Five Best Choices for Python Learning Software
Five recommendations for choosing Python learning software, specific code examples are required
As a simple, easy-to-learn and powerful programming language, Python is favored by more and more people Love and attention. For beginners, choosing a good Python learning software will improve learning efficiency and experience. This article will recommend five Python learning software worth trying, and attach specific code examples so that readers can better understand and use them.
1. Python official website (www.python.org)
The Python official website is the official website of the Python programming language, providing the latest Python version downloads, official documents, tutorials and sample codes Resources such as this are an excellent platform for learning Python. The following is a simple sample code that demonstrates how to use Python's basic syntax to output "Hello World!":
print("Hello World!")
2. Jupyter Notebook (jupyter.org)
Jupyter Notebook is an open source An interactive notebook that supports multiple programming languages, including Python. It provides an interactive environment through the browser, where you can write and execute code directly, and add text, pictures, mathematical formulas, etc. The following is a sample code to draw a simple line chart in Jupyter Notebook:
import matplotlib.pyplot as plt x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10] plt.plot(x, y) plt.xlabel('x') plt.ylabel('y') plt.title('Simple Line Plot') plt.show()
3. Pycharm (www.jetbrains.com/pycharm/)
Pycharm is a powerful The Python integrated development environment (IDE) provides a wealth of functions and plug-ins, including code auto-completion, syntax highlighting, debuggers, etc. The following is a sample code that demonstrates how to use Pycharm to create a simple Python class definition:
class Rectangle: def __init__(self, width, height): self.width = width self.height = height def area(self): return self.width * self.height r = Rectangle(4, 5) print("The area of the rectangle is:", r.area())
4. Visual Studio Code (code.visualstudio.com)
Visual Studio Code is a free An open source lightweight code editor that supports multiple programming languages, including Python. It is characterized by simplicity and ease of use, rich functions, and mature plug-in ecology. The following is a sample code to implement a simple calculator function in Visual Studio Code:
def add(x, y): return x + y def subtract(x, y): return x - y def multiply(x, y): return x * y def divide(x, y): return x / y num1 = 10 num2 = 5 print("The sum is:", add(num1, num2)) print("The difference is:", subtract(num1, num2)) print("The product is:", multiply(num1, num2)) print("The quotient is:", divide(num1, num2))
5. Anaconda (www.anaconda.com)
Anaconda is a software for data science and The Python distribution for machine learning includes multiple commonly used Python libraries and tools, such as NumPy, Pandas, Scikit-learn, etc. It provides a complete set of data analysis and scientific computing environments, suitable for data processing, visualization and model training. The following is a sample code that shows how to use Anaconda's NumPy library for array operations:
import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) print("The sum of the arrays is:", np.add(a, b)) print("The difference of the arrays is:", np.subtract(a, b)) print("The product of the arrays is:", np.multiply(a, b)) print("The quotient of the arrays is:", np.divide(a, b))
The above are five Python learning software worth trying. They have their own characteristics in terms of functionality and usage experience. I hope that the code examples provided in this article can help beginners better understand and master Python programming. No matter which software you choose as a learning tool, continuous learning and practice are the most effective ways to improve your abilities. I hope every Python learner can continue to make progress in the world of programming!
The above is the detailed content of Five Best Choices for Python Learning Software. For more information, please follow other related articles on the PHP Chinese website!

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To run an ipynb file in PyCharm: open the ipynb file, create a Python environment (optional), run the code cell, use an interactive environment.

Solutions to PyCharm crashes include: check memory usage and increase PyCharm's memory limit; update PyCharm to the latest version; check plug-ins and disable or uninstall unnecessary plug-ins; reset PyCharm settings; disable hardware acceleration; reinstall PyCharm; contact Support staff asked for help.

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