brauliobarahona · April 21, 2020 19:15
diff --git a/_get_swissgrid_data.py b/_get_swissgrid_data.py
 import os
 import pandas as pd
 from datetime import datetime
 	
 url='https://www.swissgrid.ch/dam/dataimport/energy-statistic/EnergieUebersichtCH-{YEAR}.xls'

 dfs = []
 for y in range(2009,datetime.now().year+1):
 	# generate url with year in it
 	url_y = url.format(YEAR=str(y))
 	
 	# use local file, if it exists
 	if os.path.exists(url_y.split('/')[-1]):
 		url_y = url_y.split('/')[-1]

 	df = pd.read_excel(url_y, sheet_name='Zeitreihen0h15', usecols='A,B', skiprows=0, header=1)
 	# keep only total consumption and timestamp (already index) columns
 	dfs.append(df)

 	#break # debugging

 # combine dataframes
 df = pd.concat(dfs)

 # set index
 df.set_index('Zeitstempel', inplace=True)

 # aggregate to daily sum
 df = df.resample('D').sum()

 # convert to daily MWh
 #df['MWh'] = df.kWh*0.001
 df['GWh'] = df.kWh*0.001*0.001

 del df['kWh']

 # output daily sums to csv
 df.to_csv('all_years.csv')

 # aggregate to get monthly average
 df = df.resample('M',convention='end').mean()
 df.to_csv('all_years_monthly.csv')
diff --git a/_get_zurich_monthly_temps.py b/_get_zurich_monthly_temps.py
 # download historical weather from meteoswiss and print as csv (tab seperated)

 import requests
 from datetime import datetime

 delim =','

 # url for historical zurich weather data
 url = 'http://www.meteoswiss.admin.ch/product/output/climate-data/homogenous-monthly-data-processing/data/homog_mo_SMA.txt'
 r = requests.get(url).text

 # loop over lines in text file. find data header line number
 for idx, val in enumerate(r.split('\r\n')):
 	if val.startswith('Year'):
 		#print(delim.join(val.split())) #print csv header
 		start = idx
 		break

 # read remaining lines as data
 data = r.split('\n')[start+1:-1]

 # print with same delimiter
 print(delim.join(('month','temperature','precipitation')))
 for d in data:
 	dd = d.split()
 	print(delim.join((dd[0]+'-'+dd[1].zfill(2)+'-01', dd[2], dd[3])))


  
diff --git a/all_years_daily.csv b/all_years_daily.csv
diff --git a/all_years_monthly.csv b/all_years_monthly.csv
diff --git a/monthly_weather_zurich.csv b/monthly_weather_zurich.csv
	import os
	import pandas as pd
	from datetime import datetime

	url='https://www.swissgrid.ch/dam/dataimport/energy-statistic/EnergieUebersichtCH-{YEAR}.xls'

	dfs = []
	for y in range(2009,datetime.now().year+1):
	# generate url with year in it
	url_y = url.format(YEAR=str(y))

	# use local file, if it exists
	if os.path.exists(url_y.split('/')[-1]):
	url_y = url_y.split('/')[-1]

	df = pd.read_excel(url_y, sheet_name='Zeitreihen0h15', usecols='A,B', skiprows=0, header=1)
	# keep only total consumption and timestamp (already index) columns
	dfs.append(df)

	#break # debugging

	# combine dataframes
	df = pd.concat(dfs)

	# set index
	df.set_index('Zeitstempel', inplace=True)

	# aggregate to daily sum
	df = df.resample('D').sum()

	# convert to daily MWh
	#df['MWh'] = df.kWh*0.001
	df['GWh'] = df.kWh0.0010.001

	del df['kWh']

	# output daily sums to csv
	df.to_csv('all_years.csv')

	# aggregate to get monthly average
	df = df.resample('M',convention='end').mean()
	df.to_csv('all_years_monthly.csv')
	# download historical weather from meteoswiss and print as csv (tab seperated)

	import requests
	from datetime import datetime

	delim =','

	# url for historical zurich weather data
	url = 'http://www.meteoswiss.admin.ch/product/output/climate-data/homogenous-monthly-data-processing/data/homog_mo_SMA.txt'
	r = requests.get(url).text

	# loop over lines in text file. find data header line number
	for idx, val in enumerate(r.split('\r\n')):
	if val.startswith('Year'):
	#print(delim.join(val.split())) #print csv header
	start = idx
	break

