许多NumPy函数都支持along axis操作,让我们以sum()函数为例: importnumpyasnp arr=np.array([[1,2,3],[4,5,6],[7,8,9]])print("Original array:\n",arr)# 沿着axis 0求和(列求和)sum_axis0=np.sum(arr,axis=0)print("Sum along axis 0:",sum_axis
import numpy as np # create a 2D array arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # function to calculate the sum of an array def sumArray(arr): return np.sum(arr) # apply the sumArray function along the rows (axis=1) result = np.apply_along_axis(sumArr...
首先来看代码如下: import numpy as npa = np.array([[0, 1],[1, 2],[2, 3],])print(a) # [3,2]print(np.sum(a, axis=0)) # [3 6]print(np.sum(a, axis=1)) # [1 3 5] 针对axis=0进行压缩,而0维有3个元素,现需要将3个元素压缩成1个元素,即[3,6]; 同理,针对axis=1进行压...
importnumpyasnp# 创建一个2x3的二维数组arr=np.array([[1,2,3],[4,5,6]])# 计算沿着axis=0的和(列和)sum_axis_0=np.sum(arr,axis=0)# 计算沿着axis=1的和(行和)sum_axis_1=np.sum(arr,axis=1)print("Original array:")print(arr)print("\nSum along axis 0:",sum_axis_0)print("Sum...
# Transpose the array transposed_arr = np.transpose(arr) [[1 4] [2 5] [3 6]] numpy.concatate:沿现有轴连接数组。 # Create two 1-dimensionalarrays arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) # Concatenate the arrays along axis 0 (default) ...
axis参数,表面意思是数轴,官网解释为“Axis or axes along which a sum is performed. 沿其执行求和的轴。” 我认为说了跟没说一样,怎么个沿其求和法? 对于二位数组,我们可以简单的记为axis=0是按列加和,axis=1是按行加和。 对于更多维度数组呢?axis是元组的情况呢?
当axis=0时,numpy沿着第0维的方向进行求和,也就是第一个元素值=a0000+a1000+a2000+a3000=11,第二个元素=a0001+a1001+a2001+a3001=5,同理可得最后的结果如下: >>>data.sum(axis=0)array([[[11,5,6],[7,9,4]],[[6,6,11],[7,10,9]],[[6,11,4],[7,12,8]]]) ...
Weeks indices after split [array([0,1, 2, 3, 4], dtype=int64), array([5, 6, 7, 8, 9], dtype=int64), array([10, 11, 12, 13, 14], dtype=int64), array([15, 16, 17, 18, 19], dtype=int64)] NumPy中,数组的维度也被称作轴。apply_along_axis 函数会调用另外一个由我们给出...
下面分别以二维和三维向量为例讲解axis参数的具体用法。 二维向量示例 首先来看代码如下: import numpy as np a = np.array([ [0, 1], [1, 2], [2, 3], ]) print(a) # [3,2] print(np.sum(a, axis=0)) # [3 6] print(np.sum(a, axis=1)) # [1 3 5] ...
a = np.array([1,5,5,2])print(np.sum(a, axis=0)) 上面代码就是把各个值加相加.默认axis为0.axis在二维以上数组中才能体现出来作用. import numpy as np a = np.array([[1, 5, 5, 2], [9, 6, 2, 8], [3, 7, 9, 1]])print(np.sum(a, axis=0)) ...