The Notebook That Worked—Until It Didn’t

PIP vs CONDA for data science wasn’t a debate I planned to have.
It started with a Jupyter notebook that ran perfectly on my laptop.
The plots rendered.
The model trained.
The metrics looked reasonable.
Then I sent the notebook to a teammate.
Same code.
Same dataset.
Same Python version.
Different results.
And one missing import error that made no sense.
That was the moment I realized something uncomfortable:
In data science, your environment isn’t background infrastructure.
It is the experiment.

How I Ended Up Using Both (Without Thinking)

I didn’t choose pip or conda deliberately.
They just… appeared.

  • pip came with Python
  • conda came with Anaconda
  • Tutorials used both interchangeably
  • Examples “just worked” (until they didn’t)

For a long time, I assumed the difference between pip and conda was mostly preference.
That assumption cost me time.

The problem wasn’t broken code.
The problem was an environment I didn’t fully understand.

Why Data Science Changes the Equation

In backend development, dependencies are usually Python-only.
In data science, they aren’t.

You’re dealing with:

  • Native libraries (BLAS, LAPACK, OpenMP)
  • GPU runtimes
  • CUDA and cuDNN
  • Platform-specific wheels
  • CPU instruction sets

This is where PIP vs CONDA for data science stops being theoretical and starts being practical.
The difference becomes visible the first time you need to install a library that wraps a C extension, or when you’re trying to get consistent matrix multiplication performance across different machines. These aren’t edge cases—they’re everyday data science workflows.

The pip Workflow I Tried to Make Work

This was my original setup:

python -m venv .venv
source .venv/bin/activate
pip install numpy pandas scikit-learn matplotlib jupyter

It looked clean.
It felt “Pythonic.”

Until I needed PyTorch with GPU support.

pip install torch torchvision torchaudio

Sometimes it worked.
Sometimes it didn’t.
Sometimes it installed a CPU build when I needed CUDA.
And when it failed, the errors were… abstract.

What pip Is Actually Good At

To be fair, pip does exactly what it promises.

PIP is excellent when:

  • Dependencies are pure Python
  • Wheels are available for your platform
  • You don’t care about system libraries
  • You value minimal tooling

For lightweight data analysis, pip can be enough.
The trouble starts when your project stops being lightweight.

The First Time Conda Felt Like Magic

The first time I tried conda for a serious ML project, the difference was immediate.

conda create -n ml-env python=3.10
conda activate ml-env
conda install numpy pandas scikit-learn pytorch cudatoolkit=11.8

I watched this line appear:

Solving environment...

And yes—it took time.
But when it finished?
Everything worked.
No missing shared libraries.
No mysterious runtime errors.
No CPU fallback when I needed GPU.
That was the moment I understood what conda optimizes for.

PIP vs CONDA for data science (What They Really Optimize)

This is the core difference most comparisons miss.
PIP optimizes for Python packages.
CONDA optimizes for environments.
That single distinction explains almost everything.

PIP assumes:

  • System libraries already exist
  • Python wheels are sufficient
  • The OS is someone else’s problem

CONDA assumes:

  • You want a self-contained ecosystem
  • System-level dependencies matter
  • Reproducibility beats elegance

Neither approach is “wrong.”
They’re solving different problems.

Where pip Starts to Crack

The breaking point for me wasn’t installation.
It was reproducibility.

I had notebooks that worked on:

  • My machine
  • But not CI
  • Or not a teammate’s laptop
  • Or not a cloud VM

PIP didn’t fail loudly.
It failed subtly.
Different BLAS implementations.
Different binary builds.
Different performance characteristics.
The notebook didn’t crash.
It just behaved differently.
That’s worse.

Here’s what that looked like in practice: I ran a gradient descent optimization on my laptop with MKL-compiled NumPy. It converged in 45 iterations. My teammate ran the same code with OpenBLAS-compiled NumPy. It converged in 52 iterations. Same algorithm, same hyperparameters, different linear algebra backend.

The results weren’t wrong—they were just inconsistent enough to erode confidence. When you’re debugging model performance, you need to trust that numerical differences come from your code, not from invisible infrastructure layers.

Where Conda Starts to Feel Heavy

Conda isn’t free either.

