numpy.lib.recfunctions

module documentation

(source)

Collection of utilities to manipulate structured arrays.

Most of these functions were initially implemented by John Hunter for matplotlib. They have been rewritten and extended for convenience.

Function	`append_fields`	Add new fields to an existing array.
Function	`apply_along_fields`	Apply function 'func' as a reduction across fields of a structured array.
Function	`assign_fields_by_name`	Assigns values from one structured array to another by field name.
Function	`drop_fields`	Return a new array with fields in `drop_names` dropped.
Function	`find_duplicates`	Find the duplicates in a structured array along a given key
Function	`flatten_descr`	Flatten a structured data-type description.
Function	`get_fieldstructure`	Returns a dictionary with fields indexing lists of their parent fields.
Function	`get_names`	Returns the field names of the input datatype as a tuple. Input datatype must have fields otherwise error is raised.
Function	`get_names_flat`	Returns the field names of the input datatype as a tuple. Input datatype must have fields otherwise error is raised. Nested structure are flattened beforehand.
Function	`join_by`	Join arrays `r1` and `r2` on key `key`.
Function	`merge_arrays`	Merge arrays field by field.
Function	`rec_append_fields`	Add new fields to an existing array.
Function	`rec_drop_fields`	Returns a new numpy.recarray with fields in `drop_names` dropped.
Function	`rec_join`	Join arrays `r1` and `r2` on keys. Alternative to join_by, that always returns a np.recarray.
Function	`recursive_fill_fields`	Fills fields from output with fields from input, with support for nested structures.
Function	`rename_fields`	Rename the fields from a flexible-datatype ndarray or recarray.
Function	`repack_fields`	Re-pack the fields of a structured array or dtype in memory.
Function	`require_fields`	Casts a structured array to a new dtype using assignment by field-name.
Function	`stack_arrays`	Superposes arrays fields by fields
Function	`structured_to_unstructured`	Converts an n-D structured array into an (n+1)-D unstructured array.
Function	`unstructured_to_structured`	Converts an n-D unstructured array into an (n-1)-D structured array.
Function	`_append_fields_dispatcher`	Undocumented
Function	`_apply_along_fields_dispatcher`	Undocumented
Function	`_assign_fields_by_name_dispatcher`	Undocumented
Function	`_drop_fields_dispatcher`	Undocumented
Function	`_find_duplicates_dispatcher`	Undocumented
Function	`_fix_defaults`	Update the fill_value and masked data of `output` from the default given in a dictionary defaults.
Function	`_fix_output`	Private function: return a recarray, a ndarray, a MaskedArray or a MaskedRecords depending on the input parameters
Function	`_get_fields_and_offsets`	Returns a flat list of (dtype, count, offset) tuples of all the scalar fields in the dtype "dt", including nested fields, in left to right order.
Function	`_get_fieldspec`	Produce a list of name/dtype pairs corresponding to the dtype fields
Function	`_izip_fields`	Returns an iterator of concatenated fields from a sequence of arrays.
Function	`_izip_fields_flat`	Returns an iterator of concatenated fields from a sequence of arrays, collapsing any nested structure.
Function	`_izip_records`	Returns an iterator of concatenated items from a sequence of arrays.
Function	`_join_by_dispatcher`	Undocumented
Function	`_keep_fields`	Return a new array keeping only the fields in `keep_names`, and preserving the order of those fields.
Function	`_merge_arrays_dispatcher`	Undocumented
Function	`_rec_append_fields_dispatcher`	Undocumented
Function	`_rec_drop_fields_dispatcher`	Undocumented
Function	`_rec_join_dispatcher`	Undocumented
Function	`_recursive_fill_fields_dispatcher`	Undocumented
Function	`_rename_fields_dispatcher`	Undocumented
Function	`_repack_fields_dispatcher`	Undocumented
Function	`_require_fields_dispatcher`	Undocumented
Function	`_stack_arrays_dispatcher`	Undocumented
Function	`_structured_to_unstructured_dispatcher`	Undocumented
Function	`_unstructured_to_structured_dispatcher`	Undocumented
Function	`_zip_descr`	Combine the dtype description of a series of arrays.
Function	`_zip_dtype`	Undocumented

@array_function_dispatch(_append_fields_dispatcher)
def append_fields(base, names, data, dtypes=None, fill_value=-1, usemask=True, asrecarray=False): (source) ¶

Add new fields to an existing array.

