add tests for nces and blocks, docstring for bea

.ci/39.yml → .ci/312.yml

@@ -2,7 +2,7 @@ name: test
 channels:
   - conda-forge
 dependencies:
-  - python =3.9
+  - python =3.12
   - pandas
   - giddy >=2.2.1
   - libpysal

.github/workflows/unittests.yml

@@ -18,13 +18,13 @@ jobs:
       matrix:
         os: [ubuntu-latest]
         environment-file:
-          - .ci/39.yml
           - .ci/310.yml
           - .ci/311.yml
+          - .ci/312.yml
         include:
-          - environment-file: .ci/310.yml
+          - environment-file: .ci/311.yml
             os: macos-latest
-          - environment-file: .ci/310.yml
+          - environment-file: .ci/311.yml
             os: windows-latest
     defaults:
       run:

docs/api.rst

@@ -25,6 +25,7 @@ available quickly with no configuration by accessing methods on the class.
 
     DataStore
     DataStore.acs
+    DataStore.bea_regions
     DataStore.blocks_2000
     DataStore.blocks_2010
     DataStore.blocks_2020

geosnap/_data.py

@@ -70,9 +70,9 @@ def __init__(self, data_dir="auto", disclaimer=False):
             )
 
     def __dir__(self):
-
         atts = [
             "acs",
+            "bea_regions",
             "blocks_2000",
             "blocks_2010",
             "blocks_2020",
@@ -90,7 +90,7 @@ def __dir__(self):
             "tracts_1990",
             "tracts_2000",
             "tracts_2010",
-            "tracts_2020"
+            "tracts_2020",
         ]
 
         return atts
@@ -107,6 +107,21 @@ def show_data_dir(self, verbose=True):
             print(self.data_dir)
         return self.data_dir
 
+    def bea_regions(self):
+        """Table that maps states to their respective BEA regions
+
+        Returns
+        -------
+        pandas.DataFrame
+            BEA region table
+        """
+        return pd.read_csv(
+            os.path.join(
+                os.path.dirname(os.path.abspath(__file__)), "io/bea_regions.csv"
+            ),
+            converters={'stfips':str}
+        )
+
     def acs(self, year=2018, level="tract", states=None):
         """American Community Survey Data (5-year estimates).
 
@@ -187,7 +202,7 @@ def seda(
         assert pooling in [
             "pool",
             "long",
-            "poolsub"
+            "poolsub",
         ], "`pool` argument must be either 'pool', 'long', or 'poolsub'"
         assert standardize in [
             "gcs",
@@ -197,7 +212,6 @@ def seda(
             fn = f"seda_{level}_{pooling}_{standardize}_4.1_corrected"
         else:
             fn = f"seda_{level}_{pooling}_{standardize}_4.1"
-
         local_path = pathlib.Path(self.data_dir, "seda", f"{fn}.parquet")
         remote_path = f"https://stacks.stanford.edu/file/druid:xv742vh9296/{fn}.csv"
         msg = (
@@ -216,8 +230,8 @@ def seda(
                         remote_path, converters={"sedasch": str, "fips": str}
                     )
                     t.sedasch = t.sedasch.str.rjust(12, "0")
-                except FileNotFoundError:
-                    raise FileNotFoundError(
+                except FileNotFoundError as e:
+                    raise FileNotFoundError from e(
                         "Unable to access local or remote SEDA data"
                     )
             elif level == "geodist":
@@ -226,8 +240,8 @@ def seda(
                         remote_path, converters={"sedalea": str, "fips": str}
                     )
                     t.sedalea = t.sedalea.str.rjust(7, "0")
-                except FileNotFoundError:
-                    raise FileNotFoundError(
+                except FileNotFoundError as e:
+                    raise FileNotFoundError from e(
                         "Unable to access local or remote SEDA data"
                     )
                 t.fips = t.fips.str.rjust(2, "0")
@@ -250,10 +264,7 @@ def nces(self, year=1516, dataset="sabs"):
         geopandas.GeoDataFrame
             geodataframe of NCES data
         """
-        if dataset == "school_districts":
-            selector = "districts"
-        else:
-            selector = dataset
+        selector = "districts" if dataset == "school_districts" else dataset
         local_path = pathlib.Path(self.data_dir, "nces", f"{dataset}_{year}.parquet")
         remote_path = f"s3://spatial-ucr/nces/{selector}/{dataset}_{year}.parquet"
         msg = "Streaming data from S3. Use `geosnap.io.store_nces()` to store the data locally for better performance"
@@ -569,16 +580,11 @@ def msa_definitions(self):
             dataframe that stores state/county --> MSA crosswalk definitions.
 
