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 npa = 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 nparray1 = 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 DataFramedf1=DataFrame(np.arange(16).reshape((4,4)),index=['a','b','c','d'],columns=['one','two','three','four']) # 创建一个dataframedf1.loc['e'] = 0 # 优雅地增加一行全0df1.ix[(df1==0).all(axis=1), :] # 找到它df1.ix[~(df1==0).all(axis=1), :] # 删了它

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