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Overview

What is Spyder?

Spyder is a free and open source scientific environment for Python. It combines advanced editing, analysis, debugging, and profiling, with data exploration, interactive execution, deep inspection, and visualization capabilities. Spyder is sponsored by open source supporters QuanSight, and NumFOCUS, as…

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Recent Reviews

TrustRadius Insights

Users have found Spyder to be a valuable tool for data science and machine learning. Its robust environment enables users to analyze, …
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Spyder Review

9 out of 10
July 08, 2021
Spyder is a great tool to work in the field of Data Science and Machine learning. Spyder is IDLE that provides us a good environment to …
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Pricing

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What is Spyder?

Spyder is a free and open source scientific environment for Python. It combines advanced editing, analysis, debugging, and profiling, with data exploration, interactive execution, deep inspection, and visualization capabilities. Spyder is sponsored by open source supporters QuanSight, and NumFOCUS,…

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Product Demos

Working with Spyder - Part 3: Optimizing code

YouTube

Working with Spyder - Part 2: Improving your code quality

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Working with Spyder - Part 1: Beyond the main panes

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First steps with Spyder - Part 3: Customization

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Spyder FAQ: How do I use additional packages if I downloaded Spyder from the standalone installers?

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First steps with Spyder - Part 2: Learning the basics

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Product Details

What is Spyder?

Spyder is a free and open source scientific environment for Python, built for scientists, engineers and data analysts. It combines advanced editing, analysis, debugging, and the profiling functionality of a comprehensive development tool with data exploration, interactive execution, deep inspection, and visualization capabilities similar to a scientific package. Spyder is sponsored by open source supporters QuanSight, and NumFOCUS, as well as individual donors.

Spyder Technical Details

Deployment TypesOn-premise
Operating SystemsWindows
Mobile ApplicationNo

Frequently Asked Questions

Spyder is a free and open source scientific environment for Python. It combines advanced editing, analysis, debugging, and profiling, with data exploration, interactive execution, deep inspection, and visualization capabilities. Spyder is sponsored by open source supporters QuanSight, and NumFOCUS, as well as individual donors.

Spyder starts at $0.

The most common users of Spyder are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(32)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Users have found Spyder to be a valuable tool for data science and machine learning. Its robust environment enables users to analyze, document, and transform data effectively. Many organizations rely on Spyder as their go-to coding platform, utilizing its features to write code, track code history, and enhance model performance. Being free and open-source, Spyder has gained a reputation as one of the best tools in the field.

Spyder finds extensive application in market research, where it is used for running Python code and conducting data analysis. Data analytics teams also utilize Spyder to build functions that can be easily shared among team members, streamlining the analysis and data cleaning processes. For those transitioning from R to Python, Spyder offers a familiar interface similar to RStudio when used via Anaconda. This makes it a popular choice among users coming from an R background.

One of the standout features of Spyder is its ability to run code line by line, facilitating efficient debugging and problem-solving for users. This functionality has proven invaluable in identifying and resolving issues within code. Additionally, Spyder has played a crucial role in helping users grasp the fundamentals of Python programming and enhance their understanding of key programming concepts.

In educational settings, Spyder is often incorporated into machine learning courses to provide students with hands-on experience coding in Python. It serves as a preferred tool for data scientists due to its similarity to RStudio and its capability to run code line by line, enabling better comprehension and mastery of Python programming techniques.

  • Well-Formatted Code Comments: Many users have appreciated the well-formatted comments in the code, which have made it easier for them to read and understand the codebase. These clear and organized comments enhance the overall readability and maintainability of the code.

  • Free and Open Source: Several reviewers have found it beneficial that Spyder is free and open source. This allows them to utilize any library in Python for their data analysis and reporting tasks without any cost implications. The availability of a wide range of libraries enhances their ability to perform complex analyses efficiently.

  • Tailored for Python: A significant number of users have mentioned that Spyder is specifically designed for Python, making it highly suitable for data analysis and reporting operations in that programming language. Its integration with Python's ecosystem ensures seamless compatibility with popular scientific computing libraries such as NumPy, Pandas, and Matplotlib.

Limited data analysis capabilities: Some users have expressed that Spyder could improve its data analysis capabilities, particularly in how it presents plots and charts. They feel that the software lacks certain features or functionalities that are essential for their analytical needs.

Lack of visually attractive user interface: Several reviewers have mentioned that they would like to see a more visually attractive user interface in Spyder. They believe that a more modern and appealing design would enhance their overall experience with the software.

Difficulty working collaboratively: A number of users desire the ability to work on projects collaboratively in real-time, but they find it challenging to do so in Spyder. They suggest adding features or functionality that would enable seamless collaboration among team members.

Attribute Ratings

Reviews

(1-2 of 2)
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Xiaotong Song | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
We use Spyder via anaconda in our organization. It is a decent python IDE that gives you a very similar feeling when an R user just transferred from R to python. The IDE allows users to run code line by line and make the debugging work much easier than doing it directly.
  • Free
  • Line by line debugging
  • Similar to Rstudio
  • Old interface
  • Not easy to change ENV
  • Only work with python
Spyder is suitable for a company that either does not have a considerable budget or does not want to spend on the tools that cost a crazy amount of money. In the meantime, have a group of real data scientists/data engineers to perform the data science analytic work.
  • Cost efficient
  • Easy debugging
  • Old fashion interface
For PyCharm, if you choose the professional edition, you will have to pay an annual fee for it. Even your company is allowing those expenses. You might find it is still not worth it to pay for that since you can get a free community version for free or the Spyder for free.
Most of data scientists or data engineers are either using ec2 on the cloud or Atom or PyCharm locally. It is a bit hard to find people who are still using Spyder and have the sight of the IDE and can help you to answer your question.
Docker, Atom, PyCharm
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Spyder is used by some data scientists at our company. I am one of these people who are still using Spyder. Spyder is a good tool if you are coming from R background since the interface can be changed to almost the same as RStudio and also allows you to run line by line.
  • Free.
  • Familiar.
  • Line execution.
  • Old style.
  • Poor layout.
  • Terminal hard to find.
If you are a data scientist coming from R background and trying to adopt Python now, Spyder might be a good first stop for you. You have to keep the same coding habits from R to Python via Spyder. In here, you can have similar to RStudio interface and code running mechanic.
  • Easy to use.
  • Quick switch from R.
  • Hard to manage env.
First of all, for PyCharm, the layout is better than Spyder from my own experience and interaction. However, Spyder can allow you to arrange the layout by yourself but the layout for PyCharm is fixed. Second, if you choose PyCharm Professional, you need to pay an annual fee to use it.
If your question is related to Python, you can find most answers almost everywhere by only searching Google. However, if the support you need is only related to Spyder, you might find it is a little bit hard to navigate down since the user base for Spyder is not too large.
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