Numpy 简明教程

NumPy - Data Types

NumPy 支持的数值类型比 Python 所支持的类型多得多。下表显示了 NumPy 中定义的不同标量数据类型。

NumPy supports a much greater variety of numerical types than Python does. The following table shows different scalar data types defined in NumPy.

Sr.No.

Data Types & Description

1

bool_ Boolean (True or False) stored as a byte

2

int_ Default integer type (same as C long; normally either int64 or int32)

3

intc Identical to C int (normally int32 or int64)

4

intp Integer used for indexing (same as C ssize_t; normally either int32 or int64)

5

int8 Byte (-128 to 127)

6

int16 Integer (-32768 to 32767)

7

int32 Integer (-2147483648 to 2147483647)

8

int64 Integer (-9223372036854775808 to 9223372036854775807)

9

uint8 Unsigned integer (0 to 255)

10

uint16 Unsigned integer (0 to 65535)

11

uint32 Unsigned integer (0 to 4294967295)

12

uint64 Unsigned integer (0 to 18446744073709551615)

13

float_ Shorthand for float64

14

float16 Half precision float: sign bit, 5 bits exponent, 10 bits mantissa

15

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

16

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

17

complex_ Shorthand for complex128

18

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

19

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

NumPy 数值类型是 dtype（数据类型）对象的实例，每个实例具有独特的特征。dtype 可用作 np.bool_、np.float32 等。

NumPy numerical types are instances of dtype (data-type) objects, each having unique characteristics. The dtypes are available as np.bool_, np.float32, etc.

Data Type Objects (dtype)

数据类型对象描述了与数组相对应的固定内存块的解读，具体取决于以下几个方面：

A data type object describes interpretation of fixed block of memory corresponding to an array, depending on the following aspects −

  1. Type of data (integer, float or Python object)

  2. Size of data

  3. Byte order (little-endian or big-endian)

  4. In case of structured type, the names of fields, data type of each field and part of the memory block taken by each field.

  5. If data type is a subarray, its shape and data type

通过在数据类型前面添加'<'或'>'来确定字节顺序。'<'表示编码是小端序（最小重要位存储在最小地址）。'>'表示编码是大端序（最大重要位存储在最小地址）。

The byte order is decided by prefixing '<' or '>' to data type. '<' means that encoding is little-endian (least significant is stored in smallest address). '>' means that encoding is big-endian (most significant byte is stored in smallest address).

dtype对象使用以下语法构造 -

A dtype object is constructed using the following syntax −

numpy.dtype(object, align, copy)

参数是 -

The parameters are −

  1. Object − To be converted to data type object

  2. Align − If true, adds padding to the field to make it similar to C-struct

  3. Copy − Makes a new copy of dtype object. If false, the result is reference to builtin data type object

Example 1

# using array-scalar type
import numpy as np
dt = np.dtype(np.int32)
print dt

输出如下 −

The output is as follows −

int32

Example 2

#int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc.
import numpy as np

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

输出如下 −

The output is as follows −

int32

Example 3

# using endian notation
import numpy as np
dt = np.dtype('>i4')
print dt

输出如下 −

The output is as follows −

>i4

以下示例显示结构化数据类型的使用。此处，应声明字段名称和相应标量数据类型。

The following examples show the use of structured data type. Here, the field name and the corresponding scalar data type is to be declared.

Example 4

# first create structured data type
import numpy as np
dt = np.dtype([('age',np.int8)])
print dt

输出如下 −

The output is as follows −

[('age', 'i1')]

Example 5

# now apply it to ndarray object
import numpy as np

dt = np.dtype([('age',np.int8)])
a = np.array([(10,),(20,),(30,)], dtype = dt)
print a

输出如下 −

The output is as follows −

[(10,) (20,) (30,)]

Example 6

# file name can be used to access content of age column
import numpy as np

dt = np.dtype([('age',np.int8)])
a = np.array([(10,),(20,),(30,)], dtype = dt)
print a['age']

输出如下 −

The output is as follows −

[10 20 30]

Example 7

以下示例定义一个名为 student 的结构化数据类型，该数据类型具有字符串字段’name'，一个 integer field 'age’和一个 float field 'marks'。此dtype应用于ndarray对象。

The following examples define a structured data type called student with a string field 'name', an integer field 'age' and a float field 'marks'. This dtype is applied to ndarray object.

import numpy as np
student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')])
print student

输出如下 −

The output is as follows −

[('name', 'S20'), ('age', 'i1'), ('marks', '<f4')])

Example 8

import numpy as np

student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')])
a = np.array([('abc', 21, 50),('xyz', 18, 75)], dtype = student)
print a

输出如下 −

The output is as follows −

[('abc', 21, 50.0), ('xyz', 18, 75.0)]

每个内置数据类型都具有唯一标识它的字符代码。

Each built-in data type has a character code that uniquely identifies it.

  1. 'b' − boolean

  2. 'i' − (signed) integer

  3. 'u' − unsigned integer

  4. 'f' − floating-point

  5. 'c' − complex-floating point

  6. 'm' − timedelta

  7. 'M' − datetime

  8. 'O' − (Python) objects

  9. 'S', 'a' − (byte-)string

  10. 'U' − Unicode

  11. 'V' − raw data (void)