import numpy as np 
import pandas as pd 
import datetime as dt
import matplotlib.pyplot as plt
import seaborn as sns
import folium
import geopandas as gpd
import plotly.express as px
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import AgglomerativeClustering
import plotly.graph_objects as go
import statsmodels.api as sm
from plotly.subplots import make_subplots
from sklearn.cluster import KMeans
from matplotlib.font_manager import FontProperties
from IPython.display import HTML as html_print
from termcolor import colored
from scipy import stats

df1= pd.read_csv("C:/Users/wambu/FaithWambui/Renewable Energy in Africa/Final Data Used/Final ISO Africa Data 1990-2021.csv")  
df2= pd.read_csv("C:/Users/wambu/FaithWambui/Renewable Energy in Africa/Final Data Used/Final ISO SDG7 Indicators Data 2000-2021.csv") 
df3= pd.read_csv("C:/Users/wambu/FaithWambui/Renewable Energy in Africa/Final Data Used/Final ISO CAGR and 2021 Data.csv")

def print_section_title(title):
    print(colored(title, 'blue', attrs=['bold', 'underline']))

def display_head_and_tail(dataframe, head=5):
    display(dataframe.head(head).style.set_caption("Head"))
    display(dataframe.tail(head).style.set_caption("Tail"))

def display_na(dataframe):
    na_df1 = dataframe.isnull().sum().reset_index()
    na_df1.columns = ['Column', 'Number of NA']
    display(na_df1.style.set_caption("Number of NA Values"))

def display_quantiles(dataframe):
    quantiles_df1 = dataframe.describe([0, 0.05, 0.50, 0.95, 0.99, 1]).T
    display(quantiles_df1.style.format("{:.2f}").set_caption("Quantiles"))

def check_df1(dataframe, head=5):
    print_section_title('Shape')
    print(dataframe.shape)
    print_section_title('Types')
    print(dataframe.dtypes.to_frame('Data Type').style.set_caption("Data Types"))
    print_section_title('Head & Tail')
    display_head_and_tail(dataframe, head)
    print_section_title('NA Values')
    display_na(dataframe)
    print_section_title('Quantiles')
    display_quantiles(dataframe)

check_df1(df1)

Shape
(1728, 8)
Types
<pandas.io.formats.style.Styler object at 0x000001BAA6124690>
Head & Tail

NA Values

Quantiles

def print_section_title(title):
    print(colored(title, 'blue', attrs=['bold', 'underline']))

def display_head_and_tail(dataframe, head=5):
    display(dataframe.head(head).style.set_caption("Head"))
    display(dataframe.tail(head).style.set_caption("Tail"))

def display_na(dataframe):
    na_df2 = dataframe.isnull().sum().reset_index()
    na_df2.columns = ['Column', 'Number of NA']
    display(na_df2.style.set_caption("Number of NA Values"))

def display_quantiles(dataframe):
    quantiles_df2 = dataframe.describe([0, 0.05, 0.50, 0.95, 0.99, 1]).T
    display(quantiles_df2.style.format("{:.2f}").set_caption("Quantiles"))

def check_df2(dataframe, head=5):
    print_section_title('Shape')
    print(dataframe.shape)
    print_section_title('Types')
    print(dataframe.dtypes.to_frame('Data Type').style.set_caption("Data Types"))
    print_section_title('Head & Tail')
    display_head_and_tail(dataframe, head)
    print_section_title('NA Values')
    display_na(dataframe)
    print_section_title('Quantiles')
    display_quantiles(dataframe)

check_df2(df2)

Shape
(1144, 9)
Types
<pandas.io.formats.style.Styler object at 0x000001BAA0A61990>
Head & Tail

NA Values

Quantiles

def print_section_title(title):
    print(colored(title, 'blue', attrs=['bold', 'underline']))

def display_head_and_tail(dataframe, head=5):
    display(dataframe.head(head).style.set_caption("Head"))
    display(dataframe.tail(head).style.set_caption("Tail"))

def display_na(dataframe):
    na_df3 = dataframe.isnull().sum().reset_index()
    na_df3.columns = ['Column', 'Number of NA']
    display(na_df3.style.set_caption("Number of NA Values"))

def display_quantiles(dataframe):
    quantiles_df3 = dataframe.describe([0, 0.05, 0.50, 0.95, 0.99, 1]).T
    display(quantiles_df3.style.format("{:.2f}").set_caption("Quantiles"))

def check_df3(dataframe, head=5):
    print_section_title('Shape')
    print(dataframe.shape)
    print_section_title('Types')
    print(dataframe.dtypes.to_frame('Data Type').style.set_caption("Data Types"))
    print_section_title('Head & Tail')
    display_head_and_tail(dataframe, head)
    print_section_title('NA Values')
    display_na(dataframe)
    print_section_title('Quantiles')
    display_quantiles(dataframe)

check_df3(df3)

Shape
(54, 7)
Types
<pandas.io.formats.style.Styler object at 0x000001BAA6A98E50>
Head & Tail

NA Values

Quantiles

# Animated choropleth map
fig = px.choropleth(df1, 
                    locations="ISO Code", 
                    locationmode="ISO-3", 
                    color="Renewable energy consumption (% of total final energy consumption)",
                    hover_name="Country Name", 
                    animation_frame="Year",  # To create the animation over years
                    color_continuous_scale=px.colors.sequential.Viridis,
                    projection="natural earth",
                    title="Progression of Renewable Energy Adoption in Africa",
                    labels={'Renewable energy consumption (% of total final energy consumption)': 'Renewable Energy Consumption (%)'})  # Custom color scale name)

 # Updating layout to focus on Africa and enlarge the map
fig.update_geos(
        showcoastlines=True, coastlinecolor="Black",
        showland=True, landcolor="white",
        showocean=True, oceancolor="white",
        showlakes=True, lakecolor="LightBlue",
        scope="africa" 
    )

    # Enlarge the figure size
fig.update_layout(
        width=1000,  # Set the width of the figure
        height=800,  # Set the height of the figure
        margin={"r":0,"t":50,"l":0,"b":0}  # Adjust margins to make the map bigger
    )

fig.write_html('animated_map.html')
# Show the map
fig.show()

african_countries = sorted(df1['Country Name'].unique().tolist())

fig = go.Figure()

for country in african_countries:
    country_data = df1[df1['Country Name'] == country]
    fig.add_trace(
        go.Scatter(
            x=country_data['Year'],
            y=country_data['Renewable energy consumption (% of total final energy consumption)'],
            mode='lines+markers',
            name=country,
            visible=True
        )
    )

# Create and add dropdown menu for multi-select
updatemenus = list([
    dict(
        buttons=[
            dict(
                args=[{'visible': [True for _ in african_countries]},
                      {'title': 'Renewable Energy Consumption in All African Countries'}],
                label="Select All",
                method='update'
            ),
            dict(
                args=[{'visible': [False for _ in african_countries]},
                      {'title': 'Renewable Energy Consumption in Selected African Countries'}],
                label="Deselect All",
                method='update'
            )
        ] + [
            dict(
                args=[{'visible': [country == c for c in african_countries]},
                      {'title': f'Renewable Energy Consumption in {country}'}],
                label=country,
                method='update'
            ) for country in african_countries
        ],
        direction="down",
        pad={"r": 10, "t": 10},
        showactive=True,
        x=0.1,
        xanchor="left",
        y=1.1,
        yanchor="top"
    ),
])

# Update layout
fig.update_layout(
    updatemenus=updatemenus,
    xaxis_title='Year',
    yaxis_title='Renewable Energy Consumption (%)',
    hovermode='x'
)

# Add range slider and selector
fig.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=5, label="Last 5 years", step="year", stepmode="backward"),
                dict(count=10, label="Last 10 years", step="year", stepmode="backward"),
                dict(count=20, label="Last 20 years", step="year", stepmode="backward"),
                dict(step="all")
            ])
        ),
        rangeslider=dict(
            visible=True
        ),
        type="date"
    )
)

# Show the figure
fig.show()

# Grouping by 'Country' and calculating the mean of 'Renewable Energy Consumption'
averaged_df1 = df1.groupby('Country Name')['Renewable energy consumption (% of total final energy consumption)'].mean().reset_index()

# Sorting by the averaged 'Renewable Energy Consumption'
sorted_averaged_df1 = averaged_df1.sort_values(by='Renewable energy consumption (% of total final energy consumption)', ascending=False)

# Displaying top 10 performers
top_performers = sorted_averaged_df1.head(10)
print("Top 10 Performers (Averaged):")
print(top_performers)

# Displaying bottom 10 performers
bottom_performers = sorted_averaged_df1.tail(10)
print("\nBottom 10 Performers (Averaged):")
print(bottom_performers)

Top 10 Performers (Averaged):
                Country Name  \
11          Congo, Dem. Rep.   
51                    Uganda   
19                  Ethiopia   
44                   Somalia   
5                    Burundi   
8   Central African Republic   
27                   Liberia   
48                  Tanzania   
24             Guinea-Bissau   
35                Mozambique   

    Renewable energy consumption (% of total final energy consumption)  
11                                          96.462500                   
51                                          94.246875                   
19                                          93.918750                   
44                                          92.740625                   
5                                           92.296875                   
8                                           91.965625                   
27                                          89.890625                   
48                                          89.028125                   
24                                          88.262500                   
35                                          87.940625                   

