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Okay, let's dive into creating plot line graphs from NumPy arrays using Matplotlib. This comprehensive tutorial will cover the basics, customization options, handling multiple plots, and more advanced techniques.

*Core Libraries and Concepts*

Before we begin, make sure you have NumPy and Matplotlib installed:



*NumPy:* NumPy (Numerical Python) provides powerful tools for working with arrays, which are essential for storing and manipulating numerical data.
*Matplotlib:* Matplotlib is a versatile plotting library in Python. Its `pyplot` module is most commonly used for creating line plots, scatter plots, bar charts, histograms, and many other types of visualizations.

*1. Basic Plotting with `pyplot.plot()`*

The foundation of line plotting in Matplotlib is the `pyplot.plot()` function. Let's start with a simple example:



*Explanation:*

1. *Import Libraries:* We import `numpy` as `np` and `matplotlib.pyplot` as `plt`.
2. *Create Data:*
`np.linspace(0, 10, 100)` creates an array of 100 evenly spaced values between 0 and 10. This serves as our x-axis data.
`np.sin(x)` calculates the sine of each value in the `x` array. This provides the corresponding y-axis data.
3. *`plt.plot(x, y)`:* This is the core plotting function. It takes two arguments:
`x`: The array of x-coordinates.
`y`: The array of y-coordinates. Matplotlib connects the points `(x[i], y[i])` with lines.
4. *Labels and Title:*
`plt.xlabel("X-axis")` sets the label for the x-axis.
`plt.ylabel("Y-axis")` sets the label for the y-axis.
`plt.title("Sine Wave")` sets the title of the plot.
5. *`plt.show()`:* This displays the plot. Without `plt.show()`, the plot might not be visible in some environments (especially when running scripts outside of interactive notebooks).

*2. Customizing the Plot*

Matplotlib offers a wide range of customization options to enhance the visual appearance of your plots.

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