Arrays (numpy)¶
- petlx.array.toarray(table, dtype=None, count=-1, sample=1000)[source]¶
Convenience function to load data from the given table into a numpy structured array. E.g.:
>>> from petl import look >>> from petlx.array import toarray >>> look(table) +-----------+-------+-------+ | 'foo' | 'bar' | 'baz' | +===========+=======+=======+ | 'apples' | 1 | 2.5 | +-----------+-------+-------+ | 'oranges' | 3 | 4.4 | +-----------+-------+-------+ | 'pears' | 7 | 0.1 | +-----------+-------+-------+ >>> a = toarray(table) >>> a array([('apples', 1, 2.5), ('oranges', 3, 4.4), ('pears', 7, 0.1)], dtype=[('foo', '|S7'), ('bar', '<i8'), ('baz', '<f8')]) >>> a['foo'] array(['apples', 'oranges', 'pears'], dtype='|S7') >>> a['bar'] array([1, 3, 7]) >>> a['baz'] array([ 2.5, 4.4, 0.1]) >>> a['foo'][0] 'apples' >>> a['bar'][1] 3 >>> a['baz'][2] 0.10000000000000001
If no datatype is specified, sample rows will be examined to infer an appropriate datatype for each field.
The datatype can be specified as a string, e.g.:
>>> a = toarray(table, dtype='a4, i2, f4') >>> a array([('appl', 1, 2.5), ('oran', 3, 4.400000095367432), ('pear', 7, 0.10000000149011612)], dtype=[('foo', '|S4'), ('bar', '<i2'), ('baz', '<f4')])
The datatype can also be partially specified, in which case datatypes will be inferred for other fields, e.g.:
>>> a = toarray(table, dtype={'foo': 'a4'}) >>> a array([('appl', 1, 2.5), ('oran', 3, 4.4), ('pear', 7, 0.1)], dtype=[('foo', '|S4'), ('bar', '<i8'), ('baz', '<f8')])