numpy.radians(x[, out]) = Convert angles from degrees to radians. Parameters: x : array_like Input array in degrees. out : ndarray, optional Output array of same shape as x. Returns: y : ndarray The corresponding radian values. See also deg2rad equivalent function Examples Convert a degree array to radians >>> deg = np.arange(12.) * 30. >>> np.radians(deg) array([ 0. , 0.52359878, 1.04719755, 1.57079633, 2.0943951 , 2.61799388, 3.1415926

numpy.rad2deg(x[, out]) = Convert angles from radians to degrees. Parameters: x : array_like Angle in radians. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. See doc.ufuncs. Returns: y : ndarray The corresponding angle in degrees. See also deg2rad Convert angles from degrees to radians. unwrap Remove large jumps in angle by wrapping. Notes New in version 1.3.0. rad2deg(x) is 180

numpy.pv(rate, nper, pmt, fv=0.0, when='end') [source] Compute the present value. Given: a future value, fv an interest rate compounded once per period, of which there are nper total a (fixed) payment, pmt, paid either at the beginning (when = {?begin?, 1}) or the end (when = {?end?, 0}) of each period Return: the value now Parameters: rate : array_like Rate of interest (per period) nper : array_like Number of compounding periods pmt : array_like Payment fv : array_like, optional

numpy.putmask(a, mask, values) Changes elements of an array based on conditional and input values. Sets a.flat[n] = values[n] for each n where mask.flat[n]==True. If values is not the same size as a and mask then it will repeat. This gives behavior different from a[mask] = values. Parameters: a : array_like Target array. mask : array_like Boolean mask array. It has to be the same shape as a. values : array_like Values to put into a where mask is True. If values is smaller than a it wi

numpy.put(a, ind, v, mode='raise') [source] Replaces specified elements of an array with given values. The indexing works on the flattened target array. put is roughly equivalent to: a.flat[ind] = v Parameters: a : ndarray Target array. ind : array_like Target indices, interpreted as integers. v : array_like Values to place in a at target indices. If v is shorter than ind it will be repeated as necessary. mode : {?raise?, ?wrap?, ?clip?}, optional Specifies how out-of-bounds indice

numpy.ptp(a, axis=None, out=None) [source] Range of values (maximum - minimum) along an axis. The name of the function comes from the acronym for ?peak to peak?. Parameters: a : array_like Input values. axis : int, optional Axis along which to find the peaks. By default, flatten the array. out : array_like Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type of the output values will be cast if necess

numpy.promote_types(type1, type2) Returns the data type with the smallest size and smallest scalar kind to which both type1 and type2 may be safely cast. The returned data type is always in native byte order. This function is symmetric and associative. Parameters: type1 : dtype or dtype specifier First data type. type2 : dtype or dtype specifier Second data type. Returns: out : dtype The promoted data type. See also result_type, dtype, can_cast Notes New in version 1.6.0. Star

numpy.prod(a, axis=None, dtype=None, out=None, keepdims=False) [source] Return the product of array elements over a given axis. Parameters: a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which a product is performed. The default, axis=None, will calculate the product of all the elements in the input array. If axis is negative it counts from the last to the first axis. New in version 1.7.0. If axis is a tuple of ints, a product is performed

numpy.ppmt(rate, per, nper, pv, fv=0.0, when='end') [source] Compute the payment against loan principal. Parameters: rate : array_like Rate of interest (per period) per : array_like, int Amount paid against the loan changes. The per is the period of interest. nper : array_like Number of compounding periods pv : array_like Present value fv : array_like, optional Future value when : {{?begin?, 1}, {?end?, 0}}, {string, int} When payments are due (?begin? (1) or ?end? (0)) See a

numpy.power(x1, x2[, out]) = First array elements raised to powers from second array, element-wise. Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be broadcastable to the same shape. Parameters: x1 : array_like The bases. x2 : array_like The exponents. Returns: y : ndarray The bases in x1 raised to the exponents in x2. Examples Cube each element in a list. >>> x1 = range(6) >>> x1 [0, 1, 2, 3, 4, 5] >>> np.power(x1,