def test_land_use(self):
# OECD -> Environment -> Resources Land Use
result = read_jsdmx(
os.path.join(self.dirpath, 'jsdmx', 'land_use.json'))
assert isinstance(result, pd.DataFrame)
result = result.loc['2010':'2011']
cols = [
'Arable land and permanent crops',
'Arable and cropland % land area', 'Total area', 'Forest',
'Forest % land area', 'Land area',
'Permanent meadows and pastures',
'Meadows and pastures % land area', 'Other areas',
'Other % land area'
]
exp_col = pd.MultiIndex.from_product(
[['Japan', 'United States'], cols], names=['Country', 'Variable'])
exp_idx = pd.DatetimeIndex(['2010', '2011'], name='Year')
values = [[
53790.0, 14.753154141525, 377800.0, np.nan, np.nan, 364600.0,
5000.0, 1.3713658804169, np.nan, np.nan, 1897990.0,
20.722767650476, 9629090.0, np.nan, np.nan, 9158960.0, 2416000.0,
26.378540795025, np.nan, np.nan
],
[
53580.0, 14.691527282698, 377800.0, np.nan, np.nan,
364700.0, 5000.0, 1.3709898546751, np.nan, np.nan,
1897990.0, 20.722767650476, 9629090.0, np.nan, np.nan,
9158960.0, 2416000.0, 26.378540795025, np.nan, np.nan
]]
values = np.array(values)
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result[exp_col], expected)
def test_quartervalue(dirpath):
# https://stats.oecd.org/sdmx-json/data/QNA/AUS+AUT+BEL+CAN+CHL.GDP+B1_
# GE.CUR+VOBARSA.Q/all?startTime=2009-Q1&endTime=2011-Q4
result = read_jsdmx(os.path.join(dirpath, "jsdmx", "oecd1.json"))
assert isinstance(result, pd.DataFrame)
expected = pd.DatetimeIndex(
[
"2009-01-01",
"2009-04-01",
"2009-07-01",
"2009-10-01",
"2010-01-01",
"2010-04-01",
"2010-07-01",
"2010-10-01",
"2011-01-01",
"2011-04-01",
"2011-07-01",
"2011-10-01",
],
dtype="datetime64[ns]",
name=u"Period",
freq=None,
)
tm.assert_index_equal(result.index, expected)
def test_tourism(self):
# OECD -> Industry and Services -> Inbound Tourism
result = read_jsdmx(os.path.join(self.dirpath, 'jsdmx',
'tourism.json'))
self.assertTrue(isinstance(result, pd.DataFrame))
exp_col = pd.MultiIndex.from_product(
[['Japan'],
[
'China', 'Hong Kong, China', 'Total international arrivals',
'Total international receipts',
'International passenger transport receipts',
'International travel receipts', 'Korea', 'Chinese Taipei',
'United States'
]],
names=['Country', 'Variable'])
exp_idx = pd.DatetimeIndex([
'2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011',
'2012'
],
name='Year')
values = np.array([[616, 300, 6138, 1550, 330, 1220, 1588, 1081, 760],
[653, 299, 6728, 1710, 340, 1370, 1747, 1275, 822],
[812, 352, 7334, 1330, 350, 980, 2117, 1309, 817],
[942, 432, 8347, 1460, 360, 1100, 2601, 1385, 816],
[1000, 550, 8351, 1430, 310, 1120, 2382, 1390, 768],
[1006, 450, 6790, 1170, 210, 960, 1587, 1024, 700],
[1413, 509, 8611, 1350, 190, 1160, 2440, 1268, 727],
[1043, 365, 6219, 1000, 100, 900, 1658, 994, 566],
[1430, 482, 8368, 1300, 100, 1200, 2044, 1467,
717]])
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
def test_land_use(self):
# OECD -> Environment -> Resources Land Use
result = read_jsdmx(
os.path.join(self.dirpath, 'jsdmx', 'land_use.json'))
self.assertTrue(isinstance(result, pd.DataFrame))
result = result.ix['2010':'2011']
exp_col = pd.MultiIndex.from_product(
[['Japan', 'United States'],
[
'Arable land and permanent crops',
'Arable and cropland % land area', 'Total area', 'Forest',
'Forest % land area', 'Land area',
'Permanent meadows and pastures',
'Meadows and pastures % land area', 'Other areas',
'Other % land area'
]],
names=['Country', 'Variable'])
exp_idx = pd.DatetimeIndex(['2010', '2011'], name='Year')
values = np.array([[
45930, 12.601, 377950, 249790, 68.529, 364500, np.nan, np.nan,
68780, 18.87, 1624330, 17.757, 9831510, 3040220, 33.236, 9147420,
2485000, 27.166, 1997870, 21.841
],
[
45610, 12.513, 377955, 249878, 68.554, 364500,
np.nan, np.nan, 69012, 18.933, 1627625, 17.793,
9831510, 3044048, 33.278, 9147420, 2485000,
27.166, 1990747, 21.763
]])
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
def test_tourism(self):
# OECD -> Industry and Services -> Inbound Tourism
result = read_jsdmx(os.path.join(self.dirpath, 'jsdmx', 'tourism.json'))
self.assertTrue(isinstance(result, pd.DataFrame))
exp_col = pd.MultiIndex.from_product([['Japan'],
['China', 'Hong Kong, China',
'Total international arrivals',
'Total international receipts',
'International passenger transport receipts',
'International travel receipts',
'Korea', 'Chinese Taipei', 'United States']],
names=['Country', 'Variable'])
exp_idx = pd.DatetimeIndex(['2004', '2005', '2006', '2007', '2008',
'2009', '2010', '2011', '2012'], name='Year')
values = np.array([[616, 300, 6138, 1550, 330, 1220, 1588, 1081, 760],
[653, 299, 6728, 1710, 340, 1370, 1747, 1275, 822],
[812, 352, 7334, 1330, 350, 980, 2117, 1309, 817],
[942, 432, 8347, 1460, 360, 1100, 2601, 1385, 816],
[1000, 550, 8351, 1430, 310, 1120, 2382, 1390, 768],
[1006, 450, 6790, 1170, 210, 960, 1587, 1024, 700],
[1413, 509, 8611, 1350, 190, 1160, 2440, 1268, 727],
[1043, 365, 6219, 1000, 100, 900, 1658, 994, 566],
[1430, 482, 8368, 1300, 100, 1200, 2044, 1467, 717]])
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
def test_tourism(dirpath):
# OECD -> Industry and Services -> Inbound Tourism
result = read_jsdmx(os.path.join(dirpath, 'jsdmx', 'tourism.json'))
assert isinstance(result, pd.DataFrame)
jp = result['Japan']
visitors = [
'China', 'Hong Kong, China', 'Total international arrivals', 'Korea',
'Chinese Taipei', 'United States'
]
exp_col = pd.Index([
'China', 'Hong Kong, China', 'Total international arrivals', 'Korea',
'Chinese Taipei', 'United States'
],
name='Variable')
exp_idx = pd.DatetimeIndex([
'2008-01-01', '2009-01-01', '2010-01-01', '2011-01-01', '2012-01-01',
'2013-01-01', '2014-01-01', '2015-01-01', '2016-01-01'
],
name='Year')
values = [
[1000000.0, 550000.0, 8351000.0, 2382000.0, 1390000.0, 768000.0],
[1006000.0, 450000.0, 6790000.0, 1587000.0, 1024000.0, 700000.0],
[1413000.0, 509000.0, 8611000.0, 2440000.0, 1268000.0, 727000.0],
[1043000.0, 365000.0, 6219000.0, 1658000.0, 994000.0, 566000.0],
[1430000.0, 482000.0, 8368000.0, 2044000.0, 1467000.0, 717000.0],
[1314000.0, 746000.0, 10364000.0, 2456000.0, 2211000.0, 799000.0],
[2409000.0, 926000.0, 13413000.0, 2755000.0, 2830000.0, 892000.0],
[4993689.0, 1524292.0, 19737409.0, 4002095.0, 3677075.0, 1033258.0],
[6373564.0, 1839193.0, 24039700.0, 5090302.0, 4167512.0, 1242719.0]
]
values = np.array(values, dtype='object')
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(jp[visitors], expected)
def _read_lines(self, out):
""" read one data from specified URL """
df = read_jsdmx(out)
try:
idx_name = df.index.name # hack for pandas 0.16.2
df.index = pd.to_datetime(df.index)
df = df.sort_index()
df = df.truncate(self.start, self.end)
df.index.name = idx_name
except ValueError:
pass
return df
def _read_lines(self, out):
""" read one data from specified URL """
df = read_jsdmx(out)
try:
idx_name = df.index.name # hack for pandas 0.16.2
