Using geopandas to plot the number and median price of Airbnb listings in each London borough

mapping-boroughs.py

import geopandas as gpd

# Importing the London borough boundary GeoJSON file as a dataframe in geopandas
map_df = gpd.read_file('data/neighbourhoods.geojson')

# Creating a dataframe of listing counts and median price by borough
borough_df = pd.DataFrame(df.groupby('borough').size())
borough_df.rename(columns={0: 'number_of_listings'}, inplace=True)
borough_df['median_price'] = df.groupby('borough').price.median().values

# Joining the dataframes
borough_map_df = map_df.set_index('neighbourhood').join(borough_df)

# Plotting the number of listings in each borough
fig1, ax1 = plt.subplots(1, figsize=(15, 6))
borough_map_df.plot(column='number_of_listings', cmap='Blues', ax=ax1)
ax1.axis('off')
ax1.set_title('Number of Airbnb listings in each London borough', fontsize=14)
sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=0, vmax=9000))
sm._A = [] # Creates an empty array for the data range
cbar = fig1.colorbar(sm)
plt.show()

# Plotting the median price of listings in each borough
fig2, ax2 = plt.subplots(1, figsize=(15, 6))
borough_map_df.plot(column='median_price', cmap='Blues', ax=ax2)
ax2.axis('off')
ax2.set_title('Median price of Airbnb listings in each London borough', fontsize=14)
sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=min(borough_map_df.median_price), vmax=max(borough_map_df.median_price)))
sm._A = [] # Creates an empty array for the data range
cbar = fig2.colorbar(sm)
plt.show()