Datasets for use with libpysal¶

As of version 4.2, libpysal has refactored the examples package to:

reduce the size of the source installation
allow the use of remote datasets from the Center for Spatial Data Science at the Unversity of Chicago, and other remotes

This notebook highlights the new functionality

Backwards compatibility is maintained¶

If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work.

For example:

from libpysal.examples import get_path

get_path("mexicojoin.dbf")

'/home/runner/micromamba/envs/docs/lib/python3.14/site-packages/libpysal/examples/mexico/mexicojoin.dbf'

An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a builtin dataset.

import libpysal

dbf = libpysal.io.open(get_path("mexicojoin.dbf"))

dbf.header

['POLY_ID',
 'AREA',
 'CODE',
 'NAME',
 'PERIMETER',
 'ACRES',
 'HECTARES',
 'PCGDP1940',
 'PCGDP1950',
 'PCGDP1960',
 'PCGDP1970',
 'PCGDP1980',
 'PCGDP1990',
 'PCGDP2000',
 'HANSON03',
 'HANSON98',
 'ESQUIVEL99',
 'INEGI',
 'INEGI2',
 'MAXP',
 'GR4000',
 'GR5000',
 'GR6000',
 'GR7000',
 'GR8000',
 'GR9000',
 'LPCGDP40',
 'LPCGDP50',
 'LPCGDP60',
 'LPCGDP70',
 'LPCGDP80',
 'LPCGDP90',
 'LPCGDP00',
 'TEST']

The function available is also available but has been updated to return a Pandas DataFrame. In addition to the builtin datasets, available will report on what datasets are available, either as builtin or remotes.

from libpysal.examples import available

df = available()

df.shape

(99, 3)

libpysal.examples.summary()

99 datasets available, 27 installed, 72 remote.

We see that there are 98 total datasets available for use with PySAL. On an initial install (i.e., examples has not been used yet), 27 of these are builtin datasets and 71 are remote. The latter can be downloaded and installed.

Downloading Remote Datasets¶

df.head()

	Name	Description	Installed
0	10740	Albuquerque, New Mexico, Census 2000 Tract Dat...	True
1	AirBnB	Airbnb rentals, socioeconomics, and crime in C...	False
2	Atlanta	Atlanta, GA region homicide counts and rates	False
3	Baltimore	Baltimore house sales prices and hedonics	False
4	Bostonhsg	Boston housing and neighborhood data	False

The remote AirBnB can be installed by calling load_example:

airbnb = libpysal.examples.load_example("AirBnB")

Downloading AirBnB to /home/runner/.local/share/pysal/AirBnB

libpysal.examples.summary()

99 datasets available, 28 installed, 71 remote.

And we see that the number of remotes as declined by one and the number of installed has increased by 1.

Trying to load an example that doesn’t exist will return None and alert the user:

libpysal.examples.load_example("dataset42")

Example not available: dataset42

Getting remote urls¶

If the url, rather than the dataset, is needed this can be obtained on a remote with get_url. As the Baltimore dataset has not yet been downloaded in this example, we can grab it’s url:

balt_url = libpysal.examples.get_url("Baltimore")
balt_url

'https://geodacenter.github.io/data-and-lab//data/baltimore.zip'

Explaining a dataset¶

libpysal.examples.explain("taz")

taz
===

Dataset used for regionalization
--------------------------------

* taz.dbf: attribute data. (k=14)
* taz.shp: Polygon shapefile. (n=4109)
* taz.shx: spatial index.

taz = libpysal.examples.load_example("taz")

Downloading taz to /home/runner/.local/share/pysal/taz

taz.get_file_list()

['/home/runner/.local/share/pysal/taz/taz-master/README.md',
 '/home/runner/.local/share/pysal/taz/taz-master/taz.dbf',
 '/home/runner/.local/share/pysal/taz/taz-master/taz.shp',
 '/home/runner/.local/share/pysal/taz/taz-master/taz.shx']

libpysal.examples.explain("Baltimore")

balt = libpysal.examples.load_example("Baltimore")

Downloading Baltimore to /home/runner/.local/share/pysal/Baltimore

libpysal.examples.available()

	Name	Description	Installed
0	10740	Albuquerque, New Mexico, Census 2000 Tract Dat...	True
1	AirBnB	Airbnb rentals, socioeconomics, and crime in C...	True
2	Atlanta	Atlanta, GA region homicide counts and rates	False
3	Baltimore	Baltimore house sales prices and hedonics	True
4	Bostonhsg	Boston housing and neighborhood data	False
...	...	...	...
94	taz	Traffic Analysis Zones in So. California	True
95	tokyo	Tokyo Mortality data	True
96	us_income	Per-capita income for the lower 48 US states 1...	True
97	virginia	Virginia counties shapefile	True
98	wmat	Datasets used for spatial weights testing	True

