Where is the income/wealth distribution concentrated?

economy

python

An analysis of where income and wealth distribution is concentrated across different population segments.

Published

Feb 21, 2026

Keywords

income, wealth

Summary

This study analyzes income and wealth distribution data sourced from the World Inequality Database (WID), segmented by country. It highlights the concentration of wealth and income within different nations, providing insights into global economic disparities.

Distributions blocks 1

The distribution of “Income” and “Wealth” chart is split into two blocks [50-50]:
• Bottom 50
• Top 50

Code

# Libraries
# ==========================================
import pandas as pd
import numpy as np
import requests
import seaborn as sns
import matplotlib.pyplot as plt

# Data Extraction - GITHUB (Countries)
# =====================================================================
# Extract JSON and bring data to a dataframe
url = 'https://raw.githubusercontent.com/guillemmaya92/world_map/main/Dim_Country.json'
response = requests.get(url)
data = response.json()
df = pd.DataFrame(data)
df = pd.DataFrame.from_dict(data, orient='index').reset_index()
df_countries = df.rename(columns={'index': 'ISO3', 'Country_Abr': 'name'})

# Data Extraction - WID (Percentiles)
# ==========================================
# Carga del archivo Parquet
df = pd.read_parquet("https://github.com/guillemmaya92/Analytics/raw/refs/heads/master/Data/WID_Percentiles.parquet")

# Data Manipulation
# =====================================================================
# Filter a year and select measure
df = df[df['country'].isin(["NO", "DK", "ES", "FR", "DE", "UK", "US", "IN", "CN", "JA", "AR", "RU", "QA", "CL", "BR", "CA", "AU", "KR", "MX"])]
df = df[df['year'] == 2021]
df['value'] = df['income']

# Grouping by percentiles
df["group"] = pd.cut(
    df["percentile"],
    bins=[0, 50, 89, 99, 100],
    labels=["bottom50", "50-90", "90-99", "top1"],
    include_lowest=True
)

# Calculate percents
df['side'] = np.where(df['group'].isin(['bottom50']), 'left', 'right')
df['value'] *= df['side'].eq('left').map({True: -1, False: 1})

# Select columns
df = df[['country', 'group', 'value']]
df = df.groupby(["country", "group"], as_index=False)["value"].sum()

# Pivot columns
df_pivot = df.pivot(index="country", columns="group", values="value").fillna(0).reset_index()

# Merge names
df_pivot = df_pivot.merge(df_countries[['ISO2', 'name']], left_on='country', right_on='ISO2', how='inner')
df_pivot = df_pivot.drop(columns=['ISO2'])

# Define column with values for individuals and professionals
df_pivot['total_left'] = df_pivot['bottom50']
df_pivot['total_right'] = df_pivot['50-90'] + df_pivot['90-99'] + df_pivot['top1']
df_pivot = df_pivot.sort_values(by='total_left', ascending=True)

# Select and order columns
order = ["name", "bottom50", "50-90", "90-99", "top1"]
dfplot = df_pivot[order]
dfplot.set_index('name', inplace=True)

print(dfplot)

# Data Visualization
# ==========================================
# Font and style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Franklin Gothic'], 'font.size': 9})
sns.set(style="white", palette="muted")

# Palette color
palette = ["#f15b4c", "#537c78", "#faa41b", "#ffd45b"]

# Create horizontal stack bar plot
ax = dfplot.plot(kind="barh", stacked=True, figsize=(10, 6), width=0.7, color=palette)

# Add title and labels
ax.text(0, 1.12, f'Where is income distribution concentrated?', fontsize=16, fontweight='bold', ha='left', transform=ax.transAxes)
ax.text(0, 1.07 , f'Income Percentile Breakdown by Country', fontsize=11, color='#262626', ha='left', transform=ax.transAxes)
ax.set_xlim(-100, 100)
xticks = np.linspace(-75, 75, 7)
plt.xticks(xticks, labels=[f"{abs(int(i))}%" for i in xticks], fontsize=8)
plt.gca().set_ylabel('')
plt.yticks(fontsize=9, color='#282828', fontweight='bold')
plt.grid(axis='x', linestyle='--', color='gray', linewidth=0.5, alpha=0.3)
plt.axvline(x=0, color='#282828', linestyle='-', linewidth=1.5, alpha=0.7)

