1+ import streamlit as st
2+ import pandas as pd
3+ import matplotlib .pyplot as plt
4+ import seaborn as sns
5+ import geopandas as gpd
6+ import plotly .express as px
7+
8+ energy_columns = ['renewables_electricity' , 'coal_electricity' , 'gas_electricity' , 'oil_electricity' , 'nuclear_electricity' , 'wind_electricity' , 'solar_electricity' , "hydro_electricity" ]
9+ # Load and prepare data
10+ @st .cache_data
11+ def load_data ():
12+ url = "https://raw.githubusercontent.com/owid/energy-data/master/owid-energy-data.csv"
13+ df = pd .read_csv (url )
14+ df ['year' ] = pd .to_datetime (df ['year' ], format = '%Y' )
15+ df .rename (columns = {
16+ "renewables_electricity" : "Renewables" ,
17+ "hydro_electricity" : "Hydro" ,
18+ "solar_electricity" : "Solar" ,
19+ "wind_electricity" : "Wind" ,
20+ "coal_electricity" : 'Coal' ,
21+ 'gas_electricity' : "Gas" ,
22+ 'oil_electricity' : "Oil" ,
23+ 'nuclear_electricity' : "Nuclear"
24+ }, inplace = True )
25+
26+ df = df [~ df .country .isin ([
27+ "G20 (Ember)" ,
28+ "OECD (Ember)" ,
29+ "Asia (Ember)" ,
30+ "G7 (Ember)" ,
31+ "OECD (EI)" ,
32+ "Asia Pacific (EI)" ,
33+ "High-income countries" ,
34+ "Europe (Ember)" ,
35+ "Non-OECD (EI)" ,
36+ "Asia" ,
37+ "North America (Ember)" ,
38+ "Upper-middle-income countries" ,
39+ "Europe (EI)" ,
40+ "Oceania (Ember)" ,
41+ "ASEAN (Ember)" ,
42+ "Lower-middle-income countries" ,
43+ "European Union (27)" ,
44+ "North America (EI)"
45+ ])]
46+
47+ return df .sort_values ('year' )
48+
49+
50+ @st .cache_data
51+ def load_world_data (map_data ):
52+ world = gpd .read_file ("ne_110m_admin_0_countries/ne_110m_admin_0_countries.dbf" )
53+ # data processing
54+ world .rename (columns = {"ADMIN" : "country" }, inplace = True )
55+ # replace country name to fit the other dataset
56+ world .country = world .country .replace (
57+ ["United States of America" , "Democratic Republic of the Congo" , "Republic of the Congo" , "United Republic of Tanzania" , "The Bahamas" , "Czechia" , "eSwatini" , "Republic of Serbia" ],
58+ ["United States" , "Democratic Republic of Congo" , "Congo" , "Tanzania" , "Bahamas" , "Czechoslovakia" , "Eswatini" , "Serbia" ]
59+ )
60+
61+ # world.SOV_A3 = world.SOV_A3.replace(["US1", "CH1", "FR1", "KA1", "GB1", "NZ1", "AU1"], ["USA", "CHN", "FRA", "KAZ", "GBR", "NZL", "AUS"])
62+ # latest_year = recent_data['year'].max() - pd.Timedelta(days=365*2)
63+ # latest_data = recent_data[recent_data['year'] == latest_year]
64+ world = world .merge (map_data , on = ['country' ])
65+ return world
66+
67+
68+ @st .cache_data
69+ def get_map_plot (energy_type_map , year_map ):
70+ match energy_type_map :
71+ case "Solar" :
72+ color_scale = "Oranges"
73+ case "Wind" :
74+ color_scale = "Greens"
75+ case "Hydro" :
76+ color_scale = "Blues"
77+ case _ :
78+ color_scale = "Viridis"
79+
80+ fig = px .choropleth (world , locations = 'ADM0_A3' , color = energy_type_map ,
81+ hover_name = 'country' , projection = 'natural earth2' , color_continuous_scale = color_scale ,
82+ title = f'{ energy_type_map .replace ("_" , " " ).title ()} { year_map } )
83+
84+ return fig
85+ df = load_data ()
86+
87+
88+ # Streamlit app
89+ st .title ('Global Energy Production Analysis' )
