Moving Average Pandas

Question

I would like to add a moving average calculation to my exchange time series   Original data from Quandl  Exchange   Quandl get  BUNDESBANK BBEX3 D SEK USD CA AC 000                         authtoken  xxxxxxx                    Value   Date                  1989-01-02  6 10500   1989-01-03  6 07500   1989-01-04  6 10750   1989-01-05  6 15250   1989-01-09  6 25500   1989-01-10  6 24250   1989-01-11  6 26250   1989-01-12  6 23250   1989-01-13  6 27750   1989-01-16  6 31250    Calculating Moving Avarage MovingAverage   pd rolling mean Exchange 5                   Value   Date             1989-01-02      NaN   1989-01-03      NaN   1989-01-04      NaN   1989-01-05      NaN   1989-01-09  6 13900   1989-01-10  6 16650   1989-01-11  6 20400   1989-01-12  6 22900   1989-01-13  6 25400   1989-01-16  6 26550   I would like to add the calculated Moving Average as a new column to the right after Value using the same index  Date   Preferably I would also like to rename the calculated moving average to MA

User · Answer

The rolling mean returns a Series you only have to add it as a new column of your DataFrame  MA  as described below    For information  the rolling mean function has been deprecated in pandas newer versions  I have used the new method in my example  see below a quote from the pandas documentation      Warning Prior to version 0 18 0  pd rolling    pd expanding    and pd ewm  were module level functions and are now deprecated  These are replaced by using the Rolling  Expanding and EWM  objects and a corresponding method call    df  MA     df rolling window 5  mean    print df                Value    MA   Date                      1989-01-02   6 11   NaN   1989-01-03   6 08   NaN   1989-01-04   6 11   NaN   1989-01-05   6 15   NaN   1989-01-09   6 25  6 14   1989-01-10   6 24  6 17   1989-01-11   6 26  6 20   1989-01-12   6 23  6 23   1989-01-13   6 28  6 25   1989-01-16   6 31  6 27

User · Answer

To get the moving average in pandas we can use cum sum and then divide by count   Here is the working example   import pandas as pd import numpy as np  df   pd DataFrame   id   range 5                       value   range 100 600 100       some other similar statistics df  cum sum     df  value   cumsum   df  count     range 1 len df  value    1  df  mov avg     df  cum sum     df  count      other statistics df  rolling mean2     df  value   rolling window 2  mean    print df    output     id  value  cum sum  count  mov avg     rolling mean2 0   0    100      100      1    100 0           NaN 1   1    200      300      2    150 0           150 0 2   2    300      600      3    200 0           250 0 3   3    400     1000      4    250 0           350 0 4   4    500     1500      5    300 0           450 0

User · Answer

A moving average can also be calculated and visualized directly in a line chart by using the following code   Example using stock price data   import pandas datareader data as web import matplotlib pyplot as plt import datetime plt style use  ggplot      Input variables start   datetime datetime 2016  1  01  end   datetime datetime 2018  3  29  stock    WFC     Extrating data df   web DataReader stock  morningstar   start  end  df   df  Close    print df   plt plot df  WFC   label   Close   plt plot df  WFC   rolling 9  mean   label   MA 9 days   plt plot df  WFC   rolling 21  mean   label   MA 21 days   plt legend loc  best   plt title  Wells Fargo nClose and Moving Averages   plt show     Tutorial on how to do this  https   youtu be XWAPpyF62Vg

User · Answer

In case you are calculating more than one moving average   for i in range 2 10      df  MA    format i     df rolling window i  mean     Then you can do an aggregate average of all the MA  df  f for f in list df  if  MA  in f   mean axis 1

[python] Moving Average Pandas

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