Making GOV.UK style plots with Python and R

pip install numpy pandas matplotlib seaborn plotly pandas

pip install folium geopandas pandas

install.packages("tidyverse")
install.packages("ggplot2")

install.packages('devtools')
devtools::install_github('ukgovdatascience/govstyle')

import matplotlib.pyplot as plt

# Adjusted years list to match the length of rate lists
years = [1971, 1974, 1977, 1980, 1983, 1986, 1989, 1992, 1995, 1998, 2001, 2004, 2007, 2010, 2013, 2016, 2019, 2021]
men_rate = [4.1, 5.0, 6.0, 8.2, 11.5, 9.6, 8.3, 6.5, 5.4, 4.9, 5.1, 6.8, 7.2, 6.3, 5.1, 4.6, 3.8, 4.0]
women_rate = [4.0, 5.5, 6.2, 7.8, 9.8, 8.7, 7.5, 6.9, 5.8, 5.2, 5.3, 6.1, 6.7, 5.8, 4.9, 4.3, 3.5, 3.6] 

# Create the figure and axis
fig, ax = plt.subplots(figsize=(10, 6))

# Plotting the two lines
ax.plot(years, men_rate, label='Men', color='darkblue', linewidth=2)
ax.plot(years, women_rate, label='Women', color='orangered', linewidth=2)

# Adding labels directly on the lines
ax.text(years[-1], men_rate[-1], 'Men', fontsize=12, verticalalignment='bottom', color='darkblue')
ax.text(years[-1], women_rate[-1], 'Women', fontsize=12, verticalalignment='top', color='orangered')

# Setting axis labels and title
ax.set_ylabel('%', fontsize=14)
ax.set_xlabel('Year', fontsize=12)

# Remove top and right spines for a cleaner look
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Enable horizontal gridlines only
ax.yaxis.grid(True, linestyle='--', alpha=0.5)
ax.xaxis.grid(False)

# Set y-axis limits and x-ticks
ax.set_ylim(0, 14)
ax.set_xticks(list(range(1971, 2022, 3)))  # Set x-tick intervals
ax.set_xlim(1971, 2021)

# Show the plot
plt.tight_layout()
plt.show()

library(ggplot2)

# Define the data
years <- c(1971, 1974, 1977, 1980, 1983, 1986, 1989, 1992, 1995, 1998, 2001, 2004, 2007, 2010, 2013, 2016, 2019, 2021)
men_rate <- c(4.1, 5.0, 6.0, 8.2, 11.5, 9.6, 8.3, 6.5, 5.4, 4.9, 5.1, 6.8, 7.2, 6.3, 5.1, 4.6, 3.8, 4.1)
women_rate <- c(4.0, 5.5, 6.2, 7.8, 9.8, 8.7, 7.5, 6.9, 5.8, 5.2, 5.3, 6.1, 6.7, 5.8, 4.9, 4.3, 3.5, 3.6)

# Create a data frame to hold the values
data <- data.frame(
  Year = rep(years, 2),
  Rate = c(men_rate, women_rate),
  Gender = rep(c("Men", "Women"), each = length(years))
)

# Generate the plot using ggplot2
p <- ggplot(data, aes(x = Year, y = Rate, color = Gender, group = Gender)) +
  geom_line(size = 1.2) +  # Adjust line thickness
  geom_text(data = subset(data, Year == 2021), aes(label = Gender), hjust = -0.1, size = 5) +
  scale_color_manual(values = c("Men" = "darkblue", "Women" = "orangered")) +
  labs(x = "Year", y = "%", title = NULL) +
  theme_minimal() +  # Minimal theme
  theme(
    panel.grid.major.y = element_line(linetype = "dashed", color = "gray", size = 0.5),  # Horizontal gridlines
    panel.grid.major.x = element_blank(),  # Remove vertical gridlines
    panel.grid.minor = element_blank(),  # Remove minor gridlines
    axis.line = element_line(color = "black"),  # Add black axis lines
    axis.title = element_text(size = 14),
    axis.text = element_text(size = 12),
    legend.position = "none"  # Remove legend
  ) +
  coord_cartesian(ylim = c(0, 14)) +  # Match y-axis limits
  scale_x_continuous(limits = c(1971, 2029), breaks = seq(1971, 2025, by = 5))  # Extend x-axis to 2025

