nums.numpy.api.nan module

nums.numpy.api.nan.nanmax(a, axis=None, out=None, keepdims=False)[source]

Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and NaN is returned for that slice.

This docstring was copied from numpy.nanmax.

Some inconsistencies with the NumS version may exist.

Parameters

  • a (BlockArray) – Array containing numbers whose maximum is desired. If a is not an array, a conversion is attempted.

  • axis ({int, tuple of int, None}, optional) – Axis or axes along which the maximum is computed. The default is to compute the maximum of the flattened array.

  • out (BlockArray, optional) – Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary.

  • 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 original a. If the value is anything but the default, then keepdims will be passed through to the max method of sub-classes of BlockArray. If the sub-classes methods does not implement keepdims any exceptions will be raised.

Returns

nanmax – An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, an BlockArray scalar is returned. The same dtype as a is returned.

Return type

BlockArray

See also

nanmin

The minimum value of an array along a given axis, ignoring any NaNs.

amax

The maximum value of an array along a given axis, propagating any NaNs.

fmax

Element-wise maximum of two arrays, ignoring any NaNs.

maximum

Element-wise maximum of two arrays, propagating any NaNs.

isnan

Shows which elements are Not a Number (NaN).

isfinite

Shows which elements are neither NaN nor infinity.

amin, fmin, minimum

Notes

NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number.

If the input has a integer type the function is equivalent to nps.max.

‘out’ is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.array([[1, 2], [3, nps.nan]])  
>>> nps.nanmax(a).get()  
array(3.)
>>> nps.nanmax(a, axis=0).get()  
array([3.,  2.])
>>> nps.nanmax(a, axis=1).get()  
array([2.,  3.])
nums.numpy.api.nan.nanmean(a, axis=None, dtype=None, out=None, keepdims=False)[source]

Compute the arithmetic mean along the specified axis, ignoring NaNs.

This docstring was copied from numpy.nanmean.

Some inconsistencies with the NumS version may exist.

Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float intermediate and return values are used for integer inputs.

For all-NaN slices, NaN is returned and a RuntimeWarning is raised.

Parameters

  • a (BlockArray) – Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.

  • axis ({int, tuple of int, None}, optional) – Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.

  • dtype (data-type, optional) – Type to use in computing the mean. For integer inputs, the default is float64; for inexact inputs, it is the same as the input dtype.

  • out (BlockArray, optional) – Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary.

  • 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 original a. If the value is anything but the default, then keepdims will be passed through to the mean or sum methods of sub-classes of BlockArray. If the sub-classes methods does not implement keepdims any exceptions will be raised.

Returns

m – If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned. Nan is returned for slices that contain only NaNs.

Return type

BlockArray, see dtype parameter above

See also

average

Weighted average

mean

Arithmetic mean taken while not ignoring NaNs

var, nanvar

Notes

The arithmetic mean is the sum of the non-NaN elements along the axis divided by the number of non-NaN elements.

Note that for floating-point input, the mean 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. Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue.

‘out’ is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.array([[1, nps.nan], [3, 4]])  
>>> nps.nanmean(a).get()  
array(2.66666667)
>>> nps.nanmean(a, axis=0).get()  
array([2.,  4.])
>>> nps.nanmean(a, axis=1).get()  
array([1.,  3.5]) # may vary
nums.numpy.api.nan.nanmin(a, axis=None, out=None, keepdims=False)[source]

Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and Nan is returned for that slice.

This docstring was copied from numpy.nanmin.

Some inconsistencies with the NumS version may exist.

Parameters

  • a (BlockArray) – Array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted.

  • axis ({int, tuple of int, None}, optional) – Axis or axes along which the minimum is computed. The default is to compute the minimum of the flattened array.

  • out (BlockArray, optional) – Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary.

  • 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 original a.

    If the value is anything but the default, then keepdims will be passed through to the min method of sub-classes of BlockArray. If the sub-classes methods does not implement keepdims any exceptions will be raised.

Returns

nanmin – An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, an BlockArray scalar is returned. The same dtype as a is returned.

Return type

BlockArray

See also

nanmax

The maximum value of an array along a given axis, ignoring any NaNs.

amin

The minimum value of an array along a given axis, propagating any NaNs.

fmin

Element-wise minimum of two arrays, ignoring any NaNs.

minimum

Element-wise minimum of two arrays, propagating any NaNs.

isnan

Shows which elements are Not a Number (NaN).

isfinite

Shows which elements are neither NaN nor infinity.

amax, fmax, maximum

Notes

NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number.

If the input has a integer type the function is equivalent to nps.min.

‘out’ is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.array([[1, 2], [3, nps.nan]])  
>>> nps.nanmin(a).get()  
arary(1.)
>>> nps.nanmin(a, axis=0).get()  
array([1.,  2.])
>>> nps.nanmin(a, axis=1).get()  
array([1.,  3.])
nums.numpy.api.nan.nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)[source]

Compute the standard deviation along the specified axis, while ignoring NaNs.

This docstring was copied from numpy.nanstd.

Some inconsistencies with the NumS version may exist.

Returns the standard deviation, a measure of the spread of a distribution, of the non-NaN array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis.

For all-NaN slices or slices with zero degrees of freedom, NaN is returned and a RuntimeWarning is raised.

