1: Define the Plan (LazyFrame). Nothing runs yet.
import polars as pl
q = (
pl.scan_csv("large_file.csv")
.filter(pl.col("value") > 100)
.with_columns(
(pl.col("new_val") * 2).alias("doubled")
)
.group_by("category")
.agg(pl.col("doubled").sum())
)

Step 2: Execute the Optimized Plan.
result = q.collect()

🧠 𝐖𝐇𝐀𝐓 𝐓𝐇𝐄 𝐐𝐔𝐄𝐑𝐘 𝐎𝐏𝐓𝐈𝐌𝐈Z𝐄𝐑 𝐃𝐎𝐄𝐒

Polars applies transformations to your plan:

Projection Pushdown: Only read the columns you use.
Predicate Pushdown: Filter rows while reading the CSV (skip rows at the source).
Expression Fusion: Combine multiple operations into a single, efficient kernel (no intermediate copies).

💰 𝐑𝐄𝐀𝐋-𝐖𝐎𝐑𝐋𝐃 𝐈𝐌𝐏𝐀𝐂𝐓 (10𝐆𝐁 𝐂𝐒𝐕 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤)