def setUp(self):
self.store = WorldBankStore()
def setUp(self):
self.store = WorldBankStore()
class TestWorldBankTestSite(tm.TestCase):
def setUp(self):
self.store = WorldBankStore()
def test_isvalid(self):
self.assertTrue(self.store.is_valid())
def test_get_gdp_per_capita(self):
resource = self.store.get('NY.GDP.PCAP.CD')
self.assertTrue(isinstance(resource, WorldBankResource))
df = resource.read()
jp = np.array([
478.99534016, 563.58675984, 633.64031517, 717.86691523,
835.65725248, 919.77668818, 1058.50356091, 1228.9092104,
1450.61965234, 1669.09819991, 2003.64704736, 2234.26166585,
2917.65897572, 3931.30162742, 4281.35992841, 4581.57438948,
5111.29514922, 6230.33568811, 8675.01399673, 8953.59152028,
9307.83929459, 10212.3781359, 9428.87465037, 10213.95827931,
10786.78618095, 11465.72578163, 16882.27395207, 20355.60522244,
24592.77200535, 24505.76729587, 25123.63178621, 28540.7714826,
31013.64714836, 35451.29751157, 38814.89437898, 42522.06659061,
37421.67385771, 34294.89897666, 30967.28808909, 34998.80997175,
37291.70615804, 32716.41867489, 31235.58818439, 33690.93772972,
36441.50449394, 35781.16626514, 34102.11477775, 34095.02343297,
37972.0557387, 39473.36750954, 43117.82967369, 46203.69803728,
46548.26963715, 38492.08889474
])
us = np.array([
2881.0997978, 2934.55277761, 3107.93741663, 3232.2080093,
3423.39628164, 3664.8018704, 3972.12308995, 4152.01983719,
4491.42430453, 4802.64248506, 5246.96174629, 5623.58844463,
6109.6924191, 6741.10113303, 7242.32420249, 7819.95897635,
8611.46146261, 9471.5286575, 10587.41604331, 11695.36335562,
12597.64550556, 13992.92269879, 14439.01512535, 15561.26813578,
17134.3157002, 18269.27926565, 19114.82386844, 20100.78872751,
21483.11445037, 22922.46545039, 23954.52342132, 24404.99484151,
25492.95555018, 26464.7832594, 27776.42650289, 28781.94969168,
30068.22720625, 31572.63521567, 32948.95125682, 34639.11983945,
36467.29542582, 37285.81592335, 38175.37638297, 39682.47224732,
41928.88613648, 44313.58524128, 46443.81019859, 48070.38468627,
48407.0769099, 46998.82041531, 48357.67356926, 49854.52266835,
51755.21484065, 53142.88963052
])
index = pd.DatetimeIndex(map(str, range(1960, 2014)), name='date')
for label, values in [('Japan', jp), ('United States', us)]:
expected = pd.Series(values, index=index)
result = df['GDP per capita (current US$)'][label]['1960':'2013']
tm.assert_series_equal(result, expected)
raw_data = resource.read(raw=True)
self.assertTrue(len(raw_data) > 0)
def test_get_co2_emit(self):
resource = self.store.get('EN.ATM.CO2E.PC')
self.assertTrue(isinstance(resource, WorldBankResource))
df = resource.read()
jp = np.array([
2.51653752, 2.98197939, 3.05973635, 3.35932078, 3.67303507,
3.91290553, 4.20626471, 4.86355785, 5.56659316, 6.33852317,
7.36808874, 7.54556103, 7.96146247, 8.47295875, 8.31387944,
7.77267069, 8.05971943, 8.21349644, 7.86685725, 8.24734789,
8.11401701, 7.90159205, 7.59990231, 7.41108575, 7.83324828,
7.5806754, 7.5340533, 7.41845835, 8.06669419, 8.3299481,
8.86239902, 8.88086258, 9.04436538, 8.90155004, 9.39514505,
9.43841991, 9.58652198, 9.52987839, 9.16909548, 9.45947022,
9.61290451, 9.45557013, 9.54726303, 9.68876079, 9.85946288,
9.69047361, 9.63791597, 9.79204079, 9.4508838, 8.62862707,
9.18565087
])
us = np.array([
15.99977916, 15.68125552, 16.0139375, 16.48276215, 16.96811858,
17.45172525, 18.12107301, 18.59831788, 19.08938916, 19.85794566,
21.11125227, 20.98020348, 21.74864198, 22.51058213, 21.50293038,
20.40222407, 21.15761537, 21.53248401, 21.96514631, 21.77411499,
20.77751491, 19.7492974, 18.56395007, 18.54180517, 18.95611586,
18.85669719, 18.70287126, 19.33406449, 19.9946205, 20.0595756,
19.10135589, 19.07931196, 19.1887997, 19.35128717, 19.46428538,
19.36385544, 19.62199049, 19.87640503, 19.77891432, 19.82400911,
20.24918916, 19.65619321, 19.6469218, 19.58465737, 19.7768452,
19.7159606, 19.22922866, 19.34957722, 18.60227269, 17.31529716,
17.56415999
])
index = pd.DatetimeIndex(map(str, range(1960, 2011)), name='date')
for label, values in [('Japan', jp), ('United States', us)]:
expected = pd.Series(values, index=index)
result = df['CO2 emissions (metric tons per capita)'][label][
'1960':'2010']
tm.assert_series_equal(result, expected)
raw_data = resource.read(raw=True)
