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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, a floating point number, or something else, etc.)

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

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

  Parameters : | (for the __new__ method; see Notes below) : shape : tuple of ints Shape of created array. dtype : data-type, optional Any object that can be interpreted as 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.

  See also

  `array`
    Construct an array.
  `zeros`
    Create an array, each element of which is zero.
  `empty`
    Create an array, but leave its allocated memory unchanged (i.e., it contains “garbage”).
  `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 exposing 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 above 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]])         #random

  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])

  Attributes

  `T` |
  `data` |
  `dtype` | Create a data type object.
  `flags` |
  `flat` |
  `imag`(val) | Return the imaginary part of the elements of the array.
  `real`(val) | Return the real part of the elements of the array.
  `size`(a[, axis]) | Return the number of elements along a given axis.
  `itemsize` |
  `nbytes` | Base object for a dictionary for look-up with any alias for an array dtype.
  `ndim`(a) | Return the number of dimensions of an array.
  `shape`(a) | Return the shape of an array.
  `strides` |
  `ctypes` |
  `base` |

  Methods

  `all`(a[, axis, out]) | Test whether all array elements along a given axis evaluate to True.
  `any`(a[, axis, out]) | Test whether any array element along a given axis evaluates to True.
  `argmax`(a[, axis]) | Indices of the maximum values along an axis.
  `argmin`(a[, axis]) | Return the indices of the minimum values along an axis.
  `argsort`(a[, axis, kind, order]) | Returns the indices that would sort an array.
  `astype` |
  `byteswap` |
  `choose`(a, choices[, out, mode]) | Construct an array from an index array and a set of arrays to choose from.
  `clip`(a, a_min, a_max[, out]) | Clip (limit) the values in an array.
  `compress`(condition, a[, axis, out]) | Return selected slices of an array along given axis.
  `conj`() | Return the complex conjugate, element-wise.
  `conjugate`() | Return the complex conjugate, element-wise.
  `copy`(a) | Return an array copy of the given object.
  `cumprod`(a[, axis, dtype, out]) | Return the cumulative product of elements along a given axis.
  `cumsum`(a[, axis, dtype, out]) | Return the cumulative sum of the elements along a given axis.
  `diagonal`(a[, offset, axis1, axis2]) | Return specified diagonals.
  `dot`(a, b) | Dot product of two arrays.
  `dump` |
  `dumps` |
  `fill` |
  `flatten` |
  `getfield` |
  `item` |
  `itemset` |
  `max`(a[, axis, out]) | Return the maximum of an array or maximum along an axis.
  `mean`(a[, axis, dtype, out]) | Compute the arithmetic mean along the specified axis.
  `min`(a[, axis, out]) | Return the minimum of an array or minimum along an axis.
  `newbyteorder` |
  `nonzero`(a) | Return the indices of the elements that are non-zero.
  `prod`(a[, axis, dtype, out]) | Return the product of array elements over a given axis.
  `ptp`(a[, axis, out]) | Range of values (maximum - minimum) along an axis.
  `put`(a, ind, v[, mode]) | Replaces specified elements of an array with given values.
  `ravel`(a[, order]) | Return a flattened array.
  `repeat`(a, repeats[, axis]) | Repeat elements of an array.
  `reshape`(a, newshape[, order]) | Gives a new shape to an array without changing its data.
  `resize`(a, new_shape) | Return a new array with the specified shape.
  `round`(a[, decimals, out]) | Round an array to the given number of decimals.
  `searchsorted`(a, v[, side]) | Find indices where elements should be inserted to maintain order.
  `setfield` |
  `setflags` |
  `sort`(a[, axis, kind, order]) | Return a sorted copy of an array.
  `squeeze`(a) | Remove single-dimensional entries from the shape of an array.
  `std`(a[, axis, dtype, out, ddof]) | Compute the standard deviation along the specified axis.
  `sum`(a[, axis, dtype, out]) | Sum of array elements over a given axis.
  `swapaxes`(a, axis1, axis2) | Interchange two axes of an array.
  `take`(a, indices[, axis, out, mode]) | Take elements from an array along an axis.
  `tofile` |
  `tolist` |
  `tostring` |
  `trace`(a[, offset, axis1, axis2, dtype, out]) | Return the sum along diagonals of the array.
  `transpose`(a[, axes]) | Permute the dimensions of an array.
  `var`(a[, axis, dtype, out, ddof]) | Compute the variance along the specified axis.
  `view` |

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- NumPy v1.5 Manual (DRAFT) »
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