# Create a pivot table to reshape the data for stacked area chart
pivot_df = df.pivot(index='year', columns='country', values='total_wealth')
# Define custom colors for the countries
custom_colors = ["#003f5c","#2f4b7c","#665191","#a05195","#d45087","#f95d6a","#ff7c43","#ffa600"]
# Define the desired order of countries
desired_order = ["United States", "China", "Japan", "Germany", "United Kingdom", "France", "India", "Other"]
# Reorder the columns of the pivot_df and custom_colors list
pivot_df = pivot_df[desired_order]
# Smooth the lines using spline interpolation
x_smooth = np.linspace(pivot_df.index.min(), pivot_df.index.max(), 300)
pivot_smooth = pd.DataFrame({country: make_interp_spline(pivot_df.index, pivot_df[country])(x_smooth)
for country in pivot_df.columns})
# Plot the stacked area chart with smoothing and custom colors
plt.figure(figsize=(8, 8)) # Set the figure size
plt.stackplot(x_smooth, pivot_smooth.values.T, labels=pivot_smooth.columns, colors=custom_colors)
# `plt.gca()` function is used to obtain a reference to the current axes on which you plot your data
ax = plt.gca()
# Annotations for the values per year
def add_annotations_year(year):
"""
Input: a year
Apply: add to the graph the total wealth of all countries at a given date
and a line from the bottom of the graph to the total value of wealth
"""
# Calculate total amount of wealth at a given year
y_end = df[df["year"]==year]["total_wealth"].sum()
# Set values in areas where the graph does not appear
# Special case for 2021: we put it on the left instead of upper the line
if year==2021:
modif_xaxis = -3.3
modif_yaxis = 20000
else:
modif_xaxis = -1.5
modif_yaxis = 26000
# Add the values, with a specific position, in bold, black and a fontsize of 10
plt.text(year+modif_xaxis,
y_end+modif_yaxis,
f'${y_end}M',
fontsize=10,
color='black',
fontweight = 'bold')
# Add line
ax.plot([year, year], # x-axis position
[0, y_end*1.05], # y-axis position (*1.05 is used to make a it little bit longer)
color='black', # Color
linewidth=1) # Width of the line
# Add a point at the top of the line
ax.plot(year, # x-axis position
y_end*1.05, # y-axis position (*1.05 is used to make a it little bit longer)
marker='o', # Style of the point
markersize=5, # Size of the point
color='black') # Color
# Add the line and the values for each of the following years
for year in [2000,2005,2010,2015,2021]:
add_annotations_year(year)
# Annotations for the values per country
def add_annotations_country(country, value_placement, amount, color):
"""
Adds an annotation to a plot at a specific location with information about a country's amount in millions.
Parameters:
country (str): The name of the country for which the annotation is being added.
value_placement (float): The vertical position where the annotation will be placed on the plot.
amount (float): The amount in millions that will be displayed in the annotation.
color (str): The color of the annotation text.
"""
plt.text(2021.5, value_placement, f'{country} ${amount}M', fontsize=10, color=color, fontweight='bold')
# We manually define the labels, values and position that will be displayed on the right of the graph
countries = ['Rest of the world', 'India', 'France', 'UK', 'Germany', 'Japan', 'China', 'USA']
values_placement = [400000, 310000, 295000, 275000, 260000, 240000, 180000, 70000]
amounts = [142841, 14225, 16159, 16261, 17489, 25692, 85107, 145793]
custom_colors.reverse() # Makes sure the colors match the country concerned
# Iterate over all countries and add the name with the right value and color
for country, value, amount, color in zip(countries, values_placement, amounts, custom_colors):
add_annotations_country(country, value, amount, color)
# Title of our graph
plt.text(2000, 320000,
'Aggregated \nHousehold \nWealth', # Title ('\n' allows you to go to the line)
fontsize=40, # High font size for style
color='black',
fontweight = 'bold')
# Credits and data information
plt.text(2000, -50000, 'Data:', fontsize=8, color='black', fontweight = 'bold')
plt.text(2001.4, -50000, 'James Davies, Rodrigo Lluberas and Anthony Shorrocks, Credit Suisse Global Wealth Databook 2022', fontsize=8, color='black')
plt.text(2000, -63000, 'Design:', fontsize=8, color='black', fontweight = 'bold')
plt.text(2001.9, -63000, 'Gilbert Fontana', fontsize=8, color='black')
# Images
def add_logo(path_to_logo, text, image_bottom_left_x, image_bottom_left_y, image_width):
"""
Adds a logo image and text to a plot at specific positions.
Parameters:
path_to_logo (str): The file path to the logo image that will be added to the plot.
text (str): The text to be added along with the logo.
image_bottom_left_x (float): The x-coordinate of the bottom-left corner of the logo image's position on the plot.
image_bottom_left_y (float): The y-coordinate of the bottom-left corner of the logo image's position on the plot.
image_width (float): The width of the logo image in the plot.
"""
logo = Image.open(path_to_logo) # Open the image
image_array = np.array(logo) # Convert to a numpy array
image_height = image_width * image_array.shape[0] / image_array.shape[1] # Calculate height based on ratio
# Add image to graph
ax_image = plt.axes([image_bottom_left_x, # Position on the x-axis
image_bottom_left_y, # Position on the y-axis
image_width, # Image width
image_height]) # Image height
ax_image.imshow(image_array) # Display the image
ax_image.axis('off') # Remove axis of the image in order to improve style
# Add text
plt.text(700, # Position on the x-axis
image_bottom_left_y+500, # Position on the y-axis
text,
fontsize=8,
color='black')
# Add github and twitter credentials
add_logo("github.png", "gilbertfontana", 0.155, -0.03, 0.03)
add_logo("twitter.png", "GilbertFontana", 0.16, -0.06, 0.02)
# Remove the y-axis frame (left, right and top spines)
ax.spines['left'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Remove the ticks and labels on the y-axis
ax.tick_params(left=False, labelleft=False)
# Display the chart
plt.show()RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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