blosc2.std#
- blosc2.std(ndarr: NDArray | NDField | C2Array | LazyExpr, axis: int | tuple[int] = 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. The default is to compute 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 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.
kwargs¶ (dict, optional) – 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
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]