ukincome.ipynb

ukincome.ipynb

import matplotlib.pyplot as plt

Create the figure

plt.figure(figsize=(12, 7))

Plot main income line and the trend

plt.plot(data["Year"], data["Work_Mean"], linestyle='-', marker='o', color='red', linewidth=2)#label="Working Households",
plt.plot(data["Year"], data["Trend_Work"], linestyle='--', color='black', linewidth=2)#label="1977–2007 Trend (3% CAGR)",

Shaded Recessions and Crises

Add shaded regions for recessions

Add precise recession markers at the bottom

plt.fill_betweenx([0, 100000], 1980 + 0/4, 1981 + 1/4, color='gray', alpha=0.5, linewidth=0) # 1980 Q1 to 1981 Q1
plt.text(1981.4, 1000, "Recession", fontsize=10, color="gray", va="center") # Label for 1980-1981 recession

plt.fill_betweenx([0, 100000], 1990 + 2/4, 1991 + 3/4, color='gray', alpha=0.5, linewidth=0) # 1990 Q3 to 1991 Q3
plt.text(1991.8, 1000, "Recession", fontsize=10, color="gray", va="center") # Label for 1990-1991 recession

plt.fill_betweenx([0, 100000], 2008 + 1/4, 2009 + 2/4, color='purple', alpha=0.5, linewidth=0) # 2008 Q2 to 2009 Q2
plt.text(2009.6, 1000, "Financial Crisis", fontsize=10, color="purple", va="center") # Label for 2008-2009 recession

plt.fill_betweenx([0, 100000], 2020 + 0/4, 2020 + 1/4, color='green', alpha=0.5, linewidth=0) # 2020 Q1 to 2020 Q2
plt.text(2020.5, 1000, "COVID", fontsize=10, color="green", va="center") # Label for 2020 recession

#plt.fill_betweenx([5000, 10000], 2016 + 2/4, 2021, color='gray', alpha=0.5, linewidth=0) # 2020 Q1 to 2020 Q2
#plt.text(2020.5, 5000, "Brexit: vote to exit", fontsize=10, color="gray", va="center") # Label for 2020 recession

plt.fill_betweenx([0, 100000], 2016.5, 2021.0, color='orange', alpha=0.25, linewidth=0)
plt.text(2018.5, 15000, "Brexit Transition", fontsize=10, color="darkorange", rotation=90, ha="center")

Titles and labels

plt.suptitle("Real Disposable Income of UK Working Households (1977–2023)", fontsize=19, y=0.98)
plt.title("Inflation-adjusted to 2022/23 prices. Data: ONS. Dashed line shows 1977–2007 growth continued.", fontsize=11, y=1.01)

plt.xlabel("Year", fontsize=12)
plt.ylabel("Mean Household Disposable Income (£)", fontsize=12)

Ticks and grid

plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
plt.grid(True, which='major', linestyle='--', alpha=0.5)

Get the 2023 actual and trend values

actual_2023 = data.loc[data["Year"] == 2023, "Work_Mean"].values[0]
trend_2023 = data.loc[data["Year"] == 2023, "Trend_Work"].values[0]

#gap

Compute gap

gap = int(trend_2023 - actual_2023)
gap_midpoint = (actual_2023 + trend_2023) / 2

Add only the box, no line

plt.text(2021.6, (actual_2023 + trend_2023) / 2,
"£30,000+ gap",
fontsize=11, fontweight='bold',
va='center', ha='left',
bbox=dict(facecolor='white', edgecolor='gray', boxstyle='round,pad=0.2'))

Legend

#plt.legend(fontsize=11, loc="upper left")

plt.ylim(bottom=0000, top=85000)

Save and show

plt.savefig("final_income_graph.png", dpi=300, bbox_inches="tight")
plt.show()