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Compute the quantiles of the given values for each quantile in `qs`.
|
|
Parameters
|
----------
|
values : np.ndarray or ExtensionArray
|
qs : np.ndarray[float64]
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interpolation : str
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Returns
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-------
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np.ndarray or ExtensionArray
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Compute the quantiles of the given values for each quantile in `qs`.
|
|
Parameters
|
----------
|
values : np.ndarray
|
For ExtensionArray, this is _values_for_factorize()[0]
|
mask : np.ndarray[bool]
|
mask = isna(values)
|
For ExtensionArray, this is computed before calling _value_for_factorize
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fill_value : Scalar
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The value to interpret fill NA entries with
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For ExtensionArray, this is _values_for_factorize()[1]
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qs : np.ndarray[float64]
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interpolation : str
|
Type of interpolation
|
|
Returns
|
-------
|
np.ndarray
|
|
Notes
|
-----
|
Assumes values is already 2D. For ExtensionArray this means np.atleast_2d
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Quantile is computed along axis=1.
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Parameters
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----------
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values : array over which to find quantiles
|
mask : ndarray[bool]
|
locations in values that should be considered missing
|
qs : np.ndarray[float64] of quantile indices to find
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na_value : scalar
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value to return for empty or all-null values
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interpolation : str
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Returns
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quantiles : scalar or array
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Wrapper for np.percentile that skips missing values.
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Parameters
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values : np.ndarray[ndim=2] over which to find quantiles
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qs : np.ndarray[float64] of quantile indices to find
|
na_value : scalar
|
value to return for empty or all-null values
|
mask : np.ndarray[bool]
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locations in values that should be considered missing
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interpolation : str
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Returns
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-------
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quantiles : scalar or array
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