Table of Contents

• 1  WORLD GREEN ENERGY GENERATION ANALYSIS
  • 1.1  What is Green Energy?
    • 1.1.1  Green Energy Types
      • 1.1.1.1  Solar Power
      • 1.1.1.2  Wind Power
      • 1.1.1.3  Hydropower
      • 1.1.1.4  Geothermal Energy
      • 1.1.1.5  Biomass
      • 1.1.1.6  Biofuels
  • 1.2  Load Dataset
  • 1.3  Data Cleaning and Structuring
    • 1.3.1  Remove unnecessary columns
    • 1.3.2  Replace TypeCode 'codes' with 'names'
    • 1.3.3  Round float columns to 2 digits
    • 1.3.4  Rename Entity to Country
    • 1.3.5  Add Total_WSO and Total columns
    • 1.3.6  Merge Data and Country DataFrames
    • 1.3.7  Save cleansed datasets
  • 1.4  Data Dictionary of Datasets
  • 1.5  Preparing aggregated data frames
    • 1.5.1  By Year
    • 1.5.2  2019 by Continent
    • 1.5.3  2019 by Country with cumulative percentage information
  • 1.6  Data Visualization
    • 1.6.1  World green energy generation by types between 1965 and 2019
    • 1.6.2  World green energy generation in 2019 by continent
    • 1.6.3  World total green energy generation map between 1965 and 2019
    • 1.6.4  Green energy generation between 1965 and 2019 by top 5 countries in 2019
    • 1.6.5  Green energy type breakdown in top 10 countries in 2019
    • 1.6.6  Top 5 producers of each green energy type in 2019
  • 1.7  References

WORLD GREEN ENERGY GENERATION ANALYSIS

What is Green Energy?

Green energy is any energy type that is generated from natural resources, such as sunlight, wind or water. It often comes from renewable energy sources although there are some differences between renewable and green energy.

The key with these energy resources are that they don’t harm the environment through factors such as releasing greenhouse gases into the atmosphere.

As a source of energy, green energy often comes from renewable energy technologies such as solar energy, wind power, geothermal energy, biomass and hydroelectric power. Each of these technologies works in different ways, whether that is by taking power from the sun, as with solar panels, or using wind turbines or the flow of water to generate energy.

In order to be deemed green energy, a resource cannot produce pollution, such as is found with fossil fuels.

This means that not all sources used by the renewable energy industry are green. For example, power generation that burns organic material from sustainable forests may be renewable, but it is not necessarily green, due to the CO2 produced by the burning process itself.

Green Energy Types

The main sources are wind energy, solar power and hydroelectric power (including tidal energy, which uses ocean energy from the tides in the sea). Solar and wind power are able to be produced on a small scale at people’s homes or alternatively, they can be generated on a larger, industrial scale.

The six most common forms are as follows:

Solar Power

This common renewable, green energy source is usually produced using photovoltaic cells that capture sunlight and turn it into electricity. Solar power is also used to heat buildings and for hot water as well as for cooking and lighting. Solar power has now become affordable enough to be used for domestic purposes including garden lighting, although it is also used on a larger scale to power entire neighbourhoods.

Solar energy is the most abundant energy source on the planet, because the sun shines down every single day. Anywhere the sun touches could be converted into solar power.

Wind Power

Particularly suited to offshore and higher altitude sites, wind energy uses the power of the flow of air around the world to push turbines that then generate electricity.

Hydropower

Also known as hydroelectric power, this type of green energy uses the flow of water in rivers, streams, dams or elsewhere to produce energy. Hydropower can even work on a small scale using the flow of water through pipes in the home or can come from evaporation, rainfall or the tides in the oceans.

Wave and tidal power is generated using large underwater turbines. These rotate with the ebbing and flowing of the moving water to create energy. The turbines are connected to electrical generators that turn this kinetic energy into electricity, which we can use. Tidal energy is the oldest form of renewable energy. It was used by the Romans when they occupied Britain to power water mills.

Exactly how ‘green’ the following three types of green energy are is dependent on how they are created.

Geothermal Energy

This type of green power uses thermal energy that has been stored just under the earth’s crust. While this resource requires drilling to access, thereby calling the environmental impact into question, it is a huge resource once tapped into. Geothermal energy has been used for bathing in hot springs for thousands of years and this same resource can be used for steam to turn turbines and generate electricity. The energy stored under the United States alone is enough to produce 10 times as much electricity as coal currently can. While some nations, such as Iceland, have easy-to-access geothermal resources, it is a resource that is reliant on location for ease of use, and to be fully ‘green’ the drilling procedures need to be closely monitored.