# Display the plot
print(p)

import matplotlib.pyplot as plt
import pandas as pd

data = pd.DataFrame({
    'Year': list(range(2009, 2024)),
    'Employment Rate': [70, 71, 71.5, 72, 73, 74, 75, 76, 77, 78, 79, 78.5, 75.9, 75.5, 74],
    'Unemployment Rate': [8, 7.8, 7.5, 7, 6.8, 6.5, 6, 5.8, 5.0, 4.5, 3.5, 2.8, 4, 3.8, 3.5]
})

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8), sharex=False)

ax1.plot(data['Year'], data['Employment Rate'], color='#005EA5', linewidth=2)
ax1.text(2010, 74, 'Employment rate', fontname="Arial", fontsize=12, color='black')
ax1.set_ylabel('Employment Rate (%)')
ax1.axvspan(2020, 2021, color='#b1b4b6', alpha=0.3)  # Highlight for COVID-19 period
ax1.text(2016, 80, 'At the start of the coronavirus (COVID-19)\n pandemic the employment rate fell . . .', fontsize=10)
ax1.set_ylim(62, 85)

ax2.plot(data['Year'], data['Unemployment Rate'], color='#f47738', linewidth=2)
ax2.text(2010, 8, 'Unemployment rate', fontsize=12, color='black')
ax2.set_ylabel('Unemployment Rate (%)')
ax2.axvspan(2020, 2021, color='#b1b4b6', alpha=0.3)  # Highlight for COVID-19 period
ax2.text(2021.2, 4.3, '. . . and the\n unemployment\n rate rose.', fontsize=10)
ax2.set_ylim(0, 10)

for ax in [ax1, ax2]:
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from matplotlib.ticker import FuncFormatter
from matplotlib.dates import DateFormatter

# Generating sample data to match the given plot pattern
np.random.seed(42)  # For reproducibility

# Generate dates
dates = pd.date_range(start="2021-08-01", end="2021-11-15")

# Adjusting the synthetic data lengths to match the number of dates (107 entries)
num_dates = len(dates)

# Create synthetic data with similar trends for each country
data = {
    "Austria": np.concatenate([np.linspace(300, 400, num_dates//3), np.linspace(400, 600, num_dates//3), np.linspace(600, 1400, num_dates - 2*(num_dates//3))]),
    "Belgium": np.concatenate([np.linspace(200, 300, num_dates//3), np.linspace(300, 500, num_dates//3), np.linspace(500, 1200, num_dates - 2*(num_dates//3))]),
    "Netherlands": np.concatenate([np.linspace(200, 300, num_dates//3), np.linspace(300, 500, num_dates//3), np.linspace(500, 1100, num_dates - 2*(num_dates//3))]),
    "UK": np.concatenate([np.linspace(400, 600, num_dates//3), np.linspace(600, 800, num_dates//3), np.linspace(800, 550, num_dates - 2*(num_dates//3))]),
    "Germany": np.concatenate([np.linspace(300, 500, num_dates//3), np.linspace(500, 700, num_dates//3), np.linspace(700, 650, num_dates - 2*(num_dates//3))]),
    "France": np.linspace(50, 200, num_dates),
    "Italy": np.linspace(50, 150, num_dates),
    "Spain": np.linspace(50, 120, num_dates)
}

# Adding slight variability (smaller noise) to each line to make them less smooth
for country in data:
    noise = np.random.normal(0, 7, num_dates)  # Adding random noise with mean 0 and standard deviation 7
    data[country] += noise

# Create DataFrame
df = pd.DataFrame(data, index=dates)

# Function to format y-axis labels with commas
def y_format(x, pos):
    return f'{int(x):,}'

# Plotting the data
plt.figure(figsize=(12, 6))
for country in df.columns:
    color = 'navy' if country == "UK" else 'grey'
    alpha_value = 1 if country == "UK" else 0.6  # Set higher transparency for non-UK lines
    plt.plot(df.index, df[country], label=country, linewidth=2 if country == "UK" else 1.5, color=color, alpha=alpha_value)

