Import and install necessary libraries
First, you need to install and import the libraries
!pip install pandas numpy matplotlib seaborn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as snsLoading your real estate data
Upload your data
Drag and drop your real estate data CSV file into the file explorer on the left side of the Deepnote interface.
Load data into the data frame
Load the data by adding the following code
df = pd.read_csv('your_real_estate_data.csv')
dfData cleaning and preparation
Check for missing values
Run the following to see if there are any missing values in your data:
df.isnull().sum()Handle missing values
Fill or drop missing values based on your needs
df = df.fillna(method='ffill')Convert data types
Ensure that your data types are correct, especially for dates
df['date'] = pd.to_datetime(df['date'])
df.dtypesExploratory data analysis (EDA)
Summary statistics
Generate summary statistics to understand your data
df.describe()Visualize data distributions
Create a histogram of property prices
plt.figure(figsize=(10, 6))
sns.histplot(df['price'], kde=True)
plt.title('Distribution of property prices')
plt.show()Compare prices by location
Use a boxplot to visualize property prices by location
plt.figure(figsize=(14, 8))
sns.boxplot(x='location', y='price', data=df)
plt.title('Property prices by location')
plt.xticks(rotation=45)
plt.show()Analyzing market trends
Price trends over time
Analyze how property prices have changed over time
plt.figure(figsize=(12, 6))
sns.lineplot(x='date', y='price', data=df)
plt.title('Property price trends over time')
plt.xticks(rotation=45)
plt.show()Sales volume trends over time
Look at the volume of property sales over time
df['month_year'] = df['date'].dt.to_period('M')
sales_volume = df.groupby('month_year').size()
plt.figure(figsize=(12, 6))
sales_volume.plot()
plt.title('Sales volume trends over time')
plt.xlabel('Month-Year')
plt.ylabel('Number of sales')
plt.xticks(rotation=45)
plt.show()Identifying key market indicators
Price per square foot
Calculate and analyze the price per square foot
df['price_per_sqft'] = df['price'] / df['sqft']
plt.figure(figsize=(14, 8))
sns.boxplot(x='location', y='price_per_sqft', data=df)
plt.title('Price per square foot by location')
plt.xticks(rotation=45)
plt.show()Days on Market
Analyze how long properties stay on the market before being sold
# Plot days on market
plt.figure(figsize=(10, 6))
sns.histplot(df['days_on_market'], kde=True)
plt.title('Distribution of days on market')
plt.show()Making data-driven decisions
Identifying market opportunities
Find locations with the best price per square foot
location_price_per_sqft = df.groupby('location')['price_per_sqft'].median().sort_values()
location_price_per_sqftFiltering properties based on criteria
Filter properties that meet certain investment criteria
price_per_sqft_threshold = 200
affordable_properties = df[df['price_per_sqft'] < price_per_sqft_threshold]
affordable_propertiesSaving and sharing your work
Export your results
Save your results to a new CSV file
df.to_csv('real_estate_market_analysis_results.csv', index=False)Share your notebook
- Click the “Share” button in Deepnote to share your notebook with others. You can provide view or edit access to your collaborators.
- Click on the Create app on the right side of the notebook configure it and share
Conclusion and next steps
Review findings: Summarize your key findings and insights from the analysis. Highlight the best market opportunities and any significant patterns observed.
Further analysis: Consider exploring more advanced studies such as predictive modeling for market trends. Consider incorporating external factors like economic indicators and regional development plans to enhance your analysis.
By following this step-by-step guide, you will be able to perform a comprehensive real estate market analysis using Python in Deepnote. This walkthrough provides a solid foundation for analyzing real estate data and making informed market decisions.