Stop Using These 5 Python Libraries in 2025

AI Generated

Let’s face it: Python is a beautiful mess.

On one hand, it’s the glue holding modern AI workflows together. On the other, it’s become a bit of a graveyard for libraries that should have retired years ago.

Now, before you raise your pitchfork, I’m not saying these libraries were bad. Most of them were brilliant in their prime. But this is 2025, and we need to talk about the tools in your stack that are quietly draining your time, your compute, and sometimes — your will to code.

So, here’s my hit list: 5 Python libraries you need to drop this year, and what to use instead.

1. Matplotlib

Why it’s on the chopping block: It’s like drawing with a chisel in the age of iPads.

Matplotlib is the granddaddy of Python plotting libraries. And I respect my elders… but not when they make me write 20 lines of code just to get a bar chart that doesn’t look like it was generated in 2008.

The truth? Matplotlib has become the PowerPoint 2003 of data viz — clunky, verbose, and visually uninspiring unless you heavily customize it.

Use Instead:

• Plotly (for interactivity and modern UI)
• Altair (for quick, declarative, grammar-based plots)
• Polars + hvPlot (if you’re moving toward faster, Arrow-native workflows)

Quick fact: Plotly Express can render a polished chart with 1 line of code. Matplotlib? More like 6, plus debugging time.

2. Requests

Why it’s on the chopping block: It’s blocking your progress — literally.

I’ll be honest: I used to swear by requests. I still reach for it when I want to fetch a simple webpage or check a status code. But in 2025, speed is the name of the game — and requests is slow, synchronous, and not built for concurrent workloads.

If your AI or data pipeline involves scraping, fetching APIs, or any kind of batch HTTP work, requests is the bottleneck.

Use Instead:

• httpx (drop-in replacement with async support)
• aiohttp (for full async workflows)
• trio + asks (if you’re feeling bold — and okay with learning curve)

Bonus: httpx supports connection pooling and HTTP/2 out of the box. requests still thinks it’s 2012.

3. pickle

Why it’s on the chopping block: It’s insecure, opaque, and a pain to debug.

Ah, pickle. The duct tape of object serialization in Python. Sure, it works — until it doesn’t.

Let’s count the issues:

• You can’t inspect the output.
• It’s not language-agnostic.
• It can execute arbitrary code (hello, security risks).
• And let’s not even start on version incompatibility between Python 3.10 and 3.12.

Use Instead:

• joblib (for ML model artifacts)
• json / orjson (for structured data)
• pydantic v2 + dataclasses-json (for typed, safe serialization)
• protobuf / msgpack (if performance and interoperability matter)

In short: if your object matters, don’t pickle it. You’re better than that.

4. BeautifulSoup

Why it’s on the chopping block: It’s the Swiss army knife you keep using as a chainsaw.

Don’t get me wrong — BeautifulSoup is elegant. For small HTML parsing tasks, it’s still handy. But anything more complex than scraping a single webpage and it turns into a brittle mess of find_all() spaghetti.

You want speed? It’s not here. You want structured DOM parsing with modern JavaScript rendering? Look elsewhere.

Use Instead:

• selectolax (ultra-fast parsing via Rust)
• parsel (for XPath and CSS queries)
• Playwright / Selenium (when JS rendering is required)
• trafilatura (for extracting clean article text — a hidden gem)

TL;DR: If your scraper breaks every time a <div> tag changes, you’re doing it wrong.

5. TensorFlow 1.x and legacy Keras

Why it’s on the chopping block: You deserve a better developer experience.

I know there are still tutorials floating around that teach TensorFlow 1.x like it’s 2017. And I get the nostalgia — static graphs, sess.run(), and tf.placeholder were... character-building.

But at this point, if you’re not using PyTorch or at least TensorFlow 2.x (with tf.keras), you're actively making your life harder.

Modern LLM workflows, custom training loops, ONNX export — they’re just smoother in PyTorch. That’s not an opinion. That’s a battle-tested fact.

Use Instead:

• PyTorch (flexibility and ecosystem)
• JAX (for TPU speed demons and pure function lovers)
• HuggingFace Transformers + Diffusers (because why reinvent?)

Also, fun fact: almost every new paper implementation from CVPR/NeurIPS now uses PyTorch or JAX. Not TensorFlow.

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