Scientific Tools
PyCharm Professional Edition helps you analyze your data with Python. Just create a scientific project, add your data, and start analyzing.
Start your analysis by running ad-hoc Python commands in the Python console. PyCharm helps you out by showing you all the variables you have created. You can also use PyCharm’s SciView to look deeper into your DataFrames and NumPy Series.
Visualizing data is an essential step in any data analysis, PyCharm helps you out by showing you your plots inside the IDE. PyCharm also keeps track of the last plots that you created, making it easy to spot changes between two plots.
After you’ve fine-tuned your commands, you can copy and paste them into a .py file, PyCharm will handle the formatting for you. You can easily divide your Python files into logical parts by defining code cells. Just create a comment that starts with #%% to start a cell.
Interactive Python Console
You can run a REPL Python console in PyCharm which offers many advantages over the standard one: on-the-fly syntax check with inspections, braces and quotes matching, and of course code completion.
Scientific Stack Support
PyCharm has built-in support for scientific libraries. It supports Pandas, Numpy, Matplotlib, and other scientific libraries, offering you best-in-class code intelligence, graphs, array viewers and much more.
Conda Integration
Keep your dependencies isolated by having separate Conda environments per project, PyCharm makes it easy for you to create and select the right environment.
Jupyter Notebook Integration
PyCharm integrates with Jupyter Notebook and delivers a solution that combines the advantages of Jupyter Notebook with the extra benefits that the most intelligent Python IDE can offer, including auto-completion, navigation, error checking, etc.
Scientific Project
Quickly get started with a new project by using PyCharm’s scientific project. This helps you set up both a Conda env and the folder structure for your next analysis; in one step.
SciView
To view a Pandas DataFrame, Pandas Series, or a NumPy array, you can run your project in a debug mode and find your data in the variables list shown in PyCharm’s graphical debugger. SciView is available from the integrated Python console as well.