移动平均线 pandas

本文介绍了移动平均线 pandas 的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我想在我的交易时间序列中添加移动平均计算.

I would like to add a moving average calculation to my exchange time series.

来自 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

我想使用相同的索引 (Date) 在 Value 之后将计算出的移动平均线作为一个新列添加到右侧.最好我还想将计算出的移动平均线重命名为 MA.

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.

推荐答案

滚动平均值返回一个 Series 您只需将其添加为 DataFrame 的新列(MA) 如下所述.

The rolling mean returns a Series you only have to add it as a new column of your DataFrame (MA) as described below.

有关信息，rolling_mean 函数已在 pandas 较新版本中被弃用.我在示例中使用了新方法，请参阅下面来自 pandas 文档.

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.

警告 0.18.0 之前的版本、pd.rolling_*、pd.expanding_* 和 pd.ewm* 是模块级函数，现在已弃用.这些通过使用 Rolling、Expanding 和 EWM. 对象以及相应的方法调用来替换.

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