You can use the following basic syntax to plot a line of best fit in Python:

**#find line of best fit
a, b = np.polyfit(x, y, 1)
#add points to plot
plt.scatter(x, y)
#add line of best fit to plot
plt.plot(x, a*x+b)
**

The following example shows how to use this syntax in practice.

**Example 1: Plot Basic Line of Best Fit in Python**

The following code shows how to plot a basic line of best fit in Python:

import numpy as np import matplotlib.pyplot as plt #define data x = np.array([1, 2, 3, 4, 5, 6, 7, 8]) y = np.array([2, 5, 6, 7, 9, 12, 16, 19]) #find line of best fit a, b = np.polyfit(x, y, 1) #add points to plot plt.scatter(x, y) #add line of best fit to plot plt.plot(x, a*x+b)

**Example 2: Plot Custom Line of Best Fit in Python**

The following code shows how to create the same line of best fit as the previous example except with the following additions:

- Customized colors for the points and the line of best fit
- Customized style and width for the line of best fit
- The equation of the fitted regression line displayed on the plot

import numpy as np import matplotlib.pyplot as plt #define data x = np.array([1, 2, 3, 4, 5, 6, 7, 8]) y = np.array([2, 5, 6, 7, 9, 12, 16, 19]) #find line of best fit a, b = np.polyfit(x, y, 1) #add points to plot plt.scatter(x, y, color='purple') #add line of best fit to plot plt.plot(x, a*x+b, color='steelblue', linestyle='--', linewidth=2) #add fitted regression equation to plot plt.text(1, 17, 'y = ' + '{:.2f}'.format(b) + ' + {:.2f}'.format(a) + 'x', size=14)

Feel free to place the fitted regression equation in whatever **(x, y)** coordinates you would like on the plot.

For this particular example, we chose (x, y) = (1, 17).

**Additional Resources**

The following tutorials explain how to fit various regression models in Python:

A Complete Guide to Linear Regression in Python

How to Perform Polynomial Regression in Python

How to Perform Quantile Regression in Python