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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` | Same as self.transpose(), except that self is returned if self.ndim < 2.
  `data` | Python buffer object pointing to the start of the array’s data.
  `dtype` | Data-type of the array’s elements.
  `flags` | Information about the memory layout of the array.
  `flat` | A 1-D iterator over the array.
  `imag` | The imaginary part of the array.
  `real` | The real part of the array.
  `size` | Number of elements in the array.
  `itemsize` | Length of one array element in bytes.
  `nbytes` | Total bytes consumed by the elements of the array.
  `ndim` | Number of array dimensions.
  `shape` | Tuple of array dimensions.
  `strides` | Tuple of bytes to step in each dimension when traversing an array.
  `ctypes` | An object to simplify the interaction of the array with the ctypes module.
  `base` | Base object if memory is from some other object.

  Methods

  `all`([axis, out]) | Returns True if all elements evaluate to True.
  `any`([axis, out]) | Returns True if any of the elements of a evaluate to True.
  `argmax`([axis, out]) | Return indices of the maximum values along the given axis.
  `argmin`([axis, out]) | Return indices of the minimum values along the given axis of a.
  `argsort`([axis, kind, order]) | Returns the indices that would sort this array.
  `astype`(dtype[, order, casting, subok, copy]) | Copy of the array, cast to a specified type.
  `byteswap`(inplace) | Swap the bytes of the array elements
  `choose`(choices[, out, mode]) | Use an index array to construct a new array from a set of choices.
  `clip`(a_min, a_max[, out]) | Return an array whose values are limited to `[a_min, a_max]`.
  `compress`(condition[, axis, out]) | Return selected slices of this array along given axis.
  `conj`() | Complex-conjugate all elements.
  `conjugate`() | Return the complex conjugate, element-wise.
  `copy`([order]) | Return a copy of the array.
  `cumprod`([axis, dtype, out]) | Return the cumulative product of the elements along the given axis.
  `cumsum`([axis, dtype, out]) | Return the cumulative sum of the elements along the given axis.
  `diagonal`([offset, axis1, axis2]) | Return specified diagonals.
  `dot`(b[, out]) | Dot product of two arrays.
  `dump`(file) | Dump a pickle of the array to the specified file.
  `dumps`() | Returns the pickle of the array as a string.
  `fill`(value) | Fill the array with a scalar value.
  `flatten`([order]) | Return a copy of the array collapsed into one dimension.
  `getfield`(dtype[, offset]) | Returns a field of the given array as a certain type.
  `item`(*args) | Copy an element of an array to a standard Python scalar and return it.
  `itemset`(*args) | Insert scalar into an array (scalar is cast to array’s dtype, if possible)
  `max`([axis, out]) | Return the maximum along a given axis.
  `mean`([axis, dtype, out]) | Returns the average of the array elements along given axis.
  `min`([axis, out]) | Return the minimum along a given axis.
  `newbyteorder`([new_order]) | Return the array with the same data viewed with a different byte order.
  `nonzero`() | Return the indices of the elements that are non-zero.
  `prod`([axis, dtype, out]) | Return the product of the array elements over the given axis
  `ptp`([axis, out]) | Peak to peak (maximum - minimum) value along a given axis.
  `put`(indices, values[, mode]) | Set `a.flat[n] = values[n]` for all n in indices.
  `ravel`([order]) | Return a flattened array.
  `repeat`(repeats[, axis]) | Repeat elements of an array.
  `reshape`(shape[, order]) | Returns an array containing the same data with a new shape.
  `resize`(new_shape[, refcheck]) | Change shape and size of array in-place.
  `round`([decimals, out]) | Return a with each element rounded to the given number of decimals.
  `searchsorted`(v[, side, sorter]) | Find indices where elements of v should be inserted in a to maintain order.
  `setfield`(val, dtype[, offset]) | Put a value into a specified place in a field defined by a data-type.
  `setflags`([write, align, uic]) | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.
  `sort`([axis, kind, order]) | Sort an array, in-place.
  `squeeze`([axis]) | Remove single-dimensional entries from the shape of a.
  `std`([axis, dtype, out, ddof]) | Returns the standard deviation of the array elements along given axis.
  `sum`([axis, dtype, out]) | Return the sum of the array elements over the given axis.
  `swapaxes`(axis1, axis2) | Return a view of the array with axis1 and axis2 interchanged.
  `take`(indices[, axis, out, mode]) | Return an array formed from the elements of a at the given indices.
  `tofile`(fid[, sep, format]) | Write array to a file as text or binary (default).
  `tolist`() | Return the array as a (possibly nested) list.
  `tostring`([order]) | Construct a Python string containing the raw data bytes in the array.
  `trace`([offset, axis1, axis2, dtype, out]) | Return the sum along diagonals of the array.
  `transpose`(*axes) | Returns a view of the array with axes transposed.
  `var`([axis, dtype, out, ddof]) | Returns the variance of the array elements, along given axis.
  `view`([dtype, type]) | New view of array with the same data.

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