python画图_matplotlib和pandas

灰太狼 2022-06-10 02:40 272阅读 0赞

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1.  \#encoding:utf-8
2.  '''''
3.  Created on 2015年9月11日
4.  
5.  @author: ZHOUMEIXU204
6.  '''
7.  
8.  \# pylab 是 matplotlib 面向对象绘图库的一个接口。它的语法和 Matlab 十分相近
9.  **import** pandas as pd 
10. **from** ggplot **import** \* 
11. **import** numpy as np 
12. **import** matplotlib.pyplot as plt 
13. df=pd.DataFrame(np.random.randn(1000,4),columns=list('ABCD')) 
14. df=df.cumsum() 
15. **print**(plt.figure()) 
16. **print**(df.plot()) 
17. **print**(plt.show()) 
18. 
19. \# print(ggplot(df,aes(x='A',y='B'))+geom\_point())
20. 
21. \# 画简单的图形
22. **from** pylab **import** \* 
23. x=np.linspace(-np.pi,np.pi,256,endpoint=True) 
24. c,s=np.cos(x),np.sin(x) 
25. plot(x,c, color="blue", linewidth=2.5, linestyle="-", label="cosine") \#label用于标签显示问题
26. plot(x,s,color="red", linewidth=2.5, linestyle="-", label="sine") 
27. show() 
28. 
29. 
30. \#散点图
31. **from** pylab **import** \* 
32. n = 1024
33. X = np.random.normal(0,1,n) 
34. Y = np.random.normal(0,1,n) 
35. scatter(X,Y) 
36. show() 
37. 
38. \#条形图
39. 
40. **from** pylab **import** \* 
41. n = 12
42. X = np.arange(n) 
43. Y1 = (1\-X/float(n)) \* np.random.uniform(0.5,1.0,n) 
44. Y2 = (1\-X/float(n)) \* np.random.uniform(0.5,1.0,n) 
45. bar(X, +Y1, facecolor='\#9999ff', edgecolor='white') 
46. bar(X, -Y2, facecolor='\#ff9999', edgecolor='white') 
47. **for** x,y **in** zip(X,Y1): 
48.  text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom') 
49. ylim(-1.25,+1.25) 
50. show() 
51. 
52. 
53. \#饼图
54. **from** pylab **import** \* 
55. n = 20
56. Z = np.random.uniform(0,1,n) 
57. pie(Z), show() 
58. 
59. 
60. 
61. \#画三维图
62. **import** numpy as np 
63. **from** mpl\_toolkits.mplot3d **import** Axes3D 
64. **from** pylab **import** \* 
65. fig=figure() 
66. ax=Axes3D(fig) 
67. x=np.arange(-4,4,0.1) 
68. y=np.arange(-4,4,0.1) 
69. x,y=np.meshgrid(x,y) 
70. R=np.sqrt(x\*\*2\+y\*\*2) 
71. z=np.sin(R) 
72. ax.plot\_surface(x,y,z,rstride=1,cstride=1,cmap='hot') 
73. show() 
74. 
75. 
76. \#用于图像显示的问题
77. 
78. **import** matplotlib.pyplot as plt 
79. **import** pandas as pd 
80. weights\_dataframe=pd.DataFrame() 
81. plt.figure() 
82. plt.plot(weights\_dataframe.weights\_ij,weights\_dataframe.weights\_x1,label='weights\_x1') 
83. plt.plot(weights\_dataframe.weights\_ij,weights\_dataframe.weights\_x0,label='weights\_x0') 
84. plt.plot(weights\_dataframe.weights\_ij,weights\_dataframe.weights\_x2,label='weights\_x2') 
85. plt.legend(loc='upper right') \#用于标签显示问题 
86. plt.xlabel(u"迭代次数", fontproperties='SimHei') 
87. plt.ylabel(u"参数变化", fontproperties='SimHei') 
88. plt.title(u"迭代次数显示", fontproperties='SimHei') \#fontproperties='SimHei' 用于可以显示中文
89. plt.show()

