BUG: read_csv not correctly infering datetime64[ns] with embedded nan/NaT

[jreback]

This is known, but putting up for reference
easy (after reading) workaround, but could be done inilne

In [28]: df = pd.DataFrame(dict({
  'A' : np.asarray(range(10),dtype='float64'), 
  'B' : pd.Timestamp('20010101') }))

In [29]: df.ix[3:6,:] = np.nan

In [30]: df
Out[30]: 
    A                   B
0   0 2001-01-01 00:00:00
1   1 2001-01-01 00:00:00
2   2 2001-01-01 00:00:00
3 NaN                 NaT
4 NaN                 NaT
5 NaN                 NaT
6 NaN                 NaT
7   7 2001-01-01 00:00:00
8   8 2001-01-01 00:00:00
9   9 2001-01-01 00:00:00

In [31]: df.dtypes
Out[31]: 
A           float64
B    datetime64[ns]
dtype: object

In [32]: df.to_csv('test.h5')

In [33]: df2 = pd.read_csv('test.h5',index_col=0)

In [34]: df2.dtypes
Out[34]: 
A    float64
B     object
dtype: object

To fix, force a conversion to datetimes

In [35]: df2['B'] = pd.to_datetime(df2['B'])

In [36]: df2
Out[36]: 
    A                   B
0   0 2001-01-01 00:00:00
1   1 2001-01-01 00:00:00
2   2 2001-01-01 00:00:00
3 NaN                 NaT
4 NaN                 NaT
5 NaN                 NaT
6 NaN                 NaT
7   7 2001-01-01 00:00:00
8   8 2001-01-01 00:00:00
9   9 2001-01-01 00:00:00

In [37]: df2.dtypes
Out[37]: 
A           float64
B    datetime64[ns]
dtype: object

[wesm]

Marked as an "enhancement" to get something better for this at some point