         """
-        local = pathlib.Path(self.data_dir, "msa_definitions.parquet")
-        remote = "s3://spatial-ucr/census/administrative/msa_definitions.parquet"
-        msg = "Streaming data from S3. Use `geosnap.io.store_census() to store the data locally for better performance"
-        try:
-            t = pd.read_parquet(local)
-        except FileNotFoundError:
-            warn(msg)
-            t = pd.read_parquet(remote, storage_options={"anon": True})
-
-        return t
+        return pd.read_csv(
+            os.path.join(
+                os.path.dirname(os.path.abspath(__file__)), "io/msa_definitions.csv"
+            )
+        )
 
     def ltdb(self):
         """Longitudinal Tract Database (LTDB).
@@ -626,5 +632,3 @@ def codebook(self):
         return pd.read_csv(
             os.path.join(os.path.dirname(os.path.abspath(__file__)), "io/variables.csv")
         )
-
-

geosnap/io/bea_regions.csv

@@ -1,111 +1,52 @@
-,geoid,metro,state,bea_region
-0,10420,Akron,OH,Great Lakes
-1,10580,Albany-Schenectady-Troy,NY,Mideast
-2,10740,Albuquerque,NM,Southwest
-3,10900,Allentown-Bethlehem-Easton,PA-NJ,Mideast
-4,12060,Atlanta-Sandy Springs-Alpharetta,GA,Southeast
-5,12260,Augusta-Richmond County,GA-SC,Southeast
-6,12420,Austin-Round Rock-Georgetown,TX,Southwest
-7,12540,Bakersfield,CA,Far West
-8,12580,Baltimore-Columbia-Towson,MD,Mideast
-9,12940,Baton Rouge,LA,Southeast
-10,13820,Birmingham-Hoover,AL,Southeast
-11,14260,Boise City,ID,Rocky Mountain
-12,14460,Boston-Cambridge-Newton,MA-NH,New England
-13,14860,Bridgeport-Stamford-Norwalk,CT,New England
-14,15380,Buffalo-Cheektowaga,NY,Mideast
-15,15980,Cape Coral-Fort Myers,FL,Southeast
-16,16700,Charleston-North Charleston,SC,Southeast
-17,16740,Charlotte-Concord-Gastonia,NC-SC,Southeast
-18,16860,Chattanooga,TN-GA,Southeast
-19,16980,Chicago-Naperville-Elgin,IL-IN-WI,Great Lakes
-20,17140,Cincinnati,OH-KY-IN,Great Lakes
-21,17460,Cleveland-Elyria,OH,Great Lakes
-22,17820,Colorado Springs,CO,Rocky Mountain
-23,17900,Columbia,SC,Southeast
-24,18140,Columbus,OH,Great Lakes
-25,19100,Dallas-Fort Worth-Arlington,TX,Southwest
-26,19430,Dayton-Kettering,OH,Great Lakes
-27,19660,Deltona-Daytona Beach-Ormond Beach,FL,Southeast
-28,19740,Denver-Aurora-Lakewood,CO,Rocky Mountain
-29,19780,Des Moines-West Des Moines,IA,Plains
-30,19820,Detroit-Warren-Dearborn,MI,Great Lakes
-31,20500,Durham-Chapel Hill,NC,Southeast
-32,21340,El Paso,TX,Southwest
-33,22180,Fayetteville,NC,Southeast
-34,22220,Fayetteville-Springdale-Rogers,AR,Southeast
-35,23420,Fresno,CA,Far West
-36,24340,Grand Rapids-Kentwood,MI,Great Lakes
-37,24660,Greensboro-High Point,NC,Southeast
-38,24860,Greenville-Anderson,SC,Southeast
-39,25420,Harrisburg-Carlisle,PA,Mideast
-40,25540,Hartford-East Hartford-Middletown,CT,New England
-41,26420,Houston-The Woodlands-Sugar Land,TX,Southwest
-42,26900,Indianapolis-Carmel-Anderson,IN,Great Lakes
-43,27140,Jackson,MS,Southeast
-44,27260,Jacksonville,FL,Southeast
-45,28140,Kansas City,MO-KS,Plains
-46,28940,Knoxville,TN,Southeast
-47,29460,Lakeland-Winter Haven,FL,Southeast
-48,29540,Lancaster,PA,Mideast
-49,29620,Lansing-East Lansing,MI,Great Lakes
-50,29820,Las Vegas-Henderson-Paradise,NV,Far West
-51,30460,Lexington-Fayette,KY,Southeast
-52,30780,Little Rock-North Little Rock-Conway,AR,Southeast
-53,31080,Los Angeles-Long Beach-Anaheim,CA,Far West
-54,31140,Louisville/Jefferson County,KY-IN,Southeast
-55,31540,Madison,WI,Great Lakes
-56,32580,McAllen-Edinburg-Mission,TX,Southwest
-57,32820,Memphis,TN-MS-AR,Southeast
-58,33100,Miami-Fort Lauderdale-Pompano Beach,FL,Southeast
-59,33340,Milwaukee-Waukesha,WI,Great Lakes
-60,33460,Minneapolis-St. Paul-Bloomington,MN-WI,Plains
-61,33700,Modesto,CA,Far West
-62,34980,Nashville-Davidson--Murfreesboro--Franklin,TN,Southeast
-63,35300,New Haven-Milford,CT,New England
-64,35380,New Orleans-Metairie,LA,Southeast