Biomass

This renewable resource also needs to be carefully managed in order to be truly labelled as a ‘green energy’ source. Biomass power plants use wood waste, sawdust and combustible organic agricultural waste to create energy. While the burning of these materials releases greenhouse gas these emissions are still far lower than those from petroleum-based fuels.

Biofuels

Rather than burning biomass as mentioned above, these organic materials can be transformed into fuel such as ethanol and biodiesel. Having supplied just 2.7% of the world’s fuel for transport in 2010, the biofuels are estimated to have the capacity to meet over 25% of global transportation fuel demand by 2050.

This notebook analyzes two datasets:

• World renewable energy generation
• Share of electricity production from renewables

Load Dataset

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'

# Disable warnings in Anaconda
import warnings
warnings.filterwarnings('ignore')

import pandas as pd
import numpy as np
import seaborn as sns

%matplotlib inline
import matplotlib.pyplot as plt
# from __future__ import division
from __future__ import division

# Import plotly
import chart_studio
chart_studio.tools.set_credentials_file(username='', api_key='')
chart_studio.tools.set_config_file(world_readable=True, sharing='public')

import plotly.io as pio
import plotly.express as px
import chart_studio.plotly as py
import plotly.graph_objects as go
from plotly.subplots import make_subplots

from plotly.offline import iplot, init_notebook_mode
init_notebook_mode(connected = True)

from sklearn.linear_model import LinearRegression

df_geo = pd.read_excel('./data/green_energy.xlsx', sheet_name = 'Geo', thousands = '.', convert_float = True)
df_wre = pd.read_excel('./data/green_energy.xlsx', sheet_name = 'WREGeneration', thousands = '.', convert_float = True)
df_wre_se = pd.read_excel('./data/green_energy.xlsx', sheet_name = 'WREShareElectricity', thousands = '.', convert_float = True)

df_geo.info()
print()
df_wre.info()
print()
df_wre_se.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 231 entries, 0 to 230
Data columns (total 14 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Name         231 non-null    object 
 1   TypeCode     231 non-null    object 
 2   IsUNMember   231 non-null    int64  
 3   Name2        223 non-null    object 
 4   Name3        14 non-null     object 
 5   Name4        4 non-null      object 
 6   Capital      227 non-null    object 
 7   Continent    231 non-null    object 
 8   Region       231 non-null    object 
 9   ISO3         231 non-null    object 
 10  ISO2         230 non-null    object 
 11  Latitude     231 non-null    float64
 12  Longitude    231 non-null    float64
 13  Description  3 non-null      object 
dtypes: float64(2), int64(1), object(11)
memory usage: 25.4+ KB

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4090 entries, 0 to 4089
Data columns (total 6 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Entity           4090 non-null   object 
 1   Year             4090 non-null   int64  
 2   Wind             4090 non-null   float64
 3   Solar            4090 non-null   float64
 4   GeoBiomassOther  4090 non-null   float64
 5   Hydro            4090 non-null   float64
dtypes: float64(4), int64(1), object(1)
memory usage: 191.8+ KB

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5395 entries, 0 to 5394
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Entity  5395 non-null   object 
 1   Year    5395 non-null   int64  
 2   Value   5395 non-null   float64
dtypes: float64(1), int64(1), object(1)
memory usage: 126.6+ KB

df_geo.head()
df_wre.head()
df_wre_se.head()

	Name	TypeCode	IsUNMember	Name2	Name3	Name4	Capital	Continent	Region	ISO3	ISO2	Latitude	Longitude	Description
0	Afghanistan	C	1	Afghanistan	NaN	NaN	Kabul	Asia	Southern Asia	AFG	AF	34.5328	69.1658	NaN
1	Albania	C	1	Albania	NaN	NaN	Tirana	Europe	Southern Europe	ALB	AL	41.3300	19.8200	NaN
2	Algeria	C	1	Algeria	NaN	NaN	Algiers	Africa	Northern Africa	DZA	DZ	36.7764	3.0586	NaN
3	American Samoa	D	0	American Samoa	NaN	NaN	Pago Pago	Oceania	Polynesia	ASM	AS	-14.2740	-170.7046	NaN
4	Andorra	C	1	Andorra	NaN	NaN	Andorra la Vella	Europe	Southern Europe	AND	AD	42.5000	1.5000	NaN

