In [1]: from shapely.geometry import Polygon
In [2]: s = geopandas.GeoSeries([Polygon([(0, 0), (1, 1), (0, 1)]), None, Polygon([])])
In [3]: s
Out[3]:
0 POLYGON ((0 0, 1 1, 0 1, 0 0))
1 None
2 POLYGON EMPTY
dtype: geometry
In spatial operations, missing geometries will typically propagate (be missing
in the result as well), while empty geometries are treated as a geometry
and the result will depend on the operation:
In [4]: s.area
Out[4]:
0 0.5
1 NaN
2 0.0
dtype: float64
In [5]: s.union(Polygon([(0, 0), (0, 1), (1, 1), (1, 0)]))
Out[5]:
0 POLYGON ((1 1, 1 0, 0 0, 0 1, 1 1))
1 None
2 POLYGON ((0 1, 1 1, 1 0, 0 0, 0 1))
dtype: geometry
In [6]: s.intersection(Polygon([(0, 0), (0, 1), (1, 1), (1, 0)]))
Out[6]:
0 POLYGON ((0 0, 0 1, 1 1, 0 0))
1 None
2 POLYGON EMPTY
dtype: geometry
The GeoSeries.isna() method will only check for missing values and not
for empty geometries:
In [7]: s.isna()
Out[7]:
0 False
1 True
2 False
dtype: bool
On the other hand, if you want to know which values are empty geometries,
you can use the GeoSeries.is_empty attribute:
In [8]: s.is_empty
Out[8]:
0 False
1 False
2 True
dtype: bool
To get only the actual geometry objects that are neither missing nor empty,
you can use a combination of both:
In [9]: s.is_empty | s.isna()
Out[9]:
0 False
1 True
2 True
dtype: bool
In [10]: s[~(s.is_empty | s.isna())]
Out[10]:
0 POLYGON ((0 0, 1 1, 0 1, 0 0))
dtype: geometry
Changes since GeoPandas v0.6.0
In GeoPandas v0.6.0, the missing data handling was refactored and made more
consistent across the library.
Historically, missing (“NA”) values in a GeoSeries could be represented by empty
geometric objects, in addition to standard representations such as None and
np.nan. At least, this was the case in GeoSeries.isna() or when a
GeoSeries got aligned in geospatial operations. But, other methods like
dropna() and fillna() did not follow this
approach and did not consider empty geometries as missing.
In GeoPandas v0.6.0, the most important change is GeoSeries.isna() no
longer treating empty as missing:
Using the small example from above, the old behaviour treated both the
empty as missing geometry as “missing”:
0 POLYGON ((0 0, 1 1, 0 1, 0 0))
1 None
2 GEOMETRYCOLLECTION EMPTY
dtype: object
>>> s.isna()
0 False
1 True
2 True
dtype: bool
Starting from GeoPandas v0.6.0, it will now only see actual missing values
as missing:
In [11]: s.isna()
Out[11]:
0 False
1 True
2 False
dtype: bool
For now, when isna() is called on a GeoSeries with empty geometries,
a warning is raised to alert the user of the changed behaviour with an
indication how to solve this.
Additionally, the behaviour of GeoSeries.align() changed to use
missing values instead of empty geometries to fill non-matching indexes.
Consider the following small toy example:
In [12]: from shapely.geometry import Point
In [13]: s1 = geopandas.GeoSeries([Point(0, 0), Point(1, 1)], index=[0, 1])
In [14]: s2 = geopandas.GeoSeries([Point(1, 1), Point(2, 2)], index=[1, 2])
In [15]: s1
Out[15]:
0 POINT (0 0)
1 POINT (1 1)
dtype: geometry
In [16]: s2
Out[16]:
1 POINT (1 1)
2 POINT (2 2)
dtype: geometry
Previously, the align method would use empty geometries to fill
values:
>>> s1_aligned, s2_aligned = s1.align(s2)
>>> s1_aligned
0 POINT (0 0)
1 POINT (1 1)
2 GEOMETRYCOLLECTION EMPTY
dtype: object
>>> s2_aligned
0 GEOMETRYCOLLECTION EMPTY
1 POINT (1 1)
2 POINT (2 2)
dtype: object
This method is used under the hood when performing spatial operations on
mis-aligned GeoSeries objects:
>>> s1.intersection(s2)
0 GEOMETRYCOLLECTION EMPTY
1 POINT (1 1)
2 GEOMETRYCOLLECTION EMPTY
dtype: object
Starting from GeoPandas v0.6.0, GeoSeries.align() will use missing
values to fill in the non-aligned indices, to be consistent with the
behaviour in pandas:
In [17]: s1_aligned, s2_aligned = s1.align(s2)
In [18]: s1_aligned
Out[18]:
0 POINT (0 0)
1 POINT (1 1)
2 None
dtype: geometry
In [19]: s2_aligned
Out[19]:
0 None
1 POINT (1 1)
2 POINT (2 2)
dtype: geometry
This has the consequence that spatial operations will also use missing
values instead of empty geometries, which can have a different behaviour
depending on the spatial operation:
In [20]: s1.intersection(s2)
Out[20]:
0 None
1 POINT (1 1)
2 None
dtype: geometry
