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在處理numpy陣列，有這個需求，故寫下此文：

使用np.argwhere和np.all來查詢索引。要使用np.delete刪除它們。

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)

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

pandas 刪除全零列

from pandas import DataFrame
df1=DataFrame(np.arange(16).reshape((4,4)),index=['a','b','c','d'],columns=['one','two','three','four'])   # 建立一個dataframe

df1.loc['e'] = 0          # 優雅地增加一行全0

df1.ix[(df10).all(axis=1), :]  # 找到它

df1.ix[~(df10).all(axis=1), :]  # 刪了它

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