- numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *, where=<no value>)#
Compute the standard deviation along the specified axis.
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.
Calculate the standard deviation of these values.
- axisNone or 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.
New in version 1.7.0.
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.
- dtypedtype, optional
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.
- outndarray, optional
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.
- ddofint, optional
Means Delta Degrees of Freedom. The divisor used in calculations is
N - ddof, where
Nrepresents the number of elements. By default ddof is zero.
- keepdimsbool, 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.
If the default value is passed, then keepdims will not be passed through to the
stdmethod of sub-classes of
ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.
- wherearray_like of bool, optional
Elements to include in the standard deviation. See
New in version 1.20.0.
- standard_deviationndarray, see dtype parameter above.
If out is None, return a new array containing the standard deviation, otherwise return a reference to the output array.
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, ddof is specified, the divisor
N - ddofis used instead. In standard statistical practice,
ddof=1provides an unbiased estimator of the variance of the infinite population.
ddof=0provides 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,
stdtakes 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
dtypekeyword can alleviate this issue.
>>> 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]]) 2.0