一.Numpy/Scipy
1 #coding=utf-8 2 import numpy 3 import scipy 4 5 x = numpy.ones((3, 4)) 6 print x 7 """ 8 [[ 1. 1. 1. 1.] 9 [ 1. 1. 1. 1.] 10 [ 1. 1. 1. 1.]] 11 """ 12 13 y = numpy.array([[1, 2], [3, 4]]) 14 print y 15 """ 16 [[1 2] 17 [3 4]] 18 """ 19 20 print numpy.linalg.det(y) #-2.0 21 22 print numpy.arange(1, 5, 0.5) #[ 1. 1.5 2. 2.5 3. 3.5 4. 4.5] 23 24 a = numpy.array([[5, 5, 5], [5, 5, 5]]) 25 b = numpy.array([[2, 2, 2], [2, 2, 2]]) 26 print a * b 27 """ 28 [[10 10 10] 29 [10 10 10]] 30 """ 31 32 print a.sum() #30 33 print a.sum(axis=0) #[10 10 10] 34 print a.sum(axis=1) #[15 15] 35 36 a = numpy.array([1, 3, 5]) 37 b = numpy.array([2, 4, 6]) 38 c = numpy.array([7, 8, 9]) 39 print numpy.where(a > 2, b, c) #[7 4 6] Numpy.where函数是三元表达式x if condition else y的矢量化版本a > 2 [False, True, True]
1 #coding=utf-8 2 import numpy 3 import scipy 4 5 def fun(x, y): 6 return (x + 1) * (y + 1) 7 8 a = numpy.fromfunction(fun, (9, 9)) 9 print a 10 """ 11 [[ 1. 2. 3. 4. 5. 6. 7. 8. 9.] 12 [ 2. 4. 6. 8. 10. 12. 14. 16. 18.] 13 [ 3. 6. 9. 12. 15. 18. 21. 24. 27.] 14 [ 4. 8. 12. 16. 20. 24. 28. 32. 36.] 15 [ 5. 10. 15. 20. 25. 30. 35. 40. 45.] 16 [ 6. 12. 18. 24. 30. 36. 42. 48. 54.] 17 [ 7. 14. 21. 28. 35. 42. 49. 56. 63.] 18 [ 8. 16. 24. 32. 40. 48. 56. 64. 72.] 19 [ 9. 18. 27. 36. 45. 54. 63. 72. 81.]] 20 """ 21 22 a = numpy.array([[1, 2, 3]]) 23 b = numpy.array([[3, 4, 5]]) 24 print numpy.add(a, b) #[[4 6 8]] 25 print numpy.multiply(a, b) #[[ 3 8 15]]
二.Pandas
1 #coding=utf-8
2 from pandas import Series
3 import pandas as pd
4 a = Series([3, 5, 7], index=[‘a‘, ‘b‘, ‘c‘])
5 print a[‘a‘] #3
6
7 data = {‘a‘:1, ‘b‘:2, ‘c‘:3}
8 sindex = [‘a‘, ‘b‘, ‘d‘]
9 Ser = Series(data, index=sindex)
10 print Ser
11 """
12 a 1
13 b 2
14 d NaN
15 dtype: float64
16 """
17 print Series.isnull(Ser)
18 """
19 a False
20 b False
21 d True
22 dtype: bool
23 """
24 print a
25 """
26 a 3
27 b 5
28 c 7
29 dtype: int64
30 """
31
32 b = {‘a‘:2, ‘b‘:3, ‘d‘:5}
33 print Series(a) + Series(b)
34 """
35 a 5
36 b 8
37 c NaN
38 d NaN
39 dtype: float64
40 """
41 data = {‘Name‘:[‘a‘, ‘b‘, ‘c‘], ‘Num‘:[1, 2, 3]}
42 a = pd.DataFrame(data)
43 """
44 Name Num
45 0 a 1
46 1 b 2
47 2 c 3
48 """
49 print a[‘Name‘]
50 print a.Name
51 """
52 0 a
53 1 b
54 2 c
55 Name: Name, dtype: object
56 """
57 print a[0:2]
58 print a[a.index < 2]
59 """
60 Name Num
61 0 a 1
62 1 b 2
63 """
64 print a.ix[1]
65 """
66 Name b
67 Num 2
68 """
69 del a[‘Name‘]
70 print a
71 """
72 Num
73 0 1
74 1 2
75 2 3
76 """
三.Matplotlib
1 import pandas as pd 2 from matplotlib.finance import quotes_historical_yahoo 3 from datetime import date 4 today = date.today() 5 start = (today.year - 1, today.month, today.day) 6 quote = quotes_historical_yahoo(‘AXP‘, start, today) 7 fields = [‘date‘, ‘open‘, ‘close‘, ‘high‘, ‘low‘, ‘volume‘] 8 df = pd.DataFrame(quote, index=range(1, len(quote) + 1), columns=fields) 9 print df.head(10) 10 """ 11 date open close high low volume 12 1 735663 79.412407 79.097240 79.924559 78.851015 6530200 13 2 735666 78.939660 79.294224 79.609392 78.742676 5846100 14 3 735667 78.526003 77.915364 78.565393 77.678990 7525000 15 4 735668 78.200986 78.250226 78.545699 77.915364 4546200 16 5 735669 78.890411 80.328364 80.761722 78.821469 9386600 17 6 735670 80.269272 79.382861 80.417009 78.742676 6919400 18 7 735673 79.658632 80.269272 80.417009 79.530598 5295100 19 8 735674 79.973799 79.835914 79.983650 79.333620 4258200 20 9 735675 79.363166 80.623836 81.076889 79.057843 6449400 21 10 735676 80.604134 80.308669 80.722325 79.717731 4677000 22 """ 23 list1 = [] 24 for i in range(0, len(quote)): 25 x = date.fromordinal(int(quote[i][0])) 26 y = date.strftime(x, ‘%y-%m-%d‘) 27 list1.append(y) 28 df = pd.DataFrame(quote, index=list1, columns=fields) 29 df = df.drop([‘date‘], axis=1) 30 print df 31 """ 32 open close high low volume 33 15-03-06 79.412407 79.097240 79.924559 78.851015 6530200 34 15-03-09 78.939660 79.294224 79.609392 78.742676 5846100 35 15-03-10 78.526003 77.915364 78.565393 77.678990 7525000 36 15-03-11 78.200986 78.250226 78.545699 77.915364 4546200 37 15-03-12 78.890411 80.328364 80.761722 78.821469 9386600 38 15-03-13 80.269272 79.382861 80.417009 78.742676 6919400 39 15-03-16 79.658632 80.269272 80.417009 79.530598 5295100 40 15-03-17 79.973799 79.835914 79.983650 79.333620 4258200 41 15-04-01 77.128302 77.997907 78.383301 76.930659 6163500 42 15-04-02 77.997907 78.758811 78.827984 77.642158 5695200 43 ........ 44 16-03-04 58.439999 58.290001 58.650002 57.810001 5407400 45 46 [252 rows x 5 columns] 47 """
时间： 2024-10-20 08:49:49