Moving Average Pandas

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

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 webimport matplotlib.pyplot as pltimport datetimeplt.style.use('ggplot')# Input variablesstart = datetime.datetime(2016, 1, 01)end = datetime.datetime(2018, 3, 29)stock = 'WFC'# Extrating datadf = 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

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)