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54 def sum(self): 55 def sum(scol): 56 return F.when( 57 F.row_number().over(self._unbounded_window) >= self._min_periods, 58 F.sum(scol).over(self._window) 59 ).otherwise(F.lit(None)) 60 61 return self._apply_as_series_or_frame(sum)
236 def mean(self): 237 # TODO, there is a lot of copy-paste with the code above 238 # TODO, we should probably define groupby.aggregate 239 func = _accumulate_groupby_mean 240 start = (0, 0) 241 if isinstance(self.grouper, Streaming): 242 func = partial(func, index=self.index) 243 example = self.root.example.groupby(self.grouper.example) 244 if self.index is not None: 245 example = example[self.index] 246 example = example.mean() 247 stream = self.root.stream.zip(self.grouper.stream) 248 stream = stream.accumulate(func, start=start, returns_state=True) 249 else: 250 func = partial(func, grouper=self.grouper, index=self.index) 251 example = self.root.example.groupby(self.grouper) 252 if self.index is not None: 253 example = example[self.index] 254 example = example.mean() 255 stream = self.root.stream.accumulate(func, start=start, 256 returns_state=True) 257 if isinstance(example, pd.DataFrame): 258 return StreamingDataFrame(stream, example) 259 else: 260 return StreamingSeries(stream, example)
103 def SUM(df, n, price='Close'): 104 """ 105 Summation 106 """ 107 sum_list = [] 108 i = 0 109 while i < len(df[price]): 110 if i + 1 < n: 111 SUM = float('NaN') 112 else: 113 start = i + 1 - n 114 end = i + 1 115 SUM = sum(df[price][start:end]) 116 sum_list.append(SUM) 117 i += 1 118 return sum_list
31 def groupby(xs, keys): 32 result = defaultdict(list) 33 for (x, key) in zip(xs, keys): 34 result[key].append(x) 35 return result
89 def SUM(Series, N): 90 return pd.Series.rolling(Series, N).sum()
230 def get_column_sum(self, column): 231 return self.spark_df.select(column).groupBy().sum().collect()[0][0]
38 def add_group_id(df, *groupby_cols, gid_colname='gid'): 39 groupby_cols = list(groupby_cols) 40 df_group = df.groupby(groupby_cols).apply(lambda g: pd.Series({ 41 'group_length': g.shape[0] 42 })).reset_index() 43 df_group[gid_colname] = df_group.index 44 df_merge = pd.merge(df, df_group, how='outer', on=groupby_cols) 45 df_merge['group_length'] = df_merge['group_length'].fillna(-1) 46 df_merge[gid_colname] = df_merge[gid_colname].fillna(-1) 47 df_merge['group_length'] = df_merge['group_length'].astype(int) 48 df_merge[gid_colname] = df_merge[gid_colname].astype(int) 49 return df_merge
2757 @typecheck(f=func_spec(1, expr_any), 2758 collection=expr_oneof(expr_set(), expr_array())) 2759 def group_by(f: Callable, collection) -> DictExpression: 2760 """Group collection elements into a dict according to a lambda function. 2761 2762 Examples 2763 -------- 2764 2765 >>> a = ['The', 'quick', 'brown', 'fox'] 2766 2767 >>> hl.eval(hl.group_by(lambda x: hl.len(x), a)) 2768 {5: ['quick', 'brown'], 3: ['The', 'fox']} 2769 2770 Parameters 2771 ---------- 2772 f : function ( (arg) -> :class:`.Expression`) 2773 Function to evaluate for each element of the collection to produce a key for the 2774 resulting dictionary. 2775 collection : :class:`.ArrayExpression` or :class:`.SetExpression` 2776 Collection expression. 2777 2778 Returns 2779 ------- 2780 :class:`.DictExpression`. 2781 Dictionary keyed by results of `f`. 2782 """ 2783 return collection.group_by(f)