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# SKEW

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The SKEW node is based on a numpy or scipy function. The description of that function is as follows: Compute the sample skewness of a dataset. For normally distributed data, the skewness should be about zero. For unimodal continuous distributions, a skewness value greater than zero means that there is more weight in the right tail of the distribution. The function 'skewtest' can be used to determine if the skewness value is close enough to zero, statistically speaking. Params: a : ndarray Input array. axis : int Default = 0. If an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output. If None, the input will be raveled before computing the statistic. bias : bool If False, then the calculations are corrected for statistical bias. nan_policy : {'propagate', 'omit', 'raise'} Defines how to handle input NaNs. - propagate : if a NaN is present in the axis slice (e.g. row) along which the statistic is computed, the corresponding entry of the output will be NaN. - omit : NaNs will be omitted when performing the calculation. If insufficient data remains in the axis slice along which the statistic is computed, the corresponding entry of the output will be NaN. - raise : if a NaN is present, a ValueError will be raised. keepdims : bool, default: False If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'
Python Code
from flojoy import OrderedPair, flojoy, Matrix, Scalar
import numpy as np

import scipy.stats

@flojoy
def SKEW(
default: OrderedPair | Matrix,
axis: int = 0,
bias: bool = True,
nan_policy: str = "propagate",
keepdims: bool = False,
) -> OrderedPair | Matrix | Scalar:
"""The SKEW node is based on a numpy or scipy function.

The description of that function is as follows:

Compute the sample skewness of a dataset.

For normally distributed data, the skewness should be about zero.
For unimodal continuous distributions, a skewness value greater than zero means that there is more weight in the right tail of the distribution. \
The function 'skewtest' can be used to determine if the skewness value is close enough to zero, statistically speaking.

Parameters
----------
a : ndarray
Input array.
axis : int
Default = 0.
If an int, the axis of the input along which to compute the statistic.
The statistic of each axis-slice (e.g. row) of the input will appear in a
corresponding element of the output.
If None, the input will be raveled before computing the statistic.
bias : bool, optional
If False, then the calculations are corrected for statistical bias.
nan_policy : {'propagate', 'omit', 'raise'}
Defines how to handle input NaNs.
- propagate : if a NaN is present in the axis slice (e.g. row) along
which the statistic is computed, the corresponding entry of the output
will be NaN.
- omit : NaNs will be omitted when performing the calculation.
If insufficient data remains in the axis slice along which the
statistic is computed, the corresponding entry of the output will be NaN.
- raise : if a NaN is present, a ValueError will be raised.
keepdims : bool, default: False
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.

Returns
-------
DataContainer
type 'ordered pair', 'scalar', or 'matrix'
"""

result = scipy.stats.skew(
a=default.y,
axis=axis,
bias=bias,
nan_policy=nan_policy,
keepdims=keepdims,
)

if isinstance(result, np.ndarray):
result = OrderedPair(x=default.x, y=result)
else:
assert isinstance(
result, np.number | float | int
), f"Expected np.number, float or int for result, got {type(result)}"
result = Scalar(c=float(result))

return result


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