Question

考虑以下DataFrame：

arrays = [['foo', 'bar', 'bar', 'bar'],
      ['A', 'B', 'C', 'D']]
tuples = list(zip(*arrays))          
columnValues = pd.MultiIndex.from_tuples(tuples)
df = pd.DataFrame(np.random.rand(4,4), columns = columnValues)
print(df)
        foo       bar                    
          A         B         C         D
0  0.859664  0.671857  0.685368  0.939156
1  0.155301  0.495899  0.733943  0.585682
2  0.124663  0.467614  0.622972  0.567858
3  0.789442  0.048050  0.630039  0.722298

我想要删除第一列，如下所示：

df.drop(df.columns[[0]], axis = 1, inplace = True)
print(df)
        bar                    
          B         C         D
0  0.671857  0.685368  0.939156
1  0.495899  0.733943  0.585682
2  0.467614  0.622972  0.567858
3  0.048050  0.630039  0.722298

这会产生预期结果，但保留列标签foo和A：

print(df.columns.levels)
[['bar', 'foo'], ['A', 'B', 'C', 'D']]

有没有办法从MultiIndex DataFrame中完全删除列，包括其标签？

编辑：正如约翰所建议的那样，我看了https://github.com/pydata/pandas/issues/12822。我得到的是它不是一个bug，但我相信建议的解决方案（https://github.com/pydata/pandas/issues/2770#issuecomment-76500001）对我不起作用。我在这里错过了什么吗？

df2 = df.drop(df.columns[[0]], axis = 1)
print(df2)
        bar                    
          B         C         D
0  0.969674  0.068575  0.688838
1  0.650791  0.122194  0.289639
2  0.373423  0.470032  0.749777
3  0.707488  0.734461  0.252820

print(df2.columns[[0]])

MultiIndex(levels=[['bar', 'foo'], ['A', 'B', 'C', 'D']],
       labels=[[0], [1]])

df2.set_index(pd.MultiIndex.from_tuples(df2.columns.values))

ValueError: Length mismatch: Expected axis has 4 elements, new values have 3 elements

Answer 1

New Answer

As of pandas 0.20, pd.MultiIndex has a method pd.MultiIndex.remove_unused_levels

df.columns = df.columns.remove_unused_levels()

Old Answer

Our savior is pd.MultiIndex.to_series()

it returns a series of tuples restricted to what is in the DataFrame

df.columns = pd.MultiIndex.from_tuples(df.columns.to_series())

从多索引数据框中删除列

1 个答案:

New Answer

Old Answer