Parameters

  • a (BlockArray) – Calculate the standard deviation of the non-NaN values.

  • axis ({int, tuple of int, None}, optional) – Axis or axes along which the standard deviation is computed. The default is to compute the standard deviation of the flattened array.

  • dtype (dtype, 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.

  • out (BlockArray, 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.

  • ddof (int, optional) – Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of non-NaN 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 original a. If this value is anything but the default it is passed through as-is to the relevant functions of the sub-classes. If these functions do not have a keepdims kwarg, a RuntimeError will be raised.

Returns

standard_deviation – If out is None, return a new array containing the standard deviation, otherwise return a reference to the output array. If ddof is >= the number of non-NaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN.

Return type

BlockArray, see dtype parameter above.

See also

var, mean, std, nanvar, nanmean

Notes

The standard deviation is the square root of the average of the squared deviations from the mean: std = sqrt(mean(abs(x - x.mean())**2)).

The average squared deviation is normally calculated as x.sum() / N, where N = len(x). If, however, 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.

‘out’ is currently not supported’

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.array([[1, nps.nan], [3, 4]])  
>>> nps.nanstd(a).get()  
array(1.24721913)
>>> nps.nanstd(a, axis=0).get()  
array([1., 0.])
>>> nps.nanstd(a, axis=1).get()  
array([0.,  0.5]) # may vary
nums.numpy.api.nan.nansum(a, axis=None, dtype=None, out=None, keepdims=False)[source]

Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.

This docstring was copied from numpy.nansum.

Some inconsistencies with the NumS version may exist.

In NumPy versions <= 1.9.0 Nan is returned for slices that are all-NaN or empty. In later versions zero is returned.

Parameters

  • a (BlockArray) – Array containing numbers whose sum is desired. If a is not an array, a conversion is attempted.

  • axis ({int, tuple of int, None}, optional) – Axis or axes along which the sum is computed. The default is to compute the sum of the flattened array.

  • dtype (data-type, optional) – The type of the returned array and of the accumulator in which the elements are summed. By default, the dtype of a is used. An exception is when a has an integer type with less precision than the platform (u)intp. In that case, the default will be either (u)int32 or (u)int64 depending on whether the platform is 32 or 64 bits. For inexact inputs, dtype must be inexact.

  • out (BlockArray, optional) – Alternate output array in which to place the result. The default is None. If provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details. The casting of NaN to integer can yield unexpected results.

  • 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 original a. If the value is anything but the default, then keepdims will be passed through to the mean or sum methods of sub-classes of BlockArray. If the sub-classes methods does not implement keepdims any exceptions will be raised.

Returns

nansum – A new array holding the result is returned unless out is specified, in which it is returned. The result has the same size as a, and the same shape as a if axis is not None or a is a 1-d array.

Return type

BlockArray.

See also

numpy.sum

Sum across array propagating NaNs.

isnan

Show which elements are NaN.

isfinite

Show which elements are not NaN or +/-inf.

Notes

If both positive and negative infinity are present, the sum will be Not A Number (NaN).

‘out’ is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.nansum(nps.array([1])).get()  
array(1)
>>> nps.nansum(nps.array([1, nps.nan])).get()  
array(1.)
>>> a = nps.array([[1, 1], [1, nps.nan]])  
>>> nps.nansum(a).get()  
array(3.)
>>> nps.nansum(a, axis=0).get()  
array([2.,  1.])
>>> nps.nansum(nps.array([1, nps.nan, nps.inf])).get()  
array(inf)
>>> nps.nansum(nps.array([1, nps.nan, nps.NINF])).get()  
array(-inf)
nums.numpy.api.nan.nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)[source]

Compute the variance along the specified axis, while ignoring NaNs.

This docstring was copied from numpy.nanvar.

Some inconsistencies with the NumS version may exist.

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.

For all-NaN slices or slices with zero degrees of freedom, NaN is returned and a RuntimeWarning is raised.

Parameters

  • a (BlockArray) – Array containing numbers whose variance is desired. If a is not an array, a conversion is attempted.

  • axis ({int, tuple of int, None}, optional) – Axis or axes along which the variance is computed. The default is to compute the variance of the flattened array.

  • dtype (data-type, optional) – Type to use in computing the variance. For arrays of integer type the default is float64; for arrays of float types it is the same as the array type.

  • out (BlockArray, optional) – Alternate output array in which to place the result. It must have the same shape as the expected output, but the type is cast if necessary.

  • ddof (int, optional) – “Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of non-NaN 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 original a.

Returns

variance – If out is None, return a new array containing the variance, otherwise return a reference to the output array. If ddof is >= the number of non-NaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN.

Return type

BlockArray, see dtype parameter above

See also

std

Standard deviation

mean

Average

var

Variance while not ignoring NaNs

nanstd, nanmean

Notes

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 - ddof is used instead. In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of a hypothetical infinite population. ddof=0 provides 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 dtype keyword can alleviate this issue.

For this function to work on sub-classes of BlockArray, they must define sum with the kwarg keepdims

‘out’ is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.array([[1, nps.nan], [3, 4]])  
>>> nps.nanvar(a).get()  
array(1.55555556)
>>> nps.nanvar(a, axis=0).get()  
array([1.,  0.])
>>> nps.nanvar(a, axis=1).get()  
array([0.,  0.25])  # may vary