Code for best fit straight line of a scatter plot in python

Question

Below is my code for scatter plotting the data in my text file  The file I am opening contains two columns  The left column is x coordinates and the right column is y coordinates  the code creates a scatter plot of x vs  y  I need a code to overplot a line of best fit to the data in the scatter plot  and none of the built in pylab function have worked for me     from matplotlib import   from pylab import    with open  file txt   as f     data    line split   for line in f readlines       out     float x   float y   for x  y in data  for i in out     scatter i 0  i 1      xlabel  X      ylabel  Y      title  My Title   show

User · Answer

A one-line version of this excellent answer to plot the line of best fit is:

plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))

Using np.unique(x) instead of x handles the case where x isn't sorted or has duplicate values.

User · Answer

Have implemented  Micah  s solution to generate a trendline with a few changes and thought I d share    Coded as a function Option for a polynomial trendline  input order 2  Function can also just return the coefficient of determination  R 2  input Rval True  More Numpy array optimisations   Code   def trendline xd  yd  order 1  c  r   alpha 1  Rval False          Make a line of best fit          Calculate trendline     coeffs   np polyfit xd  yd  order       intercept   coeffs -1      slope   coeffs -2      power   coeffs 0  if order    2 else 0      minxd   np min xd      maxxd   np max xd       xl   np array  minxd  maxxd       yl   power   xl    2   slope   xl   intercept       Plot trendline     plt plot xl  yl  c  alpha alpha        Calculate R Squared     p   np poly1d coeffs       ybar   np sum yd    len yd      ssreg   np sum  p xd  - ybar     2      sstot   np sum  yd - ybar     2      Rsqr   ssreg   sstot      if not Rval           Plot R 2 value         plt text 0 8   maxxd   0 2   minxd  0 8   np max yd    0 2   np min yd                      R 2    0 2f     Rsqr      else           Return the R 2 value          return Rsqr

User · Answer

You can use numpy s polyfit  I use the following  you can safely remove the bit about coefficient of determination and error bounds  I just think it looks nice       usr bin python3  import numpy as np import matplotlib pyplot as plt import csv  with open  example csv    r   as f      data    row for row in csv reader f       xd    float row 0   for row in data      yd    float row 1   for row in data     sort the data reorder   sorted range len xd    key   lambda ii  xd ii   xd    xd ii  for ii in reorder  yd    yd ii  for ii in reorder     make the scatter plot plt scatter xd  yd  s 30  alpha 0 15  marker  o      determine best fit line par   np polyfit xd  yd  1  full True   slope par 0  0  intercept par 0  1  xl    min xd   max xd   yl    slope xx   intercept  for xx in xl     coefficient of determination  plot text variance   np var yd  residuals   np var   slope xx   intercept - yy   for xx yy in zip xd yd    Rsqr   np round 1-residuals variance  decimals 2  plt text  9 max xd   1 min xd   9 max yd   1 min yd    R 2    0 2f    Rsqr  fontsize 30   plt xlabel  X Description   plt ylabel  Y Description      error bounds yerr    abs slope xx   intercept - yy   for xx yy in zip xd yd   par   np polyfit xd  yerr  2  full True   yerrUpper     xx slope intercept   par 0  0  xx  2   par 0  1  xx   par 0  2   for xx yy in zip xd yd   yerrLower     xx slope intercept - par 0  0  xx  2   par 0  1  xx   par 0  2   for xx yy in zip xd yd    plt plot xl  yl   -r   plt plot xd  yerrLower   --r   plt plot xd  yerrUpper   --r   plt show

User · Answer

from sklearn linear model import LinearRegression  X  Y   x reshape -1 1   y reshape -1 1  plt plot  X  LinearRegression   fit X  Y  predict X

User · Answer

Assuming line of best fit for a set of points is   y   a   b   x   where   b     sum xi   yi  - n   xbar   ybar     sum  xi - xbar  2  a   ybar - b   xbar   Code and plot    sample points  X    0  5  10  15  20  Y    0  7  10  13  20     solve for a and b def best fit X  Y        xbar   sum X  len X      ybar   sum Y  len Y      n   len X    or len Y       numer   sum  xi yi for xi yi in zip X  Y    - n   xbar   ybar     denum   sum  xi  2 for xi in X   - n   xbar  2      b   numer   denum     a   ybar - b   xbar      print  best fit line  ny      2f       2f x  format a  b        return a  b    solution a  b   best fit X  Y   best fit line   y   0 80   0 92x    plot points and fit line import matplotlib pyplot as plt plt scatter X  Y  yfit    a   b   xi for xi in X  plt plot X  yfit      UPDATE   notebook version

[python] Code for best fit straight line of a scatter plot in python

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