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1.  <pre name="code"**class**="python">**import** matplotlib.pyplot as plt 
2.  **from** numpy.random **import** random 
3.  
4.  colors = \['b', 'c', 'y', 'm', 'r'\] 
5.  
6.  lo = plt.scatter(random(10), random(10), marker='x', color=colors\[0\]) 
7.  ll = plt.scatter(random(10), random(10), marker='o', color=colors\[0\]) 
8.  l = plt.scatter(random(10), random(10), marker='o', color=colors\[1\]) 
9.  a = plt.scatter(random(10), random(10), marker='o', color=colors\[2\]) 
10. h = plt.scatter(random(10), random(10), marker='o', color=colors\[3\]) 
11. hh = plt.scatter(random(10), random(10), marker='o', color=colors\[4\]) 
12. ho = plt.scatter(random(10), random(10), marker='x', color=colors\[4\]) 
13. 
14. plt.legend((lo, ll, l, a, h, hh, ho), 
15.  ('Low Outlier', 'LoLo', 'Lo', 'Average', 'Hi', 'HiHi', 'High Outlier'), 
16.  scatterpoints=1, 
17.  loc='lower left', 
18.  ncol=3, 
19.  fontsize=8) 
20. 
21. plt.show()

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1.  <span style="font-family: Arial, Helvetica, sans-serif;">\#pandas中画图 </span>

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1.  \#画累和图
2.  **import** pandas as pd 
3.  **import** numpy as np 
4.  **import** matplotlib.pyplot as plt 
5.  ts=pd.Series(np.random.randn(1000),index=pd.date\_range('1/1/2000',periods=1000)) 
6.  ts=ts.cumsum() 
7.  ts.plot() 
8.  plt.show() 
9.  
10. 
11. 
12. df=pd.DataFrame(np.random.randn(1000,4),index=ts.index,columns=list('ABCD')) 
13. df=df.cumsum() 
14. df.plot() 
15. plt.show() 
16. 
17. 
18. 
19. 
20. \#画柱状图
21. df2 = pd.DataFrame(np.random.rand(10, 4), columns=\['a', 'b', 'c', 'd'\]) 
22. df2.plot(kind='bar') \#分开并列线束
23. df2.plot(kind='bar', stacked=True) \#四个在同一个里面显示 百分比的形式
24. df2.plot(kind='barh', stacked=True)\#纵向显示
25. plt.show() 
26. 
27. 
28. df4=pd.DataFrame(\{ 'a':np.random.randn(1000)+1,'b':np.random.randn(1000),'c':np.random.randn(1000)-1\},columns=list('abc')) 
29. df4.plot(kind='hist', alpha=0.5) 
30. df4.plot(kind='hist', stacked=True, bins=20) 
31. df4\['a'\].plot(kind='hist', orientation='horizontal',cumulative=True) \#cumulative是按顺序排序，加上这个
32. plt.show() 
33. 
34. 
35. \#Area Plot
36. df = pd.DataFrame(np.random.rand(10, 4), columns=\['a', 'b', 'c', 'd'\]) 
37. df.plot(kind='area') 
38. df.plot(kind='area',stacked=False) 
39. plt.show() 
40. 
41. 
42. \#散点图
43. df = pd.DataFrame(np.random.rand(50, 4), columns=\['a', 'b', 'c', 'd'\]) 
44. df.plot(kind='scatter', x='a', y='b') 
45. df.plot(kind='scatter', x='a', y='b',color='DarkBlue', label='Group 1') 
46. 
47. \#饼图
48. df = pd.DataFrame(3 \* np.random.rand(4, 2), index=\['a', 'b', 'c', 'd'\], columns=\['x', 'y'\]) 
49. df.plot(kind='pie', subplots=True, figsize=(8, 4)) 
50. df.plot(kind='pie', subplots=True,autopct='%.2f',figsize=(8, 4)) \#显示百分比
51. plt.show() 
52. 
53. 
54. 
55. \#画矩阵散点图
56. df = pd.DataFrame(np.random.randn(1000, 4), columns=\['a', 'b', 'c', 'd'\]) 
57. pd.scatter\_matrix(df, alpha=0.2, figsize=(6, 6), diagonal='kde') 
58. plt.show()

[view plain]: http://blog.csdn.net/luoyexuge/article/details/49069225#