Is Spyder Faster Than Jupyter

Python Programming

Recently, I’ve been exploring different Python IDEs for my development projects, and one question that came to my mind was whether Spyder or Jupyter is faster. Both these IDEs are popular among Python developers, but they have different strengths and weaknesses. In this article, I’ll share my personal experiences and delve deep into the performance aspects of Spyder and Jupyter.

Introduction to Spyder and Jupyter

Spyder is an open-source IDE that is specifically designed for scientific computing and data analysis. It provides a feature-rich environment with tools for debugging, profiling, and exploring data. Spyder is built on top of Qt and incorporates the power of the IPython kernel.

On the other hand, Jupyter is an interactive computing platform that enables users to create and share documents called notebooks. These notebooks can contain live code, equations, visualizations, and narrative text. Jupyter supports several programming languages, including Python, and is widely used for data exploration, data analysis, and machine learning.

Performance Comparison: Spyder vs Jupyter

When it comes to performance, there are several factors to consider. Let’s dive deep into each of these factors:

Startup Time

One noticeable difference between Spyder and Jupyter is their startup time. Spyder takes longer to start compared to Jupyter. This is because Spyder loads a lot of modules and dependencies upfront, which can increase the overall startup time. On the other hand, Jupyter has a relatively faster startup time as it only needs to initialize the Jupyter kernel and the web interface.

Code Execution Speed

Both Spyder and Jupyter use the same IPython kernel for code execution, which means that the performance of executing code should be similar. However, in my experience, I found that Spyder tends to be slightly faster when it comes to executing code. This could be due to the optimized code editor and the tight integration with the IPython kernel.

Memory Usage

Memory usage is an important aspect to consider, especially when working with large datasets or running resource-intensive computations. In my tests, I found that Jupyter consumes less memory compared to Spyder. This could be because Jupyter notebooks have a granular execution model, where each cell can be executed independently, allowing for better memory management.

Workflow and Productivity

While performance is a crucial factor, it’s also important to consider the workflow and productivity aspects of the IDEs. Spyder is known for its feature-rich environment that provides powerful debugging tools, variable exploration, and integrated documentation. This makes it an excellent choice for scientific computing and data analysis projects. On the other hand, Jupyter’s interactive notebooks allow for seamless integration of code, visualizations, and explanations, making it an ideal choice for exploratory data analysis and interactive data storytelling.


In conclusion, the question of whether Spyder is faster than Jupyter doesn’t have a straightforward answer. While Spyder may have a slower startup time, it offers a rich feature set and optimized code execution. On the other hand, Jupyter has a faster startup time, lower memory usage, and a unique interactive notebook experience. Ultimately, the choice between these IDEs depends on your specific use case and preferences.