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)

其它查看：https://moonbooks.org/Articles/How-to-remove-array-rows-that-contain-only-0-in-python/

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

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