The names of the fields are given with the names arguments, the corresponding values with the data arguments. If a single field is appended, names, data and dtypes do not have to be lists but just values.

Parameters
base:`array`	Input array to extend.
names:`string`, `sequence`	String or sequence of strings corresponding to the names of the new fields.
data:`array` or `sequence` of `arrays`	Array or sequence of arrays storing the fields to add to the base.
dtypes:`sequence` of `datatypes`, optional	Datatype or sequence of datatypes. If None, the datatypes are estimated from the `data`.
fill_value:{float}, optional	Filling value used to pad missing data on the shorter arrays.
usemask:{False, True}, optional	Whether to return a masked array or not.
asrecarray:{False, True}, optional	Whether to return a recarray (MaskedRecords) or not.

@array_function_dispatch(_apply_along_fields_dispatcher)
def apply_along_fields(func, arr): (source) ¶

Apply function 'func' as a reduction across fields of a structured array.

This is similar to apply_along_axis, but treats the fields of a structured array as an extra axis. The fields are all first cast to a common type following the type-promotion rules from numpy.result_type applied to the field's dtypes.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
...              dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
>>> rfn.apply_along_fields(np.mean, b)
array([ 2.66666667,  5.33333333,  8.66666667, 11.        ])
>>> rfn.apply_along_fields(np.mean, b[['x', 'z']])
array([ 3. ,  5.5,  9. , 11. ])

Parameters
func:`function`	Function to apply on the "field" dimension. This function must support an `axis` argument, like np.mean, np.sum, etc.
arr:`ndarray`	Structured array for which to apply func.
Returns
`ndarray`	out - Result of the recution operation

@array_function_dispatch(_assign_fields_by_name_dispatcher)
def assign_fields_by_name(dst, src, zero_unassigned=True): (source) ¶

Assigns values from one structured array to another by field name.

Normally in numpy >= 1.14, assignment of one structured array to another copies fields "by position", meaning that the first field from the src is copied to the first field of the dst, and so on, regardless of field name.

This function instead copies "by field name", such that fields in the dst are assigned from the identically named field in the src. This applies recursively for nested structures. This is how structure assignment worked in numpy >= 1.6 to <= 1.13.

Parameters
dst:`ndarray`
src:`ndarray`	The source and destination arrays during assignment.
zero_unassigned:`bool`, optional	If True, fields in the dst for which there was no matching field in the src are filled with the value 0 (zero). This was the behavior of numpy <= 1.13. If False, those fields are not modified.

@array_function_dispatch(_drop_fields_dispatcher)
def drop_fields(base, drop_names, usemask=True, asrecarray=False): (source) ¶

Return a new array with fields in drop_names dropped.

Nested fields are supported.

Changed in version 1.18.0: drop_fields returns an array with 0 fields if all fields are dropped, rather than returning None as it did previously.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
...   dtype=[('a', np.int64), ('b', [('ba', np.double), ('bb', np.int64)])])
>>> rfn.drop_fields(a, 'a')
array([((2., 3),), ((5., 6),)],
      dtype=[('b', [('ba', '<f8'), ('bb', '<i8')])])
>>> rfn.drop_fields(a, 'ba')
array([(1, (3,)), (4, (6,))], dtype=[('a', '<i8'), ('b', [('bb', '<i8')])])
>>> rfn.drop_fields(a, ['ba', 'bb'])
array([(1,), (4,)], dtype=[('a', '<i8')])

Parameters
base:`array`	Input array
drop_names:`string` or `sequence`	String or sequence of strings corresponding to the names of the fields to drop.
usemask:{False, True}, optional	Whether to return a masked array or not.
asrecarray:`string` or `sequence`, optional	Whether to return a recarray or a mrecarray (`asrecarray=True`) or a plain ndarray or masked array with flexible dtype. The default is False.

