Python Pandas - Dataframe可以有多个索引吗？

Question

I have a dataset in CSV which I read with:

df = pd.read_csv(requestfile, header=[0,1], parse_dates= [0])

The following Dataframe is in following format [0..8759]:

                   time output direct diffuse temperature
                 UTC     kW  kW/m2   kW/m2       deg C
0    2014-01-01 00:00:00  0.000  0.000   0.000       1.495
1    2014-01-01 01:00:00  0.000  0.000   0.000       1.543
2    2014-01-01 02:00:00  0.000  0.000   0.000       1.517

Now I want do things with it using https://github.com/renewables-ninja/gsee (gsee.pv.run_plant_model), however I receive the following error:

File "C:\Data\Solar\gsee-master\gsee\trigon.py", line 183, in aperture_irradiance
sunrise_set_times = sun_rise_set_times(direct.index, coords)

File "C:\Data\Solar\gsee-master\gsee\trigon.py", line 56, in sun_rise_set_times
dtindex = pd.DatetimeIndex(datetime_index.to_series().map(pd.Timestamp.date).unique())

File "C:\Users\XX\Anaconda3\lib\site-packages\pandas\core\series.py", line 2177, in map
new_values = map_f(values, arg)

File "pandas\src\inference.pyx", line 1207, in pandas.lib.map_infer (pandas\lib.c:66124)
TypeError: descriptor 'date' requires a 'datetime.datetime' object but received a 'int'

So I assumed the fault is in my default index, so I modified the CSV-reading to use the 'time' column as index:

df = pd.read_csv(requestfile, header=[0,1], index_col=0, parse_dates= [0])

time                output direct diffuse temperature
UTC                     kW  kW/m2   kW/m2       deg C
2014-01-01 00:00:00  0.000  0.000   0.000       1.495
2014-01-01 01:00:00  0.000  0.000   0.000       1.543

Now the error I get is following:

File "C:\Users\XX\Anaconda3\lib\site-packages\pandas\core\frame.py", line 402, in _init_dict
return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)

File "C:\Users\XX\Anaconda3\lib\site-packages\pandas\core\frame.py", line 5398, in _arrays_to_mgr
index = extract_index(arrays)

File "C:\Users\XX\Anaconda3\lib\site-packages\pandas\core\frame.py", line 5437, in extract_index
raise ValueError('If using all scalar values, you must pass'

ValueError: If using all scalar values, you must pass an index

So if I understood correctly, the first error is because my index is just numbers [0..8759] in INT when it should be in datetime-format, and my second error is because my index is in datetime-format and

index = extract_index(arrays)

doesn't have the orginal index [0..8759]. Or have I completely understood the scalar value error wrong? Would it be possible to have 2 indexes for the DataFrame, one [0..8759] and other ['time']-column? How would this be translated to pd.read_csv function or by other method?

If it is any help, I also do the following with the DataFrame (which don't show for some beginner mistake when I call the DataFrame df) (but they are used by the run_plant_model function and) :

df.global_horizontal = df.direct + df.diffuse
df.diffuse_fraction = df.diffuse / df.global_horizontal
df.diffuse_fraction = df.diffuse_fraction.fillna(0)

EDIT: I now properly added the latest columns to the dataframe. It did not have any effect on the error.

Function call:

gsee.pv.run_plant_model(df, site.coords, angle, azimuth, tracking, 
                        capacity, technology, system_loss, 
                        angles=None, include_raw_data=False)    

I believe the original question might have been bad:

C:\Users\XX\Anaconda3\lib\site-packages\pandas\indexes\base.py:2683: RuntimeWarning: Cannot compare type 'Timestamp' with type 'str', sort order is undefined for incomparable objects
return this.join(other, how=how, return_indexers=return_indexers)

So I have 'str' where I should have 'Timestamp'?

Answer 1

Ok, I found the error and the original question was bad:

Solution:

df = pd.read_csv(requestfile, index_col=[0], parse_dates=[0], skiprows=[1])

Headers were left out, and I added the read_csv to skip the row containing units in 'str'. So the problem was one of the functions used was trying to compare 'Timestamp' with the unit row ('str').