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    Python offers several libraries for creating and visualizing 3D graphics, making it a versatile tool for data visualization, scientific computing, and more. Here are some popular libraries and examples of how to use them:

    Matplotlib

    Matplotlib is a widely-used library for 2D and 3D plotting in Python. It is built on NumPy arrays and integrates well with the broader SciPy stack. Although it mainly creates static 3D objects, it can be integrated with other software to produce interactive 3D plots.

    Example: 3D Histogram

    import matplotlib.pyplot as plt
    import numpy as np

    fig = plt.figure()
    ax = fig.add_subplot(projection='3d')
    x3 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    y3 = [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
    z3 = np.zeros(10)
    dx = np.ones(10)
    dy = np.ones(10)
    dz = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    ax.bar3d(x3, y3, z3, dx, dy, dz, zsort='average')
    plt.show()

    This code creates a 3D histogram using Matplotlib. The bar3d method is used to plot the histogram, and the zsort parameter arranges the bars properly.

    Plotly

    Is Python good for 3D modeling?
    Python is also well-suited for 3D modeling due to its readability, ease of use, and large number of libraries and packages for working with 3D objects and visualization. Python’s NumPy library provides support for numerical operations on arrays and matrices, making it a popular choice for scientific computing.
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