Bottom 10 Performers (Averaged):
    Country Name  \
14      Djibouti   
7     Cape Verde   
33     Mauritius   
34       Morocco   
50       Tunisia   
45  South Africa   
15         Egypt   
28         Libya   
42    Seychelles   
0        Algeria   

    Renewable energy consumption (% of total final energy consumption)  
14                                          30.284375                   
7                                           25.125000                   
33                                          20.950000                   
34                                          15.546875                   
50                                          13.703125                   
45                                          12.275000                   
15                                           7.053125                   
28                                           2.690625                   
42                                           1.653125                   
0                                            0.312500

# Creating subplots for better comparison
fig, axes = plt.subplots(2, 1, figsize=(12, 6), sharex=True)

# Top Performers Plot
sns.barplot(x='Renewable energy consumption (% of total final energy consumption)', y='Country Name', 
            data=top_performers, palette='Greens_d', ax=axes[0])
axes[0].set_title('Top 10 Performers in Renewable Energy Consumption', fontsize=12)
axes[0].set_xlabel('')  # Remove x-axis label for the top plot to avoid redundancy
axes[0].set_ylabel('Country', fontsize=10)
axes[0].bar_label(axes[0].containers[0], fmt='%.2f', label_type='edge')  # Add data labels

# Bottom Performers Plot
sns.barplot(x='Renewable energy consumption (% of total final energy consumption)', y='Country Name', 
            data=bottom_performers, palette='Reds_d', ax=axes[1])
axes[1].set_title('Bottom 10 Performers in Renewable Energy Consumption', fontsize=12)
axes[1].set_xlabel('Renewable Energy Consumption (%Total Final)', fontsize=10)
axes[1].set_ylabel('Country', fontsize=12)
axes[1].bar_label(axes[1].containers[0], fmt='%.2f', label_type='edge')  # Add data labels

# Adjust layout and show the plot

plt.show()

# Calculate the annual change
df1['Annual Change'] = df1.groupby('Country Name')['Renewable energy consumption (% of total final energy consumption)'].diff()

# Group by Country and periods of 10 years to get the periodic changes
period_years = 10 
periodic_changes = df1.groupby(['Country Name', (df1['Year'] // period_years) * period_years])['Annual Change'].sum().reset_index()

# Get the top 10 countries based on the total change rate
top_countries = periodic_changes.groupby('Country Name')['Annual Change'].sum().sort_values(ascending=False).head(10)

# Plot the bar chart and capture the Axes object
ax = top_countries.plot(kind='bar', figsize=(10, 5), title='Most Improved Countries', xlabel='Country', ylabel='Total Change Rate')
plt.xticks(rotation=45)

# Add labels on top of the bars
for i, v in enumerate(top_countries):
    ax.text(i, v + 0.01, f"{v:.2f}", ha='center', va='bottom', fontsize=12, color='black')

# Show the plot
plt.show()

df1['Annual Change'] = df1.groupby('Country Name')['Renewable energy consumption (% of total final energy consumption)'].diff()

period_years = 10  
periodic_changes = df1.groupby(['Country Name', (df1['Year'] // period_years) * period_years])['Annual Change'].sum().reset_index()

bottom_countries = periodic_changes.groupby('Country Name')['Annual Change'].sum().sort_values().head(10)

ax=bottom_countries.plot(kind='bar', figsize=(12, 6), title='Least Improving Countries', xlabel='Country', ylabel='Total Change Rate')

# Add labels on top of the bars
for i, v in enumerate(bottom_countries):
    ax.text(i, v + 0.01, f"{v:.2f}", ha='center', va='top', fontsize=12, color='black')
    
plt.xticks(rotation=45)
plt.show()

# Sorting the dataframe
df1 = df1.sort_values(['Country Name', 'Year'])

# Calculating the year-over-year change
df1['YoY_Change'] = df1.groupby('Country Name')['Renewable energy consumption (% of total final energy consumption)'].pct_change() * 100

df1['YoY_Change'] = df1['YoY_Change'].fillna(0)

# Calculating the simple average growth rate
average_growth_rate = df1['YoY_Change'].mean()

print(f"Simple Average Growth Rate: {average_growth_rate:.2f}%")


# Plotting the average renewable energy percentage over time
yearly_average = df1.groupby('Year')['Renewable energy consumption (% of total final energy consumption)'].mean()
plt.figure(figsize=(12, 6))
plt.plot(yearly_average.index, yearly_average.values)
plt.title('Average Renewable Energy Consumption Across African Countries')
plt.xlabel('Year')
plt.ylabel('Average Renewable Energy Consumption (%Total Final)')
plt.show()

C:\Users\wambu\AppData\Local\Temp\ipykernel_11112\3870675418.py:5: FutureWarning:

The default fill_method='ffill' in SeriesGroupBy.pct_change is deprecated and will be removed in a future version. Either fill in any non-leading NA values prior to calling pct_change or specify 'fill_method=None' to not fill NA values.

Simple Average Growth Rate: -0.62%

# Plotting the distribution of growth rates
plt.figure(figsize=(12, 6))
plt.hist(df1['YoY_Change'], bins=50)
plt.title('Distribution of Year-over-Year Changes in Renewable Energy Consumption')
plt.xlabel('Year-over-Year Change (%)')
plt.ylabel('Frequency')
plt.show()

# List of African countries
african_countries = sorted(df1['Country Name'].unique().tolist())

# Create figure
fig = go.Figure()

# Loop through each African country and add a trace
for country in african_countries:
    country_data = df1[df1['Country Name'] == country]
    fig.add_trace(
        go.Scatter(
            x=country_data['GDP/Capita'],
            y=country_data['Renewable energy consumption (% of total final energy consumption)'],
            mode='markers',
            name=country,
            text=country_data['Year'],  # Display the year on hover
            visible=True
        )
    )

# Create and add dropdown menu for multi-select
updatemenus = list([
    dict(
        buttons=[
            dict(
                args=[{'visible': [True for _ in african_countries]},
                      {'title': 'GDP per Capita vs Renewable Energy Consumption in All African Countries'}],
                label="Select All",
                method='update'
            ),
            dict(
                args=[{'visible': [False for _ in african_countries]},
                      {'title': 'GDP per Capita vs Renewable Energy Consumption in Selected African Countries'}],
                label="Deselect All",
                method='update'
            )
        ] + [
            dict(
                args=[{'visible': [country == c for c in african_countries]},
                      {'title': f'GDP per Capita vs Renewable Energy Consumption in {country}'}],
                label=country,
                method='update'
            ) for country in african_countries
        ],
        direction="down",
        pad={"r": 10, "t": 10},
        showactive=True,
        x=0.1,
        xanchor="left",
        y=1.1,
        yanchor="top"
    ),
])

# Update layout
fig.update_layout(
    updatemenus=updatemenus,
    xaxis_title='GDP per Capita (USD)',
    yaxis_title='Renewable Energy Consumption (%Total Final)',
    title='GDP per Capita vs Renewable Energy Consumption in African Countries',
    hovermode='closest',
    xaxis=dict(type='log')  # Optional: Use logarithmic scale for GDP per Capita
)

# Add range slider and selector for year (optional)
fig.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=5, label="Last 5 years", step="year", stepmode="backward"),
                dict(count=10, label="Last 10 years", step="year", stepmode="backward"),
                dict(count=20, label="Last 20 years", step="year", stepmode="backward"),
                dict(step="all")
            ])
        ),
        rangeslider=dict(
            visible=True
        ),
        type="linear"  # Keep it linear for GDP per Capita
    )
)

# Show the figure
fig.show()

african_countries = sorted(df1['Country Name'].unique().tolist())

fig = go.Figure()

# Loop through each country to create traces for both FDI/GDP and Renewable Energy
for country in african_countries:
    country_data = df1[df1['Country Name'] == country]
    
    # FDI/GDP Ratio trace
    fig.add_trace(
        go.Bar(
            x=country_data['Year'],
            y=country_data['FDI/GDP (%)'],
            name=f'{country} FDI/GDP%',
            visible=True
        )
    )
    
    # Renewable Energy Consumption trace
    fig.add_trace(
        go.Bar(
            x=country_data['Year'],
            y=country_data['Renewable energy consumption (% of total final energy consumption)'],
            name=f'{country} Renewable Energy %',
            visible=True
        )
    )

# Create dropdown menu for multi-select
updatemenus = list([
    dict(
        buttons=[
            dict(
                args=[{'visible': [True for _ in range(2 * len(african_countries))]},
                      {'title': 'FDI/GDP% vs Renewable Energy in All African Countries'}],
                label="Select All",
                method='update'
            ),
            dict(
                args=[{'visible': [False for _ in range(2 * len(african_countries))]},
                      {'title': 'FDI/GDP% vs Renewable Energy in Selected African Countries'}],
                label="Deselect All",
                method='update'
            )
        ] + [
            dict(
                args=[{'visible': [(country == c or country == c) for c in african_countries for _ in range(2)]},
                      {'title': f'FDI/GDP% vs Renewable Energy in {country}'}],
                label=country,
                method='update'
            ) for country in african_countries
        ],
        direction="down",
        pad={"r": 10, "t": 10},
        showactive=True,
        x=0.1,
        xanchor="left",
        y=1.1,
        yanchor="top"
    ),
])

