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Polynomial Fit in Python

Create a polynomial fit / regression in Python 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

# Scientific libraries
import numpy as np

points = np.array([(1, 1), (2, 4), (3, 1), (9, 3)])

# get x and y vectors
x = points[:,0]
y = points[:,1]

# calculate polynomial
z = np.polyfit(x, y, 3)
f = np.poly1d(z)
print f

# calculate new x's and y's
x_new = np.linspace(x[0], x[-1], 50)
y_new = f(x_new)

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

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

annotation = go.Annotation(
                  x=6,
                  y=-4.5,
                  text='$\textbf{Fit}: 0.43X^3 - 0.56X^2 + 16.78X + 10.61$',
                  showarrow=False
                  )
layout = go.Layout(
                title='Polynomial 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='Polynomial-Fit-in-python')
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