如何按列访问 NumPy 数组
通过特定的列索引访问基于 NumPy 的数组可以通过 索引 来实现。让我们详细讨论一下。
NumPy 遵循标准的基于 0 的索引。
NumPy 中的行和列类似于 Python 列表
示例:
Given array : 1 13 6
9 4 7
19 16 2
Input: print(NumPy_array_name[ :,2])
# printing *2nd* column
Output: [6 7 2]
Input: x = NumPy_array_name[ :,1]
print(x)
# storing 1st column into variable x
Output: [13 4 16]
方法#1:使用切片选择
语法:
为列: numpy_Array_name[:， 列
*为排:numpy _ Array _ name【T3】排，T5:】*
Python 3
# Python code to select row and column
# in NumPy
import numpy as np
array = [[1, 13, 6], [9, 4, 7], [19, 16, 2]]
# defining array
arr = np.array(array)
print('printing array as it is')
print(arr)
print('printing 0th row')
print(arr[0, :])
print('printing 2nd column')
print(arr[:, 2])
# multiple columns or rows can be selected as well
print('selecting 0th and 1st row simultaneously')
print(arr[:,[0,1]])
*输出:*
printing array as it is
[[ 1 13 6]
[ 9 4 7]
[19 16 2]]
printing 0th row
[ 1 13 6]
printing 2nd column
[6 7 2]
selecting 0th and 1st row simultaneously
[[ 1 13]
[ 9 4]
[19 16]]
*方法 2:使用省略号*
*语法:*
*列*:numpy _ Array _ name【……，列】
*第*行:numpy_Array_name【第 … 行】
其中“ … ”表示给定行或列中没有元素
*注意:这不是一个非常实用的*方法，但是你必须尽可能了解。
Python 3
# program to select row and column
# in numpy using ellipsis
import numpy as np
# defining array
array = [[1, 13, 6], [9, 4, 7], [19, 16, 2]]
# converting to numpy array
arr = np.array(array)
print('printing array as it is')
print(arr)
print('selecting 0th column')
print(arr[..., 0])
print('selecting 1st row')
print(arr[1, ...])
*输出:*
printing array as it is
[[ 1 13 6]
[ 9 4 7]
[19 16 2]]
selecting 0th column
[ 1 9 19]
selecting 1st row
[9 4 7]
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