@array_function_dispatch(_find_duplicates_dispatcher)
def find_duplicates(a, key=None, ignoremask=True, return_index=False): (source) ¶

Find the duplicates in a structured array along a given key

Examples

>>> from numpy.lib import recfunctions as rfn
>>> ndtype = [('a', int)]
>>> a = np.ma.array([1, 1, 1, 2, 2, 3, 3],
...         mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
>>> rfn.find_duplicates(a, ignoremask=True, return_index=True)
(masked_array(data=[(1,), (1,), (2,), (2,)],
             mask=[(False,), (False,), (False,), (False,)],
       fill_value=(999999,),
            dtype=[('a', '<i8')]), array([0, 1, 3, 4]))

Parameters
a:`array-like`	Input array
key:{string, None}, optional	Name of the fields along which to check the duplicates. If None, the search is performed by records
ignoremask:{True, False}, optional	Whether masked data should be discarded or considered as duplicates.
return_index:{False, True}, optional	Whether to return the indices of the duplicated values.

def flatten_descr(ndtype): (source) ¶

Flatten a structured data-type description.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> ndtype = np.dtype([('a', '<i4'), ('b', [('ba', '<f8'), ('bb', '<i4')])])
>>> rfn.flatten_descr(ndtype)
(('a', dtype('int32')), ('ba', dtype('float64')), ('bb', dtype('int32')))

def get_fieldstructure(adtype, lastname=None, parents=None): (source) ¶

Returns a dictionary with fields indexing lists of their parent fields.

This function is used to simplify access to fields nested in other fields.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> ndtype =  np.dtype([('A', int),
...                     ('B', [('BA', int),
...                            ('BB', [('BBA', int), ('BBB', int)])])])
>>> rfn.get_fieldstructure(ndtype)
... # XXX: possible regression, order of BBA and BBB is swapped
{'A': [], 'B': [], 'BA': ['B'], 'BB': ['B'], 'BBA': ['B', 'BB'], 'BBB': ['B', 'BB']}

Parameters
adtype:`np.dtype`	Input datatype
lastname:optional	Last processed field name (used internally during recursion).
parents:`dictionary`	Dictionary of parent fields (used interbally during recursion).

def get_names(adtype): (source) ¶

Returns the field names of the input datatype as a tuple. Input datatype must have fields otherwise error is raised.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names(np.empty((1,), dtype=[('A', int)]).dtype)
('A',)
>>> rfn.get_names(np.empty((1,), dtype=[('A',int), ('B', float)]).dtype)
('A', 'B')
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> rfn.get_names(adtype)
('a', ('b', ('ba', 'bb')))

Parameters
adtype:`dtype`	Input datatype

def get_names_flat(adtype): (source) ¶

Returns the field names of the input datatype as a tuple. Input datatype must have fields otherwise error is raised. Nested structure are flattened beforehand.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names_flat(np.empty((1,), dtype=[('A', int)]).dtype) is None
False
>>> rfn.get_names_flat(np.empty((1,), dtype=[('A',int), ('B', str)]).dtype)
('A', 'B')
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> rfn.get_names_flat(adtype)
('a', 'b', 'ba', 'bb')

Parameters
adtype:`dtype`	Input datatype

@array_function_dispatch(_join_by_dispatcher)
def join_by(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None, usemask=True, asrecarray=False): (source) ¶

Join arrays r1 and r2 on key key.

The key should be either a string or a sequence of string corresponding to the fields used to join the array. An exception is raised if the key field cannot be found in the two input arrays. Neither r1 nor r2 should have any duplicates along key: the presence of duplicates will make the output quite unreliable. Note that duplicates are not looked for by the algorithm.

Notes

The output is sorted along the key.
A temporary array is formed by dropping the fields not in the key for the two arrays and concatenating the result. This array is then sorted, and the common entries selected. The output is constructed by filling the fields with the selected entries. Matching is not preserved if there are some duplicates...