self.assertTrue(len(raw_data) > 0)
class TestWorldBankTestSite(tm.TestCase):
def setUp(self):
self.store = WorldBankStore()
def test_isvalid(self):
self.assertTrue(self.store.is_valid())
def test_get_gdp_per_capita(self):
resource = self.store.get('NY.GDP.PCAP.CD')
self.assertTrue(isinstance(resource, WorldBankResource))
df = resource.read()
jp = np.array([
478.99534016, 563.58675984, 633.64031517, 717.86691523,
835.65725248, 919.77668818, 1058.50356091, 1228.9092104,
1450.61965234, 1669.09819991, 2003.64704736, 2234.26166585,
2917.65897572, 3931.30162742, 4281.35992841, 4581.57438948,
5111.29514922, 6230.33568811, 8675.01399673, 8953.59152028,
9307.83929459, 10212.3781359, 9428.87465037, 10213.95827931,
10786.78618095, 11465.72578163, 16882.27395207, 20355.60522244,
24592.77200535, 24505.76729587, 25123.63178621, 28540.7714826,
31013.64714836, 35451.29751157, 38814.89437898, 42522.06659061,
37421.67385771, 34294.89897666, 30967.28808909, 34998.80997175,
37291.70615804, 32716.41867489, 31235.58818439, 33690.93772972,
36441.50449394, 35781.16626514, 34102.11477775, 34095.02343297,
37972.0557387, 39473.36750954, 43117.82967369, 46203.69803728,
46548.26963715, 38492.08889474
])
us = np.array([
2881.0997978, 2934.55277761, 3107.93741663, 3232.2080093,
3423.39628164, 3664.8018704, 3972.12308995, 4152.01983719,
4491.42430453, 4802.64248506, 5246.96174629, 5623.58844463,
6109.6924191, 6741.10113303, 7242.32420249, 7819.95897635,
8611.46146261, 9471.5286575, 10587.41604331, 11695.36335562,
12597.64550556, 13992.92269879, 14439.01512535, 15561.26813578,
17134.3157002, 18269.27926565, 19114.82386844, 20100.78872751,
21483.11445037, 22922.46545039, 23954.52342132, 24404.99484151,
25492.95555018, 26464.7832594, 27776.42650289, 28781.94969168,
30068.22720625, 31572.63521567, 32948.95125682, 34639.11983945,
36467.29542582, 37285.81592335, 38175.37638297, 39682.47224732,
41928.88613648, 44313.58524128, 46443.81019859, 48070.38468627,
48407.0769099, 46998.82041531, 48357.67356926, 49854.52266835,
51755.21484065, 53142.88963052
])
index = pd.DatetimeIndex(map(str, range(1960, 2014)), name='date')
for label, values in [('Japan', jp), ('United States', us)]:
expected = pd.Series(values, index=index)
result = df['GDP per capita (current US$)'][label]['1960':'2013']
tm.assert_series_equal(result, expected)
raw_data = resource.read(raw=True)
self.assertTrue(len(raw_data) > 0)
def test_get_co2_emit(self):
resource = self.store.get('EN.ATM.CO2E.PC')
self.assertTrue(isinstance(resource, WorldBankResource))
df = resource.read()
jp = np.array([
2.51653752, 2.98197939, 3.05973635, 3.35932078, 3.67303507,
3.91290553, 4.20626471, 4.86355785, 5.56659316, 6.33852317,
7.36808874, 7.54556103, 7.96146247, 8.47295875, 8.31387944,
7.77267069, 8.05971943, 8.21349644, 7.86685725, 8.24734789,
8.11401701, 7.90159205, 7.59990231, 7.41108575, 7.83324828,
7.5806754, 7.5340533, 7.41845835, 8.06669419, 8.3299481,
8.86239902, 8.88086258, 9.04436538, 8.90155004, 9.39514505,
9.43841991, 9.58652198, 9.52987839, 9.16909548, 9.45947022,
9.61290451, 9.45557013, 9.54726303, 9.68876079, 9.85946288,
9.69047361, 9.63791597, 9.79204079, 9.4508838, 8.62862707,
9.18565087
])
us = np.array([
15.99977916, 15.68125552, 16.0139375, 16.48276215, 16.96811858,
17.45172525, 18.12107301, 18.59831788, 19.08938916, 19.85794566,
21.11125227, 20.98020348, 21.74864198, 22.51058213, 21.50293038,
20.40222407, 21.15761537, 21.53248401, 21.96514631, 21.77411499,
20.77751491, 19.7492974, 18.56395007, 18.54180517, 18.95611586,
18.85669719, 18.70287126, 19.33406449, 19.9946205, 20.0595756,
19.10135589, 19.07931196, 19.1887997, 19.35128717, 19.46428538,
19.36385544, 19.62199049, 19.87640503, 19.77891432, 19.82400911,
20.24918916, 19.65619321, 19.6469218, 19.58465737, 19.7768452,
19.7159606, 19.22922866, 19.34957722, 18.60227269, 17.31529716,
17.56415999
])
index = pd.DatetimeIndex(map(str, range(1960, 2011)), name='date')
for label, values in [('Japan', jp), ('United States', us)]:
expected = pd.Series(values, index=index)
result = df['CO2 emissions (metric tons per capita)'][label][
'1960':'2010']
tm.assert_series_equal(result, expected)
raw_data = resource.read(raw=True)
self.assertTrue(len(raw_data) > 0)
