PyCharm running shortcut key list analysis

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Release: 2024-02-23 16:48:04
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PyCharm running shortcut key list analysis

PyCharm is a powerful Python integrated development environment that can improve development efficiency by skillfully using shortcut keys. This article will give you a detailed analysis of the commonly used running shortcut keys in PyCharm, and provide specific code examples to help you better master these techniques.

  1. Run the current file:
    Shortcut key: Shift F10
    This shortcut key can quickly run the currently opened Python file. For example, after you edit a Python script, press Shift F10 to execute the script directly in PyCharm.
print("Hello, PyCharm!")
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  1. Run the current line of code:
    Shortcut key: Ctrl Shift F10
    Use this shortcut key to run only the current line of code The line of code where the cursor is located. This is very convenient when debugging code.
num1 = 10
num2 = 20
result = num1 + num2
print(result)
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  1. Debug run:
    Shortcut key: Shift F9
    This shortcut key can quickly enter the debugging mode and modify the code Perform line-by-line debugging. In debug mode, you can view the values ​​of variables, set breakpoints, etc.
for i in range(5):
    print(i)
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  1. Stop running:
    Shortcut key: Ctrl F2
    Press this shortcut key to stop running immediately program. When the program has an infinite loop or freezes, you can use this shortcut key to forcefully stop the execution of the program.
while True:
    pass
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  1. Rerun the last program:
    Shortcut key: Shift F10
    Press this shortcut key to rerun The program executed last time was very convenient and fast.
print("Hello, World!")
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  1. Run the previous code block:
    Shortcut key: Ctrl Shift F10
    This shortcut key can run before the cursor The entire code block is very useful.
def add(num1, num2):
    return num1 + num2
    
result = add(5, 3)
print(result)
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By mastering the above commonly used running shortcut keys in PyCharm, combined with the demonstration of code examples, I believe that everyone can use these techniques more skillfully and improve Python development efficiency. I hope this article is helpful to everyone, thank you for reading!

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