def test_append(self):
pth = '__test_append__.h5'
try:
store = HDFStore(pth)
df = tm.makeTimeDataFrame()
store.append('df1', df[:10])
store.append('df1', df[10:])
tm.assert_frame_equal(store['df1'], df)
store.put('df2', df[:10], table=True)
store.append('df2', df[10:])
tm.assert_frame_equal(store['df2'], df)
wp = tm.makePanel()
store.append('wp1', wp.ix[:,:10,:])
store.append('wp1', wp.ix[:,10:,:])
tm.assert_panel_equal(store['wp1'], wp)
except:
raise
finally:
store.close()
os.remove(pth)
def test_append(self):
pth = '__test_append__.h5'
try:
store = HDFStore(pth)
df = tm.makeTimeDataFrame()
store.append('df1', df[:10])
store.append('df1', df[10:])
tm.assert_frame_equal(store['df1'], df)
store.put('df2', df[:10], table=True)
store.append('df2', df[10:])
tm.assert_frame_equal(store['df2'], df)
wp = tm.makePanel()
store.append('wp1', wp.ix[:, :10, :])
store.append('wp1', wp.ix[:, 10:, :])
tm.assert_panel_equal(store['wp1'], wp)
except:
raise
finally:
store.close()
os.remove(pth)
def _check_roundtrip_table(self, obj, comparator):
store = HDFStore(self.scratchpath, 'w')
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
sorted_obj = _test_sort(obj)
comparator(retrieved, sorted_obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_wide_table_dups(self):
wp = tm.makePanel()
try:
store = HDFStore(self.scratchpath)
store._quiet = True
store.put('panel', wp, table=True)
store.put('panel', wp, table=True, append=True)
recons = store['panel']
tm.assert_panel_equal(recons, wp)
finally:
store.close()
os.remove(self.scratchpath)
def test_wide_table_dups(self):
wp = tm.makePanel()
try:
store = HDFStore(self.scratchpath)
store._quiet = True
store.put('panel', wp, table=True)
store.put('panel', wp, table=True, append=True)
recons = store['panel']
tm.assert_panel_equal(recons, wp)
finally:
store.close()
os.remove(self.scratchpath)
def hsave(self,fname):
from pandas.io.pytables import HDFStore
LOGGER.debug('Saving HDF in %s\n%s',fname,self.report())
store = HDFStore(fname,mode='w')
for k,v in self.items():
if re.match('^__',k):
continue
if isinstance(v,np.ndarray):
v = Series(v)
LOGGER.debug('Saving HDF for %s',k)
store.put(k,v)
if self._missing:
store['_MISSING']=Series(self._missing)
store.close()
def _check_roundtrip_table(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
# sorted_obj = _test_sort(obj)
comparator(retrieved, obj)
finally:
store.close()
os.remove(self.scratchpath)
def _check_roundtrip_table(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
sorted_obj = _test_sort(obj)
comparator(retrieved, sorted_obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_append(self):
pth = '__test_append__.h5'
try:
store = HDFStore(pth)
df = tm.makeTimeDataFrame()
store.append('df1', df[:10])
store.append('df1', df[10:])
tm.assert_frame_equal(store['df1'], df)
store.put('df2', df[:10], table=True)
store.append('df2', df[10:])
tm.assert_frame_equal(store['df2'], df)
store.append('/df3', df[:10])
store.append('/df3', df[10:])
tm.assert_frame_equal(store['df3'], df)
# this is allowed by almost always don't want to do it
import warnings
import tables
warnings.filterwarnings('ignore', category=tables.NaturalNameWarning)
store.append('/df3 foo', df[:10])
store.append('/df3 foo', df[10:])
tm.assert_frame_equal(store['df3 foo'], df)
warnings.filterwarnings('always', category=tables.NaturalNameWarning)
# panel
wp = tm.makePanel()
store.append('wp1', wp.ix[:,:10,:])
store.append('wp1', wp.ix[:,10:,:])
tm.assert_panel_equal(store['wp1'], wp)
# ndim
p4d = tm.makePanel4D()
store.append('p4d', p4d.ix[:,:,:10,:])
store.append('p4d', p4d.ix[:,:,10:,:])
tm.assert_panel4d_equal(store['p4d'], p4d)
except:
raise
finally:
store.close()
os.remove(pth)
def test_walk_groups(self):
with tm.ensure_clean('walk_groups.hdf') as filename:
store = HDFStore(filename, 'w')
dfs = {
'df1': pd.DataFrame([1, 2, 3]),
'df2': pd.DataFrame([4, 5, 6]),
'df3': pd.DataFrame([6, 7, 8]),
'df4': pd.DataFrame([9, 10, 11]),
}
store.put('/first_group/df1', dfs['df1'])
store.put('/first_group/df2', dfs['df2'])
store.put('/second_group/df3', dfs['df3'])
store.put('/second_group/third_group/df4', dfs['df4'])
expect = {
'/': ({'first_group', 'second_group'}, set()),
'/first_group': (set(), {'df1', 'df2'}),
'/second_group': ({'third_group'}, {'df3'}),
'/second_group/third_group': (set(), {'df4'}),
}
for path, groups, frames in store.walk_groups():
self.assertIn(path, expect)
expect_groups, expect_frames = expect[path]
self.assertEqual(expect_groups, set(groups))
self.assertEqual(expect_frames, set(frames))
for frame in frames:
frame_path = '/'.join([path, frame])
df = store.get(frame_path)
self.assert_(df.equals(dfs[frame]))
def test_walk_groups(self):
with tm.ensure_clean('walk_groups.hdf') as filename:
store = HDFStore(filename, 'w')
dfs = {
'df1': pd.DataFrame([1,2,3]),
'df2': pd.DataFrame([4,5,6]),
'df3': pd.DataFrame([6,7,8]),
'df4': pd.DataFrame([9,10,11]),
}
store.put('/first_group/df1', dfs['df1'])
store.put('/first_group/df2', dfs['df2'])
store.put('/second_group/df3', dfs['df3'])
store.put('/second_group/third_group/df4', dfs['df4'])
expect = {
'/': ({'first_group', 'second_group'}, set()),
'/first_group': (set(), {'df1', 'df2'}),
'/second_group': ({'third_group'}, {'df3'}),
'/second_group/third_group': (set(), {'df4'}),
}
for path, groups, frames in store.walk_groups():
self.assertIn(path, expect)
expect_groups, expect_frames = expect[path]
self.assertEqual(expect_groups, set(groups))
self.assertEqual(expect_frames, set(frames))
for frame in frames:
frame_path = '/'.join([path, frame])
df = store.get(frame_path)
self.assert_(df.equals(dfs[frame]))
def test_append(self):
pth = '__test_append__.h5'
try:
store = HDFStore(pth)
df = tm.makeTimeDataFrame()
store.append('df1', df[:10])
store.append('df1', df[10:])
tm.assert_frame_equal(store['df1'], df)
store.put('df2', df[:10], table=True)
store.append('df2', df[10:])
tm.assert_frame_equal(store['df2'], df)
store.append('/df3', df[:10])
store.append('/df3', df[10:])
tm.assert_frame_equal(store['df3'], df)
# this is allowed by almost always don't want to do it
import warnings
import tables
warnings.filterwarnings('ignore',
category=tables.NaturalNameWarning)
store.append('/df3 foo', df[:10])
store.append('/df3 foo', df[10:])
tm.assert_frame_equal(store['df3 foo'], df)
warnings.filterwarnings('always',
category=tables.NaturalNameWarning)
wp = tm.makePanel()
store.append('wp1', wp.ix[:, :10, :])
store.append('wp1', wp.ix[:, 10:, :])
tm.assert_panel_equal(store['wp1'], wp)
except:
raise
finally:
store.close()
os.remove(pth)
class TestHDFStore(unittest.TestCase):
path = '__test__.h5'
scratchpath = '__scratch__.h5'
def setUp(self):
self.store = HDFStore(self.path)
def tearDown(self):
self.store.close()
os.remove(self.path)
def test_factory_fun(self):
try:
with get_store(self.scratchpath) as tbl:
raise ValueError('blah')
except ValueError:
pass
with get_store(self.scratchpath) as tbl:
tbl['a'] = tm.makeDataFrame()
with get_store(self.scratchpath) as tbl:
self.assertEquals(len(tbl), 1)
self.assertEquals(type(tbl['a']), DataFrame)
os.remove(self.scratchpath)
def test_len_keys(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.assertEquals(len(self.store), 4)
self.assert_(set(self.store.keys()) == set(['a', 'b', 'c', 'd']))
def test_repr(self):
repr(self.store)
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
repr(self.store)
def test_contains(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeDataFrame()
self.assert_('a' in self.store)
self.assert_('b' in self.store)
self.assert_('c' not in self.store)
def test_reopen_handle(self):
self.store['a'] = tm.makeTimeSeries()
self.store.open('w', warn=False)
self.assert_(self.store.handle.isopen)
self.assertEquals(len(self.store), 0)
def test_flush(self):
self.store['a'] = tm.makeTimeSeries()
self.store.flush()
def test_get(self):
self.store['a'] = tm.makeTimeSeries()
left = self.store.get('a')
right = self.store['a']
tm.assert_series_equal(left, right)
self.assertRaises(AttributeError, self.store.get, 'b')
def test_put(self):
ts = tm.makeTimeSeries()
df = tm.makeTimeDataFrame()
self.store['a'] = ts
self.store['b'] = df[:10]
self.store.put('c', df[:10], table=True)
# not OK, not a table
self.assertRaises(ValueError, self.store.put, 'b', df[10:], append=True)
# node does not currently exist, test _is_table_type returns False in
# this case
self.assertRaises(ValueError, self.store.put, 'f', df[10:], append=True)
# OK
self.store.put('c', df[10:], append=True)
# overwrite table
self.store.put('c', df[:10], table=True, append=False)
tm.assert_frame_equal(df[:10], self.store['c'])
def test_put_compression(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df, table=True, compression='zlib')
tm.assert_frame_equal(self.store['c'], df)
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='zlib')
def test_put_compression_blosc(self):
tm.skip_if_no_package('tables', '2.2', app='blosc support')
df = tm.makeTimeDataFrame()
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='blosc')
self.store.put('c', df, table=True, compression='blosc')
tm.assert_frame_equal(self.store['c'], df)
def test_put_integer(self):
# non-date, non-string index
df = DataFrame(np.random.randn(50, 100))
self._check_roundtrip(df, tm.assert_frame_equal)
def test_append(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df[:10], table=True)
self.store.append('c', df[10:])
tm.assert_frame_equal(self.store['c'], df)
def test_append_diff_item_order(self):
wp = tm.makePanel()
wp1 = wp.ix[:, :10, :]
wp2 = wp.ix[['ItemC', 'ItemB', 'ItemA'], 10:, :]
self.store.put('panel', wp1, table=True)
self.assertRaises(Exception, self.store.put, 'panel', wp2,
append=True)
def test_remove(self):
ts = tm.makeTimeSeries()
df = tm.makeDataFrame()
self.store['a'] = ts
self.store['b'] = df
self.store.remove('a')
self.assertEquals(len(self.store), 1)
tm.assert_frame_equal(df, self.store['b'])
self.store.remove('b')
self.assertEquals(len(self.store), 0)
def test_remove_where_not_exist(self):
crit1 = {
'field' : 'index',
'op' : '>',
'value' : 'foo'
}
self.store.remove('a', where=[crit1])
def test_remove_crit(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = {
'field' : 'index',
'op' : '>',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
self.store.remove('wp', where=[crit1])
self.store.remove('wp', where=[crit2])
result = self.store['wp']
expected = wp.truncate(after=date).reindex(minor=['B', 'C'])
tm.assert_panel_equal(result, expected)
def test_series(self):
s = tm.makeStringSeries()
self._check_roundtrip(s, tm.assert_series_equal)
ts = tm.makeTimeSeries()
self._check_roundtrip(ts, tm.assert_series_equal)
def test_sparse_series(self):
s = tm.makeStringSeries()
s[3:5] = np.nan
ss = s.to_sparse()
self._check_roundtrip(ss, tm.assert_series_equal,
check_series_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_roundtrip(ss2, tm.assert_series_equal,
check_series_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_roundtrip(ss3, tm.assert_series_equal,
check_series_type=True)
def test_sparse_frame(self):
s = tm.makeDataFrame()
s.ix[3:5, 1:3] = np.nan
s.ix[8:10, -2] = np.nan
ss = s.to_sparse()
self._check_roundtrip(ss, tm.assert_frame_equal,
check_frame_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_roundtrip(ss2, tm.assert_frame_equal,
check_frame_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_roundtrip(ss3, tm.assert_frame_equal,
check_frame_type=True)
def test_sparse_panel(self):
items = ['x', 'y', 'z']
p = Panel(dict((i, tm.makeDataFrame()) for i in items))
sp = p.to_sparse()
self._check_roundtrip(sp, tm.assert_panel_equal,
check_panel_type=True)
sp2 = p.to_sparse(kind='integer')
self._check_roundtrip(sp2, tm.assert_panel_equal,
check_panel_type=True)
sp3 = p.to_sparse(fill_value=0)
self._check_roundtrip(sp3, tm.assert_panel_equal,
check_panel_type=True)
def test_float_index(self):
# GH #454
index = np.random.randn(10)
s = Series(np.random.randn(10), index=index)
self._check_roundtrip(s, tm.assert_series_equal)
def test_tuple_index(self):
# GH #492
col = np.arange(10)
idx = [(0.,1.), (2., 3.), (4., 5.)]
