Visualizations with Python

This project utilizes Python visualizations packages, such as Plotly Express, HVPlot and PyPlot/Matplotlib to create an interactive dashboard exploring the 2010-2016 San Francisco housing market. Uses MapBox API to grab data and create visualizations.

Visualizations of San Francisco Real Estate Housing Market:

Average Housing Units/Year

Average Price/Neighborhood (with Interactive Dropdown Selector for Neighborhood)

First, group by year and neighborhood and then create a new dataframe of the mean values:

mean_values = sfo_data.groupby([sfo_data.index, "neighborhood"]).mean()
mean_values.reset_index(inplace=True)

Then use HVPlot to create an interactive line chart of the Average Price/Sq. Foot, with a dropdown selector for the neighborhood:

mean_values.hvplot.line(x="year", y="sale_price_sqr_foot", xlabel= "Year", ylabel="Average Price/Square Foot", groupby="neighborhood")

The above code results in the following interactive line chart:

Top 10 Most Expensive Neighborhoods

Parallel Coordinates Plot to Interactively Filter and Explore Various Factors Related to Sales Price

# Parallel Coordinates Plot
px.parallel_coordinates(ten_most_expensive_df, color="sale_price_sqr_foot", color_continuous_scale=px.colors.sequential.Inferno, title='Average House Value/Neighborhood', labels={'neighborhood': "Neighborhood", 'sale_price_sqr_foot':'Sales Price/Square Foot', 'housing_units':'Housing Units', 'gross_rent':'Gross Rent'})

The above code results in the following interactive parallel coordinates plot:

Parallel Categories Plot to Interactively Filter and Explore Various Factors Related to Sales Price

# Parallel Categories Plot
px.parallel_categories(ten_most_expensive_df, color="sale_price_sqr_foot", color_continuous_scale=px.colors.sequential.Inferno, title='Average House Value/Neighborhood', labels={'neighborhood': "Neighborhood", 'sale_price_sqr_foot':'Sales Price/Square Foot', 'housing_units':'Housing Units', 'gross_rent':'Gross Rent'})

The above code results in the following interactive parallel coordinates plot:

Average Value/Neighborhood utilizing MapBox API

First, calculate the mean values for each neighborhood:

mean_neighborhoods = sfo_data.groupby("neighborhood").mean()
mean_neighborhoods = mean_neighborhoods.reset_index()

Then join average values with the neighborhood locations (Lat/Long loaded in with MapBox API):

values_and_locations_df = pd.concat([df_neighborhood_locations, mean_neighborhoods['sale_price_sqr_foot'], mean_neighborhoods['housing_units'], mean_neighborhoods['gross_rent']], axis=1).dropna()

Lastly, create a scatter plot through mapbox to analyze neighborhood info:

map_plot = px.scatter_mapbox(values_and_locations_df, lat="Lat", lon="Lon", size="sale_price_sqr_foot", color="gross_rent", color_continuous_scale=px.colors.cyclical.IceFire, size_max=15, zoom=3, width=1000, hover_name="Neighborhood", title="Average Price/Square Foot and Gross Rent in San Francisco")

The above code results in the following interactive MapBox visualization: