numpy.can_cast(from, totype, casting = 'safe') Returns True if cast between data types can occur according to the casting rule. If from is a scalar or array scalar, also returns True if the scalar value can be cast without overflow or truncation to an integer. Parameters: from : dtype, dtype specifier, scalar, or array Data type, scalar, or array to cast from. totype : dtype or dtype specifier Data type to cast to. casting : {?no?, ?equiv?, ?safe?, ?same_kind?, ?unsafe?}, optional Con

numpy.busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None) First adjusts the date to fall on a valid day according to the roll rule, then applies offsets to the given dates counted in valid days. New in version 1.7.0. Parameters: dates : array_like of datetime64[D] The array of dates to process. offsets : array_like of int The array of offsets, which is broadcast with dates. roll : {?raise?, ?nat?, ?forward?, ?following?, ?backward?

numpy.busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None) Counts the number of valid days between begindates and enddates, not including the day of enddates. If enddates specifies a date value that is earlier than the corresponding begindates date value, the count will be negative. New in version 1.7.0. Parameters: begindates : array_like of datetime64[D] The array of the first dates for counting. enddates : array_like of datetime64[D] The arr

class numpy.busdaycalendar [source] A business day calendar object that efficiently stores information defining valid days for the busday family of functions. The default valid days are Monday through Friday (?business days?). A busdaycalendar object can be specified with any set of weekly valid days, plus an optional ?holiday? dates that always will be invalid. Once a busdaycalendar object is created, the weekmask and holidays cannot be modified. New in version 1.7.0. Parameters: weekma

numpy.broadcast_to(array, shape, subok=False) [source] Broadcast an array to a new shape. Parameters: array : array_like The array to broadcast. shape : tuple The shape of the desired array. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default). Returns: broadcast : array A readonly view on the original array with the given shape. It is typically not contiguous. Furthermore, more than

numpy.broadcast_arrays(*args, **kwargs) [source] Broadcast any number of arrays against each other. Parameters: `*args` : array_likes The arrays to broadcast. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned arrays will be forced to be a base-class array (default). Returns: broadcasted : list of arrays These arrays are views on the original arrays. They are typically not contiguous. Furthermore, more than one element of a broadcasted ar

class numpy.broadcast [source] Produce an object that mimics broadcasting. Parameters: in1, in2, ... : array_like Input parameters. Returns: b : broadcast object Broadcast the input parameters against one another, and return an object that encapsulates the result. Amongst others, it has shape and nd properties, and may be used as an iterator. Examples Manually adding two vectors, using broadcasting: >>> x = np.array([[1], [2], [3]]) >>> y = np.array([4, 5, 6]) >

numpy.bmat(obj, ldict=None, gdict=None) [source] Build a matrix object from a string, nested sequence, or array. Parameters: obj : str or array_like Input data. Names of variables in the current scope may be referenced, even if obj is a string. ldict : dict, optional A dictionary that replaces local operands in current frame. Ignored if obj is not a string or gdict is None. gdict : dict, optional A dictionary that replaces global operands in current frame. Ignored if obj is not a stri

numpy.blackman(M) [source] Return the Blackman window. The Blackman window is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. It is close to optimal, only slightly worse than a Kaiser window. Parameters: M : int Number of points in the output window. If zero or less, an empty array is returned. Returns: out : ndarray The window, with the maximum value normalized to one (the value one appears only i

numpy.bitwise_xor(x1, x2[, out]) = Compute the bit-wise XOR of two arrays element-wise. Computes the bit-wise XOR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ^. Parameters: x1, x2 : array_like Only integer and boolean types are handled. Returns: out : array_like Result. See also logical_xor, bitwise_and, bitwise_or binary_repr Return the binary representation of the input number as a string. Examples