NumPy
argmax()
返回矩阵/数组的最大值的索引
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| import numpy as np
a = np.arange(12).reshape(3,4)
a
np.argmax(a)
np.argmax(a, axis=0)
np.argmax(a, axis=1)
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argmin()
返回矩阵/数组的最小值的索引
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| import numpy as np
a = np.arange(12).reshape(3,4)
a
np.argmin(a, axis=0)
np.argmin(a, axis=1)
a[2, 2] = 2
a
np.argmin(a, axis=0)
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mean()
求均值
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| import numpy as np
lst = [2,3,4,5,6]
np.mean(lst)
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求中位数
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| import numpy as np
lst = [2,1,4,5,2,5,7,8]
np.median(lst)
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newaxis
用于扩展数组的维度
https://stackoverflow.com/questions/29241056/how-does-numpy-newaxis-work-and-when-to-use-it
中的图片简单易懂
ptp()
最大值与最小值的差
reshape()
改变矩阵/数组的维数
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| import numpy as np
a = np.arange(12).reshape(3, 4)
a
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reshape(m,-1)
将数组形状变为m行（列数相应确定）；元素个数必须是m的整数倍
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| a = np.arange(12).reshape(1, -1)
a
a = np.arange(12).reshape(3, -1)
a
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reshape(-1,n)
将数组形状变为n列（行数相应确定）；元素个数必须是n的整数倍
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| a = np.arange(12).reshape(-1, 4)
a
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tile()
tile(A, B)：重复A B次
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| import numpy as np
np.tile([1, 2],5)
np.tile([3, 4],(2,1))
np.tile([1, 2], (3, 4))
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参考资料
