GeoPandas Compatibility#

cuSpatial supports any geometry format supported by GeoPandas. Load geometry information from a GeoPandas.GeoSeries or GeoPandas.GeoDataFrame.

>>> gpdf = geopandas.read_file('arbitrary.txt')
    cugpdf = cuspatial.from_geopandas(gpdf)

or

>>> cugpdf = cuspatial.GeoDataFrame(gpdf)
class cuspatial.GeoDataFrame(data: Optional[geopandas.geodataframe.GeoDataFrame] = None)#

A GPU GeoDataFrame object.

Methods

groupby(*args, **kwargs)

Group DataFrame using a mapper or by a Series of columns.

to_geopandas([nullable])

Returns a new GeoPandas GeoDataFrame object from the coordinates in the cuspatial GeoDataFrame.

to_pandas([nullable])

Calls self.to_geopandas, converting GeoSeries columns into GeoPandas columns and cudf.Series columns into pandas.Series columns, and returning a pandas.DataFrame.

cummax

cummin

cumprod

cumsum

cummax(axis=None, *args, **kwargs)#

Return cumulative max of the IndexedFrame.

Parameters
axis: {index (0), columns(1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
IndexedFrame

Examples

Series

>>> import cudf
>>> ser = cudf.Series([1, 5, 2, 4, 3])
>>> ser.cumsum()
0    1
1    6
2    8
3    12
4    15

DataFrame

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
>>> s.cumsum()
    a   b
0   1   7
1   3  15
2   6  24
3  10  34
cummin(axis=None, *args, **kwargs)#

Return cumulative min of the IndexedFrame.

Parameters
axis: {index (0), columns(1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
IndexedFrame

Examples

Series

>>> import cudf
>>> ser = cudf.Series([1, 5, 2, 4, 3])
>>> ser.cumsum()
0    1
1    6
2    8
3    12
4    15

DataFrame

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
>>> s.cumsum()
    a   b
0   1   7
1   3  15
2   6  24
3  10  34
cumprod(axis=None, *args, **kwargs)#

Return cumulative product of the IndexedFrame.

Parameters
axis: {index (0), columns(1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
IndexedFrame

Examples

Series

>>> import cudf
>>> ser = cudf.Series([1, 5, 2, 4, 3])
>>> ser.cumsum()
0    1
1    6
2    8
3    12
4    15

DataFrame

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
>>> s.cumsum()
    a   b
0   1   7
1   3  15
2   6  24
3  10  34
cumsum(axis=None, *args, **kwargs)#

Return cumulative sum of the IndexedFrame.

Parameters
axis: {index (0), columns(1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
IndexedFrame

Examples

Series

>>> import cudf
>>> ser = cudf.Series([1, 5, 2, 4, 3])
>>> ser.cumsum()
0    1
1    6
2    8
3    12
4    15

DataFrame

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
>>> s.cumsum()
    a   b
0   1   7
1   3  15
2   6  24
3  10  34
groupby(*args, **kwargs)#

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters
bymapping, function, label, or list of labels

Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align() method). If a cupy array is passed, the values are used as-is determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.

levelint, level name, or sequence of such, default None

If the axis is a MultiIndex (hierarchical), group by a particular level or levels.

as_indexbool, default True

For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output.

sortbool, default False

Sort result by group key. Differ from Pandas, cudf defaults to False for better performance. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group.

dropnabool, optional

If True (default), do not include the “null” group.

Returns
DataFrameGroupBy

Returns a groupby object that contains information about the groups.

Examples

>>> import cudf
>>> import pandas as pd
>>> df = cudf.DataFrame({
...     'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...     'Max Speed': [380., 370., 24., 26.],
... })
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0
>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...           ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = cudf.DataFrame({'Max Speed': [390., 350., 30., 20.]},
...     index=index)
>>> df
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()
        Max Speed
Type
Wild         185.0
Captive      210.0
to_geopandas(nullable=False)#

Returns a new GeoPandas GeoDataFrame object from the coordinates in the cuspatial GeoDataFrame.

