Structured arrays¶
Introduction¶
Numpy provides powerful capabilities to create arrays of structured datatype. These arrays permit one to manipulate the data by named fields. A simple example will show what is meant.:
>>> x = np.array([(1,2.,'Hello'), (2,3.,"World")],
... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])
>>> x
array([(1, 2.0, 'Hello'), (2, 3.0, 'World')],
dtype=[('foo', '>i4'), ('bar', '>f4'), ('baz', '|S10')])
Here we have created a one-dimensional array of length 2. Each element of this array is a structure that contains three items, a 32-bit integer, a 32-bit float, and a string of length 10 or less. If we index this array at the second position we get the second structure:
>>> x[1]
(2,3.,"World")
Conveniently, one can access any field of the array by indexing using the string that names that field.
>>> y = x['bar']
>>> y
array([ 2., 3.], dtype=float32)
>>> y[:] = 2*y
>>> y
array([ 4., 6.], dtype=float32)
>>> x
array([(1, 4.0, 'Hello'), (2, 6.0, 'World')],
dtype=[('foo', '>i4'), ('bar', '>f4'), ('baz', '|S10')])
In these examples, y is a simple float array consisting of the 2nd field in the structured type. But, rather than being a copy of the data in the structured array, it is a view, i.e., it shares exactly the same memory locations. Thus, when we updated this array by doubling its values, the structured array shows the corresponding values as doubled as well. Likewise, if one changes the structured array, the field view also changes:
>>> x[1] = (-1,-1.,"Master")
>>> x
array([(1, 4.0, 'Hello'), (-1, -1.0, 'Master')],
dtype=[('foo', '>i4'), ('bar', '>f4'), ('baz', '|S10')])
>>> y
array([ 4., -1.], dtype=float32)
Defining Structured Arrays¶
One defines a structured array through the dtype object. There are several alternative ways to define the fields of a record. Some of these variants provide backward compatibility with Numeric, numarray, or another module, and should not be used except for such purposes. These will be so noted. One specifies record structure in one of four alternative ways, using an argument (as supplied to a dtype function keyword or a dtype object constructor itself). This argument must be one of the following: 1) string, 2) tuple, 3) list, or 4) dictionary. Each of these is briefly described below.
1) String argument. In this case, the constructor expects a comma-separated list of type specifiers, optionally with extra shape information. The fields are given the default names ‘f0’, ‘f1’, ‘f2’ and so on. The type specifiers can take 4 different forms:
a) b1, i1, i2, i4, i8, u1, u2, u4, u8, f2, f4, f8, c8, c16, a<n>
(representing bytes, ints, unsigned ints, floats, complex and
fixed length strings of specified byte lengths)
b) int8,...,uint8,...,float16, float32, float64, complex64, complex128
(this time with bit sizes)
c) older Numeric/numarray type specifications (e.g. Float32).
Don't use these in new code!
d) Single character type specifiers (e.g H for unsigned short ints).
Avoid using these unless you must. Details can be found in the
Numpy book
These different styles can be mixed within the same string (but why would you want to do that?). Furthermore, each type specifier can be prefixed with a repetition number, or a shape. In these cases an array element is created, i.e., an array within a record. That array is still referred to as a single field. An example:
>>> x = np.zeros(3, dtype='3int8, float32, (2,3)float64')
>>> x
array([([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),
([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),
([0, 0, 0], 0.0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])],
dtype=[('f0', '|i1', 3), ('f1', '>f4'), ('f2', '>f8', (2, 3))])
By using strings to define the record structure, it precludes being able to name the fields in the original definition. The names can be changed as shown later, however.
2) Tuple argument: The only relevant tuple case that applies to record structures is when a structure is mapped to an existing data type. This is done by pairing in a tuple, the existing data type with a matching dtype definition (using any of the variants being described here). As an example (using a definition using a list, so see 3) for further details):
>>> x = np.zeros(3, dtype=('i4',[('r','u1'), ('g','u1'), ('b','u1'), ('a','u1')]))
>>> x
array([0, 0, 0])
>>> x['r']
array([0, 0, 0], dtype=uint8)
In this case, an array is produced that looks and acts like a simple int32 array, but also has definitions for fields that use only one byte of the int32 (a bit like Fortran equivalencing).
3) List argument: In this case the record structure is defined with a list of tuples. Each tuple has 2 or 3 elements specifying: 1) The name of the field (‘’ is permitted), 2) the type of the field, and 3) the shape (optional). For example:
>>> x = np.zeros(3, dtype=[('x','f4'),('y',np.float32),('value','f4',(2,2))])
>>> x
array([(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]]),
(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]]),
(0.0, 0.0, [[0.0, 0.0], [0.0, 0.0]])],
dtype=[('x', '>f4'), ('y', '>f4'), ('value', '>f4', (2, 2))])
4) Dictionary argument: two different forms are permitted. The first consists of a dictionary with two required keys (‘names’ and ‘formats’), each having an equal sized list of values. The format list contains any type/shape specifier allowed in other contexts. The names must be strings. There are two optional keys: ‘offsets’ and ‘titles’. Each must be a correspondingly matching list to the required two where offsets contain integer offsets for each field, and titles are objects containing metadata for each field (these do not have to be strings), where the value of None is permitted. As an example:
>>> x = np.zeros(3, dtype={'names':['col1', 'col2'], 'formats':['i4','f4']})
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[('col1', '>i4'), ('col2', '>f4')])
The other dictionary form permitted is a dictionary of name keys with tuple values specifying type, offset, and an optional title.