# Update layout
fig.update_layout(
    updatemenus=updatemenus,
    barmode='group',  # Group bars together for comparison
    xaxis_title='Year',
    yaxis_title='Values',
    hovermode='x'
)

# Add range slider and selector
fig.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=5, label="Last 5 years", step="year", stepmode="backward"),
                dict(count=10, label="Last 10 years", step="year", stepmode="backward"),
                dict(count=20, label="Last 20 years", step="year", stepmode="backward"),
                dict(step="all")
            ])
        ),
        rangeslider=dict(
            visible=True
        ),
        type="date"
    )
)

# Show the figure
fig.show()

heatmap_data = df2.pivot(index='Country Name', columns='Year', values='Renewable energy consumption (% of total final energy consumption)')

# Create the heatmap
plt.figure(figsize=(10, 10))
sns.heatmap(heatmap_data, annot=False, cmap="viridis")
plt.title('Renewable  Energy Consumption (% of total final energy consumption)')
plt.xlabel('Year')
plt.ylabel('Country Name')
plt.xticks(rotation=45)
plt.yticks(rotation=0)
plt.show()

heatmap_data = df2.pivot(index='Country Name', columns='Year', values='Renewable Capacity(Watts/capita)')

# Create the heatmap
plt.figure(figsize=(10, 10))
sns.heatmap(heatmap_data, annot=False, cmap="Blues")
plt.title('Distribution of Renewable  Capacity (Watts/Capita)')
plt.xlabel('Year')
plt.ylabel('Country Name')
plt.xticks(rotation=45)
plt.yticks(rotation=0)
plt.show()

heatmap_data = df2.pivot(index='Country Name', columns='Year', values='Renewable Support(USD-M)')

# Create the heatmap
plt.figure(figsize=(10, 10))
sns.heatmap(heatmap_data, annot=False, cmap="Greens")
plt.title('Renewable Investment Support (USD-M)')
plt.xlabel('Year')
plt.ylabel('Country Name')
plt.xticks(rotation=45)
plt.yticks(rotation=0)
plt.show()

heatmap_data = df2.pivot(index='Country Name', columns='Year', values='Energy Intensity Level(MJ_PER_GDP_CON_PPP_USD)')

# Create the heatmap
plt.figure(figsize=(10, 10))
sns.heatmap(heatmap_data, annot=False, cmap="Reds")
plt.title('Energy Intensity Level')
plt.xlabel('Year')
plt.ylabel('Country Name')
plt.xticks(rotation=45)
plt.yticks(rotation=0)
plt.show()

heatmap_data = df2.pivot(index='Country Name', columns='Year', values='Elec Access (%)')

# Create the heatmap
plt.figure(figsize=(10, 10))
sns.heatmap(heatmap_data, annot=False, cmap="RdYlGn")
plt.title('Elec Access (%)')
plt.xlabel('Year')
plt.ylabel('Country Name')
plt.xticks(rotation=45)
plt.yticks(rotation=0)
plt.show()

heatmap_data = df2.pivot(index='Country Name', columns='Year', values=' Clean Energy  (%)')

# Create the heatmap
plt.figure(figsize=(10, 10))
sns.heatmap(heatmap_data, annot=False, cmap="YlGnBu")
plt.title('Clean Energy Access (%)')
plt.xlabel('Year')
plt.ylabel('Country Name')
plt.xticks(rotation=45)
plt.yticks(rotation=0)
plt.show()

# Function to calculate CAGR
def calculate_cagr(start_value, end_value, num_years):
    return (end_value / start_value) ** (1 / num_years) - 1

# Filter the DataFrame for the years 1990 to 2021
df1_filtered = df1[(df1['Year'] >= 1990) & (df1['Year'] <= 2021)]

# Get first and last year for each country within the filtered range
first_last = df1_filtered.groupby('Country Name').agg({
    'Year': ['first', 'last'],
    'Renewable energy consumption (% of total final energy consumption)': ['first', 'last']
})

# Calculate CAGR for each country
first_last['CAGR'] = calculate_cagr(
    first_last[('Renewable energy consumption (% of total final energy consumption)', 'first')],
    first_last[('Renewable energy consumption (% of total final energy consumption)', 'last')],
    first_last[('Year', 'last')] - first_last[('Year', 'first')]
)

# Calculate average CAGR across all countries
average_cagr = first_last['CAGR'].mean() * 100

print(f"Average CAGR across countries (1990-2021): {average_cagr:.2f}%")

# Sort countries by CAGR
cagr_sorted = first_last.sort_values('CAGR', ascending=False)

# Print sorted CAGR values for each country
print(cagr_sorted[['CAGR']])

Average CAGR across countries (1990-2021): -0.91%
                              CAGR
                                  
Country Name                      
Zimbabwe                  0.008185
Gabon                     0.004967
Somalia                   0.003239
Congo, Rep.               0.002835
Cape Verde                0.002218
South Sudan               0.001846
Eswatini                  0.001620
Congo, Dem. Rep.          0.001475
Liberia                   0.001452
Libya                     0.000000
Zambia                    0.000000
Rwanda                   -0.000283
Guinea-Bissau            -0.000440
Cameroon                 -0.000963
Central African Republic -0.001047
Madagascar               -0.001359
Togo                     -0.001509
Uganda                   -0.001925
Ethiopia                 -0.001933
Nigeria                  -0.002876
Djibouti                 -0.003441
Eritrea                  -0.003477
Kenya                    -0.003934
Burundi                  -0.004172
Niger                    -0.004449
Malawi                   -0.005364
Tanzania                 -0.005638
Sudan                    -0.005908
Mozambique               -0.006113
Burkina Faso             -0.006185
Mali                     -0.006518
Sao Tome and Principe    -0.007109
Tunisia                  -0.007172
Cote d'Ivoire            -0.007544
Gambia                   -0.007824
Chad                     -0.007942
Sierra Leone             -0.008383
Guinea                   -0.009392
Angola                   -0.010027
Namibia                  -0.011173
Egypt                    -0.012468
Lesotho                  -0.013989
Senegal                  -0.014401
South Africa             -0.017182
Benin                    -0.017329
Botswana                 -0.018576
Comoros                  -0.018714
Algeria                  -0.022111
Morocco                  -0.022968
Ghana                    -0.023145
Mauritania               -0.024047
Seychelles               -0.028754
Mauritius                -0.053378
Equatorial Guinea        -0.086082

# Sort countries by CAGR for plotting
cagr_sorted = first_last.sort_values('CAGR', ascending=False)

# Reset index for easier plotting
cagr_sorted = cagr_sorted.reset_index()

# Plotting the CAGR values using a bar chart
plt.figure(figsize=(12, 8))
sns.barplot(x='CAGR', y='Country Name', data=cagr_sorted, palette='viridis')

# Adding labels and title
plt.xlabel('CAGR (1990-2021)')
plt.ylabel('Country Name')
plt.title('CAGR of Renewable Energy Consumption by Country (1990-2021)')

# Show the plot
plt.show()

# Define the function to calculate CAGR
def calculate_cagr(start_value, end_value, num_years):
    return (end_value / start_value) ** (1 / num_years) - 1

# Define the rolling window size (e.g., 5-year CAGR)
rolling_window = 5

# Create an empty list to store results
cagr_results = []

# Loop through each country to calculate staggered CAGRs
for country in df1['Country Name'].unique():
    country_data = df1[df1['Country Name'] == country].sort_values('Year')
    
    # Calculate CAGR for each rolling window
    for i in range(len(country_data) - rolling_window):
        start_year = country_data.iloc[i]['Year']
        end_year = country_data.iloc[i + rolling_window]['Year']
        start_value = country_data.iloc[i]['Renewable energy consumption (% of total final energy consumption)']
        end_value = country_data.iloc[i + rolling_window]['Renewable energy consumption (% of total final energy consumption)']
        
        cagr_value = calculate_cagr(start_value, end_value, end_year - start_year)
        
        # Store the results in the list
        cagr_results.append({
            'Country Name': country,
            'Start Year': start_year,
            'End Year': end_year,
            'CAGR': cagr_value
        })

# Convert the list of results to a DataFrame
cagr_staggered = pd.DataFrame(cagr_results)

# Display the staggered CAGR results
print(cagr_staggered)

     Country Name  Start Year  End Year      CAGR
0         Algeria        1990      1995  0.148698
1         Algeria        1991      1996  0.059224
2         Algeria        1992      1997  0.107566
3         Algeria        1993      1998  0.000000
4         Algeria        1994      1999  0.045640
...           ...         ...       ...       ...
1453     Zimbabwe        2012      2017  0.012662
1454     Zimbabwe        2013      2018  0.001007
1455     Zimbabwe        2014      2019  0.000495
1456     Zimbabwe        2015      2020  0.008038
1457     Zimbabwe        2016      2021  0.001708

[1458 rows x 4 columns]

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces for each country
for country in cagr_staggered['Country Name'].unique():
    country_data = cagr_staggered[cagr_staggered['Country Name'] == country]
    fig.add_trace(
        go.Scatter(x=country_data['End Year'], y=country_data['CAGR'], name=country, mode='lines'),
        secondary_y=False,
    )

