ndarray.all(axis=None, out=None, keepdims=False) Returns True if all elements evaluate to True. Refer to numpy.all for full documentation. See also numpy.all equivalent function

numpy.place(arr, mask, vals) [source] Change elements of an array based on conditional and input values. Similar to np.copyto(arr, vals, where=mask), the difference is that place uses the first N elements of vals, where N is the number of True values in mask, while copyto uses the elements where mask is True. Note that extract does the exact opposite of place. Parameters: arr : ndarray Array to put data into. mask : array_like Boolean mask array. Must have the same size as a. vals : 1-

numpy.polynomial.hermite.hermder(c, m=1, scl=1, axis=0) [source] Differentiate a Hermite series. Returns the Hermite series coefficients c differentiated m times along axis. At each iteration the result is multiplied by scl (the scaling factor is for use in a linear change of variable). The argument c is an array of coefficients from low to high degree along each axis, e.g., [1,2,3] represents the series 1*H_0 + 2*H_1 + 3*H_2 while [[1,2],[1,2]] represents 1*H_0(x)*H_0(y) + 1*H_1(x)*H_0(y)

generic.setflags() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

chararray.lstrip(chars=None) [source] For each element in self, return a copy with the leading characters removed. See also char.lstrip

recarray.compress(condition, axis=None, out=None) Return selected slices of this array along given axis. Refer to numpy.compress for full documentation. See also numpy.compress equivalent function

numpy.ma.mask_rows(a, axis=None) [source] Mask rows of a 2D array that contain masked values. This function is a shortcut to mask_rowcols with axis equal to 0. See also mask_rowcols Mask rows and/or columns of a 2D array. masked_where Mask where a condition is met. Examples >>> import numpy.ma as ma >>> a = np.zeros((3, 3), dtype=np.int) >>> a[1, 1] = 1 >>> a array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) >>> a = ma.masked_equal(a

numpy.polynomial.laguerre.lagval2d(x, y, c) [source] Evaluate a 2-D Laguerre series at points (x, y). This function returns the values: The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array a one is implicitly ap

recarray.round(decimals=0, out=None) Return a with each element rounded to the given number of decimals. Refer to numpy.around for full documentation. See also numpy.around equivalent function

MaskedArray.imag Imaginary part.