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 < 或 > 来决定的。 < 意味着小端法(最小值存储在最小的地址，即低位组放在最前面)。> 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.)]

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