Finding moving average from data points in Python

Question

I am playing in Python a bit again  and I found a neat book with examples  One of the examples is to plot some data  I have a  txt file with two columns and I have the data  I plotted the data just fine  but in the exercise it says  Modify your program further to calculate and plot the running average of the data  defined by    Y k  frac 1  2r  sum  m -r  r y  k m     where r 5 in this case  and the y k is the second column in the data file   Have the program plot both the original data and the running average on the same graph   So far I have this   from pylab import plot  ylim  xlim  show  xlabel  ylabel from numpy import linspace  loadtxt  data   loadtxt  sunspots txt   float  r 5 0  x   data   0  y   data   1   plot x y  xlim 0 1000  xlabel  Months since Jan 1749    ylabel  No  of Sun spots   show     So how do I calculate the sum  In Mathematica it s simple since it s symbolic manipulation  Sum i   i 0 10   for example   but how to calculate sum in python which takes every ten points in the data and averages it  and does so until the end of points   I looked at the book  but found nothing that would explain this       heltonbiker s code did the trick     D  from   future   import division from pylab import plot  ylim  xlim  show  xlabel  ylabel  grid from numpy import linspace  loadtxt  ones  convolve import numpy as numpy  data   loadtxt  sunspots txt   float   def movingaverage interval  window size       window  numpy ones int window size   float window size      return numpy convolve interval  window   same    x   data   0  y   data   1    plot x y  k    y av   movingaverage y  10  plot x  y av  r   xlim 0 1000  xlabel  Months since Jan 1749    ylabel  No  of Sun spots   grid True  show     And I got this      Thank you very much

User · Answer

As numpy convolve is pretty slow  those who need a fast performing solution might prefer an easier to understand cumsum approach  Here is the code   cumsum vec   numpy cumsum numpy insert data  0  0    ma vec    cumsum vec window width   - cumsum vec  -window width     window width   where data contains your data  and ma vec will contain moving averages of window width length   On average  cumsum is about 30-40 times faster than convolve

User · Answer

ravgs    sum data i i 5   5  for i in range len data -4     This isn t the most efficient approach but it will give your answer and I m unclear if your window is 5 points or 10  If its 10  replace each 5 with 10 and the 4 with 9

User · Answer

A moving average is a convolution  and numpy will be faster than most pure python operations  This will give you the 10 point moving average    import numpy as np smoothed   np convolve data  np ones 10  10    I would also strongly suggest using the great pandas package if you are working with timeseries data  There are some nice moving average operations built in

User · Answer

Before reading this answer  bear in mind that there is another answer below  from Roman Kh  which uses numpy cumsum and is MUCH MUCH FASTER than this one      Best One common way to apply a moving sliding average  or any other sliding window function  to a signal is by using numpy convolve     def movingaverage interval  window size       window   numpy ones int window size   float window size      return numpy convolve interval  window   same     Here  interval is your x array  and window size is the number of samples to consider  The window will be centered on each sample  so it takes samples before and after the current sample in order to calculate the average  Your code would become   plot x y  xlim 0 1000   x av   movingaverage interval  r  plot x av  y   xlabel  Months since Jan 1749    ylabel  No  of Sun spots   show     Hope this helps

User · Answer

My Moving Average function  without numpy function   from   future   import division    must be on first line of script  class Solution      def Moving Avg self A           m   A 0          B              B append m          for i in range 1 len A                m    m   i   A i    i 1              B append m          return B

User · Answer

There is a problem with the accepted answer  I think we need to use  valid  instead of  same  here   - return numpy convolve interval  window   same      As an Example try out the MA of this data-set    1 5 7 2 6 7 8 2 2 7 8 3 7 3 7 3 15 6  - the result should be  4 2 5 4 6 0 5 0 5 0 5 2 5 4 4 4 5 4 5 6 5 6 4 6 7 0 6 8   but having  same  gives us an incorrect output of  2 6 3 0 4 2 5 4 6 0 5 0 5 0 5 2 5 4 4 4 5 4 5 6 5 6   4 6 7 0 6 8 6 2 4 8   Rusty code to try this out -   result    dataset  1 5 7 2 6 7 8 2 2 7 8 3 7 3 7 3 15 6  window size 5 for index in xrange len dataset        if index  lt  len dataset -window size           tmp  dataset index   dataset index 1   dataset index 2   dataset index 3   dataset index 4   5 0         result append tmp      else        pass  result  movingaverage y  window size     Try this with valid  amp  same and see whether the math makes sense    See also -  http   sentdex com sentiment-analysisbig-data-and-python-tutorials-algorithmic-trading how-to-chart-stocks-and-forex-doing-your-own-financial-charting calculate-simple-moving-average-sma-python

User · Answer

I think something like   aves    sum data i i 6   for i in range 0  len data   5     But I always have to double check the indices are doing what I expect  The range you want is  0  5  10       and data 0 6  will give you data 0    data 5   ETA  oops  and you want ave rather than sum  of course  So actually using your code and the formula   r   5 x   data   0  y1   data   1  y2    ave y1 i-r i r   for i in range r  len y1   2 r   y    y1  y2

[python] Finding moving average from data points in Python

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