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RealEstateListingsProcessor.py
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RealEstateListingsProcessor.py
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# Developed by Team BitFuture
# Website: www.team-bitfuture.de | Email: [email protected]
# Lead Developer: Ossenbrück
# Website: ossenbrück.de | Email: hi@ossenbrück.de
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
from datetime import datetime
class RealEstateListingsProcessor:
"""
RealEstateListingsProcessor: Your Friendly Neighborhood Market Analyzer 🏘️📊
This class is designed to process and analyze real estate listings data.
It's like having a personal real estate expert in your pocket, minus the
expensive suit and cheesy smile. 😉
Key Features:
- Data loading and cleaning (because let's face it, raw data is messy 🧹)
- Statistical analysis (numbers that actually mean something! 🤓)
- Visualization generation (pretty pictures for your reports 📈)
- Report creation (impress your boss with markdown magic ✨)
"""
def __init__(self):
"""
Initialize the RealEstateListingsProcessor
Sets up two main attributes:
- self.data: Where all our lovely data will live 📦
- self.report: A dictionary to store our insightful findings 🔍
"""
self.data = None # Our data playground
self.report = {} # Where the magic insights happen
def load_data(self, folder_path):
"""
Load and Clean the Real Estate Data
Parameters:
- folder_path: Path to the folder containing our Excel files 📁
This method does the dirty work:
1. Finds all Excel files in the specified folder
2. Loads them into a single DataFrame
3. Cleans up the mess (hello, NaN values 👋)
Raises:
- ValueError: If no Excel files are found (sad pandas 🐼)
"""
all_files = list(Path(folder_path).glob("*.xlsx"))
if not all_files:
raise ValueError(f"No Excel files found in '{folder_path}'. Did you forget to save? 🤔")
df_list = [pd.read_excel(file) for file in all_files]
self.data = pd.concat(df_list, ignore_index=True)
# Clean up our data - no room for NaNs in this house! 🧽
self.data['Price'] = pd.to_numeric(self.data['Price'], errors='coerce')
self.data['SquareMeters'] = pd.to_numeric(self.data['SquareMeters'], errors='coerce')
self.data.dropna(subset=['Price', 'SquareMeters'], inplace=True)
def process_data(self):
"""
Process the Data and Extract Key Insights
This is where we put on our data scientist hat 🎩
and crunch those numbers:
- Calculate price per square meter (for those fancy price/m² conversations)
- Compute average prices (how much does a dream home cost these days?)
- Find the most expensive and cheapest properties (for when you win the lottery 🎉 or need a fixer-upper 🔨)
- Calculate median prices and price ranges (statistically sound and impressive in reports)
"""
# Price per square meter - the real estate equivalent of 'price per pound' 😄
self.data['PricePerSqm'] = self.data['Price'] / self.data['SquareMeters']
self.report['average_price'] = self.data['Price'].mean()
self.report['average_price_per_sqm'] = self.data['PricePerSqm'].mean()
self.report['number_of_properties'] = len(self.data)
self.report['most_expensive_property'] = self.data.loc[self.data['Price'].idxmax()]
self.report['cheapest_property'] = self.data.loc[self.data['Price'].idxmin()]
self.report['median_price'] = self.data['Price'].median()
self.report['price_range'] = self.data['Price'].max() - self.data['Price'].min()
def generate_visualizations(self):
"""
Create Eye-Catching Visualizations
Because sometimes, a picture is worth a thousand spreadsheet cells.
We'll create two key plots:
1. A scatter plot of price vs. square meters (aka the "dream home finder")
2. A histogram of price per square meter (or "how much bang for your buck")
Warning: May cause sudden urges to buy or sell property
"""
# The "dream home finder" plot
plt.figure(figsize=(10, 6))
plt.scatter(self.data['SquareMeters'], self.data['Price'])
plt.title('Price vs. Size: The Dream Home Finder')
plt.xlabel('Size (m²) - From Cozy to Palatial')
plt.ylabel('Price (€) - From Piggy Bank to Bank Vault')
plt.savefig('price_vs_sqm.png')
plt.close()
# The "bang for your buck" plot
plt.figure(figsize=(10, 6))
self.data['PricePerSqm'].hist(bins=20)
plt.title('Price per m²: How Much Bang for Your Buck?')
plt.xlabel('Price per m² (€) - The True Cost of Your Dance Space')
plt.ylabel('Frequency - Popular Price Points')
plt.savefig('price_per_sqm_distribution.png')
plt.close()
def generate_report(self):
"""
Generate the Final Report 📝✨
Compile all our hard-earned insights into a markdown report.
This method will:
- Summarize key market statistics
- Highlight the creme de la creme and the bargain bin of properties
- Include our dazzling visualizations
The result? A report so good, it might be mistaken for actual market expertise! 🕵️♂️📈
"""
report_md = f"""
# Real Estate Market Analysis Report 🏘️📊
Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
## Market Overview 🌆
- Total properties analyzed: {self.report['number_of_properties']} 🏠
- Average property price: {self.report['average_price']:,.2f} € 💰
- Average price per m²: {self.report['average_price_per_sqm']:,.2f} €/m² 📏
- Median property price: {self.report['median_price']:,.2f} € (perfectly balanced, as all things should be)
- Price range: {self.report['price_range']:,.2f} € (from "first home" to "lottery win")
## Property Extremes 🎢
### Most Expensive Property 🏰💎
- Price: {self.report['most_expensive_property']['Price']:,.2f} €
- Size: {self.report['most_expensive_property']['SquareMeters']:,.2f} m²
- Location: {self.report['most_expensive_property']['Location']}
### Most Affordable Property 🏠💡
- Price: {self.report['cheapest_property']['Price']:,.2f} €
- Size: {self.report['cheapest_property']['SquareMeters']:,.2f} m²
- Location: {self.report['cheapest_property']['Location']}
## Market Visualizations 📈🖼️
Remember, in real estate as in life, it's all about location, location, location! 🗺️🏠🗺️
"""
with open('real-estate-report.md', 'w', encoding='utf-8') as f:
f.write(report_md)