	Entity	Year	Wind	Solar	GeoBiomassOther	Hydro
0	Algeria	1965	0.0	0.0	0.0	0.400
1	Algeria	1966	0.0	0.0	0.0	0.355
2	Algeria	1967	0.0	0.0	0.0	0.410
3	Algeria	1968	0.0	0.0	0.0	0.563
4	Algeria	1969	0.0	0.0	0.0	0.361

	Entity	Year	Value
0	Afghanistan	2000	66.81
1	Afghanistan	2001	84.12
2	Afghanistan	2002	80.79
3	Afghanistan	2003	67.09
4	Afghanistan	2004	63.20

Data Cleaning and Structuring

Remove unnecessary columns

df_geo.drop(['Name2', 'Name3', 'Name4', 'IsUNMember', 'ISO2', 'Capital', 'Description'], axis = 1, inplace = True)

df_geo.head()

	Name	TypeCode	Continent	Region	ISO3	Latitude	Longitude
0	Afghanistan	C	Asia	Southern Asia	AFG	34.5328	69.1658
1	Albania	C	Europe	Southern Europe	ALB	41.3300	19.8200
2	Algeria	C	Africa	Northern Africa	DZA	36.7764	3.0586
3	American Samoa	D	Oceania	Polynesia	ASM	-14.2740	-170.7046
4	Andorra	C	Europe	Southern Europe	AND	42.5000	1.5000

Replace TypeCode 'codes' with 'names'

df_geo['TypeCode'].replace({'C': 'Country', 'D': 'Dependency', 'U': 'Unknown'}, inplace = True)
df_geo.head()

	Name	TypeCode	Continent	Region	ISO3	Latitude	Longitude
0	Afghanistan	Country	Asia	Southern Asia	AFG	34.5328	69.1658
1	Albania	Country	Europe	Southern Europe	ALB	41.3300	19.8200
2	Algeria	Country	Africa	Northern Africa	DZA	36.7764	3.0586
3	American Samoa	Dependency	Oceania	Polynesia	ASM	-14.2740	-170.7046
4	Andorra	Country	Europe	Southern Europe	AND	42.5000	1.5000

Round float columns to 2 digits

col_names = ['Wind', 'Solar', 'Hydro', 'GeoBiomassOther']

for col_name in col_names:
    df_wre[col_name] = df_wre[col_name].round(2)

df_wre.head()

	Entity	Year	Wind	Solar	GeoBiomassOther	Hydro
0	Algeria	1965	0.0	0.0	0.0	0.40
1	Algeria	1966	0.0	0.0	0.0	0.36
2	Algeria	1967	0.0	0.0	0.0	0.41
3	Algeria	1968	0.0	0.0	0.0	0.56
4	Algeria	1969	0.0	0.0	0.0	0.36

Rename Entity to Country

Since we will be only using the matching country records from the df_wre and df_wre_se datasets, we rename their Entity column to Country.

df_wre.rename(columns = {'Entity': 'Country'}, inplace = True)
df_wre_se.rename(columns = {'Entity': 'Country'}, inplace = True)

df_wre_se['Value'] = df_wre_se['Value'].round(2)

df_wre_se.head()

	Country	Year	Value
0	Afghanistan	2000	66.81
1	Afghanistan	2001	84.12
2	Afghanistan	2002	80.79
3	Afghanistan	2003	67.09
4	Afghanistan	2004	63.20

Add Total_WSO and Total columns

df_wre['Total_WSO'] = df_wre.apply(lambda x: x['Wind'] + x['Solar'] + x['GeoBiomassOther'], axis = 1)
df_wre['Total'] = df_wre.apply(lambda x: x['Hydro'] + x['Wind'] + x['Solar'] + x['GeoBiomassOther'], axis = 1)

df_wre

	Country	Year	Wind	Solar	GeoBiomassOther	Hydro	Total_WSO	Total
0	Algeria	1965	0.00	0.00	0.00	0.40	0.00	0.40
1	Algeria	1966	0.00	0.00	0.00	0.36	0.00	0.36
2	Algeria	1967	0.00	0.00	0.00	0.41	0.00	0.41
3	Algeria	1968	0.00	0.00	0.00	0.56	0.00	0.56
4	Algeria	1969	0.00	0.00	0.00	0.36	0.00	0.36
...	...	...	...	...	...	...	...	...
4085	Vietnam	2015	0.18	0.01	0.07	57.17	0.26	57.43
4086	Vietnam	2016	0.21	0.01	0.07	66.05	0.29	66.34
4087	Vietnam	2017	0.27	0.01	0.08	88.76	0.36	89.12
4088	Vietnam	2018	0.31	0.11	0.08	84.49	0.50	84.99
4089	Vietnam	2019	0.49	4.18	0.08	65.56	4.75	70.31

