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from scipy.optimize import fsolve,root fx=lambda x: x**980-5.01*x**979+7.39*x**978-3.388*x*977-x**3+5.01*x**2-7.398*x+3.388 #函数被调用4000次 x1=fsolve(fx,1 阅读全文
posted @ 2024-10-15 17:47
方~~
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import numpy as np a = np.random.rand(6,8) np.savetxt("data2_43_1.txt",a) np.savetxt("data2_43_2.csv",a,delimiter=',') b=np.loadtxt("data2_43_1.txt") 阅读全文
posted @ 2024-10-15 17:46
方~~
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with open('data2_2.txt') as fp: L1=[];L2=[] for line in fp: L1.append(len(line)) L2.append(len(line.strip())) data = [str(num) + '\t'for num in L2] pr 阅读全文
posted @ 2024-10-15 17:45
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import pandas as pd import numpy as np a = pd.DataFrame(np.random.randint(1,6,(5,3)), index=['a','b','c','d','e'], columns=['one','two','three']) a.lo 阅读全文
posted @ 2024-10-15 17:44
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import pandas as pd import numpy as np d = pd.DataFrame(np.random.randint(1,6,(10,4)),columns = list("ABCD")) d1 = d[:4] d2 = d[4:] dd= pd.concat([d1, 阅读全文
posted @ 2024-10-15 17:43
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import pandas as pd a = pd.read_csv("data2_38_2.csv",usecols=range(1,5)) b = pd.read_excel("data2_38_3.xlsx","Sheet2",usecols=range(1,5)) print("学号：30 阅读全文
posted @ 2024-10-15 17:42
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import pandas as pd import numpy as np datas = pd.date_range(start='20191101',end='20191124',freq='D') a1 = pd.DataFrame(np.random.randn(24,4),index = 阅读全文
posted @ 2024-10-15 17:41
方~~
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import pandas as pd import numpy as np datas = pd.date_range(start='20191101',end='20191124',freq = 'D') a1 = pd.DataFrame(np.random.randn(24,4),index 阅读全文
posted @ 2024-10-15 17:40
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import numpy as np a = np.eye(4) b = np.rot90(a) c,d = np.linalg.eig(b) print("特征值：",c) print("特征向量：\n",d) print("学号：3008") 结果如下 阅读全文
posted @ 2024-10-15 17:38
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import numpy as np a = np.array([[3,1],[1,2],[1,1]]) b = np.array([9,8,6]) x = np.linalg.pinv(a) @ b print(np.round(x,4)) print("学号:3008") 结果如下 阅读全文
posted @ 2024-10-15 17:37
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