带字符串列的HDFStore会产生问题

我有一个熊猫数据帧myDF的几个字符串列(其dtype为object)和许多数字列.我尝试了以下方法:

d=pandas.HDFStore("C:\\PF\\Temp.h5")
d['test']=myDF

我得到了这个结果:

C:\PF\WinPython-64bit-3.3.3.3\python-3.3.3.amd64\lib\site-packages\pandas\io\pytables.py:2446: PerformanceWarning: 

your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block2_values] 
[items->[0, 1, 3, 4, 5, 6, 9, 10, 292, 411, 412, 477, 478, 479, 495, 572, 581, 590, 599, 608, 617, 626, 635]]

  warnings.warn(ws, PerformanceWarning)

看起来每个列都是一个字符串的问题.例如,如果我尝试

myDF[0].dtype

我明白了

Out[38]: dtype('O')

如何解决问题,即更改dtypefor string列,以便HDFStore可以将其视为字符串列？

*编辑*

更多信息请求

>>> pandas.__version__
Out[49]: '0.13.1'

>>> tables.__version__
Out[53]: '3.1.0'

构建pandas数据框如下:

pandas.read_csv(fName,sep="|",header=None,low_memory=False)

当我尝试

myDF.info()

我明白了

Int64Index: 153895 entries, 0 to 153894
Data columns (total 644 columns):
0      object
1      object
2      int64
3      object
4      object
5      object
6      object
7      int64
8      float64
9      object
10     object
11     float64
12     float64
13     float64
14     float64
...
...
642    float64
643    float64
dtypes: float64(619), int64(2), object(23)

所有字符串列均已读为 object

仅当列中包含混合类型时才会发生此警告.不只是字符串,而是字符串AND号.

In [2]: DataFrame({ 'A' : [1.0,'foo'] }).to_hdf('test.h5','df',mode='w')
pandas/io/pytables.py:2439: PerformanceWarning: 
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block0_values] [items->['A']]

  warnings.warn(ws, PerformanceWarning)

In [3]: df = DataFrame({ 'A' : [1.0,'foo'] })

In [4]: df
Out[4]: 
     A
0    1
1  foo

[2 rows x 1 columns]

In [5]: df.dtypes
Out[5]: 
A    object
dtype: object

In [6]: df['A']
Out[6]: 
0      1
1    foo
Name: A, dtype: object

In [7]: df['A'].values
Out[7]: array([1.0, 'foo'], dtype=object)

因此,您需要确保不要在列中混合使用

如果您有需要转换的列,则可以执行以下操作:

In [9]: columns = ['A']

In [10]: df.loc[:,columns] = df[columns].applymap(str)

In [11]: df
Out[11]: 
     A
0  1.0
1  foo

[2 rows x 1 columns]

In [12]: df['A'].values
Out[12]: array(['1.0', 'foo'], dtype=object)