4090 rows × 8 columns

Merge Data and Country DataFrames

df_wre2 = pd.merge(df_wre, df_geo[['Name', 'TypeCode', 'Continent', 'Region', 'ISO3', 'Latitude', 'Longitude']], 
                   left_on='Country', right_on='Name')
df_wre2.drop(['Name'], axis = 1, inplace = True)

df_wre2.head()

	Country	Year	Wind	Solar	GeoBiomassOther	Hydro	Total_WSO	Total	TypeCode	Continent	Region	ISO3	Latitude	Longitude
0	Algeria	1965	0.0	0.0	0.0	0.40	0.0	0.40	Country	Africa	Northern Africa	DZA	36.7764	3.0586
1	Algeria	1966	0.0	0.0	0.0	0.36	0.0	0.36	Country	Africa	Northern Africa	DZA	36.7764	3.0586
2	Algeria	1967	0.0	0.0	0.0	0.41	0.0	0.41	Country	Africa	Northern Africa	DZA	36.7764	3.0586
3	Algeria	1968	0.0	0.0	0.0	0.56	0.0	0.56	Country	Africa	Northern Africa	DZA	36.7764	3.0586
4	Algeria	1969	0.0	0.0	0.0	0.36	0.0	0.36	Country	Africa	Northern Africa	DZA	36.7764	3.0586

df_wre_se2 = pd.merge(df_wre_se, df_geo[['Name', 'TypeCode', 'Continent', 'Region', 'ISO3', 'Latitude', 'Longitude']], 
                      left_on='Country', right_on='Name')
df_wre_se2.drop(['Name'], axis = 1, inplace = True)

df_wre_se2.head()

	Country	Year	Value	TypeCode	Continent	Region	ISO3	Latitude	Longitude
0	Afghanistan	2000	66.81	Country	Asia	Southern Asia	AFG	34.5328	69.1658
1	Afghanistan	2001	84.12	Country	Asia	Southern Asia	AFG	34.5328	69.1658
2	Afghanistan	2002	80.79	Country	Asia	Southern Asia	AFG	34.5328	69.1658
3	Afghanistan	2003	67.09	Country	Asia	Southern Asia	AFG	34.5328	69.1658
4	Afghanistan	2004	63.20	Country	Asia	Southern Asia	AFG	34.5328	69.1658

Save cleansed datasets

df_wre = df_wre2.copy()
df_wre_se = df_wre_se2.copy()

df_wre.to_csv('./data/wre_generation.csv', index = False)
df_wre_se.to_csv('./data/wre_share_of_electricity.csv', index = False)

Data Dictionary of Datasets

Column Name	Type	UOM	Description
Country	string		Country name
Year	int		1965-2019
Wind	float	terawatt-hours (TWh)	Wind generation
Solar	float	terawatt-hours (TWh)	Solar generation
GeoBiomassOther	float	terawatt-hours (TWh)	Geothermal, Biomass and Other generation
Hydro	float	terawatt-hours (TWh)	Hydro generation
Total_WSO	float
Total	float
TypeCode	string		Country, Dependency, Unknown
Continent	string
Region	string
Latitude	string
Longitude	string

Preparing aggregated data frames

ge_cols_full = ['Hydro', 'Wind', 'Solar', 'GeoBiomassOther', 'Total_WSO', 'Total']
ge_cols = ['Hydro', 'Wind', 'Solar', 'GeoBiomassOther']

