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# numpy.recarray

class numpy.recarray(shape, dtype=None, buf=None, offset=0, strides=None, formats=None, names=None, titles=None, byteorder=None, aligned=False, order='C')

  Construct an ndarray that allows field access using attributes.

  Arrays may have a data-types containing fields, analogous to columns in a spread sheet. An example is `[(x, int), (y, float)]`, where each entry in the array is a pair of `(int, float)`. Normally, these attributes are accessed using dictionary lookups such as `arr['x']` and `arr['y']`. Record arrays allow the fields to be accessed as members of the array, using `arr.x` and `arr.y`.

  Parameters:
    shape tuple

      Shape of output array.

    dtype data-type, optional

      The desired data-type. By default, the data-type is determined from formats, names, titles, aligned and byteorder.

    formats list of data-types, optional

      A list containing the data-types for the different columns, e.g. `['i4', 'f8', 'i4']`. formats does not support the new convention of using types directly, i.e. `(int, float, int)`. Note that formats must be a list, not a tuple. Given that formats is somewhat limited, we recommend specifying `dtype` instead.

    names tuple of str, optional

      The name of each column, e.g. `('x', 'y', 'z')`.

    buf buffer, optional

      By default, a new array is created of the given shape and data-type. If buf is specified and is an object exposing the buffer interface, the array will use the memory from the existing buffer. In this case, the offset and `strides` keywords are available.

  Attributes:
    `T`

      View of the transposed array.

    `base`

      Base object if memory is from some other object.

    `ctypes`

      An object to simplify the interaction of the array with the ctypes module.

    `data`

      Python buffer object pointing to the start of the array’s data.

    device
    `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.

    `itemsize`

      Length of one array element in bytes.

    `mT`

      View of the matrix transposed array.

    `nbytes`

      Total bytes consumed by the elements of the array.

    `ndim`

      Number of array dimensions.

    `real`

      The real part of the array.

    `shape`

      Tuple of array dimensions.

    `size`

      Number of elements in the array.

    `strides`

      Tuple of bytes to step in each dimension when traversing an array.

  Methods

  `all`([axis, out, keepdims, where]) | Returns True if all elements evaluate to True.
  `any`([axis, out, keepdims, where]) | Returns True if any of the elements of a evaluate to True.
  `argmax`([axis, out, keepdims]) | Return indices of the maximum values along the given axis.
  `argmin`([axis, out, keepdims]) | Return indices of the minimum values along the given axis.
  `argpartition`(kth[, axis, kind, order]) | Returns the indices that would partition this array.
  `argsort`([axis, kind, order, stable]) | 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`([min, max, out]) | Return an array whose values are limited to `[min, 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`(other, /[, out]) | Refer to `numpy.dot` for full documentation.
  `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.
  `max`([axis, out, keepdims, initial, where]) | Return the maximum along a given axis.
  `mean`([axis, dtype, out, keepdims, where]) | Returns the average of the array elements along given axis.
  `min`([axis, out, keepdims, initial, where]) | Return the minimum along a given axis.
  `nonzero`() | Return the indices of the elements that are non-zero.
  `partition`(kth[, axis, kind, order]) | Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array.
  `prod`([axis, dtype, out, keepdims, initial, ...]) | Return the product of the array elements over the 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`(a.reshape) | Returns an array containing the same data with a new shape.
  `resize`(a.resize) | 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, WRITEBACKIFCOPY, respectively.
  `sort`([axis, kind, order, stable]) | Sort an array in-place.
  `squeeze`([axis]) | Remove axes of length one from a.
  `std`([axis, dtype, out, ddof, keepdims, ...]) | Returns the standard deviation of the array elements along given axis.
  `sum`([axis, dtype, out, keepdims, initial, where]) | 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.
  `to_device`(device, /, *[, stream]) | For Array API compatibility.
  `tobytes`([order]) | Construct Python bytes containing the raw data bytes in the array.
  `tofile`(fid, /[, sep, format]) | Write array to a file as text or binary (default).
  `tolist`() | Return the array as an `a.ndim`-levels deep nested list of Python scalars.
  `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, keepdims, ...]) | Returns the variance of the array elements, along given axis.
  `view`([dtype][, type]) | New view of array with the same data.

  field |

  Returns:
    rec recarray

      Empty array of the given shape and type.

  Other Parameters:
    titles tuple of str, optional

      Aliases for column names. For example, if names were `('x', 'y', 'z')` and titles is `('x_coordinate', 'y_coordinate', 'z_coordinate')`, then `arr['x']` is equivalent to both `arr.x` and `arr.x_coordinate`.

    byteorder{‘<’, ‘>’, ‘=’}, optional

      Byte-order for all fields.

    aligned bool, optional

      Align the fields in memory as the C-compiler would.

    strides tuple of ints, optional

      Buffer (buf) is interpreted according to these strides (strides define how many bytes each array element, row, column, etc. occupy in memory).

    offset int, optional

      Start reading buffer (buf) from this offset onwards.

    order{‘C’, ‘F’}, optional

      Row-major (C-style) or column-major (Fortran-style) order.

  See also

  `numpy.rec.fromrecords`

    Construct a record array from data.

  `numpy.record`

    fundamental data-type for `recarray`.

  `numpy.rec.format_parser`

    determine data-type from formats, names, titles.

  Notes

  This constructor can be compared to `empty`: it creates a new record array but does not fill it with data. To create a record array from data, use one of the following methods:

  1. Create a standard ndarray and convert it to a record array, using `arr.view(np.recarray)`

  2. Use the buf keyword.

  3. Use np.rec.fromrecords.

  Examples

  Create an array with two fields, `x` and `y`:

      >>> import numpy as np
      >>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', '<f8'), ('y', '<i8')])
      >>> x
      array([(1., 2), (3., 4)], dtype=[('x', '<f8'), ('y', '<i8')])

      >>> x['x']
      array([1., 3.])

  View the array as a record array:

      >>> x = x.view(np.recarray)

      >>> x.x
      array([1., 3.])

      >>> x.y
      array([2, 4])

  Create a new, empty record array:

      >>> np.recarray((2,),
      ... dtype=[('x', int), ('y', float), ('z', int)])
      rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),
             (3471280, 1.2134086255804012e-316, 0)],
            dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])