python-scipyHow do I use Python Scipy to perform a linear fit?

Using Scipy to perform a linear fit is quite easy. First, import the scipy.optimize module which contains the curve_fit function:

from scipy.optimize import curve_fit

Then, define a function that describes the linear model:

def linear_model(x, a, b):
    return a * x + b

Next, define the data points to fit:

x_data = [0, 1, 2, 3]
y_data = [1, 3, 5, 7]

Finally, use the curve_fit function to fit the linear model to the data:

params, params_covariance = curve_fit(linear_model, x_data, y_data)

The params variable contains the two parameters of the linear model, a and b, which are the slope and intercept respectively. For example, the output of the above code is:

params = [2. 1.]

This means that the linear model is y = 2x + 1.

Code explanation

**

1. from scipy.optimize import curve_fit - imports the curve_fit function from the scipy.optimize module
2. def linear_model(x, a, b): - defines a function that describes the linear model
3. x_data = [0, 1, 2, 3] - defines the x-values of the data points
4. y_data = [1, 3, 5, 7] - defines the y-values of the data points
5. params, params_covariance = curve_fit(linear_model, x_data, y_data) - uses the curve_fit function to fit the linear model to the data

## Helpful links

• Scipy Optimize Documentation
• Python Curve Fitting Tutorial

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