df.to_hdf5() in table append mode very slow with data_columns=True, append=True, index=False 
#### Code Sample, a copy-pastable example if possible

```python
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randint(123,456, size=(300,225)), columns=['COL'+str(i) for i in range(225)])
df.loc[:,:'COL10'] = df.loc[:,:'COL10'].astype(str)
df.loc[:,'COL11':'COL20'] = df.loc[:,'COL11':'COL20'].astype(float)

df.to_hdf(r'test.h5', 'df', mode='w', data_columns=True, append=True, index=False)
# <-- Quite Fast on first run creation of the table.

df.to_hdf(r'test.h5', 'df', mode='a', data_columns=True, append=True, index=False)
# <-- 3.6 Minutes for 2nd run trying to append to the table. Note index=False

```
#### Problem description

``df.to_hdf()``  In Append Table mode used to work very quickly when you set each column as a data column and want _no_ index created. ``data_columns=True, append=True, index=False``. On the new version of Pandas and PyTables it seems to be taking a very long to append additional rows.

#### Expected Output

Appending HDF5 Table rows  with ''data_columns=True, append=True, index=False'' should create all columns as data columns, but not create an index and should be quite fast.


#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.24.1
pytest: 4.0.0
pip: 19.0.3
setuptools: 40.6.2
Cython: 0.29
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.12.0
xarray: 0.11.0
IPython: 7.1.1
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: 1.6.2
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.10
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.1.6
pandas_gbq: None
pandas_datareader: None
gcsfs: None

</details>
