Show Sidebar Hide Sidebar

Linear Fit in matplotlib

Create a polynomial fit / regression in Matplotlib and add a line of best fit to your chart

# Learn about API authentication here: https://plot.ly/python/getting-started
# Find your api_key here: https://plot.ly/settings/api

import plotly.plotly as py
import plotly.graph_objs as go

# MatPlotlib
import matplotlib.pyplot as plt
from matplotlib import pylab

# Scientific libraries
from numpy import arange,array,ones
from scipy import stats


xi = arange(0,9)
A = array([ xi, ones(9)])

# (Almost) linear sequence
y = [19, 20, 20.5, 21.5, 22, 23, 23, 25.5, 24]

# Generated linear fit
slope, intercept, r_value, p_value, std_err = stats.linregress(xi,y)
line = slope*xi+intercept

plt.plot(xi,y,'o', xi, line)
pylab.title('Linear Fit with Matplotlib')
ax = plt.gca()
ax.set_axis_bgcolor((0.898, 0.898, 0.898))
fig = plt.gcf()
py.plot_mpl(fig, filename='linear-Fit-with-matplotlib')
Still need help?
Contact Us

For guaranteed 24 hour response turnarounds, upgrade to a Developer Support Plan.