36 Como hago :

import pandas as pd
import matplotlib.pyplot as plt

f = "../data/002_processed/esolmet_2018.parquet"
tmx = pd.read_parquet(f)
tmx

	Ib	Ig	Id	uv	To	hr	ws	p
Fecha
2018-01-01 00:00:00	0.057	0.0	0.0	0.001	18.93	41.57	1.253	879.0692
2018-01-01 00:10:00	0.002	0.0	0.0	0.001	18.76	41.00	0.418	879.4363
2018-01-01 00:20:00	0.170	0.0	0.0	0.001	18.92	40.96	0.955	879.5181
2018-01-01 00:30:00	0.371	0.0	0.0	0.001	18.52	42.46	1.823	879.5826
2018-01-01 00:40:00	0.305	0.0	0.0	0.001	18.49	42.43	2.149	879.6826
...	...	...	...	...	...	...	...	...
2018-12-31 23:10:00	0.125	0.0	0.0	0.000	18.88	59.60	2.145	875.5595
2018-12-31 23:20:00	0.000	0.0	0.0	0.000	18.71	59.67	1.638	875.5595
2018-12-31 23:30:00	0.044	0.0	0.0	0.000	18.52	58.75	1.923	875.2889
2018-12-31 23:40:00	0.170	0.0	0.0	0.000	18.36	60.62	2.089	875.0606
2018-12-31 23:50:00	0.003	0.0	0.0	0.000	17.99	60.76	0.744	875.1424

51173 rows × 8 columns

tmx.groupby(by=[tmx.index.month, tmx.index.hour])[["To"]].mean()

		To
Fecha	Fecha
1	0	17.609032
	1	16.852581
	2	16.151613
	3	15.510699
	4	15.109892
...	...	...
12	19	20.902043
	20	19.904570
	21	19.291290
	22	18.719301
	23	18.224570

288 rows × 1 columns

Un dia promedio de cada mes incluyendo las horas y minutos desde el inicio
Un dia promedio con tooodos los datos

# import numpy as np
resumen_mensual = (
    tmx.groupby(pd.Grouper(freq="ME"))
       .agg(
           Ig_sum  = ("Ig", "sum"),          # energía/irradiación acumulada (si aplica)
           To_mean = ("To", "mean"),         # temp. media mensual
           ws_max  = ("ws", "max"),          # racha máxima
           uv_p95  = ("uv", lambda s: s.quantile(0.95))  # percentil 95 de UV
       )
       .rename_axis("Mes")
)
resumen_mensual

	Ig_sum	To_mean	ws_max	uv_p95
Mes
2018-01-31	926337.180	20.019848	8.220	13.2100
2018-02-28	994650.038	23.522289	5.536	19.3700
2018-03-31	1264863.590	25.841069	6.705	22.1785
2018-04-30	1249487.622	26.052600	16.170	22.8505
2018-05-31	1024351.873	25.793943	8.850	24.2900
2018-06-30	1133183.176	23.487846	10.480	0.0000
2018-07-31	1386001.756	20.822079	9.810	9.0310
2018-08-31	1263105.947	22.601895	5.969	0.0000
2018-09-30	1188995.082	22.765825	10.910	0.0000
2018-10-31	1075005.059	22.670980	9.180	0.0000
2018-11-30	865874.218	21.068598	4.401	0.0000
2018-12-31	929714.212	20.362943	5.847	0.0000