admin管理员组

文章数量:1487745

基于geopandas的精美地图绘制：geoplot

温馨提示

由于可视化代码过长隐藏，可点击运行Fork查看 若没有成功加载可视化图，点击运行可以查看 ps:隐藏代码在【代码已被隐藏】所在行，点击所在行，可以看到该行的最右角，会出现个三角形，点击查看即可

前言

项目目标

本项目旨在介绍geoplot的地图绘图示例。

项目方法

在以下内容中，将基于geoplot官方示例展示地图绘图。

安装与导入库

代码语言：javascript代码运行次数：0运行复制
!pip install geoplot -i /
代码语言：javascript代码运行次数：0运行复制
Looking in indexes: /
Collecting geoplot
  Downloading .5.1-py3-none-any.whl (28 kB)
Requirement already satisfied: cartopy in /opt/conda/lib/python3.9/site-packages (from geoplot) (0.23.0)
Requirement already satisfied: mapclassify>=2.1 in /opt/conda/lib/python3.9/site-packages (from geoplot) (2.5.0)
Requirement already satisfied: geopandas>=0.9.0 in /opt/conda/lib/python3.9/site-packages (from geoplot) (0.14.3)
Requirement already satisfied: seaborn in /opt/conda/lib/python3.9/site-packages (from geoplot) (0.11.2)
Collecting contextily>=1.0.0
  Downloading .6.2-py3-none-any.whl (17 kB)
Requirement already satisfied: pandas in /opt/conda/lib/python3.9/site-packages (from geoplot) (2.0.3)
Requirement already satisfied: matplotlib>=3.1.2 in /opt/conda/lib/python3.9/site-packages (from geoplot) (3.8.3)
Requirement already satisfied: requests in /opt/conda/lib/python3.9/site-packages (from contextily>=1.0.0->geoplot) (2.27.1)
Requirement already satisfied: joblib in /opt/conda/lib/python3.9/site-packages (from contextily>=1.0.0->geoplot) (1.3.2)
Requirement already satisfied: xyzservices in /opt/conda/lib/python3.9/site-packages (from contextily>=1.0.0->geoplot) (2023.10.1)
Collecting mercantile
  Downloading .2.1-py3-none-any.whl (14 kB)
Collecting geopy
  Downloading .4.1-py3-none-any.whl (125 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m125.4/125.4 kB[0m [31m4.3 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: pillow in /opt/conda/lib/python3.9/site-packages (from contextily>=1.0.0->geoplot) (9.4.0)
Requirement already satisfied: rasterio in /opt/conda/lib/python3.9/site-packages (from contextily>=1.0.0->geoplot) (1.3.6)
Requirement already satisfied: shapely>=1.8.0 in /opt/conda/lib/python3.9/site-packages (from geopandas>=0.9.0->geoplot) (1.8.5.post1)
Requirement already satisfied: pyproj>=3.3.0 in /opt/conda/lib/python3.9/site-packages (from geopandas>=0.9.0->geoplot) (3.4.1)
Requirement already satisfied: fiona>=1.8.21 in /opt/conda/lib/python3.9/site-packages (from geopandas>=0.9.0->geoplot) (1.9.1)
Requirement already satisfied: packaging in /opt/conda/lib/python3.9/site-packages (from geopandas>=0.9.0->geoplot) (23.2)
Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.9/site-packages (from mapclassify>=2.1->geoplot) (0.24.2)
Requirement already satisfied: numpy>=1.3 in /opt/conda/lib/python3.9/site-packages (from mapclassify>=2.1->geoplot) (1.26.4)
Requirement already satisfied: networkx in /opt/conda/lib/python3.9/site-packages (from mapclassify>=2.1->geoplot) (2.8)
Requirement already satisfied: scipy>=1.0 in /opt/conda/lib/python3.9/site-packages (from mapclassify>=2.1->geoplot) (1.11.4)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=3.1.2->geoplot) (0.11.0)
Requirement already satisfied: importlib-resources>=3.2.0 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=3.1.2->geoplot) (5.7.1)
Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=3.1.2->geoplot) (4.33.3)
Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=3.1.2->geoplot) (1.2.0)
Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=3.1.2->geoplot) (1.4.2)
Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=3.1.2->geoplot) (2.8.2)
Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=3.1.2->geoplot) (3.0.9)
Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.9/site-packages (from pandas->geoplot) (2024.1)
Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.9/site-packages (from pandas->geoplot) (2022.1)
Requirement already satisfied: pyshp>=2.3 in /opt/conda/lib/python3.9/site-packages (from cartopy->geoplot) (2.3.1)
Requirement already satisfied: cligj>=0.5 in /opt/conda/lib/python3.9/site-packages (from fiona>=1.8.21->geopandas>=0.9.0->geoplot) (0.7.2)
Requirement already satisfied: certifi in /opt/conda/lib/python3.9/site-packages (from fiona>=1.8.21->geopandas>=0.9.0->geoplot) (2024.2.2)
Requirement already satisfied: setuptools in /opt/conda/lib/python3.9/site-packages (from fiona>=1.8.21->geopandas>=0.9.0->geoplot) (62.2.0)
Requirement already satisfied: attrs>=19.2.0 in /opt/conda/lib/python3.9/site-packages (from fiona>=1.8.21->geopandas>=0.9.0->geoplot) (21.4.0)
Requirement already satisfied: click-plugins>=1.0 in /opt/conda/lib/python3.9/site-packages (from fiona>=1.8.21->geopandas>=0.9.0->geoplot) (1.1.1)
Requirement already satisfied: munch>=2.3.2 in /opt/conda/lib/python3.9/site-packages (from fiona>=1.8.21->geopandas>=0.9.0->geoplot) (4.0.0)
Requirement already satisfied: click~=8.0 in /opt/conda/lib/python3.9/site-packages (from fiona>=1.8.21->geopandas>=0.9.0->geoplot) (8.1.3)
Requirement already satisfied: zipp>=3.1.0 in /opt/conda/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib>=3.1.2->geoplot) (3.8.0)
Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib>=3.1.2->geoplot) (1.16.0)
Collecting geographiclib<3,>=1.52
  Downloading .0-py3-none-any.whl (40 kB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m40.3/40.3 kB[0m [31m14.4 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: snuggs>=1.4.1 in /opt/conda/lib/python3.9/site-packages (from rasterio->contextily>=1.0.0->geoplot) (1.4.7)
Requirement already satisfied: affine in /opt/conda/lib/python3.9/site-packages (from rasterio->contextily>=1.0.0->geoplot) (2.4.0)
Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.9/site-packages (from requests->contextily>=1.0.0->geoplot) (2.0.12)
Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.9/site-packages (from requests->contextily>=1.0.0->geoplot) (3.3)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.9/site-packages (from requests->contextily>=1.0.0->geoplot) (1.26.9)
Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.9/site-packages (from scikit-learn->mapclassify>=2.1->geoplot) (3.1.0)
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
代码语言：javascript代码运行次数：0运行复制
import geopandas as gpd
import geoplot as gplt
usa_cities = gpd.read_file(gplt.datasets.get_path('usa_cities'))
usa_cities.head()

