使用pandas和PyTables。首先从excel导入包含整数和浮点列的表，以及包含字符串甚至元组的其他列。 excel导入中的选项数量有限，遗憾的是，与csv导入过程不同，数据类型必须在导入后从其推断类型转换，并且不能在过程中指定。
话虽如此，所有非数字显然都是作为unicode文本导入的，这与以后导出到HDF5不兼容。有没有一种简单的方法可以将所有unicode列（以及所有列标题）转换为兼容HDF5的字符串格式？
更多细节：
>>> metaFrame.head()
ProjectName Company ContactName \
LocationID
935 PCS Petaluma High School Site Testco Test Name
937 PCS Casa Grande High School Testco Test Name
3465 FUSD Fowler High School Testco Test Name
3466 FUSD Sutter Middle School Testco Test Name
3467 FUSD Fremont Elementary School Testco Test Name
Contactemail \
LocationID
935 test.address@email.com
937 test.address@email.com
3465 test.address@email.com
3466 test.address@email.com
3467 test.address@email.com
Link Systemsize(kW) \
LocationID
935 https://internal.testco.com/locations/935/syst... NaN
937 https://internal.testco.com/locations/937/syst... 675.39
3465 https://internal.testco.com/locations/3465/sys... 384.30
3466 https://internal.testco.com/locations/3466/sys... 198.90
3467 https://internal.testco.com/locations/3467/sys... 35.10
SystemCheckStartdate SystemCheckActive \
LocationID
935 2013-10-01 00:00:00 True
937 2013-10-01 00:00:00 True
3465 2013-10-01 00:00:00 True
3466 2013-10-01 00:00:00 True
3467 2013-10-01 00:00:00 True
YTDProductionPriortostartdate NumberofInverters/cktsmonitored \
LocationID
935 NaN NaN
937 NaN NaN
3465 NaN NaN
3466 NaN NaN
3467 NaN NaN
InverterMfg InverterModel \
LocationID
935 PV Powered : PVP260KW NaN
937 PV Powered : PVP260KW NaN
3465 Advanced Energy Industries : Solaron 333kW (31... NaN
3466 PV Powered : PVP260KW NaN
3467 PV Powered : PVP35KW-480 NaN
InverterCECefficiency ModuleMfg Modulemodel \
LocationID
935 97.0 NaN NaN
937 97.0 NaN NaN
3465 97.5 NaN NaN
3466 97.0 NaN NaN
3467 96.0 NaN NaN
Moduleirradiancefactor Moduleirradiancefactorslope \
LocationID
935 NaN NaN
937 NaN NaN
3465 NaN NaN
3466 NaN NaN
3467 NaN NaN
StraightLineIntercept ModuleTemp-PwrDerate MeterDK
LocationID
935 NaN 0.005 3291 ...
937 NaN 0.005 11548 ...
3465 NaN 0.005 19248 ...
3466 NaN 0.005 15846 ...
3467 NaN 0.005 15847 ...
[5 rows x 27 columns]
>>> metaFrame.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 43 entries, 935 to 5844
Data columns (total 27 columns):
ProjectName 43 non-null values
Company 43 non-null values
ContactName 43 non-null values
Contactemail 43 non-null values
Link 43 non-null values
Systemsize(kW) 42 non-null values
SystemCheckStartdate 37 non-null values
SystemCheckActive 43 non-null values
YTDProductionPriortostartdate 0 non-null values
NumberofInverters/cktsmonitored 2 non-null values
InverterMfg 42 non-null values
InverterModel 8 non-null values
InverterCECefficiency 33 non-null values
ModuleMfg 0 non-null values
Modulemodel 0 non-null values
Moduleirradiancefactor 0 non-null values
Moduleirradiancefactorslope 0 non-null values
StraightLineIntercept 0 non-null values
ModuleTemp-PwrDerate 43 non-null values
MeterDK 43 non-null values
Genfieldname 43 non-null values
WSDK 34 non-null values
WSirradianceField 43 non-null values
WSCellTempField 43 non-null values
MiscDerate 1 non-null values
InverterDKs 37 non-null values
Invertergenfields 37 non-null values
dtypes: bool(1), datetime64[ns](1), float64(9), object(16)