data = np.random.randn(30).reshape((3, 10))
DF = DataFrame(data, index=idx, columns=col)
self._check_roundtrip(DF, tm.assert_frame_equal)
def test_index_types(self):
values = np.random.randn(2)
func = lambda l, r : tm.assert_series_equal(l, r, True, True, True)
ser = Series(values, [0, 'y'])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime.today(), 0])
self._check_roundtrip(ser, func)
ser = Series(values, ['y', 0])
self._check_roundtrip(ser, func)
from datetime import date
ser = Series(values, [date.today(), 'a'])
self._check_roundtrip(ser, func)
ser = Series(values, [1.23, 'b'])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 1.53])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 5])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime(2012, 1, 1), datetime(2012, 1, 2)])
self._check_roundtrip(ser, func)
def test_timeseries_preepoch(self):
if sys.version_info[0] == 2 and sys.version_info[1] < 7:
raise nose.SkipTest
dr = bdate_range('1/1/1940', '1/1/1960')
ts = Series(np.random.randn(len(dr)), index=dr)
try:
self._check_roundtrip(ts, tm.assert_series_equal)
except OverflowError:
raise nose.SkipTest('known failer on some windows platforms')
def test_frame(self):
df = tm.makeDataFrame()
# put in some random NAs
df.values[0, 0] = np.nan
df.values[5, 3] = np.nan
self._check_roundtrip_table(df, tm.assert_frame_equal)
self._check_roundtrip(df, tm.assert_frame_equal)
self._check_roundtrip_table(df, tm.assert_frame_equal,
compression=True)
self._check_roundtrip(df, tm.assert_frame_equal,
compression=True)
tdf = tm.makeTimeDataFrame()
self._check_roundtrip(tdf, tm.assert_frame_equal)
self._check_roundtrip(tdf, tm.assert_frame_equal,
compression=True)
# not consolidated
df['foo'] = np.random.randn(len(df))
self.store['df'] = df
recons = self.store['df']
self.assert_(recons._data.is_consolidated())
# empty
self.assertRaises(ValueError, self._check_roundtrip, df[:0],
tm.assert_frame_equal)
def test_can_serialize_dates(self):
rng = [x.date() for x in bdate_range('1/1/2000', '1/30/2000')]
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
self._check_roundtrip(frame, tm.assert_frame_equal)
def test_timezones(self):
rng = date_range('1/1/2000', '1/30/2000', tz='US/Eastern')
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
self.assert_(recons.index.equals(rng))
self.assertEquals(rng.tz, recons.index.tz)
finally:
store.close()
os.remove(self.scratchpath)
def test_store_hierarchical(self):
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo', 'bar'])
frame = DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
self._check_roundtrip(frame, tm.assert_frame_equal)
self._check_roundtrip(frame.T, tm.assert_frame_equal)
self._check_roundtrip(frame['A'], tm.assert_series_equal)
# check that the names are stored
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
assert(recons.index.names == ['foo', 'bar'])
finally:
store.close()
os.remove(self.scratchpath)
def test_store_index_name(self):
df = tm.makeDataFrame()
df.index.name = 'foo'
try:
store = HDFStore(self.scratchpath)
store['frame'] = df
recons = store['frame']
assert(recons.index.name == 'foo')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_series_name(self):
df = tm.makeDataFrame()
series = df['A']
try:
store = HDFStore(self.scratchpath)
store['series'] = series
recons = store['series']
assert(recons.name == 'A')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_mixed(self):
def _make_one():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one()
df2 = _make_one()
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
self.store['obj'] = df1
tm.assert_frame_equal(self.store['obj'], df1)
self.store['obj'] = df2
tm.assert_frame_equal(self.store['obj'], df2)
# storing in Table not yet supported
self.assertRaises(Exception, self.store.put, 'foo',
df1, table=True)
# check that can store Series of all of these types
self._check_roundtrip(df1['obj1'], tm.assert_series_equal)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal)
self._check_roundtrip(df1['int1'], tm.assert_series_equal)
# try with compression
self._check_roundtrip(df1['obj1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['int1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1, tm.assert_frame_equal,
compression=True)
def test_wide(self):
wp = tm.makePanel()
self._check_roundtrip(wp, tm.assert_panel_equal)
def test_wide_table(self):
wp = tm.makePanel()
self._check_roundtrip_table(wp, tm.assert_panel_equal)
def test_wide_table_dups(self):
wp = tm.makePanel()
try:
store = HDFStore(self.scratchpath)
store._quiet = True
store.put('panel', wp, table=True)
store.put('panel', wp, table=True, append=True)
recons = store['panel']
tm.assert_panel_equal(recons, wp)
finally:
store.close()
os.remove(self.scratchpath)
def test_long(self):
def _check(left, right):
tm.assert_panel_equal(left.to_panel(), right.to_panel())
wp = tm.makePanel()
self._check_roundtrip(wp.to_frame(), _check)
# empty
self.assertRaises(ValueError, self._check_roundtrip, wp.to_frame()[:0],
_check)
def test_longpanel(self):
pass
def test_overwrite_node(self):
self.store['a'] = tm.makeTimeDataFrame()
ts = tm.makeTimeSeries()
self.store['a'] = ts
tm.assert_series_equal(self.store['a'], ts)
def test_panel_select(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = {
'field' : 'index',
'op' : '>=',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
result = self.store.select('wp', [crit1, crit2])
expected = wp.truncate(before=date).reindex(minor=['A', 'D'])
tm.assert_panel_equal(result, expected)
def test_frame_select(self):
df = tm.makeTimeDataFrame()
self.store.put('frame', df, table=True)
date = df.index[len(df) // 2]
crit1 = {
'field' : 'index',
'op' : '>=',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
crit3 = {
'field' : 'column',
'value' : 'A'
}
result = self.store.select('frame', [crit1, crit2])
expected = df.ix[date:, ['A', 'D']]
tm.assert_frame_equal(result, expected)
result = self.store.select('frame', [crit3])
expected = df.ix[:, ['A']]
tm.assert_frame_equal(result, expected)
# can't select if not written as table
self.store['frame'] = df
self.assertRaises(Exception, self.store.select,
'frame', [crit1, crit2])
def test_select_filter_corner(self):
df = DataFrame(np.random.randn(50, 100))
df.index = ['%.3d' % c for c in df.index]
df.columns = ['%.3d' % c for c in df.columns]
self.store.put('frame', df, table=True)
crit = {
'field' : 'column',
'value' : df.columns[:75]
}
result = self.store.select('frame', [crit])
tm.assert_frame_equal(result, df.ix[:, df.columns[:75]])
def _check_roundtrip(self, obj, comparator, compression=False, **kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_roundtrip_table(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
sorted_obj = _test_sort(obj)
comparator(retrieved, sorted_obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_legacy_read(self):
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy.h5'), 'r')
store['a']
store['b']
store['c']
store['d']
store.close()
def test_store_datetime_fractional_secs(self):
dt = datetime(2012, 1, 2, 3, 4, 5, 123456)
series = Series([0], [dt])
self.store['a'] = series
self.assertEquals(self.store['a'].index[0], dt)
def test_tseries_indices_series(self):
idx = tm.makeDateIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
idx = tm.makePeriodIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
def test_tseries_indices_frame(self):
idx = tm.makeDateIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
idx = tm.makePeriodIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
def test_unicode_index(self):
unicode_values = [u'\u03c3', u'\u03c3\u03c3']
s = Series(np.random.randn(len(unicode_values)), unicode_values)
self._check_roundtrip(s, tm.assert_series_equal)
class TesttHDFStore(unittest.TestCase):
path = '__test__.h5'
scratchpath = '__scratch__.h5'
def setUp(self):
self.store = HDFStore(self.path)
def tearDown(self):
self.store.close()
os.remove(self.path)
def test_len(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.assertEquals(len(self.store), 4)
def test_repr(self):
repr(self.store)
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
repr(self.store)
def test_reopen_handle(self):
self.store['a'] = tm.makeTimeSeries()
self.store.open('w', warn=False)
self.assert_(self.store.handle.isopen)
self.assertEquals(len(self.store), 0)
def test_flush(self):
self.store['a'] = tm.makeTimeSeries()
self.store.flush()
def test_get(self):
self.store['a'] = tm.makeTimeSeries()
left = self.store.get('a')
right = self.store['a']
tm.assert_series_equal(left, right)
self.assertRaises(AttributeError, self.store.get, 'b')
def test_put(self):
ts = tm.makeTimeSeries()
df = tm.makeTimeDataFrame()
self.store['a'] = ts
self.store['b'] = df[:10]
self.store.put('c', df[:10], table=True)
# not OK, not a table
self.assertRaises(ValueError, self.store.put, 'b', df[10:], append=True)
# node does not currently exist, test _is_table_type returns False in
# this case
self.assertRaises(ValueError, self.store.put, 'f', df[10:], append=True)
# OK
self.store.put('c', df[10:], append=True)
# overwrite table
self.store.put('c', df[:10], table=True, append=False)
tm.assert_frame_equal(df[:10], self.store['c'])
def test_put_compression(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df, table=True, compression='zlib')
tm.assert_frame_equal(self.store['c'], df)
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='zlib')
def test_put_compression_blosc(self):
tm.skip_if_no_package('tables', '2.2', app='blosc support')
df = tm.makeTimeDataFrame()
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='blosc')
self.store.put('c', df, table=True, compression='blosc')
tm.assert_frame_equal(self.store['c'], df)
def test_put_integer(self):
# non-date, non-string index
df = DataFrame(np.random.randn(50, 100))
self._check_roundtrip(df, tm.assert_frame_equal)
def test_append(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df[:10], table=True)
self.store.append('c', df[10:])
tm.assert_frame_equal(self.store['c'], df)
def test_append_diff_item_order(self):
wp = tm.makePanel()
wp1 = wp.ix[:, :10, :]
wp2 = wp.ix[['ItemC', 'ItemB', 'ItemA'], 10:, :]
self.store.put('panel', wp1, table=True)
self.assertRaises(Exception, self.store.put, 'panel', wp2,
append=True)
def test_remove(self):
ts = tm.makeTimeSeries()
df = tm.makeDataFrame()
self.store['a'] = ts
self.store['b'] = df
self.store.remove('a')
self.assertEquals(len(self.store), 1)
tm.assert_frame_equal(df, self.store['b'])
self.store.remove('b')
self.assertEquals(len(self.store), 0)
def test_remove_where_not_exist(self):
crit1 = {
'field' : 'index',
'op' : '>',
'value' : 'foo'
}
self.store.remove('a', where=[crit1])
def test_remove_crit(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = {
'field' : 'index',
'op' : '>',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
self.store.remove('wp', where=[crit1])
self.store.remove('wp', where=[crit2])
result = self.store['wp']
expected = wp.truncate(after=date).reindex(minor=['B', 'C'])
tm.assert_panel_equal(result, expected)
def test_series(self):
s = tm.makeStringSeries()
self._check_roundtrip(s, tm.assert_series_equal)
ts = tm.makeTimeSeries()
self._check_roundtrip(ts, tm.assert_series_equal)
def test_timeseries_preepoch(self):
if sys.version_info[0] == 2 and sys.version_info[1] < 7:
raise nose.SkipTest
dr = DateRange('1/1/1940', '1/1/1960')
ts = Series(np.random.randn(len(dr)), index=dr)
try:
self._check_roundtrip(ts, tm.assert_series_equal)
except OverflowError:
raise nose.SkipTest('known failer on some windows platforms')
def test_frame(self):
df = tm.makeDataFrame()
# put in some random NAs
df.values[0, 0] = np.nan
df.values[5, 3] = np.nan
self._check_roundtrip_table(df, tm.assert_frame_equal)
self._check_roundtrip(df, tm.assert_frame_equal)
self._check_roundtrip_table(df, tm.assert_frame_equal,
compression=True)
self._check_roundtrip(df, tm.assert_frame_equal,
compression=True)
tdf = tm.makeTimeDataFrame()
self._check_roundtrip(tdf, tm.assert_frame_equal)
self._check_roundtrip(tdf, tm.assert_frame_equal,
compression=True)
# not consolidated
df['foo'] = np.random.randn(len(df))
self.store['df'] = df
recons = self.store['df']
self.assert_(recons._data.is_consolidated())
# empty
self.assertRaises(ValueError, self._check_roundtrip, df[:0],
tm.assert_frame_equal)
def test_can_serialize_dates(self):
rng = [x.date() for x in DateRange('1/1/2000', '1/30/2000')]
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
self._check_roundtrip(frame, tm.assert_frame_equal)
def test_store_hierarchical(self):
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo', 'bar'])
frame = DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
self._check_roundtrip(frame, tm.assert_frame_equal)
self._check_roundtrip(frame.T, tm.assert_frame_equal)
self._check_roundtrip(frame['A'], tm.assert_series_equal)
# check that the names are stored
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
assert(recons.index.names == ['foo', 'bar'])
finally:
store.close()
os.remove(self.scratchpath)
def test_store_index_name(self):
df = tm.makeDataFrame()
df.index.name = 'foo'
try:
store = HDFStore(self.scratchpath)
store['frame'] = df
recons = store['frame']
assert(recons.index.name == 'foo')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_series_name(self):
df = tm.makeDataFrame()
series = df['A']
try:
store = HDFStore(self.scratchpath)
store['series'] = series
recons = store['series']
assert(recons.name == 'A')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_mixed(self):
def _make_one():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one()
df2 = _make_one()
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
self.store['obj'] = df1
tm.assert_frame_equal(self.store['obj'], df1)
self.store['obj'] = df2
tm.assert_frame_equal(self.store['obj'], df2)
# storing in Table not yet supported
self.assertRaises(Exception, self.store.put, 'foo',
df1, table=True)
# check that can store Series of all of these types
self._check_roundtrip(df1['obj1'], tm.assert_series_equal)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal)
self._check_roundtrip(df1['int1'], tm.assert_series_equal)
# try with compression
self._check_roundtrip(df1['obj1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['int1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1, tm.assert_frame_equal,
compression=True)
def test_wide(self):
wp = tm.makePanel()
self._check_roundtrip(wp, tm.assert_panel_equal)
def test_wide_table(self):
wp = tm.makePanel()
self._check_roundtrip_table(wp, tm.assert_panel_equal)
def test_wide_table_dups(self):
wp = tm.makePanel()
try:
store = HDFStore(self.scratchpath)
store._quiet = True
store.put('panel', wp, table=True)
store.put('panel', wp, table=True, append=True)
recons = store['panel']
tm.assert_panel_equal(recons, wp)
finally:
store.close()
os.remove(self.scratchpath)
def test_long(self):
def _check(left, right):
tm.assert_panel_equal(left.to_wide(),
right.to_wide())
wp = tm.makePanel()
self._check_roundtrip(wp.to_long(), _check)
# empty
self.assertRaises(ValueError, self._check_roundtrip, wp.to_long()[:0],
_check)
def test_longpanel(self):
pass
def test_overwrite_node(self):
self.store['a'] = tm.makeTimeDataFrame()
ts = tm.makeTimeSeries()
self.store['a'] = ts
tm.assert_series_equal(self.store['a'], ts)
def test_panel_select(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = {
'field' : 'index',
'op' : '>=',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
result = self.store.select('wp', [crit1, crit2])
expected = wp.truncate(before=date).reindex(minor=['A', 'D'])
tm.assert_panel_equal(result, expected)
def test_frame_select(self):
df = tm.makeTimeDataFrame()
self.store.put('frame', df, table=True)
date = df.index[len(df) // 2]
crit1 = {
'field' : 'index',