# Add range slider
fig.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=1, label="1y", step="year", stepmode="backward"),
                dict(count=5, label="5y", step="year", stepmode="backward"),
                dict(count=10, label="10y", step="year", stepmode="backward"),
                dict(step="all")
            ])
        ),
        rangeslider=dict(visible=True),
        type="date"
    )
)

# Update layout
fig.update_layout(
    title='Staggered CAGR of Renewable Energy Consumption (%Total Final) for Multiple Countries',
    xaxis_title='End Year',
    yaxis_title='CAGR',
    legend_title='Country Name',
    hovermode="x unified"
)

# Add dropdown menu for country selection
updatemenus = [
    dict(
        buttons=list([
            dict(label='All Countries',
                 method='update',
                 args=[{'visible': [True] * len(fig.data)},
                       {'title': 'All Countries'}]),
        ] + [
            dict(label=country,
                 method='update',
                 args=[{'visible': [country == trace.name for trace in fig.data]},
                       {'title': f'Staggered CAGR of Renewable Energy Consumption for {country}'}])
            for country in cagr_staggered['Country Name'].unique()
        ]),
        direction="down",
        pad={"r": 10, "t": 10},
        showactive=True,
        x=0.1,
        xanchor="left",
        y=1.1,
        yanchor="top"
    ),
]

fig.update_layout(updatemenus=updatemenus)

# Show the plot
fig.show()

df1_cluster = df1.groupby('Country Name')['Renewable energy consumption (% of total final energy consumption)'].mean().reset_index()

kmeans = KMeans(n_clusters=4, n_init=7, random_state=1)

df1_cluster['Cluster'] = kmeans.fit_predict(df1_cluster[['Renewable energy consumption (% of total final energy consumption)']])

fig = px.scatter(df1_cluster, x='Country Name', y='Renewable energy consumption (% of total final energy consumption)', color='Cluster',
                 title='Cluster Analysis of Countries Based on Renewable Energy Consumption (1990-2021)',
                 labels={'Renewable energy consumption (% of total final energy consumption)': 'Average Renewable Energy Consumption (%)'},
                 hover_data=['Country Name'])

fig.update_layout(xaxis={'categoryorder':'total ascending'})
fig.show()

# Group the countries by their assigned cluster and print the countries in each cluster
for cluster in range(kmeans.n_clusters):
    countries_in_cluster = df1_cluster[df1_cluster['Cluster'] == cluster]['Country Name'].tolist()
    print(f"Cluster {cluster + 0}: {', '.join(countries_in_cluster)}")

Cluster 0: Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Eritrea, Ethiopia, Gabon, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Nigeria, Rwanda, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zambia
Cluster 1: Botswana, Cape Verde, Djibouti, Equatorial Guinea, Lesotho, Mauritania, Namibia, Sao Tome and Principe, Senegal, South Sudan
Cluster 2: Angola, Benin, Comoros, Congo, Rep., Cote d'Ivoire, Eswatini, Gambia, Ghana, Kenya, Sudan, Zimbabwe
Cluster 3: Algeria, Egypt, Libya, Mauritius, Morocco, Seychelles, South Africa, Tunisia

# Create a scatter plot
fig = px.scatter(df3, x='Renewable energy consumption (% of total final energy consumption)',
                 y='CAGR (1990-2021)',
                 trendline='ols',  # Add a linear regression trendline
                 hover_data=['Country Name'],  # Show country name on hover
                 title='Renewable Energy Consumption vs. CAGR')

# Customize the plot as needed
fig.update_layout(xaxis_title='Renewable Energy Consumption (%Total Final)-2021 ',
                  yaxis_title='CAGR (1990-2021)')

# Show the plot
fig.show()

# Create a scatter plot with hover information
fig = px.scatter(df3, x='FDI/GDP (%)', y='CAGR (1990-2021)',
                 hover_data=['Country Name'],  # Show country name on hover
                 title='Risk-Reward Matrix')

# Customize the plot layout
fig.update_layout(xaxis_title='FDI/GDP (Investment Attractiveness)',
                  yaxis_title='CAGR (Potential Reward)')

# Add reference lines
fig.add_hline(y=0, line_color='red', line_dash='dash', annotation_text="Zero Growth")
fig.add_vline(x=df3['FDI/GDP (%)'].mean(), line_color='red', line_dash='dash',
              annotation_text=f"Average FDI/GDP ({df3['FDI/GDP (%)'].mean():.2f})")

# Save as an interactive HTML file
fig.write_html("risk-reward matrix.html")

# Show the interactive plot
fig.show()

#Calculate correlation between FDI/GDP and renewable energy percentage
correlation = df3['FDI/GDP (%)'].corr(df3['Renewable energy consumption (% of total final energy consumption)'])
print(f"Correlation between FDI/GDP and renewable energy percentage: {correlation}")

Correlation between FDI/GDP and renewable energy percentage: 0.01494076407871146

df3 = df3.rename(columns={

    'Renewable energy consumption (% of total final energy consumption)': 'Renewable Energy Consumption',
})
corr_matrix = df3[['CAGR (1990-2021)', 'Renewable Energy Consumption', 'FDI/GDP (%)']].corr()

# Create the interactive heatmap
fig = px.imshow(
    corr_matrix,  # Data source
    x=corr_matrix.columns,  # Column labels (variable names)
     y=corr_matrix.columns,  # Row labels (reversed for readability)
    text_auto=True,  # Display correlation values on the chart
    zmin=-1,  # Minimum color value for the colormap
    zmax=1,  # Maximum color value for the colormap
    title='Correlation Heatmap',
)

# Customize color scale and colorbar
fig.update_traces(colorscale='RdBu_r')  # Use a red-blue colormap for positive/negative correlations
fig.update_layout(coloraxis=dict(colorbar=dict(title="Correlation Coefficient")))  # Customize colorbar

# Customize appearance
fig.update_layout(
    width=800,  # Increase figure width
    height=600,  # Increase figure height
    xaxis_title_font_size=12,  # Increase font size for x-axis title
    yaxis_title_font_size=12,  # Increase font size for y-axis title
    font=dict(size=14),  # Increase overall font size for text and labels
    title_font=dict(size=20),  # Increase font size for the title
    title_x=0.5,  # Center the title
    margin=dict(l=80, r=80, t=100, b=80),  # Adjust margins for better spacing
    xaxis=dict(tickangle=-45),  # Rotate x-axis labels for better readability
)

# Show the interactive heatmap
fig.show()

df2 = df2.rename(columns={
    'Country Name': 'Country',
    'Renewable energy consumption (% of total final energy consumption)': 'Renewable Energy Consumption',
    'Renewable Capacity(Watts/capita)': 'Renewable Capacity',
    'Renewable Support(USD-M)': 'Renewable Support',
    'Energy Intensity Level(MJ_PER_GDP_CON_PPP_USD)': 'Energy Intensity',
    ' Clean Energy  (%)': 'Clean Energy (%)',
    'Elec Access (%)': 'Electricity Access (%)'})

# Function to compute correlations for a specific year
def compute_correlations(year):
    year_data = df2[df2['Year'] == year]
    return year_data.corr(method='pearson', numeric_only=True)['Renewable Energy Consumption'].sort_values(ascending=False)
    
# Compute correlations for the most recent year
latest_year = df2['Year'].max()
correlations = compute_correlations(latest_year)

print(f"Correlations with Renewable Energy Consumption (Year {latest_year}):")
print(correlations)

# Visualize correlations
plt.figure(figsize=(10, 5))
correlations.plot(kind='bar')
plt.title(f'Correlation with Renewable Energy Consumption (Year {latest_year})')
plt.ylabel('Correlation Coefficient')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Correlations with Renewable Energy Consumption (Year 2021):
Renewable Energy Consumption    1.000000
Energy Intensity                0.546470
Renewable Support               0.117629
Renewable Capacity             -0.259934
Electricity Access (%)         -0.626136
Clean Energy (%)               -0.675254
Year                                 NaN
Name: Renewable Energy Consumption, dtype: float64

# Select indicators for composite score
indicators = ['Renewable Energy Consumption', 'Renewable Capacity', 'Clean Energy (%)', 'Electricity Access (%)', 'Renewable Support','Energy Intensity']

# Function to normalize data
def normalize(series):
    return (series - series.min()) / (series.max() - series.min())

# Create composite score for the most recent year
latest_year = df2['Year'].max()
latest_data = df2[df2['Year'] == latest_year].copy()  # Create a full copy of the data

# Normalize indicators and create composite score
normalized_indicators = []
for indicator in indicators:
    if indicator in latest_data.columns:
        latest_data[f'{indicator}_normalized'] = normalize(latest_data[indicator])
        normalized_indicators.append(f'{indicator}_normalized')
    else:
        print(f"Warning: {indicator} not found in the dataframe. Skipping this indicator.")

if normalized_indicators:
    latest_data['Composite_Score'] = latest_data[normalized_indicators].mean(axis=1)
else:
    print("Error: No indicators available for composite score calculation.")
    exit()

# Assuming you have the composite score calculated in latest_data['Composite_Score']
latest_data.sort_values(by='Composite_Score', ascending=False, inplace=True)

plt.figure(figsize=(12, 6))
plt.bar(latest_data['Country'], latest_data['Composite_Score'])
plt.xlabel('Country')
plt.ylabel('Composite Score')
plt.title('Composite Score for SDG 7 Indicators in Renewable Energy Adoption')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