>>> x = np.zeros(3, dtype={'col1':('i1',0,'title 1'), 'col2':('f4',1,'title 2')})
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[(('title 1', 'col1'), '|i1'), (('title 2', 'col2'), '>f4')])
Accessing and modifying field names¶
The field names are an attribute of the dtype object defining the structure. For the last example:
>>> x.dtype.names
('col1', 'col2')
>>> x.dtype.names = ('x', 'y')
>>> x
array([(0, 0.0), (0, 0.0), (0, 0.0)],
dtype=[(('title 1', 'x'), '|i1'), (('title 2', 'y'), '>f4')])
>>> x.dtype.names = ('x', 'y', 'z') # wrong number of names
<type 'exceptions.ValueError'>: must replace all names at once with a sequence of length 2
Accessing field titles¶
The field titles provide a standard place to put associated info for fields. They do not have to be strings.
>>> x.dtype.fields['x'][2]
'title 1'
Accessing multiple fields at once¶
You can access multiple fields at once using a list of field names:
>>> x = np.array([(1.5,2.5,(1.0,2.0)),(3.,4.,(4.,5.)),(1.,3.,(2.,6.))],
dtype=[('x','f4'),('y',np.float32),('value','f4',(2,2))])
Notice that x is created with a list of tuples.
>>> x[['x','y']]
array([(1.5, 2.5), (3.0, 4.0), (1.0, 3.0)],
dtype=[('x', '<f4'), ('y', '<f4')])
>>> x[['x','value']]
array([(1.5, [[1.0, 2.0], [1.0, 2.0]]), (3.0, [[4.0, 5.0], [4.0, 5.0]]),
(1.0, [[2.0, 6.0], [2.0, 6.0]])],
dtype=[('x', '<f4'), ('value', '<f4', (2, 2))])
The fields are returned in the order they are asked for.:
>>> x[['y','x']]
array([(2.5, 1.5), (4.0, 3.0), (3.0, 1.0)],
dtype=[('y', '<f4'), ('x', '<f4')])
Filling structured arrays¶
Structured arrays can be filled by field or row by row.
>>> arr = np.zeros((5,), dtype=[('var1','f8'),('var2','f8')])
>>> arr['var1'] = np.arange(5)
If you fill it in row by row, it takes a take a tuple (but not a list or array!):
>>> arr[0] = (10,20)
>>> arr
array([(10.0, 20.0), (1.0, 0.0), (2.0, 0.0), (3.0, 0.0), (4.0, 0.0)],
dtype=[('var1', '<f8'), ('var2', '<f8')])
Record Arrays¶
For convenience, numpy provides “record arrays” which allow one to access fields of structured arrays by attribute rather than by index. Record arrays are structured arrays wrapped using a subclass of ndarray, numpy.recarray, which allows field access by attribute on the array object, and record arrays also use a special datatype, numpy.record, which allows field access by attribute on the individual elements of the array.
The simplest way to create a record array is with numpy.rec.array:
>>> recordarr = np.rec.array([(1,2.,'Hello'),(2,3.,"World")],
... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])
>>> recordarr.bar
array([ 2., 3.], dtype=float32)
>>> recordarr[1:2]
rec.array([(2, 3.0, 'World')],
dtype=[('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')])
>>> recordarr[1:2].foo
array([2], dtype=int32)
>>> recordarr.foo[1:2]
array([2], dtype=int32)
>>> recordarr[1].baz
'World'
numpy.rec.array can convert a wide variety of arguments into record arrays, including normal structured arrays:
>>> arr = array([(1,2.,'Hello'),(2,3.,"World")],
... dtype=[('foo', 'i4'), ('bar', 'f4'), ('baz', 'S10')])
>>> recordarr = np.rec.array(arr)
The numpy.rec module provides a number of other convenience functions for creating record arrays, see record array creation routines.
A record array representation of a structured array can be obtained using the appropriate view:
>>> arr = np.array([(1,2.,'Hello'),(2,3.,"World")],
... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'a10')])
>>> recordarr = arr.view(dtype=dtype((np.record, arr.dtype)),
... type=np.recarray)
For convenience, viewing an ndarray as type np.recarray will automatically convert to np.record datatype, so the dtype can be left out of the view:
>>> recordarr = arr.view(np.recarray)
>>> recordarr.dtype
dtype((numpy.record, [('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')]))
To get back to a plain ndarray both the dtype and type must be reset. The following view does so, taking into account the unusual case that the recordarr was not a structured type:
>>> arr2 = recordarr.view(recordarr.dtype.fields or recordarr.dtype, np.ndarray)
Record array fields accessed by index or by attribute are returned as a record array if the field has a structured type but as a plain ndarray otherwise.
>>> recordarr = np.rec.array([('Hello', (1,2)),("World", (3,4))],
... dtype=[('foo', 'S6'),('bar', [('A', int), ('B', int)])])
>>> type(recordarr.foo)
<type 'numpy.ndarray'>
>>> type(recordarr.bar)
<class 'numpy.core.records.recarray'>
Note that if a field has the same name as an ndarray attribute, the ndarray attribute takes precedence. Such fields will be inaccessible by attribute but may still be accessed by index.