std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *, where=<no value>)
Returns the standard deviation, a measure of the spread of a distribution, of the array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis.
The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean(x))
, where x = abs(a - a.mean())**2
.
The average squared deviation is typically calculated as x.sum() / N
, where N = len(x)
. If, however, :None:None:`ddof`
is specified, the divisor N - ddof
is used instead. In standard statistical practice, ddof=1
provides an unbiased estimator of the variance of the infinite population. ddof=0
provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even with ddof=1
, it will not be an unbiased estimate of the standard deviation per se.
Note that, for complex numbers, std
takes the absolute value before squaring, so that the result is always real and nonnegative.
For floating-point input, the std is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-accuracy accumulator using the dtype
keyword can alleviate this issue.
Calculate the standard deviation of these values.
Axis or axes along which the standard deviation is computed. The default is to compute the standard deviation of the flattened array.
If this is a tuple of ints, a standard deviation is performed over multiple axes, instead of a single axis or all the axes as before.
Type to use in computing the standard deviation. For arrays of integer type the default is float64, for arrays of float types it is the same as the array type.
Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary.
Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof
, where N
represents the number of elements. By default :None:None:`ddof`
is zero.
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.
If the default value is passed, then :None:None:`keepdims`
will not be passed through to the std
method of sub-classes of ndarray
, however any non-default value will be. If the sub-class' method does not implement :None:None:`keepdims`
any exceptions will be raised.
Elements to include in the standard deviation. See :None:None:`~numpy.ufunc.reduce`
for details.
If :None:None:`out`
is None, return a new array containing the standard deviation, otherwise return a reference to the output array.
Compute the standard deviation along the specified axis.
>>> a = np.array([[1, 2], [3, 4]])
... np.std(a) 1.1180339887498949 # may vary
>>> np.std(a, axis=0) array([1., 1.])
>>> np.std(a, axis=1) array([0.5, 0.5])
In single precision, std() can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32)
... a[0, :] = 1.0
... a[1, :] = 0.1
... np.std(a) 0.45000005
Computing the standard deviation in float64 is more accurate:
>>> np.std(a, dtype=np.float64) 0.44999999925494177 # may vary
Specifying a where argument:
>>> a = np.array([[14, 8, 11, 10], [7, 9, 10, 11], [10, 15, 5, 10]])
... np.std(a) 2.614064523559687 # may vary
>>> np.std(a, where=[[True], [True], [False]]) 2.0See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.nanstd
numpy.ma.core.MaskedArray.std
pandas.core.window.rolling.Rolling.std
pandas.core.generic.NDFrame._add_numeric_operations.<locals>.std
dask.array.random.RandomState.lognormal
dask.array.core.Array.std
numpy.mean
dask.array.reductions.std
skimage.restoration.unwrap.unwrap_phase
numpy.std
numpy.ma.core.var
numpy.nanvar
numpy.matrixlib.defmatrix.matrix.std
dask.array.random.RandomState.normal
pandas.core.window.expanding.Expanding.std
numpy.var
dask.array.random.RandomState.gumbel
numpy.ma.core.MaskedArray.var
numpy.ma.core.std
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them