python科学计算——numpy - webdancer's Blog
python科学计算——numpy
numpy是python进行科学计算的一个基础包，它的核心功能是提供ndarray对象，一个实现了n维数组的对象，与python自带的list相比，数组大小初始化时候确定，而且是单一类型（当然也可以使用 对象，这样也能包含多种对象），速度更快，大多数科学计算的程序可能都是基于numpy的。
1.基本的思想有两个：vectorization和broadcasting.
- vectorization：说的是可以不适用for,而像线性代数里面提供的操作一样进行计算；
- broadcasting:指的是对于element-by-element的计算，可以自动进行；
2.数据类型：支持丰富的数值类型，如下：
| Data type | Description |
|---|---|
| bool_ | Boolean (True or False) stored as a byte |
| int_ | Default integer type (same as C long; normally either int64 or int32) |
| intc | Identical to C int (normally int32 or int64) |
| intp | Integer used for indexing (same as C ssize_t; normally either int32 or int64) |
| int8 | Byte (-128 to 127) |
| int16 | Integer (-32768 to 32767) |
| int32 | Integer (-2147483648 to 2147483647) |
| int64 | Integer (-9223372036854775808 to 9223372036854775807) |
| uint8 | Unsigned integer (0 to 255) |
| uint16 | Unsigned integer (0 to 65535) |
| uint32 | Unsigned integer (0 to 4294967295) |
| uint64 | Unsigned integer (0 to 18446744073709551615) |
| float_ | Shorthand for float64. |
| float16 | Half precision float: sign bit, 5 bits exponent, 10 bits mantissa |
| float32 | Single precision float: sign bit, 8 bits exponent, 23 bits mantissa |
| float64 | Double precision float: sign bit, 11 bits exponent, 52 bits mantissa |
| complex_ | Shorthand for complex128. |
| complex64 | Complex number, represented by two 32-bit floats (real and imaginary components) |
| complex128 | Complex number, represented by two 64-bit floats (real and imaginary components) |
3.创建数组：有5中基本的方式：
- Conversion from other Python structures (e.g., lists, tuples)
- Intrinsic numpy array array creation objects (e.g., arange, ones, zeros, etc.)
- Reading arrays from disk, either from standard or custom formats: genfromtxt
- Creating arrays from raw bytes through the use of strings or buffers
- Use of special library functions (e.g., random)
4.索引：
import numpy as np
x = np.arange(10)
x[0] ##从零开始索引,返回0
x.shape = (2,5)
x[1,2] ##这是与python的list，tuple不同的地方，支持多维的索引，返回7
x[0]等价于x[0,:]
x.shape=(10)
x[2:5] #与python的list一样，支持切片操作；
5.broadcasting:描述的是在进行算术操作时，Array的大小不一致的情况，numpy如何处理；将短的进行扩展，然后进行处理。
>>> a = np.array([1.0, 2.0, 3.0]) >>> b = 2.0 >>> a * b array([ 2., 4., 6.])
When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions, and works its way forward. Two dimensions are compatible when
- they are equal, or
- one of them is 1
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