Parameters
nullable: matches the cudf `to_pandas` signature, not yet supported.
to_pandas(nullable=False)#

Calls self.to_geopandas, converting GeoSeries columns into GeoPandas columns and cudf.Series columns into pandas.Series columns, and returning a pandas.DataFrame.

Parameters
nullable: matches the cudf `to_pandas` signature, not yet supported.
class cuspatial.GeoSeries(data: geopandas.geoseries.GeoSeries, index: Optional[Union[cudf.core.index.Index, pandas.core.indexes.base.Index]] = None, dtype=None, name=None, nan_as_null=True)#

cuspatial.GeoSeries enables GPU-backed storage and computation of shapely-like objects. Our goal is to give feature parity with GeoPandas. At this time, only from_geopandas and to_geopandas are directly supported. cuspatial GIS, indexing, and trajectory functions depend on the arrays stored in the GeoArrowBuffers object, accessible with the points, multipoints, lines, and polygons accessors.

>>> cuseries.points
    xy:
    0   -1.0
    1    0.0
    dtype: float64
Attributes
lines

Access the LineArray of the underlying GeoArrowBuffers.

multipoints

Access the MultiPointArray of the underlying GeoArrowBuffers.

points

Access the PointsArray of the underlying GeoArrowBuffers.

polygons

Access the PolygonArray of the underlying GeoArrowBuffers.

Methods

to_geopandas([nullable])

Returns a new GeoPandas GeoSeries object from the coordinates in the cuspatial GeoSeries.

to_pandas()

Treats to_pandas and to_geopandas as the same call, which improves compatibility with pandas.

cummax

cummin

cumprod

cumsum

cummax(axis=None, *args, **kwargs)#

Return cumulative max of the IndexedFrame.

Parameters
axis: {index (0), columns(1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
IndexedFrame

Examples

Series

>>> import cudf
>>> ser = cudf.Series([1, 5, 2, 4, 3])
>>> ser.cumsum()
0    1
1    6
2    8
3    12
4    15

DataFrame

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
>>> s.cumsum()
    a   b
0   1   7
1   3  15
2   6  24
3  10  34
cummin(axis=None, *args, **kwargs)#

Return cumulative min of the IndexedFrame.

Parameters
axis: {index (0), columns(1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
IndexedFrame

Examples

Series

>>> import cudf
>>> ser = cudf.Series([1, 5, 2, 4, 3])
>>> ser.cumsum()
0    1
1    6
2    8
3    12
4    15

DataFrame

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
>>> s.cumsum()
    a   b
0   1   7
1   3  15
2   6  24
3  10  34
cumprod(axis=None, *args, **kwargs)#

Return cumulative product of the IndexedFrame.

Parameters
axis: {index (0), columns(1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
IndexedFrame

Examples

Series

>>> import cudf
>>> ser = cudf.Series([1, 5, 2, 4, 3])
>>> ser.cumsum()
0    1
1    6
2    8
3    12
4    15

DataFrame

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
>>> s.cumsum()
    a   b
0   1   7
1   3  15
2   6  24
3  10  34
cumsum(axis=None, *args, **kwargs)#

Return cumulative sum of the IndexedFrame.

Parameters
axis: {index (0), columns(1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
IndexedFrame

Examples

Series

>>> import cudf
>>> ser = cudf.Series([1, 5, 2, 4, 3])
>>> ser.cumsum()
0    1
1    6
2    8
3    12
4    15

DataFrame

>>> import cudf
>>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
>>> s.cumsum()
    a   b
0   1   7
1   3  15
2   6  24
3  10  34
property lines#

Access the LineArray of the underlying GeoArrowBuffers.

property multipoints#

Access the MultiPointArray of the underlying GeoArrowBuffers.

property points#

Access the PointsArray of the underlying GeoArrowBuffers.

property polygons#

Access the PolygonArray of the underlying GeoArrowBuffers.

to_geopandas(nullable=False)#

Returns a new GeoPandas GeoSeries object from the coordinates in the cuspatial GeoSeries.

to_pandas()#

Treats to_pandas and to_geopandas as the same call, which improves compatibility with pandas.