90+ # st.write(df.columns)
91+ # st.write([col.split("_")[0].capitalize() for col in energy_columns])
92+ # Sidebar for user input
93+ st .sidebar .header ('Filter Data' )
94+ start_year = st .sidebar .slider ('Start Year' , 1900 , 2023 , 1980 )
95+ end_year = st .sidebar .slider ('End Year' , 1900 , 2022 , 2023 )
96+
97+ # Filter data based on selected years
98+ filtered_df = df [(df ['year' ].dt .year >= start_year ) & (df ['year' ].dt .year <= end_year )]
99+
100+ # Global trend plot
101+ st .header ('Global Energy Production Trends' )
102+ energy_types = st .multiselect (
103+ 'Select Energy Types' ,
104+ [col .split ("_" )[0 ].capitalize () for col in energy_columns ],
105+ default = ["Solar" , "Wind" , "Hydro" ]
106+ )
107+
108+ global_trend = filtered_df .groupby ('year' )[energy_types ].mean ()
109+
110+ fig = px .line (global_trend .reset_index (), x = 'year' , y = energy_types ,
111+ title = 'Global Energy Production Trends' )
112+ fig .update_yaxes (title = "Electricity Production (TWh)" )
113+ st .plotly_chart (fig )
114+
115+ # Map of energy production
116+ st .header ('Energy Production Map' )
117+ energy_type_map = st .selectbox ('Select Energy Type for Map' , energy_types )
118+ year_map = st .slider ('Select Year for Map' , start_year , end_year , end_year - 10 )
119+
120+
121+ map_data = filtered_df [filtered_df ['year' ].dt .year == year_map ]
122+ world = load_world_data (map_data )
123+
124+ fig = get_map_plot (energy_type_map , year_map )
125+ fig .update_layout (margin = {"r" :0 ,"t" :0 ,"l" :0 ,"b" :0 })
126+ st .plotly_chart (fig )
127+
128+ # Top countries comparison
129+ st .header ('Top Countries Comparison' )
130+ n_countries = st .slider ('Number of top countries to compare' , 1 , 20 , 15 )
131+ top_countries = filtered_df .groupby ('country' )[energy_types [0 ]].mean ().nlargest (n_countries ).index .tolist ()
132+
133+ st .write (top_countries )
134+
135+ top_countries_data = filtered_df [filtered_df ['country' ].isin (top_countries )]
136+
137+ fig = px .line (top_countries_data , x = 'year' , y = energy_types [0 ], color = 'country' ,
138+ title = f'Top { n_countries } { energy_types [0 ].replace ("_" , " " ).title ()} )
139+ st .plotly_chart (fig )
140+
141+ # Energy mix comparison
142+ st .header ('Energy Mix Comparison' )
143+ selected_country = st .selectbox ('Select a Country' , df ['country' ].unique (), index = None )
144+ if selected_country == None :
145+ selected_country = "Italy"
146+
147+ country_data = filtered_df [filtered_df ['country' ] == selected_country ]
148+ # st.write(country_data)
149+
150+
151+ energy_mix = country_data [energy_types ]
152+ energy_mix ['year' ] = country_data .year
153+
154+ fig = px .area (energy_mix .dropna (), x = 'year' , y = energy_types ,
155+ title = f'Energy Mix for { selected_country } )
156+ fig .update_yaxes (title = "Electricity Production (TWh)" )
157+ st .plotly_chart (fig )
158+ # st.write(energy_mix)
159+
160+
161+ st .write ("""
162+ This Streamlit app provides an interactive analysis of global energy production trends.
163+ You can use the sidebar and various selectors to customize the visualizations and explore different aspects of energy production data.
164+ """ )
0 commit comments