Linear regression with matplotlib / numpy


I'm trying to generate a linear regression on a scatter plot I have generated, however my data is in list format, and all of the examples I can find of using polyfit require using arange. arange doesn't accept lists though. I have searched high and low about how to convert a list to an array and nothing seems clear. Am I missing something?

Following on, how best can I use my list of integers as inputs to the polyfit?

here is the polyfit example I am following:

from pylab import * 

x = arange(data) 
y = arange(data) 

m,b = polyfit(x, y, 1) 

plot(x, y, 'yo', x, m*x+b, '--k') 
3/8/2017 1:45:27 PM

Accepted Answer

arange generates lists (well, numpy arrays); type help(np.arange) for the details. You don't need to call it on existing lists.

>>> x = [1,2,3,4]
>>> y = [3,5,7,9] 
>>> m,b = np.polyfit(x, y, 1)
>>> m
>>> b

I should add that I tend to use poly1d here rather than write out "m*x+b" and the higher-order equivalents, so my version of your code would look something like this:

import numpy as np
import matplotlib.pyplot as plt

x = [1,2,3,4]
y = [3,5,7,10] # 10, not 9, so the fit isn't perfect

fit = np.polyfit(x,y,1)
fit_fn = np.poly1d(fit) 
# fit_fn is now a function which takes in x and returns an estimate for y

plt.plot(x,y, 'yo', x, fit_fn(x), '--k')
plt.xlim(0, 5)
plt.ylim(0, 12)
6/18/2015 3:04:17 PM

This code:

from scipy.stats import linregress

linregress(x,y) #x and y are arrays or lists.

gives out a list with the following:

slope : float
slope of the regression line
intercept : float
intercept of the regression line
r-value : float
correlation coefficient
p-value : float
two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero
stderr : float
Standard error of the estimate


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