# Calculate progress over time
first_year = df2['Year'].min()
progress_df2 = df2[df2['Year'].isin([first_year, latest_year])].pivot(index='Country', columns='Year', values='Renewable Energy Consumption')
progress_df2['Progress'] = progress_df2[latest_year] - progress_df2[first_year]

# Merge progress with latest data
analysis_df2 = pd.merge(latest_data, progress_df2['Progress'], on='Country')

# Identify top performers (top 10% in both composite score and progress)
composite_threshold = analysis_df2['Composite_Score'].quantile(0.9)
progress_threshold = analysis_df2['Progress'].quantile(0.9)

top_performers = analysis_df2[(analysis_df2['Composite_Score'] >= composite_threshold) & 
                             (analysis_df2['Progress'] >= progress_threshold)]

print("\nTop Performing Countries:")
print(top_performers[['Country', 'Composite_Score', 'Progress']])

Top Performing Countries:
    Country  Composite_Score  Progress
0     Gabon         0.650817      18.5
4  Zimbabwe         0.494736      12.9
5  Eswatini         0.473948       5.3

# Compare top performers with others
analysis_df2['Is_Top_Performer'] = analysis_df2['Country'].isin(top_performers['Country'])

# Example: Compare Renewable Capacity
plt.figure(figsize=(10, 5))
sns.boxplot(x='Is_Top_Performer', y='Renewable Energy Consumption', data=analysis_df2)
plt.title('Renewable Energy Consumption: Top Performers vs Others')
plt.show()

# Statistical test
top_capacity = analysis_df2[analysis_df2['Is_Top_Performer']]['Renewable Energy Consumption']
others_capacity = analysis_df2[~analysis_df2['Is_Top_Performer']]['Renewable Energy Consumption']

t_stat, p_value = stats.ttest_ind(top_capacity, others_capacity)
print(f"\nT-test for Renewable Energy Consumption (Top Performers vs Others):")
print(f"T-statistic: {t_stat}, p-value: {p_value}")

T-test for Renewable Energy Consumption (Top Performers vs Others):
T-statistic: 1.3838594585609165, p-value: 0.17254826982701596

# Compare top performers with others
analysis_df2['Is_Top_Performer'] = analysis_df2['Country'].isin(top_performers['Country'])

# Example: Compare Renewable Capacity
plt.figure(figsize=(10, 5))
sns.boxplot(x='Is_Top_Performer', y='Renewable Capacity', data=analysis_df2)
plt.title('Renewable Capacity: Top Performers vs Others')
plt.show()

# Statistical test
top_capacity = analysis_df2[analysis_df2['Is_Top_Performer']]['Renewable Capacity']
others_capacity = analysis_df2[~analysis_df2['Is_Top_Performer']]['Renewable Capacity']

t_stat, p_value = stats.ttest_ind(top_capacity, others_capacity)
print(f"\nT-test for Renewable Capacity (Top Performers vs Others):")
print(f"T-statistic: {t_stat}, p-value: {p_value}")

T-test for Renewable Capacity (Top Performers vs Others):
T-statistic: 2.783349819800951, p-value: 0.00757348175122658

# Compare top performers with others
analysis_df2['Is_Top_Performer'] = analysis_df2['Country'].isin(top_performers['Country'])

# Example: Compare Renewable Capacity
plt.figure(figsize=(10, 5))
sns.boxplot(x='Is_Top_Performer', y='Clean Energy (%)', data=analysis_df2)
plt.title('Clean Energy (%): Top Performers vs Others')
plt.show()

# Statistical test
top_capacity = analysis_df2[analysis_df2['Is_Top_Performer']]['Clean Energy (%)']
others_capacity = analysis_df2[~analysis_df2['Is_Top_Performer']]['Clean Energy (%)']

t_stat, p_value = stats.ttest_ind(top_capacity, others_capacity)
print(f"\nT-test for Clean Energy (%) (Top Performers vs Others):")
print(f"T-statistic: {t_stat}, p-value: {p_value}")

T-test for Clean Energy (%) (Top Performers vs Others):
T-statistic: nan, p-value: nan

# Compare top performers with others
analysis_df2['Is_Top_Performer'] = analysis_df2['Country'].isin(top_performers['Country'])

# Example: Compare Renewable Capacity
plt.figure(figsize=(10, 5))
sns.boxplot(x='Is_Top_Performer', y='Electricity Access (%)', data=analysis_df2)
plt.title('Electricity Access (%): Top Performers vs Others')
plt.show()

# Statistical test
top_capacity = analysis_df2[analysis_df2['Is_Top_Performer']]['Electricity Access (%)']
others_capacity = analysis_df2[~analysis_df2['Is_Top_Performer']]['Electricity Access (%)']

t_stat, p_value = stats.ttest_ind(top_capacity, others_capacity)
print(f"\nT-test for Electricity Access (%) (Top Performers vs Others):")
print(f"T-statistic: {t_stat}, p-value: {p_value}")

T-test for Electricity Access (%) (Top Performers vs Others):
T-statistic: 1.2344626640083942, p-value: 0.2228009622887451

# Compare top performers with others
analysis_df2['Is_Top_Performer'] = analysis_df2['Country'].isin(top_performers['Country'])

# Example: Compare Renewable Capacity
plt.figure(figsize=(10, 5))
sns.boxplot(x='Is_Top_Performer', y='Energy Intensity', data=analysis_df2)
plt.title('Energy Intensity (%): Top Performers vs Others')
plt.show()

# Statistical test
top_capacity = analysis_df2[analysis_df2['Is_Top_Performer']]['Energy Intensity']
others_capacity = analysis_df2[~analysis_df2['Is_Top_Performer']]['Energy Intensity']

t_stat, p_value = stats.ttest_ind(top_capacity, others_capacity)
print(f"\nT-test for Energy Intensity (Top Performers vs Others):")
print(f"T-statistic: {t_stat}, p-value: {p_value}")

T-test for Energy Intensity (Top Performers vs Others):
T-statistic: 1.5377336561449886, p-value: 0.13041863289148292

# Compare top performers with others
analysis_df2['Is_Top_Performer'] = analysis_df2['Country'].isin(top_performers['Country'])

# Example: Compare Renewable Capacity
plt.figure(figsize=(10, 5))
sns.boxplot(x='Is_Top_Performer', y='Renewable Support', data=analysis_df2)
plt.title('Renewable Support: Top Performers vs Others')
plt.show()

# Statistical test
top_capacity = analysis_df2[analysis_df2['Is_Top_Performer']]['Renewable Support']
others_capacity = analysis_df2[~analysis_df2['Is_Top_Performer']]['Renewable Support']

t_stat, p_value = stats.ttest_ind(top_capacity, others_capacity)
print(f"\nT-test for Renewable Support (Top Performers vs Others):")
print(f"T-statistic: {t_stat}, p-value: {p_value}")

T-test for Renewable Support (Top Performers vs Others):
T-statistic: -0.6250798682680001, p-value: 0.5347611347854928

# Select a top performer for demonstration
example_country = top_performers['Country'].iloc[0]

country_data = df2[df2['Country'] == example_country]

plt.figure(figsize=(12, 6))
plt.plot(country_data['Year'], country_data['Renewable Energy Consumption'])
plt.title(f'Renewable Energy Consumption Over Time: {example_country}')
plt.xlabel('Year')
plt.ylabel('Renewable Energy Consumption')
plt.show()

# Select a top performer for demonstration
example_country = top_performers['Country'].iloc[1]

country_data = df2[df2['Country'] == example_country]

plt.figure(figsize=(12, 6))
plt.plot(country_data['Year'], country_data['Renewable Energy Consumption'])
plt.title(f'Renewable Energy Consumption Over Time: {example_country}')
plt.xlabel('Year')
plt.ylabel('Renewable Energy Consumption')
plt.show()

# Select a top performer for demonstration
example_country = top_performers['Country'].iloc[2]

country_data = df2[df2['Country'] == example_country]

plt.figure(figsize=(12, 6))
plt.plot(country_data['Year'], country_data['Renewable Energy Consumption'])
plt.title(f'Renewable Energy Consumption Over Time: {example_country}')
plt.xlabel('Year')
plt.ylabel('Renewable Energy Consumption')
plt.show()

# Get the top three performers
top_three_countries = top_performers['Country'].iloc[:3]

# Initialize a figure for plotting
plt.figure(figsize=(12, 6))

# Loop through the top three countries and plot their data
for country in top_three_countries:
    country_data = df2[df2['Country'] == country]
    plt.plot(country_data['Year'], country_data['Renewable Energy Consumption'], label=country)

# Add title and labels
plt.title('Renewable Energy Consumption Over Time: Top 3 Countries')
plt.xlabel('Year')
plt.ylabel('Renewable Energy Consumption')
plt.legend(title='Country')

# Show the plot
plt.show()

df1 = df1.rename(columns={
    'Renewable energy consumption (% of total final energy consumption)': 'Renewable Energy Consumption',
    'FDI/GDP (%)': 'FDI/GDP',
})

def correlation_analysis(df1):
    # Calculate correlation while ignoring missing values
    correlation = df1['Renewable Energy Consumption'].corr(df1['GDP Growth Rate'], method='pearson', min_periods=1)
    