# Add individual and professional text
plt.text(0.25, 1.02, 'Bottom 50', fontsize=9.5, fontweight='bold', va='center', ha='center', transform=ax.transAxes, color="#f15b4c")
plt.text(0.75, 1.02, 'Top 50', fontsize=9.5, fontweight='bold', va='center', ha='center', transform=ax.transAxes, color="#537c78")

# Add strict regulation zone
ynum = 5
ax.axvspan(-100, 100, ymin=0, ymax=ynum/len(dfplot), color='gray', alpha=0.1)
plt.axhline(y=ynum-0.5, color='#282828', linestyle='--', linewidth=0.5, alpha=0.3)
plt.text(-100, ynum-0.4, 'Countries with progressive tax systems', fontsize=6, fontweight='bold', color="gray")

# Add values for total bottom50 bars
for i, (city, total) in enumerate(zip(dfplot.index, df_pivot['total_left'])):
    ax.text(total - 1, i, f'{abs(total):.0f}', va='center', ha='right', fontsize=7, color='grey', fontweight='bold')

# Add values for total top50 bars
for i, (city, total) in enumerate(zip(dfplot.index, df_pivot['total_right'])):
    ax.text(total + 1, i, f'{total:.0f} ', va='center', ha='left', fontsize=7, color='grey', fontweight='bold')

# Add values for individual bars (top1)
for i, (city, center, top9, top1) in enumerate(zip(dfplot.index, df_pivot["50-90"], df_pivot["90-99"], df_pivot["top1"])):
    ax.text(center+top9+(top1/2), i, f'{abs(top1):.0f}', va='center', ha='center', fontsize=7, color='grey')

# Add values for individual bars (top9)
for i, (city, center, top9) in enumerate(zip(dfplot.index, df_pivot["50-90"], df_pivot["90-99"])):
    ax.text(center+(top9/2), i, f'{abs(top9):.0f}', va='center', ha='center', fontsize=7, color='white')

# Add values for individual bars (center)
for i, (city, center) in enumerate(zip(dfplot.index, df_pivot["50-90"])):
    ax.text(center / 2, i, f'{abs(center):.0f}', va='center', ha='center', fontsize=7, color='white')

# Legend configuration
plt.plot([], [], label="Percentiles: ", color='white')
plt.legend(
    loc='lower center', 
    bbox_to_anchor=(0.5, -0.12),
    ncol=7,
    fontsize=8,
    frameon=False,
    handlelength=1,
    handleheight=1,
    borderpad=0.2,
    columnspacing=0.2
)

# Add Data Source
plt.text(0, -0.135, 'Data Source:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 23
plt.text(0, -0.135, space + 'World Inequality Database (WID)', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')

# Remove spines
for spine in plt.gca().spines.values():
    spine.set_visible(False)

 # Adjust layout
plt.tight_layout()

# Plot it! :)
plt.show()

Code

# Libraries
# ==========================================
import pandas as pd
import numpy as np
import requests
import seaborn as sns
import matplotlib.pyplot as plt

# Data Extraction - GITHUB (Countries)
# =====================================================================
# Extract JSON and bring data to a dataframe
url = 'https://raw.githubusercontent.com/guillemmaya92/world_map/main/Dim_Country.json'
response = requests.get(url)
data = response.json()
df = pd.DataFrame(data)
df = pd.DataFrame.from_dict(data, orient='index').reset_index()
df_countries = df.rename(columns={'index': 'ISO3', 'Country_Abr': 'name'})

# Data Extraction - WID (Percentiles)
# ==========================================
# Carga del archivo Parquet
df = pd.read_parquet("https://github.com/guillemmaya92/Analytics/raw/refs/heads/master/Data/WID_Percentiles.parquet")

# Data Manipulation
# =====================================================================
# Filter a year and select measure
df = df[df['country'].isin(["NO", "DK", "ES", "FR", "DE", "UK", "US", "IN", "CN", "JA", "AR", "RU", "QA", "CL", "BR", "CA", "AU", "KR", "MX"])]
df = df[df['year'] == 2021]
df['value'] = df['wealth']

