Reading and writing files

Vector data cubes do not have an interoperable file format that can be used to save any Xarray object with geometric dimensions to a file and read it back or in other software like {stars} in the R-Spatial ecosystem.

CF conventions and NetCDF offer a way of saving a Dataset with a single dimension indexed by geometry, but this still needs to be implemented in Xvec. While we are working on a solution (see this issue), some options can be used in the meantime.

Pickle, joblib

As with any other file, you can serialize and deserialize Xarray objects with the pickle and joblib libraries. However, keep in mind that such a file needs to be read in an environment with the same or similar versions of all dependencies to avoid issues.

import pickle
import joblib

import geopandas as gpd
import xarray as xr
import xvec

from geodatasets import get_path

Create an example Dataset based on the US demographic data as in the introductory notebook.

counties = gpd.read_file(get_path("geoda.natregimes"))

cube = xr.Dataset(
    data_vars=dict(
        population=(["county", "year"], counties[["PO60", "PO70", "PO80", "PO90"]]),
        unemployment=(["county", "year"], counties[["UE60", "UE70", "UE80", "UE90"]]),
        divorce=(["county", "year"], counties[["DV60", "DV70", "DV80", "DV90"]]),
        age=(["county", "year"], counties[["MA60", "MA70", "MA80", "MA90"]]),
    ),
    coords=dict(county=counties.geometry, year=[1960, 1970, 1980, 1990]),
).xvec.set_geom_indexes("county", crs=counties.crs)
cube

<xarray.Dataset>
Dimensions:       (county: 3085, year: 4)
Coordinates:
  * county        (county) object POLYGON ((-95.34258270263672 48.54670333862...
  * year          (year) int64 1960 1970 1980 1990
Data variables:
    population    (county, year) int64 4304 3987 3764 4076 ... 43766 55800 65077
    unemployment  (county, year) float64 7.9 9.0 5.903 ... 5.444 7.018 5.489
    divorce       (county, year) float64 1.859 2.62 3.747 ... 2.725 4.782 7.415
    age           (county, year) float64 28.8 30.5 34.5 ... 26.4 28.97 35.33
Indexes:
    county        GeometryIndex (crs=EPSG:4326)

xarray.Dataset

• Dimensions:
  • county: 3085
  • year: 4
• Coordinates: (2)
  • county
    (county)
    object
    POLYGON ((-95.34258270263672 48....

    crs :

    epsg:4326

    array([<POLYGON ((-95.343 48.547, -95.341 48.715, -95.094 48.717, -95.095 48.912, -...>,
           <POLYGON ((-118.851 47.95, -118.847 48.478, -118.869 48.479, -118.87 48.647,...>,
           <POLYGON ((-117.438 48.044, -117.541 48.043, -117.536 47.79, -117.607 47.798...>,
           ...,
           <POLYGON ((-76.396 37.108, -76.403 37.091, -76.434 37.089, -76.464 37.103, -...>,
           <POLYGON ((-77.532 38.565, -77.721 38.84, -77.707 38.877, -77.678 38.885, -7...>,
           <POLYGON ((-111.372 44.745, -111.368 45.35, -111.349 45.349, -111.349 45.635...>],
          dtype=object)

  • year
    (year)
    int64
    1960 1970 1980 1990

    array([1960, 1970, 1980, 1990])

• Data variables: (4)
  • population
    (county, year)
    int64
    4304 3987 3764 ... 55800 65077

    array([[  4304,   3987,   3764,   4076],
           [  3889,   3655,   5811,   6295],
           [ 17884,  17405,  28979,  30948],
           ...,
           [ 21583,  33203,  44189,  53427],
           [ 50164, 111102, 166665, 250377],
           [ 39260,  43766,  55800,  65077]])

  • unemployment
    (county, year)
    float64
    7.9 9.0 5.903 ... 5.444 7.018 5.489

    array([[ 7.9       ,  9.        ,  5.9025788 ,  3.89479009],
           [ 8.2       , 15.4       , 15.42288557, 16.8115942 ],
           [10.1       ,  9.        , 13.57406431, 10.70079408],
           ...,
           [ 2.5       ,  3.10506508,  5.03704493,  4.1028878 ],
           [ 3.3       ,  2.36155862,  3.70888552,  3.35398107],
           [ 5.21803859,  5.44429066,  7.01794317,  5.48855304]])

