![]() Return an array formed from the elements of a at the given indices. Any other value for axis represents the dimension along which If axis is None, then the array is treated as a 1-DĪrray. Return a copy of the array collapsed into one dimension.įor array methods that take an axis keyword, it defaults to Return a view of the array with axis1 and axis2 interchanged. Returns a view of the array with axes transposed. Returns an array containing the same data with a new shape. Replaced with n integers which will be interpreted as an n-tuple. Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.įor reshape, resize, and transpose, the single tuple argument may be Returns a field of the given array as a certain type. Ndarray.astype(dtype)Ĭopy of the array, cast to a specified type. Returns the pickle of the array as a string. Write array to a file as text or binary (default).ĭump a pickle of the array to the specified file. Insert scalar into an array (scalar is cast to array's dtype, if possible)Ī compatibility alias for tobytes, with exactly the same behavior.Ĭonstruct Python bytes containing the raw data bytes in the array. Return the array as an a.ndim-levels deep nested list of Python scalars. (Each method’s docstring has aįor the following methods there are also corresponding functions inĬopy an element of an array to a standard Python scalar and return it. The array in some fashion, typically returning an array result. Total bytes consumed by the elements of the array.īase object if memory is from some other object.Īn object to simplify the interaction of the array with the ctypes module.Īn ndarray object has many methods which operate on or with Python buffer object pointing to the start of the array's data. Tuple of bytes to step in each dimension when traversing an array. Information about the memory layout of the array. The following attributes contain information about the memory layout ![]() Information on each attribute is given below. The exposed attributes are the core parts of anĪrray and only some of them can be reset meaningfully without creatingĪ new array. You to get and sometimes set intrinsic properties of the array withoutĬreating a new array. Generally, accessing an array through its attributes allows Array attributes #Īrray attributes reflect information that is intrinsic to the array Irregularly strided array is passed in to such algorithms, a copy However, some algorithms require single-segment arrays. ![]() Several algorithms in NumPy work on arbitrarily strided arrays. ![]()
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