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process_co2.py
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process_co2.py
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import pandas as pd
# import pandas_datareader as pdr
# import matplotlib.pyplot as plt
import plotly.express as px
import datetime
import panel as pn
countries = ['World','United States','Russia','China','Australia','Japan','Germany','India','United Kingdom', 'France','Indonesia','Iceland']
greenhouse_gases=['CarbonDioxide','Methane','NitrousOxide','FluorinatedGases']
gases_pct=[79.4,11.5,6.2,3.0]
country_component =['country','year','iso_code','population',
'co2','co2_per_gdp','co2_per_capita','cement_co2','coal_co2','oil_co2','gas_co2',
'methane']
class Process_CO2 :
def __init__(self) :
# maunaloa data
#self.mloafile = 'https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv'
self.mloafile = 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_gl.txt'
self.mloa_df = ''
self.mix_df = ''
self.create_df()
# energy mix --- needs pre-processing
self.energyfile = 'per-capita-energy-stacked.csv'
self.create_energy_df()
self.create_co2_mix_df()
def create_df (self):
mloa_df = pd.read_csv(self.mloafile,skiprows=40,delimiter=r"\s ")
print(mloa_df.head())
mloa_df=mloa_df.drop(mloa_df.iloc[:,7:],axis=1)
mloa_df.columns=['Yr','Mn','DecYr','CO2','CO2_unc','CO2_trend','trend_unc']
#self.mloa_df=mloa_df[mloa_df.Yr > 1960]
tmpdate=pd.to_datetime(dict(year=mloa_df.Yr,month=mloa_df.Mn,day=15))
print(type(tmpdate))
mloa_df['Date']=tmpdate
self.mloa_df=mloa_df
def create_energy_df (self):
dfmix = pd.read_csv(self.energyfile)
self.mix_df = dfmix[dfmix.Entity.isin(countries)]
self.mix_df.columns=['country','code','year','coal','oil','gas','nuclear','hydro','wind','solar','other']
self.mix_df=self.mix_df.dropna()
def create_co2_mix_df(self) :
# df = pd.read_csv('https://raw.githubusercontent.com/owid/co2-data/master/owid-co2-data.csv')
# cache data to improve dashboard performance
if 'data' not in pn.state.cache.keys():
df = pd.read_csv('https://raw.githubusercontent.com/owid/co2-data/master/owid-co2-data.csv')
pn.state.cache['data'] = df.copy()
else:
df = pn.state.cache['data']
# data cleanup
df=df.fillna(0)
df_major=df[(df['country'].isin(countries)) & (df['year']>1940)]
df_countries_full=df[df.iso_code!='0']
self.df_countries = df_major
self.co2_comp_world=df[df['country']=='World']
self.df_countries_full=df_countries_full[country_component]
#print(self.co2_comp_world.columns)
self.co2_comp_df = df
def limit_dates (self, min_year, max_year):
min = datetime.date (min_year,1,1)
max = datetime.date (max_year,12,31)
newdf = self.mloa_df[(self.mloa_df.Yr >= min_year) & (self.mloa_df.Yr<=max_year)]
return newdf
# my_co2 = Process_CO2()
# df = my_co2.create_df()
# newdf = my_co2.limit_dates(2000,2020)
# print(df.head(10))
# fig = px.line(newdf,x='Date',y=['CO2','CO2_trend'],range_y=[300,450])
# fig.show()