  • divorce
    (county, year)
    float64
    1.859 2.62 3.747 ... 4.782 7.415

    array([[ 1.85897436,  2.61980831,  3.74659401,  7.38853503],
           [ 2.86327845,  3.68600683,  6.6254417 , 11.54313487],
           [ 2.71144662,  2.97637795,  5.44822257,  9.12321222],
           ...,
           [ 1.11322292,  1.55032333,  3.25763881,  4.85074627],
           [ 1.67239146,  1.46834319,  3.74220727,  5.75017712],
           [ 2.77758585,  2.72460364,  4.78226375,  7.4145588 ]])

  • age
    (county, year)
    float64
    28.8 30.5 34.5 ... 26.4 28.97 35.33

    array([[28.8       , 30.5       , 34.5       , 35.5       ],
           [25.9       , 27.1       , 27.2       , 32.8       ],
           [29.6       , 31.8       , 28.7       , 34.5       ],
           ...,
           [24.4       , 24.4       , 29.8       , 34.        ],
           [22.3       , 21.8       , 25.66666667, 28.46666667],
           [28.25      , 26.4       , 28.96666667, 35.33333333]])

• Indexes: (2)
  • year
    PandasIndex

    PandasIndex(Int64Index([1960, 1970, 1980, 1990], dtype='int64', name='year'))

  • county
    GeometryIndex (crs=EPSG:4326)

    <xvec.index.GeometryIndex object at 0x12bd85060>

• Attributes: (0)

Pickle

Save the object from memory to a file using the built-in pickle package:

with open("cube.pickle", "wb") as f:
    pickle.dump(cube, f)

And read it back to memory:

with open("cube.pickle", "rb") as f:
    cube2 = pickle.load(f)

cube2

<xarray.Dataset>
Dimensions:       (county: 3085, year: 4)
Coordinates:
  * county        (county) object POLYGON ((-95.34258270263672 48.54670333862...
  * year          (year) int64 1960 1970 1980 1990
Data variables:
    population    (county, year) int64 4304 3987 3764 4076 ... 43766 55800 65077
    unemployment  (county, year) float64 7.9 9.0 5.903 ... 5.444 7.018 5.489
    divorce       (county, year) float64 1.859 2.62 3.747 ... 2.725 4.782 7.415
    age           (county, year) float64 28.8 30.5 34.5 ... 26.4 28.97 35.33
Indexes:
    county        GeometryIndex (crs=EPSG:4326)

xarray.Dataset

• Dimensions:
  • county: 3085
  • year: 4
• Coordinates: (2)
  • county
    (county)
    object
    POLYGON ((-95.34258270263672 48....

    crs :

    epsg:4326

    array([<POLYGON ((-95.343 48.547, -95.341 48.715, -95.094 48.717, -95.095 48.912, -...>,
           <POLYGON ((-118.851 47.95, -118.847 48.478, -118.869 48.479, -118.87 48.647,...>,
           <POLYGON ((-117.438 48.044, -117.541 48.043, -117.536 47.79, -117.607 47.798...>,
           ...,
           <POLYGON ((-76.396 37.108, -76.403 37.091, -76.434 37.089, -76.464 37.103, -...>,
           <POLYGON ((-77.532 38.565, -77.721 38.84, -77.707 38.877, -77.678 38.885, -7...>,
           <POLYGON ((-111.372 44.745, -111.368 45.35, -111.349 45.349, -111.349 45.635...>],
          dtype=object)

  • year
    (year)
    int64
    1960 1970 1980 1990

    array([1960, 1970, 1980, 1990])

• Data variables: (4)
  • population
    (county, year)
    int64
    4304 3987 3764 ... 55800 65077

    array([[  4304,   3987,   3764,   4076],
           [  3889,   3655,   5811,   6295],
           [ 17884,  17405,  28979,  30948],
           ...,
           [ 21583,  33203,  44189,  53427],
           [ 50164, 111102, 166665, 250377],
           [ 39260,  43766,  55800,  65077]])