I’ve waited minutes staring at:

Solving environment...

I’ve dealt with:

  • Channel conflicts
  • Priority ordering issues
  • Bloated environments
  • Slow environment creation

And conda environments can feel… opaque.

Once they work, you stop touching them.
That makes them hard to reason about later.

Conda trades transparency for stability.

A Side-by-Side That Actually Matters

Here’s how the difference played out for me in practice.

Areapipconda
Python-only packagesExcellentGood
Native librariesFragileStrong
GPU supportManualFirst-class
ReproducibilityDepends on wheelsHigh
Environment sizeSmallLarge
Setup speedFastSlower
Debugging failuresHardEasier

This table didn’t convince me.
Experience did.

The Mistake I Made (And Had to Unlearn)

My mistake was trying to force one tool to fit every workflow.

I tried:

  • pip for GPU-heavy ML projects
  • conda for lightweight API experiments

Both felt wrong.
pip struggled silently.
conda felt excessive.
The tools weren’t bad.
My expectations were.

I kept thinking I needed to “pick a side.” That pip users were doing it wrong, or that conda users were overcomplicating things. The reality is messier and more practical. Some projects genuinely benefit from conda’s guarantees. Others don’t need that overhead.

The turning point came when I stopped asking “which is better” and started asking “which problem am I solving right now.” A quick data exploration script? pip is fine. A reproducible research pipeline that needs to run identically on three different clusters? conda makes sense.

Using Them Together (When It Actually Works)

Here’s something most guides don’t mention: you can use both.
Carefully.

The workflow that works for me:

conda create -n project python=3.10
conda activate project
conda install numpy pandas scikit-learn pytorch cudatoolkit
pip install some-pure-python-package

The rule: conda for anything touching native code, pip for pure Python packages not in conda channels.

This hybrid approach has risks. If you’re not careful, you can create dependency conflicts that neither tool can resolve. But when done deliberately, it gives you conda’s stability for the foundation and pip’s breadth for the edges.

What I Actually Do Now

This is the part most articles skip.

Here’s my current rule of thumb:

  • Exploration, notebooks, ML, GPU work → conda
  • Light analysis, scripts, tooling → pip
  • Shared research environments → conda
  • Disposable experiments → pip

Once I stopped treating this as a binary choice, everything got easier.

Why Data Scientists Feel This Pain More

Data science workflows amplify environment problems.

You’re constantly:

  • Switching machines
  • Sharing notebooks
  • Re-running old experiments
  • Mixing Python with native code

A backend service failing is obvious.
A model behaving slightly differently is not.
That’s why pip vs conda for data science isn’t just a tooling debate.
It’s a correctness debate.

When pip Is Still the Right Choice

pip is still perfectly fine if:

  • You’re doing exploratory analysis only
  • You don’t need GPU support
  • Your dependencies are pure Python
  • You value minimal setup

Not every notebook needs conda.

When Conda Is Worth the Cost

Conda is worth it when:

  • You rely on compiled libraries
  • You need consistent results across machines
  • You’re doing serious ML or numerical work
  • “It runs” isn’t good enough—you need “it behaves the same”
Conda isn’t faster—but it’s steadier.

Common Pitfalls (And How I Learned to Avoid Them)

Through trial and error, I learned to watch for these issues:

With PIP: Forgetting to check which CUDA version a package expects. Installing TensorFlow or PyTorch without verifying GPU compatibility. Assuming a package will work the same way across different operating systems.

With CONDA: Mixing conda-forge and defaults channels without understanding priority. Creating environments that balloon to multiple gigabytes. Forgetting to export environment files before a major dependency update breaks something.

The fix isn’t avoiding these tools—it’s understanding their failure modes.

The Lesson I Took Forward

The biggest lesson wasn’t about pip or conda.
It was this:
In data science, environments are part of the experiment.
If you can’t explain your environment,
you can’t fully trust your results.
Once I accepted that, the choice between pip and conda stopped feeling confusing.

Final Thoughts

PIP vs CONDA for data science isn’t about which tool is better.
It’s about which problems you’re trying to avoid.
pip minimizes friction.
conda minimizes surprises.
I still use both.
I just no longer expect them to behave the same.
And once you understand that difference,
your notebooks stop lying to you.

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