-65,35620,New York-Newark-Jersey City,NY-NJ-PA,Mideast
-66,35840,North Port-Sarasota-Bradenton,FL,Southeast
-67,36260,Ogden-Clearfield,UT,Rocky Mountain
-68,36420,Oklahoma City,OK,Southwest
-69,36540,Omaha-Council Bluffs,NE-IA,Plains
-70,36740,Orlando-Kissimmee-Sanford,FL,Southeast
-71,37100,Oxnard-Thousand Oaks-Ventura,CA,Far West
-72,37340,Palm Bay-Melbourne-Titusville,FL,Southeast
-73,37980,Philadelphia-Camden-Wilmington,PA-NJ-DE-MD,Mideast
-74,38060,Phoenix-Mesa-Chandler,AZ,Southwest
-75,38300,Pittsburgh,PA,Mideast
-76,38860,Portland-South Portland,ME,New England
-77,38900,Portland-Vancouver-Hillsboro,OR-WA,Far West
-78,39100,Poughkeepsie-Newburgh-Middletown,NY,Mideast
-79,39300,Providence-Warwick,RI-MA,New England
-80,39340,Provo-Orem,UT,Rocky Mountain
-81,39580,Raleigh-Cary,NC,Southeast
-82,40060,Richmond,VA,Southeast
-83,40140,Riverside-San Bernardino-Ontario,CA,Far West
-84,40380,Rochester,NY,Mideast
-85,40900,Sacramento-Roseville-Folsom,CA,Far West
-86,41620,Salt Lake City,UT,Rocky Mountain
-87,41700,San Antonio-New Braunfels,TX,Southwest
-88,41740,San Diego-Chula Vista-Carlsbad,CA,Far West
-89,41860,San Francisco-Oakland-Berkeley,CA,Far West
-90,41940,San Jose-Sunnyvale-Santa Clara,CA,Far West
-91,42220,Santa Rosa-Petaluma,CA,Far West
-92,42540,Scranton--Wilkes-Barre,PA,Mideast
-93,42660,Seattle-Tacoma-Bellevue,WA,Far West
-94,44060,Spokane-Spokane Valley,WA,Far West
-95,44140,Springfield,MA,New England
-96,41180,St. Louis,MO-IL,Plains
-97,44700,Stockton,CA,Far West
-98,45060,Syracuse,NY,Mideast
-99,45300,Tampa-St. Petersburg-Clearwater,FL,Southeast
-100,45780,Toledo,OH,Great Lakes
-101,46060,Tucson,AZ,Southwest
-102,46140,Tulsa,OK,Southwest
-103,46520,Urban Honolulu,HI,Far West
-104,47260,Virginia Beach-Norfolk-Newport News,VA-NC,Southeast
-105,47900,Washington-Arlington-Alexandria,DC-VA-MD-WV,Mideast
-106,48620,Wichita,KS,Plains
-107,49180,Winston-Salem,NC,Southeast
-108,49340,Worcester,MA-CT,New England
-109,49660,Youngstown-Warren-Boardman,OH-PA,Great Lakes
+geoid,name,stfips,state
+91000,"New England",09000,"Connecticut"
+91000,"New England",23000,"Maine"
+91000,"New England",25000,"Massachusetts"
+91000,"New England",33000,"New Hampshire"
+91000,"New England",44000,"Rhode Island"
+91000,"New England",50000,"Vermont"
+92000,"Mideast",10000,"Delaware"
+92000,"Mideast",11000,"District of Columbia"
+92000,"Mideast",24000,"Maryland"
+92000,"Mideast",34000,"New Jersey"
+92000,"Mideast",36000,"New York"
+92000,"Mideast",42000,"Pennsylvania"
+93000,"Great Lakes",17000,"Illinois"
+93000,"Great Lakes",18000,"Indiana"
+93000,"Great Lakes",26000,"Michigan"
+93000,"Great Lakes",39000,"Ohio"
+93000,"Great Lakes",55000,"Wisconsin"
+94000,"Plains",19000,"Iowa"
+94000,"Plains",20000,"Kansas"
+94000,"Plains",27000,"Minnesota"
+94000,"Plains",29000,"Missouri"
+94000,"Plains",31000,"Nebraska"
+94000,"Plains",38000,"North Dakota"
+94000,"Plains",46000,"South Dakota"
+95000,"Southeast",01000,"Alabama"
+95000,"Southeast",05000,"Arkansas"
+95000,"Southeast",12000,"Florida"
+95000,"Southeast",13000,"Georgia"
+95000,"Southeast",21000,"Kentucky"
+95000,"Southeast",22000,"Louisiana"
+95000,"Southeast",28000,"Mississippi"
+95000,"Southeast",37000,"North Carolina"
+95000,"Southeast",45000,"South Carolina"
+95000,"Southeast",47000,"Tennessee"
+95000,"Southeast",51000,"Virginia"
+95000,"Southeast",54000,"West Virginia"
+96000,"Southwest",04000,"Arizona"
+96000,"Southwest",35000,"New Mexico"
+96000,"Southwest",40000,"Oklahoma"
+96000,"Southwest",48000,"Texas"
+97000,"Rocky Mountain",08000,"Colorado"
+97000,"Rocky Mountain",16000,"Idaho"
+97000,"Rocky Mountain",30000,"Montana"
+97000,"Rocky Mountain",49000,"Utah"
+97000,"Rocky Mountain",56000,"Wyoming"
+98000,"Far West",02000,"Alaska"
+98000,"Far West",06000,"California"
+98000,"Far West",15000,"Hawaii"
+98000,"Far West",32000,"Nevada"
+98000,"Far West",41000,"Oregon"
+98000,"Far West",53000,"Washington"