# Add country names at the end of each line
for country in df.columns:
    plt.text(df.index[-1], df[country].values[-1], country, fontsize=12, color='navy' if country == "UK" else 'grey', alpha=alpha_value, va='center')

# Formatting the plot to match the visual style
plt.xlabel(None)
plt.ylabel("Cases per 100,000 people")
plt.title(None)

# Customizing x-axis date format
date_format = DateFormatter("%d %b")  # Format as "01 Aug", "21 Aug", etc.
plt.gca().xaxis.set_major_formatter(date_format)

# Customizing the y-axis format to include commas
plt.gca().yaxis.set_major_formatter(FuncFormatter(y_format))

# Set x-tick intervals to match the original chart
plt.xticks(pd.to_datetime(['2021-08-01', '2021-08-21', '2021-09-10', '2021-09-30', '2021-10-20', '2021-11-09']), rotation=0)

# Customizing the horizontal gridlines to be faint grey
plt.grid(axis='y', color='lightgrey', linestyle='-', linewidth=0.5, alpha=0.5)

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

# Remove x and y axis tick marks
plt.tick_params(axis='both', which='both', length=0)  # Set the length of tick marks to 0

# Remove legend
plt.legend().set_visible(False)

# Display the plot
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt

# Data
categories = ['Dog', 'Cat', 'Hamster', 'Dolphin', 'Other']
values = [25, 22, 20, 18, 17]

# Create the plot
plt.figure(figsize=(12, 6))
plt.bar(categories, values, color='#12436D', width=0.6, zorder=3)

# Adding faint horizontal grey gridlines
plt.grid(axis='y', color='#e0e0e0', linestyle='-', linewidth=0.7, zorder=0)

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

# Remove x and y axis tick marks
plt.tick_params(axis='both', which='both', length=0)  # Set the length of tick marks to 0

# Adding labels and title
plt.ylabel('Number of 6 year olds')
plt.xlabel('')
plt.title('Favorite Animals of 6 Year Olds')
plt.show()

# Load necessary library
library(ggplot2)

# Data
categories <- c("Dog", "Cat", "Hamster", "Dolphin", "Other")
values <- c(25, 22, 20, 18, 17)

# Create a data frame
data <- data.frame(categories, values)

# Reorder categories based on values (largest to smallest)
data$categories <- factor(data$categories, levels = data$categories[order(-data$values)])

# Create the plot
ggplot(data, aes(x = categories, y = values)) +
  geom_bar(stat = "identity", fill = "#12436D", width = 0.6) +  # Dark blue bars with custom width
  theme_minimal(base_size = 15) +  # Minimal theme with base text size
  theme(
    panel.grid.major.y = element_line(color = "#e0e0e0", size = 0.7),  # Faint horizontal gridlines
    panel.grid.minor = element_blank(),  # Remove minor gridlines
    panel.grid.major.x = element_blank(),  # Remove vertical gridlines
    axis.ticks = element_blank(),  # Remove axis tick marks
    axis.line = element_blank(),  # Remove axis lines
    plot.title = element_text(hjust = 0.5)  # Center the plot title
  ) +
  labs(
    x = NULL,  # Remove x-axis label
    y = "Number of 6 year olds",  # Set y-axis label
    title = "Favorite Animals of 6 Year Olds"  # Set plot title
  )

import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.ticker as mticker

# Sample DataFrame
data = {
    'County': [
        'Monmouthshire', 'Vale of Glamorgan', 'Cardiff', 'Ceredigion', 'Powys', 'Isle of Anglesey',
        'Pembrokeshire', 'Newport', 'Conwy', 'Flintshire', 'Bridgend', 'Median for all Wales',
        'Gwynedd', 'Wrexham', 'Swansea', 'Denbighshire', 'Torfaen', 'Carmarthenshire',
        'Caerphilly', 'Neath Port Talbot', 'Rhondda Cynon Taf', 'Merthyr Tydfil', 'Blaenau Gwent'
    ],
    'Ranking': [
        400000, 350000, 320000, 300000, 280000, 260000, 240000, 220000, 200000, 180000, 160000,
        140000, 130000, 120000, 110000, 100000, 90000, 80000, 70000, 60000, 50000, 40000, 30000
    ],
    'Deviation': [
        100000, 80000, 60000, 40000, 30000, 20000, 10000, 5000, 3000, 1000, 500, 0,
        -500, -1000, -3000, -5000, -7000, -9000, -10000, -20000, -30000, -40000, -50000
    ]
}

