TheNeuralBit commented on a change in pull request #12982:
URL: https://github.com/apache/beam/pull/12982#discussion_r501362686
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File path: sdks/python/apache_beam/dataframe/frames.py
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@@ -34,6 +36,124 @@ def __array__(self, dtype=None):
between = frame_base._elementwise_method('between')
+ @frame_base.args_to_kwargs(pd.Series)
+ @frame_base.populate_defaults(pd.Series)
+ def std(self, axis, skipna, level, ddof, **kwargs):
+ if level is not None:
+ raise NotImplementedError("per-level aggregation")
+ if skipna:
+ self = self.dropna()
+
+ # See the online, numerically stable formulae at
+ # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+ def compute_moments(x):
+ n = len(x)
+ m = x.std(ddof=0)**2 * n
+ s = x.sum()
+ return pd.DataFrame(dict(m=[m], s=[s], n=[n]))
+
+ def combine_moments(data):
+ m = s = n = 0.0
+ for datum in data.itertuples():
+ if datum.n == 0:
+ continue
+ elif n == 0:
+ m, s, n = datum.m, datum.s, datum.n
+ else:
+ m += datum.m + (s / n - datum.s / datum.n)**2 * n * datum.n / (
+ n + datum.n)
+ s += datum.s
+ n += datum.n
+ if n <= ddof:
+ return float('nan')
+ else:
+ return math.sqrt(m / (n - ddof))
+
+ moments = expressions.ComputedExpression(
+ 'compute_moments',
+ compute_moments, [self._expr],
+ requires_partition_by=partitionings.Nothing())
+ with expressions.allow_non_parallel_operations(True):
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ 'combine_moments',
+ combine_moments, [moments],
+ requires_partition_by=partitionings.Singleton()))
+
+ @frame_base.args_to_kwargs(pd.Series)
+ @frame_base.populate_defaults(pd.Series)
+ def corr(self, other, method, min_periods):
+ if method == 'pearson': # Note that this is the default.
+ x = self.dropna()
+ y = other.dropna()
+
+ # Do this first to filter to the entries that are present on both sides.
+ def join(x, y):
+ return pd.concat([x, y], axis=1, join='inner').rename(
+ lambda c: 'xy'[c], axis=1)
+
+ # Use the formulae from
Review comment:
I think the reference to pearson correlation coefficient is no longer
necessary after this was moved to `_cov_aligned`, which can be understood just
from https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online.
It would still be helpful to be more specific here though.
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