Parameters
key:{string, sequence}	A string or a sequence of strings corresponding to the fields used for comparison.
r1:`arrays`	Structured arrays.
r2:`arrays`	Structured arrays.
jointype:{'inner', 'outer', 'leftouter'}, optional	If 'inner', returns the elements common to both r1 and r2. If 'outer', returns the common elements as well as the elements of r1 not in r2 and the elements of not in r2. If 'leftouter', returns the common elements and the elements of r1 not in r2.
r1postfix:`string`, optional	String appended to the names of the fields of r1 that are present in r2 but absent of the key.
r2postfix:`string`, optional	String appended to the names of the fields of r2 that are present in r1 but absent of the key.
defaults:{dictionary}, optional	Dictionary mapping field names to the corresponding default values.
usemask:{True, False}, optional	Whether to return a MaskedArray (or MaskedRecords is `asrecarray==True`) or a ndarray.
asrecarray:{False, True}, optional	Whether to return a recarray (or MaskedRecords if `usemask==True`) or just a flexible-type ndarray.

@array_function_dispatch(_merge_arrays_dispatcher)
def merge_arrays(seqarrays, fill_value=-1, flatten=False, usemask=False, asrecarray=False): (source) ¶

Merge arrays field by field.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> rfn.merge_arrays((np.array([1, 2]), np.array([10., 20., 30.])))
array([( 1, 10.), ( 2, 20.), (-1, 30.)],
      dtype=[('f0', '<i8'), ('f1', '<f8')])

>>> rfn.merge_arrays((np.array([1, 2], dtype=np.int64),
...         np.array([10., 20., 30.])), usemask=False)
 array([(1, 10.0), (2, 20.0), (-1, 30.0)],
         dtype=[('f0', '<i8'), ('f1', '<f8')])
>>> rfn.merge_arrays((np.array([1, 2]).view([('a', np.int64)]),
...               np.array([10., 20., 30.])),
...              usemask=False, asrecarray=True)
rec.array([( 1, 10.), ( 2, 20.), (-1, 30.)],
          dtype=[('a', '<i8'), ('f1', '<f8')])

Notes

Without a mask, the missing value will be filled with something, depending on what its corresponding type:
- -1 for integers
- -1.0 for floating point numbers
- '-' for characters
- '-1' for strings
- True for boolean values
XXX: I just obtained these values empirically

Parameters
seqarrays:`sequence` of `ndarrays`	Sequence of arrays
fill_value:{float}, optional	Filling value used to pad missing data on the shorter arrays.
flatten:{False, True}, optional	Whether to collapse nested fields.
usemask:{False, True}, optional	Whether to return a masked array or not.
asrecarray:{False, True}, optional	Whether to return a recarray (MaskedRecords) or not.

@array_function_dispatch(_rec_append_fields_dispatcher)
def rec_append_fields(base, names, data, dtypes=None): (source) ¶

Add new fields to an existing array.

See Also

join_by: equivalent function

@array_function_dispatch(_recursive_fill_fields_dispatcher)
def recursive_fill_fields(input, output): (source) ¶

Fills fields from output with fields from input, with support for nested structures.

Notes

output should be at least the same size as input

Examples

>>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, 10.), (2, 20.)], dtype=[('A', np.int64), ('B', np.float64)])
>>> b = np.zeros((3,), dtype=a.dtype)
>>> rfn.recursive_fill_fields(a, b)
array([(1, 10.), (2, 20.), (0,  0.)], dtype=[('A', '<i8'), ('B', '<f8')])

Parameters
input:`ndarray`	Input array.
output:`ndarray`	Output array.

@array_function_dispatch(_rename_fields_dispatcher)
def rename_fields(base, namemapper): (source) ¶

Rename the fields from a flexible-datatype ndarray or recarray.

Nested fields are supported.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))],
...   dtype=[('a', int),('b', [('ba', float), ('bb', (float, 2))])])
>>> rfn.rename_fields(a, {'a':'A', 'bb':'BB'})
array([(1, (2., [ 3., 30.])), (4, (5., [ 6., 60.]))],
      dtype=[('A', '<i8'), ('b', [('ba', '<f8'), ('BB', '<f8', (2,))])])

Parameters
base:`ndarray`	Input array whose fields must be modified.
namemapper:`dictionary`	Dictionary mapping old field names to their new version.

@array_function_dispatch(_repack_fields_dispatcher)
def repack_fields(a, align=False, recurse=False): (source) ¶

Re-pack the fields of a structured array or dtype in memory.

