numpy.genfromtxt(fname, dtype=, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None) [source] Load data from a text file, with missing values handled as specified. Each line past the first skip_header lines is split a

numpy.fv(rate, nper, pmt, pv, when='end') [source] Compute the future value. Given: a present value, pv 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 at the end of the nper periods Parameters: rate : scalar or array_like of shape(M, ) Rate of interest as decimal (not per cent) per period nper : scalar or array_like of

numpy.full(shape, fill_value, dtype=None, order='C') [source] Return a new array of given shape and type, filled with fill_value. Parameters: shape : int or sequence of ints Shape of the new array, e.g., (2, 3) or 2. fill_value : scalar Fill value. dtype : data-type, optional The desired data-type for the array, e.g., np.int8. Default is float, but will change to np.array(fill_value).dtype in a future release. order : {?C?, ?F?}, optional Whether to store multidimensional data in C-

numpy.full_like(a, fill_value, dtype=None, order='K', subok=True) [source] Return a full array 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. fill_value : scalar Fill value. dtype : data-type, optional Overrides the data type of the result. 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? i

numpy.fromstring(string, dtype=float, count=-1, sep='') A new 1-D array initialized from raw binary or text data in a string. Parameters: string : str A string containing the data. dtype : data-type, optional The data type of the array; default: float. For binary input data, the data must be in exactly this format. count : int, optional Read this number of dtype elements from the data. If this is negative (the default), the count will be determined from the length of the data. sep :

numpy.fromregex(file, regexp, dtype) [source] Construct an array from a text file, using regular expression parsing. The returned array is always a structured array, and is constructed from all matches of the regular expression in the file. Groups in the regular expression are converted to fields of the structured array. Parameters: file : str or file File name or file object to read. regexp : str or regexp Regular expression used to parse the file. Groups in the regular expression corr

numpy.frompyfunc(func, nin, nout) Takes an arbitrary Python function and returns a Numpy ufunc. Can be used, for example, to add broadcasting to a built-in Python function (see Examples section). Parameters: func : Python function object An arbitrary Python function. nin : int The number of input arguments. nout : int The number of objects returned by func. Returns: out : ufunc Returns a Numpy universal function (ufunc) object. Notes The returned ufunc always returns PyObject ar

numpy.fromiter(iterable, dtype, count=-1) Create a new 1-dimensional array from an iterable object. Parameters: iterable : iterable object An iterable object providing data for the array. dtype : data-type The data-type of the returned array. count : int, optional The number of items to read from iterable. The default is -1, which means all data is read. Returns: out : ndarray The output array. Notes Specify count to improve performance. It allows fromiter to pre-allocate the ou

numpy.fromfile(file, dtype=float, count=-1, sep='') Construct an array from data in a text or binary file. A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the tofile method can be read using this function. Parameters: file : file or str Open file object or filename. dtype : data-type Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in th

numpy.fromfunction(function, shape, **kwargs) [source] Construct an array by executing a function over each coordinate. The resulting array therefore has a value fn(x, y, z) at coordinate (x, y, z). Parameters: function : callable The function is called with N parameters, where N is the rank of shape. Each parameter represents the coordinates of the array varying along a specific axis. For example, if shape were (2, 2), then the parameters in turn be (0, 0), (0, 1), (1, 0), (1, 1). shape