  • unemployment
    (county, year)
    float64
    7.9 9.0 5.903 ... 5.444 7.018 5.489

    array([[ 7.9       ,  9.        ,  5.9025788 ,  3.89479009],
           [ 8.2       , 15.4       , 15.42288557, 16.8115942 ],
           [10.1       ,  9.        , 13.57406431, 10.70079408],
           ...,
           [ 2.5       ,  3.10506508,  5.03704493,  4.1028878 ],
           [ 3.3       ,  2.36155862,  3.70888552,  3.35398107],
           [ 5.21803859,  5.44429066,  7.01794317,  5.48855304]])

  • divorce
    (county, year)
    float64
    1.859 2.62 3.747 ... 4.782 7.415

    array([[ 1.85897436,  2.61980831,  3.74659401,  7.38853503],
           [ 2.86327845,  3.68600683,  6.6254417 , 11.54313487],
           [ 2.71144662,  2.97637795,  5.44822257,  9.12321222],
           ...,
           [ 1.11322292,  1.55032333,  3.25763881,  4.85074627],
           [ 1.67239146,  1.46834319,  3.74220727,  5.75017712],
           [ 2.77758585,  2.72460364,  4.78226375,  7.4145588 ]])

  • age
    (county, year)
    float64
    28.8 30.5 34.5 ... 26.4 28.97 35.33

    array([[28.8       , 30.5       , 34.5       , 35.5       ],
           [25.9       , 27.1       , 27.2       , 32.8       ],
           [29.6       , 31.8       , 28.7       , 34.5       ],
           ...,
           [24.4       , 24.4       , 29.8       , 34.        ],
           [22.3       , 21.8       , 25.66666667, 28.46666667],
           [28.25      , 26.4       , 28.96666667, 35.33333333]])

• Indexes: (2)
  • year
    PandasIndex

    PandasIndex(Int64Index([1960, 1970, 1980, 1990], dtype='int64', name='year'))

  • county
    GeometryIndex (crs=EPSG:4326)

    <xvec.index.GeometryIndex object at 0x12cb37640>

• Attributes: (0)

Joblib

Larger objects may be better to save using the joblib package that allows compression.

with open("cube.joblib.compressed", "wb") as f:
    joblib.dump(cube, f, compress=True)

And decompress and read it back to memory:

with open("cube.joblib.compressed", "rb") as f:
    cube3 = joblib.load(f)

cube3

<xarray.Dataset>
Dimensions:       (county: 3085, year: 4)
Coordinates:
  * county        (county) object POLYGON ((-95.34258270263672 48.54670333862...
  * year          (year) int64 1960 1970 1980 1990
Data variables:
    population    (county, year) int64 4304 3987 3764 4076 ... 43766 55800 65077
    unemployment  (county, year) float64 7.9 9.0 5.903 ... 5.444 7.018 5.489
    divorce       (county, year) float64 1.859 2.62 3.747 ... 2.725 4.782 7.415
    age           (county, year) float64 28.8 30.5 34.5 ... 26.4 28.97 35.33
Indexes:
    county        GeometryIndex (crs=EPSG:4326)

xarray.Dataset

• Dimensions:
  • county: 3085
  • year: 4
• Coordinates: (2)
  • county
    (county)
    object
    POLYGON ((-95.34258270263672 48....

    crs :

    epsg:4326

    array([<POLYGON ((-95.343 48.547, -95.341 48.715, -95.094 48.717, -95.095 48.912, -...>,
           <POLYGON ((-118.851 47.95, -118.847 48.478, -118.869 48.479, -118.87 48.647,...>,
           <POLYGON ((-117.438 48.044, -117.541 48.043, -117.536 47.79, -117.607 47.798...>,
           ...,
           <POLYGON ((-76.396 37.108, -76.403 37.091, -76.434 37.089, -76.464 37.103, -...>,
           <POLYGON ((-77.532 38.565, -77.721 38.84, -77.707 38.877, -77.678 38.885, -7...>,
           <POLYGON ((-111.372 44.745, -111.368 45.35, -111.349 45.349, -111.349 45.635...>],
          dtype=object)

  • year
    (year)
    int64
    1960 1970 1980 1990

    array([1960, 1970, 1980, 1990])

