TrustRadius
PyCharm for Big Data Analytics
https://www.trustradius.com/static-code-analysisPyCharmUnspecified8.967101
David Crawford profile photo
June 12, 2018

PyCharm for Big Data Analytics

Score 9 out of 101
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Overall Satisfaction with PyCharm

PyCharm is being used by the Business Intelligence department because Python is their language used for big data analytics. We needed a comprehensive IDE for Python in order to utilize good debugging tools and plugin managers, and we needed a one-stop-shop to take care of all of the Python dependencies including Anaconda.
  • Debugging tools are great, and coming from experience with other IDEs, this was a breeze and was absolutely needed.
  • Switching Python versions easily from 2.x-2.x whenever we needed.
  • The package manager was great, and I'm very visual and appreciate good UIs that let me see what I have installed and manage them.
  • The easily added scratch pads are great, especially if I don't want to make another project to try a simple snippet.
  • You can't fire it up on a big project and expect to get right into the game. PyCharm has to scan everything every time it runs, which can take time and is very annoying.
  • If you have many versions of Python, it can take some fiddling to get PyCharm to recognize them all in the proper order.
  • Great project turnaround times when we were able to use the debugging tools and other features offered by an IDE.
  • We have a small BI developer group, and the licensing was reasonable and easily managed by the machines.
There isn't a ton of great competition for PyCharm, aside from text editors. Atom came closest to functionality but required a lot of plugins to be added, and it's not stable when interacting with massive data frames. A full Python IDE with commercial support was what we needed, and most of the tools out there couldn't offer a good experience, good support, or a well-made tool.
It's great for data analytics due to the debugging tools allowing you to step into any part of your process. It worked well with Anaconda and we could visually inspect data frames and how they were evolving with our functions.