bundles / numpy 2.4.4 / numpy / nansum
_ArrayFunctionDispatcher
numpy:nansum
Signature
def nansum ( a , axis = None , dtype = None , out = None , keepdims = <no value> , initial = <no value> , where = <no value> ) Summary
Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.
Extended Summary
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: array_likeArray containing numbers whose sum is desired. If
ais not an array, a conversion is attempted.axis: {int, tuple of int, None}, optionalAxis or axes along which the sum is computed. The default is to compute the sum of the flattened array.
dtype: data-type, optionalThe type of the returned array and of the accumulator in which the elements are summed. By default, the dtype of
ais used. An exception is whenahas 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: ndarray, optionalAlternate 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. Seeufuncs-output-typefor more details. The casting of NaN to integer can yield unexpected results.keepdims: bool, optionalIf 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
keepdimswill be passed through to themeanor sum methods of sub-classes ofndarray. If the sub-classes methods does not implementkeepdimsany exceptions will be raised.initial: scalar, optionalStarting value for the sum. See
~numpy.ufunc.reducefor details.where: array_like of bool, optionalElements to include in the sum. See
~numpy.ufunc.reducefor details.
Returns
nansum: ndarray.A new array holding the result is returned unless
outis specified, in which it is returned. The result has the same size asa, and the same shape asaifaxisis not None orais a 1-d array.
Notes
If both positive and negative infinity are present, the sum will be Not A Number (NaN).
Examples
import numpy as np
✓np.nansum(1) np.nansum([1]) np.nansum([1, np.nan])✗
a = np.array([[1, 1], [1, np.nan]])
✓np.nansum(a) np.nansum(a, axis=0) np.nansum([1, np.nan, np.inf]) np.nansum([1, np.nan, -np.inf])✗
with np.errstate(invalid="ignore"): np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present✓
See also
- isfinite
Show which elements are not NaN or +/-inf.
- isnan
Show which elements are NaN.
- numpy.sum
Sum across array propagating NaNs.
Aliases
-
numpy.nansum