An article explaining in detail the basic data types of the Python data analysis module Numpy

Introduction to Numpy

​NumPy (Numerical Python) is an extension library of the Python language that supports a large number of dimensional array and matrix operations. In addition, it also provides a large number of mathematical function libraries for array operations.

NumPy​ is a very fast mathematics library, mainly used for array calculations, including:

• A powerful N-dimensional Array object ndarray
• Broadcast function function
• Tool for integrating C/C/Fortran code
• Linear algebra, Fourier transform, random number generation and other functions

NumPy Ndarray object

• The most important feature of NumPy is its N The dimensional array object ndarray is a collection of a series of data of the same type. The index of the elements in the collection starts with the 0 subscript.
• ndarray object is a multi-dimensional array used to store elements of the same type. Each element in the array
• ndarray has the same storage size area in memory

numpy object creation:

numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)

Data type conversion

Copy

Minimum dimension

##subok

NumPy data type

##Name	Description
object	array or Nested array
dtype	array element Data type, optional
copy	Whether the object Need to copy, optional
order	Create array The style, C is the row direction, F is the column direction, A is any direction (default)
subok	Default returns an array consistent with the base class type
##ndmin	Specify the minimum dimension of the generated array

##Boolean data type (True or False) ##int_##int64Integer (-9223372036854775808 to 9223372036854775807) ##uint8uint16##uint32##Complex number, representing a double 64-bit floating point number (real part and imaginary part )

数据类型对象 (dtype)

数据类型对象（numpy.dtype 类的实例）用来描述与数组对应的内存区域是如何使用，它描述了数据的以下几个方面：

• 数据的类型（整数，浮点数或者 Python 对象）
• 数据的大小（例如， 整数使用多少个字节存储）
• 数据的字节顺序（小端法或大端法）
• 在结构化类型的情况下，字段的名称、每个字段的数据类型和每个字段所取的内存块的部分
• 如果数据类型是子数组，那么它的形状和数据类型是什么。

字节顺序是通过对数据类型预先设定 来决定的。 意味着大端法(最重要的字节存储在最小的地址，即高位组放在最前面)。

dtype 对象是使用以下语法构造的：

numpy.dtype(object, align, copy)

object - 要转换为的数据类型对象
align - 如果为 true，填充字段使其类似 C 的结构体。
copy - 复制 dtype 对象 ，如果为 false，则是对内置数据类型对象的引用

每个内建类型都有一个唯一定义它的字符代码

Name	Description
##bool_
##Default integer type ( Similar to long, int32 or int64 in C language)	##intc
The same as the int type of C, usually int32 or int 64	##intp
Integer type used for indexing (similar to C's ssize_t, usually still int32 or int64)	int8	Bytes (-128 to 127)
##int16	Integer (-32768 to 32767)
##int32	Integer (-2147483648 to 2147483647)
Unsigned integer (0 to 255)
Unsigned integer (0 to 65535)
Unsigned integer (0 to 4294967295)	##uint64
Unsigned integer (0 to 18446744073709551615)
float_	##Abbreviation for float64 type
float16	##Half-precision floating point number, including: 1 sign bit, 5 exponent bits , 10 mantissa digits
float32	Single precision Floating point number, including: 1 sign bit, 8 exponent bits, 23 mantissa bits
float64	Double precision floating point number, including: 1 sign bit, 11 exponent bits, 52 mantissa bits
complex_	Abbreviation of complex128 type, that is, 128-bit complex number
complex64	Complex number, representing a double 32-bit floating point number (real part and imaginary part)
complex128

字符	对应类型
b	布尔型
i	(有符号) 整型
u	无符号整型 integer
f	浮点型
c	复数浮点型
m	timedelta（时间间隔）
M	datetime（日期时间）
O	(Python) 对象
S, a	(byte-)字符串
U	Unicode
V	原始数据 (void)

dt = np.dtype(np.int32)
print(dt)

输出：
int32


dt = np.dtype('i4')
print(dt)

输出：
int32


dt = np.dtype([('age', np.int8)])
print(dt)

输出：
[('age', 'i1')]

结构化数据类型

student = np.dtype([('name','S20'), ('age','i1'), ('score', 'f4')])
a = np.array([('xm', 10, 98.123456789), ('xh', 8, 99.111111111), ('xl', '9', 100)], dtype=student)
print(a)

输出：
[(b'xm', 10,98.12346 ) (b'xh',8,99.111115) (b'xl',9, 100.)]

The above is the detailed content of An article explaining in detail the basic data types of the Python data analysis module Numpy. For more information, please follow other related articles on the PHP Chinese website!