基于geopandas的精美地图绘制：geoplot

!pip install geoplot -i https://pypi.mirrors.ustc.edu.cn/simple/

Looking in indexes: https://pypi.mirrors.ustc.edu.cn/simple/
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Installing collected packages: mercantile, geographiclib, geopy, contextily, geoplot
Successfully installed contextily-1.6.2 geographiclib-2.0 geoplot-0.5.1 geopy-2.4.1 mercantile-1.2.1

import geopandas as gpd
import geoplot as gplt
usa_cities = gpd.read_file(gplt.datasets.get_path('usa_cities'))
usa_cities.head()

continental_usa_cities = usa_cities.query('STATE not in ["HI", "AK", "PR"]')
gplt.pointplot(continental_usa_cities)

<Axes: >

contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa'))
gplt.polyplot(contiguous_usa)

<Axes: >

ax = gplt.polyplot(contiguous_usa)
gplt.pointplot(continental_usa_cities, ax=ax)

<Axes: >

import geoplot.crs as gcrs

ax = gplt.polyplot(contiguous_usa, projection=gcrs.AlbersEqualArea())
gplt.pointplot(continental_usa_cities, ax=ax)

<GeoAxes: >

ax = gplt.polyplot(
    contiguous_usa, projection=gcrs.AlbersEqualArea(),
    edgecolor='white', facecolor='lightgray',
    figsize=(12, 8)
)
gplt.pointplot(
    continental_usa_cities, ax=ax, hue='ELEV_IN_FT', cmap='Blues',
    scheme='quantiles',
    scale='ELEV_IN_FT', limits=(1, 10),
    legend=True, legend_var='scale',
    legend_kwargs={'frameon': False},
    legend_values=[-110, 1750, 3600, 5500, 7400],
    legend_labels=['-110 feet', '1750 feet', '3600 feet', '5500 feet', '7400 feet']
)
ax.set_title('Cities in the Continental United States by Elevation', fontsize=16)

Text(0.5, 1.0, 'Cities in the Continental United States by Elevation')

gplt.choropleth(
    contiguous_usa, hue='population', projection=gcrs.AlbersEqualArea(),
    edgecolor='white', linewidth=1,
    cmap='Greens', legend=True,
    scheme='FisherJenks',
    legend_labels=[
        '<3 million', '3-6.7 million', '6.7-12.8 million',
        '12.8-25 million', '25-37 million'
    ]
)

<GeoAxes: >

import geoplot.crs as gcrs
import matplotlib.pyplot as plt
import mapclassify as mc

scheme = mc.Quantiles(continental_usa_cities['POP_2010'], k=5)
proj = gcrs.AlbersEqualArea()

ax = gplt.polyplot(
    contiguous_usa,
    zorder=-1,
    linewidth=1,
    projection=proj,
    edgecolor='white',
    facecolor='lightgray',
    figsize=(12, 12)
)

gplt.pointplot(
    continental_usa_cities,
    scale='POP_2010',
    limits=(2, 30),
    hue='POP_2010',
    cmap='Blues',
    scheme=scheme,
    legend=True,
    legend_var='scale',
    legend_values=[8000000, 2000000, 1000000, 100000],
    legend_labels=['8 million', '2 million', '1 million', '100 thousand'],
    legend_kwargs={'frameon': False, 'loc': 'lower right'},
    ax=ax
)

plt.title("Cities in the contiguous United States, 2010")

Text(0.5, 1.0, 'Cities in the contiguous United States, 2010')

import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt

dc_roads = gpd.read_file(gplt.datasets.get_path('dc_roads'))

gplt.sankey(
    dc_roads, projection=gcrs.AlbersEqualArea(),
    scale='aadt', limits=(0.1, 10), color='black'
)

plt.title("Streets in Washington DC by Average Daily Traffic, 2015")

Text(0.5, 1.0, 'Streets in Washington DC by Average Daily Traffic, 2015')

import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt

# load the data
nyc_boroughs = gpd.read_file(gplt.datasets.get_path('nyc_boroughs'))
tickets = gpd.read_file(gplt.datasets.get_path('nyc_parking_tickets'))

proj = gcrs.AlbersEqualArea(central_latitude=40.7128, central_longitude=-74.0059)


def plot_state_to_ax(state, ax):
    gplt.choropleth(
        tickets.set_index('id').loc[:, [state, 'geometry']],
        hue=state, cmap='Blues',
        linewidth=0.0, ax=ax
    )
    gplt.polyplot(
        nyc_boroughs, edgecolor='black', linewidth=0.5, ax=ax
    )


f, axarr = plt.subplots(2, 2, figsize=(12, 13), subplot_kw={'projection': proj})

plt.suptitle('Parking Tickets Issued to State by Precinct, 2016', fontsize=16)
plt.subplots_adjust(top=0.95)

plot_state_to_ax('ny', axarr[0][0])
axarr[0][0].set_title('New York (n=6,679,268)')

plot_state_to_ax('nj', axarr[0][1])
axarr[0][1].set_title('New Jersey (n=854,647)')

plot_state_to_ax('pa', axarr[1][0])
axarr[1][0].set_title('Pennsylvania (n=215,065)')

plot_state_to_ax('ct', axarr[1][1])
axarr[1][1].set_title('Connecticut (n=126,661)')

Text(0.5, 1.0, 'Connecticut (n=126,661)')

import pandas as pd
import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt
import mapclassify as mc

# load the data
obesity_by_state = pd.read_csv(gplt.datasets.get_path('obesity_by_state'), sep='\t')
contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa'))
contiguous_usa['Obesity Rate'] = contiguous_usa['state'].map(
    lambda state: obesity_by_state.query("State == @state").iloc[0]['Percent']
)
scheme = mc.Quantiles(contiguous_usa['Obesity Rate'], k=5)


ax = gplt.cartogram(
    contiguous_usa,
    scale='Obesity Rate', limits=(0.75, 1),
    projection=gcrs.AlbersEqualArea(central_longitude=-98, central_latitude=39.5),
    hue='Obesity Rate', cmap='Reds', scheme=scheme,
    linewidth=0.5,
    legend=True, legend_kwargs={'loc': 'lower right'}, legend_var='hue',
    figsize=(12, 7)
)
gplt.polyplot(contiguous_usa, facecolor='lightgray', edgecolor='None', ax=ax)

plt.title("Adult Obesity Rate by State, 2013")

Text(0.5, 1.0, 'Adult Obesity Rate by State, 2013')