diff options
Diffstat (limited to 'doc/source')
| -rw-r--r-- | doc/source/reference/c-api/types-and-structures.rst | 5 | ||||
| -rw-r--r-- | doc/source/user/basics.io.genfromtxt.rst | 14 | ||||
| -rw-r--r-- | doc/source/user/basics.rec.rst | 11 |
3 files changed, 17 insertions, 13 deletions
diff --git a/doc/source/reference/c-api/types-and-structures.rst b/doc/source/reference/c-api/types-and-structures.rst index 605a4ae71..1ea47b498 100644 --- a/doc/source/reference/c-api/types-and-structures.rst +++ b/doc/source/reference/c-api/types-and-structures.rst @@ -286,6 +286,11 @@ PyArrayDescr_Type and PyArray_Descr array like behavior. Each bit in this member is a flag which are named as: + .. c:member:: int alignment + + Non-NULL if this type is an array (C-contiguous) of some other type + + .. dedented to allow internal linking, pending a refactoring diff --git a/doc/source/user/basics.io.genfromtxt.rst b/doc/source/user/basics.io.genfromtxt.rst index 8fe7565aa..6a1ba75dd 100644 --- a/doc/source/user/basics.io.genfromtxt.rst +++ b/doc/source/user/basics.io.genfromtxt.rst @@ -231,9 +231,7 @@ When ``dtype=None``, the type of each column is determined iteratively from its data. We start by checking whether a string can be converted to a boolean (that is, if the string matches ``true`` or ``false`` in lower cases); then whether it can be converted to an integer, then to a float, -then to a complex and eventually to a string. This behavior may be changed -by modifying the default mapper of the -:class:`~numpy.lib._iotools.StringConverter` class. +then to a complex and eventually to a string. The option ``dtype=None`` is provided for convenience. However, it is significantly slower than setting the dtype explicitly. @@ -514,15 +512,15 @@ output array will then be a :class:`~numpy.ma.MaskedArray`. Shortcut functions ================== -In addition to :func:`~numpy.genfromtxt`, the :mod:`numpy.lib.npyio` module +In addition to :func:`~numpy.genfromtxt`, the ``numpy.lib.npyio`` module provides several convenience functions derived from :func:`~numpy.genfromtxt`. These functions work the same way as the original, but they have different default values. -:func:`~numpy.npyio.recfromtxt` +``numpy.lib.npyio.recfromtxt`` Returns a standard :class:`numpy.recarray` (if ``usemask=False``) or a - :class:`~numpy.ma.mrecords.MaskedRecords` array (if ``usemaske=True``). The + ``numpy.ma.mrecords.MaskedRecords`` array (if ``usemaske=True``). The default dtype is ``dtype=None``, meaning that the types of each column will be automatically determined. -:func:`~numpy.npyio.recfromcsv` - Like :func:`~numpy.npyio.recfromtxt`, but with a default ``delimiter=","``. +``numpy.lib.npyio.recfromcsv`` + Like ``numpy.lib.npyio.recfromtxt``, but with a default ``delimiter=","``. diff --git a/doc/source/user/basics.rec.rst b/doc/source/user/basics.rec.rst index 1e6f30506..7f487f39b 100644 --- a/doc/source/user/basics.rec.rst +++ b/doc/source/user/basics.rec.rst @@ -579,12 +579,13 @@ As an optional convenience numpy provides an ndarray subclass, attribute instead of only by index. Record arrays use a special datatype, :class:`numpy.record`, that allows field access by attribute on the structured scalars obtained from the array. -The :mod:`numpy.rec` module provides functions for creating recarrays from +The ``numpy.rec`` module provides functions for creating recarrays from various objects. Additional helper functions for creating and manipulating structured arrays can be found in :mod:`numpy.lib.recfunctions`. -The simplest way to create a record array is with ``numpy.rec.array``:: +The simplest way to create a record array is with +:func:`numpy.rec.array <numpy.core.records.array>`:: >>> recordarr = np.rec.array([(1, 2., 'Hello'), (2, 3., "World")], ... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')]) @@ -600,14 +601,14 @@ The simplest way to create a record array is with ``numpy.rec.array``:: >>> recordarr[1].baz b'World' -:func:`numpy.rec.array` can convert a wide variety of arguments into record -arrays, including structured arrays:: +:func:`numpy.rec.array <numpy.core.records.array>` can convert a wide variety +of arguments into record arrays, including structured arrays:: >>> arr = np.array([(1, 2., 'Hello'), (2, 3., "World")], ... dtype=[('foo', 'i4'), ('bar', 'f4'), ('baz', 'S10')]) >>> recordarr = np.rec.array(arr) -The :mod:`numpy.rec` module provides a number of other convenience functions for +The ``numpy.rec`` module provides a number of other convenience functions for creating record arrays, see :ref:`record array creation routines <routines.array-creation.rec>`. |