numpy.atleast_1d
numpy.atleast_1d(*arys) [source]
将输入转换为至少一维的数组。
标量输入被转换为一维数组,而高维输入被保留。
参数 : | arys1, arys2, … :array_like 一个或多个输入数组。 |
返回值 : | ret :ndarray 一个数组或数组列表,每个数组均带有 |
例子
1)标量输入
import numpy as np a = 5 result = np.atleast_1d(a) print(result) # 输出: [5]
2)数组输入
import numpy as np b = np.array([1, 2, 3]) result = np.atleast_1d(b) print(result) # 输出: [1 2 3]
3)多个输入
import numpy as np c = 4 d = [5, 6] e = np.array([[7, 8], [9, 10]]) result = np.atleast_1d(c, d, e) for arr in result: print(arr) # 输出: # [4] # [5 6] # [[ 7 8] # [ 9 10]]
4)处理0维数组
import numpy as np f = np.array(3) result = np.atleast_1d(f) print(result) # 输出: [3]
5)使用示例
import numpy as np # 将标量转换为至少一维的数组 result1 = np.atleast_1d(1.0) print(result1) # 输出: array([1.]) # 输入是多维数组,输出保持不变 x = np.arange(9.0).reshape(3, 3) result2 = np.atleast_1d(x) print(result2) # 输出: # array([[0., 1., 2.], # [3., 4., 5.], # [6., 7., 8.]]) # 检查输入与输出是否为同一对象 same_object = np.atleast_1d(x) is x print(same_object) # 输出: True # 多个输入,包括标量和列表 result4 = np.atleast_1d(1, [3, 4]) for arr in result4: print(arr) # 输出: # array([1]) # array([3, 4])