Best fit line python numpy
You can use the following basic syntax to plot a line of best fit in Python: Show
#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 PythonThe 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 PythonThe following code shows how to create the same line of best fit as the previous example except with the following additions:
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 ResourcesThe following tutorials explain how to fit various regression models in Python: A Complete Guide to Linear Regression in Python In this Python tutorial, we will discuss How to plot the best-fit line in matplotlib in python, and we will also cover the following topics:
The best fit line in a 2-dimensional graph refers to a line that defines the optimal relationship of the x-axis and y-axis coordinates of the data points plotted as a scatter plot on the graph. The best fit line or optimal relationship can be achieved by minimizing the distances of the data points from the purposed line. A linear equation represents a line mathematically. The normal equation of the line is as follow: (A * x) + (B * y) + C = 0
But, the most commonly used form of a line is the intercept-slope form, which is as follows: y = (m * x) + c
We can convert a normal form to the slope-intercept form as follows: (A * x) + (B * y) + C = 0 (B * y) = -C – (A * x) y = (-(A * x) – C) / B y = ((-A / B) * x) + (-C / B) On comparing this equation with the slope-intercept form of a line. We get, m = (-A / B) and c = (-C / B) We will be using the slope-intercept form of the line throughout this post. The most commonly used method to find the parameters of a line to best fit the given data points is the least square method in regression analysis. The simple regression analysis is the method of specifying a relationship between a single numeric dependent variable (Here, y) and a numeric independent variable (Here, x). Read: Matplotlib subplot tutorial Matplotlib best fit lineWe can plot a line that fits best to the scatter data points in matplotlib. First, we need to find the parameters of the line that makes it the best fit. We will be doing it by applying the vectorization concept of linear algebra. First, let’s understand the algorithm that we will be using to find the parameters of the best fit line. The equation of the line is: y = (m * x) + c Let’s change this into y = theta0 + (theta1 * x); Here, theta0 and theta1 are the parameters representing the c (intercept) and m (slope) respectively of the line. Now, let’s change this equation into the vector form:
Now, the equation in vector form will be like this: y = X . theta We can calculate and get the optimal parameter values (theta0 and theta1) for the given data points by using the least square method equation in vector form, that is as follows: theta = (XT . X)-1 . (XT . y); Here, XT is the transpose of the matrix X, and (XT . X)-1 is the inverse of the resulted matrix from (XT . X) Now, let’s implement this algorithm using python and plot the resulted line. Matplotlib best fit lineRead: Matplotlib plot bar chart Matplotlib best fit line using numpy.polyfit()We can plot the best fit line to given data points using the numpy.polyfit() function. This function is a pre-defined function that takes 3 mandatory arguments as x-coordinate values (as an iterable), y-coordinate values (as an iterable), and degree of the equation (1 for linear, 2 for quadratic, 3 for cubic, …). The syntax is as follows:
Now, let’s take a look at the example and understand the implementation of the function. Matplotlib best fit line using numpy.polyfit()Read: What is matplotlib inline Matplotlib best fit line histogramWe can fit the distribution of a histogram and plot that curve/line in python. We can use the library scipy in python, the steps to do the task are given below:
Let’s follow the above Matplotlib best fit line histogramRead: Python plot multiple lines using Matplotlib Matplotlib best fit curveWe can plot a curve that fits best to the given data points in the python if the data points when scatter plotted on the graph show some upper degree curve trend (quadratic, cubic, …). We can use the numpy.polyfit() function. This function actually returns the best fit curve for any polynomial trend. As we have discussed this function in the earlier topic, so let’s practice an example for better understanding: Matplotlib best fit curveRead: Matplotlib plot a line Matplotlib best fit line to scatterWe have already discussed two different methods, for getting the best fit line to scatter. So, let’s do another method to get the best fit line. We can use the pre-defined linear regression model in sklearn librery’s/module’s linear_model sub-module to get the best fit line for the given data points. The steps to create a model and get the best fit line parameters are as follows:
Matplotlib best fit line to scatterYou may also like to read the following tutorials.
In this Python tutorial, we have discussed, How to plot the best-fit line in matplotlib in python, and we have also covered the following topics:
Python is one of the most popular languages in the United States of America. I have been working with Python for a long time and I have expertise in working with various libraries on Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… I have experience in working with various clients in countries like United States, Canada, United Kingdom, Australia, New Zealand, etc. Check out my profile. How do you find the line of best fit in Python?Use numpy.. x = np. array([1, 3, 5, 7]). y = np. array([ 6, 3, 9, 5 ]). m, b = np. polyfit(x, y, 1) m = slope, b = intercept.. plt. plot(x, y, 'o') create scatter plot.. plt. plot(x, m*x + b) add line of best fit.. How do you fit a graph in Python?data = dataframe. values. ... . x, y = data[:, 4], data[:, -1] # curve fit.. popt, _ = curve_fit(objective, x, y) # summarize the parameter values.. print('y = %.5f * x + %.5f' % (a, b)) # plot input vs output.. pyplot. scatter(x, y) ... . x_line = arange(min(x), max(x), 1) ... . y_line = objective(x_line, a, b). How do I find the line of best fit?The equation of a line of best fit can be represented as y=mx+b y = m x + b , where m is the slope and b is the y-intercept.
How do you fit a regression line in Python?How to plot a linear regression line on a scatter plot in Python. x = np. array([1, 3, 5, 7]) generate data. y = np. array([ 6, 3, 9, 5 ]). plt. plot(x, y, 'o') create scatter plot.. m, b = np. polyfit(x, y, 1) m = slope, b=intercept.. plt. plot(x, m*x + b) add line of best fit.. |