The memory layout of structured datatypes allows fields at arbitrary byte offsets. This means the fields can be separated by padding bytes, their offsets can be non-monotonically increasing, and they can overlap.

This method removes any overlaps and reorders the fields in memory so they have increasing byte offsets, and adds or removes padding bytes depending on the align option, which behaves like the align option to numpy.dtype.

If align=False, this method produces a "packed" memory layout in which each field starts at the byte the previous field ended, and any padding bytes are removed.

If align=True, this methods produces an "aligned" memory layout in which each field's offset is a multiple of its alignment, and the total itemsize is a multiple of the largest alignment, by adding padding bytes as needed.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> def print_offsets(d):
...     print("offsets:", [d.fields[name][1] for name in d.names])
...     print("itemsize:", d.itemsize)
...
>>> dt = np.dtype('u1, <i8, <f8', align=True)
>>> dt
dtype({'names': ['f0', 'f1', 'f2'], 'formats': ['u1', '<i8', '<f8'], 'offsets': [0, 8, 16], 'itemsize': 24}, align=True)
>>> print_offsets(dt)
offsets: [0, 8, 16]
itemsize: 24
>>> packed_dt = rfn.repack_fields(dt)
>>> packed_dt
dtype([('f0', 'u1'), ('f1', '<i8'), ('f2', '<f8')])
>>> print_offsets(packed_dt)
offsets: [0, 1, 9]
itemsize: 17

Parameters
a:`ndarray` or `dtype`	array or dtype for which to repack the fields.
align:`boolean`	If true, use an "aligned" memory layout, otherwise use a "packed" layout.
recurse:`boolean`	If True, also repack nested structures.
Returns
`ndarray` or `dtype`	repacked - Copy of `a` with fields repacked, or `a` itself if no repacking was needed.

@array_function_dispatch(_require_fields_dispatcher)
def require_fields(array, required_dtype): (source) ¶

Casts a structured array to a new dtype using assignment by field-name.

This function assigns from the old to the new array by name, so the value of a field in the output array is the value of the field with the same name in the source array. This has the effect of creating a new ndarray containing only the fields "required" by the required_dtype.

If a field name in the required_dtype does not exist in the input array, that field is created and set to 0 in the output array.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> a = np.ones(4, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')])
>>> rfn.require_fields(a, [('b', 'f4'), ('c', 'u1')])
array([(1., 1), (1., 1), (1., 1), (1., 1)],
  dtype=[('b', '<f4'), ('c', 'u1')])
>>> rfn.require_fields(a, [('b', 'f4'), ('newf', 'u1')])
array([(1., 0), (1., 0), (1., 0), (1., 0)],
  dtype=[('b', '<f4'), ('newf', 'u1')])

Parameters
array	Undocumented
required_dtype:`dtype`	datatype for output array
a:`ndarray`	array to cast
Returns
`ndarray`	out - array with the new dtype, with field values copied from the fields in the input array with the same name

@array_function_dispatch(_stack_arrays_dispatcher)
def stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False, autoconvert=False): (source) ¶

Superposes arrays fields by fields

Examples

>>> from numpy.lib import recfunctions as rfn
>>> x = np.array([1, 2,])
>>> rfn.stack_arrays(x) is x
True
>>> z = np.array([('A', 1), ('B', 2)], dtype=[('A', '|S3'), ('B', float)])
>>> zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
...   dtype=[('A', '|S3'), ('B', np.double), ('C', np.double)])
>>> test = rfn.stack_arrays((z,zz))
>>> test
masked_array(data=[(b'A', 1.0, --), (b'B', 2.0, --), (b'a', 10.0, 100.0),
                   (b'b', 20.0, 200.0), (b'c', 30.0, 300.0)],
             mask=[(False, False,  True), (False, False,  True),
                   (False, False, False), (False, False, False),
                   (False, False, False)],
       fill_value=(b'N/A', 1.e+20, 1.e+20),
            dtype=[('A', 'S3'), ('B', '<f8'), ('C', '<f8')])

Parameters
arrays:`array` or `sequence`	Sequence of input arrays.
defaults:`dictionary`, optional	Dictionary mapping field names to the corresponding default values.
usemask:{True, False}, optional	Whether to return a MaskedArray (or MaskedRecords is `asrecarray==True`) or a ndarray.
asrecarray:{False, True}, optional	Whether to return a recarray (or MaskedRecords if `usemask==True`) or just a flexible-type ndarray.
autoconvert:{False, True}, optional	Whether automatically cast the type of the field to the maximum.

