Polars vs pandas in 2026: When the Speed Gap Actually Matters

Your `groupby` has been spinning for ninety seconds, you've checked Reddit twice, and somewhere a tab is whispering *"you should be using Polars."* I tutor a lot of people through this exact moment. So let me give you the honest version — not the benchmark-porn version — of where pandas and Polars stand in 2026, and how to decide without rewriting your whole life. ## The numbers are real (and the internet is being weird about them) Let's get the headline out of the way, because the gist of it is true. On the long-running H2O.ai database-ops benchmark (now maintained by DuckDB Labs), Polars is consistently one of the fastest DataFrame libraries, and it routinely beats pandas by a wide margin on group-bys, joins, and sorts at large row counts — often several times faster, and sometimes much more on the heaviest queries. It also tends to use far less memory, which is frequently the difference between a job finishing and your kernel dying. (Be skeptical of the suspiciously clean "exactly 15x, 0.45s vs 12.5s" numbers floating around blog posts. The real benchmark is run on a beefy cloud server, not a laptop, and the per-query gap varies a lot by operation — so treat "many times faster on big data" as the honest takeaway, not a single magic multiplier.) Why? Two reasons that are easy to remember: 1. **Polars is written in Rust and runs every core on your laptop in parallel.** pandas, by default, mostly uses one. 2. **Polars is lazy.** It looks at your *entire* query before running a single row, then quietly rewrites it to be faster. That second one is the real magic, and it's the part most "Polars is fast!" posts skip. Let me show you. ## The mental model: a sous-chef who reads the whole recipe first pandas is an eager line cook. You say "chop the onions" — it chops *all* the onions, right now, even the ones the dish doesn't need. Then "now filter to soups only" — too late, onions already chopped. Polars (in lazy mode) is a sous-chef who reads the entire recipe *before touching a knife*. It sees you only want soups, so it never chops the salad onions in the first place. This is called **predicate and projection pushdown**, and it's why Polars often reads less data off disk than pandas even reads into memory. Here's the same task in both. pandas first: ```python import pandas as pd df = pd.read_csv("sales.csv") # reads ALL columns, ALL rows df = df[df["region"] == "EU"] # then filters out = df.groupby("product")["revenue"].mean() ``` Now Polars, lazy: ```python import polars as pl out = ( pl.scan_csv("sales.csv") # nothing read yet — just a plan .filter(pl.col("region") == "EU") .group_by("product") .agg(pl.col("revenue").mean()) .collect() # NOW it runs the optimized plan ) ``` Notice `scan_csv` instead of `read_csv`, and the `.collect()` at the end. Between those two lines, Polars builds a query plan, realizes it only needs three columns and only EU rows, and reads *just that* from disk. The pandas version hauled the entire file into RAM before it filtered a thing. If you've used SQL, this will feel familiar — because it basically is SQL's query optimizer, living inside your DataFrame. ## "But I know pandas in my sleep" Good news: pandas in 2026 is not the pandas you complained about in 2021. pandas 2.x added a **PyArrow backend**, which gives you proper string types, better nulls, and chunky speed-ups on a lot of operations: ```python df = pd.read_csv("sales.csv", dtype_backend="pyarrow") ``` That one keyword can quietly make your existing code faster without touching anything else. So before you rewrite 4,000 lines, try this first. And the ecosystem reality matters: by PyPI's own download stats, pandas still pulls **well over ten times as many weekly downloads as Polars** — pandas sits in the hundreds of millions of installs per week, Polars in the low tens of millions. Every tutorial, every Stack Overflow answer, every `scikit-learn` example assumes pandas. When something breaks at 11pm, that gravity is worth real money. ## So when do I actually reach for Polars? Here's the rule of thumb I give my students. Reach for Polars when **two or more** of these are true: - Your data is **over ~1 GB**, or your `groupby`/join genuinely makes you wait. - You're writing a **new pipeline** (greenfield — no migration cost). - You'll run this thing **repeatedly** (a daily ETL, not a one-off chart). - You care about **memory** — you're hitting "kernel died" on a 16 GB machine. Stay on pandas when: - The data is small-to-medium and you're just *exploring*. - You're deep in the **ML stack** — `scikit-learn`, `statsmodels`, most plotting — which all speak pandas natively. - **Team familiarity** matters more than shaving 8 seconds. This is also what the industry is doing. New pipelines written in 2025–2026 increasingly *default* to Polars — its own site lists production users like Optiver, Netflix, Microsoft, and G-Research — while existing pandas codebases get migrated *selectively*, only where the ROI is obvious. The common thread is teams with genuinely large data and hard latency budgets, exactly where pandas starts to hurt. ## The cheat sheet for your fingers The syntax differences are smaller than the hype suggests. A quick translation table: | pandas | Polars | | --- | --- | | `df[df.x > 5]` | `df.filter(pl.col("x") > 5)` | | `df.groupby("k").agg(...)` | `df.group_by("k").agg(...)` | | `df["a"] = df["b"] * 2` | `df.with_columns((pl.col("b") * 2).alias("a"))` | | `df.merge(other, on="id")` | `df.join(other, on="id")` | The big shift isn't vocabulary — it's that in Polars you build one **expression chain** and let the engine optimize it, rather than mutating a DataFrame step by step. Once that clicks, you stop missing the old way. And you don't have to pick a side, by the way. A very 2026 move is to use both: pull and crunch the heavy stuff in Polars, then `.to_pandas()` at the end to hand a tidy frame to `scikit-learn`. ```python clean = ( pl.scan_parquet("events/*.parquet") .filter(pl.col("event") == "purchase") .group_by("user_id") .agg(pl.col("amount").sum()) .collect() ) features = clean.to_pandas() # now feed your ML model as usual ``` ## Takeaways - The speed gap is **real** and often large on big data, driven by Rust, multi-core, and lazy query optimization. - Try `dtype_backend="pyarrow"` on your existing pandas code *before* rewriting anything. - Switch to Polars for **big, repeated, memory-heavy, greenfield** work; keep pandas for **exploration and the ML stack**. - `scan_*` + `.collect()` is the Polars superpower — learn that pattern first. - You can mix them. Polars to crunch, pandas to model. Speed is nice, but the goal was never "fast" — it was *getting your answer before you lose your train of thought.* Pick the tool that does that for the dataset in front of you today. Want to work through your own dataset together — and figure out whether Polars actually helps your specific bottleneck? I tutor 1:1 → /book-session.