# convert date and time info to datetime object
def cleanUpDateTime(df):
# creates a function to convert from string to datetime info
# still need to add info to keep time zone info
df['DateTime'] = ""
for n in range(df.shape[0]):
y = df.Date[n].replace("T", "")
z = y.replace("Z", "")
df['DateTime'][n] = datetime.strptime(z, '%Y%m')
return (df)
ng_tx = cleanUpDateTime(ng_tx)
elec_tx = cleanUpDateTime(elec_tx)
# set the datetime info to be the index
ng_tx = ng_tx.set_index(['DateTime'])
elec_tx = elec_tx.set_index(['DateTime'])
# save csv files of monthly natural gas and electricity prices
ng_tx.to_csv('CSV_Files/MonthlyNatGasPrices.csv')
elec_tx.to_csv('CSV_Files/MonthlyElectricityPrices.csv')
print('convertedIndex', ng_tx.head(5))
# make a figure
def plotGasAndElecPrices(priceData, figName):
plt.figure(figsize=(5, 3))
# add a matplotlib converter to get rid of this error
fig, ax = plt.subplots()
# find min and max demands
max_tex = max(tx.demand)
print('Maximum demand, Texas:', max_tex)
min_tex = min(tx.demand)
print('Minimum demand, Texas:', min_tex)
max_tex_south = max(tx_s.demand)
print('Maximum demand, South Central Texas:', max_tex_south)
min_tex_south = min(tx_s.demand)
print('Minimum demand, South Central Texas:', min_tex_south)
# find scale factor between South Central Texas and Texas
ratio = max_tex_south / max_tex
print('Max ERCOT South Central/Max ALL Texas:', ratio)
# set the DateTime to be the index
tx = tx.set_index(['DateTime'])
tx_s = tx_s.set_index(['DateTime'])
# slice 2018 from ALL-Texas demand, save it to csv file
txSlice = tx['2019-01-01 00:00:00':'2018-01-01 00:00:00']
tx2018 = txSlice.iloc[::-1]
tx2018.to_csv('CSV_Files/HourlyTxDemand2018.csv')
# create a notional 2018 hourly electrical demand for ERCOT SCEN based on ratio of peaks
tx_SCEN = tx2018.copy()
tx_SCEN["demand"] *= ratio
tx_SCEN.to_csv('CSV_Files/HourlyTxScenDemand2018-Notional.csv')
print(tx_SCEN.head())
tx_SCEN = cleanUpDateTime(tx_SCEN)
print(tx_SCEN.head())