    # Plot the scatterplot, automatically skipping missing values
    plt.figure(figsize=(10, 6))
    sns.scatterplot(x='Renewable Energy Consumption', y='GDP Growth Rate', data=df1)
    
    # Title with the correlation value
    plt.title(f'Correlation between Renewable Energy % and GDP Growth Rate: {correlation:.2f}')
    
    # Display the plot
    plt.show()

# Call the function
correlation_analysis(df1)

def regression_analysis(df1):
    # Create lagged variables
    for lag in range(1, 4):
        df1[f'renewable_energy_consumption_lag{lag}'] = df1.groupby('Country Name')['Renewable Energy Consumption'].shift(lag)
        df1[f'FDI/GDP_lag{lag}'] = df1.groupby('Country Name')['FDI/GDP'].shift(lag)
    
    # Drop rows with missing values
    df1.dropna(inplace=True)
    
    # Prepare the data for regression
    X = df1[['renewable_energy_consumption_lag1', 'renewable_energy_consumption_lag2', 'renewable_energy_consumption_lag3',
             'FDI/GDP_lag1', 'FDI/GDP_lag2', 'FDI/GDP_lag3']]
    y = df1['GDP Growth Rate']
    
    # Add a constant to the independent variables
    X = sm.add_constant(X)
    
    # Fit the model
    model = sm.OLS(y, X).fit()
    
    # Print the summary
    print(model.summary())

# Call the function
regression_analysis(df1)

                            OLS Regression Results                            
==============================================================================
Dep. Variable:        GDP Growth Rate   R-squared:                       0.039
Model:                            OLS   Adj. R-squared:                  0.035
Method:                 Least Squares   F-statistic:                     8.895
Date:                Tue, 12 Nov 2024   Prob (F-statistic):           1.55e-09
Time:                        13:21:27   Log-Likelihood:                -5688.6
No. Observations:                1322   AIC:                         1.139e+04
Df Residuals:                    1315   BIC:                         1.143e+04
Df Model:                           6                                         
Covariance Type:            nonrobust                                         
=====================================================================================================
                                        coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------------------------
const                                 3.6284      1.172      3.097      0.002       1.330       5.927
renewable_energy_consumption_lag1    -0.2877      0.216     -1.333      0.183      -0.711       0.136
renewable_energy_consumption_lag2     0.1786      0.302      0.591      0.555      -0.414       0.771
renewable_energy_consumption_lag3     0.1523      0.216      0.706      0.480      -0.271       0.575
FDI/GDP_lag1                          0.2458      0.060      4.100      0.000       0.128       0.363
FDI/GDP_lag2                          0.0039      0.064      0.061      0.952      -0.121       0.129
FDI/GDP_lag3                          0.1363      0.060      2.277      0.023       0.019       0.254
==============================================================================
Omnibus:                     1319.850   Durbin-Watson:                   1.939
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           213605.145
Skew:                           4.262   Prob(JB):                         0.00
Kurtosis:                      64.686   Cond. No.                         283.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

# Prepare the data for fitting the model
X = df1['Renewable Energy Consumption']
y = df1['GDP Growth Rate']

# Add a constant to the predictor variable (required for statsmodels)
X = sm.add_constant(X)

# Fit the model
model = sm.OLS(y, X).fit()

# Plot the scatter plot
plt.figure(figsize=(10, 6))
plt.scatter(df1['Renewable Energy Consumption'], df1['GDP Growth Rate'], label='Data Points')

# Plot the fitted line
plt.plot(df1['Renewable Energy Consumption'], model.fittedvalues, color='red', label='Fitted Line')

# Add labels and title
plt.xlabel('Renewable Energy Consumption')
plt.ylabel('GDP Growth Rate')
plt.title('Regression Analysis: Renewable Energy Consumption vs. GDP Growth Rate')
plt.legend()

# Show the plot
plt.show()

def select_case_studies(df1):
    # Calculate the growth rate of renewable energy percentage
    df1['Renewable Energy Growth'] = df1.groupby('Country Name')['Renewable Energy Consumption'].pct_change()
    
    # Drop rows where Renewable Energy Growth is NaN
    df1.dropna(subset=['Renewable Energy Growth'], inplace=True)
    
    # Calculate the average growth rate for each country, skipping missing values
    avg_growth = df1.groupby('Country Name')['Renewable Energy Growth'].mean().sort_values(ascending=False)
    
    # Select top 5 countries with the highest average growth
    top_countries = avg_growth.head()
    
    # Display the result
    print("Top 5 countries with significant renewable energy growth:")
    print(top_countries)
    
    # Return the list of top countries
    return top_countries.index.tolist()

# Call the function
selected_countries = select_case_studies(df1)

Top 5 countries with significant renewable energy growth:
Country Name
Libya         0.019200
Gabon         0.010612
Zimbabwe      0.008969
Eritrea       0.004330
Seychelles    0.003791
Name: Renewable Energy Growth, dtype: float64

# Plot renewable energy consumption over time for case study countries
plt.figure(figsize=(12, 6))
for country in selected_countries:
    country_data = df1[df1['Country Name'] == country]
    plt.plot(country_data['Year'], country_data['Renewable Energy Consumption'], label=country)

plt.title('Renewable Energy Consumption Over Time for Case Study Countries')
plt.xlabel('Year')
plt.ylabel('Renewable Energy Consumption(%)')
plt.legend()
plt.show()

# Display the first few rows of the data
print(df3.head())

   Country Name  \
0       Algeria   
1        Angola   
2         Benin   
3      Botswana   
4  Burkina Faso   

   Renewable energy consumption (% of total final energy consumption)  \
0                                                0.1                    
1                                               52.9                    
2                                               54.5                    
3                                               27.4                    
4                                               71.2                    

  ISO Code  CAGR (1990-2021)  CAGR (2016-2021)  FDI/GDP (%)  GDP/Capita  
0      DZA         -0.022111          0.000000        0.467       11000  
1      AGO         -0.010027          0.019206       -6.550        5910  
2      BEN         -0.017329          0.037213        1.960        3320  
3      BWA         -0.018576          0.005189       -1.700       14900  
4      BFA         -0.006185         -0.015796       -0.407        2180

df3 = df3.rename(columns={
    'Renewable energy consumption (% of total final energy consumption)': 'Renewable Energy Consumption',
    'CAGR (2016-2021)': 'Renewable Growth Rate',
    'GDP/Capita':'GDP per Capita'
})

# Separate features for clustering
features = ['Renewable Energy Consumption', 'Renewable Growth Rate', 'GDP per Capita']
X = df3[features]

# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Perform hierarchical clustering
n_clusters =4  # Adjust based on your needs
clustering = AgglomerativeClustering(n_clusters=n_clusters)
df3['Cluster'] = clustering.fit_predict(X_scaled)

# Create an interactive scatter plot
fig = px.scatter(df3, 
                 x='Renewable Energy Consumption', 
                 y='Renewable Growth Rate', 
                 color='Cluster', 
                 size='GDP per Capita', 
                 hover_name='Country Name',
                 title='Country Clusters based on Renewable Energy and Growth',
                 labels={'Renewable Energy Consumption': 'Renewable Energy %', 
                         'Renewable Growth Rate': 'Growth Rate'})

# Customize the color scale
fig.update_traces(marker=dict(opacity=0.7), 
                  selector=dict(mode='markers'))

# Update layout for better interactivity
fig.update_layout(legend_title_text='Cluster',
                  xaxis=dict(title='Renewable Energy Consumption (%)'),
                  yaxis=dict(title='Growth Rate'),
                  coloraxis_colorbar=dict(title="Cluster"))

# Save as an interactive HTML file
fig.write_html("country_clusters.html")

# Show the interactive plot
fig.show()

# Print a list of clusters with the countries assigned to each
clustered_countries = df3.groupby('Cluster')['Country Name'].apply(list)
print("Countries in each cluster:")
for cluster, countries in clustered_countries.items():
    print(f"Cluster {cluster}: {countries}")

Countries in each cluster:
Cluster 0: ['Algeria', 'Botswana', 'Cape Verde', 'Egypt', 'Equatorial Guinea', 'Libya', 'Mauritius', 'Morocco', 'Namibia', 'Seychelles', 'South Africa', 'Tunisia']
Cluster 1: ['Angola', 'Benin', 'Congo, Rep.', "Cote d'Ivoire", 'Djibouti', 'Eswatini', 'Gabon', 'Gambia', 'Ghana', 'Kenya', 'Sao Tome and Principe', 'Senegal', 'South Sudan', 'Sudan']
Cluster 2: ['Comoros', 'Lesotho', 'Mauritania']
Cluster 3: ['Burkina Faso', 'Burundi', 'Cameroon', 'Central African Republic', 'Chad', 'Congo, Dem. Rep.', 'Eritrea', 'Ethiopia', 'Guinea', 'Guinea-Bissau', 'Liberia', 'Madagascar', 'Malawi', 'Mali', 'Mozambique', 'Niger', 'Nigeria', 'Rwanda', 'Sierra Leone', 'Somalia', 'Tanzania', 'Togo', 'Uganda', 'Zambia', 'Zimbabwe']

