numpy.seterr(all=None, divide=None, over=None, under=None, invalid=None) [source] Set how floating-point errors are handled. Note that operations on integer scalar types (such as int16) are handled like floating point, and are affected by these settings. Parameters: all : {?ignore?, ?warn?, ?raise?, ?call?, ?print?, ?log?}, optional Set treatment for all types of floating-point errors at once: ignore: Take no action when the exception occurs. warn: Print a RuntimeWarning (via the Python w

numpy.is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None) Calculates which of the given dates are valid days, and which are not. New in version 1.7.0. Parameters: dates : array_like of datetime64[D] The array of dates to process. weekmask : str or array_like of bool, optional A seven-element array indicating which of Monday through Sunday are valid days. May be specified as a length-seven list or array, like [1,1,1,1,1,0,0]; a length-seven string, like ?11111

numpy.matlib.rand(*args) [source] Return a matrix of random values with given shape. Create a matrix of the given shape and propagate it with random samples from a uniform distribution over [0, 1). Parameters: *args : Arguments Shape of the output. If given as N integers, each integer specifies the size of one dimension. If given as a tuple, this tuple gives the complete shape. Returns: out : ndarray The matrix of random values with shape given by *args. See also randn, numpy.rando

numpy.core.defchararray.isnumeric(a) [source] For each element, return True if there are only numeric characters in the element. Calls unicode.isnumeric element-wise. Numeric characters include digit characters, and all characters that have the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION ONE FIFTH. Parameters: a : array_like, unicode Input array. Returns: out : ndarray, bool Array of booleans of same shape as a. See also unicode.isnumeric

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.random.randint(low, high=None, size=None, dtype='l') Return random integers from low (inclusive) to high (exclusive). Return random integers from the ?discrete uniform? distribution of the specified dtype in the ?half-open? interval [low, high). If high is None (the default), then results are from [0, low). Parameters: low : int Lowest (signed) integer to be drawn from the distribution (unless high=None, in which case this parameter is the highest such integer). high : int, optiona

numpy.ma.mask_rowcols(a, axis=None) [source] Mask rows and/or columns of a 2D array that contain masked values. Mask whole rows and/or columns of a 2D array that contain masked values. The masking behavior is selected using the axis parameter. If axis is None, rows and columns are masked. If axis is 0, only rows are masked. If axis is 1 or -1, only columns are masked. Parameters: a : array_like, MaskedArray The array to mask. If not a MaskedArray instance (or if no array elements are mas

HermiteE.integ(m=1, k=[], lbnd=None) [source] Integrate. Return a series instance that is the definite integral of the current series. Parameters: m : non-negative int The number of integrations to perform. k : array_like Integration constants. The first constant is applied to the first integration, the second to the second, and so on. The list of values must less than or equal to m in length and any missing values are set to zero. lbnd : Scalar The lower bound of the definite integra

MaskedArray.conj() Complex-conjugate all elements. Refer to numpy.conjugate for full documentation. See also numpy.conjugate equivalent function

Polynomials in NumPy can be created, manipulated, and even fitted using the Using the Convenience Classes of the numpy.polynomial package, introduced in NumPy 1.4. Prior to NumPy 1.4, numpy.poly1d was the class of choice and it is still available in order to maintain backward compatibility. However, the newer Polynomial package is more complete than numpy.poly1d and its convenience classes are better behaved in the numpy environment. Therefore Polynomial is recommended for new coding. Transit