Exploratory analysis of global CO2 emissions

#reading file into data frame
emissions = pd.read_csv('GCB2022v27_MtCO2_flat.csv')
emissions.head()

emissions.tail()

emissions.sample(20)

#checking number of rows and columns in the data frame
emissions.shape

#checking for column data types and number of null values
emissions.info()

#checking for duplicate rows
sum(emissions.duplicated())

#listing the countries included in the data
emissions.Country.unique()

#listing the years included in the data
emissions.Year.unique()

emissions.columns

global_emissions

#summary statistics for global emissions
global_emissions.describe()

global_emissions.hist(figsize = (10,10));

#plotting the distribution of global Co2 emissions
plt.figure(figsize=[14,6])
bins=np.arange(0,global_emissions['total'].max()+2000, 2000)
plt.hist(data=global_emissions, x='total',bins=bins, edgecolor='black')
plt.title('Distribution of global CO2 emissions'.title(), fontsize=14,weight='bold')
plt.xlabel('Emissions in Mt',fontsize=10, weight='bold')
plt.ylabel('Count',fontsize=10, weight='bold')
plt.plot();

#plotting the distribution of global Co2 emissions from coal
plt.figure(figsize=[14,6])
bins=np.arange(0,global_emissions['coal'].max()+1000, 500)
plt.hist(data=global_emissions, x='coal',bins=bins, edgecolor='black')
plt.title('Distribution of global CO2 emissions from coal'.title(), fontsize=14,weight='bold')
plt.xlabel('Emissions in Mt',fontsize=10, weight='bold')
plt.ylabel('Count',fontsize=10, weight='bold')
plt.plot();

#plotting the distribution of global Co2 emissions from oil
plt.figure(figsize=[14,6])
bins=np.arange(0,global_emissions['oil'].max()+1000, 500)
plt.hist(data=global_emissions, x='oil',bins=bins, edgecolor='black')
plt.title('Distribution of global CO2 emissions from oil'.title(), fontsize=14,weight='bold')
plt.xlabel('Emissions in Mt',fontsize=10, weight='bold')
plt.ylabel('Count',fontsize=10, weight='bold')
plt.plot();

#plotting the distribution of global Co2 emissions from gas
plt.figure(figsize=[14,6])
bins=np.arange(0,global_emissions['gas'].max()+1000, 500)
plt.hist(data=global_emissions, x='gas',bins=bins, edgecolor='black')
plt.title('Distribution of global CO2 emissions from gas'.title(), fontsize=14,weight='bold')
plt.xlabel('Emissions in Mt',fontsize=10, weight='bold')
plt.ylabel('Count',fontsize=10, weight='bold')
plt.plot();

#plotting the distribution of global Co2 emissions from cement
plt.figure(figsize=[14,6])
bins=np.arange(0,global_emissions['cement'].max()+500, 125)
plt.hist(data=global_emissions, x='cement',bins=bins, edgecolor='black')
plt.title('Distribution of global CO2 emissions from cement'.title(), fontsize=14,weight='bold')
plt.xlabel('Emissions in Mt',fontsize=10, weight='bold')
plt.ylabel('Count',fontsize=10, weight='bold')
plt.plot();

#plotting the distribution of global Co2 emissions from flaring
plt.figure(figsize=[14,6])
bins=np.arange(0,global_emissions['flaring'].max()+100, 25)
plt.hist(data=global_emissions, x='flaring',bins=bins, edgecolor='black')
plt.title('Distribution of global CO2 emissions from flaring'.title(), fontsize=14,weight='bold')
plt.xlabel('Emissions in Mt',fontsize=10, weight='bold')
plt.ylabel('Count',fontsize=10, weight='bold')
plt.plot();

#plotting a line graph to show the trend in global emissions
global_emissions
x=global_emissions.year
y=global_emissions.total
plt.figure(figsize = [14,6])
plt.xlim(1700, 2050)
plt.title('Global Co2 emissions over time (1750-2021)',fontsize=14,weight='bold')
plt.xlabel('Year',fontsize=10, weight='bold')
plt.ylabel('Total emissions in Mt', fontsize=10, weight='bold')
plt.plot(x,y);

#plotting a line graph to show the trend in global emissions over the last decade
x=decade_global_emissions.year
y=decade_global_emissions.total
plt.figure(figsize = [14,6])
plt.xlim(2010,2022)
plt.title('Global Co2 emissions over the past decade (2011-2021)',fontsize=14,weight='bold')
plt.xlabel('Year',fontsize=10, weight='bold')
plt.ylabel('Total emissions in Mt', fontsize=10, weight='bold')
plt.plot(x,y);

global_emissions.columns

#creating a data frame that only consists industry emission records
industry_emissions = global_emissions.drop(['country', 'country_code', 'year','total','per_capita'], axis = 1)
industry_emissions.head()

#plotting bargraph to show each sector's contribution to Co2 emissions
plt.figure(figsize = [14,6])
industry_emissions.sum().plot(kind='bar',edgecolor='black')
plt.title('Global Industry contribution to Co2 emissions',fontsize=14,weight='bold')
plt.xlabel('Industry',fontsize=10,weight='bold')
plt.xticks(rotation=45)
plt.ylabel('Total emissions in Mt',fontsize=10,weight='bold');