df.index = pd.to_datetime(df.index)
df = df.sort_index()
df = df.truncate(self.start, self.end)
df.index.name = idx_name
except ValueError:
pass
return df
def fetch_data(url, name):
resp = _urlopen(url)
resp = resp.read()
resp = resp.decode('utf-8')
data = read_jsdmx(resp)
try:
idx_name = data.index.name # hack for pandas 0.16.2
data.index = pd.to_datetime(data.index)
data = data.sort_index()
data = data.truncate(start, end)
data.index.name = idx_name
except ValueError:
pass
return data
def _read_lines(self, out):
""" read one data from specified URL """
df = read_jsdmx(out)
try:
idx_name = df.index.name # hack for panda 0.16.2
df.index = pd.to_datetime(df.index, errors="ignore")
for col in df:
df[col] = pd.to_numeric(df[col], errors="ignore")
df = df.sort_index()
df = df.truncate(self.start, self.end)
df.index.name = idx_name
except ValueError:
pass
return df
def fetch_data(url, name):
resp = _urlopen(url)
resp = resp.read()
resp = resp.decode("utf-8")
data = read_jsdmx(resp)
try:
idx_name = data.index.name # hack for pandas 0.16.2
data.index = pd.to_datetime(data.index)
data = data.sort_index()
data = data.truncate(start, end)
data.index.name = idx_name
except ValueError:
pass
return data
def test_land_use(self):
# OECD -> Environment -> Resources Land Use
result = read_jsdmx(os.path.join(self.dirpath, 'jsdmx', 'land_use.json'))
self.assertTrue(isinstance(result, pd.DataFrame))
result = result.ix['2010':'2011']
exp_col = pd.MultiIndex.from_product([['Japan', 'United States'],
['Arable land and permanent crops',
'Arable and cropland % land area',
'Total area', 'Forest', 'Forest % land area',
'Land area', 'Permanent meadows and pastures',
'Meadows and pastures % land area',
'Other areas', 'Other % land area']],
names=['Country', 'Variable'])
exp_idx = pd.DatetimeIndex(['2010', '2011'], name='Year')
values = np.array([[45930, 12.601, 377950, 249790, 68.529, 364500, np.nan, np.nan,
68780, 18.87, 1624330, 17.757, 9831510, 3040220, 33.236, 9147420,
2485000, 27.166, 1997870, 21.841],
[45610, 12.513, 377955, 249878, 68.554, 364500, np.nan, np.nan,
69012, 18.933, 1627625, 17.793, 9831510, 3044048, 33.278, 9147420,
2485000, 27.166, 1990747, 21.763]])
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result, expected)
def test_land_use(dirpath):
# OECD -> Environment -> Resources Land Use
result = read_jsdmx(os.path.join(dirpath, "jsdmx", "land_use.json"))
assert isinstance(result, pd.DataFrame)
result = result.loc["2010":"2011"]
cols = [
"Arable land and permanent crops",
"Arable and cropland % land area",
"Total area",
"Forest",
"Forest % land area",
"Land area",
"Permanent meadows and pastures",
"Meadows and pastures % land area",
"Other areas",
"Other % land area",
]
exp_col = pd.MultiIndex.from_product([["Japan", "United States"], cols],
names=["Country", "Variable"])
exp_idx = pd.DatetimeIndex(["2010", "2011"], name="Year")
values = [
[
53790.0,
14.753154141525,
377800.0,
np.nan,
np.nan,
364600.0,
5000.0,
1.3713658804169,
np.nan,
np.nan,
1897990.0,
20.722767650476,
9629090.0,
np.nan,
np.nan,
9158960.0,
2416000.0,
26.378540795025,
np.nan,
np.nan,
],
[
53580.0,
14.691527282698,
377800.0,
np.nan,
np.nan,
364700.0,
5000.0,
1.3709898546751,
np.nan,
np.nan,
1897990.0,
20.722767650476,
9629090.0,
np.nan,
np.nan,
9158960.0,
2416000.0,
26.378540795025,
np.nan,
np.nan,
],
]
values = np.array(values)
expected = pd.DataFrame(values, index=exp_idx, columns=exp_col)
tm.assert_frame_equal(result[exp_col], expected)
