一、隨機生成八隻股票兩週的交易日漲幅數據
>>> import numpy as np
>>> stock_change=np.random.normal(loc=0,scale=1,size=(8,10))#這裏用正態分佈隨機數
>>> stock_change
array([[-1.1343391 , 1.65608029, -0.68222626, -0.4288315 , 0.19948599,
-0.61926558, 0.21516773, 0.7767751 , -0.26167027, -0.43091756],
[-0.29634732, 0.13890544, 0.45824194, 0.3184083 , -1.87233628,
-0.02544499, -1.7498489 , -0.49005395, -0.21800702, -0.67063429],
[-1.9517504 , 0.15462343, -0.08247815, -0.1475269 , 0.0056942 ,
-1.12796392, 0.59864058, 2.00443335, -0.44038852, 0.72490669],
[ 0.26197967, 1.22252265, -0.29807856, 0.16792887, -1.40108093,
0.86089209, -0.95521308, 1.63804955, 0.57357237, 1.05756125],
[ 0.79324084, 3.37549996, -0.43210609, -0.16073839, 0.64017597,
-0.39662389, 1.8220427 , -0.15538366, 0.2457401 , 0.16393938],
[ 0.20220074, -1.58643882, 1.52836969, 0.35649477, -1.18283582,
-0.2627466 , -1.62134852, -0.62051081, -0.03113397, -0.42981914],
[ 2.24448065, 0.24069295, -2.69817108, -1.60745796, 0.81643552,
-0.91676992, 1.33626747, 0.20102252, 1.02987241, 0.57122122],
[-0.03591242, -1.51726478, -0.01696559, -1.96890186, 0.21840141,
-1.35285592, 0.52658141, -0.64944062, -1.84926774, 1.03067173]])
二、獲取第一個股票的前三個交易日的漲跌幅數據
>>> stock_change[0,0:3]
array([-1.1343391 , 1.65608029, -0.68222626])#二維數組索引
tips：三維的數組怎麼索引
>>> a1=np.array([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]])
>>> a1
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])
>>> #(2,2,3)
>>> a1.shape
(2, 2, 3)
>>> #索引9
>>> a1[1,0,2]
9
>>> a1[1,0,2]=100#把這個位置的數改成100
>>> a1
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 100],
[ 10, 11, 12]]])
>>>
三、形狀修改（把股票的股票行，日期列反過來變成日期行，股票列）
1.用ndarray.reshape轉換，會發現只是把數據重新分割變成10行8列，並沒有達到我們想要的效果，且返回新的數組，原始數據沒有改
>>> stock_change
array([[-1.1343391 , 1.65608029, -0.68222626, -0.4288315 , 0.19948599,
-0.61926558, 0.21516773, 0.7767751 , -0.26167027, -0.43091756],
[-0.29634732, 0.13890544, 0.45824194, 0.3184083 , -1.87233628,
-0.02544499, -1.7498489 , -0.49005395, -0.21800702, -0.67063429],
[-1.9517504 , 0.15462343, -0.08247815, -0.1475269 , 0.0056942 ,
-1.12796392, 0.59864058, 2.00443335, -0.44038852, 0.72490669],
[ 0.26197967, 1.22252265, -0.29807856, 0.16792887, -1.40108093,
0.86089209, -0.95521308, 1.63804955, 0.57357237, 1.05756125],
[ 0.79324084, 3.37549996, -0.43210609, -0.16073839, 0.64017597,
-0.39662389, 1.8220427 , -0.15538366, 0.2457401 , 0.16393938],
[ 0.20220074, -1.58643882, 1.52836969, 0.35649477, -1.18283582,
-0.2627466 , -1.62134852, -0.62051081, -0.03113397, -0.42981914],
[ 2.24448065, 0.24069295, -2.69817108, -1.60745796, 0.81643552,
-0.91676992, 1.33626747, 0.20102252, 1.02987241, 0.57122122],
[-0.03591242, -1.51726478, -0.01696559, -1.96890186, 0.21840141,
-1.35285592, 0.52658141, -0.64944062, -1.84926774, 1.03067173]])
>>> stock_change.shape
(8, 10)
>>> stock_change.reshape((10,8))
array([[-1.1343391 , 1.65608029, -0.68222626, -0.4288315 , 0.19948599,
-0.61926558, 0.21516773, 0.7767751 ],
[-0.26167027, -0.43091756, -0.29634732, 0.13890544, 0.45824194,
