Python IDEs For Data Science

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.
2. Spyder – Spyder is an open source 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.
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.
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.
5. Pycharm – Pycharm is an 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.
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.
7. Thonny – Thonny is an IDE that was developed at The University of Tartu for Python. It is created for beginners that are learning to programme 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.
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.
9. Geany – Geany is a free text editor that supports Python and contains IDE features as well. 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.