方案一:python 从日期列表中选出最大的_从Python列表中获取N个最小或最大元素的快速方法... 方案二:Python pandas.rolling_max方法代码示例 # 需要导入模块: import pandas [as 别名] # 或者: from pandas import rolling_max [as 别名] def getWilliam(close, high, low): ''' 计算威廉指数 :param Data...
# 需要导入模块: import pandas [as 别名]# 或者: from pandas importrolling_max[as 别名]defdrawdown(x):### Returns a ts of drawdowns for a time series x## rolling max with infinite windowmaxx=pd.rolling_max(x,99999999, min_periods=1)return(x - maxx)/maxx 开发者ID:robcarver17,项目名...
tr = pd.concat([(pd.rolling_max(xdf.high,2) - pd.rolling_min(xdf.close,2))*self.multiplier, (pd.rolling_max(xdf.close,2) - pd.rolling_min(xdf.low,2))*self.multiplier, xdf.high - xdf.close, xdf.close - xdf.low], join='outer', axis=1).max(axis=1)else: tr= pd.concat([...
Trying to get the rolling max in presence of NaN's. For some reason, not getting the expected output. Any thoughts! # Import libraryimportpandasaspd# Datax = pd.Series([1,2,np.nan,4,np.nan])# Get rolling maxout = x.rolling(window=2).max()print(out)print()# Get rolling maxout =...
It looks like you need cummax() instead of .rolling(N).max() In [29]: df['new'] = df.groupby('id').value.cummax() In [30]: df Out[30]: id value new 0 1 3 3 1 1 6 6 2 1 3 6 3 2 2 2 4 2 1 2 Timing (using brand new Pandas version 0.20.1): In [3]: df...
你可以使用其他聚合函数,例如max、min、std等: df['Rolling_Max']=df['Sales_Qty'].rolling(window=3).max()df['Rolling_Min']=df['Sales_Qty'].rolling(window=3).min()df['Rolling_Std']=df['Sales_Qty'].rolling(window=3).std()print(df) ...
- rolling rolling_max - resample data.set_index(data.date).resample('M) df.index = pd.DatetimeIndex(df.index) - shift - diff - groupby.agg - groupby.trasfrom - groupby.apply - concat((df,df),join='inner',axis) - append 针对表的拼接 - pd.merge(df,df ,how,on) out取并集 inner取...
您需要将其向前移动140行，以便窗口 * 包括接下来的140行 *，而不仅仅是第一行，第二行，第三行等...
Rolling.median(**kwargs):计算滚动中位数。 Rolling.var([ddof]):计算无偏滚动方差。 Rolling.std([ddof]):计算滚动标准偏差。 Rolling.min(args, *kwargs):计算滚动最小值。 Rolling.max(args, *kwargs):滚动最大值 Rolling.corr([other, pairwise]):滚动样本相关性 Rolling.cov([other, pairwise, ddo...
rolling_max 移动窗口的最大值 pandas.rolling_min(arg, window, min_periods=None, freq=None, center=False, how='min', **kwargs) 1 rolling_corr 移动窗口的相关系数 pandas.rolling_corr(arg1, arg2=None, window=None, min_periods=None, freq=None, center=False, pairwise=None, how=None) ...