# Grouping by percentiles
df["group"] = pd.cut(
    df["percentile"],
    bins=[0, 50, 89, 99, 100],
    labels=["bottom50", "50-90", "90-99", "top1"],
    include_lowest=True
)

# Calculate percents
df['side'] = np.where(df['group'].isin(['bottom50']), 'left', 'right')
df['value'] *= df['side'].eq('left').map({True: -1, False: 1})

# Select columns
df = df[['country', 'group', 'value']]
df = df.groupby(["country", "group"], as_index=False)["value"].sum()

# Pivot columns
df_pivot = df.pivot(index="country", columns="group", values="value").fillna(0).reset_index()

# Merge names
df_pivot = df_pivot.merge(df_countries[['ISO2', 'name']], left_on='country', right_on='ISO2', how='inner')
df_pivot = df_pivot.drop(columns=['ISO2'])

# Define column with values for individuals and professionals
df_pivot['total_left'] = df_pivot['bottom50']
df_pivot['total_right'] = df_pivot['50-90'] + df_pivot['90-99'] + df_pivot['top1']
df_pivot = df_pivot.sort_values(by='total_left', ascending=True)

# Select and order columns
order = ["name", "bottom50", "50-90", "90-99", "top1"]
dfplot = df_pivot[order]
dfplot.set_index('name', inplace=True)

print(dfplot)

# Data Visualization
# ==========================================
# Font and style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Franklin Gothic'], 'font.size': 9})
sns.set(style="white", palette="muted")

# Palette color
palette = ["#f15b4c", "#537c78", "#faa41b", "#ffd45b"]

# Create horizontal stack bar plot
ax = dfplot.plot(kind="barh", stacked=True, figsize=(10, 6), width=0.7, color=palette)

# Add title and labels
ax.text(0, 1.12, f'Where is wealth distribution concentrated?', fontsize=16, fontweight='bold', ha='left', transform=ax.transAxes)
ax.text(0, 1.07 , f'Wealth Percentile Breakdown by Country', fontsize=11, color='#262626', ha='left', transform=ax.transAxes)
ax.set_xlim(-100, 100)
xticks = np.linspace(-75, 75, 7)
plt.xticks(xticks, labels=[f"{abs(int(i))}%" for i in xticks], fontsize=8)
plt.gca().set_ylabel('')
plt.yticks(fontsize=9, color='#282828', fontweight='bold')
plt.grid(axis='x', linestyle='--', color='gray', linewidth=0.5, alpha=0.3)
plt.axvline(x=0, color='#282828', linestyle='-', linewidth=1.5, alpha=0.7)

# Add individual and professional text
plt.text(0.25, 1.02, 'Bottom 50', fontsize=9.5, fontweight='bold', va='center', ha='center', transform=ax.transAxes, color="#f15b4c")
plt.text(0.75, 1.02, 'Top 50', fontsize=9.5, fontweight='bold', va='center', ha='center', transform=ax.transAxes, color="#537c78")

# Add strict regulation zone
ynum = 0
ax.axvspan(-100, 100, ymin=0, ymax=ynum/len(dfplot), color='gray', alpha=0.1)
plt.axhline(y=ynum-0.5, color='#282828', linestyle='--', linewidth=0.5, alpha=0.3)
plt.text(-100, ynum-0.4, 'Countries with fair distribution on wealth (indeed, no one...)', fontsize=6, fontweight='bold', color="gray")

# Add values for total bottom50 bars
for i, (city, total) in enumerate(zip(dfplot.index, df_pivot['total_left'])):
    ax.text(total - 1, i, f'{abs(total):.0f}', va='center', ha='right', fontsize=7, color='grey', fontweight='bold')

# Add values for total top50 bars
for i, (city, total) in enumerate(zip(dfplot.index, df_pivot['total_right'])):
    ax.text(total + 1, i, f'{total:.0f} ', va='center', ha='left', fontsize=7, color='grey', fontweight='bold')