'op' : '>=',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
crit3 = {
'field' : 'column',
'value' : 'A'
}
result = self.store.select('frame', [crit1, crit2])
expected = df.ix[date:, ['A', 'D']]
tm.assert_frame_equal(result, expected)
result = self.store.select('frame', [crit3])
expected = df.ix[:, ['A']]
tm.assert_frame_equal(result, expected)
# can't select if not written as table
self.store['frame'] = df
self.assertRaises(Exception, self.store.select,
'frame', [crit1, crit2])
def test_select_filter_corner(self):
df = DataFrame(np.random.randn(50, 100))
df.index = ['%.3d' % c for c in df.index]
df.columns = ['%.3d' % c for c in df.columns]
self.store.put('frame', df, table=True)
crit = {
'field' : 'column',
'value' : df.columns[:75]
}
result = self.store.select('frame', [crit])
tm.assert_frame_equal(result, df.ix[:, df.columns[:75]])
def _check_roundtrip(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj)
finally:
store.close()
os.remove(self.scratchpath)
def _check_roundtrip_table(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
sorted_obj = _test_sort(obj)
comparator(retrieved, sorted_obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_legacy_read(self):
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy.h5'), 'r')
store['a']
store['b']
store['c']
store['d']
store.close()
class TestHDFStore(unittest.TestCase):
path = '__test__.h5'
scratchpath = '__scratch__.h5'
def setUp(self):
self.store = HDFStore(self.path)
def tearDown(self):
self.store.close()
os.remove(self.path)
def test_factory_fun(self):
try:
with get_store(self.scratchpath) as tbl:
raise ValueError('blah')
except ValueError:
pass
with get_store(self.scratchpath) as tbl:
tbl['a'] = tm.makeDataFrame()
with get_store(self.scratchpath) as tbl:
self.assertEquals(len(tbl), 1)
self.assertEquals(type(tbl['a']), DataFrame)
os.remove(self.scratchpath)
def test_keys(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.store['foo/bar'] = tm.makePanel()
self.assertEquals(len(self.store), 5)
self.assert_(set(self.store.keys()) == set(['/a', '/b', '/c', '/d', '/foo/bar']))
def test_repr(self):
repr(self.store)
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.store['foo/bar'] = tm.makePanel()
self.store.append('e', tm.makePanel())
repr(self.store)
str(self.store)
def test_contains(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeDataFrame()
self.store['foo/bar'] = tm.makeDataFrame()
self.assert_('a' in self.store)
self.assert_('b' in self.store)
self.assert_('c' not in self.store)
self.assert_('foo/bar' in self.store)
self.assert_('/foo/bar' in self.store)
self.assert_('/foo/b' not in self.store)
self.assert_('bar' not in self.store)
def test_versioning(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeDataFrame()
df = tm.makeTimeDataFrame()
self.store.remove('df1')
self.store.append('df1', df[:10])
self.store.append('df1', df[10:])
self.assert_(self.store.root.a._v_attrs.pandas_version == '0.10')
self.assert_(self.store.root.b._v_attrs.pandas_version == '0.10')
self.assert_(self.store.root.df1._v_attrs.pandas_version == '0.10')
# write a file and wipe its versioning
self.store.remove('df2')
self.store.append('df2', df)
self.store.get_node('df2')._v_attrs.pandas_version = None
self.store.select('df2')
self.store.select('df2', [ Term('index','>',df.index[2]) ])
def test_meta(self):
raise nose.SkipTest('no meta')
meta = { 'foo' : [ 'I love pandas ' ] }
s = tm.makeTimeSeries()
s.meta = meta
self.store['a'] = s
self.assert_(self.store['a'].meta == meta)
df = tm.makeDataFrame()
df.meta = meta
self.store['b'] = df
self.assert_(self.store['b'].meta == meta)
# this should work, but because slicing doesn't propgate meta it doesn
self.store.remove('df1')
self.store.append('df1', df[:10])
self.store.append('df1', df[10:])
results = self.store['df1']
#self.assert_(getattr(results,'meta',None) == meta)
# no meta
df = tm.makeDataFrame()
self.store['b'] = df
self.assert_(hasattr(self.store['b'],'meta') == False)
def test_reopen_handle(self):
self.store['a'] = tm.makeTimeSeries()
self.store.open('w', warn=False)
self.assert_(self.store.handle.isopen)
self.assertEquals(len(self.store), 0)
def test_flush(self):
self.store['a'] = tm.makeTimeSeries()
self.store.flush()
def test_get(self):
self.store['a'] = tm.makeTimeSeries()
left = self.store.get('a')
right = self.store['a']
tm.assert_series_equal(left, right)
left = self.store.get('/a')
right = self.store['/a']
tm.assert_series_equal(left, right)
self.assertRaises(KeyError, self.store.get, 'b')
def test_put(self):
ts = tm.makeTimeSeries()
df = tm.makeTimeDataFrame()
self.store['a'] = ts
self.store['b'] = df[:10]
self.store['foo/bar/bah'] = df[:10]
self.store['foo'] = df[:10]
self.store['/foo'] = df[:10]
self.store.put('c', df[:10], table=True)
# not OK, not a table
self.assertRaises(ValueError, self.store.put, 'b', df[10:], append=True)
# node does not currently exist, test _is_table_type returns False in
# this case
self.assertRaises(ValueError, self.store.put, 'f', df[10:], append=True)
# OK
self.store.put('c', df[10:], append=True)
# overwrite table
self.store.put('c', df[:10], table=True, append=False)
tm.assert_frame_equal(df[:10], self.store['c'])
def test_put_string_index(self):
index = Index([ "I am a very long string index: %s" % i for i in range(20) ])
s = Series(np.arange(20), index = index)
df = DataFrame({ 'A' : s, 'B' : s })
self.store['a'] = s
tm.assert_series_equal(self.store['a'], s)
self.store['b'] = df
tm.assert_frame_equal(self.store['b'], df)
# mixed length
index = Index(['abcdefghijklmnopqrstuvwxyz1234567890'] + [ "I am a very long string index: %s" % i for i in range(20) ])
s = Series(np.arange(21), index = index)
df = DataFrame({ 'A' : s, 'B' : s })
self.store['a'] = s
tm.assert_series_equal(self.store['a'], s)
self.store['b'] = df
tm.assert_frame_equal(self.store['b'], df)
def test_put_compression(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df, table=True, compression='zlib')
tm.assert_frame_equal(self.store['c'], df)
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='zlib')
def test_put_compression_blosc(self):
tm.skip_if_no_package('tables', '2.2', app='blosc support')
df = tm.makeTimeDataFrame()
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='blosc')
self.store.put('c', df, table=True, compression='blosc')
tm.assert_frame_equal(self.store['c'], df)
def test_put_integer(self):
# non-date, non-string index
df = DataFrame(np.random.randn(50, 100))
self._check_roundtrip(df, tm.assert_frame_equal)
def test_append(self):
df = tm.makeTimeDataFrame()
self.store.remove('df1')
self.store.append('df1', df[:10])
self.store.append('df1', df[10:])
tm.assert_frame_equal(self.store['df1'], df)
self.store.remove('df2')
self.store.put('df2', df[:10], table=True)
self.store.append('df2', df[10:])
tm.assert_frame_equal(self.store['df2'], df)
self.store.remove('df3')
self.store.append('/df3', df[:10])
self.store.append('/df3', df[10:])
tm.assert_frame_equal(self.store['df3'], df)
# this is allowed by almost always don't want to do it
warnings.filterwarnings('ignore', category=tables.NaturalNameWarning)
self.store.remove('/df3 foo')
self.store.append('/df3 foo', df[:10])
self.store.append('/df3 foo', df[10:])
tm.assert_frame_equal(self.store['df3 foo'], df)
warnings.filterwarnings('always', category=tables.NaturalNameWarning)
# panel
wp = tm.makePanel()
self.store.remove('wp1')
self.store.append('wp1', wp.ix[:,:10,:])
self.store.append('wp1', wp.ix[:,10:,:])
tm.assert_panel_equal(self.store['wp1'], wp)
# ndim
p4d = tm.makePanel4D()
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:,:,:10,:])
self.store.append('p4d', p4d.ix[:,:,10:,:])
tm.assert_panel4d_equal(self.store['p4d'], p4d)
# test using axis labels
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:,:,:10,:], axes=['items','major_axis','minor_axis'])
self.store.append('p4d', p4d.ix[:,:,10:,:], axes=['items','major_axis','minor_axis'])
tm.assert_panel4d_equal(self.store['p4d'], p4d)
# test using differnt number of items on each axis
p4d2 = p4d.copy()
p4d2['l4'] = p4d['l1']
p4d2['l5'] = p4d['l1']
self.store.remove('p4d2')
self.store.append('p4d2', p4d2, axes=['items','major_axis','minor_axis'])
tm.assert_panel4d_equal(self.store['p4d2'], p4d2)
# test using differt order of items on the non-index axes
self.store.remove('wp1')
wp_append1 = wp.ix[:,:10,:]
self.store.append('wp1', wp_append1)
wp_append2 = wp.ix[:,10:,:].reindex(items = wp.items[::-1])
self.store.append('wp1', wp_append2)
tm.assert_panel_equal(self.store['wp1'], wp)
def test_append_frame_column_oriented(self):
# column oriented
df = tm.makeTimeDataFrame()
self.store.remove('df1')
self.store.append('df1', df.ix[:,:2], axes = ['columns'])
self.store.append('df1', df.ix[:,2:])
tm.assert_frame_equal(self.store['df1'], df)
result = self.store.select('df1', 'columns=A')
expected = df.reindex(columns=['A'])
tm.assert_frame_equal(expected, result)
# this isn't supported
self.assertRaises(Exception, self.store.select, 'df1', ('columns=A', Term('index','>',df.index[4])))
# selection on the non-indexable
result = self.store.select('df1', ('columns=A', Term('index','=',df.index[0:4])))
expected = df.reindex(columns=['A'],index=df.index[0:4])
tm.assert_frame_equal(expected, result)
def test_ndim_indexables(self):
""" test using ndim tables in new ways"""
p4d = tm.makePanel4D()
def check_indexers(key, indexers):
for i,idx in enumerate(indexers):
self.assert_(getattr(getattr(self.store.root,key).table.description,idx)._v_pos == i)
# append then change (will take existing schema)
indexers = ['items','major_axis','minor_axis']
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:,:,:10,:], axes=indexers)
self.store.append('p4d', p4d.ix[:,:,10:,:])
tm.assert_panel4d_equal(self.store.select('p4d'),p4d)
check_indexers('p4d',indexers)
# same as above, but try to append with differnt axes
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:,:,:10,:], axes=indexers)
self.store.append('p4d', p4d.ix[:,:,10:,:], axes=['labels','items','major_axis'])
tm.assert_panel4d_equal(self.store.select('p4d'),p4d)
check_indexers('p4d',indexers)
# pass incorrect number of axes
self.store.remove('p4d')
self.assertRaises(Exception, self.store.append, 'p4d', p4d.ix[:,:,:10,:], axes=['major_axis','minor_axis'])
# different than default indexables #1
indexers = ['labels','major_axis','minor_axis']
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:,:,:10,:], axes=indexers)
self.store.append('p4d', p4d.ix[:,:,10:,:])
tm.assert_panel4d_equal(self.store['p4d'], p4d)
check_indexers('p4d',indexers)
# different than default indexables #2
indexers = ['major_axis','labels','minor_axis']
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:,:,:10,:], axes=indexers)
self.store.append('p4d', p4d.ix[:,:,10:,:])
tm.assert_panel4d_equal(self.store['p4d'], p4d)
check_indexers('p4d',indexers)
# partial selection
result = self.store.select('p4d',['labels=l1'])
expected = p4d.reindex(labels = ['l1'])
tm.assert_panel4d_equal(result, expected)
# partial selection2
result = self.store.select('p4d',[Term('labels=l1'), Term('items=ItemA'), Term('minor_axis=B')])
expected = p4d.reindex(labels = ['l1'], items = ['ItemA'], minor_axis = ['B'])
tm.assert_panel4d_equal(result, expected)
# non-existant partial selection
result = self.store.select('p4d',[Term('labels=l1'), Term('items=Item1'), Term('minor_axis=B')])
expected = p4d.reindex(labels = ['l1'], items = [], minor_axis = ['B'])
tm.assert_panel4d_equal(result, expected)
def test_append_with_strings(self):
wp = tm.makePanel()
wp2 = wp.rename_axis(dict([ (x,"%s_extra" % x) for x in wp.minor_axis ]), axis = 2)
self.store.append('s1', wp, min_itemsize = 20)
self.store.append('s1', wp2)
expected = concat([ wp, wp2], axis = 2)
expected = expected.reindex(minor_axis = sorted(expected.minor_axis))
tm.assert_panel_equal(self.store['s1'], expected)
# test dict format
self.store.append('s2', wp, min_itemsize = { 'minor_axis' : 20 })
self.store.append('s2', wp2)
expected = concat([ wp, wp2], axis = 2)
expected = expected.reindex(minor_axis = sorted(expected.minor_axis))
tm.assert_panel_equal(self.store['s2'], expected)
# apply the wrong field (similar to #1)
self.store.append('s3', wp, min_itemsize = { 'major_axis' : 20 })
self.assertRaises(Exception, self.store.append, 's3')
# test truncation of bigger strings
self.store.append('s4', wp)
self.assertRaises(Exception, self.store.append, 's4', wp2)
# avoid truncation on elements
df = DataFrame([[123,'asdqwerty'], [345,'dggnhebbsdfbdfb']])
self.store.append('df_big',df, min_itemsize = { 'values' : 1024 })
tm.assert_frame_equal(self.store.select('df_big'), df)
# appending smaller string ok
df2 = DataFrame([[124,'asdqy'], [346,'dggnhefbdfb']])
self.store.append('df_big',df2)
expected = concat([ df, df2 ])
tm.assert_frame_equal(self.store.select('df_big'), expected)
# avoid truncation on elements
df = DataFrame([[123,'asdqwerty'], [345,'dggnhebbsdfbdfb']])
self.store.append('df_big2',df, min_itemsize = { 'values' : 10 })
tm.assert_frame_equal(self.store.select('df_big2'), df)
# bigger string on next append
self.store.append('df_new',df, min_itemsize = { 'values' : 16 })
df_new = DataFrame([[124,'abcdefqhij'], [346, 'abcdefghijklmnopqrtsuvwxyz']])
self.assertRaises(Exception, self.store.append, 'df_new',df_new)
def test_create_table_index(self):
wp = tm.makePanel()
self.store.append('p5', wp)
self.store.create_table_index('p5')
assert(self.store.handle.root.p5.table.cols.major_axis.is_indexed == True)
assert(self.store.handle.root.p5.table.cols.minor_axis.is_indexed == False)
# default optlevels
assert(self.store.handle.root.p5.table.cols.major_axis.index.optlevel == 6)
assert(self.store.handle.root.p5.table.cols.major_axis.index.kind == 'medium')
# let's change the indexing scheme
self.store.create_table_index('p5')
assert(self.store.handle.root.p5.table.cols.major_axis.index.optlevel == 6)
assert(self.store.handle.root.p5.table.cols.major_axis.index.kind == 'medium')
self.store.create_table_index('p5', optlevel=9)
assert(self.store.handle.root.p5.table.cols.major_axis.index.optlevel == 9)
assert(self.store.handle.root.p5.table.cols.major_axis.index.kind == 'medium')
self.store.create_table_index('p5', kind='full')
assert(self.store.handle.root.p5.table.cols.major_axis.index.optlevel == 9)
assert(self.store.handle.root.p5.table.cols.major_axis.index.kind == 'full')
self.store.create_table_index('p5', optlevel=1, kind='light')
assert(self.store.handle.root.p5.table.cols.major_axis.index.optlevel == 1)
assert(self.store.handle.root.p5.table.cols.major_axis.index.kind == 'light')
df = tm.makeTimeDataFrame()
self.store.append('f', df[:10])
self.store.append('f', df[10:])
self.store.create_table_index('f')
# try to index a non-table
self.store.put('f2', df)
self.assertRaises(Exception, self.store.create_table_index, 'f2')
# try to change the version supports flag
from pandas.io import pytables
pytables._table_supports_index = False
self.assertRaises(Exception, self.store.create_table_index, 'f')
# test out some versions
original = tables.__version__
for v in ['2.2','2.2b']:
pytables._table_mod = None
pytables._table_supports_index = False
tables.__version__ = v
self.assertRaises(Exception, self.store.create_table_index, 'f')
for v in ['2.3.1','2.3.1b','2.4dev','2.4',original]:
pytables._table_mod = None
pytables._table_supports_index = False
tables.__version__ = v
self.store.create_table_index('f')
pytables._table_mod = None
pytables._table_supports_index = False
tables.__version__ = original
def test_big_table(self):
raise nose.SkipTest('no big table')
# create and write a big table
wp = Panel(np.random.randn(20, 1000, 1000), items= [ 'Item%s' % i for i in xrange(20) ],
major_axis=date_range('1/1/2000', periods=1000), minor_axis = [ 'E%s' % i for i in xrange(1000) ])
