def total_capacity_per_country():
data = read_power_plants()
data = join_on_iso_3(data, 'country', two_americas=False)
capacity_per_country = data.groupby(['country']).sum()['capacity_mw']
number_per_country = data.groupby(
['country']).count()['country_long'].reset_index()
country_names = capacity_per_country.axes[0].to_frame().reset_index(
drop=True)
capacity_per_country = capacity_per_country.reset_index(drop=True)
country_names['quantity'] = number_per_country['country_long']
country_names['capacity_mw'] = capacity_per_country
country_names = join_on_iso_3(country_names, 'country', two_americas=False)
return country_names
def timeline():
co = carbon_data().dropna()
co = join_on_iso_3(co, 'Code')
co = co.groupby(['Continent', 'Year']).sum().reset_index()
important_year = [
[1914, 1918, "#C0BCB5"],
[1929, 1933, "#4A6C6F"],
[1939, 1945, "#846075"],
[1956, 1960, "#AF5D63"],
[1989, 1992, "#ED474A"],
[2008, 2010, "#9649CB"]
]
fig = go.Figure()
for c in get_continents():
fig.add_trace(go.Scatter(**scatter_fig(co, c)))
fig.update_yaxes(range=[0, 22 * 10 ** 9])
fig.update_layout(
xaxis=dict(
autorange=True,
rangeslider=dict(
autorange=True)
),
shapes=[shapes(*years) for years in important_year]
)
# fig.write_html('tmp3.html', auto_open=True)
return plotly.offline.plot(figure_or_data=fig, include_plotlyjs=False, output_type='div')
def map_scatter_plot():
data = read_power_plants()
power_plant_types = get_power_plants_types(data)
data = join_on_iso_3(data, 'country')
limits = [[1, 99], [100, 999], [1000, 9999], [10000, 99999]]
data.sort_values('capacity_mw', ascending=False)
data = set_new_limits(data)
data = set_text_for_map_plot(data)
fig = go.Figure()
charts, charts_orders = set_charts(data, limits, power_plant_types)
for c in charts:
fig.add_trace(c)
buttons = [
dict(
label=pp,
method="update",
args=[{
"visible": set_visible_by_option(pp, charts_orders)
}, {
"title":
f'Locations of {pp} Power Plants in the World<br>(Hover Power Plant for Details)'
}]) for pp in power_plant_types
]
fig.update_layout(
title=
f'Locations of {power_plant_types[0]} Power Plants in the World<br>(Hover Power Plant for Details)',
updatemenus=[dict(active=0, buttons=buttons)])
return plotly.offline.plot(figure_or_data=fig,
include_plotlyjs=False,
output_type='div')
def main():
co = carbon_data().dropna()
co = join_on_iso_3(co, 'Code')
min_year, max_year = 1850, co['Year'].max()
for year in range(min_year, max_year):
data = co[co['Year'] == year].dropna(subset=['Continent'])
data = data[data['Annual CO2 emissions (tonnes)'] > 0]
data['rank'] = data['Annual CO2 emissions (tonnes)'].rank()
def get_data_from_year(current_year):
co = data_by_year(current_year)
pop = get_pop_by_year(current_year)
data = join_gdp_by_iso3(co, 'Code', current_year)
data = join_on_iso_3(data, 'Code')
data = data.join(pop.set_index("code_pop"), on='Code').dropna(
subset=[str(current_year), 'Country Name', f'{str(current_year)}_pop'])
data['co2_per_capta'] = data['Annual CO2 emissions (tonnes)'] / data[
f'{str(current_year)}_pop']
data['co2_per_capta_str'] = data['co2_per_capta'].map(
'{:,.2f} Tonnes / per capta'.format)
data[f'{str(current_year)}_pop_str'] = data[
f'{str(current_year)}_pop'].map('{:,.0f}'.format)
data[f"{str(current_year)}_str"] = data[str(current_year)].map(
'${:,.2f}'.format)
return data