# Add values for individual bars (top1)
for i, (city, center, top9, top1) in enumerate(zip(dfplot.index, df_pivot["50-90"], df_pivot["90-99"], df_pivot["top1"])):
    ax.text(center+top9+(top1/2), i, f'{abs(top1):.0f}', va='center', ha='center', fontsize=7, color='grey')

# Add values for individual bars (top9)
for i, (city, center, top9) in enumerate(zip(dfplot.index, df_pivot["50-90"], df_pivot["90-99"])):
    ax.text(center+(top9/2), i, f'{abs(top9):.0f}', va='center', ha='center', fontsize=7, color='white')

# Add values for individual bars (center)
for i, (city, center) in enumerate(zip(dfplot.index, df_pivot["50-90"])):
    ax.text(center / 2, i, f'{abs(center):.0f}', va='center', ha='center', fontsize=7, color='white')

# Legend configuration
plt.plot([], [], label="Percentiles: ", color='white')
plt.legend(
    loc='lower center', 
    bbox_to_anchor=(0.5, -0.12),
    ncol=7,
    fontsize=8,
    frameon=False,
    handlelength=1,
    handleheight=1,
    borderpad=0.2,
    columnspacing=0.2
)

# Add Data Source
plt.text(0, -0.135, 'Data Source:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 23
plt.text(0, -0.135, space + 'World Inequality Database (WID)', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')

# Remove spines
for spine in plt.gca().spines.values():
    spine.set_visible(False)

 # Adjust layout
plt.tight_layout()

# Plot it! :)
plt.show()

Distributions blocks 2

The distribution of “Income” and “Wealth” chart is split into two blocks [90-10]:
• Bottom 90
• Top 10

Code

# Libraries
# ==========================================
import pandas as pd
import numpy as np
import requests
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

# Data Extraction - GITHUB (Countries)
# =====================================================================
# Extract JSON and bring data to a dataframe
url = 'https://raw.githubusercontent.com/guillemmaya92/world_map/main/Dim_Country.json'
response = requests.get(url)
data = response.json()
df = pd.DataFrame(data)
df = pd.DataFrame.from_dict(data, orient='index').reset_index()
df_countries = df.rename(columns={'index': 'ISO3', 'Country_Abr': 'name'})

# Data Extraction - WID (Percentiles)
# ==========================================
# Carga del archivo Parquet
df = pd.read_parquet("https://github.com/guillemmaya92/Analytics/raw/refs/heads/master/Data/WID_Percentiles.parquet")

# Data Manipulation
# =====================================================================
# Filter a year and select measure
df = df[df['country'].isin(["NO", "DK", "ES", "FR", "DE", "UK", "US", "IN", "CN", "JA", "AR", "RU", "QA", "CL", "BR", "CA", "AU", "KR", "MX", "ZA"])]
df = df[df['year'] == 2021]
df['value'] = df['income']

# Grouping by percentiles
df["group"] = pd.cut(
    df["percentile"],
    bins=[0, 50, 89, 99, 100],
    labels=["bottom50", "50-90", "90-99", "top1"],
    include_lowest=True
)

# Calculate percents
df['side'] = np.where(df['group'].isin(['bottom50', '50-90']), 'left', 'right')
df['value'] *= df['side'].eq('left').map({True: -1, False: 1})

# Select columns
df = df[['country', 'group', 'value']]
df = df.groupby(["country", "group"], as_index=False)["value"].sum()

# Pivot columns
df_pivot = df.pivot(index="country", columns="group", values="value").fillna(0).reset_index()

# Merge names
df_pivot = df_pivot.merge(df_countries[['ISO2', 'name']], left_on='country', right_on='ISO2', how='inner')
df_pivot = df_pivot.drop(columns=['ISO2'])

# Define column with values for individuals and professionals
df_pivot['total_left'] = df_pivot['bottom50'] + df_pivot['50-90']
df_pivot['total_right'] = df_pivot['90-99'] + df_pivot['top1']
df_pivot = df_pivot.sort_values(by='total_left', ascending=True)

# Select and order columns
order = ["name", "50-90", "bottom50", "90-99", "top1"]
dfplot = df_pivot[order]
dfplot.set_index('name', inplace=True)

print(dfplot)