wp.ix[:,100:200,300:400] = np.nan
try:
store = HDFStore(self.scratchpath)
store._debug_memory = True
store.append('wp',wp)
recons = store.select('wp')
finally:
store.close()
os.remove(self.scratchpath)
def test_append_diff_item_order(self):
raise nose.SkipTest('append diff item order')
wp = tm.makePanel()
wp1 = wp.ix[:, :10, :]
wp2 = wp.ix[['ItemC', 'ItemB', 'ItemA'], 10:, :]
self.store.put('panel', wp1, table=True)
self.assertRaises(Exception, self.store.put, 'panel', wp2,
append=True)
def test_table_index_incompatible_dtypes(self):
df1 = DataFrame({'a': [1, 2, 3]})
df2 = DataFrame({'a': [4, 5, 6]},
index=date_range('1/1/2000', periods=3))
self.store.put('frame', df1, table=True)
self.assertRaises(Exception, self.store.put, 'frame', df2,
table=True, append=True)
def test_table_values_dtypes_roundtrip(self):
df1 = DataFrame({'a': [1, 2, 3]}, dtype = 'f8')
self.store.append('df1', df1)
assert df1.dtypes == self.store['df1'].dtypes
df2 = DataFrame({'a': [1, 2, 3]}, dtype = 'i8')
self.store.append('df2', df2)
assert df2.dtypes == self.store['df2'].dtypes
# incompatible dtype
self.assertRaises(Exception, self.store.append, 'df2', df1)
def test_table_mixed_dtypes(self):
# frame
def _make_one_df():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['bool3'] = True
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one_df()
self.store.append('df1_mixed', df1)
tm.assert_frame_equal(self.store.select('df1_mixed'), df1)
# panel
def _make_one_panel():
wp = tm.makePanel()
wp['obj1'] = 'foo'
wp['obj2'] = 'bar'
wp['bool1'] = wp['ItemA'] > 0
wp['bool2'] = wp['ItemB'] > 0
wp['int1'] = 1
wp['int2'] = 2
return wp.consolidate()
p1 = _make_one_panel()
self.store.append('p1_mixed', p1)
tm.assert_panel_equal(self.store.select('p1_mixed'), p1)
# ndim
def _make_one_p4d():
wp = tm.makePanel4D()
wp['obj1'] = 'foo'
wp['obj2'] = 'bar'
wp['bool1'] = wp['l1'] > 0
wp['bool2'] = wp['l2'] > 0
wp['int1'] = 1
wp['int2'] = 2
return wp.consolidate()
p4d = _make_one_p4d()
self.store.append('p4d_mixed', p4d)
tm.assert_panel4d_equal(self.store.select('p4d_mixed'), p4d)
def test_remove(self):
ts = tm.makeTimeSeries()
df = tm.makeDataFrame()
self.store['a'] = ts
self.store['b'] = df
self.store.remove('a')
self.assertEquals(len(self.store), 1)
tm.assert_frame_equal(df, self.store['b'])
self.store.remove('b')
self.assertEquals(len(self.store), 0)
# pathing
self.store['a'] = ts
self.store['b/foo'] = df
self.store.remove('foo')
self.store.remove('b/foo')
self.assertEquals(len(self.store), 1)
self.store['a'] = ts
self.store['b/foo'] = df
self.store.remove('b')
self.assertEquals(len(self.store), 1)
# __delitem__
self.store['a'] = ts
self.store['b'] = df
del self.store['a']
del self.store['b']
self.assertEquals(len(self.store), 0)
def test_remove_where(self):
# non-existance
crit1 = Term('index','>','foo')
self.store.remove('a', where=[crit1])
# try to remove non-table (with crit)
# non-table ok (where = None)
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
self.store.remove('wp', [('minor_axis', ['A', 'D'])])
rs = self.store.select('wp')
expected = wp.reindex(minor_axis = ['B','C'])
tm.assert_panel_equal(rs,expected)
# empty where
self.store.remove('wp')
self.store.put('wp', wp, table=True)
# deleted number (entire table)
n = self.store.remove('wp', [])
assert(n == 120)
# non - empty where
self.store.remove('wp')
self.store.put('wp', wp, table=True)
self.assertRaises(Exception, self.store.remove,
'wp', ['foo'])
# selectin non-table with a where
#self.store.put('wp2', wp, table=False)
#self.assertRaises(Exception, self.store.remove,
# 'wp2', [('column', ['A', 'D'])])
def test_remove_crit(self):
wp = tm.makePanel()
# group row removal
date4 = wp.major_axis.take([ 0,1,2,4,5,6,8,9,10 ])
crit4 = Term('major_axis',date4)
self.store.put('wp3', wp, table=True)
n = self.store.remove('wp3', where=[crit4])
assert(n == 36)
result = self.store.select('wp3')
expected = wp.reindex(major_axis = wp.major_axis-date4)
tm.assert_panel_equal(result, expected)
# upper half
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = Term('major_axis','>',date)
crit2 = Term('minor_axis',['A', 'D'])
n = self.store.remove('wp', where=[crit1])
assert(n == 56)
n = self.store.remove('wp', where=[crit2])
assert(n == 32)
result = self.store['wp']
expected = wp.truncate(after=date).reindex(minor=['B', 'C'])
tm.assert_panel_equal(result, expected)
# individual row elements
self.store.put('wp2', wp, table=True)
date1 = wp.major_axis[1:3]
crit1 = Term('major_axis',date1)
self.store.remove('wp2', where=[crit1])
result = self.store.select('wp2')
expected = wp.reindex(major_axis=wp.major_axis-date1)
tm.assert_panel_equal(result, expected)
date2 = wp.major_axis[5]
crit2 = Term('major_axis',date2)
self.store.remove('wp2', where=[crit2])
result = self.store['wp2']
expected = wp.reindex(major_axis=wp.major_axis-date1-Index([date2]))
tm.assert_panel_equal(result, expected)
date3 = [wp.major_axis[7],wp.major_axis[9]]
crit3 = Term('major_axis',date3)
self.store.remove('wp2', where=[crit3])
result = self.store['wp2']
expected = wp.reindex(major_axis=wp.major_axis-date1-Index([date2])-Index(date3))
tm.assert_panel_equal(result, expected)
# corners
self.store.put('wp4', wp, table=True)
n = self.store.remove('wp4', where=[Term('major_axis','>',wp.major_axis[-1])])
result = self.store.select('wp4')
tm.assert_panel_equal(result, wp)
def test_terms(self):
wp = tm.makePanel()
p4d = tm.makePanel4D()
self.store.put('wp', wp, table=True)
self.store.put('p4d', p4d, table=True)
# some invalid terms
terms = [
[ 'minor', ['A','B'] ],
[ 'index', ['20121114'] ],
[ 'index', ['20121114', '20121114'] ],
]
for t in terms:
self.assertRaises(Exception, self.store.select, 'wp', t)
self.assertRaises(Exception, Term.__init__)
self.assertRaises(Exception, Term.__init__, 'blah')
self.assertRaises(Exception, Term.__init__, 'index')
self.assertRaises(Exception, Term.__init__, 'index', '==')
self.assertRaises(Exception, Term.__init__, 'index', '>', 5)
# panel
result = self.store.select('wp',[ Term('major_axis<20000108'), Term('minor_axis', '=', ['A','B']) ])
expected = wp.truncate(after='20000108').reindex(minor=['A', 'B'])
tm.assert_panel_equal(result, expected)
# p4d
result = self.store.select('p4d',[ Term('major_axis<20000108'), Term('minor_axis', '=', ['A','B']), Term('items', '=', ['ItemA','ItemB']) ])
expected = p4d.truncate(after='20000108').reindex(minor=['A', 'B'],items=['ItemA','ItemB'])
tm.assert_panel4d_equal(result, expected)
# valid terms
terms = [
dict(field = 'major_axis', op = '>', value = '20121114'),
('major_axis', '20121114'),
('major_axis', '>', '20121114'),
(('major_axis', ['20121114','20121114']),),
('major_axis', datetime(2012,11,14)),
'major_axis>20121114',
'major_axis>20121114',
'major_axis>20121114',
(('minor_axis', ['A','B']),),
(('minor_axis', ['A','B']),),
((('minor_axis', ['A','B']),),),
(('items', ['ItemA','ItemB']),),
('items=ItemA'),
]
for t in terms:
self.store.select('wp', t)
self.store.select('p4d', t)
# valid for p4d only
terms = [
(('labels', '=', ['l1','l2']),),
Term('labels', '=', ['l1','l2']),
]
for t in terms:
self.store.select('p4d', t)
def test_series(self):
s = tm.makeStringSeries()
self._check_roundtrip(s, tm.assert_series_equal)
ts = tm.makeTimeSeries()
self._check_roundtrip(ts, tm.assert_series_equal)
ts2 = Series(ts.index, Index(ts.index, dtype=object))
self._check_roundtrip(ts2, tm.assert_series_equal)
ts3 = Series(ts.values, Index(np.asarray(ts.index, dtype=object),
dtype=object))
self._check_roundtrip(ts3, tm.assert_series_equal)
def test_sparse_series(self):
s = tm.makeStringSeries()
s[3:5] = np.nan
ss = s.to_sparse()
self._check_roundtrip(ss, tm.assert_series_equal,
check_series_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_roundtrip(ss2, tm.assert_series_equal,
check_series_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_roundtrip(ss3, tm.assert_series_equal,
check_series_type=True)
def test_sparse_frame(self):
s = tm.makeDataFrame()
s.ix[3:5, 1:3] = np.nan
s.ix[8:10, -2] = np.nan
ss = s.to_sparse()
self._check_double_roundtrip(ss, tm.assert_frame_equal,
check_frame_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_double_roundtrip(ss2, tm.assert_frame_equal,
check_frame_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_double_roundtrip(ss3, tm.assert_frame_equal,
check_frame_type=True)
def test_sparse_panel(self):
items = ['x', 'y', 'z']
p = Panel(dict((i, tm.makeDataFrame().ix[:2, :2]) for i in items))
sp = p.to_sparse()
self._check_double_roundtrip(sp, tm.assert_panel_equal,
check_panel_type=True)
sp2 = p.to_sparse(kind='integer')
self._check_double_roundtrip(sp2, tm.assert_panel_equal,
check_panel_type=True)
sp3 = p.to_sparse(fill_value=0)
self._check_double_roundtrip(sp3, tm.assert_panel_equal,
check_panel_type=True)
def test_float_index(self):
# GH #454
index = np.random.randn(10)
s = Series(np.random.randn(10), index=index)
self._check_roundtrip(s, tm.assert_series_equal)
def test_tuple_index(self):
# GH #492
col = np.arange(10)
idx = [(0.,1.), (2., 3.), (4., 5.)]
data = np.random.randn(30).reshape((3, 10))
DF = DataFrame(data, index=idx, columns=col)
self._check_roundtrip(DF, tm.assert_frame_equal)
def test_index_types(self):
values = np.random.randn(2)
func = lambda l, r : tm.assert_series_equal(l, r, True, True, True)
ser = Series(values, [0, 'y'])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime.today(), 0])
self._check_roundtrip(ser, func)
ser = Series(values, ['y', 0])
self._check_roundtrip(ser, func)
from datetime import date
ser = Series(values, [date.today(), 'a'])
self._check_roundtrip(ser, func)
ser = Series(values, [1.23, 'b'])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 1.53])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 5])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime(2012, 1, 1), datetime(2012, 1, 2)])
self._check_roundtrip(ser, func)
def test_timeseries_preepoch(self):
if sys.version_info[0] == 2 and sys.version_info[1] < 7:
raise nose.SkipTest
dr = bdate_range('1/1/1940', '1/1/1960')
ts = Series(np.random.randn(len(dr)), index=dr)
try:
self._check_roundtrip(ts, tm.assert_series_equal)
except OverflowError:
raise nose.SkipTest('known failer on some windows platforms')
def test_frame(self):
df = tm.makeDataFrame()
# put in some random NAs
df.values[0, 0] = np.nan
df.values[5, 3] = np.nan
self._check_roundtrip_table(df, tm.assert_frame_equal)
self._check_roundtrip(df, tm.assert_frame_equal)
self._check_roundtrip_table(df, tm.assert_frame_equal,
compression=True)
self._check_roundtrip(df, tm.assert_frame_equal,
compression=True)
tdf = tm.makeTimeDataFrame()
self._check_roundtrip(tdf, tm.assert_frame_equal)
self._check_roundtrip(tdf, tm.assert_frame_equal,
compression=True)
# not consolidated
df['foo'] = np.random.randn(len(df))
self.store['df'] = df
recons = self.store['df']
self.assert_(recons._data.is_consolidated())
# empty
self._check_roundtrip(df[:0], tm.assert_frame_equal)
def test_empty_series_frame(self):
s0 = Series()
s1 = Series(name='myseries')
df0 = DataFrame()
df1 = DataFrame(index=['a', 'b', 'c'])
df2 = DataFrame(columns=['d', 'e', 'f'])
self._check_roundtrip(s0, tm.assert_series_equal)
self._check_roundtrip(s1, tm.assert_series_equal)
self._check_roundtrip(df0, tm.assert_frame_equal)
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
def test_can_serialize_dates(self):
rng = [x.date() for x in bdate_range('1/1/2000', '1/30/2000')]
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
self._check_roundtrip(frame, tm.assert_frame_equal)
def test_timezones(self):
rng = date_range('1/1/2000', '1/30/2000', tz='US/Eastern')
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
self.assert_(recons.index.equals(rng))
self.assertEquals(rng.tz, recons.index.tz)
finally:
store.close()
os.remove(self.scratchpath)
def test_fixed_offset_tz(self):
rng = date_range('1/1/2000 00:00:00-07:00', '1/30/2000 00:00:00-07:00')
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
self.assert_(recons.index.equals(rng))
self.assertEquals(rng.tz, recons.index.tz)
finally:
store.close()
os.remove(self.scratchpath)
def test_store_hierarchical(self):
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo', 'bar'])
frame = DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
self._check_roundtrip(frame, tm.assert_frame_equal)
self._check_roundtrip(frame.T, tm.assert_frame_equal)
self._check_roundtrip(frame['A'], tm.assert_series_equal)
# check that the names are stored
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
assert(recons.index.names == ['foo', 'bar'])
finally:
store.close()
os.remove(self.scratchpath)
def test_store_index_name(self):
df = tm.makeDataFrame()
df.index.name = 'foo'
try:
store = HDFStore(self.scratchpath)
store['frame'] = df
recons = store['frame']
assert(recons.index.name == 'foo')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_series_name(self):
df = tm.makeDataFrame()
series = df['A']
try:
store = HDFStore(self.scratchpath)
store['series'] = series
recons = store['series']
assert(recons.name == 'A')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_mixed(self):
def _make_one():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one()
df2 = _make_one()
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
self.store['obj'] = df1
tm.assert_frame_equal(self.store['obj'], df1)
self.store['obj'] = df2
tm.assert_frame_equal(self.store['obj'], df2)
# check that can store Series of all of these types
self._check_roundtrip(df1['obj1'], tm.assert_series_equal)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal)
self._check_roundtrip(df1['int1'], tm.assert_series_equal)
# try with compression
self._check_roundtrip(df1['obj1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['int1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1, tm.assert_frame_equal,
compression=True)
def test_wide(self):
wp = tm.makePanel()
self._check_roundtrip(wp, tm.assert_panel_equal)
def test_wide_table(self):
wp = tm.makePanel()
self._check_roundtrip_table(wp, tm.assert_panel_equal)
def test_wide_table_dups(self):
wp = tm.makePanel()
try:
store = HDFStore(self.scratchpath)
store._quiet = True
store.put('panel', wp, table=True)
store.put('panel', wp, table=True, append=True)
recons = store['panel']
tm.assert_panel_equal(recons, wp)
finally:
store.close()
os.remove(self.scratchpath)
def test_long(self):
def _check(left, right):
tm.assert_panel_equal(left.to_panel(), right.to_panel())
wp = tm.makePanel()
self._check_roundtrip(wp.to_frame(), _check)
# empty
# self._check_roundtrip(wp.to_frame()[:0], _check)
def test_longpanel(self):
pass
def test_overwrite_node(self):
self.store['a'] = tm.makeTimeDataFrame()
ts = tm.makeTimeSeries()
self.store['a'] = ts
tm.assert_series_equal(self.store['a'], ts)
def test_select(self):
wp = tm.makePanel()
# put/select ok
self.store.remove('wp')
self.store.put('wp', wp, table=True)
self.store.select('wp')
# non-table ok (where = None)
self.store.remove('wp')
self.store.put('wp2', wp, table=False)
self.store.select('wp2')
# selection on the non-indexable with a large number of columns
wp = Panel(np.random.randn(100, 100, 100), items = [ 'Item%03d' % i for i in xrange(100) ],
major_axis=date_range('1/1/2000', periods=100), minor_axis = [ 'E%03d' % i for i in xrange(100) ])
self.store.remove('wp')
self.store.append('wp', wp)
items = [ 'Item%03d' % i for i in xrange(80) ]
result = self.store.select('wp', Term('items', items))
expected = wp.reindex(items = items)
tm.assert_panel_equal(expected, result)
# selectin non-table with a where
#self.assertRaises(Exception, self.store.select,
# 'wp2', ('column', ['A', 'D']))
def test_panel_select(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = ('major_axis','>=',date)
crit2 = ('minor_axis', '=', ['A', 'D'])
