9 matplotlib

matplotlib

参考：https://matplotlib.org/index.html

In [1]:

import matplotlib.pyplot as plt
import numpy as np

 

plt.plot()绘制线性图¶

• 绘制单条线形图
• 绘制多条线形图
• 设置坐标系的比例plt.figure(figsize=(a,b))
• 设置图例legend()
• 设置轴的标识
• 图例保存
  • fig = plt.figure()
  • plt.plot(x,y)
  • figure.savefig（）
• 曲线的样式和风格（自学）

In [2]:

#绘制单条线形图
x = np.array([1,2,3,4,5])
y = x + 3

plt.plot(x,y)

Out[2]:

[<matplotlib.lines.Line2D at 0x111dc3f28>]

 

In [3]:

#绘制多条线形图
plt.plot(x,y)
plt.plot(x+1,y-2)

Out[3]:

[<matplotlib.lines.Line2D at 0x111e38b00>]

 

In [4]:

plt.plot(x,y,x+1,y-2)

Out[4]:

[<matplotlib.lines.Line2D at 0x111f80a20>,
 <matplotlib.lines.Line2D at 0x111f80be0>]

 

In [7]:

#设置坐标系的比例plt.figure(figsize=(a,b))
plt.figure(figsize=(5,9))#放置在绘图的plot方法之前
plt.plot(x,y)

Out[7]:

[<matplotlib.lines.Line2D at 0x1120aebe0>]

 

In [14]:

#设置图例legend()
plt.plot(x,y,label='x,y')
plt.plot(x+1,y-2,label='x+1,y-2')
plt.legend() #图例生效

Out[14]:

<matplotlib.legend.Legend at 0x11693a5f8>

 

In [17]:

#设置轴的标识
plt.plot(x,y)
plt.xlabel('temp')
plt.ylabel('dist')
plt.title('dist&temp')

Out[17]:

Text(0.5,1,'dist&temp')

 

In [20]:

#图例保存
#fig = plt.figure()
#plt.plot(x,y)
#figure.savefig（）
fig = plt.figure()  #该对象的创建一定要放置在plot绘图之前
plt.plot(x,y,label='x,y')
fig.savefig('./123.png')

 

In [1]:

##曲线的样式和风格（自学）
plt.plot(x,y,c='red',alpha=0.1)

 

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-cb8d3856a8e3> in <module>
      1 ##曲线的样式和风格（自学）
----> 2plt.plot(x,y,c='red',alpha=0.1)

NameError: name 'plt' is not defined

 

柱状图：plt.bar()¶

• 参数：第一个参数是索引。第二个参数是数据值。第三个参数是条形的宽度

In [28]:

plt.bar(x,y)

Out[28]:

<BarContainer object of 5 artists>

 

 

直方图¶

• 是一个特殊的柱状图，又叫做密度图
• plt.hist()的参数
  • bins
    可以是一个bin数量的整数值，也可以是表示bin的一个序列。默认值为10
  • normed
    如果值为True，直方图的值将进行归一化处理，形成概率密度，默认值为False
  • color
    指定直方图的颜色。可以是单一颜色值或颜色的序列。如果指定了多个数据集合,例如DataFrame对象，颜色序列将会设置为相同的顺序。如果未指定，将会使用一个默认的线条颜色
  • orientation
    通过设置orientation为horizontal创建水平直方图。默认值为vertical

In [34]:

data = [1,1,2,2,2,3,4,5,6,6,6,6,6,6,7,8,9,0]
plt.hist(data,bins=20) # bin表示柱子的个数

Out[34]:

(array([1., 0., 2., 0., 3., 0., 1., 0., 1., 0., 0., 1., 0., 6., 0., 1., 0.,
        1., 0., 1.]),
 array([0.  , 0.45, 0.9 , 1.35, 1.8 , 2.25, 2.7 , 3.15, 3.6 , 4.05, 4.5 ,
        4.95, 5.4 , 5.85, 6.3 , 6.75, 7.2 , 7.65, 8.1 , 8.55, 9.  ]),
 <a list of 20 Patch objects>)

 

 

