Interpolation in Python Plot

Make sure you have Python installed on your machine. You can install the required library using the following command:
Interpolation is useful when you have a set of discrete data points and need to estimate values between them. This can be particularly valuable in scenarios such as data smoothing, function approximation, or creating continuous curves from sparse data.
Let's create a simple example dataset. We'll use the sine function and add some noise to simulate real-world data.
Now, let's interpolate between these data points using interp1d.
In this example, we've used both linear and cubic interpolation methods. The resulting plot will show the original data points, the linearly interpolated curve, and the smoothly interpolated cubic curve.
Interpolation is a powerful tool for estimating values between known data points, and Python provides convenient libraries to perform various interpolation techniques. Experiment with different interpolation methods and apply them to your datasets for smoother and more accurate representations of your data.
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