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# DESCRIBE

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The DESCRIBE node is based on a numpy or scipy function. The description of that function is as follows: Compute several descriptive statistics of the passed array. Params: select_return : This function has returns multiple objects ['nobs', 'mean', 'variance', 'skewness', 'kurtosis']. Select the desired one to return. See the respective function docs for descriptors. a : array_like Input data. axis : int or None Axis along which statistics are calculated. Default is 0. If None, compute over the whole array 'a'. ddof : int Delta degrees of freedom (only for variance). Default is 1. bias : bool If False, then the skewness and kurtosis calculations are corrected for statistical bias. nan_policy : {'propagate', 'raise', 'omit'} Defines how to handle when input contains nan. The following options are available (default is 'propagate'): 'propagate': returns nan 'raise': throws an error 'omit': performs the calculations ignoring nan values Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'
Python Code
from flojoy import OrderedPair, flojoy, Matrix, Scalar
import numpy as np
from typing import Literal

import scipy.stats

@flojoy
def DESCRIBE(
default: OrderedPair | Matrix,
axis: int = 0,
ddof: int = 1,
bias: bool = True,
nan_policy: str = "propagate",
select_return: Literal["nobs", "mean", "variance", "skewness", "kurtosis"] = "nobs",
) -> OrderedPair | Matrix | Scalar:
"""The DESCRIBE node is based on a numpy or scipy function.

The description of that function is as follows:

Compute several descriptive statistics of the passed array.

Parameters
----------
select_return : This function has returns multiple objects ['nobs', 'mean', 'variance', 'skewness', 'kurtosis'].
Select the desired one to return.
See the respective function docs for descriptors.
a : array_like
Input data.
axis : int or None, optional
Axis along which statistics are calculated. Default is 0.
If None, compute over the whole array 'a'.
ddof : int, optional
Delta degrees of freedom (only for variance). Default is 1.
bias : bool, optional
If False, then the skewness and kurtosis calculations are corrected for statistical bias.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan.
The following options are available (default is 'propagate'):
'propagate': returns nan
'raise': throws an error
'omit': performs the calculations ignoring nan values

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

result = scipy.stats.describe(
a=default.y,
axis=axis,
ddof=ddof,
bias=bias,
nan_policy=nan_policy,
)

return_list = ["nobs", "mean", "variance", "skewness", "kurtosis"]
if isinstance(result, tuple):
res_dict = {}
num = min(len(result), len(return_list))
for i in range(num):
res_dict[return_list[i]] = result[i]
result = res_dict[select_return]
else:
result = result._asdict()
result = result[select_return]

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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