# Create DataFrame
df = pd.DataFrame(data)

# Set the county names as the index
df.set_index('County', inplace=True)

# Set the figure size with increased width to accommodate labels
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(18, 8))

# Plot the Ranking bar chart
df.sort_values('Ranking', ascending=True).plot.barh(
    y='Ranking', ax=ax1, color='#12436D', legend=False, zorder=3
)
ax1.set_title('Ranking')
ax1.set_xlabel('£')

# Highlight the "Median for all Wales" row in the specified color (#f46a25)
median_index = df.index.get_loc('Median for all Wales')
ax1.get_children()[median_index].set_color('#f46a25')

# Remove the y-axis label ('County')
ax1.set_ylabel('')

# Adjust x-axis for the left chart to use 100,000 increments up to 425,000
ax1.set_xlim(0, 425000)  # Set the x-axis limit from 0 to 425,000
ax1.set_xticks(range(0, 426000, 100000))  # Set x-ticks at 0, 100,000, 200,000, ..., 425,000

# Plot the Deviation bar chart
df.sort_values('Deviation', ascending=True).plot.barh(
    y='Deviation', ax=ax2, color='#12436D', legend=False, zorder=3
)
ax2.set_title('Deviation')
ax2.set_xlabel('£')

# Highlight the "Median for all Wales" row in the specified color (#f46a25)
median_index_deviation = df.index.get_loc('Median for all Wales')
ax2.get_children()[median_index_deviation].set_color('#f46a25')

# Remove the y-axis label ('County')
ax2.set_ylabel('')

# Remove all spines (box borders) and add vertical gridlines
for ax in [ax1, ax2]:
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_visible(False)
    ax.spines['bottom'].set_visible(False)

    # Add light gray vertical gridlines behind the bars using zorder=1
    ax.grid(True, axis='x', color='lightgray', linestyle='--', linewidth=0.7, zorder=1)

    # Remove tick marks
    ax.tick_params(axis='both', which='both', length=0)

    # Set x-axis formatter to include commas
    ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f'{int(x):,}'))

# Adjust subplot spacing and margins to prevent labels from being cut off
plt.subplots_adjust(left=0.13, right=0.976, top=0.9, bottom=0.1, wspace=0.39)

# Show the plot
plt.show()

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
import numpy as np
import matplotlib.ticker as mticker  # Import ticker for formatting

# Seed for reproducibility
np.random.seed(42)

# Create a DataFrame with a nuanced pattern
data = {
    'Category': np.random.choice(['Urban', 'Rural'], 50),  # Randomly assign categories
}

# Assign household incomes with Urban starting from 20,000 upwards
data['Household Income'] = [
    np.random.randint(20000, 100000) if cat == 'Urban' else np.random.randint(20000, 70000)
    for cat in data['Category']
]

# Assign house prices to be generally higher in Rural areas, lower in Urban areas
data['House Prices'] = [
    np.random.randint(300000, 500000) if cat == 'Rural' else np.random.randint(100000, 300000)
    for cat in data['Category']
]

# Convert to a DataFrame for easier manipulation
df = pd.DataFrame(data)

# Introduce a general correlation: Higher incomes should have higher house prices
# Modify 25% of the data points to ensure high income leads to high house prices
high_income_mask = df['Household Income'] > 70000
df.loc[high_income_mask, 'House Prices'] += np.random.randint(100000, 200000, high_income_mask.sum())

# Create a scatter plot with GOV.UK styling for two categories
fig, ax = plt.subplots(figsize=(10, 6))

# Plot scatter points by category with GOV.UK style colors
sns.scatterplot(x='Household Income', y='House Prices', hue='Category', data=df, s=100, ax=ax, palette=['#12436D', '#f46a25'])