# Function to identify the leader in each cluster
def identify_leader(cluster_df3):
    # Adjust the scoring method based on specific priorities
    cluster_df3['Score'] = (
        cluster_df3['Renewable Energy Consumption'] + 
        cluster_df3['Renewable Growth Rate'] * 10 + 
        cluster_df3['GDP per Capita'] / 1000
    )
    return cluster_df3.loc[cluster_df3['Score'].idxmax()]

# Identify leaders for each cluster
leaders = df3.groupby('Cluster').apply(identify_leader)
print("\nPotential Regional Leaders:")
print(leaders[['Country Name', 'Renewable Energy Consumption', 'Renewable Growth Rate', 'GDP per Capita']])

# Create an interactive scatter plot with regional leaders highlighted
fig = px.scatter(df3, 
                 x='Renewable Energy Consumption', 
                 y='Renewable Growth Rate', 
                 color='Cluster', 
                 size='GDP per Capita', 
                 hover_name='Country Name',
                 title='Country Clusters with Regional Leaders',
                 labels={'Renewable Energy Consumption': 'Renewable Energy (%)', 
                         'Renewable Growth Rate': 'Growth Rate'})

# Highlight the regional leaders
fig.add_trace(px.scatter(leaders, 
                         x='Renewable Energy Consumption', 
                         y='Renewable Growth Rate',
                         size='GDP per Capita',
                         color_discrete_sequence=['red'], 
                         hover_name='Country Name',
                         symbol_sequence=['star']).data[0])

# Update layout for better interactivity and clarity
fig.update_traces(marker=dict(opacity=0.5),
                  selector=dict(mode='markers'))
fig.update_layout(legend_title_text='Cluster',
                  xaxis=dict(title='Renewable Energy Consumption'),
                  yaxis=dict(title='Renewable Growth Rate'),
                  coloraxis_colorbar=dict(title="Cluster"),
                  legend=dict(itemsizing='constant'))
# Save as an interactive HTML file
fig.write_html("regional_leaders.html")

# Show the interactive plot
fig.show()

Potential Regional Leaders:
             Country Name  Renewable Energy Consumption  \
Cluster                                                   
0                Botswana                          27.4   
1                   Gabon                          91.3   
2                 Comoros                          41.2   
3        Congo, Dem. Rep.                          96.3   

         Renewable Growth Rate  GDP per Capita  
Cluster                                         
0                     0.005189           14900  
1                     0.022969           13800  
2                    -0.084321            3230  
3                    -0.001447            1070

kenya_data = df1[df1['Country Name'] == 'Kenya']

fig = go.Figure(go.Scatter(
    x=kenya_data['Year'],
    y=kenya_data['Renewable energy consumption (% of total final energy consumption)'],
    mode='lines+markers',
    name='Renewable Energy Consumption '
))

fig.update_layout(
    title='Renewable Energy Consumption in Kenya 1990-2021',
    xaxis_title='Year',
    yaxis_title='Renewable Energy Consumption (%)',
    hovermode='x'
)

# Define a list of countries to compare with Kenya
countries_to_compare = ['Kenya', 'Gabon', 'Zimbabwe', 'Eswatini']

# Filter the DataFrame using loc for efficient selection
compare_data = df1.loc[df1['Country Name'].isin(countries_to_compare)]
traces = []  # List to store traces for each country

for country in countries_to_compare:
    country_data = df1[df1['Country Name'] == country]
    traces.append(go.Scatter(
        x=country_data['Year'],
        y=country_data['Renewable energy consumption (% of total final energy consumption)'],
        mode='lines',
        name=country
    ))

fig = go.Figure(data=traces)

fig.update_layout(
    title='Renewable Energy Consumption in Kenya compared with the Top Three Countries in Africa',
    xaxis_title='Year',
    yaxis_title='Renewable Energy Consumption (%)',
    hovermode='x'
)

fig.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=[
                dict(count=5, label="Last 5 years", step="year", stepmode="backward"),
                dict(count=10, label="Last 10 years", step="year", stepmode="backward"),
                dict(count=20, label="Last 20 years", step="year", stepmode="backward"),
                dict(step="all")
            ]
        ),
        rangeslider=dict(
            visible=True
        ),
        type="date"
    )
)

top_countries = df1['Country Name'].value_counts().head(20).index

filtered_data = df1[df1['Country Name'].isin(top_countries)]

kenya_data = df1[df1['Country Name'] == 'Kenya']

filtered_data_updated = pd.concat([filtered_data, kenya_data])

heatmap_data= filtered_data_updated.pivot(index='Country Name', columns='Year', values='Renewable energy consumption (% of total final energy consumption)')

plt.figure(figsize=(10, 5))
sns.heatmap(heatmap_data, annot=False, cmap="viridis")
plt.title('Distribution of Renewable Energy Consumption for the Top 20 Countries and Kenya (Heatmap)')
plt.xlabel('Year')
plt.ylabel('Country Name')
plt.xticks(rotation=45)
plt.yticks(rotation=0)
plt.show()

# Filter data for Kenya
kenya_data = df3[df3['Country Name'] == 'Kenya']

# Get top 5 with highest and lowest consumption (assuming sorted by year)
top_5_highest = df3.nlargest(5, 'Renewable energy consumption (% of total final energy consumption)')  # Use nlargest for efficient selection
top_5_lowest = df3.nsmallest(5, 'Renewable energy consumption (% of total final energy consumption)')   # Use nsmallest for efficient selection

# Combine Kenya data with top 5 highest and lowest
combined_data = pd.concat([top_5_lowest, kenya_data, top_5_highest])

# Assign colors based on ranking
colors = []
for entity in combined_data['Country Name']:
    if entity in top_5_lowest['Country Name'].values:
        colors.append('red')
    elif entity in kenya_data['Country Name'].values:
        colors.append('green')
    else:
        colors.append('blue')

# Create the bar chart with labels
plt.figure(figsize=(16, 8))
bars = plt.bar(combined_data['Country Name'], combined_data['Renewable energy consumption (% of total final energy consumption)'], color=colors)
for bar in bars:
    height = bar.get_height()
    # Improved text positioning logic
    text_x = bar.get_x() + bar.get_width() / 2
    plt.text(text_x, height + 0.5, f'{height:.2f}', ha='center', fontsize=12, fontweight='bold')

# Set plot title, labels, and formatting
plt.title(f'Kenya and Countries with the Highest and Lowest Renewable Energy Consumption Rates in 2021', fontsize=16, fontweight='bold')
plt.xlabel('Country', fontsize=14, fontweight='bold')
plt.ylabel('Renewable Energy Consumption (%)', fontsize=14, fontweight='bold')

plt.xticks(rotation=45)
plt.grid(True, linestyle='--', alpha=0.7)
plt.tick_params(axis='both', which='major', labelsize=12, width=2)

plt.tight_layout()
plt.show()

	Year	Country Name	ISO Code	Renewable energy consumption (% of total final energy consumption)	GDP/Capita	GDP	GDP Growth Rate	FDI/GDP (%)
1723	2017	Zimbabwe	ZWE	82.000000	2330	17600.000000	-14.146341	1.750000
1724	2018	Zimbabwe	ZWE	79.700000	2400	34200.000000	94.318182	2.100000
1725	2019	Zimbabwe	ZWE	81.000000	2200	21800.000000	-36.257310	1.140000
1726	2020	Zimbabwe	ZWE	84.100000	1990	21500.000000	-1.376147	0.699000
1727	2021	Zimbabwe	ZWE	82.400000	2120	28400.000000	32.093023	0.881000

	count	mean	std	min	0%	5%	50%	95%	99%	100%	max
Year	1728.00	2005.50	9.24	1990.00	1990.00	1991.00	2005.50	2020.00	2021.00	2021.00	2021.00
Renewable energy consumption (% of total final energy consumption)	1703.00	62.04	29.99	0.10	0.10	3.00	74.30	94.50	96.90	98.30	98.30
GDP/Capita	1728.00	4873.90	5689.83	387.00	387.00	849.00	2630.00	16665.00	28100.00	36300.00	36300.00
GDP	1686.00	27941.43	66002.38	72.30	72.30	492.50	6675.00	129750.00	390800.00	574000.00	574000.00
GDP Growth Rate	1632.00	7.09	18.90	-84.10	-84.10	-18.09	6.34	32.59	71.12	305.52	305.52
FDI/GDP (%)	1661.00	3.70	9.08	-82.90	-82.90	-0.33	1.83	12.10	39.42	162.00	162.00

	Year	Country Name	ISO Code	Renewable energy consumption (% of total final energy consumption)	Renewable Capacity(Watts/capita)	Renewable Support(USD-M)	Energy Intensity Level(MJ_PER_GDP_CON_PPP_USD)	Clean Energy (%)	Elec Access (%)
0	2000	Angola	AGO	73.800000	14.369780	0.000000	3.770000	40.900000	24.200000
1	2001	Angola	AGO	72.800000	13.911210	0.000000	3.740000	41.350000	20.000000
2	2002	Angola	AGO	70.800000	13.454910	0.000000	3.410000	41.500000	26.300000
3	2003	Angola	AGO	65.600000	13.003400	0.050000	3.560000	42.100000	27.400000
4	2004	Angola	AGO	62.300000	26.401130	0.040000	3.230000	41.800000	28.400000