99 rows × 3 columns

Working with an example dataset¶

explain will render maps for an example if available

from libpysal.examples import explain

explain("Tampa1")

from libpysal.examples import load_example

tampa1 = load_example("Tampa1")

Downloading Tampa1 to /home/runner/.local/share/pysal/Tampa1

tampa1.installed

True

tampa1.get_file_list()

['/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.xlsx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.kml',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.prj',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.geojson',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shp',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.kml',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/gdb',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sqlite',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.gpkg',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mif',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.xlsx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbn',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.dbf',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbx',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.dbf',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbn',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.geojson',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mid',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sqlite',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shp',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.prj',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mid',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.gpkg',
 '/home/runner/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mif',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',
 '/home/runner/.local/share/pysal/Tampa1/__MACOSX/._TampaMSA']

tampa_counties_shp = tampa1.load("tampa_counties.shp")

tampa_counties_shp

<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7f5eaecc1be0>

import geopandas

tampa_df = geopandas.read_file(tampa1.get_path("tampa_counties.shp"))

%matplotlib inline
tampa_df.plot()

<Axes: >

../../_images/5b334ac337abada3c9eb788216992a74c9774d19b304a29c6e2beecdc254eccc.png

Other Remotes¶

In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories.

libpysal.examples.explain("Rio Grande do Sul")

Rio_Grande_do_Sul
======================

Cities of the Brazilian State of Rio Grande do Sul
-------------------------------------------------------

* 43MUE250GC_SIR.dbf: attribute data (k=2)
* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)
* 43MUE250GC_SIR.shx: spatial index
* 43MUE250GC_SIR.cpg: encoding file 
* 43MUE250GC_SIR.prj: projection information 
* map_RS_BR.dbf: attribute data (k=3)
* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)
* map_RS_BR.prj: projection information
* map_RS_BR.shx: spatial index



Source: Renan Xavier Cortes 
Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495

Note that the explain function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality.

rio = libpysal.examples.load_example("Rio Grande do Sul")

Downloading Rio Grande do Sul to /home/runner/.local/share/pysal/Rio_Grande_do_Sul