• Data variables: (4)
  • population
    (county, year)
    int64
    4304 3987 3764 ... 55800 65077

    array([[  4304,   3987,   3764,   4076],
           [  3889,   3655,   5811,   6295],
           [ 17884,  17405,  28979,  30948],
           ...,
           [ 21583,  33203,  44189,  53427],
           [ 50164, 111102, 166665, 250377],
           [ 39260,  43766,  55800,  65077]])

  • unemployment
    (county, year)
    float64
    7.9 9.0 5.903 ... 5.444 7.018 5.489

    array([[ 7.9       ,  9.        ,  5.9025788 ,  3.89479009],
           [ 8.2       , 15.4       , 15.42288557, 16.8115942 ],
           [10.1       ,  9.        , 13.57406431, 10.70079408],
           ...,
           [ 2.5       ,  3.10506508,  5.03704493,  4.1028878 ],
           [ 3.3       ,  2.36155862,  3.70888552,  3.35398107],
           [ 5.21803859,  5.44429066,  7.01794317,  5.48855304]])

  • divorce
    (county, year)
    float64
    1.859 2.62 3.747 ... 4.782 7.415

    array([[ 1.85897436,  2.61980831,  3.74659401,  7.38853503],
           [ 2.86327845,  3.68600683,  6.6254417 , 11.54313487],
           [ 2.71144662,  2.97637795,  5.44822257,  9.12321222],
           ...,
           [ 1.11322292,  1.55032333,  3.25763881,  4.85074627],
           [ 1.67239146,  1.46834319,  3.74220727,  5.75017712],
           [ 2.77758585,  2.72460364,  4.78226375,  7.4145588 ]])

  • age
    (county, year)
    float64
    28.8 30.5 34.5 ... 26.4 28.97 35.33

    array([[28.8       , 30.5       , 34.5       , 35.5       ],
           [25.9       , 27.1       , 27.2       , 32.8       ],
           [29.6       , 31.8       , 28.7       , 34.5       ],
           ...,
           [24.4       , 24.4       , 29.8       , 34.        ],
           [22.3       , 21.8       , 25.66666667, 28.46666667],
           [28.25      , 26.4       , 28.96666667, 35.33333333]])

• Indexes: (2)
  • year
    PandasIndex

    PandasIndex(Int64Index([1960, 1970, 1980, 1990], dtype='int64', name='year'))

  • county
    GeometryIndex (crs=EPSG:4326)

    <xvec.index.GeometryIndex object at 0x12c952fb0>

• Attributes: (0)

In this case, the cube.pickle file has 1.8 MB while the cube.joblib.compressed has 823 KB.

GIS file formats

Another option is to convert the data to a GeoDataFrame and save it to any geospatial file format or a GeoParquet if there are multiple geometry columns.

Long table form

Long table form needs to save each value in a row with all its dimension coordinates. While this option is possible, it can be inefficient, leading to a high degree of repetition and very long tables. However, it is readily supported by Xarray and Xvec.

long = cube.xvec.to_geodataframe()
long

	county	population	unemployment	divorce	age
year
1960	POLYGON ((-95.34258 48.54670, -95.34081 48.715...	4304	7.900000	1.858974	28.800000
1970	POLYGON ((-95.34258 48.54670, -95.34081 48.715...	3987	9.000000	2.619808	30.500000
1980	POLYGON ((-95.34258 48.54670, -95.34081 48.715...	3764	5.902579	3.746594	34.500000
1990	POLYGON ((-95.34258 48.54670, -95.34081 48.715...	4076	3.894790	7.388535	35.500000
1960	POLYGON ((-118.85050 47.94969, -118.84732 48.4...	3889	8.200000	2.863278	25.900000
...	...	...	...	...	...
1990	POLYGON ((-77.53178 38.56506, -77.72094 38.840...	250377	3.353981	5.750177	28.466667
1960	POLYGON ((-111.37152 44.74516, -111.36839 45.3...	39260	5.218039	2.777586	28.250000
1970	POLYGON ((-111.37152 44.74516, -111.36839 45.3...	43766	5.444291	2.724604	26.400000
1980	POLYGON ((-111.37152 44.74516, -111.36839 45.3...	55800	7.017943	4.782264	28.966667
1990	POLYGON ((-111.37152 44.74516, -111.36839 45.3...	65077	5.488553	7.414559	35.333333

12340 rows × 5 columns

You can see that the result is longer than the original GeoDataFrame that had each year encoded as a column.