result = self.store.select('wp', [crit1, crit2])
expected = wp.truncate(before=date).reindex(minor=['A', 'D'])
tm.assert_panel_equal(result, expected)
result = self.store.select('wp', [ 'major_axis>=20000124', ('minor_axis', '=', ['A','B']) ])
expected = wp.truncate(before='20000124').reindex(minor=['A', 'B'])
tm.assert_panel_equal(result, expected)
def test_frame_select(self):
df = tm.makeTimeDataFrame()
self.store.put('frame', df, table=True)
date = df.index[len(df) // 2]
crit1 = ('index','>=',date)
crit2 = ('columns',['A', 'D'])
crit3 = ('columns','A')
result = self.store.select('frame', [crit1, crit2])
expected = df.ix[date:, ['A', 'D']]
tm.assert_frame_equal(result, expected)
result = self.store.select('frame', [crit3])
expected = df.ix[:, ['A']]
tm.assert_frame_equal(result, expected)
# other indicies for a frame
# integer
df = DataFrame(dict(A = np.random.rand(20), B = np.random.rand(20)))
self.store.append('df_int', df)
self.store.select('df_int', [ Term("index<10"), Term("columns", "=", ["A"]) ])
df = DataFrame(dict(A = np.random.rand(20), B = np.random.rand(20), index = np.arange(20,dtype='f8')))
self.store.append('df_float', df)
self.store.select('df_float', [ Term("index<10.0"), Term("columns", "=", ["A"]) ])
# can't select if not written as table
#self.store['frame'] = df
#self.assertRaises(Exception, self.store.select,
# 'frame', [crit1, crit2])
def test_select_filter_corner(self):
df = DataFrame(np.random.randn(50, 100))
df.index = ['%.3d' % c for c in df.index]
df.columns = ['%.3d' % c for c in df.columns]
self.store.put('frame', df, table=True)
crit = Term('columns', df.columns[:75])
result = self.store.select('frame', [crit])
tm.assert_frame_equal(result, df.ix[:, df.columns[:75]])
def _check_roundtrip(self, obj, comparator, compression=False, **kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_double_roundtrip(self, obj, comparator, compression=False,
**kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
store['obj'] = retrieved
again = store['obj']
comparator(again, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_roundtrip_table(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
# sorted_obj = _test_sort(obj)
comparator(retrieved, obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_legacy_read(self):
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy.h5'), 'r')
store['a']
store['b']
store['c']
store['d']
store.close()
def test_legacy_table_read(self):
# legacy table types
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy_table.h5'), 'r')
store.select('df1')
store.select('df2')
store.select('wp1')
# force the frame
store.select('df2', typ = 'legacy_frame')
# old version (this still throws an exception though)
import warnings
warnings.filterwarnings('ignore', category=IncompatibilityWarning)
self.assertRaises(Exception, store.select, 'wp1', Term('minor_axis','=','B'))
warnings.filterwarnings('always', category=IncompatibilityWarning)
store.close()
def test_legacy_table_write(self):
# legacy table types
pth = curpath()
df = tm.makeDataFrame()
wp = tm.makePanel()
store = HDFStore(os.path.join(pth, 'legacy_table.h5'), 'a')
self.assertRaises(Exception, store.append, 'df1', df)
self.assertRaises(Exception, store.append, 'wp1', wp)
store.close()
def test_store_datetime_fractional_secs(self):
dt = datetime(2012, 1, 2, 3, 4, 5, 123456)
series = Series([0], [dt])
self.store['a'] = series
self.assertEquals(self.store['a'].index[0], dt)
def test_tseries_indices_series(self):
idx = tm.makeDateIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
idx = tm.makePeriodIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
def test_tseries_indices_frame(self):
idx = tm.makeDateIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
idx = tm.makePeriodIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
def test_unicode_index(self):
unicode_values = [u'\u03c3', u'\u03c3\u03c3']
s = Series(np.random.randn(len(unicode_values)), unicode_values)
self._check_roundtrip(s, tm.assert_series_equal)
def test_store_datetime_mixed(self):
df = DataFrame({'a': [1,2,3], 'b': [1.,2.,3.], 'c': ['a', 'b', 'c']})
ts = tm.makeTimeSeries()
df['d'] = ts.index[:3]
self._check_roundtrip(df, tm.assert_frame_equal)
def test_cant_write_multiindex_table(self):
# for now, #1848
df = DataFrame(np.random.randn(10, 4),
index=[np.arange(5).repeat(2),
np.tile(np.arange(2), 5)])
self.assertRaises(Exception, self.store.put, 'foo', df, table=True)
class TesttHDFStore(unittest.TestCase):
path = '__test__.h5'
scratchpath = '__scratch__.h5'
def setUp(self):
self.store = HDFStore(self.path)
def tearDown(self):
self.store.close()
os.remove(self.path)
def test_len(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makeWidePanel()
self.assertEquals(len(self.store), 4)
def test_repr(self):
repr(self.store)
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makeWidePanel()
repr(self.store)
def test_reopen_handle(self):
self.store['a'] = tm.makeTimeSeries()
self.store.open('w', warn=False)
self.assert_(self.store.handle.isopen)
self.assertEquals(len(self.store), 0)
def test_flush(self):
self.store['a'] = tm.makeTimeSeries()
self.store.flush()
def test_get(self):
self.store['a'] = tm.makeTimeSeries()
left = self.store.get('a')
right = self.store['a']
tm.assert_series_equal(left, right)
self.assertRaises(AttributeError, self.store.get, 'b')
def test_put(self):
ts = tm.makeTimeSeries()
df = tm.makeTimeDataFrame()
self.store['a'] = ts
self.store['b'] = df[:10]
self.store.put('c', df[:10], table=True)
# not OK, not a table
self.assertRaises(ValueError, self.store.put, 'b', df[10:], append=True)
# node does not currently exist, test _is_table_type returns False in
# this case
self.assertRaises(ValueError, self.store.put, 'f', df[10:], append=True)
# OK
self.store.put('c', df[10:], append=True)
# overwrite table
self.store.put('c', df[:10], table=True, append=False)
tm.assert_frame_equal(df[:10], self.store['c'])
def test_put_compression(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df, table=True, compression='blosc')
tm.assert_frame_equal(self.store['c'], df)
self.store.put('c', df, table=True, compression='zlib')
tm.assert_frame_equal(self.store['c'], df)
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='blosc')
def test_put_integer(self):
# non-date, non-string index
df = DataFrame(np.random.randn(50, 100))
self._check_roundtrip(df, tm.assert_frame_equal)
def test_append(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df[:10], table=True)
self.store.append('c', df[10:])
tm.assert_frame_equal(self.store['c'], df)
def test_remove(self):
ts = tm.makeTimeSeries()
df = tm.makeDataFrame()
self.store['a'] = ts
self.store['b'] = df
self.store.remove('a')
self.assertEquals(len(self.store), 1)
tm.assert_frame_equal(df, self.store['b'])
self.store.remove('b')
self.assertEquals(len(self.store), 0)
def test_remove_crit(self):
wp = tm.makeWidePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = {
'field' : 'index',
'op' : '>',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
self.store.remove('wp', where=[crit1])
self.store.remove('wp', where=[crit2])
result = self.store['wp']
expected = wp.truncate(after=date).reindex(minor=['B', 'C'])
tm.assert_panel_equal(result, expected)
def test_series(self):
s = tm.makeStringSeries()
self._check_roundtrip(s, tm.assert_series_equal)
ts = tm.makeTimeSeries()
self._check_roundtrip(ts, tm.assert_series_equal)
def test_timeseries_preepoch(self):
if sys.version_info[0] == 2 and sys.version_info[1] < 7:
raise nose.SkipTest
dr = DateRange('1/1/1940', '1/1/1960')
ts = Series(np.random.randn(len(dr)), index=dr)
self._check_roundtrip(ts, tm.assert_series_equal)
def test_frame(self):
df = tm.makeDataFrame()
# put in some random NAs
df.values[0, 0] = np.nan
df.values[5, 3] = np.nan
self._check_roundtrip_table(df, tm.assert_frame_equal)
self._check_roundtrip(df, tm.assert_frame_equal)
tdf = tm.makeTimeDataFrame()
self._check_roundtrip(tdf, tm.assert_frame_equal)
def test_store_mixed(self):
def _make_one():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one()
df2 = _make_one()
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
self.store['obj'] = df1
tm.assert_frame_equal(self.store['obj'], df1)
self.store['obj'] = df2
tm.assert_frame_equal(self.store['obj'], df2)
# storing in Table not yet supported
self.assertRaises(Exception, self.store.put, 'foo',
df1, table=True)
# check that can store Series of all of these types
self._check_roundtrip(df1['obj1'], tm.assert_series_equal)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal)
self._check_roundtrip(df1['int1'], tm.assert_series_equal)
def test_wide(self):
wp = tm.makeWidePanel()
self._check_roundtrip(wp, tm.assert_panel_equal)
def test_wide_table(self):
wp = tm.makeWidePanel()
self._check_roundtrip_table(wp, tm.assert_panel_equal)
def test_long(self):
def _check(left, right):
tm.assert_panel_equal(left.to_wide(),
right.to_wide())
wp = tm.makeWidePanel()
self._check_roundtrip(wp.to_long(), _check)
def test_longpanel(self):
pass
def test_overwrite_node(self):
self.store['a'] = tm.makeTimeDataFrame()
ts = tm.makeTimeSeries()
self.store['a'] = ts
tm.assert_series_equal(self.store['a'], ts)
def test_panel_select(self):
wp = tm.makeWidePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = {
'field' : 'index',
'op' : '>=',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
result = self.store.select('wp', [crit1, crit2])
expected = wp.truncate(before=date).reindex(minor=['A', 'D'])
tm.assert_panel_equal(result, expected)
def test_frame_select(self):
df = tm.makeTimeDataFrame()
self.store.put('frame', df, table=True)
date = df.index[len(df) // 2]
crit1 = {
'field' : 'index',
'op' : '>=',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
crit3 = {
'field' : 'column',
'value' : 'A'
}
result = self.store.select('frame', [crit1, crit2])
expected = df.ix[date:, ['A', 'D']]
tm.assert_frame_equal(result, expected)
result = self.store.select('frame', [crit3])
expected = df.ix[:, ['A']]
tm.assert_frame_equal(result, expected)
# can't select if not written as table
self.store['frame'] = df
self.assertRaises(Exception, self.store.select,
'frame', [crit1, crit2])
def test_select_filter_corner(self):
df = DataFrame(np.random.randn(50, 100))
df.index = ['%.3d' % c for c in df.index]
df.columns = ['%.3d' % c for c in df.columns]
self.store.put('frame', df, table=True)
crit = {
'field' : 'column',
'value' : df.columns[:75]
}
result = self.store.select('frame', [crit])
tm.assert_frame_equal(result, df.ix[:, df.columns[:75]])
def _check_roundtrip(self, obj, comparator):
store = HDFStore(self.scratchpath, 'w')
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj)
finally:
store.close()
os.remove(self.scratchpath)
def _check_roundtrip_table(self, obj, comparator):
store = HDFStore(self.scratchpath, 'w')
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
sorted_obj = _test_sort(obj)
comparator(retrieved, sorted_obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_legacy_read(self):
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy.h5'), 'r')
store['a']
store['b']
store['c']
store['d']
store.close()
class TestHDFStore(unittest.TestCase):
path = '__test__.h5'
scratchpath = '__scratch__.h5'
def setUp(self):
self.store = HDFStore(self.path)
def tearDown(self):
self.store.close()
os.remove(self.path)
def test_factory_fun(self):
try:
with get_store(self.scratchpath) as tbl:
raise ValueError('blah')
except ValueError:
pass
with get_store(self.scratchpath) as tbl:
tbl['a'] = tm.makeDataFrame()
with get_store(self.scratchpath) as tbl:
self.assertEquals(len(tbl), 1)
self.assertEquals(type(tbl['a']), DataFrame)
os.remove(self.scratchpath)
def test_keys(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.store['foo/bar'] = tm.makePanel()
self.assertEquals(len(self.store), 5)
self.assert_(
set(self.store.keys()) == set(['/a', '/b', '/c', '/d', '/foo/bar'
]))
def test_repr(self):
repr(self.store)
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.store['foo/bar'] = tm.makePanel()
self.store.append('e', tm.makePanel())
repr(self.store)
str(self.store)
def test_contains(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeDataFrame()
self.store['foo/bar'] = tm.makeDataFrame()
self.assert_('a' in self.store)
self.assert_('b' in self.store)
self.assert_('c' not in self.store)
self.assert_('foo/bar' in self.store)
self.assert_('/foo/bar' in self.store)
self.assert_('/foo/b' not in self.store)
self.assert_('bar' not in self.store)
def test_reopen_handle(self):
self.store['a'] = tm.makeTimeSeries()
self.store.open('w', warn=False)
self.assert_(self.store.handle.isopen)
self.assertEquals(len(self.store), 0)
def test_flush(self):
self.store['a'] = tm.makeTimeSeries()
self.store.flush()
def test_get(self):
self.store['a'] = tm.makeTimeSeries()
left = self.store.get('a')
right = self.store['a']
tm.assert_series_equal(left, right)
left = self.store.get('/a')
right = self.store['/a']
tm.assert_series_equal(left, right)
self.assertRaises(KeyError, self.store.get, 'b')
def test_put(self):
ts = tm.makeTimeSeries()
df = tm.makeTimeDataFrame()
self.store['a'] = ts
self.store['b'] = df[:10]
self.store['foo/bar/bah'] = df[:10]
self.store['foo'] = df[:10]
self.store['/foo'] = df[:10]
self.store.put('c', df[:10], table=True)
# not OK, not a table
self.assertRaises(ValueError,
self.store.put,
'b',
df[10:],
append=True)
# node does not currently exist, test _is_table_type returns False in
# this case
self.assertRaises(ValueError,
self.store.put,
'f',
df[10:],
append=True)
# OK
self.store.put('c', df[10:], append=True)
# overwrite table
self.store.put('c', df[:10], table=True, append=False)
tm.assert_frame_equal(df[:10], self.store['c'])
def test_put_compression(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df, table=True, compression='zlib')
tm.assert_frame_equal(self.store['c'], df)
# can't compress if table=False
self.assertRaises(ValueError,
self.store.put,
'b',
df,
table=False,
compression='zlib')
def test_put_compression_blosc(self):
tm.skip_if_no_package('tables', '2.2', app='blosc support')
df = tm.makeTimeDataFrame()
# can't compress if table=False
self.assertRaises(ValueError,
self.store.put,
'b',
df,
table=False,
compression='blosc')
self.store.put('c', df, table=True, compression='blosc')
tm.assert_frame_equal(self.store['c'], df)
def test_put_integer(self):
# non-date, non-string index
df = DataFrame(np.random.randn(50, 100))
self._check_roundtrip(df, tm.assert_frame_equal)
def test_append(self):
pth = '__test_append__.h5'
try:
store = HDFStore(pth)
df = tm.makeTimeDataFrame()
store.append('df1', df[:10])
store.append('df1', df[10:])
tm.assert_frame_equal(store['df1'], df)
store.put('df2', df[:10], table=True)
store.append('df2', df[10:])
tm.assert_frame_equal(store['df2'], df)
store.append('/df3', df[:10])
store.append('/df3', df[10:])
tm.assert_frame_equal(store['df3'], df)
# this is allowed by almost always don't want to do it
import warnings
import tables
warnings.filterwarnings('ignore',
category=tables.NaturalNameWarning)
store.append('/df3 foo', df[:10])
store.append('/df3 foo', df[10:])
tm.assert_frame_equal(store['df3 foo'], df)
warnings.filterwarnings('always',
category=tables.NaturalNameWarning)
wp = tm.makePanel()
store.append('wp1', wp.ix[:, :10, :])
store.append('wp1', wp.ix[:, 10:, :])
tm.assert_panel_equal(store['wp1'], wp)
except:
raise
finally:
store.close()
os.remove(pth)
def test_append_with_strings(self):
wp = tm.makePanel()
wp2 = wp.rename_axis(dict([(x, "%s_extra" % x)
for x in wp.minor_axis]),
axis=2)
self.store.append('s1', wp, min_itemsize=20)
self.store.append('s1', wp2)
expected = concat([wp, wp2], axis=2)
expected = expected.reindex(minor_axis=sorted(expected.minor_axis))
tm.assert_panel_equal(self.store['s1'], expected)
