poly1d.__call__(val) [source]

poly1d.integ(m=1, k=0) [source] Return an antiderivative (indefinite integral) of this polynomial. Refer to polyint for full documentation. See also polyint equivalent function

poly1d.deriv(m=1) [source] Return a derivative of this polynomial. Refer to polyder for full documentation. See also polyder equivalent function

Basics poly1d(c_or_r[, r, variable]) A one-dimensional polynomial class. polyval(p, x) Evaluate a polynomial at specific values. poly(seq_of_zeros) Find the coefficients of a polynomial with the given sequence of roots. roots(p) Return the roots of a polynomial with coefficients given in p. Fitting polyfit(x, y, deg[, rcond, full, w, cov]) Least squares polynomial fit. Calculus polyder(p[, m]) Return the derivative of the specified order of a polynomial. polyint(p[, m, k]) Return a

pad(array, pad_width, mode, **kwargs) Pads an array.

Aliases for functions which may be accelerated by Scipy. Scipy can be built to use accelerated or otherwise improved libraries for FFTs, linear algebra, and special functions. This module allows developers to transparently support these accelerated functions when scipy is available but still support users who have only installed Numpy. Linear algebra cholesky(a) Cholesky decomposition. det(a) Compute the determinant of an array. eig(a) Compute the eigenvalues and right eigenvectors of a sq

numpy.zeros_like(a, dtype=None, order='K', subok=True) [source] Return an array of zeros with the same shape and type as a given array. Parameters: a : array_like The shape and data-type of a define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result. New in version 1.6.0. order : {?C?, ?F?, ?A?, or ?K?}, optional Overrides the memory layout of the result. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran c

numpy.zeros(shape, dtype=float, order='C') Return a new array of given shape and type, filled with zeros. Parameters: shape : int or sequence of ints Shape of the new array, e.g., (2, 3) or 2. dtype : data-type, optional The desired data-type for the array, e.g., numpy.int8. Default is numpy.float64. order : {?C?, ?F?}, optional Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. Returns: out : ndarray Array of zeros with the g

numpy.where(condition[, x, y]) Return elements, either from x or y, depending on condition. If only condition is given, return condition.nonzero(). Parameters: condition : array_like, bool When True, yield x, otherwise yield y. x, y : array_like, optional Values from which to choose. x and y need to have the same shape as condition. Returns: out : ndarray or tuple of ndarrays If both x and y are specified, the output array contains elements of x where condition is True, and elements

numpy.vstack(tup) [source] Stack arrays in sequence vertically (row wise). Take a sequence of arrays and stack them vertically to make a single array. Rebuild arrays divided by vsplit. Parameters: tup : sequence of ndarrays Tuple containing arrays to be stacked. The arrays must have the same shape along all but the first axis. Returns: stacked : ndarray The array formed by stacking the given arrays. See also stack Join a sequence of arrays along a new axis. hstack Stack array