	# read remaining lines as data
	data = r.split('\n')[start+1:-1]

	# print with same delimiter
	print(delim.join(('month','temperature','precipitation')))
	for d in data:
	dd = d.split()
	print(delim.join((dd[0]+'-'+dd[1].zfill(2)+'-01', dd[2], dd[3])))
	Zeitstempel	GWh
	2009-01-31	181.89132123158066
	2009-02-28	179.90775635632139
	2009-03-31	166.7508874169032
	2009-04-30	142.07002526050002
	2009-05-31	136.91866071274197
	2009-06-30	136.93097605736668
	2009-07-31	134.0020588288387
	2009-08-31	134.2390176104516
	2009-09-30	144.87924559420003
	2009-10-31	153.97093015783875
	2009-11-30	165.16846558319995
	2009-12-31	174.69543670367747
	2010-01-31	182.93598689893545
	2010-02-28	183.07513932225007
	2010-03-31	173.2999861000968
	2010-04-30	152.643216968
	2010-05-31	145.79467737406452
	2010-06-30	144.73639416346668
	2010-07-31	137.93949904854838
	2010-08-31	138.59728959809678
	2010-09-30	148.4618076199667
	2010-10-31	158.50856520825812
	2010-11-30	173.61066394840003
	2010-12-31	185.73032865119356
	2011-01-31	183.88897004516136
	2011-02-28	181.9097098380715
	2011-03-31	171.1870322500967
	2011-04-30	146.2848935645667
	2011-05-31	146.4389869201936
	2011-06-30	142.1266659939
	2011-07-31	137.16842517648388
	2011-08-31	140.38293517941938
	2011-09-30	147.5018577377667
	2011-10-31	153.92731508016126
	2011-11-30	169.7470950941
	2011-12-31	173.11853660870966
	2012-01-31	179.2182984460323
	2012-02-29	194.58914434386216
	2012-03-31	163.28857445819355
	2012-04-30	151.8206864402667
	2012-05-31	142.08877488593544
	2012-06-30	141.6952252143
	2012-07-31	134.84148677351612
	2012-08-31	138.16817154651613
	2012-09-30	145.13486418493335
	2012-10-31	156.6377171499355
	2012-11-30	168.9285657228667
	2012-12-31	176.34787582461286
	2013-01-31	181.27618320683874
	2013-02-28	187.4602608481072
	2013-03-31	173.81969380009681
	2013-04-30	158.23553297276666
	2013-05-31	146.04094416654843
	2013-06-30	143.42766254
	2013-07-31	136.98761179409675
	2013-08-31	135.1935060784516
	2013-09-30	145.14485205023334
	2013-10-31	153.83340675380649
	2013-11-30	172.6064872906333
	2013-12-31	176.04504248274193
	2014-01-31	177.4945657031936
	2014-02-28	177.31626394814285
	2014-03-31	163.1336122866129
	2014-04-30	147.6236681
	2014-05-31	145.504863395871
	2014-06-30	139.36379593106665
	2014-07-31	136.72075074006455
	2014-08-31	135.4224777103871
	2014-09-30	146.11533019716666
	2014-10-31	152.032588165
	2014-11-30	164.32734801503335
	2014-12-31	171.2005758649355
	2015-01-31	179.89219631096776
	2015-02-28	186.35292336817858
	2015-03-31	167.08860754496774
	2015-04-30	148.98445824353334
	2015-05-31	140.3388121989355
	2015-06-30	140.7547688359
	2015-07-31	137.66203059209673
	2015-08-31	135.96407092493547
	2015-09-30	145.99648839346665
	2015-10-31	154.8881071027742
	2015-11-30	164.8850850937
	2015-12-31	168.37899358296775
	2016-01-31	177.2828638033871
	2016-02-29	175.6642857335172
	2016-03-31	168.452092324
	2016-04-30	153.0116958538
	2016-05-31	141.79313575680646
	2016-06-30	141.33018447093335
	2016-07-31	132.67535006377418
	2016-08-31	133.78289589980645
	2016-09-30	141.46324541910002
	2016-10-31	153.59431193906454
	2016-11-30	170.1842347203333
	2016-12-31	175.73548828887098
	2017-01-31	191.44154939758067
	2017-02-28	176.59988522960717
	2017-03-31	161.24704695458067
	2017-04-30	146.3588479731667
	2017-05-31	143.41021936251613
	2017-06-30	138.81714214870001
	2017-07-31	132.02644500974193
	2017-08-31	135.72823003596775
	2017-09-30	145.5531329057
	2017-10-31	149.553205057
	2017-11-30	172.5532032719
	2017-12-31	178.25891160712905
	2018-01-31	172.5359185554516
	2018-02-28	185.21761174003575
	2018-03-31	173.75484832429035
	2018-04-30	144.8615010186
	2018-05-31	139.1921557934516
	2018-06-30	139.08294539213333
	2018-07-31	133.73835160500002
	2018-08-31	135.9371888781613
	2018-09-30	140.6732387318667
	2018-10-31	150.1898308683548
	2018-11-30	167.92421167960003
	2018-12-31	169.77448336083876
	2019-01-31	182.66328946283872
	2019-02-28	175.77399360625
	2019-03-31	162.77824454422574
	2019-04-30	149.75469923653336
	2019-05-31	146.54890049112902
	2019-06-30	135.98438914436667
	2019-07-31	133.5515627311936
	2019-08-31	132.68179247580642
	2019-09-30	139.67571234789997
	2019-10-31	148.89436652183875
	2019-11-30	167.37409185166666
	2019-12-31	166.496929833742
	2020-01-31	174.12190733441932
	2020-02-29	168.65245498827585
	2020-03-31	154.6213379664839