================ by Jawad Haider
Chpt 4 - Visualization with Matplotlib¶
Example: California Cities¶
Example: California Cities¶
Recall that in “Customizing Plot Legends”, we demonstrated the use of size and color in a scatter plot to convey information about the location, size, and population of California cities. Here, we’ll create this plot again, but using Basemap to put the data in context.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
| Unnamed: 0 | city | latd | longd | elevation_m | elevation_ft | population_total | area_total_sq_mi | area_land_sq_mi | area_water_sq_mi | area_total_km2 | area_land_km2 | area_water_km2 | area_water_percent | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Adelanto | 34.576111 | -117.432778 | 875.0 | 2871.0 | 31765 | 56.027 | 56.009 | 0.018 | 145.107 | 145.062 | 0.046 | 0.03 |
| 1 | 1 | AgouraHills | 34.153333 | -118.761667 | 281.0 | 922.0 | 20330 | 7.822 | 7.793 | 0.029 | 20.260 | 20.184 | 0.076 | 0.37 |
| 2 | 2 | Alameda | 37.756111 | -122.274444 | NaN | 33.0 | 75467 | 22.960 | 10.611 | 12.349 | 59.465 | 27.482 | 31.983 | 53.79 |
| 3 | 3 | Albany | 37.886944 | -122.297778 | NaN | 43.0 | 18969 | 5.465 | 1.788 | 3.677 | 14.155 | 4.632 | 9.524 | 67.28 |
| 4 | 4 | Alhambra | 34.081944 | -118.135000 | 150.0 | 492.0 | 83089 | 7.632 | 7.631 | 0.001 | 19.766 | 19.763 | 0.003 | 0.01 |
# extract the data we're interested in
lat=cities['latd'].values
lon=cities['longd'].values
population=cities['population_total'].values
area=cities['area_total_km2'].values
# 1. Draw the map background
fig=plt.figure(figsize=(8,8))
m=Basemap(projection='lcc', resolution='h',
lat_0=37.5, lon_0=-119,
width=1E6, height=1.2E6)
m.shadedrelief()
m.drawcoastlines(color='gray')
m.drawcountries(color='gray')
m.drawstates(color='gray')
# 2. scatter city data, with color reflecting population and size reflecting area
m.scatter(lon, lat, latlon=True,
c=np.log10(population),s=area,
cmap='Reds',alpha=0.5)
# 3. Create colobar and legned
plt.colorbar(label=r'$log_{10}({\rm population})$')
plt.clim(3,7)
# 4. make legend with dummy points
for a in [100,300,500]:
plt.scatter([],[],c='k',alpha=0.5,s=a,label=str(a)+'km$^2$')
plt.legend(scatterpoints=1, frameon=False,
labelspacing=1, loc='lower left')
<matplotlib.legend.Legend at 0x7f0308093b50>
