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4 Top Python IDE for Data Scientist

Jupyter Notebook IDE alternative you want to use

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Photo by Shamsudeen Adedokun on Unsplash
Photo by Shamsudeen Adedokun on Unsplash

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IDE or Integrated Development Environment is a code programming tool used for writing, testing, debugging, and intuitively compile code. Any people who work with the programming language would need an IDE to make their job easier. Well, you could code in the notepad, but obviously, why you want to do that? – There are many IDE developed that support our works.

For you who already familiar with the Jupyter Notebook, it is also an IDE that is interactive, beginner-friendly, and could be used for presentation. It is a great tool for any level of Data Scientist. However, you would realize that Jupyter Notebook lacks any development and debugging purposes. This is because Jupyter Notebook is mainly developed for testing and document sharing rather than code development. Hence you might want to look into another IDE that still suits Data Scientist needs but could use for development.

In this article, I want to share my top 4 IDE for Data Scientist that I used in a different situation. Let’s get into it.


1. Spyder

Scientific Python Development Environment or Spyder is a free, open-source Python IDE. If you are installing the Anaconda, one of the software available to install for you is Spyder. So, what is special about Spyder? Spyder is an IDE built specifically for Data Science. The GUI is simple, but the learning curve is not steep that If you are a beginner, you might master it easily. Let’s try to take a look at the overall GUI.

Spyder GUI (Image from Author)
Spyder GUI (Image from Author)

Note how this interface is quite similar to RStudio; That’s why, if you’re switching between Matlab or R to Python, you will find it easier. Let’s try to break down the GUI at a glance.

Spyder Code Cell (Image from Author)
Spyder Code Cell (Image from Author)

Above is the environment where you write your code. Notice that there is an auto-completion feature with all the common code you use during data analysis. Moreover, there is a syntax highlighting feature that help you find any variable or similar code swiftly. Let’s go to the other feature available on Spyder.

Spyder Feature Toolbar (Image from Author)
Spyder Feature Toolbar (Image from Author)

Above is four default tabs that could help you navigate the data science project you have. Note that there are more tabs available once you used them more frequently. The help tab would be your best friend for a beginner because it shows you the Spyder Tutorial.

Help Tab (Image from Author)
Help Tab (Image from Author)

The help feature also extends to online help, which allows you to search for specific information about libraries. In the next tab, you would also have the variable explorer.

Variable Tab (Image from Author)
Variable Tab (Image from Author)

And if you produce any plots, it is served within the plots tabs. You could explore all the plots you produce here and saved the plot.

Plots tab (Image from Author)
Plots tab (Image from Author)

There are still many tabs produce after you exploring the IDE more. Everything you need is all in the tutorial, and it is easy to follow.

In my opinion, you would like to use Spyder IDE if you previously come from RStudio IDE and try to find something similar to it in Python.


2. Thonny

Thonny is an IDE developed for learning and teaching programming. It’s developed at The University of Tartu, which you can download for free on the Bitbucket repository.

Thonny IDE (Image by Author)
Thonny IDE (Image by Author)

Thonny is very nice for beginners because it can show you the step-through statements and expressions with the assistant page’s help. Thonny also offers a nice editing function where a new window is opened with local variables and the code being shown separately from your main code.

Assistant tab on Thonny (Image by Author)
Assistant tab on Thonny (Image by Author)

All the tools you can explore are available in the view tab, which is shown below.

Thonny View Tab (Image by Author)
Thonny View Tab (Image by Author)

Moreover, Thonny offers you the Python script debugging process, which runs neatly. Overall, the purpose of Thonny is to give you a good understanding of how Python works under the hood and help you simplify the coding process. In my opinion, Thonny is suitable for a beginner who wants to learn more about the Python language.


3. Atom

Atom is an open-source IDE developed by GitHub. The developers call it a "hackable text editor for the 21st Century" because Atom enables users to install third-party packages and themes. You can customize the features and looks of the editor as you prefer, so it is a highly versatile IDE. Moreover, Atom is both beginner-friendly and highly customizable for an experienced developer.

Atom IDE (Image by Author)
Atom IDE (Image by Author)

Above is the screen showing up when you open the IDE for the first time. On this screen, you are served by all the tutorial and Atom features you could use. Here are some features I thought were the Atom main selling point for Data Scientist:

  • The Version Control with Git and GitHub tracking panel is embedded on the IDE.
Git panel (Image by Author)
Git panel (Image by Author)
  • The Third-Party Package Installer on Atom IDE
Third-Party Installer (Image by Author)
Third-Party Installer (Image by Author)

The best package you could install for a Data scientist is Hydrogen. It allows you to possess the Jupyter Kernel power on the Atom IDE.

Hydrogen Package (Image created by Author)
Hydrogen Package (Image created by Author)
  • Code Collaboration with Teletype
Teletype panel (Image created by Author)
Teletype panel (Image created by Author)

4. PyCharm

PyCharm is an IDE made by JetBrain whom also develop the famous Java IDE, the IntelliJ IDEA.

PyCharm is perfect for those who already have experience in development or programming language with another IDE. Pycharm, like Anaconda distribution, also integrates its tools and libraries such as NumPy and Matplotlib, allowing you to work with an array of viewers and interactive plots. Also, just like Atom IDE we have discussed previously, PyCharm provides version-controlling integration and debugging capability.

Besides, PyCharm supports many other development programming languages such as Node.js, HTML/CSS, and many more. This makes PyCharm versatile enough to use in integration with web and app development language.

You can download PyCharm here. There are two versions available, but you might want the Community version as it is the free one (The features are still superb, so no worries).

. PyCharm IDE (Image created by Author)
. PyCharm IDE (Image created by Author)

Moreover, PyCharm provides enhancement on their IDE by allowing you to install various Plugins.

Plugin on PyCharm
Plugin on PyCharm

You might want to explore more with PyCharm if you are an experienced Data Scientist who wants to dwell more in the development area. Otherwise, you could use another IDE I have mentioned previously.


Conclusion

As a Data Scientist, you might have the experience to develop your data science project with the Jupyter Notebook. Even though it is a great IDE, you would realize that Jupyter Notebook lacks few features such as development and debugging purposes.

There are many alternative IDE as a data scientist you could use, but my top 4 are:

  1. Spyder
  2. Thonny
  3. Atom
  4. PyCharm

I hope it helps!


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