By Year

df_wre_year = df_wre.groupby('Year')[ge_cols_full].sum().reset_index()
df_wre_year['Hydro %'] = round(df_wre_year['Hydro'] / df_wre_year['Total'] * 100, 1)
df_wre_year['Wind %'] = round(df_wre_year['Wind'] / df_wre_year['Total'] * 100, 1)
df_wre_year['Solar %'] = round(df_wre_year['Solar'] / df_wre_year['Total'] * 100, 1)
df_wre_year['GeoBiomassOther %'] = round(df_wre_year['GeoBiomassOther'] / df_wre_year['Total'] * 100, 1)

df_wre_year.tail()

	Year	Hydro	Wind	Solar	GeoBiomassOther	Total_WSO	Total	Hydro %	Wind %	Solar %	GeoBiomassOther %
50	2015	3576.21	823.34	254.34	519.12	1596.80	5173.01	69.1	15.9	4.9	10.0
51	2016	3687.44	954.00	323.63	527.79	1805.42	5492.86	67.1	17.4	5.9	9.6
52	2017	3731.78	1130.13	440.66	562.56	2133.35	5865.13	63.6	19.3	7.5	9.6
53	2018	3823.73	1256.28	574.27	593.38	2423.93	6247.66	61.2	20.1	9.2	9.5
54	2019	3883.94	1412.70	708.03	629.01	2749.74	6633.68	58.5	21.3	10.7	9.5

2019 by Continent

# Group dataset by Continent for 2019
df_wre_continent_2019 = df_wre[df_wre.Year == 2019].groupby('Continent')[ge_cols_full].sum().reset_index()
df_wre_continent_2019.sort_values('Total', ascending = False, inplace = True)

df_wre_continent_2019['Hydro %'] = round(df_wre_continent_2019['Hydro'] / df_wre_continent_2019['Total'] * 100, 1)
df_wre_continent_2019['Wind %'] = round(df_wre_continent_2019['Wind'] / df_wre_continent_2019['Total'] * 100, 1)
df_wre_continent_2019['Solar %'] = round(df_wre_continent_2019['Solar'] / df_wre_continent_2019['Total'] * 100, 1)
df_wre_continent_2019['GeoBiomassOther %'] = round(df_wre_continent_2019['GeoBiomassOther'] / df_wre_continent_2019['Total'] * 100, 1)

df_wre_continent_2019.head()

	Continent	Hydro	Wind	Solar	GeoBiomassOther	Total_WSO	Total	Hydro %	Wind %	Solar %	GeoBiomassOther %
1	Asia	1819.26	514.94	395.65	238.95	1149.54	2968.80	61.3	17.3	13.3	8.0
2	Europe	704.03	438.59	144.33	207.97	790.89	1494.92	47.1	29.3	9.7	13.9
3	North America	676.93	354.87	125.11	96.90	576.88	1253.81	54.0	28.3	10.0	7.7
5	South America	628.28	68.08	13.72	72.75	154.55	782.83	80.3	8.7	1.8	9.3
4	Oceania	39.88	21.78	18.08	12.01	51.87	91.75	43.5	23.7	19.7	13.1

2019 by Country with cumulative percentage information

# Group dataset by Country for 2019
df_wre_country_2019 = df_wre[df_wre.Year == 2019].groupby(['Country', 'Continent'])[ge_cols_full].sum().reset_index()
df_wre_country_2019.sort_values('Total', ascending = False, inplace = True)

df_wre_country_2019['Cum. Total %'] = (df_wre_country_2019.Total.cumsum() / df_wre_country_2019.Total.sum()) * 100
df_wre_country_2019['Cum. Total_WSO %'] = (df_wre_country_2019.Total_WSO.cumsum() / df_wre_country_2019.Total_WSO.sum()) * 100
df_wre_country_2019.head(5)

	Country	Continent	Hydro	Wind	Solar	GeoBiomassOther	Total_WSO	Total	Cum. Total %	Cum. Total_WSO %
12	China	Asia	1269.67	405.70	223.80	102.83	732.33	2002.00	30.179327	26.632700
74	United States	North America	271.16	303.10	108.36	78.34	489.80	760.96	41.650487	44.445293
8	Brazil	South America	399.30	55.83	5.56	56.25	117.64	516.94	49.443145	48.723516
10	Canada	North America	381.98	34.17	4.31	10.81	49.29	431.27	55.944363	50.516049
28	India	Asia	161.85	63.31	46.27	25.35	134.93	296.78	60.418199	55.423058