id

POP_2010

ELEV_IN_FT

STATE

geometry

0

53

40888.0

1611.0

ND

POINT (-101.29627 48.23251)

1

101

52838.0

830.0

ND

POINT (-97.03285 47.92526)

2

153

15427.0

1407.0

ND

POINT (-98.70844 46.91054)

3

177

105549.0

902.0

ND

POINT (-96.78980 46.87719)

4

192

17787.0

2411.0

ND

POINT (-102.78962 46.87918)

代码语言：javascript代码运行次数：0运行复制
continental_usa_cities = usa_cities.query('STATE not in ["HI", "AK", "PR"]')
gplt.pointplot(continental_usa_cities)
代码语言：javascript代码运行次数：0运行复制
<Axes: >
代码语言：javascript代码运行次数：0运行复制
contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa'))
gplt.polyplot(contiguous_usa)
代码语言：javascript代码运行次数：0运行复制
<Axes: >
代码语言：javascript代码运行次数：0运行复制
ax = gplt.polyplot(contiguous_usa)
gplt.pointplot(continental_usa_cities, ax=ax)
代码语言：javascript代码运行次数：0运行复制
<Axes: >
代码语言：javascript代码运行次数：0运行复制
import geoplot.crs as gcrs

ax = gplt.polyplot(contiguous_usa, projection=gcrs.AlbersEqualArea())
gplt.pointplot(continental_usa_cities, ax=ax)
代码语言：javascript代码运行次数：0运行复制
<GeoAxes: >
代码语言：javascript代码运行次数：0运行复制
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)
代码语言：javascript代码运行次数：0运行复制
Text(0.5, 1.0, 'Cities in the Continental United States by Elevation')
代码语言：javascript代码运行次数：0运行复制
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'
    ]
)
代码语言：javascript代码运行次数：0运行复制
<GeoAxes: >
代码语言：javascript代码运行次数：0运行复制
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")
代码语言：javascript代码运行次数：0运行复制
Text(0.5, 1.0, 'Cities in the contiguous United States, 2010')
代码语言：javascript代码运行次数：0运行复制
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")
代码语言：javascript代码运行次数：0运行复制
Text(0.5, 1.0, 'Streets in Washington DC by Average Daily Traffic, 2015')
代码语言：javascript代码运行次数：0运行复制
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)')
代码语言：javascript代码运行次数：0运行复制
Text(0.5, 1.0, 'Connecticut (n=126,661)')
代码语言：javascript代码运行次数：0运行复制
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")
代码语言：javascript代码运行次数：0运行复制
Text(0.5, 1.0, 'Adult Obesity Rate by State, 2013')

小结

虽然许久不更新，但geoplot依然能绘制颜值在线的地图，当然还有更多功能有待发掘

本文参与 腾讯云自媒体同步曝光计划，分享自微信公众号。原始发表：2024-10-01，如有侵权请联系 cloudcommunity@tencent 删除地图condageopandasimportpython3

本文标签： 基于geopandas的精美地图绘制geoplot

版权声明：本文标题：基于geopandas的精美地图绘制：geoplot 内容由网友自发贡献，该文观点仅代表作者本人， 转载请联系作者并注明出处：http://www.betaflare.com/shuma/1755005266a3182371.html， 本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌抄袭侵权/违法违规的内容，一经查实，本站将立刻删除。