Detailed discussion on array reshaping, merging and splitting methods in Numpy

The following article will share with you a detailed discussion of array reshaping, merging and splitting methods in Numpy. It has a good reference value and I hope it will be helpful to everyone. Let’s take a look together

1. Array reshaping

##1.1 Convert a one-dimensional array into a two-dimensional array

This can be achieved through the reshape() function. Assume that data is a one-dimensional array array of type numpy.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), now convert it into a two-dimensional array with 2 rows and 5 columns. The code is as follows:

data.reshape((2,5))

One dimension of the shape as a parameter Can be -1, which means that the size of the dimension is inferred from the data itself, so the above code is equivalent to:

data.reshape((2,-1))

1.2 Convert a two-dimensional array to a one-dimensional array

The operation of converting a multi-dimensional array into a one-dimensional array is usually called flattening or raveling, so there are two functions that can for selection. The execution code is as follows:

data.ravel() # 不会产生源数据的副本
data.flatten() # 总是返回数据的副本

I don’t understand the difference between these two points very thoroughly. If anyone knows what to say, comments and exchanges are welcome.

2. Merging and splitting arrays

##2.1 Merging arraysnumpy provides many array merging methods. Here we only introduce the most commonly used one, the concatenate method. The code is as follows:

arr1 = np.array([[1,2,3], [4,5,6]])
arr2 = np.array([[7,8,9], [10,11,12]])
data = np.concatenate([arr1, arr2], axis=0) # axis参数指明合并的轴向，0表示按行，1表示按列

2.2 Array splitting

Only the split function is introduced here

np.split(data, [1], axis=0 )#data is the split array, [1] is the split row number or column number, axis indicates splitting by column or row (the default is 0, that is, splitting by row)

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