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81 def mean(self): 82 def mean(scol): 83 return F.when( 84 F.row_number().over(self._unbounded_window) >= self._min_periods, 85 F.mean(scol).over(self._window) 86 ).otherwise(F.lit(None)) 87 88 return self._apply_as_series_or_frame(mean)

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273 def rolling_mean_by_h(x, h, w, name): 274 """Compute a rolling mean of x, after first aggregating by h. 275 276 Right-aligned. Computes a single mean for each unique value of h. Each 277 mean is over at least w samples. 278 279 Parameters 280 ---------- 281 x: Array. 282 h: Array of horizon for each value in x. 283 w: Integer window size (number of elements). 284 name: Name for metric in result dataframe 285 286 Returns 287 ------- 288 Dataframe with columns horizon and name, the rolling mean of x. 289 """ 290 # Aggregate over h 291 df = pd.DataFrame({'x': x, 'h': h}) 292 df2 = ( 293 df.groupby('h').agg(['mean', 'count']).reset_index().sort_values('h') 294 ) 295 xm = df2['x']['mean'].values 296 ns = df2['x']['count'].values 297 hs = df2['h'].values 298 299 res_h = [] 300 res_x = [] 301 # Start from the right and work backwards 302 i = len(hs) - 1 303 while i >= 0: 304 # Construct a mean of at least w samples. 305 n = int(ns[i]) 306 xbar = float(xm[i]) 307 j = i - 1 308 while ((n < w) and j >= 0): 309 # Include points from the previous horizon. All of them if still 310 # less than w, otherwise just enough to get to w. 311 n2 = min(w - n, ns[j]) 312 xbar = xbar * (n / (n + n2)) + xm[j] * (n2 / (n + n2)) 313 n += n2 314 j -= 1 315 if n < w: 316 # Ran out of horizons before enough points. 317 break 318 res_h.append(hs[i]) 319 res_x.append(xbar) 320 i -= 1 321 res_h.reverse() 322 res_x.reverse() 323 return pd.DataFrame({'horizon': res_h, name: res_x})

85 def sma(data, span=100): 86 """Computes and returns the simple moving average. 87 88 Note: the moving average is computed on all columns. 89 90 :Input: 91 :data: pandas.DataFrame with stock prices in columns 92 :span: int (defaul: 100), number of days/values over which 93 the average is computed 94 95 :Output: 96 :sma: pandas.DataFrame of simple moving average 97 """ 98 return data.rolling(window=span, center=False).mean()

14 def period_mean(data, freq): 15 ''' 16 Method to calculate mean for each frequency 17 ''' 18 return np.array( 19 [np.mean(data[i::freq]) for i in range(freq)])

2155 def hpat_pandas_series_mean_impl(self, axis=None, skipna=None, level=None, numeric_only=None): 2156 if skipna is None: 2157 skipna = True 2158 2159 if skipna: 2160 return numpy.nanmean(self._data) 2161 2162 return self._data.mean()

314 def __mean(xarr): 315 """mean = x.sum() / len(x)""" #interface is [lb,ub]; not lb,ub 316 from numpy import mean 317 return mean(xarr)

174 @rabbit 175 def mean(self): 176 """Finds The Arithmetic Mean.""" 177 return sum(self.units)/float(len(self))