Data Visualization

InteractiveShell.ast_node_interactivity = 'last'

pio.templates['custom'] = go.layout.Template(
    layout_colorway=['#4285F4', '#DB4437', '#F4B400', '#0F9D58',
                     '#185ABC', '#B31412', '#EA8600', '#137333',
                     '#d2e3fc', '#ceead6']
)

pio.templates.default = 'custom'

World green energy generation by types between 1965 and 2019

fig = go.Figure()

annotations = []

def getAnnoDict(df, ndxYear, col_name, xanchor):
    ndx = df[df.Year == ndxYear].index[-1]
    val = df.iloc[ndx,][col_name + ' %']
    return dict(x=ndxYear, y=df.iloc[ndx,][col_name],
                xanchor=xanchor, yanchor='middle', text='{}%'.format(val), 
                font=dict(family='Arial', size=14), showarrow=False)

for i in range(len(ge_cols)):
    col_name = ge_cols[i]
    _ = fig.add_trace(go.Scatter(x = df_wre_year['Year'], y = df_wre_year[ge_cols[i]], name = ge_cols[i], 
                                 marker_size = 6, marker_line_width = 0))
    
    # labeling the left_side of the plot
    if col_name in ['Hydro']:
        annotations.append(getAnnoDict(df_wre_year, 1965, col_name, xanchor='right'))
        annotations.append(getAnnoDict(df_wre_year, 1970, col_name, xanchor='right'))
        annotations.append(getAnnoDict(df_wre_year, 1980, col_name, xanchor='right'))

    if col_name in ['Hydro', 'GeoBiomassOther']:
        annotations.append(getAnnoDict(df_wre_year, 1990, col_name, xanchor='right'))
        annotations.append(getAnnoDict(df_wre_year, 2000, col_name, xanchor='right'))
        annotations.append(getAnnoDict(df_wre_year, 2010, col_name, xanchor='right'))

    annotations.append(getAnnoDict(df_wre_year, 2015, col_name, xanchor='right'))

    # labeling the right side of the plot
    annotations.append(getAnnoDict(df_wre_year, 2019, col_name, xanchor='left'))

    
_ = fig.update_xaxes(title_text = 'Year')
_ = fig.update_yaxes(title_text = 'TWh')

_ = fig.update_traces(mode = 'lines+markers')
_ = fig.update_layout(showlegend = True,
                      title_text = 'World green energy generation by types between 1965 and 2019',
                      annotations=annotations)

fig.show()

World green energy generation in 2019 by continent

# Chart by continent and type
chart_data = []
for i in range(len(ge_cols)):
    chart_data.append(go.Bar(name = ge_cols[i], x = df_wre_continent_2019['Continent'], y = df_wre_continent_2019[ge_cols[i]]))

fig = go.Figure(data = chart_data)

_ = fig.update_xaxes(title_text = 'Continents')
_ = fig.update_yaxes(title_text = ' TWh')

_ = fig.update_layout(barmode='stack',
                      title_text = 'World green energy generation in 2019 by continent and type')

fig.show()

# Pareto charts for each continent
continents = ['Asia', 'Europe', 'North America', 'South America']

fig = make_subplots(rows = 2, cols = 2,
                   specs = [[{"secondary_y": True}, {"secondary_y": True}],
                            [{"secondary_y": True}, {"secondary_y": True}]],
                   subplot_titles = continents)


for i in range(len(continents)):
    rowNo = 1 if i < 2 else 2
    colNo = 1 if i in [0, 2] else 2
    
    df_tmp = df_wre_country_2019[df_wre_country_2019.Continent == continents[i]]
    df_tmp['Cum. Total %'] = (df_tmp.Total.cumsum() / df_tmp.Total.sum()) * 100
    df_tmp = df_tmp.nlargest(10 if continents[i] == 'Europe' else 7, columns = 'Total')
    df_pareto = [80] * df_tmp.size
    
    _ = fig.add_trace(go.Bar(x = df_tmp['Country'], y = df_tmp['Total'], name = continents[i]),
                     secondary_y = False, row = rowNo, col = colNo)
    _ = fig.add_trace(go.Scatter(x = df_tmp['Country'], y = df_tmp['Cum. Total %'],
                                 marker_size = 4, marker_line_width = 0, showlegend=False), 
                      secondary_y = True, row = rowNo, col = colNo)
    _ = fig.add_trace(go.Scatter(x = df_tmp['Country'], y = df_pareto, 
                                 line = dict(dash = 'dash'), hoverinfo = 'none', showlegend=False), 
                      secondary_y = True, row = rowNo, col = colNo)
    _ = fig.update_yaxes(title_text = '<b>Cum. %</b>', row = rowNo, col = colNo, secondary_y=True)
    _ = fig.update_yaxes(title_text = 'TWh', row = rowNo, col = colNo)

