See also

Note

XXX: Combine `numpy.doc.indexing` with material from
“Guide to Numpy” (section 2.1 Data-Type descriptors)?
Or incorporate the material directly here?

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 data-type.

Data type | Description |
---|---|

bool | Boolean (True or False) stored as a byte |

int | Platform integer (normally either int32 or int64) |

int8 | Byte (-128 to 127) |

int16 | Integer (-32768 to 32767) |

int32 | Integer (-2147483648 to 2147483647) |

int64 | Integer (9223372036854775808 to 9223372036854775807) |

uint8 | Unsigned integer (0 to 255) |

uint16 | Unsigned integer (0 to 65535) |

uint32 | Unsigned integer (0 to 4294967295) |

uint64 | Unsigned integer (0 to 18446744073709551615) |

float | Shorthand for float64. |

float32 | Single precision float: sign bit, 8 bits exponent, 23 bits mantissa |

float64 | Double precision float: sign bit, 11 bits exponent, 52 bits mantissa |

complex | Shorthand for complex128. |

complex64 | Complex number, represented by two 32-bit floats (real and imaginary components) |

complex128 | Complex number, represented by two 64-bit floats (real and imaginary components) |

Numpy numerical types are instances of `dtype` (data-type) 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 in the table above, are explored in
section *link_here*.

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. 32-bit
vs. 64-bit machines). This should be taken into account when interfacing
with low-level code (such as C or Fortran) where the raw memory is addressed.

Data-types 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:

```
>>> import numpy as np
>>> 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)
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` and
that `float` is `np.float`. The other data-types 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 bit-width and its byte-order. See xxx for details. 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, int)
True
>>> np.issubdtype(d, float)
False
```

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.