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Merge pull request #158 from omenSi/groupby
Groupby ported to nim
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,201 +1,13 @@ | ||
| from collections import defaultdict | ||
| from datetime import date, time, datetime, timedelta # noqa | ||
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| class GroupbyFunction(object): | ||
| pass | ||
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| class Limit(GroupbyFunction): | ||
| def __init__(self): | ||
| self.value = None | ||
| self.f = None | ||
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| def update(self, value): | ||
| if value is None: | ||
| pass | ||
| elif self.value is None: | ||
| self.value = value | ||
| else: | ||
| self.value = self.f((value, self.value)) | ||
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| class Max(Limit): | ||
| def __init__(self): | ||
| super().__init__() | ||
| self.f = max | ||
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| class Min(Limit): | ||
| def __init__(self): | ||
| super().__init__() | ||
| self.f = min | ||
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| class Sum(GroupbyFunction): | ||
| def __init__(self): | ||
| self.value = 0 | ||
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| def update(self, value): | ||
| if isinstance(value, (type(None), date, time, datetime, str)): | ||
| raise ValueError(f"Sum of {type(value)} doesn't make sense.") | ||
| self.value += value | ||
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| class Product(GroupbyFunction): | ||
| def __init__(self) -> None: | ||
| self.value = 1 | ||
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| def update(self, value): | ||
| self.value *= value | ||
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| class First(GroupbyFunction): | ||
| empty = (None,) | ||
| # we will never receive a tuple, so using (None,) as the initial | ||
| # value will assure that IF None is the first value, then it can | ||
| # be captured correctly. | ||
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| def __init__(self): | ||
| self.value = self.empty | ||
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| def update(self, value): | ||
| if self.value is First.empty: | ||
| self.value = value | ||
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| class Last(GroupbyFunction): | ||
| def __init__(self): | ||
| self.value = None | ||
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| def update(self, value): | ||
| self.value = value | ||
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| class Count(GroupbyFunction): | ||
| def __init__(self): | ||
| self.value = 0 | ||
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| def update(self, value): | ||
| self.value += 1 | ||
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| class CountUnique(GroupbyFunction): | ||
| def __init__(self): | ||
| self.items = set() | ||
| self.value = None | ||
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| def update(self, value): | ||
| self.items.add(value) | ||
| self.value = len(self.items) | ||
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| class Average(GroupbyFunction): | ||
| def __init__(self): | ||
| self.sum = 0 | ||
| self.count = 0 | ||
| self.value = 0 | ||
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| def update(self, value): | ||
| if isinstance(value, (date, time, datetime, str)): | ||
| raise ValueError(f"Sum of {type(value)} doesn't make sense.") | ||
| if value is not None: | ||
| self.sum += value | ||
| self.count += 1 | ||
| self.value = self.sum / self.count | ||
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| class StandardDeviation(GroupbyFunction): | ||
| """ | ||
| Uses J.P. Welfords (1962) algorithm. | ||
| For details see https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online_algorithm | ||
| """ | ||
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| def __init__(self): | ||
| self.count = 0 | ||
| self.mean = 0 | ||
| self.c = 0.0 | ||
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| def update(self, value): | ||
| if isinstance(value, (date, time, datetime, str)): | ||
| raise ValueError(f"Std.dev. of {type(value)} doesn't make sense.") | ||
| if value is not None: | ||
| self.count += 1 | ||
| dt = value - self.mean | ||
| self.mean += dt / self.count | ||
| self.c += dt * (value - self.mean) | ||
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| @property | ||
| def value(self): | ||
| if self.count <= 1: | ||
| return 0.0 | ||
| variance = self.c / (self.count - 1) | ||
| return variance ** (1 / 2) | ||
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| class Histogram(GroupbyFunction): | ||
| def __init__(self): | ||
| self.hist = defaultdict(int) | ||
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| def update(self, value): | ||
| self.hist[value] += 1 | ||
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| class Median(Histogram): | ||
| def __init__(self): | ||
| super().__init__() | ||
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| @property | ||
| def value(self): | ||
| if not self.hist: | ||
| raise ValueError("No data.") | ||
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| keys = len(self.hist.keys()) | ||
| if keys == 1: | ||
| for k in self.hist: | ||
| return k | ||
| elif keys % 2 == 0: | ||
| A, B, total, midpoint = None, None, 0, sum(self.hist.values()) / 2 | ||
| for k, v in sorted(self.hist.items()): | ||
| total += v | ||
| A, B = B, k | ||
| if total > midpoint: | ||
| return (A + B) / 2 | ||
| else: | ||
| midpoint = sum(self.hist.values()) / 2 | ||
| total = 0 | ||
| for k, v in sorted(self.hist.items()): | ||
| total += v | ||
| if total > midpoint: | ||
| return k | ||
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| class Mode(Histogram): | ||
| def __init__(self): | ||
| super().__init__() | ||
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| @property | ||
| def value(self): | ||
| L = [(v, k) for k, v in self.hist.items()] | ||
| L.sort(reverse=True) | ||
| _, most_frequent = L[0] # top of the list. | ||
| return most_frequent | ||
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| class GroupBy(object): | ||
| max = Max # shortcuts to avoid having to type a long list of imports. | ||
| min = Min | ||
| sum = Sum | ||
| product = Product | ||
| first = First | ||
| last = Last | ||
| count = Count | ||
| count_unique = CountUnique | ||
| avg = Average | ||
| stdev = StandardDeviation | ||
| median = Median | ||
| mode = Mode | ||
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| functions = [Max, Min, Sum, First, Last, Product, Count, CountUnique, Average, StandardDeviation, Median, Mode] | ||
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| function_names = {f.__name__: f for f in functions} | ||
| max = "max" | ||
| min = "min" | ||
| sum = "sum" | ||
| product = "product" | ||
| first = "first" | ||
| last = "last" | ||
| count = "count" | ||
| count_unique = "count_unique" | ||
| avg = "avg" | ||
| stdev = "stdev" | ||
| median = "median" | ||
| mode = "mode" |
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