# Data Visualization
# ==========================================
# Font and style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Franklin Gothic'], 'font.size': 9})
sns.set(style="white", palette="muted")

# Palette color
palette = ["#537c78", "#f15b4c", "#faa41b", "#ffd45b"]

# Create horizontal stack bar plot
ax = dfplot.plot(kind="barh", stacked=True, figsize=(10, 6), width=0.7, color=palette)

# Add title and labels
ax.text(0, 1.12, f'Where is income distribution concentrated?', fontsize=16, fontweight='bold', ha='left', transform=ax.transAxes)
ax.text(0, 1.07 , f'Income Percentile Breakdown by Country', fontsize=11, color='#262626', ha='left', transform=ax.transAxes)
ax.set_xlim(-100, 100)
xticks = np.linspace(-75, 75, 7)
plt.xticks(xticks, labels=[f"{abs(int(i))}%" for i in xticks], fontsize=8)
plt.gca().set_ylabel('')
plt.yticks(fontsize=9, color='#282828', fontweight='bold')
plt.grid(axis='x', linestyle='--', color='gray', linewidth=0.5, alpha=0.3)
plt.axvline(x=0, color='#282828', linestyle='-', linewidth=1.5, alpha=0.7)

# Add individual and professional text
plt.text(0.25, 1.02, 'Bottom 90', fontsize=9.5, fontweight='bold', va='center', ha='center', transform=ax.transAxes, color="#526b69")
plt.text(0.75, 1.02, 'Top 10', fontsize=9.5, fontweight='bold', va='center', ha='center', transform=ax.transAxes, color="#b58231")

# Add strict regulation zone
ynum = 7
ax.axvspan(-100, 100, ymin=0, ymax=ynum/len(dfplot), color='gray', alpha=0.1)
plt.axhline(y=ynum-0.5, color='#282828', linestyle='--', linewidth=0.5, alpha=0.3)
plt.text(+100, ynum-0.4, 'Countries with progressive tax systems', fontsize=6, fontweight='bold', color="gray", ha="right")

# Add values for total bottom50 bars
for i, (city, total) in enumerate(zip(dfplot.index, df_pivot['total_left'])):
    ax.text(total - 1, i, f'{abs(total):.0f}', va='center', ha='right', fontsize=7, color='grey', fontweight='bold')

# Add values for total top50 bars
for i, (city, total) in enumerate(zip(dfplot.index, df_pivot['total_right'])):
    ax.text(total + 1, i, f'{total:.0f} ', va='center', ha='left', fontsize=7, color='grey', fontweight='bold')

# Add values for individual bars (top1)
for i, (city, center, top9, top1) in enumerate(zip(dfplot.index, df_pivot["50-90"], df_pivot["90-99"], df_pivot["top1"])):
    ax.text(top9+(top1/2), i, f'{abs(top1):.0f}', va='center', ha='center', fontsize=7, color='grey')

# Add values for individual bars (top9)
for i, (city, center, top9) in enumerate(zip(dfplot.index, df_pivot["50-90"], df_pivot["90-99"])):
    ax.text((top9/2), i, f'{abs(top9):.0f}', va='center', ha='center', fontsize=7, color='white')

# Add values for individual bars (center)
for i, (city, bottom50, center) in enumerate(zip(dfplot.index, df_pivot["bottom50"], df_pivot["50-90"])):
    ax.text(center+(bottom50/2), i, f'{abs(bottom50):.0f}', va='center', ha='center', fontsize=7, color='white')

# Add values for individual bars (center)
for i, (city, center) in enumerate(zip(dfplot.index, df_pivot["50-90"])):
    ax.text((center/2), i, f'{abs(center):.0f}', va='center', ha='center', fontsize=7, color='white')

# Configurar la leyenda manualmente con cuadrados
handles = [
    mpatches.Patch(color=palette[1], label="Bottom 50", linewidth=2),
    mpatches.Patch(color=palette[0], label="(50-90]", linewidth=2),
    mpatches.Patch(color=palette[2], label="(90-99]", linewidth=2),
    mpatches.Patch(color=palette[3], label="Top 1", linewidth=2)
]