# Calculate and plot separate linear trends and R² values for each category
for i, category in enumerate(df['Category'].unique()):
    # Filter data for each category
    category_data = df[df['Category'] == category]
    
    # Perform linear regression
    X = category_data[['Household Income']]
    y = category_data['House Prices']
    reg = LinearRegression().fit(X, y)
    y_pred = reg.predict(X)
    r2 = r2_score(y, y_pred)
    
    # Plot linear trend line for each category
    sns.regplot(x='Household Income', y='House Prices', data=category_data, scatter=False, ax=ax,
                line_kws={'lw': 2, 'linestyle': '--'}, color='#12436D' if category == 'Urban' else '#f46a25')
    
    # Display R² for each category with adjusted positions
    common_x_pos = df['Household Income'].min() - 3000  # Fixed x position for neat alignment
    y_pos = category_data['House Prices'].max()
    
    # Adjust R² label y position to avoid overlap and set x position to common_x_pos
    y_offset = 10000 if i == 0 else -10000  # Offset R² label: Up for Urban, Down for Rural
    plt.text(common_x_pos, y_pos + y_offset, f'{category} $R^2$ = {r2:.2f}', fontsize=12, color='#12436D' if category == 'Urban' else '#f46a25')

# Set the GOV.UK style labels and title
ax.set_xlabel('Household Income (£)', fontsize=14, weight='bold')
ax.set_ylabel('House Prices (£)', fontsize=14, weight='bold')

# Set the formatter for x and y axes to include commas
ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f'{int(x):,}'))
ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda y, _: f'{int(y):,}'))

# Set the GOV.UK styling colors for axes and background
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_linewidth(1.5)
ax.spines['bottom'].set_linewidth(1.5)
ax.spines['left'].set_color('black')
ax.spines['bottom'].set_color('black')

# Adjust tick parameters for GOV.UK styling
ax.tick_params(axis='both', which='major', labelsize=12, color='black')

# Show the plot
plt.tight_layout()
plt.show()

# Load required libraries
library(ggplot2)
library(dplyr)
library(broom)
library(scales)  # For comma formatting

# Set seed for reproducibility
set.seed(42)

# Create a DataFrame with a nuanced pattern
data <- data.frame(
  Category = sample(c("Urban", "Rural"), 50, replace = TRUE)  # Randomly assign categories
)

# Assign household incomes with Urban starting from 20,000 upwards
data$Household_Income <- ifelse(
  data$Category == "Urban", 
  sample(20000:100000, 50, replace = TRUE), 
  sample(20000:70000, 50, replace = TRUE)
)

# Assign house prices to be generally higher in Rural areas, lower in Urban areas
data$House_Prices <- ifelse(
  data$Category == "Rural", 
  sample(300000:500000, 50, replace = TRUE), 
  sample(100000:300000, 50, replace = TRUE)
)

# Introduce a general correlation: Higher incomes should have higher house prices
# Modify 25% of the data points to ensure high income leads to high house prices
high_income_mask <- data$Household_Income > 70000
data$House_Prices[high_income_mask] <- data$House_Prices[high_income_mask] + sample(100000:200000, sum(high_income_mask), replace = TRUE)

# Define the custom GOV.UK color palette
govuk_palette <- c(Urban = "#12436D", Rural = "#f46a25")

# Create a scatter plot with GOV.UK styling for two categories
ggplot(data, aes(x = Household_Income, y = House_Prices, color = Category)) +
  geom_point(size = 3, alpha = 0.7) +
  
  # Add separate linear trends and display R² values for each category
  geom_smooth(method = "lm", se = FALSE, linetype = "dashed", size = 1.2, aes(color = Category)) +
  
  # Apply the custom GOV.UK color palette
  scale_color_manual(values = govuk_palette) +
  
  # Custom labels and title
  labs(
    x = "Household Income (£)",
    y = "House Prices (£)",
    title = "House Prices vs. Household Income by Category"
  ) +
  
