8 examples of 'group by sum pandas' in Python

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54def 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)
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236def 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)
103def 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
31def groupby(xs, keys):
32 result = defaultdict(list)
33 for (x, key) in zip(xs, keys):
34 result[key].append(x)
35 return result
89def SUM(Series, N):
90 return pd.Series.rolling(Series, N).sum()
230def get_column_sum(self, column):
231 return self.spark_df.select(column).groupBy().sum().collect()[0][0]
38def 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()))
2759def 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)

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