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Best Python IDEs For Data Science in 2024

Last Updated : 27 Feb, 2024
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It is easier for anyone to take a decision if they have any existing data regarding that, and as Data-driven decision-making is increasing in companies, the demand for efficient and powerful Python IDEs is increasing for Data Science. And it is very important to select the correct Python IDE for Data Science due to the unique capabilities and features of each IDE.


In, this article we will first understand the overview of data science and explore the best Python IDEs for Data Science and data analytics. From user interfaces to integration with data frameworks like Pandas and NumPy, our list of Python IDEs will cater to a diverse range of data science tasks.

Best Python IDEs For Data Science in 2024

Data Science is a field that is used to study and understand data and draw various conclusions with the help of different scientific processes. Python is a popular language that is quite useful for data science because of its capacity for statistical analysis and its easy readability. Python also has various packages for machine learning, natural language processing, data visualization, data analysis, etc. that make it suited for data science.

Some of the Python IDE’s that are used for Data Science are given as follows:

1. Jupyter Notebook

Jupyter Notebook is an open-source IDE that is used to create Jupyter documents that can be created and shared with live codes. Also, it is a web-based interactive computational environment. The Jupyter Notebook can support various languages that are popular in data science such as Python, Julia, Scala, R, etc. This is known as IDE for Data Science.

To learn more, refer to this article – Jupyter Notebook

Jupyter Notebook

2. Spyder

Spyder is an open-source Python IDE that was originally created and developed by Pierre Raybaut in 2009. It can be integrated with many different Python packages such as NumPy, SymPy, SciPy, pandas, IPython, etc. The Spyder editor also supports code introspection, code completion, syntax highlighting, horizontal and vertical splitting, etc. It is considered one of the best Python IDEs for data science field.


3. Sublime text

Sublime text is a proprietary code editor and it supports a Python API. Some of the features of Sublime Text are project-specific preferences, quick navigation, supportive plugins for cross-platform, etc. While the Sublime text is quite fast and has a good support group, it is not available for free. Perhaps a lot of good features are available as well, and you can build a basic project in it. For more advance and complex loaded features, you have to take its premium subscription. Also, it is one of the best choices among Data Scientists.

To learn more, refer to this article – Sublime text

Sublime text

4. Visual Studio Code

Visual Studio Code is a code editor that was developed by Microsoft. It was developed using Electron but it does not use Atom. Some of the features of Visual Studio Code are embedded Git control, intelligent code completion, support for debugging, syntax highlighting, code refactoring, etc. It is also quite fast and lightweight as well. Data Scientist also prefers this IDE over other IDEs, due to its extensive feature list.

To learn more, refer to this article – Visual Studio Code

Visual Studio Code

5. Pycharm

Pycharm is a Python IDE developed by JetBrains and created specifically for Python. It has various features such as code analysis, integrated unit tester, integrated Python debugger, support for web frameworks, etc. Pycharm is particularly useful in machine learning because it supports libraries such as Pandas, Matplotlib, Scikit-Learn, NumPy, etc.

To learn more, refer to this article – Pycharm


6. Rodeo

Rodeo is an open-source IDE that was developed by Yhat for data science in Python. So Rodeo includes Python tutorials and also cheat sheets that can be used for reference if required. Some of the features of Rodeo are syntax highlighting, auto-completion, easy interaction with data frames and plots, built-in IPython support, etc. It is the best Python IDE for data science students.Rodeo

7. Thonny

Thonny is a Python IDE that was developed at The University of Tartu for Python. It is created for beginners that are learning to program in Python or for those that are teaching it. Some of the features of Thonny are statement stepping without breakpoints, simple pip GUI, line numbers, live variables during debugging, etc. You can use it as Python IDE for Data Science to leans the basic.


8. Atom

Atom is an open-source text and code editor that was developed using Electron. It has multiple features such as a sleek interface, a file system browser, various extensions, etc. Atom also has an extension that can support Python while it is running and work as a Python IDE, where you can work for data science.

To learn more, refer to this article- Atom


9. Geany

Geany is a free text editor that supports Python and contains IDE features as well and it is considered as one of the best Python IDEs for data science. It was originally authored by Enrico Tröger in C and C++. Some of the features of Geany are Symbol lists, Auto-completion, Syntax highlighting, Code navigation, Multiple document support, etc.

To learn more, refer to this article- Geany



So, to sum it all up, Python IDEs for data science are like super cool tools that make coding easier and more fun! With these special programs, data scientists can write and test their code all in one place, to create amazing stuff and solve real-world problems. Also, each IDE has its unique features and capabilities, and the IDEs which are specifically made for Python are best suited for data science. So, if you want to dive into the world of data science and be a coding geek, don’t forget to pick the best Python IDE that suits you and get ready for some coding magic!

FAQs on Best Python IDEs For Data Science

What are the top Python IDEs For Data Science?

These are the best Python IDEs for Data Science

1. Jupyter Notebook
2. Spyder
3. Sublime text
4. Visual Studio Code
5. Pycharm

What Python topics are needed for data science?

These are the must-known Python topics before learning data science

  1. Integers and Floating-Point Numbers in Python
  2. Strings in Python
  3. Boolean values in Python
  4. Operators in Python
  5. F-string formatting in Python
  6. Data Types in Python
  7. List Comprehension in Python

What are IDEs?

IDEs (Integrated Development Environments) are special computer programs or software applications that help programmers write and test their code easily in one place, making it simpler to create awesome software applications!

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