Anarraywithsub-array being deletedasper the mentionedobjectalong a given axis. 代码1:从一维数组中删除 Python实现 # Python Program illustrating # numpy.delete() importnumpyasgeek #Working on 1D arr=geek.arange(5) print("arr : ",arr) print("Shape : ",arr.shape) # deletion from 1D array ...
Numpy.delete(arr, obj, axis) 复制 函数说明 序号参数及说明 1 arr 输入数组 2 obj 可以是切片、整数或整数数组,表示要从输入数组中删除的子数组 3 axis 删除给定子数组的轴。如果没有给出, arr 被展平 例子 import numpy as np a = np.arange(12).reshape(3,4) print 'First array:'...
Numpy.delete(arr, obj, axis) Where, 例子(Example) import numpy as np a = np.arange(12).reshape(3,4) print 'First array:' print a print '\n' print 'Array flattened before delete operation as axis not used:' print np.delete(a,5) print '\n' print 'Column 2 deleted:' print np....
# Python Program illustrating# numpy.delete()importnumpyasgeek#Working on 1Darr = geek.arange(12).reshape(3,4) print("arr : \n", arr) print("Shape : ", arr.shape)# deletion from 2D arraya = geek.delete(arr,1,0)''' [[ 0 1 2 3] [ 4 5 6 7] -> deleted [ 8 9 10 11]...
Numpy.delete(arr, obj, axis) 1. import numpy as np a = np.arange(12).reshape(3,4) print 'First array:' print a print '\n' print 'Array flattened before delete operation as axis not used:' print np.delete(a,5) print '\n' ...
] b = [3,5,6] a = np.array(a) b = np.array(b) a_b_column = np.column...
[9,10,11,12]])>>>np.delete(arr,1,0) array([[1,2,3,4], [9,10,11,12]]) >>>np.delete(arr, np.s_[::2],1) array([[2,4], [6,8], [10,12]])>>>np.delete(arr, [1,3,5],None) array([1,3,5,7,8,9,10,11,12]) 文档:numpy.delete.html...
在下文中一共展示了numpy.delete方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。 示例1: draw_heatmap ▲点赞 6▼ # 需要导入模块: import numpy [as 别名]# 或者: from numpy importdelete[as 别名]defdraw_heat...
Python # Python Program illustrating# numpy.delete()importnumpyasgeek#Working on 1Darr=geek.arange(12).reshape(3,4)print("arr : \n",arr)print("Shape : ",arr.shape)# deletion from 2D arraya=geek.delete(arr,1,0)''' [[ 0 1 2 3] [ 4 5 6 7] -> deleted [ 8 9 10 11]] '...
deffun_ndarray():a=1-6-b=,5,6]a=np.array(a)b=np.array(b)a_b_column=npcolumn_stackab#左右根据列拼接 a_b_row=np.row_stack((a,b))#上下按照行拼接print('a_b_column')print(a_b_column)print('a_b_row')print(a_b_row) ...