Visualize the trend of the Rental Housing Protection Act's priority payment amount in a Python line graph (2025)
If the Rental Protection Act Priority Payment AmountHave you heard of the Important steps to protect your tenants' security depositsThis is a program that gives you priority protection for a portion of your deposit if your landlord goes bankrupt or goes to auction.
Today we'll be using Python How to use line graphs to visualize trends in Priority Payments by regionline by line, so even Python beginners can easily follow along. So let's get started.
What is the Rent Protection Act Priority Payment Amount?
The Rent Protection Act's Priority Reimbursement Amount protects tenants from losing their security deposit due to a landlord's financial difficulties. Even if your home goes to auction or is foreclosed on, you'll still get your Gives you the right to have a portion of your deposit reimbursed before other creditorsin the file.
Prioritized Reimbursement Amount by Region in 2025
- Seoul: Deposit of KRW 165 million or less → Priority repayment amount of KRW 55 million
- Metropolitan Overcrowding Control Areas: Deposit of KRW 145 million or less → Priority repayment amount of KRW 48 million
- Metropolitan and major cities: Deposit of KRW 85 million or less → Priority repayment amount of KRW 28 million
- Other regions: Deposit of KRW 75 million or less → Priority repayment amount of KRW 25 million
Legal provisions related to the Rent Protection Act's priority payment amount
Under Section 8 of the Residential Tenancies Protection Act, a small tenant may have a portion of their security deposit protected before other creditors. This provision provides that in the event of a home auction or short sale, a portion of the security deposit is prioritized for reimbursement in order to protect the tenant's security deposit.
As mentioned above, the specific priority payment amount and small tenant thresholds are set by presidential decree, and the amount cannot exceed one-twoth of the value of the house.
Visualize Priority Payments Trends with Python Line Graphs
Now let's get serious and visualize the above data using a Python line graph! Using the code below, we can see the change in priority payments from 2001 to 2023 at a glance.
Python code examples
import matplotlib.pyplot as plt
Clean up the # data
years = [2001, 2008, 2014, 2018, 2021, 2023]
seoul = [2000, 2700, 3400, 3700, 4500, 5500]
capital_region = [1600, 2200, 2700, 3400, 4000, 4800]
metropolitan = [1200, 1800, 2200, 2800, 2500, 2800]
other_regions = [1000, 1500, 1900, 2500, 2000, 2500]
Draw a # line graph
plt.figure(figsize=(12, 8))
plt.plot(years, seoul, marker='o', label='Seoul')
plt.plot(years, capital_region, marker='o', label='Capital Region')
plt.plot(years, metropolitan, marker='o', label='Metropolitan Cities')
plt.plot(years, other_regions, marker='o', label='Other Regions')
Add amount labels to the # data points
for year, s, c, m, o in zip(years, seoul, capital_region, metropolitan, other_regions):
plt.text(year, s, f'{s}', ha='right')
plt.text(year, c, f'{c}', ha='right')
plt.text(year, m, f'{m}', ha='right')
plt.text(year, o, f'{o}', ha='right')
Set up the # graph
plt.title('Changes in Priority Compensation Amount by Region (2001-2023)')
plt.xlabel('Year')
plt.ylabel('Priority Compensation Amount (10,000 KRW)')
plt.legend()
plt.grid(True)
Output the # graph
plt.tight_layout()
plt.show()Conclusion: Understanding data is easier with Python line graphs!
Today, we've used a Python line graph to visualize the changes in the Rent Protection Act priority payment amount. Visualizing data makes it much more intuitive!
By the way, you use bar graphs almost as much as line graphs, right? Changing Perceptions of Marital Childbearing: Analyzing Data with Stacked Bar Graphs Check out the post to find out!
# Code Explained in detail
1. Import the required libraries
import matplotlib.pyplot as plt
matplotlib.pyplotis the main module used to plot graphs in Python.2. Prepare your data
years = [2001, 2008, 2014, 2018, 2021, 2023] seoul = [2000, 2700, 3400, 3700, 4500, 5500] capital_region = [1600, 2200, 2700, 3400, 4000, 4800] metropolitan = [1200, 1800, 2200, 2800, 2500, 2800] other_regions = [1000, 1500, 1900, 2500, 2000, 2500]
years: Year data to display on the x-axis.seoul,capital_region,metropolitan,other_regions: Preferred payment amount data for each region (y-axis).3. Set graph size
plt.figure(figsize=(12, 8))
- Set the overall size of the graph to 12 inches wide by 8 inches tall.
4. Draw a line graph
plt.plot(years, seoul, marker='o', label='Seoul') plt.plot(years, capital_region, marker='o', label='Capital Region') plt.plot(years, metropolitan, marker='o', label='Metropolitan Cities') plt.plot(years, other_regions, marker='o', label='Other Regions')
- Represent each dataset as a line graph, highlighting data points with dots (markers).
labelis the name that will appear in the legend.5. Add data value labels
for year, s, c, m, o in zip(years, seoul, capital_region, metropolitan, other_regions): plt.text(year, s, f'{s}', ha='right') plt.text(year, c, f'{c}', ha='right') plt.text(year, m, f'{m}', ha='right') plt.text(year, o, f'{o}', ha='right')
zipto iterate over the year and each region's data in a bundle.plt.text()displays a value next to each data point.ha='right'sets the text alignment to right.6. Add a graph title and axis labels
plt.title('Changes in Priority Compensation Amount by Region (2001-2023)') plt.xlabel('Year') plt.ylabel('Priority Compensation Amount (10,000 KRW)')
- Add a graph title and x- and y-axis labels.
7. Add legends and grids
plt.legend() plt.grid(True)
plt.legend(): Adds a legend to the graph.plt.grid(True): Displays a grid in the background for better readability.8. Graph Output
plt.tight_layout() plt.show()
tight_layout(): Automatically adjusts the spacing between graph elements.show(): Prints the finished graph to the screen.This code provides a clear visual representation of the change in priority payments by region over the years. We've labeled each data point with a value and applied clean styling to make it easy for beginners to understand the data.
