1. IDE说明：
所有的案例用Anacoda中的Jupiter工具进行交互式讲解。
2. 版本和安装：
NumPy从如下网站安装：http://sourceforge.net/projects/numpy/files。
我们通过环境查看相关的版本。如果电脑上安装了Anaconda的话这些版本基本上都是最新版本的。
如果Anaconda的库不是最新的可以通过Prompt进行安装和更新。可以参照如下博客，非常简单。https://blog.csdn.net/xiexu911/article/details/80282440
3. 我们通过Anaconda打开Jupiter或spyder打开进行讲解。
4. 第一个简单操作：通过对比Python和NumPy的计算观察NumPy的运算速度：
from datetime import datetime
import numpy as np
# 纯Python写的程序
def pythonsum(n):
a = []
b = []
c = []
for i in range(n):
a.append(i)
b.append(i)
c.append(a[i]**2 + b[i]**3)
return c
# NumPy写的程序
def numpysum(n):
a = np.arange(n,dtype=object) ** 2
b = np.arange(n,dtype=object) ** 3
c = a + b
return c
# 进行比较测试
size = 30000
start = datetime.now()
c1 = pythonsum(size)
delta = datetime.now()-start
print("The last 2 elements of the sum",c1[-2:])
print("PythonSum elapsed time in microsecond",delta.microseconds)
start = datetime.now()
c2 = numpysum(size)
delta = datetime.now()-start
print("The last 2 elements of the sum",c2[-2:])
print("NumPySum elapsed time in microsecond",delta.microseconds)
# 10000的情况下：
#The last 2 elements of the sum [999500079996, 999800010000]
#PythonSum elapsed time in microsecond 15625
#The last 2 elements of the sum [999500079996 999800010000]
#NumPySum elapsed time in microsecond 15623
# 20000的情况下：
#The last 2 elements of the sum [7998000159996, 7999200020000]
#PythonSum elapsed time in microsecond 31247
#The last 2 elements of the sum [7998000159996 7999200020000]
#NumPySum elapsed time in microsecond 0
# 30000的情况下：
#The last 2 elements of the sum [26995500239996, 26998200030000]
#PythonSum elapsed time in microsecond 46871
#The last 2 elements of the sum [26995500239996 26998200030000]
#NumPySum elapsed time in microsecond 0
我们发现越是数据大NumPy的优势就能够体现出来了。注意我们用NumPy的时候规定dtype = object是为了放置数组的溢出，这个在很多教材中都没有提及。如果不写，在数值过大的时候，数组会产生溢出，导致计算的记过不一样。
第二个简单操作：通过help查看NumPy的帮助文档：
# -*- coding: utf-8 -*- """ Spyder Editor This is a temporary script file. """ import numpy as np help(np.arange) #Help on built-in function arange in module numpy.core.multiarray: # #arange(...) # arange([start,] stop[, step,], dtype=None) # # Return evenly spaced values within a given interval. # # Values are generated within the half-open interval ``[start, stop)`` # (in other words, the interval including `start` but excluding `stop`). # For integer arguments the function is equivalent to the Python built-in # `range <http://docs.python.org/lib/built-in-funcs.html>`_ function, # but returns an ndarray rather than a list. # # When using a non-integer step, such as 0.1, the results will often not # be consistent. It is better to use ``linspace`` for these cases. # # Parameters # ---------- # start : number, optional # Start of interval. The interval includes this value. The default # start value is 0. # stop : number # End of interval. The interval does not include this value, except # in some cases where `step` is not an integer and floating point # round-off affects the length of `out`. # step : number, optional # Spacing between values. For any output `out`, this is the distance # between two adjacent values, ``out[i+1] - out[i]``. The default # step size is 1. If `step` is specified as a position argument, # `start` must also be given. # dtype : dtype # The type of the output array. If `dtype` is not given, infer the data # type from the other input arguments. # # Returns # ------- # arange : ndarray # Array of evenly spaced values. # # For floating point arguments, the length of the result is # ``ceil((stop - start)/step)``. Because of floating point overflow, # this rule may result in the last element of `out` being greater # than `stop`. # # See Also # -------- # linspace : Evenly spaced numbers with careful handling of endpoints. # ogrid: Arrays of evenly spaced numbers in N-dimensions. # mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions. # # Examples # -------- #np.arange(3) # array([0, 1, 2]) #np.arange(3.0) # array([ 0., 1., 2.]) #np.arange(3,7) # array([3, 4, 5, 6]) #np.arange(3,7,2) # array([3, 5])