You can then save it using to_file or to_parquet.

long.to_file("cube.gpkg")

or

long.to_parquet("cube.parquet")

You can then re-create the Dataset using the from_dataframe method with the geometry column set as an index.

xr.Dataset.from_dataframe(long.set_index("county", append=True)).xvec.set_geom_indexes(
    "county", crs=long.crs
)

<xarray.Dataset>
Dimensions:       (year: 4, county: 3085)
Coordinates:
  * year          (year) int64 1960 1970 1980 1990
  * county        (county) object POLYGON ((-95.34258270263672 48.54670333862...
Data variables:
    population    (year, county) int64 4304 3889 17884 ... 53427 250377 65077
    unemployment  (year, county) float64 7.9 8.2 10.1 7.5 ... 4.103 3.354 5.489
    divorce       (year, county) float64 1.859 2.863 2.711 ... 4.851 5.75 7.415
    age           (year, county) float64 28.8 25.9 29.6 ... 34.0 28.47 35.33
Indexes:
    county        GeometryIndex (crs=EPSG:4326)

xarray.Dataset

• Dimensions:
  • year: 4
  • county: 3085
• Coordinates: (2)
  • year
    (year)
    int64
    1960 1970 1980 1990

    array([1960, 1970, 1980, 1990])

  • county
    (county)
    object
    POLYGON ((-95.34258270263672 48....

    crs :

    epsg:4326

    array([<POLYGON ((-95.343 48.547, -95.341 48.715, -95.094 48.717, -95.095 48.912, -...>,
           <POLYGON ((-118.851 47.95, -118.847 48.478, -118.869 48.479, -118.87 48.647,...>,
           <POLYGON ((-117.438 48.044, -117.541 48.043, -117.536 47.79, -117.607 47.798...>,
           ...,
           <POLYGON ((-76.396 37.108, -76.403 37.091, -76.434 37.089, -76.464 37.103, -...>,
           <POLYGON ((-77.532 38.565, -77.721 38.84, -77.707 38.877, -77.678 38.885, -7...>,
           <POLYGON ((-111.372 44.745, -111.368 45.35, -111.349 45.349, -111.349 45.635...>],
          dtype=object)

• Data variables: (4)
  • population
    (year, county)
    int64
    4304 3889 17884 ... 250377 65077

    array([[  4304,   3889,  17884, ...,  21583,  50164,  39260],
           [  3987,   3655,  17405, ...,  33203, 111102,  43766],
           [  3764,   5811,  28979, ...,  44189, 166665,  55800],
           [  4076,   6295,  30948, ...,  53427, 250377,  65077]])

  • unemployment
    (year, county)
    float64
    7.9 8.2 10.1 ... 4.103 3.354 5.489

    array([[ 7.9       ,  8.2       , 10.1       , ...,  2.5       ,
             3.3       ,  5.21803859],
           [ 9.        , 15.4       ,  9.        , ...,  3.10506508,
             2.36155862,  5.44429066],
           [ 5.9025788 , 15.42288557, 13.57406431, ...,  5.03704493,
             3.70888552,  7.01794317],
           [ 3.89479009, 16.8115942 , 10.70079408, ...,  4.1028878 ,
             3.35398107,  5.48855304]])

  • divorce
    (year, county)
    float64
    1.859 2.863 2.711 ... 5.75 7.415

    array([[ 1.85897436,  2.86327845,  2.71144662, ...,  1.11322292,
             1.67239146,  2.77758585],
           [ 2.61980831,  3.68600683,  2.97637795, ...,  1.55032333,
             1.46834319,  2.72460364],
           [ 3.74659401,  6.6254417 ,  5.44822257, ...,  3.25763881,
             3.74220727,  4.78226375],
           [ 7.38853503, 11.54313487,  9.12321222, ...,  4.85074627,
             5.75017712,  7.4145588 ]])

