在处理numpy数组，有这个需求，故写下此文：

使用np.argwhere和np.all来查找索引。要使用np.delete删除它们。

示例1

import numpy as np

a = np.array([[1, 2, 0, 3, 0],

[4, 5, 0, 6, 0],

[7, 8, 0, 9, 0]])

idx = np.argwhere(np.all(a[..., :] == 0, axis=0))

a2 = np.delete(a, idx, axis=1)

print(a2)

"""

[[1 2 3]

[4 5 6]

[7 8 9]]

"""

示例2

import numpy as np

array1 = np.array([[1,0,1,0,0,0,0,0,0,1,1,0,0,0,1,1,0,1,0,0],

[0,1,1,0,0,1,1,1,1,0,0,0,1,0,1,0,0,1,1,1],

[0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0,1,1],

[0,1,1,0,0,1,1,1,1,0,1,1,1,0,0,1,0,0,1,1],

[0,0,1,0,0,1,1,1,0,1,0,1,1,0,1,1,0,0,1,0],

[1,0,1,0,0,0,1,0,0,1,1,1,1,0,1,1,0,0,1,0],

[1,0,1,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,1,1],

[0,1,0,0,1,0,0,0,1,0,1,1,1,0,1,0,0,1,1,0],

[0,1,0,0,1,0,0,1,1,0,1,1,1,0,0,1,0,1,0,0],

[1,0,0,0,0,1,0,1,0,0,0,1,1,0,0,1,0,1,0,0]])

mask = (array1 == 0).all(0)

column_indices = np.where(mask)[0]

array1 = array1[:,~mask]

print("raw array", array1.shape) # raw array (10, 20)

print("after array",array1.shape) # after array (10, 17)

print("=====x=====\n",array1)

pandas 删除全零列

from pandas import DataFrame

df1=DataFrame(np.arange(16).reshape((4,4)),index=['a','b','c','d'],columns=['one','two','three','four']) # 创建一个dataframe

df1.loc['e'] = 0 # 优雅地增加一行全0

df1.ix[(df1==0).all(axis=1), :] # 找到它

df1.ix[~(df1==0).all(axis=1), :] # 删了它

到此这篇关于Numpy(Pandas)删除全为零的列的方法的文章就介绍到这了,更多相关Numpy删除全为零的列内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！