var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>)¶
Compute the variance along the specified axis.
Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, otherwise over the specified axis.
- a : array_like
Array containing numbers whose variance is desired. If a is not an array, a conversion is attempted.
- axis : None or int or tuple of ints, optional
New in version 1.7.0.
- dtype : data-type, optional
Type to use in computing the variance. For arrays of integer type the default is
float32; for arrays of float types it is the same as the array type.
- out : ndarray, optional
- ddof : int, optional
N - ddof, where
Nrepresents the number of elements. By default ddof is zero.
- keepdims : bool, optional
If the default value is passed, then keepdims will not be passed through to the
varmethod 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.
- variance : ndarray, see dtype parameter above
out=None, returns a new array containing the variance; otherwise, a reference to the output array is returned.
The variance is the average of the squared deviations from the mean, i.e.,
var = mean(abs(x - x.mean())**2).
The mean is normally 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 a hypothetical infinite population.
ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables.
Note that for complex numbers, the absolute value is taken before squaring, so that the result is always real and nonnegative.
For floating-point input, the variance 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.var(a) 1.25 >>> np.var(a, axis=0) array([1., 1.]) >>> np.var(a, axis=1) array([0.25, 0.25])
In single precision, var() can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32) >>> a[0, :] = 1.0 >>> a[1, :] = 0.1 >>> np.var(a) 0.20250003
Computing the variance in float64 is more accurate:
>>> np.var(a, dtype=np.float64) 0.20249999932944759 # may vary >>> ((1-0.55)**2 + (0.1-0.55)**2)/2 0.2025