# Configuración de la leyenda
plt.legend(
    handles=handles,
    loc='lower center', 
    bbox_to_anchor=(0.5, -0.12),
    ncol=4,  # Para que los elementos queden en una fila
    fontsize=8,
    frameon=False,
    handlelength=0.5,
    handleheight=0.5,
    borderpad=0.2,
    columnspacing=0.4
)

# Add Data Source
plt.text(0, -0.135, 'Data Source:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 23
plt.text(0, -0.135, space + 'World Inequality Database (WID)', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')

# Remove spines
for spine in plt.gca().spines.values():
    spine.set_visible(False)

 # Adjust layout
plt.tight_layout()

# Plot it! :)
plt.show()

Code

# Libraries
# ==========================================
import pandas as pd
import numpy as np
import requests
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

# Data Extraction - GITHUB (Countries)
# =====================================================================
# Extract JSON and bring data to a dataframe
url = 'https://raw.githubusercontent.com/guillemmaya92/world_map/main/Dim_Country.json'
response = requests.get(url)
data = response.json()
df = pd.DataFrame(data)
df = pd.DataFrame.from_dict(data, orient='index').reset_index()
df_countries = df.rename(columns={'index': 'ISO3', 'Country_Abr': 'name'})

# Data Extraction - WID (Percentiles)
# ==========================================
# Carga del archivo Parquet
df = pd.read_parquet("https://github.com/guillemmaya92/Analytics/raw/refs/heads/master/Data/WID_Percentiles.parquet")

# Data Manipulation
# =====================================================================
# Filter a year and select measure
df = df[df['country'].isin(["NO", "DK", "ES", "FR", "DE", "UK", "US", "IN", "CN", "JA", "AR", "RU", "QA", "CL", "BR", "CA", "AU", "KR", "MX", "ZA"])]
df = df[df['year'] == 2021]
df['value'] = df['wealth']

# Grouping by percentiles
df["group"] = pd.cut(
    df["percentile"],
    bins=[0, 50, 89, 99, 100],
    labels=["bottom50", "50-90", "90-99", "top1"],
    include_lowest=True
)

# Calculate percents
df['side'] = np.where(df['group'].isin(['bottom50', '50-90']), 'left', 'right')
df['value'] *= df['side'].eq('left').map({True: -1, False: 1})

# Select columns
df = df[['country', 'group', 'value']]
df = df.groupby(["country", "group"], as_index=False)["value"].sum()
df['value'] = np.where((df['group'] == 'bottom50') & (df['value'] >= 0), np.nan, df['value'])

# Pivot columns
df_pivot = df.pivot(index="country", columns="group", values="value").fillna(0).reset_index()

# Merge names
df_pivot = df_pivot.merge(df_countries[['ISO2', 'name']], left_on='country', right_on='ISO2', how='inner')
df_pivot = df_pivot.drop(columns=['ISO2'])

# Define column with values for individuals and professionals
df_pivot['total_left'] = df_pivot['bottom50'] + df_pivot['50-90']
df_pivot['total_right'] = df_pivot['90-99'] + df_pivot['top1']
df_pivot = df_pivot.sort_values(by='total_left', ascending=True)

# Select and order columns
order = ["name", "50-90", "bottom50", "90-99", "top1"]
dfplot = df_pivot[order]
dfplot.set_index('name', inplace=True)

print(dfplot)

# Data Visualization
# ==========================================
# Font and style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Franklin Gothic'], 'font.size': 9})
sns.set(style="white", palette="muted")

# Palette color
palette = ["#537c78", "#f15b4c", "#faa41b", "#ffd45b"]

# Create horizontal stack bar plot
ax = dfplot.plot(kind="barh", stacked=True, figsize=(10, 6), width=0.7, color=palette)

# Add title and labels
ax.text(0, 1.12, f'Where is income distribution concentrated?', fontsize=16, fontweight='bold', ha='left', transform=ax.transAxes)
ax.text(0, 1.07 , f'Income Percentile Breakdown by Country', fontsize=11, color='#262626', ha='left', transform=ax.transAxes)
ax.set_xlim(-100, 100)
xticks = np.linspace(-75, 75, 7)
plt.xticks(xticks, labels=[f"{abs(int(i))}%" for i in xticks], fontsize=8)
plt.gca().set_ylabel('')
plt.yticks(fontsize=9, color='#282828', fontweight='bold')
plt.grid(axis='x', linestyle='--', color='gray', linewidth=0.5, alpha=0.3)
plt.axvline(x=0, color='#282828', linestyle='-', linewidth=1.5, alpha=0.7)

