blosc2.std#

blosc2.std(ndarr: NDArray | NDField | C2Array | LazyExpr, axis: int | tuple[int] | None = None, dtype: dtype = None, ddof: int = 0, keepdims: bool = False, **kwargs: dict) → ndarray | NDArray | int | float | bool#

Return the standard deviation along the specified axis.

Parameters:

  • ndarr (NDArray or NDField or C2Array or LazyExpr) – The input array or expression.

  • axis (int or tuple of ints, optional) – Axis or axes along which the standard deviation is computed. By default, axis=None computes the standard deviation of the flattened array.

  • dtype (np.dtype, optional) – Type to use in computing the standard deviation. For integer inputs, the default is float32; for floating point inputs, it is the same as the input dtype.

  • ddof (int, optional) – Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. By default, ddof is zero.

  • keepdims (bool, optional) – If set to True, the reduced axes are left in the result as dimensions with size one. This ensures that the result will broadcast correctly against the input array.

  • kwargs (dict, optional) – Additional keyword arguments that are supported by the empty() constructor.

Returns:

std_along_axis – The standard deviation of the elements along the axis.

Return type:

np.ndarray or NDArray or scalar

References

np.std

Examples

>>> import numpy as np
>>> import blosc2
>>> # Create an instance of NDArray with some data
>>> array = np.array([[1, 2, 3], [4, 5, 6]])
>>> nd_array = blosc2.asarray(array)
>>> # Compute the standard deviation of the entire array
>>> std_all = blosc2.std(nd_array)
>>> print("Standard deviation of the entire array:", std_all)
Standard deviation of the entire array: 1.707825127659933
>>> # Compute the standard deviation along axis 0 (columns)
>>> std_axis0 = blosc2.std(nd_array, axis=0)
>>> print("Standard deviation along axis 0:", std_axis0)
Standard deviation along axis 0: [1.5 1.5 1.5]
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