geosnap/tests/test_constructors.py

@@ -35,10 +35,13 @@ def test_nces_sabs():
     assert sabs.shape == (75128, 15)
 
 
-def test_acs():
+def test_acs_tract():
     acs = io.get_acs(store, fips="11", years=[2018], level="tract")
     assert acs.shape == (179, 157)
 
+def test_acs_blockgroup():
+    acs = io.get_acs(store, fips="11", years=[2018], level="bg")
+    assert acs.shape == (450, 38)
 
 @pytest.mark.skipif(not LTDB, reason="unable to locate LTDB data")
 def test_ltdb_from_boundary():

geosnap/tests/test_datastore.py

@@ -6,22 +6,18 @@ def test_data_dir():
     loc = datasets.show_data_dir()
     assert len(loc) > 5
 
-
 def test_acs():
     df = datasets.acs(year=2012, states=["11"])
     assert df.shape == (179, 104)
 
-
 def test_tracts90():
     df = datasets.tracts_1990(states=["11"])
     assert df.shape == (192, 164)
 
-
 def test_tracts00():
     df = datasets.tracts_2000(states=["11"])
     assert df.shape == (188, 192)
 
-
 def test_tracts10():
     df = datasets.tracts_2010(states=["11"])
     assert df.shape == (179, 194)
@@ -30,25 +26,36 @@ def test_tracts20():
     df = datasets.tracts_2020(states=["11"])
     assert df.shape == (206, 15)
 
-
 def test_counties():
     assert datasets.counties().shape == (3233, 2)
 
-
 def test_states():
     assert datasets.states().shape == (51, 3)
 
-
 def test_msas():
     df = datasets.msas()
     assert df.shape == (939, 4)
 
-
 def test_msa_defs():
     df = datasets.msa_definitions()
     assert df.shape == (1916, 13)
 
-
 def test_codebook():
     df = datasets.codebook()
     assert df.shape == (194, 12)
+
+def test_bea():
+    df = datasets.bea_regions()
+    assert df.shape == (51, 4)
+
+def test_blocks_2000():
+    df = datasets.blocks_2000(states=['11'])
+    assert df.shape == (5674, 3)
+
+def test_blocks_2010():
+    df = datasets.blocks_2010(states=['11'])
+    assert df.shape == (6507, 5)
+
+def test_blocks_2020():
+    df = datasets.blocks_2020(states=['11'])
+    assert df.shape == (6012, 7)