libpysal.examples.remote_datasets.datasets  # a listing of all remotes

{'AirBnB': <libpysal.examples.base.Example at 0x7f5ef1bdf0e0>,
 'Atlanta': <libpysal.examples.base.Example at 0x7f5ef1a9e0d0>,
 'Baltimore': <libpysal.examples.base.Example at 0x7f5ef1a9e350>,
 'Bostonhsg': <libpysal.examples.base.Example at 0x7f5ef1924c30>,
 'Buenosaires': <libpysal.examples.base.Example at 0x7f5ef1924d60>,
 'Charleston1': <libpysal.examples.base.Example at 0x7f5ef19459d0>,
 'Charleston2': <libpysal.examples.base.Example at 0x7f5ef1945bf0>,
 'Chicago Health': <libpysal.examples.base.Example at 0x7f5ef1e6ee50>,
 'Chicago commpop': <libpysal.examples.base.Example at 0x7f5ef1937650>,
 'Chicago parcels': <libpysal.examples.base.Example at 0x7f5ef19809b0>,
 'Chile Labor': <libpysal.examples.base.Example at 0x7f5ef1980aa0>,
 'Chile Migration': <libpysal.examples.base.Example at 0x7f5ef1949e10>,
 'Cincinnati': <libpysal.examples.base.Example at 0x7f5ef1949fd0>,
 'Cleveland': <libpysal.examples.base.Example at 0x7f5ef1f4add0>,
 'Columbus': <libpysal.examples.base.Example at 0x7f5ef1a1f650>,
 'Elections': <libpysal.examples.base.Example at 0x7f5ef1a1f590>,
 'Grid100': <libpysal.examples.base.Example at 0x7f5f5ccca990>,
 'Groceries': <libpysal.examples.base.Example at 0x7f5ef301a4c0>,
 'Guerry': <libpysal.examples.base.Example at 0x7f5ef1ac3890>,
 'Health+': <libpysal.examples.base.Example at 0x7f5ef1ac3a70>,
 'Health Indicators': <libpysal.examples.base.Example at 0x7f5ef1ac3d90>,
 'Hickory1': <libpysal.examples.base.Example at 0x7f5ef1ac3610>,
 'Hickory2': <libpysal.examples.base.Example at 0x7f5ef1ac3b10>,
 'Home Sales': <libpysal.examples.base.Example at 0x7f5ef1ac3c50>,
 'Houston': <libpysal.examples.base.Example at 0x7f5ef1ac3cf0>,
 'Juvenile': <libpysal.examples.base.Example at 0x7f5ef1ac3570>,
 'Lansing1': <libpysal.examples.base.Example at 0x7f5ef1ac3e30>,
 'Lansing2': <libpysal.examples.base.Example at 0x7f5ef1ac3ed0>,
 'Laozone': <libpysal.examples.base.Example at 0x7f5ef19a4690>,
 'LasRosas': <libpysal.examples.base.Example at 0x7f5ef19a49b0>,
 'Liquor Stores': <libpysal.examples.base.Example at 0x7f5ef19a4410>,
 'Malaria': <libpysal.examples.base.Example at 0x7f5ef19a4cd0>,
 'Milwaukee1': <libpysal.examples.base.Example at 0x7f5ef19a40f0>,
 'Milwaukee2': <libpysal.examples.base.Example at 0x7f5ef19a4190>,
 'NCOVR': <libpysal.examples.base.Example at 0x7f5ef19a4230>,
 'Natregimes': <libpysal.examples.base.Example at 0x7f5ef19a42d0>,
 'NDVI': <libpysal.examples.base.Example at 0x7f5ef19a4370>,
 'Nepal': <libpysal.examples.base.Example at 0x7f5ef19a44b0>,
 'NYC': <libpysal.examples.base.Example at 0x7f5ef19a4550>,
 'NYC Earnings': <libpysal.examples.base.Example at 0x7f5ef19a4730>,
 'NYC Education': <libpysal.examples.base.Example at 0x7f5ef19a47d0>,
 'NYC Neighborhoods': <libpysal.examples.base.Example at 0x7f5ef19a4870>,
 'NYC Socio-Demographics': <libpysal.examples.base.Example at 0x7f5ef19a4910>,
 'Ohiolung': <libpysal.examples.base.Example at 0x7f5ef19a4a50>,
 'Orlando1': <libpysal.examples.base.Example at 0x7f5ef19a4af0>,
 'Orlando2': <libpysal.examples.base.Example at 0x7f5ef19a4b90>,
 'Oz9799': <libpysal.examples.base.Example at 0x7f5ef19a4c30>,
 'Phoenix ACS': <libpysal.examples.base.Example at 0x7f5ef19a4d70>,
 'Pittsburgh': <libpysal.examples.base.Example at 0x7f5ef19a4e10>,
 'Police': <libpysal.examples.base.Example at 0x7f5ef19a4eb0>,
 'Sacramento1': <libpysal.examples.base.Example at 0x7f5ef19a4f50>,
 'Sacramento2': <libpysal.examples.base.Example at 0x7f5ef19a4ff0>,
 'SanFran Crime': <libpysal.examples.base.Example at 0x7f5ef19a5090>,
 'Savannah1': <libpysal.examples.base.Example at 0x7f5ef19a5130>,
 'Savannah2': <libpysal.examples.base.Example at 0x7f5ef19a51d0>,
 'Scotlip': <libpysal.examples.base.Example at 0x7f5ef19a5270>,
 'Seattle1': <libpysal.examples.base.Example at 0x7f5ef19a5310>,
 'Seattle2': <libpysal.examples.base.Example at 0x7f5ef19a53b0>,
 'SIDS': <libpysal.examples.base.Example at 0x7f5ef19a5450>,
 'SIDS2': <libpysal.examples.base.Example at 0x7f5ef19a54f0>,
 'Snow': <libpysal.examples.base.Example at 0x7f5ef19a5590>,
 'South': <libpysal.examples.base.Example at 0x7f5ef19a5630>,
 'Spirals': <libpysal.examples.base.Example at 0x7f5ef19a56d0>,
 'StLouis': <libpysal.examples.base.Example at 0x7f5ef19a5770>,
 'Tampa1': <libpysal.examples.base.Example at 0x7f5ef19a5810>,
 'US SDOH': <libpysal.examples.base.Example at 0x7f5ef19a58b0>,
 'Rio Grande do Sul': <libpysal.examples.base.Example at 0x7f5ef19a5950>,
 'nyc_bikes': <libpysal.examples.base.Example at 0x7f5ef19a59f0>,
 'taz': <libpysal.examples.base.Example at 0x7f5ef19a5a90>,
 'clearwater': <libpysal.examples.base.Example at 0x7f5ef19a5b30>,
 'newHaven': <libpysal.examples.base.Example at 0x7f5ef19a5bd0>,
 'chicagoSDOH': <libpysal.examples.base.Example at 0x7f5ef19a5c70>}