y = np.expand_dims(x, axis=(0, 1)) print("新数组:", y) # 输出:[[]] print("新数组形状:", y.shape) # 输出:(1, 1, 2) ```通过这些示例,我们可以看到 numpy.expand_dims 函数在处理数组形状时的灵活性,特别是在需要与其他数组进行特定维度操作时,它能够非常方便地扩展数组的维度。0 0 ...
expand_dims(a, axis)中,a为numpy数组,axis为需添加维度的轴,a.shape将在该轴显示为1,通过索引调用a中元素时,该轴对应的索引一直为0。废话少说,实操为证: 本人使用jupyter notebook软件编程 1.一维数组:即向量 如上图所示,axis=0对应的shape为6,axis=1对应的shape为空。如下图,在axis=0添加维度,即shape...
numpy.expand_dims(a,axis) Expand the shape of an array. Insert a new axis that will appear at theaxisposition in the expanded array shape. Note Previous to NumPy 1.13.0, neither axis < -a.ndim - 1 nor axis > a.ndim raised errors or put the new axis where documented. Those axis v...
numpy.expand_dims(arr, axis) 其中: arr:输入数组 axis:新轴插入的位置 importnumpyasnp x = np.array(([1,2], [3,4])) print(x) y = np.expand_dims(x, axis=0) print(y) print(x.shape, y.shape) y = np.expand_dims(x, axis=1) print(...
3. 示例示例 1：import numpy as npa = np.array([1, 2, 3])# 在第一维之前插入新的轴b = np.expand_dims(a, axis=)print(b)输出结果：[[1 2 3]]示例 2：import numpy as npa = np.array([1, 2, 3])# 在第二维之前插入新的轴b = np.expand_dims(a, axis=1)print(b)...
np.expand_dims(x,axis = 0):在x的第一维度上插入一个维度,axis=1,在x的第二个维度上插入一个维度 例如: x = np.array([[1,2,3],[4,5,6]]) print (x) print (x.shape) 结果: [[1 2 3] [4 5 6]] (2, 3) axis = 0: ...
1)数组创建 import numpy as np # array函数创建一个一维数组 arr1 = np.array([1, 2, 3, 4, 5]) print("一维数组:", arr1) # 创建一个二维数组 arr2 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) print("二维数组:\n", arr2) ...
[[0 1 2 3]] 调用broadcast_to 函数之后: [[0 1 2 3] [0 1 2 3] [0 1 2 3] [0 1 2 3]] numpy.expand_dims numpy.expand_dims 函数通过在指定位置插入新的轴来扩展数组形状,函数格式如下: numpy.expand_dims(arr, axis) 参数说明: ...
2.expand_dims(a, axis) 就是在axis的那一个轴上把数据加上去,这个数据在axis这个轴的0位置。 例如原本为一维的2个数据,axis=0,则shape变为(1,2),axis=1则shape变为(2,1) 再例如 原本为 (2,3),axis=0,则shape变为(1,2,3),axis=1则shape变为(2,1,3) ...
02 数组形状修改函数1. ndarray.reshape 函数在不改变数据的条件下修改形状,参数如下: ndarray.reshape(arr, newshape, order) import numpy as np a = np.arange(8) print(a) b = a.reshape(4, 2) print(b) [0 1 2 3 4 5 6 7] [[0 1] ...