Data & Analytics Blog


Duking It Out: Jupyter vs Spyder Data Analysis Software

Posted by LPA Software Solutions on Jun 18, 2020 10:46:00 AM

At least 2.5 quintillion bytes of data get created every day. This includes everything from videos to text and more. This barrage of info has earned the name big data, a name that has quickly become a buzzword in a variety of industries.

Businesses need the best possible programs to organize and analyze all of the information they manage so they can use it. There are a variety of options, and it can be difficult to choose between them.

Data analysis is one of the best ways to ensure your information is usable. Two of the most popular data analysis systems on the market today are Jupyter and Spyder. Seeing the two options duking it out is one of the best ways to decide which is the best option for you.

Read on as we compare the pros and cons of Sypder vs. Jupyter to see who will emerge as the best data analysis system. 

Duking It Out: Jupyter vs. Spyder

At least 53% of companies around the world and 55% of US companies are using big data analytics. Businesses that use big data noticed an increase in profit of 8-10% and an overall cost reduction of 10%. This is part of the reason why the big data industry will be worth $77 billion by 2023. The 

A data analysis system lets you access and use all the information available in the nebulous online world of big data. There are several options, but it's important to choose the best one for you.

Jupyter and Spyder are both effective options for accessing this wealth of information, but they differ in many ways. Letting them duke it out is the most effective way to quickly compare the two to ensure you make the right choice.


2.5 million Jupyter notebooks were shared in 2018, up from only 200,000 in 2015. Knowing what Jupyter notebook is lets you understand its rise in popularity. 

The free, open-source computing environment supports over 40 programming languages. It has three components: the notebook application for writing and running codes, kernels that control the system, and documents that contain all the notebook's code and its corresponding kernels.

Jupyter software is easy to install. Users just have to execute the Python Pip command, and it comes included with the installation of the Anaconda system.

Jupyter is an interactive platform allowing large teams to work together with a wide variety of different types of data. This makes it easier for large teams to work together to combine their information.

Jupyter isn't picky about what file formats you use. This makes it easy to share your data between team members and customers and ensure they can actually open and read it.

The software supports PDF, HTML, and JSON or JavaScript Object Notification formats for exporting. It's also easy to convert files into whatever other formats you need, including HTML and Markdown. 

Other benefits include improved exploratory data analysis, cell caching, a feedback system, and an easy learning curve. The interactive program lets an entire team easily manage their data.

Problems may arise from Jupyter software. It can frustrate programmers by running code cells out of order. It also makes it harder to organize and test code. Extra steps like moving analysis code to external files, defining key variables, and restarting and running the notebook are required.

The world of data analysis is constantly changing, and what is true about a program today may not be tomorrow. For more information, find out more about Jupyter notebook and how it's changing.


Spyder is free, cross-platform, and open-source. It has a multi-language text editor with plenty of features, and users can add even more by installing plugins. 

Spyder's text editor comes with a range of included features. Some of the most useful are syntax highlighting, integrated libraries, and code completion.

Sypder makes it easy to find what you're looking for in your code. It features a go-to definition feature for finding component definitions and an outline explorer as well.

Sypder lets users create their own code cells. These sections of lines can then all be executed at the same time.

Spyder includes an internal debugger known as PDB. It shows breakpoints and lets you edit their variables.

Spyder also has a Git version control system. This feature helps make sure you're using the latest version and that outdated components aren't affecting the software.

Spyder also makes it easy for newbies to begin. It includes a wide range of easily accessible help documents to explain all the essential features. 

There are also drawbacks to this software. It requires additional plugins to get the most features and doesn't support as many file types or formats.

Sypder vs. Jupyter

It's almost impossible to say whether Spyder or Jupyter is the better system. If it were a popularity contest, Jupyter would definitely win.

There are aspects of each software that may make them better for a particular business, and these are what you should use to choose between the two.

If you have a large team that manages several types of data, then your winner is Jupyter. It's made for collaboration and easy sharing, making it a popular option even outside of the data analytics field.

If you're a more scientific operation or lack technical knowledge, Spyder may be the best choice for you. The program was specifically designed for data analysis and makes it easy to write, analyze, and test code. It also includes a wide range of scientific libraries, making it able to handle complex terms and functions. Its help features make it easy for anyone to learn.

Where to Get Data Analysis Help

Choosing the right software is a process of watching them duking it out. You can compare their pros and cons to see which is the best fit for you.

Spyder and Jupyter are free, open-source options that make managing data easier. Large operations can communicate better with Jupyter, but Spyder makes it easy to create and manage complex code.

Even with the right software, managing large amounts of data can feel like an impossible task without a team to help you.

Check out our project portfolio to see who we've already helped, and let us develop a winning data strategy for your business today.

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Topics: Planning Analytics, Data Science & AI