0.3184083 , -1.87233628, -0.02544499],
[-1.7498489 , -0.49005395, -0.21800702, -0.67063429, -1.9517504 ,
0.15462343, -0.08247815, -0.1475269 ],
[ 0.0056942 , -1.12796392, 0.59864058, 2.00443335, -0.44038852,
0.72490669, 0.26197967, 1.22252265],
[-0.29807856, 0.16792887, -1.40108093, 0.86089209, -0.95521308,
1.63804955, 0.57357237, 1.05756125],
[ 0.79324084, 3.37549996, -0.43210609, -0.16073839, 0.64017597,
-0.39662389, 1.8220427 , -0.15538366],
[ 0.2457401 , 0.16393938, 0.20220074, -1.58643882, 1.52836969,
0.35649477, -1.18283582, -0.2627466 ],
[-1.62134852, -0.62051081, -0.03113397, -0.42981914, 2.24448065,
0.24069295, -2.69817108, -1.60745796],
[ 0.81643552, -0.91676992, 1.33626747, 0.20102252, 1.02987241,
0.57122122, -0.03591242, -1.51726478],
[-0.01696559, -1.96890186, 0.21840141, -1.35285592, 0.52658141,
-0.64944062, -1.84926774, 1.03067173]])
>>> stock_change.shape
(8, 10)#原始數組沒有改
2.用ndarray.resize同ndarray.reshape，但沒有返回新的數組且改變原始數組
3.用ndarray.T(轉置)才能達到我們想要的效果。
>>> stock_change.T
array([[-1.1343391 , -0.29634732, -1.9517504 , 0.26197967, 0.79324084,
0.20220074, 2.24448065, -0.03591242],
[ 1.65608029, 0.13890544, 0.15462343, 1.22252265, 3.37549996,
-1.58643882, 0.24069295, -1.51726478],
[-0.68222626, 0.45824194, -0.08247815, -0.29807856, -0.43210609,
1.52836969, -2.69817108, -0.01696559],
[-0.4288315 , 0.3184083 , -0.1475269 , 0.16792887, -0.16073839,
0.35649477, -1.60745796, -1.96890186],
[ 0.19948599, -1.87233628, 0.0056942 , -1.40108093, 0.64017597,
-1.18283582, 0.81643552, 0.21840141],
[-0.61926558, -0.02544499, -1.12796392, 0.86089209, -0.39662389,
-0.2627466 , -0.91676992, -1.35285592],
[ 0.21516773, -1.7498489 , 0.59864058, -0.95521308, 1.8220427 ,
-1.62134852, 1.33626747, 0.52658141],
[ 0.7767751 , -0.49005395, 2.00443335, 1.63804955, -0.15538366,
-0.62051081, 0.20102252, -0.64944062],
[-0.26167027, -0.21800702, -0.44038852, 0.57357237, 0.2457401 ,
-0.03113397, 1.02987241, -1.84926774],
[-0.43091756, -0.67063429, 0.72490669, 1.05756125, 0.16393938,
-0.42981914, 0.57122122, 1.03067173]])
>>> stock_change.T.shape
(10, 8)
四、類型修改
用ndarray.astype（type）
>>> stock_change.astype("int32")#修改成整數
array([[-1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, -1, 0, -1, 0, 0, 0],
[-1, 0, 0, 0, 0, -1, 0, 2, 0, 0],
[ 0, 1, 0, 0, -1, 0, 0, 1, 0, 1],
[ 0, 3, 0, 0, 0, 0, 1, 0, 0, 0],
[ 0, -1, 1, 0, -1, 0, -1, 0, 0, 0],
[ 2, 0, -2, -1, 0, 0, 1, 0, 1, 0],
[ 0, -1, 0, -1, 0, -1, 0, 0, -1, 1]])
>>> stock_change.tostring()#本地序列化
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>>>
五、去重
用numpy.unique
>>> temp=np.array([[1,2,3,4],[3,4,5,6]])
>>> temp
array([[1, 2, 3, 4],
[3, 4, 5, 6]])
'''>>> set(temp)#直接用set會報錯，set只能用在一維數組
Traceback (most recent call last):
~~File "<pyshell#26>", line 1, in <module>
set(temp)
TypeError: unhashable type: 'numpy.ndarray'~~
'''
>>> temp
array([[1, 2, 3, 4],
[3, 4, 5, 6]])
>>> set(temp.flatten())
{1, 2, 3, 4, 5, 6}####當然你可以把它降維之後再用set
>>> np.unique(temp)#一般用這個
array([1, 2, 3, 4, 5, 6])