I am very new to uproot and Python, but hopefully catching up quickly.
I am wondering why the method .pandas() is creating such a weird table from a TH2D histogram:
myhisto = file["angular_distr_el/ID3_mol_e0_valid/EN_gate/check_cthetaEE_x"]
type(myhisto)
outputs:
uproot.rootio.TH2D
Finally, myhisto.pandas() returns:
count variance
cos(theta) electron energy [eV]
[-inf, -1.0) [-inf, 10.0) 0.0 0.0
[10.0, 10.15) 0.0 0.0
[10.15, 10.3) 0.0 0.0
[10.3, 10.45) 0.0 0.0
[10.45, 10.6) 0.0 0.0
... ... ... ...
[1.0, inf) [24.4, 24.549999999999997) 0.0 0.0
[24.549999999999997, 24.7) 0.0 0.0
[24.7, 24.85) 0.0 0.0
[24.85, 25.0) 0.0 0.0
[25.0, inf) 0.0 0.0
2244 rows × 2 columns
and myhisto.columns returns:
Index(['count', 'variance'], dtype='object')
Where can I find the documentation of the method .pandas() to understand what it is doing? Is there a way to reorganise myhisto in a DataFrame with the right columns?
After some fun but desperate browsing, I understand which type of object it is. It is a very clever way of creating sorted MultiIndex DataFrames. Just typing myhisto.index is possible to see it directly:
MultiIndex([([-inf, -1.0), [-inf, 10.0)),
([-inf, -1.0), [10.0, 10.15)),
([-inf, -1.0), [10.15, 10.3)),
([-inf, -1.0), [10.3, 10.45)),
([-inf, -1.0), [10.45, 10.6)),
([-inf, -1.0), [10.6, 10.75)),
([-inf, -1.0), [10.75, 10.9)),
([-inf, -1.0), [10.9, 11.05)),
([-inf, -1.0), [11.05, 11.2)),
([-inf, -1.0), [11.2, 11.35)),
...
( [1.0, inf), [23.65, 23.799999999999997)),
( [1.0, inf), [23.799999999999997, 23.95)),
( [1.0, inf), [23.95, 24.1)),
( [1.0, inf), [24.1, 24.25)),
( [1.0, inf), [24.25, 24.4)),
( [1.0, inf), [24.4, 24.549999999999997)),
( [1.0, inf), [24.549999999999997, 24.7)),
( [1.0, inf), [24.7, 24.85)),
( [1.0, inf), [24.85, 25.0)),
( [1.0, inf), [25.0, inf))],
names=['cos(theta)', 'electron energy [eV]'], length=2244)
The solution is to unstack or create a pivot table of the DataFrame. For this specific object, a pivot table is better, because of the presence of counts AND variance as columns in the original DataFrame. As an example:
myhisto.unstack()
count ... variance
electron energy [eV] [-inf, 10.0) [10.0, 10.15) [10.15, 10.3) [10.3, 10.45) [10.45, 10.6) [10.6, 10.75) [10.75, 10.9) [10.9, 11.05) [11.05, 11.2) [11.2, 11.35) ... [23.65, 23.799999999999997) [23.799999999999997, 23.95) [23.95, 24.1) [24.1, 24.25) [24.25, 24.4) [24.4, 24.549999999999997) [24.549999999999997, 24.7) [24.7, 24.85) [24.85, 25.0) [25.0, inf)
cos(theta)
[-inf, -1.0) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
[-1.0, -0.9) 0.0 1.0 1.0 0.0 0.0 2.0 0.0 2.0 0.0 1.0 ... 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
[-0.9, -0.8) 0.0 0.0 3.0 3.0 0.0 0.0 0.0 0.0 1.0 1.0 ... 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0
[-0.8, -0.7) 0.0 0.0 1.0 2.0 0.0 1.0 1.0 2.0 1.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
[-0.7, -0.6) 0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0
[-0.6, -0.5) 0.0 1.0 1.0 1.0 0.0 0.0 2.0 1.0 0.0 3.0 ... 0.0 1.0 0.0 1.0 1.0
**22 rows × 204 columns**
vs.
pivot_pipanda = pipanda.pivot_table(values="count", index="cos(theta)", columns="electron energy [eV]")
electron energy [eV] [-inf, 10.0) [10.0, 10.15) [10.15, 10.3) [10.3, 10.45) [10.45, 10.6) [10.6, 10.75) [10.75, 10.9) [10.9, 11.05) [11.05, 11.2) [11.2, 11.35) ... [23.65, 23.799999999999997) [23.799999999999997, 23.95) [23.95, 24.1) [24.1, 24.25) [24.25, 24.4) [24.4, 24.549999999999997) [24.549999999999997, 24.7) [24.7, 24.85) [24.85, 25.0) [25.0, inf)
cos(theta)
[-inf, -1.0) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
[-1.0, -0.9) 0.0 1.0 1.0 0.0 0.0 2.0 0.0 2.0 0.0 1.0 ... 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
[-0.9, -0.8) 0.0 0.0 3.0 3.0 0.0 0.0 0.0 0.0 1.0 1.0 ... 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0
[-0.8, -0.7) 0.0 0.0 1.0 2.0 0.0 1.0 1.0 2.0 1.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
[-0.7, -0.6) 0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0
[-0.6, -0.5) 0.0 1.0 1.0 1.0 0.0 0.0 2.0 1.0 0.0 3.0 ... 0.0 1.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 0.0
[-0.5, -0.3999999999999999) 0.0 0.0 2.0 0.0 1.0 1.0 3.0 2.0 3.0 1.0 ... 3.0 0.0 0.0 0.0 0.0 2.0 0.0 1.0 1.0 0.0
and from here the standard methods of pandas are available!
(To play with slicing techniques like loc[] and iloc[]: https://www.youtube.com/watch?v=tcRGa2soc-c)