numpy.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. Parameters: a : array_like Array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted. axis : int, optional Axis along which the minimum is computed. The default is to compute the minimum of the flattened array. out :

numpy.nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=False) [source] Compute the median along the specified axis, while ignoring NaNs. Returns the median of the array elements. New in version 1.9.0. Parameters: a : array_like Input array or object that can be converted to an array. axis : {int, sequence of int, None}, optional Axis or axes along which the medians are computed. The default is to compute the median along a flattened version of the array. A sequence of

numpy.nanmean(a, axis=None, dtype=None, out=None, keepdims=False) [source] Compute the arithmetic mean along the specified axis, ignoring NaNs. Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs. For all-NaN slices, NaN is returned and a RuntimeWarning is raised. New in version 1.8.0. Parameters: a : array_like Array containing numbers w

numpy.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. Parameters: a : array_like Array containing numbers whose maximum is desired. If a is not an array, a conversion is attempted. axis : int, optional Axis along which the maximum is computed. The default is to compute the maximum of the flattened array. o

numpy.nanargmin(a, axis=None) [source] Return the indices of the minimum values in the specified axis ignoring NaNs. For all-NaN slices ValueError is raised. Warning: the results cannot be trusted if a slice contains only NaNs and Infs. Parameters: a : array_like Input data. axis : int, optional Axis along which to operate. By default flattened input is used. Returns: index_array : ndarray An array of indices or a single index value. See also argmin, nanargmax Examples >>&

numpy.nanargmax(a, axis=None) [source] Return the indices of the maximum values in the specified axis ignoring NaNs. For all-NaN slices ValueError is raised. Warning: the results cannot be trusted if a slice contains only NaNs and -Infs. Parameters: a : array_like Input data. axis : int, optional Axis along which to operate. By default flattened input is used. Returns: index_array : ndarray An array of indices or a single index value. See also argmax, nanargmin Examples >>

numpy.multiply(x1, x2[, out]) = Multiply arguments element-wise. Parameters: x1, x2 : array_like Input arrays to be multiplied. Returns: y : ndarray The product of x1 and x2, element-wise. Returns a scalar if both x1 and x2 are scalars. Notes Equivalent to x1 * x2 in terms of array broadcasting. Examples >>> np.multiply(2.0, 4.0) 8.0 >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.multiply(x1, x2) array([[ 0., 1., 4.]

numpy.msort(a) [source] Return a copy of an array sorted along the first axis. Parameters: a : array_like Array to be sorted. Returns: sorted_array : ndarray Array of the same type and shape as a. See also sort Notes np.msort(a) is equivalent to np.sort(a, axis=0).

numpy.moveaxis(a, source, destination) [source] Move axes of an array to new positions. Other axes remain in their original order. Parameters: a : np.ndarray The array whose axes should be reordered. source : int or sequence of int Original positions of the axes to move. These must be unique. destination : int or sequence of int Destination positions for each of the original axes. These must also be unique. Returns: result : np.ndarray Array with moved axes. This array is a view o

numpy.modf(x[, out1, out2]) = Return the fractional and integral parts of an array, element-wise. The fractional and integral parts are negative if the given number is negative. Parameters: x : array_like Input array. Returns: y1 : ndarray Fractional part of x. y2 : ndarray Integral part of x. Notes For integer input the return values are floats. Examples >>> np.modf([0, 3.5]) (array([ 0. , 0.5]), array([ 0., 3.])) >>> np.modf(-0.5) (-0.5, -0)