# test dict format
self.store.append('s2', wp, min_itemsize={'column': 20})
self.store.append('s2', wp2)
expected = concat([wp, wp2], axis=2)
expected = expected.reindex(minor_axis=sorted(expected.minor_axis))
tm.assert_panel_equal(self.store['s2'], expected)
# apply the wrong field (similar to #1)
self.store.append('s3', wp, min_itemsize={'index': 20})
self.assertRaises(Exception, self.store.append, 's3')
# test truncation of bigger strings
self.store.append('s4', wp)
self.assertRaises(Exception, self.store.append, 's4', wp2)
def test_create_table_index(self):
wp = tm.makePanel()
self.store.append('p5', wp)
self.store.create_table_index('p5')
assert (self.store.handle.root.p5.table.cols.index.is_indexed == True)
assert (
self.store.handle.root.p5.table.cols.column.is_indexed == False)
df = tm.makeTimeDataFrame()
self.store.append('f', df[:10])
self.store.append('f', df[10:])
self.store.create_table_index('f')
# create twice
self.store.create_table_index('f')
# try to index a non-table
self.store.put('f2', df)
self.assertRaises(Exception, self.store.create_table_index, 'f2')
# try to change the version supports flag
from pandas.io import pytables
pytables._table_supports_index = False
self.assertRaises(Exception, self.store.create_table_index, 'f')
def test_append_diff_item_order(self):
wp = tm.makePanel()
wp1 = wp.ix[:, :10, :]
wp2 = wp.ix[['ItemC', 'ItemB', 'ItemA'], 10:, :]
self.store.put('panel', wp1, table=True)
self.assertRaises(Exception, self.store.put, 'panel', wp2, append=True)
def test_table_index_incompatible_dtypes(self):
df1 = DataFrame({'a': [1, 2, 3]})
df2 = DataFrame({'a': [4, 5, 6]},
index=date_range('1/1/2000', periods=3))
self.store.put('frame', df1, table=True)
self.assertRaises(Exception,
self.store.put,
'frame',
df2,
table=True,
append=True)
def test_table_values_dtypes_roundtrip(self):
df1 = DataFrame({'a': [1, 2, 3]}, dtype='f8')
self.store.append('df1', df1)
assert df1.dtypes == self.store['df1'].dtypes
df2 = DataFrame({'a': [1, 2, 3]}, dtype='i8')
self.store.append('df2', df2)
assert df2.dtypes == self.store['df2'].dtypes
# incompatible dtype
self.assertRaises(Exception, self.store.append, 'df2', df1)
def test_table_mixed_dtypes(self):
# frame
def _make_one_df():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['bool3'] = True
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one_df()
self.store.append('df1_mixed', df1)
tm.assert_frame_equal(self.store.select('df1_mixed'), df1)
# panel
def _make_one_panel():
wp = tm.makePanel()
wp['obj1'] = 'foo'
wp['obj2'] = 'bar'
wp['bool1'] = wp['ItemA'] > 0
wp['bool2'] = wp['ItemB'] > 0
wp['int1'] = 1
wp['int2'] = 2
return wp.consolidate()
p1 = _make_one_panel()
self.store.append('p1_mixed', p1)
tm.assert_panel_equal(self.store.select('p1_mixed'), p1)
def test_remove(self):
ts = tm.makeTimeSeries()
df = tm.makeDataFrame()
self.store['a'] = ts
self.store['b'] = df
self.store.remove('a')
self.assertEquals(len(self.store), 1)
tm.assert_frame_equal(df, self.store['b'])
self.store.remove('b')
self.assertEquals(len(self.store), 0)
# pathing
self.store['a'] = ts
self.store['b/foo'] = df
self.store.remove('foo')
self.store.remove('b/foo')
self.assertEquals(len(self.store), 1)
self.store['a'] = ts
self.store['b/foo'] = df
self.store.remove('b')
self.assertEquals(len(self.store), 1)
# __delitem__
self.store['a'] = ts
self.store['b'] = df
del self.store['a']
del self.store['b']
self.assertEquals(len(self.store), 0)
def test_remove_where(self):
# non-existance
crit1 = Term('index', '>', 'foo')
self.store.remove('a', where=[crit1])
# try to remove non-table (with crit)
# non-table ok (where = None)
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
self.store.remove('wp', [('column', ['A', 'D'])])
rs = self.store.select('wp')
expected = wp.reindex(minor_axis=['B', 'C'])
tm.assert_panel_equal(rs, expected)
# empty where
self.store.remove('wp')
self.store.put('wp', wp, table=True)
self.store.remove('wp', [])
# non - empty where
self.store.remove('wp')
self.store.put('wp', wp, table=True)
self.assertRaises(Exception, self.store.remove, 'wp', ['foo'])
# selectin non-table with a where
self.store.put('wp2', wp, table=False)
self.assertRaises(Exception, self.store.remove, 'wp2',
[('column', ['A', 'D'])])
def test_remove_crit(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = Term('index', '>', date)
crit2 = Term('column', ['A', 'D'])
self.store.remove('wp', where=[crit1])
self.store.remove('wp', where=[crit2])
result = self.store['wp']
expected = wp.truncate(after=date).reindex(minor=['B', 'C'])
tm.assert_panel_equal(result, expected)
# test non-consecutive row removal
wp = tm.makePanel()
self.store.put('wp2', wp, table=True)
date1 = wp.major_axis[1:3]
date2 = wp.major_axis[5]
date3 = [wp.major_axis[7], wp.major_axis[9]]
crit1 = Term('index', date1)
crit2 = Term('index', date2)
crit3 = Term('index', date3)
self.store.remove('wp2', where=[crit1])
self.store.remove('wp2', where=[crit2])
self.store.remove('wp2', where=[crit3])
result = self.store['wp2']
ma = list(wp.major_axis)
for d in date1:
ma.remove(d)
ma.remove(date2)
for d in date3:
ma.remove(d)
expected = wp.reindex(major=ma)
tm.assert_panel_equal(result, expected)
def test_terms(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
# some invalid terms
terms = [
['minor', ['A', 'B']],
['index', ['20121114']],
['index', ['20121114', '20121114']],
]
for t in terms:
self.assertRaises(Exception, self.store.select, 'wp', t)
self.assertRaises(Exception, Term.__init__)
self.assertRaises(Exception, Term.__init__, 'blah')
self.assertRaises(Exception, Term.__init__, 'index')
self.assertRaises(Exception, Term.__init__, 'index', '==')
self.assertRaises(Exception, Term.__init__, 'index', '>', 5)
result = self.store.select(
'wp',
[Term('major_axis<20000108'),
Term('minor_axis', '=', ['A', 'B'])])
expected = wp.truncate(after='20000108').reindex(minor=['A', 'B'])
tm.assert_panel_equal(result, expected)
# valid terms
terms = [
dict(field='index', op='>', value='20121114'),
('index', '20121114'),
('index', '>', '20121114'),
(('index', ['20121114', '20121114']), ),
('index', datetime(2012, 11, 14)),
'index>20121114',
'major>20121114',
'major_axis>20121114',
(('minor', ['A', 'B']), ),
(('minor_axis', ['A', 'B']), ),
((('minor_axis', ['A', 'B']), ), ),
(('column', ['A', 'B']), ),
]
for t in terms:
self.store.select('wp', t)
def test_series(self):
s = tm.makeStringSeries()
self._check_roundtrip(s, tm.assert_series_equal)
ts = tm.makeTimeSeries()
self._check_roundtrip(ts, tm.assert_series_equal)
ts2 = Series(ts.index, Index(ts.index, dtype=object))
self._check_roundtrip(ts2, tm.assert_series_equal)
ts3 = Series(ts.values,
Index(np.asarray(ts.index, dtype=object), dtype=object))
self._check_roundtrip(ts3, tm.assert_series_equal)
def test_sparse_series(self):
s = tm.makeStringSeries()
s[3:5] = np.nan
ss = s.to_sparse()
self._check_roundtrip(ss,
tm.assert_series_equal,
check_series_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_roundtrip(ss2,
tm.assert_series_equal,
check_series_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_roundtrip(ss3,
tm.assert_series_equal,
check_series_type=True)
def test_sparse_frame(self):
s = tm.makeDataFrame()
s.ix[3:5, 1:3] = np.nan
s.ix[8:10, -2] = np.nan
ss = s.to_sparse()
self._check_double_roundtrip(ss,
tm.assert_frame_equal,
check_frame_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_double_roundtrip(ss2,
tm.assert_frame_equal,
check_frame_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_double_roundtrip(ss3,
tm.assert_frame_equal,
check_frame_type=True)
def test_sparse_panel(self):
items = ['x', 'y', 'z']
p = Panel(dict((i, tm.makeDataFrame().ix[:2, :2]) for i in items))
sp = p.to_sparse()
self._check_double_roundtrip(sp,
tm.assert_panel_equal,
check_panel_type=True)
sp2 = p.to_sparse(kind='integer')
self._check_double_roundtrip(sp2,
tm.assert_panel_equal,
check_panel_type=True)
sp3 = p.to_sparse(fill_value=0)
self._check_double_roundtrip(sp3,
tm.assert_panel_equal,
check_panel_type=True)
def test_float_index(self):
# GH #454
index = np.random.randn(10)
s = Series(np.random.randn(10), index=index)
self._check_roundtrip(s, tm.assert_series_equal)
def test_tuple_index(self):
# GH #492
col = np.arange(10)
idx = [(0., 1.), (2., 3.), (4., 5.)]
data = np.random.randn(30).reshape((3, 10))
DF = DataFrame(data, index=idx, columns=col)
self._check_roundtrip(DF, tm.assert_frame_equal)
def test_index_types(self):
values = np.random.randn(2)
func = lambda l, r: tm.assert_series_equal(l, r, True, True, True)
ser = Series(values, [0, 'y'])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime.today(), 0])
self._check_roundtrip(ser, func)
ser = Series(values, ['y', 0])
self._check_roundtrip(ser, func)
from datetime import date
ser = Series(values, [date.today(), 'a'])
self._check_roundtrip(ser, func)
ser = Series(values, [1.23, 'b'])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 1.53])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 5])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime(2012, 1, 1), datetime(2012, 1, 2)])
self._check_roundtrip(ser, func)
def test_timeseries_preepoch(self):
if sys.version_info[0] == 2 and sys.version_info[1] < 7:
raise nose.SkipTest
dr = bdate_range('1/1/1940', '1/1/1960')
ts = Series(np.random.randn(len(dr)), index=dr)
try:
self._check_roundtrip(ts, tm.assert_series_equal)
except OverflowError:
raise nose.SkipTest('known failer on some windows platforms')
def test_frame(self):
df = tm.makeDataFrame()
# put in some random NAs
df.values[0, 0] = np.nan
df.values[5, 3] = np.nan
self._check_roundtrip_table(df, tm.assert_frame_equal)
self._check_roundtrip(df, tm.assert_frame_equal)
self._check_roundtrip_table(df,
tm.assert_frame_equal,
compression=True)
self._check_roundtrip(df, tm.assert_frame_equal, compression=True)
tdf = tm.makeTimeDataFrame()
self._check_roundtrip(tdf, tm.assert_frame_equal)
self._check_roundtrip(tdf, tm.assert_frame_equal, compression=True)
# not consolidated
df['foo'] = np.random.randn(len(df))
self.store['df'] = df
recons = self.store['df']
self.assert_(recons._data.is_consolidated())
# empty
self._check_roundtrip(df[:0], tm.assert_frame_equal)
def test_empty_series_frame(self):
s0 = Series()
s1 = Series(name='myseries')
df0 = DataFrame()
df1 = DataFrame(index=['a', 'b', 'c'])
df2 = DataFrame(columns=['d', 'e', 'f'])
self._check_roundtrip(s0, tm.assert_series_equal)
self._check_roundtrip(s1, tm.assert_series_equal)
self._check_roundtrip(df0, tm.assert_frame_equal)
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
def test_can_serialize_dates(self):
rng = [x.date() for x in bdate_range('1/1/2000', '1/30/2000')]
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
self._check_roundtrip(frame, tm.assert_frame_equal)
def test_timezones(self):
rng = date_range('1/1/2000', '1/30/2000', tz='US/Eastern')
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
self.assert_(recons.index.equals(rng))
self.assertEquals(rng.tz, recons.index.tz)
finally:
store.close()
os.remove(self.scratchpath)
def test_fixed_offset_tz(self):
rng = date_range('1/1/2000 00:00:00-07:00', '1/30/2000 00:00:00-07:00')
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
self.assert_(recons.index.equals(rng))
self.assertEquals(rng.tz, recons.index.tz)
finally:
store.close()
os.remove(self.scratchpath)
def test_store_hierarchical(self):
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo', 'bar'])
frame = DataFrame(np.random.randn(10, 3),
index=index,
columns=['A', 'B', 'C'])
self._check_roundtrip(frame, tm.assert_frame_equal)
self._check_roundtrip(frame.T, tm.assert_frame_equal)
self._check_roundtrip(frame['A'], tm.assert_series_equal)
# check that the names are stored
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
assert (recons.index.names == ['foo', 'bar'])
finally:
store.close()
os.remove(self.scratchpath)
def test_store_index_name(self):
df = tm.makeDataFrame()
df.index.name = 'foo'
try:
store = HDFStore(self.scratchpath)
store['frame'] = df
recons = store['frame']
assert (recons.index.name == 'foo')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_series_name(self):
df = tm.makeDataFrame()
series = df['A']
try:
store = HDFStore(self.scratchpath)
store['series'] = series
recons = store['series']
assert (recons.name == 'A')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_mixed(self):
def _make_one():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one()
df2 = _make_one()
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
self.store['obj'] = df1
tm.assert_frame_equal(self.store['obj'], df1)
self.store['obj'] = df2
tm.assert_frame_equal(self.store['obj'], df2)
# check that can store Series of all of these types
self._check_roundtrip(df1['obj1'], tm.assert_series_equal)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal)
self._check_roundtrip(df1['int1'], tm.assert_series_equal)
# try with compression
self._check_roundtrip(df1['obj1'],
tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['bool1'],
tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['int1'],
tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1, tm.assert_frame_equal, compression=True)
def test_wide(self):
wp = tm.makePanel()
self._check_roundtrip(wp, tm.assert_panel_equal)
def test_wide_table(self):
wp = tm.makePanel()
self._check_roundtrip_table(wp, tm.assert_panel_equal)
def test_wide_table_dups(self):
wp = tm.makePanel()
try:
store = HDFStore(self.scratchpath)
store._quiet = True
store.put('panel', wp, table=True)
store.put('panel', wp, table=True, append=True)
recons = store['panel']
tm.assert_panel_equal(recons, wp)
finally:
store.close()
os.remove(self.scratchpath)
def test_long(self):
def _check(left, right):
tm.assert_panel_equal(left.to_panel(), right.to_panel())
wp = tm.makePanel()
self._check_roundtrip(wp.to_frame(), _check)
# empty
# self._check_roundtrip(wp.to_frame()[:0], _check)
def test_longpanel(self):
pass
def test_overwrite_node(self):
self.store['a'] = tm.makeTimeDataFrame()
ts = tm.makeTimeSeries()
self.store['a'] = ts
tm.assert_series_equal(self.store['a'], ts)
def test_select(self):
wp = tm.makePanel()
# put/select ok
self.store.put('wp', wp, table=True)
self.store.select('wp')
# non-table ok (where = None)
self.store.put('wp2', wp, table=False)
self.store.select('wp2')
# selectin non-table with a where
self.assertRaises(Exception, self.store.select, 'wp2',
('column', ['A', 'D']))
def test_panel_select(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = ('index', '>=', date)
crit2 = ('column', '=', ['A', 'D'])
result = self.store.select('wp', [crit1, crit2])
expected = wp.truncate(before=date).reindex(minor=['A', 'D'])
tm.assert_panel_equal(result, expected)
result = self.store.select(
'wp', ['major_axis>=20000124', ('minor_axis', '=', ['A', 'B'])])
expected = wp.truncate(before='20000124').reindex(minor=['A', 'B'])
tm.assert_panel_equal(result, expected)
def test_frame_select(self):
df = tm.makeTimeDataFrame()
self.store.put('frame', df, table=True)
date = df.index[len(df) // 2]
crit1 = ('index', '>=', date)
crit2 = ('column', ['A', 'D'])
crit3 = ('column', 'A')
result = self.store.select('frame', [crit1, crit2])
expected = df.ix[date:, ['A', 'D']]
tm.assert_frame_equal(result, expected)
result = self.store.select('frame', [crit3])
expected = df.ix[:, ['A']]
tm.assert_frame_equal(result, expected)
# can't select if not written as table
self.store['frame'] = df
self.assertRaises(Exception, self.store.select, 'frame',
[crit1, crit2])
def test_select_filter_corner(self):
df = DataFrame(np.random.randn(50, 100))
df.index = ['%.3d' % c for c in df.index]
df.columns = ['%.3d' % c for c in df.columns]
self.store.put('frame', df, table=True)
crit = Term('column', df.columns[:75])
result = self.store.select('frame', [crit])
