Python算法庫的安裝過(guò)程

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Python算法庫包含以下幾個(gè)程序包，官方下載地址：
https://pypi.python.org/pypi

下載的算法庫要與安裝的Python版本一致，比如安裝的是Python3.7-64位版本，下載的算法庫要為cp37...win_amd64，否則安裝時(shí)會(huì )報錯。Python算法庫的安裝順序為：NumPy->SciPy->Matplotlib->Scikit-Learn。

NumPy：
array processing for numbers, strings, records, and objects.
NumPy是一個(gè)開(kāi)源的Python科學(xué)計算庫。使用NumPy，就可以很自然地使用數組和矩陣。NumPy包含很多實(shí)用的數學(xué)函數，涵蓋線(xiàn)性代數運算、傅里葉變換和隨機數生成等功能。NumPy通常與SciPy和Matplotlib一起使用，這種組合廣泛用于替代MatLab（一個(gè)流行的技術(shù)計算平臺），Python作為MatLab的替代方案，現在被視為一種更加現代和完整的編程語(yǔ)言。

安裝：
C:\Program Files\Python37\Scripts>pip install d:\numpy-1.15.2-cp37-none-win_amd64.whl
Processing d:\numpy-1.15.2-cp37-none-win_amd64.whl
Installing collected packages: numpy
Successfully installed numpy-1.15.2

SciPy：
Scientific Library for Python.
SciPy是一個(gè)開(kāi)源的Python科學(xué)計算庫，建立在Numpy之上。它增加的功能包括數值積分、最優(yōu)化、統計和一些專(zhuān)用函數。SciPy函數庫在NumPy庫的基礎上增加了眾多的數學(xué)、科學(xué)以及工程計算中常用的庫函數。例如插值運算、線(xiàn)性代數、常微分方程數值求解、信號處理、圖像處理、稀疏矩陣等等。

安裝：
C:\Program Files\Python37\Scripts>pip install d:\scipy-1.1.0-cp37-none-win_amd64.whl
Processing d:\scipy-1.1.0-cp37-none-win_amd64.whl
Requirement already satisfied: numpy>=1.8.2 in c:\program files\python37\lib\site-packages (from scipy==1.1.0) (1.15.2)
Installing collected packages: scipy
Successfully installed scipy-1.1.0

Matplotlib：
Python plotting package.
Matplotlib是一個(gè)Python 2D繪圖庫，它可以在各種平臺上以各種硬拷貝格式和交互式環(huán)境生成具有出版品質(zhì)的圖形。Matplotlib只需幾行代碼即可生成曲線(xiàn)圖、直方圖、曲餅圖、散點(diǎn)圖等。

安裝：
C:\Program Files\Python37\Scripts>pip install d:\matplotlib-3.0.0-cp37-cp37m-win_amd64.whl
Processing d:\matplotlib-3.0.0-cp37-cp37m-win_amd64.whl
Collecting kiwisolver>=1.0.1 (from matplotlib==3.0.0)
  Downloading https://files.pythonhosted.org/packages/7c/be/7ae355b45699460e369ebf88d86058fca26827933974cc3f6b6b7800a324/kiwisolver-1.0.1-cp37-none-win_amd64.whl (57kB)
    100% |████████████████████████████████| 61kB 55kB/s
Collecting python-dateutil>=2.1 (from matplotlib==3.0.0)
  Downloading https://files.pythonhosted.org/packages/cf/f5/af2b09c957ace60dcfac112b669c45c8c97e32f94aa8b56da4c6d1682825/python_dateutil-2.7.3-py2.py3-none-any.whl (211kB)
    100% |████████████████████████████████| 215kB 74kB/s
Collecting cycler>=0.10 (from matplotlib==3.0.0)
  Downloading https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl
Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 (from matplotlib==3.0.0)
  Downloading https://files.pythonhosted.org/packages/2b/4a/f06b45ab9690d4c37641ec776f7ad691974f4cf6943a73267475b05cbfca/pyparsing-2.2.2-py2.py3-none-any.whl (57kB)
    100% |████████████████████████████████| 61kB 131kB/s
Requirement already satisfied: numpy>=1.10.0 in c:\program files\python37\lib\site-packages (from matplotlib==3.0.0) (1.15.2)
Requirement already satisfied: setuptools in c:\program files\python37\lib\site-packages (from kiwisolver>=1.0.1->matplotlib==3.0.0) (39.0.1)
Collecting six>=1.5 (from python-dateutil>=2.1->matplotlib==3.0.0)
  Downloading https://files.pythonhosted.org/packages/67/4b/141a581104b1f6397bfa78ac9d43d8ad29a7ca43ea90a2d863fe3056e86a/six-1.11.0-py2.py3-none-any.whl
Installing collected packages: kiwisolver, six, python-dateutil, cycler, pyparsing, matplotlib
Successfully installed cycler-0.10.0 kiwisolver-1.0.1 matplotlib-3.0.0 pyparsing-2.2.2 python-dateutil-2.7.3 six-1.11.0

Scikit-Learn：
A set of python modules for machine learning and data mining.
scikit-learn(簡(jiǎn)記sklearn)是用python實(shí)現的機器學(xué)習算法庫。sklearn可以實(shí)現數據預處理、分類(lèi)、回歸、降維、模型選擇等常用的機器學(xué)習算法。sklearn是基于NumPy, SciPy, matplotlib的。

安裝：
C:\Program Files\Python37\Scripts>pip install d:\scikit_learn-0.20.0-cp37-cp37m-win_amd64.whl
Processing d:\scikit_learn-0.20.0-cp37-cp37m-win_amd64.whl
Requirement already satisfied: scipy>=0.13.3 in c:\program files\python37\lib\site-packages (from scikit-learn==0.20.0) (1.1.0)
Requirement already satisfied: numpy>=1.8.2 in c:\program files\python37\lib\site-packages (from scikit-learn==0.20.0) (1.15.2)
Installing collected packages: scikit-learn
Successfully installed scikit-learn-0.20.0

可以簡(jiǎn)單測試一下算法庫安裝后的效果，代碼如下：

#導入NumPy庫
import numpy as np
#導入Matplotlib庫
import matplotlib.pyplot as plt
import math
#定義序列端點(diǎn)和樣本數
x = np.linspace(-math.pi, math.pi, 100)
#定義函數
y0 = x / x - 1
y1 = np.sin(x)
y2 = np.cos(x)
y3 = x**2 - 2 * x - 1
#繪制曲線(xiàn)
plt.plot(x, y0, color = 'black', linewidth = 0.5)
plt.plot(x, y1, label = '$y=sin(x)$', color = 'red', linewidth = 0.5)
plt.plot(x, y2, label = '$y=cos(x)$', color = 'green', linewidth = 0.5)
plt.plot(x, y3, label = '$y=x^2-2x+1$', color = 'blue', linewidth = 0.5)
#定義坐標
plt.xlabel('Time(s)')
plt.ylabel('Volt')
plt.xlim(-4, 4)
plt.ylim(-2, 2)
#標題和圖示
plt.title('PyPlot')
plt.legend()
#顯示繪圖
plt.show()