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The content of this article is about the attributes and creation matrix of Numpy in python. It has certain reference value. Friends in need can refer to it. I hope it will be helpful to you.
ndarray.ndim: Dimension
ndarray.shape: Shape
ndarray.size: Number of elements
ndarray.dtype: Element data type
ndarray.itemsize: byte size
Create array:
a = np.array([2,23,4]) # list 1d print(a) # [2 23 4]
Specify data type:
a = np.array([2,23,4],dtype=np.int) print(a.dtype) # int 64
dtype The types that can be specified are int32, float, float32, If not followed by a number, the default is 64
a = np.zeros((3,4)) # 数据全为0,3行4列 """
a = np.ones((3,4),dtype = np.int) # 数据为1,3行4列
a = np.empty((3,4)) # 数据为empty,3行4列
empty type: the initial content is random, depending on the state of the memory
a = np.arange(10,20,2) # 10-19 的数据,2步长
a = np.arange(12).reshape((3,4)) # 3行4列,0到11
reshape modifies the data shape, such as 3 rows and 4 columns
a = np.linspace(1,10,20) # 开始端1,结束端10,且分割成20个数据,生成线段
linspace The amount of data can be determined, but arrage cannot determine the amount of data. At the same time, linspace can also use reshape to define the structure.
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How does Python numpy extract the specified rows and columns of the matrix?
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