# Linear Fit in Python/v3

Create a linear fit / regression in Python and add a line of best fit to your chart.

See our Version 4 Migration Guide for information about how to upgrade.

#### New to Plotly?¶

You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!

#### Version Check¶

Note: Linear fits are available in version 1.9.2+
Run pip install plotly --upgrade to update your Plotly version

In :
import plotly
plotly.__version__

Out:
'1.12.12'

### Linear Fit¶

In :
# Learn about API authentication here: https://plotly.com/python/getting-started
# Find your api_key here: https://plotly.com/settings/api

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

# 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

# Creating the dataset, and generating the plot
trace1 = go.Scatter(
x=xi,
y=y,
mode='markers',
marker=go.Marker(color='rgb(255, 127, 14)'),
name='Data'
)

trace2 = go.Scatter(
x=xi,
y=line,
mode='lines',
marker=go.Marker(color='rgb(31, 119, 180)'),
name='Fit'
)

annotation = go.Annotation(
x=3.5,
y=23.5,
text='$R^2 = 0.9551,\\Y = 0.716X + 19.18$',
showarrow=False,
font=go.Font(size=16)
)
layout = go.Layout(
title='Linear Fit in Python',
plot_bgcolor='rgb(229, 229, 229)',
xaxis=go.XAxis(zerolinecolor='rgb(255,255,255)', gridcolor='rgb(255,255,255)'),
yaxis=go.YAxis(zerolinecolor='rgb(255,255,255)', gridcolor='rgb(255,255,255)'),
annotations=[annotation]
)

data = [trace1, trace2]
fig = go.Figure(data=data, layout=layout)

py.plot(fig, filename='Linear-Fit-in-python')

Out:
u'https://plotly.com/~PythonPlotBot/162' 