将列名从int转换为pandas中的字符串

问题描述

我有一个带有混合列名的pandas数据框:

I have a pandas dataframe with mixed column names:

1,2,3,4,5，'Class'

1,2,3,4,5, 'Class'

当我将此数据帧保存到h5file时，它表示性能将由于混合类型而受到影响.如何在pandas中将整数转换为字符串?

When I save this dataframe to h5file, it says that the performance will be affected due to mixed types. How do I convert the integer to string in pandas?

推荐答案

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]: df
Out[27]:
          1         2         3         4         5     Class
0  0.773423  0.865091  0.614956  0.219458  0.837748  0.862177
1  0.544805  0.535341  0.323215  0.929041  0.042705  0.759294
2  0.215638  0.251063  0.648350  0.353999  0.986773  0.483313

In [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:\Anaconda3\lib\site-packages\pandas\io\pytables.py:260: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed-integer,key->axis0] [items->None]

  f(store)
C:\Anaconda3\lib\site-packages\pandas\io\pytables.py:260: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map 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]:

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