  • age
    (year, county)
    float64
    28.8 25.9 29.6 ... 34.0 28.47 35.33

    array([[28.8       , 25.9       , 29.6       , ..., 24.4       ,
            22.3       , 28.25      ],
           [30.5       , 27.1       , 31.8       , ..., 24.4       ,
            21.8       , 26.4       ],
           [34.5       , 27.2       , 28.7       , ..., 29.8       ,
            25.66666667, 28.96666667],
           [35.5       , 32.8       , 34.5       , ..., 34.        ,
            28.46666667, 35.33333333]])

• Indexes: (2)
  • year
    PandasIndex

    PandasIndex(Int64Index([1960, 1970, 1980, 1990], dtype='int64', name='year'))

  • county
    GeometryIndex (crs=EPSG:4326)

    <xvec.index.GeometryIndex object at 0x12df9bcd0>

• Attributes: (0)

Wide table form

The other option is to create a wide table form which is what we started with. Each combination of coordinate and data variables except geometry is encoded as a column (e.g. ["population_1960", "population_1970", "population_1980", "population_1990"] or using the MultiIndex like below). However, this works only when the Dataset or DataArray is indexed by one geometry dimension only.

To create a valid GeoDataFrame, you would need to do something like this:

wide = cube.xvec.to_geodataframe(long=False)
wide

	county	population				unemployment				divorce				age
year		1960	1970	1980	1990	1960	1970	1980	1990	1960	1970	1980	1990	1960	1970	1980	1990
0	POLYGON ((-95.34258 48.54670, -95.34081 48.715...	4304	3987	3764	4076	7.900000	9.000000	5.902579	3.894790	1.858974	2.619808	3.746594	7.388535	28.80	30.5	34.500000	35.500000
1	POLYGON ((-118.85050 47.94969, -118.84732 48.4...	3889	3655	5811	6295	8.200000	15.400000	15.422886	16.811594	2.863278	3.686007	6.625442	11.543135	25.90	27.1	27.200000	32.800000
2	POLYGON ((-117.43777 48.04422, -117.54113 48.0...	17884	17405	28979	30948	10.100000	9.000000	13.574064	10.700794	2.711447	2.976378	5.448223	9.123212	29.60	31.8	28.700000	34.500000
3	POLYGON ((-118.97096 47.93928, -118.97293 47.9...	25520	25867	30639	33350	7.500000	10.500000	12.700195	10.203544	3.372041	4.090386	7.122333	9.245627	29.40	31.1	31.200000	35.000000
4	POLYGON ((-117.43750 49.00000, -117.03098 49.0...	6914	6025	8580	8915	10.600000	13.400000	18.148999	14.991023	3.008988	4.010695	5.287860	10.158351	31.20	33.8	31.300000	36.100000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3080	POLYGON ((-114.51984 33.02767, -114.55830 33.0...	46235	60827	90554	120739	4.600000	4.800000	8.012906	10.910351	3.702838	4.160574	5.730308	7.798840	25.70	24.6	27.900000	33.900000
3081	POLYGON ((-107.19484 34.58352, -107.71780 34.5...	39085	40576	61115	69029	6.100000	3.800000	8.375127	11.002271	1.880621	2.274648	5.372952	9.078873	20.10	21.7	25.500000	30.450000
3082	POLYGON ((-76.39569 37.10771, -76.40270 37.090...	21583	33203	44189	53427	2.500000	3.105065	5.037045	4.102888	1.113223	1.550323	3.257639	4.850746	24.40	24.4	29.800000	34.000000
3083	POLYGON ((-77.53178 38.56506, -77.72094 38.840...	50164	111102	166665	250377	3.300000	2.361559	3.708886	3.353981	1.672391	1.468343	3.742207	5.750177	22.30	21.8	25.666667	28.466667
3084	POLYGON ((-111.37152 44.74516, -111.36839 45.3...	39260	43766	55800	65077	5.218039	5.444291	7.017943	5.488553	2.777586	2.724604	4.782264	7.414559	28.25	26.4	28.966667	35.333333

3085 rows × 17 columns

And a re-creation of the Dataset.

wide.set_index("county", append=True).stack().reset_index(
    level=0, drop=True
).to_xarray().xvec.set_geom_indexes("county", crs=wide.crs)

