In this tutorial, we will look at how to add a trendline to a matplotlib scatter plot with the help of some examples.

## Steps to add a trendline to a plot

To add a trendline to a plot in matplotlib –

- First, plot your scatter plot with the relevant points using the matplotlib pyplot’s
`scatter()`

function. - Create the trendline with the help of the
`numpy.polyfit()`

and the`numpy.plot1d()`

functions. - Add the trendline to the matplotlib plot using the
`matplotlib.pyplot.plot()`

function.

The important part here is to get the trendline which we do with the help of the numpy functions `numpy.polyfit()`

and `numpy.plot1d()`

.

The `numpy.polyfit()`

function is used to get a least squares polynomial fit. We pass the `x`

and `y`

values and the degree of the polynomial fit. For a line, use `1`

as the degree of fit. The `numpy.polyfit()`

function returns the coefficients of the polynomial which we then pass to the `numpy.plot1d()`

function which creates a polynomial function from the given coefficients.

To get the trendline points, we use the resulting function from `numpy.plot1d()`

and pass the x values to get the corresponding trendline points which we then plot on our matplotlib plot.

Let’s now look at an example of using the above steps.

## Example 1 – Add trendline to a plot

Let’s use the data of US dollar to Indian Rupee conversion to plot our scatter plot and get a trendline. We’ll use the conversion rates from 2011 to 2020.

import numpy as np import matplotlib.pyplot as plt # x values - years x = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020] # y values - 1 USD in INR y = [46.67, 53.44, 56.57, 62.33, 62.97, 66.46, 67.79, 70.09, 70.39, 76.38] # plot x and y on scatter plot plt.scatter(x, y) plt.xlabel('Year') plt.ylabel('1 USD in INR') # get the trendline coefficients z = np.polyfit(x, y, 1) # get the polynomial to generate the trendline p = np.poly1d(z) # add trendline to the plot plt.plot(x, p(x))

Output:

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Here, we first used the maplotlib pyplot’s `plt.scatter()`

function to plot our scatter plot with the given x and y values. We then generated our trendline using the numpy `polyfit()`

and `ploy1d()`

functions and then used the `plt.plot()`

function to add the trendline to our matplotlib plot.

## Example 2 – Add higher order trendcurve to the plot

In the above example, we added a trendline (a straight line) that best fits the scatter plot. You can similarly add fit curves with higher-degree polynomials to the plots.

The only change you need to make is to pass the degree you want the fit curve to have to the `numpy.polyfit()`

function. For example, to add a quadratic fit curve use the degree as 2, to add a cubic fit curve, use the degree as 3, etc.

Let’s add a quadratic fit line to the plot.

# plot x and y on scatter plot plt.scatter(x, y) plt.xlabel('Year') plt.ylabel('1 USD in INR') # get the trendline coefficients z = np.polyfit(x, y, 2) # get the polynomial to generate the trendline p = np.poly1d(z) # add trendline to the plot plt.plot(x, p(x))

Output:

You can see that the fit line now is a quadratic curve and not a straight line.

Let’s add a cubic fit curve to the above points.

# plot x and y on scatter plot plt.scatter(x, y) plt.xlabel('Year') plt.ylabel('1 USD in INR') # get the trendline coefficients z = np.polyfit(x, y, 3) # get the polynomial to generate the trendline p = np.poly1d(z) # add trendline to the plot plt.plot(x, p(x))

Output:

You can see that we get a fit curve on the plot.

You might also be interested in –

- Plot a Line Chart in Python with Matplotlib
- Create a Scatter Plot in Python with Matplotlib
- Change Background Color of Plot in Matplotlib
- Change Font Size of elements in a Matplotlib plot

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