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)