@array_function_dispatch(_structured_to_unstructured_dispatcher)
def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'): (source) ¶

Converts an n-D structured array into an (n+1)-D unstructured array.

The new array will have a new last dimension equal in size to the number of field-elements of the input array. If not supplied, the output datatype is determined from the numpy type promotion rules applied to all the field datatypes.

Nested fields, as well as each element of any subarray fields, all count as a single field-elements.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> a = np.zeros(4, dtype=[('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
>>> a
array([(0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.]),
       (0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.])],
      dtype=[('a', '<i4'), ('b', [('f0', '<f4'), ('f1', '<u2')]), ('c', '<f4', (2,))])
>>> rfn.structured_to_unstructured(a)
array([[0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.]])

>>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
...              dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
>>> np.mean(rfn.structured_to_unstructured(b[['x', 'z']]), axis=-1)
array([ 3. ,  5.5,  9. , 11. ])

Parameters
arr:`ndarray`	Structured array or dtype to convert. Cannot contain object datatype.
dtype:`dtype`, optional	The dtype of the output unstructured array.
copy:`bool`, optional	See copy argument to `numpy.ndarray.astype`. If true, always return a copy. If false, and `dtype` requirements are satisfied, a view is returned.
casting:{'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional	See casting argument of `numpy.ndarray.astype`. Controls what kind of data casting may occur.
Returns
`ndarray`	unstructured - Unstructured array with one more dimension.

@array_function_dispatch(_unstructured_to_structured_dispatcher)
def unstructured_to_structured(arr, dtype=None, names=None, align=False, copy=False, casting='unsafe'): (source) ¶

Converts an n-D unstructured array into an (n-1)-D structured array.

The last dimension of the input array is converted into a structure, with number of field-elements equal to the size of the last dimension of the input array. By default all output fields have the input array's dtype, but an output structured dtype with an equal number of fields-elements can be supplied instead.

Nested fields, as well as each element of any subarray fields, all count towards the number of field-elements.

Examples

>>> from numpy.lib import recfunctions as rfn
>>> dt = np.dtype([('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
>>> a = np.arange(20).reshape((4,5))
>>> a
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19]])
>>> rfn.unstructured_to_structured(a, dt)
array([( 0, ( 1.,  2), [ 3.,  4.]), ( 5, ( 6.,  7), [ 8.,  9.]),
       (10, (11., 12), [13., 14.]), (15, (16., 17), [18., 19.])],
      dtype=[('a', '<i4'), ('b', [('f0', '<f4'), ('f1', '<u2')]), ('c', '<f4', (2,))])

Parameters
arr:`ndarray`	Unstructured array or dtype to convert.
dtype:`dtype`, optional	The structured dtype of the output array
names:`list` of `strings`, optional	If dtype is not supplied, this specifies the field names for the output dtype, in order. The field dtypes will be the same as the input array.
align:`boolean`, optional	Whether to create an aligned memory layout.
copy:`bool`, optional	See copy argument to `numpy.ndarray.astype`. If true, always return a copy. If false, and `dtype` requirements are satisfied, a view is returned.
casting:{'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional	See casting argument of `numpy.ndarray.astype`. Controls what kind of data casting may occur.
Returns
`ndarray`	structured - Structured array with fewer dimensions.

def _append_fields_dispatcher(base, names, data, dtypes=None, fill_value=None, usemask=None, asrecarray=None): (source) ¶

Undocumented

def _apply_along_fields_dispatcher(func, arr): (source) ¶

Undocumented

def _assign_fields_by_name_dispatcher(dst, src, zero_unassigned=None): (source) ¶

Undocumented

def _drop_fields_dispatcher(base, drop_names, usemask=None, asrecarray=None): (source) ¶

Undocumented

def _find_duplicates_dispatcher(a, key=None, ignoremask=None, return_index=None): (source) ¶

