Numpy Tutorial - NumPy Data Type and Conversion

  1. NumPy Data Type
  2. Data Type Conversion

Data type - dtype in NumPy is different from the primitive data types in Python, for example, dtype has the type with higher resolution that is useful in the data calculation.

NumPy Data Type

Data Type Description
bool Boolean
int8 8-bit signed integer
int16 16-bit signed integer
int32 32-bit signed integer
int64 64-bit signed integer
uint8 8-bit unsigned integer
uint16 16-bit unsigned integer
uint32 32-bit unsigned integer
uint64 64-bit unsigned integer
float16 16-bit floating point number
float32 32-bit floating point number
float64 64-bit floating point number
complex64 64-bit complex number
complex128 128-bit complex number

When creating a new ndarray data, you can define the data type of the element by string or or data type constants in the numpy library.

import numpy as np

# by string
test = np.array([4, 5, 6], dtype='int64')

# by data type constant in numpy
test = np.array([7, 8, 8], dtype=np.int64)

Data Type Conversion

After the data instance is created, you can change the type of the element to another type with astype() method, such as from integer to floating and so on.

>>> import numpy as np
>>> test = np.array([11, 12, 13, 14], dtype="int32")
>>> x = test.astype('float32')
>>> x
array([11., 12., 13., 14.], dtype=float32)
>>> test, test.dtype
(array([11, 12, 13, 14]), dtype('int32'))

The data type conversion method will only return a new array instance, and the data and information of the original array instance has not changed.

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