dtype

ndarray is a container for homogeneous data, i.e. all elements must be of the same type. Each array has a dtype, an object that describes the data type of the array:

import numpy as np


data = np.random.randn(7, 3)
dt = data.dtype
dt

dtype('float64')

NumPy data types:

Type	Type code	Description
int8, uint8	i1, u1	Signed and unsigned 8-bit (1-byte) integer types
int16, uint16	i2, u2	Signed and unsigned 16-Bit (2 Byte) integer types
int32, uint32	i4, u4	Signed and unsigned 32-Bit (4 Byte) integer types
int64, uint64	i8, u8	Signed and unsigned 64-Bit (8 Byte) integer types
float16	f2	Standard floating point with half precision
float32	f4 or f	Standard floating point with single precision; compatible with C float
float64	f8 or d	Standard floating point with double precision; compatible with C double and Python float object
complex64, complex128, complex256	c8, c16, c32	Complex numbers represented by two 32, 64 or 128 floating point numbers respectively
bool	?	Boolean type that stores the values True and False
object	O	Python object type; a value can be any Python object
string_	S	ASCII string type with fixed length (1 byte per character); to create a string type with length 7, for example, use S7; longer inputs are truncated without warning
unicode_	U	Unicode type with fixed length where the number of bytes is platform-specific; uses the same specification semantics as string_, e.g. U7

Determine the number of elements with itemsize:

dt.itemsize

8

Determine the name of the data type:

dt.name

'float64'

Check data type:

dt.type is np.float64

True

Change data type with astype:

data_float32 = data.astype(np.float32)
data_float32

array([[ 0.44477868,  1.7366465 , -2.0396285 ],
       [ 0.65273875, -0.11706501, -2.3253074 ],
       [-1.3416812 , -1.1469622 ,  0.04803479],
       [-0.08298384, -0.02865864,  1.0284923 ],
       [ 0.59293705,  0.5345401 , -1.717722  ],
       [-1.1971567 , -0.4091349 , -0.03829814],
       [ 1.030255  ,  0.9890015 , -0.4749484 ]], dtype=float32)