# Add individual and professional text
plt.text(0.25, 1.02, 'Bottom 90', fontsize=9.5, fontweight='bold', va='center', ha='center', transform=ax.transAxes, color="#526b69")
plt.text(0.75, 1.02, 'Top 10', fontsize=9.5, fontweight='bold', va='center', ha='center', transform=ax.transAxes, color="#b58231")

# Add strict regulation zone
ynum = 0
ax.axvspan(-100, 100, ymin=0, ymax=ynum/len(dfplot), color='gray', alpha=0.1)
plt.axhline(y=ynum-0.5, color='#282828', linestyle='--', linewidth=0.5, alpha=0.3)
plt.text(-100, ynum-0.4, 'Countries with fair distribution on wealth (indeed, no one...)', fontsize=5, fontweight='bold', color="gray", ha="left")

# Add values for total bottom50 bars
for i, (city, total) in enumerate(zip(dfplot.index, df_pivot['total_left'])):
    ax.text(total - 1, i, f'{abs(total):.0f}', va='center', ha='right', fontsize=7, color='grey', fontweight='bold')

# Add values for total top50 bars
for i, (city, total) in enumerate(zip(dfplot.index, df_pivot['total_right'])):
    ax.text(total + 1, i, f'{total:.0f} ', va='center', ha='left', fontsize=7, color='grey', fontweight='bold')

# Add values for individual bars (top1)
for i, (city, center, top9, top1) in enumerate(zip(dfplot.index, df_pivot["50-90"], df_pivot["90-99"], df_pivot["top1"])):
    ax.text(top9+(top1/2), i, f'{abs(top1):.0f}', va='center', ha='center', fontsize=7, color='grey')

# Add values for individual bars (top9)
for i, (city, center, top9) in enumerate(zip(dfplot.index, df_pivot["50-90"], df_pivot["90-99"])):
    ax.text((top9/2), i, f'{abs(top9):.0f}', va='center', ha='center', fontsize=7, color='white')

# Add values for individual bars (bottom50)
for i, (city, bottom50, center) in enumerate(zip(dfplot.index, df_pivot["bottom50"], df_pivot["50-90"])):
    value = abs(bottom50)
    if round(value) != 0:  # Solo muestra el texto si el valor redondeado no es cero
        ax.text(center + (bottom50 / 2), i, f'{value:.0f}', va='center', ha='center', fontsize=7, color='white')

# Add values for individual bars (center)
for i, (city, center) in enumerate(zip(dfplot.index, df_pivot["50-90"])):
    ax.text((center/2), i, f'{abs(center):.0f}', va='center', ha='center', fontsize=7, color='white')

# Configurar la leyenda manualmente con cuadrados
handles = [
    mpatches.Patch(color=palette[1], label="Bottom 50", linewidth=2),
    mpatches.Patch(color=palette[0], label="(50-90]", linewidth=2),
    mpatches.Patch(color=palette[2], label="(90-99]", linewidth=2),
    mpatches.Patch(color=palette[3], label="Top 1", linewidth=2)
]

# Configuración de la leyenda
plt.legend(
    handles=handles,
    loc='lower center', 
    bbox_to_anchor=(0.5, -0.12),
    ncol=4,  # Para que los elementos queden en una fila
    fontsize=8,
    frameon=False,
    handlelength=0.5,
    handleheight=0.5,
    borderpad=0.2,
    columnspacing=0.4
)

# Add Data Source
plt.text(0, -0.135, 'Data Source:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 23
plt.text(0, -0.135, space + 'World Inequality Database (WID)', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')

# Remove spines
for spine in plt.gca().spines.values():
    spine.set_visible(False)

 # Adjust layout
plt.tight_layout()

# Plot it! :)
plt.show()