tm.assert_frame_equal(result, df.ix[:, df.columns[:75]])
def _check_roundtrip(self, obj, comparator, compression=False, **kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_double_roundtrip(self,
obj,
comparator,
compression=False,
**kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
store['obj'] = retrieved
again = store['obj']
comparator(again, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_roundtrip_table(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
# sorted_obj = _test_sort(obj)
comparator(retrieved, obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_legacy_read(self):
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy.h5'), 'r')
store['a']
store['b']
store['c']
store['d']
store.close()
def test_legacy_table_read(self):
# legacy table types
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy_table.h5'), 'r')
store.select('df1')
store.select('df2')
store.select('wp1')
store.close()
def test_legacy_table_write(self):
# legacy table types
pth = curpath()
df = tm.makeDataFrame()
wp = tm.makePanel()
store = HDFStore(os.path.join(pth, 'legacy_table.h5'), 'a')
self.assertRaises(Exception, store.append, 'df1', df)
self.assertRaises(Exception, store.append, 'wp1', wp)
store.close()
def test_store_datetime_fractional_secs(self):
dt = datetime(2012, 1, 2, 3, 4, 5, 123456)
series = Series([0], [dt])
self.store['a'] = series
self.assertEquals(self.store['a'].index[0], dt)
def test_tseries_indices_series(self):
idx = tm.makeDateIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
idx = tm.makePeriodIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
def test_tseries_indices_frame(self):
idx = tm.makeDateIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
idx = tm.makePeriodIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
def test_unicode_index(self):
unicode_values = [u'\u03c3', u'\u03c3\u03c3']
s = Series(np.random.randn(len(unicode_values)), unicode_values)
self._check_roundtrip(s, tm.assert_series_equal)
def test_store_datetime_mixed(self):
df = DataFrame({
'a': [1, 2, 3],
'b': [1., 2., 3.],
'c': ['a', 'b', 'c']
})
ts = tm.makeTimeSeries()
df['d'] = ts.index[:3]
self._check_roundtrip(df, tm.assert_frame_equal)
def test_cant_write_multiindex_table(self):
# for now, #1848
df = DataFrame(
np.random.randn(10, 4),
index=[np.arange(5).repeat(2),
np.tile(np.arange(2), 5)])
self.assertRaises(Exception, self.store.put, 'foo', df, table=True)
class TestHDFStore(unittest.TestCase):
path = '__test__.h5'
scratchpath = '__scratch__.h5'
def setUp(self):
self.store = HDFStore(self.path)
def tearDown(self):
self.store.close()
os.remove(self.path)
def test_factory_fun(self):
try:
with get_store(self.scratchpath) as tbl:
raise ValueError('blah')
except ValueError:
pass
with get_store(self.scratchpath) as tbl:
tbl['a'] = tm.makeDataFrame()
with get_store(self.scratchpath) as tbl:
self.assertEquals(len(tbl), 1)
self.assertEquals(type(tbl['a']), DataFrame)
os.remove(self.scratchpath)
def test_keys(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.store['foo/bar'] = tm.makePanel()
self.assertEquals(len(self.store), 5)
self.assert_(set(self.store.keys()) == set(['/a', '/b', '/c', '/d', '/foo/bar']))
def test_repr(self):
repr(self.store)
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.store['foo/bar'] = tm.makePanel()
self.store.append('e', tm.makePanel())
repr(self.store)
str(self.store)
def test_contains(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeDataFrame()
self.store['foo/bar'] = tm.makeDataFrame()
self.assert_('a' in self.store)
self.assert_('b' in self.store)
self.assert_('c' not in self.store)
self.assert_('foo/bar' in self.store)
self.assert_('/foo/bar' in self.store)
self.assert_('/foo/b' not in self.store)
self.assert_('bar' not in self.store)
def test_reopen_handle(self):
self.store['a'] = tm.makeTimeSeries()
self.store.open('w', warn=False)
self.assert_(self.store.handle.isopen)
self.assertEquals(len(self.store), 0)
def test_flush(self):
self.store['a'] = tm.makeTimeSeries()
self.store.flush()
def test_get(self):
self.store['a'] = tm.makeTimeSeries()
left = self.store.get('a')
right = self.store['a']
tm.assert_series_equal(left, right)
left = self.store.get('/a')
right = self.store['/a']
tm.assert_series_equal(left, right)
self.assertRaises(KeyError, self.store.get, 'b')
def test_put(self):
ts = tm.makeTimeSeries()
df = tm.makeTimeDataFrame()
self.store['a'] = ts
self.store['b'] = df[:10]
self.store['foo/bar/bah'] = df[:10]
self.store['foo'] = df[:10]
self.store['/foo'] = df[:10]
self.store.put('c', df[:10], table=True)
# not OK, not a table
self.assertRaises(ValueError, self.store.put, 'b', df[10:], append=True)
# node does not currently exist, test _is_table_type returns False in
# this case
self.assertRaises(ValueError, self.store.put, 'f', df[10:], append=True)
# OK
self.store.put('c', df[10:], append=True)
# overwrite table
self.store.put('c', df[:10], table=True, append=False)
tm.assert_frame_equal(df[:10], self.store['c'])
def test_put_compression(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df, table=True, compression='zlib')
tm.assert_frame_equal(self.store['c'], df)
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='zlib')
def test_put_compression_blosc(self):
tm.skip_if_no_package('tables', '2.2', app='blosc support')
df = tm.makeTimeDataFrame()
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='blosc')
self.store.put('c', df, table=True, compression='blosc')
tm.assert_frame_equal(self.store['c'], df)
def test_put_integer(self):
# non-date, non-string index
df = DataFrame(np.random.randn(50, 100))
self._check_roundtrip(df, tm.assert_frame_equal)
def test_append(self):
pth = '__test_append__.h5'
try:
store = HDFStore(pth)
df = tm.makeTimeDataFrame()
store.append('df1', df[:10])
store.append('df1', df[10:])
tm.assert_frame_equal(store['df1'], df)
store.put('df2', df[:10], table=True)
store.append('df2', df[10:])
tm.assert_frame_equal(store['df2'], df)
store.append('/df3', df[:10])
store.append('/df3', df[10:])
tm.assert_frame_equal(store['df3'], df)
# this is allowed by almost always don't want to do it
import warnings
import tables
warnings.filterwarnings('ignore', category=tables.NaturalNameWarning)
store.append('/df3 foo', df[:10])
store.append('/df3 foo', df[10:])
tm.assert_frame_equal(store['df3 foo'], df)
warnings.filterwarnings('always', category=tables.NaturalNameWarning)
# panel
wp = tm.makePanel()
store.append('wp1', wp.ix[:,:10,:])
store.append('wp1', wp.ix[:,10:,:])
tm.assert_panel_equal(store['wp1'], wp)
# ndim
p4d = tm.makePanel4D()
store.append('p4d', p4d.ix[:,:,:10,:])
store.append('p4d', p4d.ix[:,:,10:,:])
tm.assert_panel4d_equal(store['p4d'], p4d)
except:
raise
finally:
store.close()
os.remove(pth)
def test_append_with_strings(self):
wp = tm.makePanel()
wp2 = wp.rename_axis(dict([ (x,"%s_extra" % x) for x in wp.minor_axis ]), axis = 2)
self.store.append('s1', wp, min_itemsize = 20)
self.store.append('s1', wp2)
expected = concat([ wp, wp2], axis = 2)
expected = expected.reindex(minor_axis = sorted(expected.minor_axis))
tm.assert_panel_equal(self.store['s1'], expected)
# test dict format
self.store.append('s2', wp, min_itemsize = { 'minor_axis' : 20 })
self.store.append('s2', wp2)
expected = concat([ wp, wp2], axis = 2)
expected = expected.reindex(minor_axis = sorted(expected.minor_axis))
tm.assert_panel_equal(self.store['s2'], expected)
# apply the wrong field (similar to #1)
self.store.append('s3', wp, min_itemsize = { 'major_axis' : 20 })
self.assertRaises(Exception, self.store.append, 's3')
# test truncation of bigger strings
self.store.append('s4', wp)
self.assertRaises(Exception, self.store.append, 's4', wp2)
def test_create_table_index(self):
wp = tm.makePanel()
self.store.append('p5', wp)
self.store.create_table_index('p5')
assert(self.store.handle.root.p5.table.cols.major_axis.is_indexed == True)
assert(self.store.handle.root.p5.table.cols.minor_axis.is_indexed == False)
df = tm.makeTimeDataFrame()
self.store.append('f', df[:10])
self.store.append('f', df[10:])
self.store.create_table_index('f')
# create twice
self.store.create_table_index('f')
# try to index a non-table
self.store.put('f2', df)
self.assertRaises(Exception, self.store.create_table_index, 'f2')
# try to change the version supports flag
from pandas.io import pytables
pytables._table_supports_index = False
self.assertRaises(Exception, self.store.create_table_index, 'f')
def test_append_diff_item_order(self):
wp = tm.makePanel()
wp1 = wp.ix[:, :10, :]
wp2 = wp.ix[['ItemC', 'ItemB', 'ItemA'], 10:, :]
self.store.put('panel', wp1, table=True)
self.assertRaises(Exception, self.store.put, 'panel', wp2,
append=True)
def test_table_index_incompatible_dtypes(self):
df1 = DataFrame({'a': [1, 2, 3]})
df2 = DataFrame({'a': [4, 5, 6]},
index=date_range('1/1/2000', periods=3))
self.store.put('frame', df1, table=True)
self.assertRaises(Exception, self.store.put, 'frame', df2,
table=True, append=True)
def test_table_values_dtypes_roundtrip(self):
df1 = DataFrame({'a': [1, 2, 3]}, dtype = 'f8')
self.store.append('df1', df1)
assert df1.dtypes == self.store['df1'].dtypes
df2 = DataFrame({'a': [1, 2, 3]}, dtype = 'i8')
self.store.append('df2', df2)
assert df2.dtypes == self.store['df2'].dtypes
# incompatible dtype
self.assertRaises(Exception, self.store.append, 'df2', df1)
def test_table_mixed_dtypes(self):
# frame
def _make_one_df():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['bool3'] = True
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one_df()
self.store.append('df1_mixed', df1)
tm.assert_frame_equal(self.store.select('df1_mixed'), df1)
# panel
def _make_one_panel():
wp = tm.makePanel()
wp['obj1'] = 'foo'
wp['obj2'] = 'bar'
wp['bool1'] = wp['ItemA'] > 0
wp['bool2'] = wp['ItemB'] > 0
wp['int1'] = 1
wp['int2'] = 2
return wp.consolidate()
p1 = _make_one_panel()
self.store.append('p1_mixed', p1)
tm.assert_panel_equal(self.store.select('p1_mixed'), p1)
def test_remove(self):
ts = tm.makeTimeSeries()
df = tm.makeDataFrame()
self.store['a'] = ts
self.store['b'] = df
self.store.remove('a')
self.assertEquals(len(self.store), 1)
tm.assert_frame_equal(df, self.store['b'])
self.store.remove('b')
self.assertEquals(len(self.store), 0)
# pathing
self.store['a'] = ts
self.store['b/foo'] = df
self.store.remove('foo')
self.store.remove('b/foo')
self.assertEquals(len(self.store), 1)
self.store['a'] = ts
self.store['b/foo'] = df
self.store.remove('b')
self.assertEquals(len(self.store), 1)
# __delitem__
self.store['a'] = ts
self.store['b'] = df
del self.store['a']
del self.store['b']
self.assertEquals(len(self.store), 0)
def test_remove_where(self):
# non-existance
crit1 = Term('index','>','foo')
self.store.remove('a', where=[crit1])
# try to remove non-table (with crit)
# non-table ok (where = None)
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
self.store.remove('wp', [('minor_axis', ['A', 'D'])])
rs = self.store.select('wp')
expected = wp.reindex(minor_axis = ['B','C'])
tm.assert_panel_equal(rs,expected)
# empty where
self.store.remove('wp')
self.store.put('wp', wp, table=True)
self.store.remove('wp', [])
# non - empty where
self.store.remove('wp')
self.store.put('wp', wp, table=True)
self.assertRaises(Exception, self.store.remove,
'wp', ['foo'])
# selectin non-table with a where
self.store.put('wp2', wp, table=False)
self.assertRaises(Exception, self.store.remove,
'wp2', [('column', ['A', 'D'])])
def test_remove_crit(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = Term('major_axis','>',date)
crit2 = Term('minor_axis',['A', 'D'])
self.store.remove('wp', where=[crit1])
self.store.remove('wp', where=[crit2])
result = self.store['wp']
expected = wp.truncate(after=date).reindex(minor=['B', 'C'])
tm.assert_panel_equal(result, expected)
# test non-consecutive row removal
wp = tm.makePanel()
self.store.put('wp2', wp, table=True)
date1 = wp.major_axis[1:3]
date2 = wp.major_axis[5]
date3 = [wp.major_axis[7],wp.major_axis[9]]
crit1 = Term('major_axis',date1)
crit2 = Term('major_axis',date2)
crit3 = Term('major_axis',date3)
self.store.remove('wp2', where=[crit1])
self.store.remove('wp2', where=[crit2])
self.store.remove('wp2', where=[crit3])
result = self.store['wp2']
ma = list(wp.major_axis)
for d in date1:
ma.remove(d)
ma.remove(date2)
for d in date3:
ma.remove(d)
expected = wp.reindex(major = ma)
tm.assert_panel_equal(result, expected)
def test_terms(self):
wp = tm.makePanel()
p4d = tm.makePanel4D()
self.store.put('wp', wp, table=True)
self.store.put('p4d', p4d, table=True)
# some invalid terms
terms = [
[ 'minor', ['A','B'] ],
[ 'index', ['20121114'] ],
[ 'index', ['20121114', '20121114'] ],
]
for t in terms:
self.assertRaises(Exception, self.store.select, 'wp', t)
self.assertRaises(Exception, Term.__init__)
self.assertRaises(Exception, Term.__init__, 'blah')
self.assertRaises(Exception, Term.__init__, 'index')
self.assertRaises(Exception, Term.__init__, 'index', '==')
self.assertRaises(Exception, Term.__init__, 'index', '>', 5)
# panel
result = self.store.select('wp',[ Term('major_axis<20000108'), Term('minor_axis', '=', ['A','B']) ])
expected = wp.truncate(after='20000108').reindex(minor=['A', 'B'])
tm.assert_panel_equal(result, expected)
# p4d
result = self.store.select('p4d',[ Term('major_axis<20000108'), Term('minor_axis', '=', ['A','B']) ])
expected = p4d.truncate(after='20000108').reindex(minor=['A', 'B'])
tm.assert_panel4d_equal(result, expected)
# valid terms
terms = [
dict(field = 'major_axis', op = '>', value = '20121114'),
('major_axis', '20121114'),
('major_axis', '>', '20121114'),
(('major_axis', ['20121114','20121114']),),
('major_axis', datetime(2012,11,14)),
'major_axis>20121114',
'major_axis>20121114',
'major_axis>20121114',
(('minor_axis', ['A','B']),),
(('minor_axis', ['A','B']),),
((('minor_axis', ['A','B']),),),
]
for t in terms:
self.store.select('wp', t)
self.store.select('p4d', t)
def test_series(self):
s = tm.makeStringSeries()
self._check_roundtrip(s, tm.assert_series_equal)
ts = tm.makeTimeSeries()
self._check_roundtrip(ts, tm.assert_series_equal)
ts2 = Series(ts.index, Index(ts.index, dtype=object))
self._check_roundtrip(ts2, tm.assert_series_equal)
ts3 = Series(ts.values, Index(np.asarray(ts.index, dtype=object),
dtype=object))
self._check_roundtrip(ts3, tm.assert_series_equal)
def test_sparse_series(self):
s = tm.makeStringSeries()
s[3:5] = np.nan
ss = s.to_sparse()
self._check_roundtrip(ss, tm.assert_series_equal,
check_series_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_roundtrip(ss2, tm.assert_series_equal,
check_series_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_roundtrip(ss3, tm.assert_series_equal,
check_series_type=True)
def test_sparse_frame(self):
s = tm.makeDataFrame()
s.ix[3:5, 1:3] = np.nan
s.ix[8:10, -2] = np.nan
ss = s.to_sparse()
self._check_double_roundtrip(ss, tm.assert_frame_equal,
check_frame_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_double_roundtrip(ss2, tm.assert_frame_equal,
check_frame_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_double_roundtrip(ss3, tm.assert_frame_equal,
check_frame_type=True)
def test_sparse_panel(self):
items = ['x', 'y', 'z']
p = Panel(dict((i, tm.makeDataFrame().ix[:2, :2]) for i in items))
sp = p.to_sparse()
self._check_double_roundtrip(sp, tm.assert_panel_equal,
check_panel_type=True)
sp2 = p.to_sparse(kind='integer')
self._check_double_roundtrip(sp2, tm.assert_panel_equal,
check_panel_type=True)
sp3 = p.to_sparse(fill_value=0)
self._check_double_roundtrip(sp3, tm.assert_panel_equal,
check_panel_type=True)
def test_float_index(self):
# GH #454
index = np.random.randn(10)
s = Series(np.random.randn(10), index=index)
self._check_roundtrip(s, tm.assert_series_equal)
def test_tuple_index(self):
# GH #492
col = np.arange(10)
idx = [(0.,1.), (2., 3.), (4., 5.)]
