def test_multi_index(self):
import rowgenerators as rg
df = rg.dataframe('census://RI/140/B17001').mi()
print(df.head())
df = rg.dataframe('census://RI/140/B11001A').mi()
print(df.head())
def test_basic_fetch(self):
import rowgenerators as rg
year = 2018
release = 5
state = 'CA'
sl = 'tract'
b11016 = rg.dataframe(f'census://{year}/{release}/{state}/{sl}/B11016')
print(b11016.head())
def test_dimensions(self):
import rowgenerators as rg
df = rg.dataframe('census://CA/140/B17001')
print(type(df))
#for c in df.table.columns:
# print(c.unique_id, c.sex, c.race, c.age, c.poverty_status)
print(df.head())
def test_split_tables(self):
# Table b24121 Detailed Occupation by Median Earnings for the Full-time, Year-round Civilian Population is split
# across multiple segments.
from publicdata.census.files.generators import Table, TableMeta, SequenceFile
from publicdata.census.files.metafiles import TableLookup
from itertools import islice
sequence_file = SequenceFile(2017, 5, 'RI', 50, 85)
print(sequence_file.header_url)
print(len(list(sequence_file.columns)))
for c in list(sequence_file.columns)[:10]:
print(c.row)
sequence_file = SequenceFile(2017, 5, 'RI', 50, 86)
print(len(list(sequence_file.columns)))
sequence_file = SequenceFile(2017, 5, 'RI', 50, 87)
print(len(list(sequence_file.columns)))
print(sequence_file.est_url)
print('\n'.join(str(e) for e in list(islice(sequence_file, 10))))
meta = TableMeta(2017, 5)
print(len(meta.tables['b24121'].columns))
tl = TableLookup(2017, 5)
print (tl.url)
table = Table(2017, 5, 'RI', 40, 'B24121')
self.assertEqual(1056, (len(list(table.file_headers))))
self.assertEqual(1056, len(list(table.columns)))
from rowgenerators import dataframe
df = dataframe('census://2016/5/RI/40/B24121')
def census_table(table, state, sl='state', year=2018, release=5):
import rowgenerators as rg
return rg.dataframe(f'census://{year}/{release}/{state}/{sl}/{table}')
def test_B02001(self):
import rowgenerators as rg
raceeth = rg.dataframe('census://2017/5/CA/tract/B02001')