	Year	Country Name	ISO Code	Renewable energy consumption (% of total final energy consumption)	Renewable Capacity(Watts/capita)	Renewable Support(USD-M)	Energy Intensity Level(MJ_PER_GDP_CON_PPP_USD)	Clean Energy (%)	Elec Access (%)
1139	2017	Zimbabwe	ZWE	82.000000	60.002570	6.450000	13.620000	30.100000	44.000000
1140	2018	Zimbabwe	ZWE	79.700000	78.965220	0.010000	13.640000	30.300000	45.400000
1141	2019	Zimbabwe	ZWE	81.000000	77.621130	0.270000	14.350000	30.300000	46.700000
1142	2020	Zimbabwe	ZWE	84.100000	76.203600	0.060000	15.050000	30.500000	52.700000
1143	2021	Zimbabwe	ZWE	82.400000	75.527820	0.010000	14.770000	30.500000	49.000000

	count	mean	std	min	0%	5%	50%	95%	99%	100%	max
Year	1144.00	2010.50	6.35	2000.00	2000.00	2001.00	2010.50	2020.00	2021.00	2021.00	2021.00
Renewable energy consumption (% of total final energy consumption)	1132.00	61.25	29.76	0.10	0.10	5.36	73.65	94.10	96.77	98.30	98.30
Renewable Capacity(Watts/capita)	1144.00	31.60	42.66	0.00	0.00	0.00	13.56	135.64	173.78	209.52	209.52
Renewable Support(USD-M)	1144.00	56.76	231.59	0.00	0.00	0.00	0.26	267.58	1052.88	4951.88	4951.88
Energy Intensity Level(MJ_PER_GDP_CON_PPP_USD)	1132.00	6.42	3.85	0.90	0.90	2.59	5.26	14.12	20.21	26.91	26.91
Clean Energy (%)	1122.00	24.66	31.82	0.00	0.00	0.30	7.50	98.10	99.80	99.90	99.90
Elec Access (%)	1128.00	43.41	29.02	0.80	0.80	6.00	40.05	99.26	100.00	100.00	100.00

	Year	Country Name	ISO Code	Renewable energy consumption (% of total final energy consumption)	GDP/Capita	GDP	GDP Growth Rate	FDI/GDP (%)
0	1990	Angola	AGO	72.300000	4260	nan	nan	nan
1	1991	Angola	AGO	71.900000	4300	nan	nan	nan
2	1992	Angola	AGO	72.700000	4030	nan	nan	nan
3	1993	Angola	AGO	71.300000	3050	5880.000000	nan	5.140000
4	1994	Angola	AGO	72.200000	3070	4430.000000	-24.659864	3.840000

	Country Name	Renewable energy consumption (% of total final energy consumption)	ISO Code	CAGR (1990-2021)	CAGR (2016-2021)	FDI/GDP (%)	GDP/Capita
0	Algeria	0.100000	DZA	-0.022111	0.000000	0.467000	11000
1	Angola	52.900000	AGO	-0.010027	0.019206	-6.550000	5910
2	Benin	54.500000	BEN	-0.017329	0.037213	1.960000	3320
3	Botswana	27.400000	BWA	-0.018576	0.005189	-1.700000	14900
4	Burkina Faso	71.200000	BFA	-0.006185	-0.015796	-0.407000	2180

	Country Name	Renewable energy consumption (% of total final energy consumption)	ISO Code	CAGR (1990-2021)	CAGR (2016-2021)	FDI/GDP (%)	GDP/Capita
49	Togo	75.100000	TGO	-0.001509	-0.014281	-1.630000	2140
50	Tunisia	11.600000	TUN	-0.007172	-0.013250	1.170000	10400
51	Uganda	91.000000	UGA	-0.001925	-0.000877	4.070000	2250
52	Zambia	83.000000	ZMB	0.000000	-0.002392	1.780000	3240
53	Zimbabwe	82.400000	ZWE	0.008185	0.001708	0.881000	2120

	count	mean	std	min	0%	5%	50%	95%	99%	100%	max
Renewable energy consumption (% of total final energy consumption)	54.00	55.68	30.24	0.10	0.10	4.47	67.25	91.96	95.82	96.30	96.30
CAGR (1990-2021)	54.00	-0.01	0.01	-0.09	-0.09	-0.03	-0.01	0.00	0.01	0.01	0.01
CAGR (2016-2021)	54.00	-0.00	0.03	-0.08	-0.08	-0.04	-0.00	0.04	0.06	0.07	0.07
FDI/GDP (%)	52.00	3.75	5.73	-6.55	-6.55	-1.66	2.05	12.00	23.82	32.80	32.80
GDP/Capita	54.00	5536.07	5795.20	396.00	396.00	988.80	3235.00	16790.00	24049.00	27600.00	27600.00

AFRICA'S TRANSITION TO SUSTAINABLE ENERGY: 1990-2021

STRATEGIC INSIGHTS FOR RENEWABLE ENERGY ADOPTION IN AFRICA

OBJECTIVE OVERVIEW

Data Overview and Setup¶

A. Data Sources and Metrics¶

UN SDG Indicators Database

Gapminder Database

B. Data Processing¶

1. Data Cleaning and Transformation

2. Data Enrichment

C. Importing Required Libraries¶

D. Loading the Dataset

Exploratory Data Analysis¶

A. Descriptive Statistics¶

B. Renewable Energy Consumption in African Countries 1990-2021¶

1. Progression of Renewable Energy Adoption¶

2. Trends in Renewable Energy Consumption¶

3. Top and Bottom Performers in Renewable Energy Consumption¶

4. Most Improved Countries¶

5. Least Improved Countries¶

6. Average Renewable Energy Growth Rate¶

C. Comparative Analysis of Renewable Energy and Socio-Economic Indicators¶

1. GDP per Capita vs. Renewable Energy Percentage¶

2. FDI/GDP % vs. Renewable Energy Percentage for African Countries 1990-2021¶

D. SDG 7 Indicators of Renewable Energy Adoption¶

1. Renewable Energy Consumption(% of total final energy consumption)¶

2. Renewable Energy Capacity¶

3. Renewable Energy Investments¶

4. Energy Intensity Level¶

5. Access to Modern Energy Services¶

6. Access to Clean Energy¶

Detailed Analysis¶

A. Strategic Energy Transition Planning¶

1. Compound Annual Growth Rate (CAGR) for Renewable Energy Consumption (1990-2021)¶

2. 5-Year Staggered Compound Annual Growth Rate (CAGR) for Renewable Energy Consumption (1990-2021)¶

3. Cluster Analysis Based on Average Renewable Energy Consumption¶

B. Investment Opportunity Identification¶

1. Renewable Energy Consumption(%) vs. CAGR (2021)¶

2. Risk-reward Matrix using FDI/GDP as a proxy for investment attractiveness¶

3. Correlation between FDI/GDP and Renewable Energy Consumption¶

4. Correlation between FDI/GDP, CAGR and Renewable Energy Consumption¶

¶

C. Sustainable Development Goal 7 Alignment¶

1. Correlation Analysis¶

2. Composite Score for SDG 7 Progress¶

3. Countries That Have Made Significant Progress¶

4. Characteristics of Top Performing Countries¶

a. Renewable Energy Consumption¶

b. Renewable Energy Capacity¶

c. Access to Clean Energy¶

d. Access to Electricity¶

e. Energy Intensity¶

f. Renewable Support¶

5. Time Series Analysis for Top Performing Countries¶

a. Gabon¶

b. Zimbabwe¶

c. Eswatini¶

The Top Three Performers¶

D. Economic Impact Assessment¶

1. Correlation Analysis between Renewable Energy Consumption and GDP Growth Rate¶

2. Regression Analysis¶

3. Case Studies of Countries with Significant Renewable Energy Growth¶

E. Regional Cooperation Framework¶

1. Hierarchical Clustering of Countries Based on Renewable Energy Consumption, Growth Rate and GDP/Capita¶

2. Potential Region Leaders in Renewable Energy Adoption¶

Renewable Energy Adoption in Africa: Kenya's Case Study¶

1. Renewable Energy Consumption in Kenya (1990-2021)¶

2. Renewable Energy Consumption in Kenya compared with the Top Three Performing Countries in Africa 1990-2021¶

3. Renewable Energy Consumption for Kenya and the Top 20 Countries¶

4. Kenya's Comparison with Countries with the Lowest and Highest Renewable Energy Consumption Rates in 2021¶

Data Overview and Setup
¶

A. Data Sources and Metrics
¶

B. Data Processing
¶

C. Importing Required Libraries
¶

Exploratory Data Analysis
¶

A. Descriptive Statistics
¶

B. Renewable Energy Consumption in African Countries 1990-2021
¶

C. Comparative Analysis of Renewable Energy and Socio-Economic Indicators
¶

D. SDG 7 Indicators of Renewable Energy Adoption
¶

Detailed Analysis
¶

A. Strategic Energy Transition Planning
¶

B. Investment Opportunity Identification
¶

C. Sustainable Development Goal 7 Alignment
¶

D. Economic Impact Assessment
¶

E. Regional Cooperation Framework
¶

Renewable Energy Adoption in Africa: Kenya's Case Study
¶