["python","numpy","matplotlib","linear-regression","curve-fitting"]
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: import numpy as np import matplotlib.pyplot as plt x = np.arange(data) y = np.arange(data) m, b = np.polyfit(x, y, 1) plt.plot(x, y, 'yo', x, m*x+b, '--k') plt.show()
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 2.0000000000000009 >>> b 0.99999999999999833 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 coef = np.polyfit(x,y,1) poly1d_fn = np.poly1d(coef) # poly1d_fn is now a function which takes in x and returns an estimate for y plt.plot(x,y, 'yo', x, poly1d_fn(x), '--k') #'--k'=black dashed line, 'yo' = yellow circle marker plt.xlim(0, 5) plt.ylim(0, 12)