THE Best IDE For Data Science

THE Best IDE For Data Science [There’s Only One Correct Answer]

Yeah, we get it – everyone tells you there are 5-10 different IDEs you can use and then gives you the pros and cons of each.

We’re not going to do that.

Regarding the best IDE for data science (especially learning data science), look no further: Spyder is the best IDE for data science.

While there are about ten more reasons Spyder should be your only choice – we’ll get into all of that below.

Looking for an IDE switch? or just getting into data science?

This article was made for you.

pointing at you
Why Spyder is the best IDE For Data Science

The majority of data science roles are closer to analyst roles than they are to programming roles.

From my experience as a data scientist, I spent way more time in analytical software like Excel than I did in things like Git and Docker.

And there’s a reason for that – I was building models, not software.

And you will be too.

This idea that you should have a set-up that a full-stack developer or a software engineer uses is just silly.

You will be getting paid to find optimizations and build models.

Why then wouldn’t you optimize your tech stack to benefit this goal?

Now that we got that out of the way (I’m looking at you VS Coders!!) let me tell you why Spyder is the best IDE for data scientists.

Well, first and foremost, it was explicitly designed for data scientists. This is why it’s offered in the Anaconda download package, the most popular data science platform.

When you start working as a data scientist, everything is datasets.


You’ll be trading datasets, modeling off new ones, and looking for old ones. Everything you touch will be influenced by some dataset.

And this makes sense; the title is literally “data scientist.”

And if you know anything about a data scientist’s actual work, you spend most of your time munging, fixing, editing, and correcting these datasets to prepare them for modeling.

You’ll often take a dataset from ingestion (you just received the dataset) to modeling, which will not resemble the original dataset in any way.

So, from going from point A to point Z, wouldn’t it be nice to have an IDE that saved values in variables?

Since you’re moving so much data from point A to point Z, having an IDE that saves the “state” of your variables is a lifesaver.

This sole reason (and many more, but this is the main one) is why Spyder is a home run for data scientists.

You can use its Variable Explorer to easily view and update variables, detect errors from their values, or create code plots with a few clicks.

Check out the image below, which saves every single variable we’re using and allows us to explore it later.

Spyder variable explorer

Now, imagine that you were coding with something like VS Code or PyCharm; you’d be in a scenario where if something goes wrong or you wanted to see how variables developed over time, you’d have to add print statements to see the output.

This gets insanely confusing, adds bloat to the code, and doesn’t help nearly as much as you think it would.

The second and “slightly smaller” reason Spyder is the best IDE for data science is because of the box it comes in.

There’s nothing worse than having to install a thousand different software programs on your computer to get things up and running.

With the anaconda download, you’ll be gifted everything that you need to get started with data science quickly.

And yes, you guessed it – Spyder sits right inside that download.

Once you’ve started coding and worked on a couple of projects, the idea behind creating virtual environments becomes incredibly important.

Basically, without diving too deep into it, you create baskets on your computer where you install specific modules. These modules will only exist in that basket and will allow you to run certain pieces of code once you’re inside the basket.

Now I bet you’re wondering where you create these baskets.

Well, you’re in for luck – because once you download the anaconda package, you’ll have the Conda environment on your laptop.

This allows you to create those baskets to install any modules and packages you want so that you can run any code you wish to.

What else could you want from one simple download?

Conda environment

So, to bring it all back home, Spyder is the best IDE for data science because it was made for data science. 

The anaconda download has everything you will ever need to be successful as a data scientist, from variable tracking to baskets to install modules into.

Why VS Code Is NOT The Best IDE For Data Science

VS Code may be a popular choice among software engineers, but it is far from the best IDE for data science.

Many of its features and plugins are wasted on data scientists since they are more tailored to developers than analysts.

VS Code also falls short with its lack of variable exploration and dataset-based tools.

Without these key features, working with and analyzing large amounts of data in VS Code can become tedious, complex, and time-consuming.

Therefore, while it is an excellent tool for coding, it isn’t as efficient or practical when dealing with datasets that play an integral part in data science.

Now I will say (and many have emailed me) if your only option at work is VS Code, then use VS Code – but if given the option, choose Spyder.

a real spider

What VS Code is Good For

VS Code is an excellent choice for software engineering tasks within a team.

software engineers coding away

It has an innovative and powerful autocompletion feature that helps speed up the coding process and a super simple folder architecture that takes zero time to learn.

The git and docker integration allows developers to compare branches, stage changes, manage containers and commit code easily.

Heavy terminal use is also fluid by default, with full support for everyday command line operations like running code, stopping code, and restarting your programs.

VS Code works so well with the terminal that one is actually integrated into the IDE.

And to top it off, it offers various development tools (made by the community) for working with docker containers and general microservices architecture.

This makes it super easy for software engineers to debug and see how APIs work between their microservices.

Should I Spend Money on an IDE For Data Science?

It really is sad – everyone on the internet has something to sell.

If you’re wondering if you should ever pay for an IDE for data science, the truth is the answer is no.

With open-source software such as Spyder and Visual Studio Code, you can access powerful tools without spending a single dime.

Both provide smart code completion, real-time analysis, debugging capabilities, and many other integrations with various packages – all powered by the community.

Plus, the interface is well-designed and user-friendly, so even beginners can get up and running quickly.

Therefore it’s hard to justify spending money on an IDE for data science or software engineering.

While I’m sure there are scenarios out there where you would need to pay for an IDE, like a custom-built software package for some niche of coding, if you’re breaking into this game, you probably don’t need it.

When Should I Use Jupyter Notebook vs. Spyder?

Jupyter Notebook and Spyder are two of the most popular choices regarding data science applications.

And luckily for you, they both come in the Anaconda download.


While both are useful for writing code and doing data science, some key differences between them should be considered when deciding which one to use.

I prefer Jupyter Notebook for hyperparameter tuning and final touch-up modeling due to its enhanced usability over Spyder. Jupyter Notebook is well suited for making small changes and updating scripts quickly to get the desired results.

I sometimes bring over code from Spyder into Jupyter Notebook (shamelessly) if I have difficulty debugging it in Spyder.

One downside to using Jupyter Notebook is that sharing it with other data scientists can be clunky or awkward compared to using conventional software like an IDE.

In these cases, using Spyder may be a better choice.

Understanding when to use Jupyter Notebook versus Spyder can help streamline your workflow and make tasks more efficient.

Generally, I’m in Spyder about 70% of the time, using jupyter notebook to primarily clean up my code.

Stewart Kaplan

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