#creating a dataframe that only consists industry emission records with the year as the index column for time-series plot
annual_industry_emissions = global_emissions.drop(['country', 'country_code','total','per_capita'], axis = 1)
annual_industry_emissions.set_index('year',inplace=True)
annual_industry_emissions.head()

#plotting a line graph to show industry emissions over time
plt.figure(figsize=[10,6])
plt.plot(annual_industry_emissions.coal,label = 'coal')
plt.plot(annual_industry_emissions.oil,label = 'oil')
plt.plot(annual_industry_emissions.gas,label = 'gas')
plt.plot(annual_industry_emissions.cement,label = 'cement')
plt.plot(annual_industry_emissions.flaring,label = 'flaring')
plt.plot(annual_industry_emissions.other,label = 'other')
plt.ylabel('Emissions in Mt',fontsize=10, weight='bold')
plt.xlabel('Year', fontsize=10, weight='bold')
plt.xlim(1700, 2050)
plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
plt.title('Global Industry Co2 emissions over time (1750-2021)', fontsize=14,weight='bold');

#plotting a line graph to show industry emissions over the last 121 years
plt.figure(figsize=[10,6])
plt.plot(centuary_annual_industry_emissions.coal,label = 'coal')
plt.plot(centuary_annual_industry_emissions.oil,label = 'oil')
plt.plot(centuary_annual_industry_emissions.gas,label = 'gas')
plt.plot(centuary_annual_industry_emissions.cement,label = 'cement')
plt.plot(centuary_annual_industry_emissions.flaring,label = 'flaring')
plt.plot(centuary_annual_industry_emissions.other,label = 'other')
plt.ylabel('Emissions in Mt',fontsize=10, weight='bold')
plt.xlabel('Year', fontsize=10, weight='bold')
#plt.xlim(1700, 2050)
plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
plt.title('Global Industry Co2 emissions over time (1901-2021)', fontsize=14,weight='bold');

country_emissions

coal_emissions = country_emissions.groupby('country')['coal'].sum().sort_values().tail(10).to_frame()
coal_emissions

#barplot of coal emissions
coal_emissions.plot(kind = 'barh',edgecolor='black');
plt.title('Countries with the highest Co2 emissions by coal', fontsize=12, weight='bold');
plt.xlabel('Total emissions in Mt', fontsize=10,weight='bold');
plt.ylabel('Country', fontsize=10, weight='bold');

oil_emissions = country_emissions.groupby('country')['oil'].sum().sort_values().tail(10).to_frame()
oil_emissions

oil_emissions.plot(kind='barh',edgecolor='black')
plt.title('Countries with the highest Co2 emissions by oil', fontsize=12,weight='bold');
plt.xlabel('Total emissions in Mt',fontsize=10,weight='bold');
plt.ylabel('Country',fontsize=10, weight='bold');

gas_emissions = country_emissions.groupby('country')['gas'].sum().sort_values().tail(10).to_frame()
gas_emissions

gas_emissions.plot(kind='barh',edgecolor='black');
plt.title('Countries with the highest Co2 emissions by gas',fontsize=12,weight='bold');
plt.xlabel('Total emissions in Mt',fontsize=10,weight='bold')
plt.ylabel('Country',fontsize=10,weight='bold');

#plotting a bar grapg of countries with the highest average emissions
ave_emissions = country_emissions.groupby('country', as_index = False)['total'].mean().sort_values(by=['total'], ascending = False).head(10)
x = ave_emissions.country
y = ave_emissions.total
plt.figure(figsize = [10,6])
plt.title('High Co2 emitting countries', fontsize =14, weight='bold')
plt.xlabel('Country',fontsize = 10, weight='bold')
plt.ylabel('Average emissions in Mt',fontsize = 10, weight='bold')
plt.xticks(rotation = 45)
plt.bar(x,y, edgecolor = 'black');

#plottig a graph to show the trends in country emissions for countries with the highest average emissions
countries =['USA','China','Russia','Germany','United Kingdom']

top_countries = country_emissions[country_emissions.country.isin(countries) == True]
annual_country_emissions = top_countries.query('year > 1900')
annual_country_emissions

#plottig a graph to show the trends in country emissions for countries with the highest average emissions
x  = annual_country_emissions.year.unique()
y1 = annual_country_emissions.query('country == "USA"').total
y2 = annual_country_emissions.query('country == "China"').total
y3 = annual_country_emissions.query('country == "Russia"').total
y4 = annual_country_emissions.query('country == "Germany"').total
y5 = annual_country_emissions.query('country == "United Kingdom"').total

plt.plot(x,y1,label='USA')
plt.plot(x,y2,label='China')
plt.plot(x,y3,label='Russia')
plt.plot(x,y4,label='Germany')
plt.plot(x,y5,label='United Kingdom')
plt.title('Top country emissions over time', fontsize = 12, weight='bold')
plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5));

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