def test_order(self):
result = read_jstat(os.path.join(self.dirpath, 'jstat', 'order.json'))
exp_idx = pd.MultiIndex.from_product([['1', '2', '3'], ['1', '2']], names=['A', 'B'])
exp_col = pd.Index(['1', '2', '3', '4'], name='C')
expected = pd.DataFrame({'1': ['A1B1C1', 'A1B2C1', 'A2B1C1', 'A2B2C1', 'A3B1C1', 'A3B2C1'],
'2': ['A1B1C2', 'A1B2C2', 'A2B1C2', 'A2B2C2', 'A3B1C2', 'A3B2C2'],
'3': ['A1B1C3', 'A1B2C3', 'A2B1C3', 'A2B2C3', 'A3B1C3', 'A3B2C3'],
'4': ['A1B1C4', 'A1B2C4', 'A2B1C4', 'A2B2C4', 'A3B1C4', 'A3B2C4']},
index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
result = read_jstat(os.path.join(self.dirpath, 'jstat', 'order.json'), typ='series')
exp_idx = pd.MultiIndex.from_product([['1', '2', '3'], ['1', '2'],
['1', '2', '3', '4']], names=['A', 'B', 'C'])
expected = pd.Series(['A1B1C1', 'A1B1C2', 'A1B1C3', 'A1B1C4',
'A1B2C1', 'A1B2C2', 'A1B2C3', 'A1B2C4',
'A2B1C1', 'A2B1C2', 'A2B1C3', 'A2B1C4',
'A2B2C1', 'A2B2C2', 'A2B2C3', 'A2B2C4',
'A3B1C1', 'A3B1C2', 'A3B1C3', 'A3B1C4',
'A3B2C1', 'A3B2C2', 'A3B2C3', 'A3B2C4'], index=exp_idx)
tm.assert_series_equal(result, expected)
result = read_jstat(os.path.join(self.dirpath, 'jstat', 'order.json'), squeeze=False)
self.assertTrue(isinstance(result, dict))
result = result['order']
exp_idx = pd.MultiIndex.from_product([['1', '2', '3'], ['1', '2']], names=['A', 'B'])
exp_col = pd.Index(['1', '2', '3', '4'], name='C')
expected = pd.DataFrame({'1': ['A1B1C1', 'A1B2C1', 'A2B1C1', 'A2B2C1', 'A3B1C1', 'A3B2C1'],
'2': ['A1B1C2', 'A1B2C2', 'A2B1C2', 'A2B2C2', 'A3B1C2', 'A3B2C2'],
'3': ['A1B1C3', 'A1B2C3', 'A2B1C3', 'A2B2C3', 'A3B1C3', 'A3B2C3'],
'4': ['A1B1C4', 'A1B2C4', 'A2B1C4', 'A2B2C4', 'A3B1C4', 'A3B2C4']},
index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
def test_us_labor(self):
result = read_jstat(os.path.join(self.dirpath, 'jstat', 'us-labor.json'))
result = result.head(n=10)
exp_idx = pd.MultiIndex.from_product([['2012'],
['Abbeville County, SC', 'Acadia Parish, LA',
'Accomack County, VA', 'Ada County, ID',
'Adair County, IA', 'Adair County, KY',
'Adair County, MO', 'Adair County, OK',
'Adams County, CO', 'Adams County, IA']],
names=['year', 'county'])
exp_col = pd.Index(['Employed', 'Labor Force', 'Unemployed',
'Unemployment rate'], name='labor')
values = np.array([[9757, 10861, 1104, 10.2],
[24391, 25826, 1435, 5.6],
[16958, 18215, 1257, 6.9],
[191379, 204182, 12803, 6.3],
[4134, 4317, 183, 4.2],
[8886, 9631, 745, 7.7],
[11480, 12219, 739, 6.0],
[9747, 10476, 729, 7.0],
[212762, 234436, 21674, 9.2],
[1999, 2090, 91, 4.4]])
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
def test_omit_values(self):
result = read_jstat(os.path.join(self.dirpath, 'jstat', 'omit-values.json'))
exp_idx = pd.MultiIndex.from_product([['1', '2', '3'], ['1', '2']], names=['A', 'B'])
exp_col = pd.Index(['1', '2', '3', '4'], name='C')
expected = pd.DataFrame({'1': ['A1B1C1', 'A1B2C1', 'A2B1C1', 'A2B2C1', 'A3B1C1', 'A3B2C1'],
'2': [np.nan, np.nan, np.nan, np.nan, np.nan, 'A3B2C2'],
'3': [np.nan, 'A1B2C3', np.nan, np.nan, np.nan, np.nan],
'4': [np.nan, np.nan, np.nan, 'A2B2C4', np.nan, np.nan]},
index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
def test_hierarchy(self):
result = read_jstat(os.path.join(self.dirpath, 'jstat', 'hierarchy.json'))
self.assertTrue(isinstance(result, pd.DataFrame))
result = result.head(n=10)
