Is PyCharm a Good Tool for Data Scientists?

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    Data science is well suited to PyCharm. The program includes the Python terminal and functions well with many scripts. PyCharm provides several outstanding features, including a debugger and superior project maintenance tools, and student licenses are readily available.


    You've come to the correct place if you want to learn more about PyCharm and whether or not it's a good tool for data science. In this article, we will go over the detailed introduction to the PyCharm tool. Let’s get started! 


    What is PyCharm? 


    An Integrated Development Environment is PyCharm (IDE). IDEs are programming tools that let you write, test, and debug code in addition to writing it. However, iNotebooks like Jupyter Notebook incorporate some of the functionality of IDEs like PyCharm.


    The main advantage of utilizing an IDE instead of writing code in Word or Notepad is the ability to run your code from within the same program. Some word processors do, however, now provide plug-ins that can test and debug code.


    PyCharm is simple for seasoned programmers and data scientists already familiar with Python or Anaconda. If you routinely use libraries like NumPy and Matplotlib, you'll find that PyCharm contains everything you need. You can explore and learn them by joining the data science certification course in Pune.


    Do You Like PyCharm?


    One of the greatest IDEs on the market, PyCharm, was created by JetBrains, the company that also created IntelliJ IDEA.


    PyCharm works well for people who already use Python, Anaconda, and other programming languages because it can handle many scripts, has strong debugging tools, and more.

    The best option for data science is PyCharm, but you might need to purchase the Professional Edition to use the data science tools.


    What attributes does it possess?


    PyCharm, a program from Jetbrains, is brimming with helpful features that will make your work easier. Features of PyCharm include:


    • Javascript, CSS, SQL, and more are supported.

    • editable code

    • Integration with Git, SVN, and Mercurial error highlighting

    • Keybinding and interface customization

    • Library of PyCharm plug-ins


    Is PyCharm a good choice for new users?


    Using PyCharm has a learning curve, but every software has one for new users. On the one hand, PyCharm is wonderful for individuals accustomed to working with Python, which can turn off those unfamiliar with the language and its applications. But everyone needs to start somewhere, and PyCharm can be a great starting point because of its functionality targeted toward easily maintainable code.


    Benefits of PyCharm


    • Integration with Python, Anaconda, Git, and Mercurial

    • supports numerous other scripts

    • fantastic debugger

    • a vast library of plug-ins

    • Free Community Edition and Student License are both available

    • Tools for project maintenance


    Drawbacks of PyCharm


    • The community edition does not include any scientific equipment.

    • It could be difficult to learn to use

    • It needs a lot of memory.


    The display of data plots, scientific libraries, and Conda integration are just a few of PyCharm's outstanding data science capabilities. However, you must have the Professional Edition to use PyCharm's data science features. Jupyter Notebook will be included with your software, enabling you to use it in conjunction with PyCharm.




    In this article we reviewed and examined PyCharm's suitability for data science, the differences between its Community and Professional editions, and how to obtain a student license. Additionally, we discussed how you could modify the code for PyCharm's Community Edition and outlined some advantages and disadvantages of PyCharm. Head over to the data science course in Pune to master PyCharm and other data science tools for a smooth project.