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# numpy.ndarray

class `numpy.``ndarray`

  An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer or a floating point number, etc.).

  Arrays should be constructed using array, zeros or empty (refer to the `See Also` section below). The parameters given here describe a low-level method for instantiating an array (ndarray(...)).

  For more information, refer to the numpy module and examine the the methods and attributes of an array.

  Parameters: | shape : tuple of ints Shape of created array. dtype : data type, optional Any object that can be interpreted a numpy data type. buffer : object exposing buffer interface, optional Used to fill the array with data. offset : int, optional Offset of array data in buffer. strides : tuple of ints, optional Strides of data in memory. order : {‘C’, ‘F’}, optional Row-major or column-major order.
  Attributes: | T : ndarray Transponent of the array. data : buffer Array data in memory. dtype : data type Data type, describing the format of the elements in the array. flags : dict Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, and others. flat : ndarray Return flattened version of the array as an iterator. The iterator allows assignments, e.g., `x.flat = 3`. imag : ndarray Imaginary part of the array. real : ndarray Real part of the array. size : int Number of elements in the array. itemsize : int The size of each element in memory (in bytes). nbytes : int The total number of bytes required to store the array data, i.e., `itemsize * size`. shape : tuple of ints Shape of the array. strides : tuple of ints The step-size required to move from one element to the next in memory. For example, a contiguous `(3, 4)` array of type `int16` in C-order has strides `(8, 2)`. This implies that to move from element to element in memory requires jumps of 2 bytes. To move from row-to-row, one needs to jump 6 bytes at a time (`2 * 4`). ctypes : ctypes object Class containing properties of the array needed for interaction with ctypes. base : ndarray If the array is a view on another array, that array is its base (unless that array is also a view). The base array is where the array data is ultimately stored.

  See also

  `array`
    Construct an array.
  `zeros`
    Create an array and fill its allocated memory with zeros.
  `empty`
    Create an array, but leave its allocated memory unchanged.
  `dtype`
    Create a data type.

  Notes

  There are two modes of creating an array using __new__:

  1. If buffer is None, then only shape, dtype, and order are used.
  2. If buffer is an object exporting the buffer interface, then all keywords are interpreted.

  No __init__ method is needed because the array is fully initialized after the __new__ method.

  Examples

  These examples illustrate the low-level ndarray constructor. Refer to the See Also section for easier ways of constructing an ndarray.

  First mode, buffer is None:

      >>> np.ndarray(shape=(2,2), dtype=float, order='F')
      array([[ -1.13698227e+002,   4.25087011e-303],
             [  2.88528414e-306,   3.27025015e-309]])

  Second mode:

      >>> np.ndarray((2,), buffer=np.array([1,2,3]),
      ...            offset=np.int_().itemsize,
      ...            dtype=int) # offset = 1*itemsize, i.e. skip first element
      array([2, 3])

### Navigation

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- NumPy v1.3 Manual (DRAFT) »
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