33 Une dos archivos en un dataframe, esolmet y ruoa

import pandas as pd
import matplotlib.pyplot as plt
from tools.importaciones import importa_excel
import glob as glob
import missingno as msno

f = "../data/001_raw/2018-2019-VIENTO-TMIX-RUOA.txt"
ruoa = pd.read_csv(f,
                   usecols=["TIMESTAMP","WSpeed_Avg","WDir_Avg"],
                   # usecols=["WDir_Avg","WSpeed_Avg"],
                   # usecols=[0,4,5],
                   skiprows=[0,2,3],
                   encoding="ANSI",
                   delimiter="\t",
                   index_col=0,
                   parse_dates=True,
                   dayfirst=True,
                  )
ruoa = ruoa.resample("10Min").mean()
ruoa = ruoa[ruoa.index.year == 2018]
ruoa.index.duplicated()

array([False, False, False, ..., False, False, False], shape=(52560,))

ruoa[ruoa.index.duplicated()]

	WSpeed_Avg	WDir_Avg
TIMESTAMP

files = glob.glob("../data/001_raw/*.xls")

esolmet = pd.concat([importa_excel(file) for file in files])
esolmet = esolmet[esolmet.index.year == 2018]
del  esolmet["Viento Vel."]
esolmet = esolmet.resample("10Min").mean()

esolmet.loc["2018-02-01"]

	Directa	Global	Difusa	UV	Temperatura	Humedad	Presiòn Bar.
Fecha
2018-02-01 00:00:00	0.003	0.0	0.0	0.001	15.38	53.71	878.2341
2018-02-01 00:10:00	0.313	0.0	0.0	0.001	15.71	50.03	878.1605
2018-02-01 00:20:00	0.029	0.0	0.0	0.001	15.57	51.52	877.9796
2018-02-01 00:30:00	0.013	0.0	0.0	0.001	15.23	52.00	877.9725
2018-02-01 00:40:00	0.001	0.0	0.0	0.001	15.28	51.80	878.0463
...	...	...	...	...	...	...	...
2018-02-01 23:10:00	0.113	0.0	0.0	0.001	19.11	41.47	878.6667
2018-02-01 23:20:00	0.248	0.0	0.0	0.001	19.57	41.13	878.7175
2018-02-01 23:30:00	0.034	0.0	0.0	0.001	19.59	41.47	878.7158
2018-02-01 23:40:00	0.033	0.0	0.0	0.001	19.38	41.95	878.7092
2018-02-01 23:50:00	0.100	0.0	0.0	0.001	19.42	41.27	878.9540

144 rows × 7 columns

esolmet[esolmet.index.duplicated()].index

DatetimeIndex([], dtype='datetime64[ns]', name='Fecha', freq='10min')

data = pd.concat([ruoa,esolmet],axis=1)
data

	WSpeed_Avg	WDir_Avg	Directa	Global	Difusa	UV	Temperatura	Humedad	Presiòn Bar.
2018-01-01 00:00:00	1.1817	300.1500	0.057	0.0	0.0	0.001	18.93	41.57	879.0692
2018-01-01 00:10:00	1.2568	306.2400	0.002	0.0	0.0	0.001	18.76	41.00	879.4363
2018-01-01 00:20:00	1.6964	348.8100	0.170	0.0	0.0	0.001	18.92	40.96	879.5181
2018-01-01 00:30:00	2.2944	348.3400	0.371	0.0	0.0	0.001	18.52	42.46	879.5826
2018-01-01 00:40:00	1.6643	210.8732	0.305	0.0	0.0	0.001	18.49	42.43	879.6826
...	...	...	...	...	...	...	...	...	...
2018-12-31 23:10:00	1.5977	331.0400	0.125	0.0	0.0	0.000	18.88	59.60	875.5595
2018-12-31 23:20:00	2.3029	323.7000	0.000	0.0	0.0	0.000	18.71	59.67	875.5595
2018-12-31 23:30:00	2.0789	186.7430	0.044	0.0	0.0	0.000	18.52	58.75	875.2889
2018-12-31 23:40:00	0.8253	80.1400	0.170	0.0	0.0	0.000	18.36	60.62	875.0606
2018-12-31 23:50:00	1.0315	276.9384	0.003	0.0	0.0	0.000	17.99	60.76	875.1424

52560 rows × 9 columns

data.to_csv("../data/002_processed/esolmet_ruoaWSWD_2018.parquet")

msno.matrix(data)