Record Arrays (numpy.rec)#

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 combinationsof these using structured types, such as:

>>>importnumpyasnp>>>a=np.array([(1,2.0),(1,2.0)],...dtype=[('x',np.int64),('y',np.float64)])>>>aarray([(1, 2.), (1, 2.)], dtype=[('x', '<i8'), ('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 analogousto columns in a spread-sheet. The different fields can be accessed asone 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.xarray([1, 1])>>>ar.yarray([2., 2.])

Functions#

array(obj[, dtype, shape, offset, strides, ...])

Construct a record array from a wide-variety of objects.

find_duplicate(list)

Find duplication in a list, return a list of duplicated elements

format_parser(formats, names, titles[, ...])

Class to convert formats, names, titles description to a dtype.

fromarrays(arrayList[, dtype, shape, ...])

Create a record array from a (flat) list of arrays

fromfile(fd[, dtype, shape, offset, ...])

Create an array from binary file data

fromrecords(recList[, dtype, shape, ...])

Create a recarray from a list of records in text form.

fromstring(datastring[, dtype, shape, ...])

Create a record array from binary data

Also, thenumpy.recarray class and thenumpy.record scalar dtype are presentin this namespace.

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