Python Is Only Slow If You are Still Using It The Way You Learned In 2018

The bottleneck isn’t the language. it’s your legacy.

I used to be the first person to complain about Python’s speed. Whenever a script took more than ten seconds to run, I’d sigh and tell my manager: “That’s just the Python tax. If we want it faster, we have to rewrite it in Go.”

I was wrong.

Last month I revisited a data pipeline I wrote back in 2018. It was slow, clunky, and ate RAM like a chrome browser. I didn’t write the logic. I just swapped the “2018 habits” for “2026 tools.” The result? a 15x speed increase with fewer lines of code.

the truth is, Python hasn’t just updated its version number, it has evolved into a high-performance-engine but only if you know which levers to pull.

Let me show you the three shifts that turned my “slow” scripts into lightning.

Photo by Artturi Jalli on Unsplash

1.Polars — Because Pandas is Now the “Slow” Option:

In 2018, import pandas as pd was the first line of every script. Today, if you’re dealing with millions of rows, Pandas is your biggest bottleneck.

The old way (Pandas):

It’s single-threaded and eager. It loads everything into memory and processes it one step at a time.

The now way (Polars):

import polars as pl

# Multithreaded, lazy evaluation, and written in rust.
df=pl.read_csv("massive_data.csv2)
result=df.filter(pl.col("sales")> 100).group_by("region").sum()

why it matters:

Polars uses all your CPU cores. On my last benchmark, a task that took pandas 40 seconds took polars under 3 seconds.

2.Pydantic v2 — Validation at the Speed of C++:

We used to spend hundreds of lines writing manual checks. If not instance of, if key in dict. It was messy and slow.

I used to write validation like this

def process_user(data):
if "id" not in data or not isinstance(data["id"],int):
raise ValueError("invalid ID")
#... more painful checks

Now?

from pydantic import BaseModel
class User(baseModel):
id: int 
email: str 
# Vadlidation happens instantly in the background.
user=User(**external_data)

Fact:

Pydantic v2 was entirely rewritten in Rust. It’s now 20x faster than the version most people were using a few years ago. You get type safely without the “speed tax.”

3.Ruff — The Linter That Saves Hours (and Sanity):

In 2018, we ran Flake8, Black, and Isort. It was a slow, multi-step process that made CI/CD pipelines crawl.

The shift:

Enter Ruff. It replaces nearly all your linting tools and it is — no joke — 100x faster.

Pro-tip:

If your “save” action in VS code takes a second to format your python file, you are living in the past. Ruff does it in milliseconds.

The bottom line:

Python isn’t slow. Your stack is.

If you stop treating Python like a legacy scripting language and start leveraging the Rust-powered ecosystem of 2026, you’ll realize the “Python tax” was actually just a “2018 tax.”

Stop refactoring logic. Start swapping libraries.

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