HDFStore with string columns gives issues

Question

I have a pandas DataFrame myDF with a few string columns (whose dtype is object) and many numeric columns. I tried the following:

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

I got this result:

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, ...]]

warnings.warn(ws, PerformanceWarning)

It seems like the issue is occurring for every column that is a string. For example if I try

myDF[0].dtype

I get

Out[38]: dtype('O')

How can I fix the issue, i.e. change the dtype for string columns so that HDFStore can treat it like a string column?

---

EDIT

More info as requested

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

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

Constructing the pandas data frame as follows:

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

When I try

myDF.info()

I get

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
...
...
642    float64
643    float64
dtypes: float64(619), int64(2), object(23)

All string columns have been read as object.

Answer 1

This warning ONLY happens if you have mixed-types IN a column. Not just strings, but string AND numbers.

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)

So, you need to ensure that you don't mix WITHIN a column

If you have columns that need conversion you can do this:

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)