  # Custom styling for GOV.UK-like appearance
  theme_minimal(base_size = 14) +
  theme(
    panel.grid = element_blank(),  # Remove gridlines
    axis.line = element_line(color = "black", size = 0.5),
    legend.position = "top",
    legend.title = element_blank(),
    axis.text = element_text(color = "black"),
    axis.title = element_text(size = 14, face = "bold", color = "black")
  ) +
  
  # Format x and y axis labels with commas
  scale_x_continuous(labels = comma) +
  scale_y_continuous(labels = comma)

# Calculate separate R² values for each category and display them on the plot
r2_values <- data %>%
  group_by(Category) %>%
  summarise(R2 = summary(lm(House_Prices ~ Household_Income))$r.squared)

# Print R² values for reference
print(r2_values)

import folium
import requests

# URL to the GeoJSON file hosted on GitHub
geojson_url = "https://raw.githubusercontent.com/shedloadofcode/data-files/refs/heads/main/LAD_DEC_2021_GB_BFC.json"

# Fetch the GeoJSON data from the URL
response = requests.get(geojson_url)
geo_data = response.json()

# Define the custom color scale and class intervals for density
classes = [0, 10000, 20000, 30000, 40000, 50000]
colorscale = ['#cfdce3', '#9fb9c8', '#7095ac', '#407291', '#104f75']

# Create a Folium map centered around the UK with CartoDB's light background
m = folium.Map(location=[54.509865, -5.118092], zoom_start=6, tiles=None)
folium.TileLayer(
    tiles='https://{s}.basemaps.cartocdn.com/light_all/{z}/{x}/{y}{r}.png',
    attr='&copy; <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors &copy; <a href="https://carto.com/attributions">CARTO</a>'
).add_to(m)

# Define color based on density values
def style_function(feature):
    density = feature['properties'].get('density', 0)
    color = colorscale[0]  # Default color
    for i, cls in enumerate(classes):
        if density > cls:
            color = colorscale[i]
    return {
        'fillOpacity': 0.7,
        'weight': 2,
        'opacity': 1,
        'color': 'white',  # Default dashed border color
        'fillColor': color,
        'dashArray': '3'
    }

# Highlight function to show black solid lines on hover
def highlight_function(feature):
    return {
        'weight': 3,
        'color': 'black',  # Change to solid black on hover
        'dashArray': '',   # Remove dashes on hover
        'fillOpacity': 0.7
    }

# Create the GeoJson layer with tooltips
geojson_layer = folium.GeoJson(
    geo_data,
    style_function=style_function,
    highlight_function=highlight_function,
    tooltip=folium.GeoJsonTooltip(
        fields=['LAD21NM', 'density'],
        aliases=['Region:', 'Density:'],
        localize=True,
        sticky=True,
        style=("background-color: white; border: 1px solid black; border-radius: 3px; "
               "box-shadow: 3px 3px 3px rgba(0,0,0,0.25); font-size: 16px; font-family: Arial;")
    ),
    zoom_on_click=True
).add_to(m)

# Custom legend for density classes
legend_html = '''
<div style="position: fixed; 
    bottom: 50px; left: 50px; width: 300px; height: 170px; 
    background-color: white; z-index: 1000; border:2px solid grey; padding: 10px;">
    <h4 style="margin:0; text-align: center;">Population Density - Not actual figures</h4>
    <div style="padding: 5px 10px;">
        <div style="background-color: #cfdce3; width: 20px; height: 20px; display: inline-block;"></div> 0 - 10,000<br>
        <div style="background-color: #9fb9c8; width: 20px; height: 20px; display: inline-block;"></div> 10,000 - 20,000<br>
        <div style="background-color: #7095ac; width: 20px; height: 20px; display: inline-block;"></div> 20,000 - 30,000<br>
        <div style="background-color: #407291; width: 20px; height: 20px; display: inline-block;"></div> 30,000 - 40,000<br>
        <div style="background-color: #104f75; width: 20px; height: 20px; display: inline-block;"></div> 40,000 - 50,000+<br>
    </div>
</div>
'''

# Add the legend to the map
m.get_root().html.add_child(folium.Element(legend_html))

# Save the map as an HTML file
m.save('choropleth_map.html')
print("Map saved as 'choropleth_map.html'")