Linear regression with matplotlib numpy

Question

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    show

User · Answer

Another quick and dirty answer is that you can just convert your list to an array using    import numpy as np arr   np asarray listname

User · Answer

arange generates lists  well  numpy arrays   type help np arange  for the details   You don t need to call it on existing lists      gt  gt  gt  x    1 2 3 4   gt  gt  gt  y    3 5 7 9    gt  gt  gt    gt  gt  gt  m b   np polyfit x  y  1   gt  gt  gt  m 2 0000000000000009  gt  gt  gt  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   plt xlim 0  5  plt ylim 0  12

User · Answer

from pylab import     import numpy as np x1   arange data   for example this is a list y1   arange data   for example this is a list  x np array x   this will convert a list in to an array y np array y  m b   polyfit x  y  1    plot x  y   yo   x  m x b   --k    show

User · Answer

import numpy as np import matplotlib pyplot as plt  from scipy import stats  x   np array  1 5 2 2 5 3 3 5 4 4 5 5 5 5 6   y   np array  10 35 12 3 13 14 0 16 17 18 2 20 20 7 22 5   gradient  intercept  r value  p value  std err   stats linregress x y  mn np min x  mx np max x  x1 np linspace mn mx 500  y1 gradient x1 intercept plt plot x y  ob   plt plot x1 y1  -r   plt show     USe this

User · Answer

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   Source

[python] Linear regression with matplotlib / numpy

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