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Record Arrays
=============
Record arrays expose the fields of structured arrays as properties.
Most commonly, ndarrays contain elements of a single type, e.g. floats,
integers, bools etc. However, it is possible for elements to be combinations
of these using structured types, such as::
>>> a = np.array([(1, 2.0), (1, 2.0)], dtype=[('x', int), ('y', float)])
>>> a
array([(1, 2.0), (1, 2.0)],
dtype=[('x', '<i4'), ('y', '<f8')])
Here, each element consists of two fields: x (and int), and y (a float).
This is known as a structured array. The different fields are analogous
to columns in a spread-sheet. The different fields can be accessed as
one would a dictionary::
>>> a['x']
array([1, 1])
>>> a['y']
array([ 2., 2.])
Record arrays allow us to access fields as properties::
>>> ar = np.rec.array(a)
>>> ar.x
array([1, 1])
>>> ar.y
array([ 2., 2.])
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Class to convert formats, names, titles description to a dtype.
After constructing the format_parser object, the dtype attribute is
the converted data-type:
``dtype = format_parser(formats, names, titles).dtype``
Attributes
----------
dtype : dtype
The converted data-type.
Parameters
----------
formats : str or list of str
The format description, either specified as a string with
comma-separated format descriptions in the form ``'f8, i4, a5'``, or
a list of format description strings in the form
``['f8', 'i4', 'a5']``.
names : str or list/tuple of str
The field names, either specified as a comma-separated string in the
form ``'col1, col2, col3'``, or as a list or tuple of strings in the
form ``['col1', 'col2', 'col3']``.
An empty list can be used, in that case default field names
('f0', 'f1', ...) are used.
titles : sequence
Sequence of title strings. An empty list can be used to leave titles
out.
aligned : bool, optional
If True, align the fields by padding as the C-compiler would.
Default is False.
byteorder : str, optional
If specified, all the fields will be changed to the
provided byte-order. Otherwise, the default byte-order is
used. For all available string specifiers, see `dtype.newbyteorder`.
See Also
--------
dtype, typename, sctype2char
Examples
--------
>>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
... ['T1', 'T2', 'T3']).dtype
dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'),
(('T3', 'col3'), '|S5')])
`names` and/or `titles` can be empty lists. If `titles` is an empty list,
titles will simply not appear. If `names` is empty, default field names
will be used.
>>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
... []).dtype
dtype([('col1', '<f8'), ('col2', '<i4'), ('col3', '|S5')])
>>> np.format_parser(['f8', 'i4', 'a5'], [], []).dtype
dtype([('f0', '<f8'), ('f1', '<i4'), ('f2', '|S5')])
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s2 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.
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.
Returns
-------
rec : recarray
Empty array of the given shape and type.
See Also
--------
rec.fromrecords : Construct a record array from data.
record : fundamental data-type for `recarray`.
format_parser : determine a 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``:
>>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)])
>>> x
array([(1.0, 2), (3.0, 4)],
dtype=[('x', '<f8'), ('y', '<i4')])
>>> 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)]) #doctest: +SKIP
rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),
(3471280, 1.2134086255804012e-316, 0)],
dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])
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W| S( sA create a record array from a (flat) list of arrays
>>> x1=np.array([1,2,3,4])
>>> x2=np.array(['a','dd','xyz','12'])
>>> x3=np.array([1.1,2,3,4])
>>> r = np.core.records.fromarrays([x1,x2,x3],names='a,b,c')
>>> print(r[1])
(2, 'dd', 2.0)
>>> x1[1]=34
>>> r.a
array([1, 2, 3, 4])
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defined
r=fromrecords([(2,3.,'abc')]*100000)
it can be slow.
If formats is None, then this will auto-detect formats. Use list of
tuples rather than list of lists for faster processing.
>>> r=np.core.records.fromrecords([(456,'dbe',1.2),(2,'de',1.3)],
... names='col1,col2,col3')
>>> print(r[0])
(456, 'dbe', 1.2)
>>> r.col1
array([456, 2])
>>> r.col2
array(['dbe', 'de'],
dtype='|S3')
>>> import pickle
>>> print(pickle.loads(pickle.dumps(r)))
[(456, 'dbe', 1.2) (2, 'de', 1.3)]
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If file is a string then that file is opened, else it is assumed
to be a file object.
>>> from tempfile import TemporaryFile
>>> a = np.empty(10,dtype='f8,i4,a5')
>>> a[5] = (0.5,10,'abcde')
>>>
>>> fd=TemporaryFile()
>>> a = a.newbyteorder('<')
>>> a.tofile(fd)
>>>
>>> fd.seek(0)
>>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
... byteorder='<')
>>> print(r[5])
(0.5, 10, 'abcde')
>>> r.shape
(10,)
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