22082448.py

# -*- coding: utf-8 -*-
""" inZK00.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1-fZUpkC2cG0OlIgHnjpZCQ1fJXRBBZBU
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv('sdg_index_2000-2022.csv')

df.head()

"""**Data:**

The sdg_index_2000-2022.csv dataset presents information on 193 UN Member States sustainability scores from 2000 to 2023 relating to 17 objectives for which the dataset has the following variables:

**country_code:** A unique identifier that links to the primary dataset.

**country:** The name of the country.

**year:** The year of the data entry.

**sdg_index_score:** The overall SDG (Sustainable Development Goals) index score of the country.

**goal_1_score:** The score for Goal 1: End poverty in all its forms everywhere.

**goal_2_score:** The score for Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture.

**goal_3_score:** The score for Goal 3: Ensure healthy lives and promote well-being for all at all ages.

**goal_4_score:** The score for Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.

**goal_5_score:** The score for Goal 5: Achieve gender equality and empower all women and girls.

**goal_6_score:** The score for Goal 6: Ensure availability and sustainable management of water and sanitation for all.

**goal_7_score:** The score for Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all.

**goal_8_score:** The score for Goal 8: Promote sustained inclusive and sustainable economic growth, full and productive employment and decent work for all.

**goal_9_score:** The score for Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation.

**goal_10_score:** The score for Goal 10: Reduce inequality within and among countries.

**goal_11_score:** The score for Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable.

**goal_12_score:** The score for Goal 12: Ensure sustainable consumption and production patterns.

**goal_13_score:** The score for Goal 13: Take urgent action to combat climate change and its impacts.

**goal_14_score:** The score for Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development.

**goal_15_score:** The score for Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems,sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.

**goal_16_score:** The score for Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable
and inclusive institutions at all levels.

**goal_17_score:** The score for Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development.
"""

world_pop=pd.read_csv('world_population.csv')

world_pop.head()

import geopandas as gpd

# Load world map data
world_map = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))

# Replace country names
country_replacements = {
    "USA": "United States",
    "Democratic Republic of the Congo": "Congo, Dem. Rep.",
    "UK": "United Kingdom",
    "Russia": "Russian Federation",
    "Venezuela": "Venezuela, RB",
    "Yemen": "Yemen, Rep."
}

world_map['name'] = world_map['name'].replace(country_replacements)

# Display the modified world map
print(world_map)

# Selecting columns CCA3 and Continent from world_pop
df_2 = world_pop[['CCA3', 'Continent']]
df_2.columns = ['country_code', 'Continent']  # Assigning column names to match the merge key

# Merging df_2 with sdg_index_2000_2022 on 'country_code'
sdg_index_2000_2022 = pd.merge(df, df_2, on='country_code')

# Display the resulting DataFrame
print(sdg_index_2000_2022)

# Grouping the data
grouped_data = sdg_index_2000_2022.groupby(['Continent', 'year']).agg(mean_c=('sdg_index_score', 'mean')).reset_index()

# Set a stylish seaborn theme
sns.set_theme(style="whitegrid")

# Choose a different color palette
colors = sns.color_palette("husl", n_colors=len(grouped_data['Continent'].unique()))

# Plotting using seaborn
plt.figure(figsize=(12, 8))
sns.lineplot(
    data=grouped_data,
    x='year',
    y='mean_c',
    hue='Continent',
    palette=colors,
    linewidth=2,
    style='Continent',
    markers=True,
    markersize=8,
    dashes=False,
)

# Updated X_label and Y_label titles for better understanding
plt.xlabel("Year", fontsize=14)
plt.ylabel("Average Sustainable Development Index Score", fontsize=14)

plt.title("Average SDG Index Score Across Continents (2000-2022)", fontsize=16)
plt.legend(title='Continent', loc='upper left', bbox_to_anchor=(1, 1))

# Adding grid for better readability
plt.grid(True, linestyle='--', alpha=0.7)

plt.savefig('/content/drive/MyDrive/line_plot1.png', bbox_inches='tight')
plt.show()

# Filter the data for the year 2022 and specified conditions for goal_1_score
filtered_data = sdg_index_2000_2022[(sdg_index_2000_2022['year'] == 2022) &
                                     (sdg_index_2000_2022['goal_1_score'] < 70) &
                                     (sdg_index_2000_2022['goal_1_score'] > 0)]

# Sort the data by 'goal_1_score' in descending order
filtered_data = filtered_data.sort_values(by='goal_1_score', ascending=False)

# Set a stylish seaborn theme
sns.set_theme()

# Define custom colors
continent_colors = {'Africa': '#FF7F0E', 'Asia': '#1F77B4', 'Europe': '#2CA02C', 'North America': '#9467BD',
                    'Oceania': '#8C564B', 'South America': '#E377C2'}

# Plotting using seaborn with customizations
plt.figure(figsize=(12, 8))
sns.barplot(
    data=filtered_data,
    x='goal_1_score',
    y='country',
    hue='Continent',
    dodge=True,
    palette=continent_colors,
    edgecolor='black',  # Add black borders to bars for better visibility
    linewidth=1.5,  # Increase bar linewidth
)

plt.title("Poverty per country in 2022", fontsize=16)
plt.xlabel("Goal 1 Score", fontsize=14)
plt.ylabel("Country", fontsize=14)

# Customize legend
plt.legend(title='Continent', title_fontsize='14', fontsize='12', loc='upper right')

# Display the plot
plt.show()

from mpl_toolkits.axes_grid1 import make_axes_locatable

# Assuming you have already loaded the world_map GeoDataFrame and the sdg_index_2000_2022 DataFrame
# If not, you should load them first

# Print column names to check
print("world_map columns:", world_map.columns)
print("dataset columns:", dataset.columns)

# Create the dataset based on the provided R code
dataset = sdg_index_2000_2022[sdg_index_2000_2022['year'] == 2022][['country', 'goal_2_score']]
dataset.columns = ['name', 'goal_2_score']  # Change 'region' to 'name' to match world_map

# Merge the GeoDataFrame and DataFrame on the 'name' column
merged_data = gpd.pd.merge(world_map, dataset, left_on='name', right_on='name', how='left')

# Load a dataset with continent centroids from Natural Earth
continents = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Plotting the map
fig, ax = plt.subplots(figsize=(12, 10))

# Specify the description for the colorbar
colorbar_description = "End Hunger - Food Security"

# Plot the map
merged_data.plot(ax=ax, column='goal_2_score', cmap='YlOrRd', linewidth=0.8, edgecolor='0.8', legend=False)

# Add label to the colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)

# Plot the colorbar vertically on the right side
sm = plt.cm.ScalarMappable(cmap='YlOrRd', norm=plt.Normalize(vmin=merged_data['goal_2_score'].min(), vmax=merged_data['goal_2_score'].max()))
sm._A = []
cbar = plt.colorbar(sm, cax=cax)

# Add label to the colorbar
cbar.set_label(colorbar_description, rotation=270, labelpad=15, fontsize=12)

# Annotate continents only once
annotated_continents = set()

for idx, row in continents.iterrows():
    continent_name = row['continent']

    # Check if the continent has already been labeled
    if continent_name not in annotated_continents:
        centroid = row['geometry'].centroid
        ax.annotate(text=continent_name, xy=(centroid.x, centroid.y),
                    xytext=(3, 3), textcoords="offset points", ha='center', fontsize=8)
        annotated_continents.add(continent_name)

ax.set_title("Hunger in 2022", fontsize=16)
plt.savefig('/content/drive/MyDrive/hunger_map_2022.png', bbox_inches='tight')
plt.show()

dataset.head()

from mpl_toolkits.axes_grid1 import make_axes_locatable

# Assuming you have already loaded the world_map GeoDataFrame and the sdg_index_2000_2022 DataFrame
# If not, you should load them first

# Create the dataset based on the provided R code
dataset = sdg_index_2000_2022[sdg_index_2000_2022['year'] == 2022][['country', 'goal_1_score']]
dataset.columns = ['name', 'goal_1_score']  # Change 'country' to 'name' to match world_map

# Merge the GeoDataFrame and DataFrame on the 'name' column
merged_data = world_map.merge(dataset, left_on='name', right_on='name', how='left')

# Load a dataset with continent centroids from Natural Earth
continents = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Plotting the map
fig, ax = plt.subplots(figsize=(12, 10))

# Specify the description for the colorbar
colorbar_description = "End Poverty In All Forms"

# Plot the map
merged_data.plot(ax=ax, column='goal_1_score', cmap='YlOrRd', linewidth=0.8, edgecolor='0.8', legend=False)

# Add label to the colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)

# Plot the colorbar vertically on the right side
sm = plt.cm.ScalarMappable(cmap='YlOrRd', norm=plt.Normalize(vmin=merged_data['goal_1_score'].min(), vmax=merged_data['goal_1_score'].max()))
sm._A = []
cbar = plt.colorbar(sm, cax=cax)

# Add label to the colorbar
cbar.set_label(colorbar_description, rotation=270, labelpad=15, fontsize=12)

# Annotate continents only once
annotated_continents = set()

for idx, row in continents.iterrows():
    continent_name = row['continent']

    # Check if the continent has already been labeled
    if continent_name not in annotated_continents:
        centroid = row['geometry'].centroid
        ax.annotate(text=continent_name, xy=(centroid.x, centroid.y),
                    xytext=(3, 3), textcoords="offset points", ha='center', fontsize=8, color='orange')  # Change color to white
        annotated_continents.add(continent_name)

ax.set_title("Poverty in 2022", fontsize=16)
plt.savefig('/content/drive/MyDrive/poverty_map_2022.png', bbox_inches='tight')

plt.show()

# Select the column for the pie chart
selected_column = 'Density (per km²)'

# Sort the DataFrame by the selected column
world_pop_sorted = world_pop.sort_values(by=selected_column, ascending=False).head(10)

# Plot a beautiful pie chart
fig, ax = plt.subplots(figsize=(10, 8))

# Define colors
colors = plt.cm.Paired(range(len(world_pop_sorted)))

# Plot the pie chart
ax.pie(world_pop_sorted[selected_column], labels=world_pop_sorted['Country/Territory'],
       autopct='%1.1f%%', startangle=90, colors=colors)

# Set aspect ratio to be equal
ax.axis('equal')

# Set title
plt.title(f'Top 10 Countries by {selected_column}', fontsize=16)

# Show the pie chart
plt.show()

# Select the columns for the pie chart
years = ['2022 Population', '2020 Population', '2015 Population', '2010 Population', '2000 Population', '1990 Population', '1980 Population', '1970 Population']

# Sum the population for each year
population_sum = world_pop[years].sum()

# Plot a beautiful pie chart
fig, ax = plt.subplots(figsize=(10, 8))

# Define colors
colors = plt.cm.Paired(range(len(years)))

# Plot the pie chart
ax.pie(population_sum, labels=years, autopct='%1.1f%%', startangle=90, colors=colors)

# Set aspect ratio to be equal
ax.axis('equal')

# Set title
plt.title('Population Distribution for Each Year', fontsize=16)

# Show the pie chart
plt.show()

# Select the columns for the pie chart
years = ['2022 Population', '2020 Population', '2015 Population', '2010 Population', '2000 Population', '1990 Population', '1980 Population', '1970 Population']

# Sum the population for each year
population_sum = world_pop[years].sum()

# Plot a beautiful pie chart
fig, ax = plt.subplots(figsize=(10, 8))

# Define colors
colors = plt.cm.Paired(range(len(years)))

# Plot the pie chart
ax.pie(population_sum, labels=years, autopct='%1.1f%%', startangle=90, colors=colors)

# Set aspect ratio to be equal
ax.axis('equal')

# Set title
plt.title('World Population Distribution for Each Year', fontsize=16)
plt.savefig('/content/drive/MyDrive/world_population_each_year.png', bbox_inches='tight')

# Show the pie chart
plt.show()