There are two ways to combine datasets in geopandas – attribute joins and spatial joins.
在 geopandas 中有两种组合数据集的方法——属性连接和空间连接。
In an attribute join, a GeoSeries or GeoDataFrame is combined with a regular pandas.Series or pandas.DataFrame based on a common variable. This is analogous to normal merging or joining in pandas.
在属性连接中，一个GeoSeries或GeoDataFrame与一个普通的pandas.Series或pandas.DataFrame基于一个共同的变量进行组合。这类似于pandas中的普通合并或连接。
In a Spatial Join, observations from two GeoSeries or GeoDataFrame are combined based on their spatial relationship to one another.
在空间连接中，来自两个GeoSeries或GeoDataFrame的观测数据根据它们之间的空间关系被结合起来。
In the following examples, we use these datasets:
在以下示例中，我们使用这些数据集：
In [1]: world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
In [2]: cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))
# For attribute join
In [3]: country_shapes = world[['geometry', 'iso_a3']]
In [4]: country_names = world[['name', 'iso_a3']]
# For spatial join
In [5]: countries = world[['geometry', 'name']]
In [6]: countries = countries.rename(columns={'name':'country'})