小开

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

小开

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)

小开

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

小开

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

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output