Undocumented

def _fix_defaults(output, defaults=None): (source) ¶

Update the fill_value and masked data of output from the default given in a dictionary defaults.

def _fix_output(output, usemask=True, asrecarray=False): (source) ¶

Private function: return a recarray, a ndarray, a MaskedArray or a MaskedRecords depending on the input parameters

def _get_fields_and_offsets(dt, offset=0): (source) ¶

Returns a flat list of (dtype, count, offset) tuples of all the scalar fields in the dtype "dt", including nested fields, in left to right order.

def _get_fieldspec(dtype): (source) ¶

Produce a list of name/dtype pairs corresponding to the dtype fields

Similar to dtype.descr, but the second item of each tuple is a dtype, not a string. As a result, this handles subarray dtypes

Can be passed to the dtype constructor to reconstruct the dtype, noting that this (deliberately) discards field offsets.

Examples

>>> dt = np.dtype([(('a', 'A'), np.int64), ('b', np.double, 3)])
>>> dt.descr
[(('a', 'A'), '<i8'), ('b', '<f8', (3,))]
>>> _get_fieldspec(dt)
[(('a', 'A'), dtype('int64')), ('b', dtype(('<f8', (3,))))]

def _izip_fields(iterable): (source) ¶

Returns an iterator of concatenated fields from a sequence of arrays.

def _izip_fields_flat(iterable): (source) ¶

Returns an iterator of concatenated fields from a sequence of arrays, collapsing any nested structure.

def _izip_records(seqarrays, fill_value=None, flatten=True): (source) ¶

Returns an iterator of concatenated items from a sequence of arrays.

Parameters
seqarrays:`sequence` of `arrays`	Sequence of arrays.
fill_value:{None, integer}	Value used to pad shorter iterables.
flatten:{True, False},	Whether to

def _join_by_dispatcher(key, r1, r2, jointype=None, r1postfix=None, r2postfix=None, defaults=None, usemask=None, asrecarray=None): (source) ¶

Undocumented

def _keep_fields(base, keep_names, usemask=True, asrecarray=False): (source) ¶

Return a new array keeping only the fields in keep_names, and preserving the order of those fields.

Parameters
base:`array`	Input array
keep_names:`string` or `sequence`	String or sequence of strings corresponding to the names of the fields to keep. Order of the names will be preserved.
usemask:{False, True}, optional	Whether to return a masked array or not.
asrecarray:`string` or `sequence`, optional	Whether to return a recarray or a mrecarray (`asrecarray=True`) or a plain ndarray or masked array with flexible dtype. The default is False.

def _merge_arrays_dispatcher(seqarrays, fill_value=None, flatten=None, usemask=None, asrecarray=None): (source) ¶

Undocumented

def _rec_append_fields_dispatcher(base, names, data, dtypes=None): (source) ¶

Undocumented

def _rec_drop_fields_dispatcher(base, drop_names): (source) ¶

Undocumented

def _rec_join_dispatcher(key, r1, r2, jointype=None, r1postfix=None, r2postfix=None, defaults=None): (source) ¶

Undocumented

def _recursive_fill_fields_dispatcher(input, output): (source) ¶

Undocumented

def _rename_fields_dispatcher(base, namemapper): (source) ¶

Undocumented

def _repack_fields_dispatcher(a, align=None, recurse=None): (source) ¶

Undocumented

def _require_fields_dispatcher(array, required_dtype): (source) ¶

Undocumented

def _stack_arrays_dispatcher(arrays, defaults=None, usemask=None, asrecarray=None, autoconvert=None): (source) ¶

Undocumented

def _structured_to_unstructured_dispatcher(arr, dtype=None, copy=None, casting=None): (source) ¶

Undocumented

def _unstructured_to_structured_dispatcher(arr, dtype=None, names=None, align=None, copy=None, casting=None): (source) ¶

Undocumented

def _zip_descr(seqarrays, flatten=False): (source) ¶

Combine the dtype description of a series of arrays.

Parameters
seqarrays:`sequence` of `arrays`	Sequence of arrays
flatten:{boolean}, optional	Whether to collapse nested descriptions.

def _zip_dtype(seqarrays, flatten=False): (source) ¶

Undocumented