PyCharm 2017.3 Help

Scientific Mode Tutorial


In this tutorial, we'll try to run and debug a Python code with graphs using PyCharm.


Make sure that the Matplotlib and numpy packages are added to your interpreter. Read the Installing, Uninstalling and Upgrading Packages section for details.


Add the Python file to the root of your project and enter the following code:

import numpy as np import matplotlib.pyplot as plt N = 50 x = np.random.rand(N) y = np.random.rand(N) colors = np.random.rand(N) area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii plt.scatter(x, y, s=area, c=colors, alpha=0.5) X = np.linspace(-np.pi, np.pi, 256,endpoint=True) C,S = np.cos(X), np.sin(X) plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-") plt.plot(X, S, color="red", linewidth=2.5, linestyle="-") plt.xlim(X.min()*1.1, X.max()*1.1) plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$']) plt.ylim(C.min()*1.1,C.max()*1.1) plt.yticks([-1, 0, +1], [r'$-1$', r'$0$', r'$+1$']) pass


Open the file for editing (F4) and right-click the editor background. On the context menu, choose Run 'sample'. The code is executed and shows two graphs in the SciView. Clicking the preview thumbnail displays the respective graph:

py matplotlib run


Let's put a breakpoint at the line:

This line appears twice in our example code, and so there will be two breakpoints.

Right-click the editor background and on the context menu choose Debug 'sample'.

You see the Debug tool window and the grey characters in the editor. This is the result of the inline debugging, which is enabled.

The line with the first breakpoint is blue highlighted. It means that the debugger has stopped at the line with the breakpoint, but has not yet executed it. If we execute this line (for example, by clicking the frames step over button on the stepping toolbar of the Debug tool window), we'll see the graph:

py matplotlib debug

Next, look at the Variables tab of the Debug tool window. If you right-click the area array, you'll see the View as Array command on the context menu.

Choosing this command results in opening the Data tab in the SciView:

py matplotlib view as array

Mind the only row of figures in the Data tab in the SciView - it's explained by the fact that the area array is one-dimensional.

Running in console

Right-click the editor background and choose the Run File in Console command:

py run file in console

This command corresponds to running a run/debug configuration for the file with the Show command line afterwards checkbox selected:

py run config for command line

When this command is run, the >>> prompt appears after the output in the Run tool window, and you can execute your own commands.


So, what has been done with the help of PyCharm?

  • The file was created and opened for editing.
  • The source code has been entered (note the powerful PyCharm's code completion!)
  • The source code has been run and debugged.
  • Finally, we ran the file in console.
Last modified: 28 March 2018

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