饼图¶

• pie()，饼图也只有一个参数x
• 饼图适合展示各部分占总体的比例，条形图适合比较各部分的大小

In [37]:

arr=[11,22,31,15]
plt.pie(arr)

Out[37]:

([<matplotlib.patches.Wedge at 0x1178be1d0>,
  <matplotlib.patches.Wedge at 0x1178be6a0>,
  <matplotlib.patches.Wedge at 0x1178beb70>,
  <matplotlib.patches.Wedge at 0x1178c60f0>],
 [Text(0.996424,0.465981,''),
  Text(-0.195798,1.08243,''),
  Text(-0.830021,-0.721848,''),
  Text(0.910034,-0.61793,'')])

 

In [36]:

arr=[0.2,0.3,0.1]
plt.pie(arr)

Out[36]:

([<matplotlib.patches.Wedge at 0x1177d0e80>,
  <matplotlib.patches.Wedge at 0x1177da390>,
  <matplotlib.patches.Wedge at 0x1177da8d0>],
 [Text(0.889919,0.646564,''),
  Text(-0.646564,0.889919,''),
  Text(-1.04616,-0.339919,'')])

 

In [38]:

arr=[11,22,31,15]
plt.pie(arr,labels=['a','b','c','d'])

Out[38]:

([<matplotlib.patches.Wedge at 0x11794aa90>,
  <matplotlib.patches.Wedge at 0x11794af60>,
  <matplotlib.patches.Wedge at 0x1179544e0>,
  <matplotlib.patches.Wedge at 0x117954a20>],
 [Text(0.996424,0.465981,'a'),
  Text(-0.195798,1.08243,'b'),
  Text(-0.830021,-0.721848,'c'),
  Text(0.910034,-0.61793,'d')])

 

In [39]:

arr=[11,22,31,15]
plt.pie(arr,labels=['a','b','c','d'],labeldistance=0.3)

Out[39]:

([<matplotlib.patches.Wedge at 0x1179e2278>,
  <matplotlib.patches.Wedge at 0x1179e2748>,
  <matplotlib.patches.Wedge at 0x1179e2c18>,
  <matplotlib.patches.Wedge at 0x1179eb198>],
 [Text(0.271752,0.127086,'a'),
  Text(-0.0533994,0.295209,'b'),
  Text(-0.226369,-0.196868,'c'),
  Text(0.248191,-0.168526,'d')])

 

In [40]:

arr=[11,22,31,15]
plt.pie(arr,labels=['a','b','c','d'],labeldistance=0.3,autopct='%.6f%%')

Out[40]:

([<matplotlib.patches.Wedge at 0x117a709e8>,
  <matplotlib.patches.Wedge at 0x117a7a128>,
  <matplotlib.patches.Wedge at 0x117a7a898>,
  <matplotlib.patches.Wedge at 0x117a83048>],
 [Text(0.271752,0.127086,'a'),
  Text(-0.0533994,0.295209,'b'),
  Text(-0.226369,-0.196868,'c'),
  Text(0.248191,-0.168526,'d')],
 [Text(0.543504,0.254171,'13.924050%'),
  Text(-0.106799,0.590419,'27.848101%'),
  Text(-0.452739,-0.393735,'39.240506%'),
  Text(0.496382,-0.337053,'18.987341%')])

 

In [41]:

arr=[11,22,31,15]
plt.pie(arr,labels=['a','b','c','d'],labeldistance=0.3,shadow=True,explode=[0.2,0.3,0.2,0.4])

Out[41]:

([<matplotlib.patches.Wedge at 0x117ab2390>,
  <matplotlib.patches.Wedge at 0x117ab2b38>,
  <matplotlib.patches.Wedge at 0x117abb390>,
  <matplotlib.patches.Wedge at 0x117abbba8>],
 [Text(0.45292,0.21181,'a'),
  Text(-0.106799,0.590419,'b'),
  Text(-0.377282,-0.328113,'c'),
  Text(0.579113,-0.393228,'d')])

 

 

散点图scatter()¶

• 因变量随自变量而变化的大致趋势

In [46]:

x = np.array([1,3,5,7,9])
y = x ** 2 - 3
plt.scatter(x,y)

Out[46]:

<matplotlib.collections.PathCollection at 0x117c1d780>