<xarray.Dataset>
Dimensions:       (county: 3085, year: 4)
Coordinates:
  * county        (county) object POLYGON ((-95.34258270263672 48.54670333862...
  * year          (year) object 1960 1970 1980 1990
Data variables:
    age           (county, year) float64 28.8 30.5 34.5 ... 26.4 28.97 35.33
    divorce       (county, year) float64 1.859 2.62 3.747 ... 2.725 4.782 7.415
    population    (county, year) int64 4304 3987 3764 4076 ... 43766 55800 65077
    unemployment  (county, year) float64 7.9 9.0 5.903 ... 5.444 7.018 5.489
Indexes:
    county        GeometryIndex (crs=EPSG:4326)

xarray.Dataset

• Dimensions:
  • county: 3085
  • year: 4
• Coordinates: (2)
  • county
    (county)
    object
    POLYGON ((-95.34258270263672 48....

    crs :

    epsg:4326

    array([<POLYGON ((-95.343 48.547, -95.341 48.715, -95.094 48.717, -95.095 48.912, -...>,
           <POLYGON ((-118.851 47.95, -118.847 48.478, -118.869 48.479, -118.87 48.647,...>,
           <POLYGON ((-117.438 48.044, -117.541 48.043, -117.536 47.79, -117.607 47.798...>,
           ...,
           <POLYGON ((-76.396 37.108, -76.403 37.091, -76.434 37.089, -76.464 37.103, -...>,
           <POLYGON ((-77.532 38.565, -77.721 38.84, -77.707 38.877, -77.678 38.885, -7...>,
           <POLYGON ((-111.372 44.745, -111.368 45.35, -111.349 45.349, -111.349 45.635...>],
          dtype=object)

  • year
    (year)
    object
    1960 1970 1980 1990

    array([1960, 1970, 1980, 1990], dtype=object)

• Data variables: (4)
  • age
    (county, year)
    float64
    28.8 30.5 34.5 ... 26.4 28.97 35.33

    array([[28.8       , 30.5       , 34.5       , 35.5       ],
           [25.9       , 27.1       , 27.2       , 32.8       ],
           [29.6       , 31.8       , 28.7       , 34.5       ],
           ...,
           [24.4       , 24.4       , 29.8       , 34.        ],
           [22.3       , 21.8       , 25.66666667, 28.46666667],
           [28.25      , 26.4       , 28.96666667, 35.33333333]])

  • divorce
    (county, year)
    float64
    1.859 2.62 3.747 ... 4.782 7.415

    array([[ 1.85897436,  2.61980831,  3.74659401,  7.38853503],
           [ 2.86327845,  3.68600683,  6.6254417 , 11.54313487],
           [ 2.71144662,  2.97637795,  5.44822257,  9.12321222],
           ...,
           [ 1.11322292,  1.55032333,  3.25763881,  4.85074627],
           [ 1.67239146,  1.46834319,  3.74220727,  5.75017712],
           [ 2.77758585,  2.72460364,  4.78226375,  7.4145588 ]])

  • population
    (county, year)
    int64
    4304 3987 3764 ... 55800 65077

    array([[  4304,   3987,   3764,   4076],
           [  3889,   3655,   5811,   6295],
           [ 17884,  17405,  28979,  30948],
           ...,
           [ 21583,  33203,  44189,  53427],
           [ 50164, 111102, 166665, 250377],
           [ 39260,  43766,  55800,  65077]])

  • unemployment
    (county, year)
    float64
    7.9 9.0 5.903 ... 5.444 7.018 5.489

    array([[ 7.9       ,  9.        ,  5.9025788 ,  3.89479009],
           [ 8.2       , 15.4       , 15.42288557, 16.8115942 ],
           [10.1       ,  9.        , 13.57406431, 10.70079408],
           ...,
           [ 2.5       ,  3.10506508,  5.03704493,  4.1028878 ],
           [ 3.3       ,  2.36155862,  3.70888552,  3.35398107],
           [ 5.21803859,  5.44429066,  7.01794317,  5.48855304]])

• Indexes: (2)
  • year
    PandasIndex

    PandasIndex(Index([1960, 1970, 1980, 1990], dtype='object', name='year'))

  • county
    GeometryIndex (crs=EPSG:4326)

    <xvec.index.GeometryIndex object at 0x12df9a9e0>

• Attributes: (0)