idx = pd.Index(['Total', 'Food and non-alcoholic beverages', 'Bread and cereal products',
'Bread', 'Cakes and biscuits', 'Breakfast cereals', 'Other cereal products',
'Meat and seafoods', 'Beef and veal', 'Pork'], name='commodity')
expected = pd.DataFrame([np.nan] * 10, index=idx)
tm.assert_frame_equal(result, expected)
def test_us_unr(self):
result = read_jstat(os.path.join(self.dirpath, 'jstat', 'us-unr.json'))
result = result.head(n=10)
exp_idx = pd.MultiIndex.from_product([['2012'],
['01001', '01003', '01005', '01007', '01009',
'01011', '01013', '01015', '01017', '01019']],
names=['year', 'county'])
exp_col = pd.Index(['Unemployment rate'], name='labor')
expected = pd.DataFrame({'Unemployment rate': [6.5, 6.8, 11.2, 7.6, 6.2,
13.4, 10.9, 7.6, 9.3, 7.1]},
index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
def test_oecd_canada(self):
results = read_jstat(os.path.join(self.dirpath, 'jstat', 'oecd-canada.json'))
self.assertEqual(len(results), 2)
result = results['oecd'].head(n=5)
exp_idx = pd.MultiIndex.from_product([['Unemployment rate'],
['Australia', 'Austria', 'Belgium', 'Canada', 'Chile']],
names=['concept', 'area'])
exp_col = pd.Index(['2003', '2004', '2005', '2006', '2007', '2008', '2009',
'2010', '2011', '2012', '2013', '2014'], name='year')
values = np.array([[5.94382629, 5.39663128, 5.04479059, 4.78936279,
4.37964939, 4.24909345, 5.5922266, 5.23066029,
5.09942294, 5.22433609, 5.50415003, 5.46286623],
[4.27855934, 4.93970775, 5.15216061, 4.72718286,
4.39973073, 3.81393363, 4.77691251, 4.39159165,
4.14358724, 4.35134579, 4.69549171, 4.74532331],
[8.15833333, 8.4, 8.48333333, 8.26666667,
7.46666667, 7.01666667, 7.89189286, 8.28317196,
7.17513878, 7.3811534, 7.6895529, 7.73544264],
[7.59461675, 7.16783395, 6.7486915, 6.3078411,
6.04984263, 6.14601466, 8.2846893, 7.98890042,
7.4536096, 7.32358421, 7.16974153, 6.88122705],
[9.5433848, 10.00149582, 9.22442255, 7.77316628,
7.15062335, 7.7872218, 10.80236438, 8.12157908,
7.10477825, 6.47746872, 6.78101031, 6.78019894]])
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
result = results['canada'].head(n=6)
exp_idx = pd.MultiIndex.from_product([['Canada'], ['2012'],
['0 to 4', '10 to 14', '15 to 19'],
['% of total of each group', 'Persons (thousands)']],
names=['country', 'year', 'age', 'concept'])
exp_col = pd.Index(['Female', 'Male', 'Total'], name='sex')
values = np.array([[5.3, 5.7, 5.5],
[940.1, 988.7, 1928.8],
[5.2, 5.6, 5.4],
[912.6, 964.7, 1877.3],
[6, 6.4, 6.2],
[1054.7, 1108.2, 2163]])
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
def test_us_gsp(self):
result = read_jstat(os.path.join(self.dirpath, 'jstat', 'us-gsp.json'))
result = result.head(n=10)
exp_idx = pd.MultiIndex.from_product([['2013'],
['Alabama', 'Alaska', 'Arizona', 'Arkansas',
'California', 'Colorado', 'Connecticut',
'Delaware', 'District of Columbia', 'Florida']],
names=['year', 'state'])
exp_col = pd.Index(['Gross State Product', 'Gross State Product as percentage of national GDP',
'Gross State Product per capita', 'Population'], name='concept')
values = np.array([[174400, 1.2, 36333, 4.8],
[45600, 0.31, 65143, 0.7],
[261300, 1.8, 40828, 6.4],
[105800, 0.73, 36483, 2.9],
[2080600, 13.34, 51914, 37.3],
[259700, 1.79, 51940, 5],
[233400, 1.61, 64833, 3.6],
[62700, 0.43, 69667, 0.9],
[104700, 0.72, 174500, 0.6],
[754000, 5.2, 40106, 18.8]])
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
