获取数据

---

用requests，请求网络数据，然后写入csv文件。

import requests
#从远程获取数据
url = "http://aima.cs.berkeley.edu/data/iris.csv"
response = requests.get(url)
#写入文件
local_file = open("iris.csv","w")
local_file.write(response.text)
local_file.close()

从文件中读取数据

---

from numpy import genfromtxt,zeros
#特征集
data = genfromtxt("iris.csv",delimiter=",",usecols=(0,1,2,3))
#分类标签
labels = genfromtxt("iris.csv",delimiter=",",usecols=(4),dtype=str)

数据为csv格式，以“,”分隔，分别为花萼的长宽，花瓣的长宽。
这里为了便于分析，学习，就叫特征0，1，2，3，最后一列为分类标签，数据格式如下：

数据样式

查看样本数据的维度，标签的种类

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#查看矩阵大小，标签种类
print(data.shape)
print(labels.shape)
print(set(labels))

结果：

矩阵大小，标签种类

根据数据开始绘图

---

这里需要引入package

from pylab import plot,show,figure,subplot,hist,xlim
import matplotlib.pyplot as plt

二维散点图

• 特征0，特征1

    plot(data[labels=="setosa",0],data[labels=="setosa",1],"bo")
    plot(data[labels=="virginica",0],data[labels=="virginica",1],"ro")
    plot(data[labels=="versicolor",0],data[labels=="versicolor",1],"go")
    show()

结果：

特征0，1

从上图看，好像并不能很好的区分种类。

• 特征0，2

    plot(data[labels=="setosa",0],data[labels=="setosa",2],"bo")
    plot(data[labels=="virginica",0],data[labels=="virginica",2],"ro")
    plot(data[labels=="versicolor",0],data[labels=="versicolor",2],"go")
    show()

结果：

特征0，2

这个就比较明显了

• 特征0，3

    plot(data[labels=="setosa",0],data[labels=="setosa",3],"bo")
    plot(data[labels=="virginica",0],data[labels=="virginica",3],"ro")
    plot(data[labels=="versicolor",0],data[labels=="versicolor",3],"go")
    show()
  

结果：

![特征0，3](https://img.haomeiwen.com/i1797187/1ae59f7b1f0f51bd.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)
同理，可以用其他的特征量来生成二位散点图

####三维散点图
---
- 特征0，2，3

ax = plt.subplot(111,projection="3d")
ax.scatter(data[labels=="setosa",0],data[labels=="setosa",2],data[labels=="setosa",3],c="b")
ax.scatter(data[labels=="virginica",0],data[labels=="virginica",2],data[labels=="virginica",3],c="r")
ax.scatter(data[labels=="versicolor",0],data[labels=="versicolor",2],data[labels=="versicolor",3],c="g")

plt.show()

结果：
  
![特征0，2，3](https://img.haomeiwen.com/i1797187/008f5382e3aad86b.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)

####直方图
---

特征0

subplot(441)
hist(data[labels=="setosa",0],color="b",alpha=0.7)
xlim(xmin0,xmax0)

subplot(445)
hist(data[labels=="virginica",0],color="r",alpha=0.7)
xlim(xmin0,xmax0)

subplot(449)
hist(data[labels=="versicolor",0],color="g",alpha=0.7)
xlim(xmin0,xmax0)

subplot(4,4,13)
hist(data[:,0],color="y",alpha=0.7)
xlim(xmin0,xmax0)

特征1

xmin1 = min(data[:,1])
xmax1 = max(data[:,1])

subplot(442)
hist(data[labels=="setosa",1],color="b",alpha=0.7)
xlim(xmin1,xmax1)

subplot(446)
hist(data[labels=="virginica",1],color="r",alpha=0.7)
xlim(xmin1,xmax1)

subplot(4,4,10)
hist(data[labels=="versicolor",1],color="g",alpha=0.7)
xlim(xmin1,xmax1)

subplot(4,4,14)
hist(data[:,1],color="y",alpha=0.7)
xlim(xmin1,xmax1)

特征2

xmin2 = min(data[:,2])
xmax2 = max(data[:,2])

subplot(443)
hist(data[labels=="setosa",2],color="b",alpha=0.7)
xlim(xmin2,xmax2)

subplot(447)
hist(data[labels=="virginica",2],color="r",alpha=0.7)
xlim(xmin2,xmax2)

subplot(4,4,11)
hist(data[labels=="versicolor",2],color="g",alpha=0.7)
xlim(xmin2,xmax2)

subplot(4,4,15)
hist(data[:,2],color="y",alpha=0.7)
xlim(xmin2,xmax2)

特征3

xmin3 = min(data[:,3])
xmax3 = max(data[:,3])

subplot(444)
hist(data[labels=="setosa",3],color="b",alpha=0.7)
xlim(xmin3,xmax3)

subplot(448)
hist(data[labels=="virginica",3],color="r",alpha=0.7)
xlim(xmin3,xmax3)

subplot(4,4,12)
hist(data[labels=="versicolor",3],color="g",alpha=0.7)
xlim(xmin3,xmax3)

subplot(4,4,16)
hist(data[:,3],color="y",alpha=0.7)
xlim(xmin3,xmax3)

show()

**这里代码为了便于理解，学习，就没进行封装**

![直方图](https://img.haomeiwen.com/i1797187/6a56c0fdb0d8f326.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)