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numpy.dtype.base

# numpy.dtype

class `numpy.``dtype`

  Create a data type object.

  A numpy array is homogeneous, and contains elements described by a dtype object. A dtype object can be constructed from different combinations of fundamental numeric types.

  Parameters: | obj Object to be converted to a data type object. align : bool, optional Add padding to the fields to match what a C compiler would output for a similar C-struct. Can be `True` only if obj is a dictionary or a comma-separated string. If a struct dtype is being created, this also sets a sticky alignment flag `isalignedstruct`. copy : bool, optional Make a new copy of the data-type object. If `False`, the result may just be a reference to a built-in data-type object.

  See also

  `result_type`

  Examples

  Using array-scalar type:

      >>> np.dtype(np.int16)
      dtype('int16')

  Structured type, one field name ‘f1’, containing int16:

      >>> np.dtype([('f1', np.int16)])
      dtype([('f1', '<i2')])

  Structured type, one field named ‘f1’, in itself containing a structured type with one field:

      >>> np.dtype([('f1', [('f1', np.int16)])])
      dtype([('f1', [('f1', '<i2')])])

  Structured type, two fields: the first field contains an unsigned int, the second an int32:

      >>> np.dtype([('f1', np.uint), ('f2', np.int32)])
      dtype([('f1', '<u4'), ('f2', '<i4')])

  Using array-protocol type strings:

      >>> np.dtype([('a','f8'),('b','S10')])
      dtype([('a', '<f8'), ('b', '|S10')])

  Using comma-separated field formats. The shape is (2,3):

      >>> np.dtype("i4, (2,3)f8")
      dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])

  Using tuples. `int` is a fixed type, 3 the field’s shape. `void` is a flexible type, here of size 10:

      >>> np.dtype([('hello',(np.int,3)),('world',np.void,10)])
      dtype([('hello', '<i4', 3), ('world', '|V10')])

  Subdivide `int16` into 2 `int8`‘s, called x and y. 0 and 1 are the offsets in bytes:

      >>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)}))
      dtype(('<i2', [('x', '|i1'), ('y', '|i1')]))

  Using dictionaries. Two fields named ‘gender’ and ‘age’:

      >>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]})
      dtype([('gender', '|S1'), ('age', '|u1')])

  Offsets in bytes, here 0 and 25:

      >>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)})
      dtype([('surname', '|S25'), ('age', '|u1')])

  Attributes

  `base` |
  `descr` | PEP3118 interface description of the data-type.
  `fields` | Dictionary of named fields defined for this data type, or `None`.
  `hasobject` | Boolean indicating whether this dtype contains any reference-counted objects in any fields or sub-dtypes.
  `isalignedstruct` | Boolean indicating whether the dtype is a struct which maintains field alignment.
  `isbuiltin` | Integer indicating how this dtype relates to the built-in dtypes.
  `isnative` | Boolean indicating whether the byte order of this dtype is native to the platform.
  `metadata` |
  `name` | A bit-width name for this data-type.
  `names` | Ordered list of field names, or `None` if there are no fields.
  `ndim` | Number of dimensions of the sub-array if this data type describes a sub-array, and `0` otherwise.
  `shape` | Shape tuple of the sub-array if this data type describes a sub-array, and `()` otherwise.
  `str` | The array-protocol typestring of this data-type object.
  `subdtype` | Tuple `(item_dtype, shape)` if this `dtype` describes a sub-array, and None otherwise.

  Methods

  `newbyteorder`([new_order]) | Return a new dtype with a different byte order.