Python 移动平均熊猫
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Moving Average Pandas
提问by Martin598
I would like to add a moving average calculation to my exchange time series.
我想在我的交易时间序列中添加移动平均计算。
Original data from Quandl
来自Quandl 的原始数据
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 Valueusing the same index (Date). Preferably I would also like to rename the calculated moving average to MA.
我想在Value使用相同的索引 ( Date)后将计算出的移动平均线作为新列添加到右侧。最好我还想将计算出的移动平均线重命名为MA.
回答by Romain
The rolling mean returns a Seriesyou only have to add it as a new column of your DataFrame(MA) as described below.
滚动平均值返回 aSeries您只需将其添加为DataFrame( MA)的新列，如下所述。
For information, the rolling_meanfunction has been deprecated in pandas newer versions. I have used the new method in my example, see below a quote from the pandas documentation.
有关信息，该rolling_mean功能已在 Pandas 较新版本中弃用。我在我的示例中使用了新方法，请参阅下面的 Pandas文档中的引用。
WarningPrior to version 0.18.0,
pd.rolling_*,pd.expanding_*, andpd.ewm*were module level functions and are now deprecated. These are replaced by using theRolling,ExpandingandEWM.objects and a corresponding method call.
警告在 0.18.0 版之前
pd.rolling_*，pd.expanding_*、 和pd.ewm*是模块级函数，现在已弃用。这些被替换为使用Rolling,Expanding和EWM.对象和相应的方法调用。
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
回答by chrisckwong821
In case you are calculating more than one moving average:
如果您计算多个移动平均线：
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
然后你可以做所有 MA 的聚合平均值
df[[f for f in list(df) if "MA" in f]].mean(axis=1)
回答by Martin598
A moving average can also be calculated and visualized directly in a line chart by using the following code:
也可以使用以下代码直接在折线图中计算和可视化移动平均线：
Example using stock price data:
使用股票价格数据的示例：
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
有关如何执行此操作的教程：https: //youtu.be/XWAPPyF62Vg
回答by Poudel
To get the moving average in pandas we can use cum_sum and then divide by count.
要获得熊猫的移动平均值，我们可以使用 cum_sum 然后除以计数。
Here is the working example:
这是工作示例：
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
输出
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
