您可以简单地使用
df.columns = df.columns.astype(str)：
In [26]: df = pd.Dataframe(np.random.random((3,6)), columns=[1,2,3,4,5,'Class'])In [27]: dfOut[27]:1 2 3 4 5 Class0 0.773423 0.865091 0.614956 0.219458 0.837748 0.8621771 0.544805 0.535341 0.323215 0.929041 0.042705 0.7592942 0.215638 0.251063 0.648350 0.353999 0.986773 0.483313In [28]: df.columns.map(type)Out[28]: array([<class 'int'>, <class 'int'>, <class 'int'>, <class 'int'>, <class 'int'>, <class 'str'>], dtype=object)In [29]: df.to_hdf("out.h5", "d1")C:Anaconda3libsite-packagespandasiopytables.py:260: PerformanceWarning: your performance may suffer as PyTables will pickle object types that it cannotmap directly to c-types [inferred_type->mixed-integer,key->axis0] [items->None] f(store)C:Anaconda3libsite-packagespandasiopytables.py:260: PerformanceWarning: your performance may suffer as PyTables will pickle object types that it cannotmap directly to c-types [inferred_type->mixed-integer,key->block0_items] [items->None] f(store)In [30]: df.columns = df.columns.astype(str)In [31]: df.columns.map(type)Out[31]: array([<class 'str'>, <class 'str'>, <class 'str'>, <class 'str'>, <class 'str'>, <class 'str'>], dtype=object)In [32]: df.to_hdf("out.h5", "d1")In [33]:
