In [5]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
In [6]:
df = pd.read_csv('Data/Global_Population_2023.csv')
df.head()
Out[6]:
| Unnamed: 0 | Country/Dependency | Population 2023 | Yearly Population Change | Net Population Change | Population Density (P/Km²) | Land Area (Km²) | Net Migrants | Fertility Rate | Median Age | Urban Population % | World Population Share % | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | India | 1,428,627,663 | 0.81 % | 11454490 | 481 | 2973190 | -486136 | 2.0 | 28.0 | 36 % | 17.76 % |
| 1 | 1 | China | 1,425,671,352 | -0.02 % | -215985 | 152 | 9388211 | -310220 | 1.2 | 39.0 | 65 % | 17.72 % |
| 2 | 2 | United States | 339,996,563 | 0.50 % | 1706706 | 37 | 9147420 | 999700 | 1.7 | 38.0 | 83 % | 4.23 % |
| 3 | 3 | Indonesia | 277,534,122 | 0.74 % | 2032783 | 153 | 1811570 | -49997 | 2.1 | 30.0 | 59 % | 3.45 % |
| 4 | 4 | Pakistan | 240,485,658 | 1.98 % | 4660796 | 312 | 770880 | -165988 | 3.3 | 21.0 | 35 % | 2.99 % |
Activities¶
1. Plot a bar chart showing the population of the top 10 most populous countries.¶
In [7]:
df['Population 2023'] = df['Population 2023'].str.replace(',', '').astype(int)
top_10_countries = df.sort_values("Population 2023", ascending=False).head(10)
# Plotting the bar chart
fig, ax = plt.subplots(figsize=(10,6))
# Plotting the bar chart
ax.bar(top_10_countries['Country/Dependency'], top_10_countries["Population 2023"], color='skyblue')
# Adding title and labels
ax.set_title('Top 10 Most Populous Countries in 2023', fontsize=14)
ax.set_xlabel('Country', fontsize=12)
ax.set_ylabel('Population (Billions)', fontsize=12)
# Adjusting x-ticks for better readability
ax.set_xticks(range(len(top_10_countries)))
ax.set_xticklabels(top_10_countries['Country/Dependency'], rotation=45, ha='right')
# Adjusting layout to prevent clipping
fig.tight_layout()
2. Find the top 3 countries with the largest net migration and visualize them using a bar chart¶
In [8]:
top_countries = df.sort_values('Net Migrants', ascending=False).head(3)
# Create a bar chart
fig, ax = plt.subplots(figsize=(10,6))
ax.bar(top_countries['Country/Dependency'], top_countries['Net Migrants'], color='green')
# Adding title and labels
ax.set_title('Top 3 Countries with the Largest Net Migration')
ax.set_xlabel('Country')
ax.set_ylabel('Net Migration')
# Show plot
plt.show()
In [11]:
df['World Population Share %'] = df['World Population Share %'].str.replace('%', '').str.strip().astype(float)
3. Create a pie chart visualizing the World Population Share of the Top 5 Countries in 2023¶
In [12]:
top_5_countries = df.sort_values('Population 2023', ascending=False).head()
# Calculate the world population share of the remaining countries
other_share = 100 - (top_5_countries['World Population Share %'].sum())
# Add the "Other" category
countries = list(top_5_countries['Country/Dependency']) + ['Other']
population_share = list(top_5_countries['World Population Share %']) + [other_share]
# Plotting the pie chart
fig, ax = plt.subplots(figsize=(8,8))
ax.pie(population_share, labels=countries, autopct='%1.1f%%', startangle=140, colors=plt.cm.Paired.colors)
ax.set_title('World Population Share of Top 5 Countries (2023)', fontsize=14)
# Show the plot
plt.show()
In [ ]:
4. Create a scatter plot showing the relationship between population and land area.¶
In [14]:
fig, ax = plt.subplots(figsize=(10,6))
# Plotting the scatter plot
ax.scatter(df['Land Area (Km²)'], df['Population 2023'], alpha=0.7, color='blue')
# Adding titles and labels
ax.set_title('Relationship Between Population and Land Area (2023)', fontsize=14)
ax.set_xlabel('Land Area (Km²)', fontsize=12)
ax.set_ylabel('Population (2023)', fontsize=12)
# Display the plot
plt.show()
With the exception of countries like China and India with extremely large populations, or Russia with significantly large land mass, most of the other countries have relatively small populations and land areas. This results in a cluster of points near the origin, indicating that many countries have smaller populations and land areas compared to the few outliers with either very large populations or expansive territories
5. Compare fertility rates across countries by similar median ages to see how fertility rates vary.¶
In [16]:
bins = [0, 20, 30, 40, 50]
labels = ['0-20', '21-30', '31-40', '41+']
df['Age Group'] = pd.cut(df['Median Age'], bins=bins, labels=labels)
# Calculate average fertility rate for each age group
average_fertility_by_age_group = df.groupby('Age Group', observed=True)['Fertility Rate'].mean()
# Plotting the results
fig, ax = plt.subplots(figsize=(10,6))
average_fertility_by_age_group.plot(kind='bar', color='pink', ax=ax)
ax.set_title('Average Fertility Rate by Age Group', fontsize=14)
ax.set_xlabel('Age Group', fontsize=12)
ax.set_ylabel('Average Fertility Rate', fontsize=12)
plt.show()
6. Visualize Median Age Distribution Across All Countries through a Histogram¶
In [20]:
fig, ax = plt.subplots(figsize=(10,6))
ax.hist(df['Median Age'], bins=15, edgecolor='black', alpha=0.7)
# Adding titles and labels
ax.set_title('Distribution of Median Ages Across Countries')
ax.set_xlabel('Median Age')
ax.set_ylabel('Frequency')
# Display the plot
plt.show()
7. Display Population and Net Population Change for Top 10 Countries Using a Grouped bar chart¶
In [27]:
fig, ax = plt.subplots(figsize=(12,8))
# Define bar width and position
bar_width = 0.35
index = range(len(top_10_countries))
# Plot Population
ax.bar(index, top_10_countries['Population 2023'], bar_width, label='Population 2023', color='blue')
# Plot Net Change
ax.bar([i + bar_width for i in index], top_10_countries['Net Population Change'], bar_width, label='Net Population Change', color='red')
# Set labels, title, and legend
ax.set_xlabel('Country')
ax.set_ylabel('Value')
ax.set_title('Population and Net Population Change for Top 10 Countries')
ax.set_xticks([i + bar_width / 2 for i in index])
ax.set_xticklabels(top_10_countries['Country/Dependency'], rotation=45, ha='right')
ax.legend()
# Show plot
plt.tight_layout()
plt.show()
The resulting grouped bar chart compares the actual population size and net population change for the top 10 countries, allowing for easy visualization of both current population figures and growth trends. It highlights which countries have the largest populations and how these populations are changing, revealing patterns of growth or decline
8. Create a histogram of the fertility rates across all countries.¶
In [32]:
average_fertility_rate = df['Fertility Rate'].mean()
fig, ax = plt.subplots(figsize=(10,6))
ax.hist(df["Fertility Rate"], bins=20, color='skyblue', edgecolor='black', alpha=0.7)
# Add a vertical line for the average fertility rate
ax.axvline(average_fertility_rate, color='red', linestyle='--', linewidth=1.5, label=f'Average Fertility Rate: {average_fertility_rate:.2f}')
# Adding title and labels
ax.set_title('Histogram of Fertility Rates Across All Countries')
ax.set_xlabel('Fertility Rate')
ax.set_ylabel('Frequency')
# Show plot
plt.show()
The End!!!✨🎉🎈🎈