Data types#
See also
Array types and conversions between types#
NumPy supports a much greater variety of numerical types than Python does. This section shows which are available, and how to modify an array’s datatype.
The primitive types supported are tied closely to those in C:
Numpy type 
C type 
Description 


Boolean (True or False) stored as a byte 


Platformdefined 


Platformdefined 


Platformdefined 


Platformdefined 


Platformdefined 


Platformdefined 


Platformdefined 


Platformdefined 


Platformdefined 


Platformdefined 

Half precision float: sign bit, 5 bits exponent, 10 bits mantissa 


Platformdefined single precision float: typically sign bit, 8 bits exponent, 23 bits mantissa 


Platformdefined double precision float: typically sign bit, 11 bits exponent, 52 bits mantissa. 


Platformdefined extendedprecision float 


Complex number, represented by two singleprecision floats (real and imaginary components) 


Complex number, represented by two doubleprecision floats (real and imaginary components). 


Complex number, represented by two extendedprecision floats (real and imaginary components). 
Since many of these have platformdependent definitions, a set of fixedsize aliases are provided (See Sized aliases).
NumPy numerical types are instances of dtype
(datatype) objects, each
having unique characteristics. Once you have imported NumPy using
>>> import numpy as np
the dtypes are available as np.bool_
, np.float32
, etc.
Advanced types, not listed above, are explored in section Structured arrays.
There are 5 basic numerical types representing booleans (bool), integers (int),
unsigned integers (uint) floating point (float) and complex. Those with numbers
in their name indicate the bitsize of the type (i.e. how many bits are needed
to represent a single value in memory). Some types, such as int
and
intp
, have differing bitsizes, dependent on the platforms (e.g. 32bit
vs. 64bit machines). This should be taken into account when interfacing
with lowlevel code (such as C or Fortran) where the raw memory is addressed.
Datatypes can be used as functions to convert python numbers to array scalars (see the array scalar section for an explanation), python sequences of numbers to arrays of that type, or as arguments to the dtype keyword that many numpy functions or methods accept. Some examples:
>>> x = np.float32(1.0)
>>> x
1.0
>>> y = np.int_([1,2,4])
>>> y
array([1, 2, 4])
>>> z = np.arange(3, dtype=np.uint8)
>>> z
array([0, 1, 2], dtype=uint8)
Array types can also be referred to by character codes, mostly to retain backward compatibility with older packages such as Numeric. Some documentation may still refer to these, for example:
>>> np.array([1, 2, 3], dtype='f')
array([1., 2., 3.], dtype=float32)
We recommend using dtype objects instead.
To convert the type of an array, use the .astype() method (preferred) or the type itself as a function. For example:
>>> z.astype(float)
array([0., 1., 2.])
>>> np.int8(z)
array([0, 1, 2], dtype=int8)
Note that, above, we use the Python float object as a dtype. NumPy knows
that int
refers to np.int_
, bool
means np.bool_
,
that float
is np.float_
and complex
is np.complex_
.
The other datatypes do not have Python equivalents.
To determine the type of an array, look at the dtype attribute:
>>> z.dtype
dtype('uint8')
dtype objects also contain information about the type, such as its bitwidth and its byteorder. The data type can also be used indirectly to query properties of the type, such as whether it is an integer:
>>> d = np.dtype(int)
>>> d
dtype('int32')
>>> np.issubdtype(d, np.integer)
True
>>> np.issubdtype(d, np.floating)
False
Array Scalars#
NumPy generally returns elements of arrays as array scalars (a scalar
with an associated dtype). Array scalars differ from Python scalars, but
for the most part they can be used interchangeably (the primary
exception is for versions of Python older than v2.x, where integer array
scalars cannot act as indices for lists and tuples). There are some
exceptions, such as when code requires very specific attributes of a scalar
or when it checks specifically whether a value is a Python scalar. Generally,
problems are easily fixed by explicitly converting array scalars
to Python scalars, using the corresponding Python type function
(e.g., int
, float
, complex
, str
, unicode
).
The primary advantage of using array scalars is that
they preserve the array type (Python may not have a matching scalar type
available, e.g. int16
). Therefore, the use of array scalars ensures
identical behaviour between arrays and scalars, irrespective of whether the
value is inside an array or not. NumPy scalars also have many of the same
methods arrays do.
Overflow Errors#
The fixed size of NumPy numeric types may cause overflow errors when a value
requires more memory than available in the data type. For example,
numpy.power
evaluates 100 ** 8
correctly for 64bit integers,
but gives 1874919424 (incorrect) for a 32bit integer.
>>> np.power(100, 8, dtype=np.int64)
10000000000000000
>>> np.power(100, 8, dtype=np.int32)
1874919424
The behaviour of NumPy and Python integer types differs significantly for
integer overflows and may confuse users expecting NumPy integers to behave
similar to Python’s int
. Unlike NumPy, the size of Python’s int
is
flexible. This means Python integers may expand to accommodate any integer and
will not overflow.
NumPy provides numpy.iinfo
and numpy.finfo
to verify the
minimum or maximum values of NumPy integer and floating point values
respectively
>>> np.iinfo(int) # Bounds of the default integer on this system.
iinfo(min=9223372036854775808, max=9223372036854775807, dtype=int64)
>>> np.iinfo(np.int32) # Bounds of a 32bit integer
iinfo(min=2147483648, max=2147483647, dtype=int32)
>>> np.iinfo(np.int64) # Bounds of a 64bit integer
iinfo(min=9223372036854775808, max=9223372036854775807, dtype=int64)
If 64bit integers are still too small the result may be cast to a floating point number. Floating point numbers offer a larger, but inexact, range of possible values.
>>> np.power(100, 100, dtype=np.int64) # Incorrect even with 64bit int
0
>>> np.power(100, 100, dtype=np.float64)
1e+200
Extended Precision#
Python’s floatingpoint numbers are usually 64bit floatingpoint numbers,
nearly equivalent to np.float64
. In some unusual situations it may be
useful to use floatingpoint numbers with more precision. Whether this
is possible in numpy depends on the hardware and on the development
environment: specifically, x86 machines provide hardware floatingpoint
with 80bit precision, and while most C compilers provide this as their
long double
type, MSVC (standard for Windows builds) makes
long double
identical to double
(64 bits). NumPy makes the
compiler’s long double
available as np.longdouble
(and
np.clongdouble
for the complex numbers). You can find out what your
numpy provides with np.finfo(np.longdouble)
.
NumPy does not provide a dtype with more precision than C’s
long double
; in particular, the 128bit IEEE quad precision
data type (FORTRAN’s REAL*16
) is not available.
For efficient memory alignment, np.longdouble
is usually stored
padded with zero bits, either to 96 or 128 bits. Which is more efficient
depends on hardware and development environment; typically on 32bit
systems they are padded to 96 bits, while on 64bit systems they are
typically padded to 128 bits. np.longdouble
is padded to the system
default; np.float96
and np.float128
are provided for users who
want specific padding. In spite of the names, np.float96
and
np.float128
provide only as much precision as np.longdouble
,
that is, 80 bits on most x86 machines and 64 bits in standard
Windows builds.
Be warned that even if np.longdouble
offers more precision than
python float
, it is easy to lose that extra precision, since
python often forces values to pass through float
. For example,
the %
formatting operator requires its arguments to be converted
to standard python types, and it is therefore impossible to preserve
extended precision even if many decimal places are requested. It can
be useful to test your code with the value
1 + np.finfo(np.longdouble).eps
.