_ = fig.update_layout(height = 1000,
                      title_text = 'World green energy generation in 2019 by continent and top countries')

fig.show()

World total green energy generation map between 1965 and 2019

df_tmp = df_wre.copy()

px.choropleth(df_tmp,
              locations="ISO3",               
              color="Total",
              hover_name="Country",  
              animation_frame="Year",
              color_continuous_scale='RdBu',
              title='World total green energy generation map between 1965 and 2019',
              height=800,
              width=900
)

Green energy generation between 1965 and 2019 by top 5 countries in 2019

df_tmp = df_wre_country_2019.nlargest(5, columns = 'Total')

fig = go.Figure()

for index, row in df_tmp.iterrows():
    df_tmp2 = df_wre[df_wre['Country'] == row['Country']]
    _ = fig.add_trace(go.Scatter(x = df_tmp2['Year'], y = df_tmp2['Total'], name = row['Country'],
                                 marker_size = 4, marker_line_width = 0))
_ = fig.update_xaxes(title_text = 'Year')
_ = fig.update_yaxes(title_text = ' TWh')

_ = fig.update_traces(mode = 'lines+markers')
_ = fig.update_layout(showlegend = True,
                      title_text = 'Green energy generation between 1965 and 2019 by top 5 countries in 2019')

fig.show()

Green energy type breakdown in top 10 countries in 2019

df_tmp = df_wre_country_2019.nlargest(10, columns = 'Total'). \
                        groupby('Country')[ge_cols + ['Total']].sum().sort_values('Total', ascending = True)

for i in df_tmp.columns:
    df_tmp[i] = round(df_tmp[i] / df_tmp['Total'] * 100, 2)
    
df_tmp = df_tmp[ge_cols]
df_tmp = df_tmp.transpose()
df_top_10 = df_tmp.copy()

fig = go.Figure()

for index, row in df_tmp.iterrows():
    fig.add_trace(go.Bar(
            x=row, y=row.index, 
            name = index,
            orientation='h'
        ))
    
fig.update_layout(barmode = 'stack',
                 title = 'Green energy type breakdown in top 10 countries in 2019')

fig.show()

Top 5 producers of each green energy type in 2019

df_final = pd.DataFrame()
for i in range(len(ge_cols)):
    df_tmp = df_wre_country_2019[['Country', ge_cols[i]]]
    df_tmp = df_tmp.nlargest(5, columns = ge_cols[i])
    
    df_tmp2 = df_tmp[['Country']]
    df_tmp2['Type'] = ge_cols[i]
    df_tmp2['Value'] = df_tmp[ge_cols[i]]
    df_final = df_final.append(df_tmp2, ignore_index=True)

df_final['No'] = df_final.index
df_final

fig = px.treemap(df_final, path=['Type', 'Country'], values='Value',
                 title = 'Top 5 producers of each green energy type in 2019')
fig.show()

df_wre_se_2019 = df_wre_se[(df_wre_se.Year == 2019)].sort_values('Value', ascending = False)
countries = df_top_10.transpose().reset_index()['Country'].unique()

fig = go.Figure()

for i in range(len(countries)):
    df_tmp = df_wre_se[df_wre_se['Country'] == countries[i]]
    _ = fig.add_trace(go.Scatter(x = df_tmp['Year'], y = df_tmp['Value'], name = countries[i], 
                                 marker_size = 6, marker_line_width = 0))
    
_ = fig.update_xaxes(title_text = 'Year')
_ = fig.update_yaxes(title_text = '% share of electricity production')

_ = fig.update_traces(mode = 'lines+markers')
_ = fig.update_layout(showlegend = True,
                      title_text = 'Share of electricity production from green sources of top 10 producers in 2019')

fig.show()

References

• WHAT IS GREEN ENERGY? (DEFINITION, TYPES AND EXAMPLES)

• A guide to renewable energy

• Plotly Python Open Source Graphing Library

• Unofficial Jupyter Notebook Extensions
  • TOC 2 Extension
• nbconvert: Convert Notebooks to other formats
  • PlotlyHTMLExporter