data = np.random.randn(30).reshape((3, 10))
DF = DataFrame(data, index=idx, columns=col)
self._check_roundtrip(DF, tm.assert_frame_equal)
def test_index_types(self):
values = np.random.randn(2)
func = lambda l, r : tm.assert_series_equal(l, r, True, True, True)
ser = Series(values, [0, 'y'])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime.today(), 0])
self._check_roundtrip(ser, func)
ser = Series(values, ['y', 0])
self._check_roundtrip(ser, func)
from datetime import date
ser = Series(values, [date.today(), 'a'])
self._check_roundtrip(ser, func)
ser = Series(values, [1.23, 'b'])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 1.53])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 5])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime(2012, 1, 1), datetime(2012, 1, 2)])
self._check_roundtrip(ser, func)
def test_timeseries_preepoch(self):
if sys.version_info[0] == 2 and sys.version_info[1] < 7:
raise nose.SkipTest
dr = bdate_range('1/1/1940', '1/1/1960')
ts = Series(np.random.randn(len(dr)), index=dr)
try:
self._check_roundtrip(ts, tm.assert_series_equal)
except OverflowError:
raise nose.SkipTest('known failer on some windows platforms')
def test_frame(self):
df = tm.makeDataFrame()
# put in some random NAs
df.values[0, 0] = np.nan
df.values[5, 3] = np.nan
self._check_roundtrip_table(df, tm.assert_frame_equal)
self._check_roundtrip(df, tm.assert_frame_equal)
self._check_roundtrip_table(df, tm.assert_frame_equal,
compression=True)
self._check_roundtrip(df, tm.assert_frame_equal,
compression=True)
tdf = tm.makeTimeDataFrame()
self._check_roundtrip(tdf, tm.assert_frame_equal)
self._check_roundtrip(tdf, tm.assert_frame_equal,
compression=True)
# not consolidated
df['foo'] = np.random.randn(len(df))
self.store['df'] = df
recons = self.store['df']
self.assert_(recons._data.is_consolidated())
# empty
self._check_roundtrip(df[:0], tm.assert_frame_equal)
def test_empty_series_frame(self):
s0 = Series()
s1 = Series(name='myseries')
df0 = DataFrame()
df1 = DataFrame(index=['a', 'b', 'c'])
df2 = DataFrame(columns=['d', 'e', 'f'])
self._check_roundtrip(s0, tm.assert_series_equal)
self._check_roundtrip(s1, tm.assert_series_equal)
self._check_roundtrip(df0, tm.assert_frame_equal)
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
def test_can_serialize_dates(self):
rng = [x.date() for x in bdate_range('1/1/2000', '1/30/2000')]
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
self._check_roundtrip(frame, tm.assert_frame_equal)
def test_timezones(self):
rng = date_range('1/1/2000', '1/30/2000', tz='US/Eastern')
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
self.assert_(recons.index.equals(rng))
self.assertEquals(rng.tz, recons.index.tz)
finally:
store.close()
os.remove(self.scratchpath)
def test_fixed_offset_tz(self):
rng = date_range('1/1/2000 00:00:00-07:00', '1/30/2000 00:00:00-07:00')
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
self.assert_(recons.index.equals(rng))
self.assertEquals(rng.tz, recons.index.tz)
finally:
store.close()
os.remove(self.scratchpath)
def test_store_hierarchical(self):
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo', 'bar'])
frame = DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
self._check_roundtrip(frame, tm.assert_frame_equal)
self._check_roundtrip(frame.T, tm.assert_frame_equal)
self._check_roundtrip(frame['A'], tm.assert_series_equal)
# check that the names are stored
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
assert(recons.index.names == ['foo', 'bar'])
finally:
store.close()
os.remove(self.scratchpath)
def test_store_index_name(self):
df = tm.makeDataFrame()
df.index.name = 'foo'
try:
store = HDFStore(self.scratchpath)
store['frame'] = df
recons = store['frame']
assert(recons.index.name == 'foo')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_series_name(self):
df = tm.makeDataFrame()
series = df['A']
try:
store = HDFStore(self.scratchpath)
store['series'] = series
recons = store['series']
assert(recons.name == 'A')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_mixed(self):
def _make_one():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one()
df2 = _make_one()
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
self.store['obj'] = df1
tm.assert_frame_equal(self.store['obj'], df1)
self.store['obj'] = df2
tm.assert_frame_equal(self.store['obj'], df2)
# check that can store Series of all of these types
self._check_roundtrip(df1['obj1'], tm.assert_series_equal)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal)
self._check_roundtrip(df1['int1'], tm.assert_series_equal)
# try with compression
self._check_roundtrip(df1['obj1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['int1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1, tm.assert_frame_equal,
compression=True)
def test_wide(self):
wp = tm.makePanel()
self._check_roundtrip(wp, tm.assert_panel_equal)
def test_wide_table(self):
wp = tm.makePanel()
self._check_roundtrip_table(wp, tm.assert_panel_equal)
def test_wide_table_dups(self):
wp = tm.makePanel()
try:
store = HDFStore(self.scratchpath)
store._quiet = True
store.put('panel', wp, table=True)
store.put('panel', wp, table=True, append=True)
recons = store['panel']
tm.assert_panel_equal(recons, wp)
finally:
store.close()
os.remove(self.scratchpath)
def test_long(self):
def _check(left, right):
tm.assert_panel_equal(left.to_panel(), right.to_panel())
wp = tm.makePanel()
self._check_roundtrip(wp.to_frame(), _check)
# empty
# self._check_roundtrip(wp.to_frame()[:0], _check)
def test_longpanel(self):
pass
def test_overwrite_node(self):
self.store['a'] = tm.makeTimeDataFrame()
ts = tm.makeTimeSeries()
self.store['a'] = ts
tm.assert_series_equal(self.store['a'], ts)
def test_select(self):
wp = tm.makePanel()
# put/select ok
self.store.put('wp', wp, table=True)
self.store.select('wp')
# non-table ok (where = None)
self.store.put('wp2', wp, table=False)
self.store.select('wp2')
# selectin non-table with a where
self.assertRaises(Exception, self.store.select,
'wp2', ('column', ['A', 'D']))
def test_panel_select(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = ('major_axis','>=',date)
crit2 = ('minor_axis', '=', ['A', 'D'])
result = self.store.select('wp', [crit1, crit2])
expected = wp.truncate(before=date).reindex(minor=['A', 'D'])
tm.assert_panel_equal(result, expected)
result = self.store.select('wp', [ 'major_axis>=20000124', ('minor_axis', '=', ['A','B']) ])
expected = wp.truncate(before='20000124').reindex(minor=['A', 'B'])
tm.assert_panel_equal(result, expected)
def test_frame_select(self):
df = tm.makeTimeDataFrame()
self.store.put('frame', df, table=True)
date = df.index[len(df) // 2]
crit1 = ('index','>=',date)
crit2 = ('columns',['A', 'D'])
crit3 = ('columns','A')
result = self.store.select('frame', [crit1, crit2])
expected = df.ix[date:, ['A', 'D']]
tm.assert_frame_equal(result, expected)
result = self.store.select('frame', [crit3])
expected = df.ix[:, ['A']]
tm.assert_frame_equal(result, expected)
# can't select if not written as table
self.store['frame'] = df
self.assertRaises(Exception, self.store.select,
'frame', [crit1, crit2])
def test_select_filter_corner(self):
df = DataFrame(np.random.randn(50, 100))
df.index = ['%.3d' % c for c in df.index]
df.columns = ['%.3d' % c for c in df.columns]
self.store.put('frame', df, table=True)
crit = Term('columns', df.columns[:75])
result = self.store.select('frame', [crit])
tm.assert_frame_equal(result, df.ix[:, df.columns[:75]])
def _check_roundtrip(self, obj, comparator, compression=False, **kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_double_roundtrip(self, obj, comparator, compression=False,
**kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
store['obj'] = retrieved
again = store['obj']
comparator(again, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_roundtrip_table(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
# sorted_obj = _test_sort(obj)
comparator(retrieved, obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_legacy_read(self):
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy.h5'), 'r')
store['a']
store['b']
store['c']
store['d']
store.close()
def test_legacy_table_read(self):
# legacy table types
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy_table.h5'), 'r')
store.select('df1')
store.select('df2')
store.select('wp1')
store.close()
def test_legacy_table_write(self):
# legacy table types
pth = curpath()
df = tm.makeDataFrame()
wp = tm.makePanel()
store = HDFStore(os.path.join(pth, 'legacy_table.h5'), 'a')
self.assertRaises(Exception, store.append, 'df1', df)
self.assertRaises(Exception, store.append, 'wp1', wp)
store.close()
def test_store_datetime_fractional_secs(self):
dt = datetime(2012, 1, 2, 3, 4, 5, 123456)
series = Series([0], [dt])
self.store['a'] = series
self.assertEquals(self.store['a'].index[0], dt)
def test_tseries_indices_series(self):
idx = tm.makeDateIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
idx = tm.makePeriodIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
def test_tseries_indices_frame(self):
idx = tm.makeDateIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
idx = tm.makePeriodIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
def test_unicode_index(self):
unicode_values = [u'\u03c3', u'\u03c3\u03c3']
s = Series(np.random.randn(len(unicode_values)), unicode_values)
self._check_roundtrip(s, tm.assert_series_equal)
def test_store_datetime_mixed(self):
df = DataFrame({'a': [1,2,3], 'b': [1.,2.,3.], 'c': ['a', 'b', 'c']})
ts = tm.makeTimeSeries()
df['d'] = ts.index[:3]
self._check_roundtrip(df, tm.assert_frame_equal)
def test_cant_write_multiindex_table(self):
# for now, #1848
df = DataFrame(np.random.randn(10, 4),
index=[np.arange(5).repeat(2),
np.tile(np.arange(2), 5)])
self.assertRaises(Exception, self.store.put, 'foo', df, table=True)
class TesttHDFStore(unittest.TestCase):
path = '__test__.h5'
scratchpath = '__scratch__.h5'
def setUp(self):
self.store = HDFStore(self.path)
def tearDown(self):
self.store.close()
os.remove(self.path)
def test_len_keys(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.assertEquals(len(self.store), 4)
self.assert_(set(self.store.keys()) == set(['a', 'b', 'c', 'd']))
def test_repr(self):
repr(self.store)
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
repr(self.store)
def test_reopen_handle(self):
self.store['a'] = tm.makeTimeSeries()
self.store.open('w', warn=False)
self.assert_(self.store.handle.isopen)
self.assertEquals(len(self.store), 0)
def test_flush(self):
self.store['a'] = tm.makeTimeSeries()
self.store.flush()
def test_get(self):
self.store['a'] = tm.makeTimeSeries()
left = self.store.get('a')
right = self.store['a']
tm.assert_series_equal(left, right)
self.assertRaises(AttributeError, self.store.get, 'b')
def test_put(self):
ts = tm.makeTimeSeries()
df = tm.makeTimeDataFrame()
self.store['a'] = ts
self.store['b'] = df[:10]
self.store.put('c', df[:10], table=True)
# not OK, not a table
self.assertRaises(ValueError, self.store.put, 'b', df[10:], append=True)
# node does not currently exist, test _is_table_type returns False in
# this case
self.assertRaises(ValueError, self.store.put, 'f', df[10:], append=True)
# OK
self.store.put('c', df[10:], append=True)
# overwrite table
self.store.put('c', df[:10], table=True, append=False)
tm.assert_frame_equal(df[:10], self.store['c'])
def test_put_compression(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df, table=True, compression='zlib')
tm.assert_frame_equal(self.store['c'], df)
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='zlib')
def test_put_compression_blosc(self):
tm.skip_if_no_package('tables', '2.2', app='blosc support')
df = tm.makeTimeDataFrame()
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='blosc')
self.store.put('c', df, table=True, compression='blosc')
tm.assert_frame_equal(self.store['c'], df)
def test_put_integer(self):
# non-date, non-string index
df = DataFrame(np.random.randn(50, 100))
self._check_roundtrip(df, tm.assert_frame_equal)
def test_append(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df[:10], table=True)
self.store.append('c', df[10:])
tm.assert_frame_equal(self.store['c'], df)
def test_append_diff_item_order(self):
wp = tm.makePanel()
wp1 = wp.ix[:, :10, :]
wp2 = wp.ix[['ItemC', 'ItemB', 'ItemA'], 10:, :]
self.store.put('panel', wp1, table=True)
self.assertRaises(Exception, self.store.put, 'panel', wp2,
append=True)
def test_remove(self):
ts = tm.makeTimeSeries()
df = tm.makeDataFrame()
self.store['a'] = ts
self.store['b'] = df
self.store.remove('a')
self.assertEquals(len(self.store), 1)
tm.assert_frame_equal(df, self.store['b'])
self.store.remove('b')
self.assertEquals(len(self.store), 0)
def test_remove_where_not_exist(self):
crit1 = {
'field' : 'index',
'op' : '>',
'value' : 'foo'
}
self.store.remove('a', where=[crit1])
def test_remove_crit(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = {
'field' : 'index',
'op' : '>',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
self.store.remove('wp', where=[crit1])
self.store.remove('wp', where=[crit2])
result = self.store['wp']
expected = wp.truncate(after=date).reindex(minor=['B', 'C'])
tm.assert_panel_equal(result, expected)
def test_series(self):
s = tm.makeStringSeries()
self._check_roundtrip(s, tm.assert_series_equal)
ts = tm.makeTimeSeries()
self._check_roundtrip(ts, tm.assert_series_equal)
def test_float_index(self):
# GH #454
index = np.random.randn(10)
s = Series(np.random.randn(10), index=index)
self._check_roundtrip(s, tm.assert_series_equal)
def test_tuple_index(self):
# GH #492
col = np.arange(10)
idx = [(0.,1.), (2., 3.), (4., 5.)]
data = np.random.randn(30).reshape((3, 10))
DF = DataFrame(data, index=idx, columns=col)
self._check_roundtrip(DF, tm.assert_frame_equal)
def test_timeseries_preepoch(self):
if sys.version_info[0] == 2 and sys.version_info[1] < 7:
raise nose.SkipTest
dr = DateRange('1/1/1940', '1/1/1960')
ts = Series(np.random.randn(len(dr)), index=dr)
try:
self._check_roundtrip(ts, tm.assert_series_equal)
except OverflowError:
raise nose.SkipTest('known failer on some windows platforms')
def test_frame(self):
df = tm.makeDataFrame()
# put in some random NAs
df.values[0, 0] = np.nan
df.values[5, 3] = np.nan
self._check_roundtrip_table(df, tm.assert_frame_equal)
self._check_roundtrip(df, tm.assert_frame_equal)
self._check_roundtrip_table(df, tm.assert_frame_equal,
compression=True)
self._check_roundtrip(df, tm.assert_frame_equal,
compression=True)
tdf = tm.makeTimeDataFrame()
self._check_roundtrip(tdf, tm.assert_frame_equal)
self._check_roundtrip(tdf, tm.assert_frame_equal,
compression=True)
# not consolidated
df['foo'] = np.random.randn(len(df))
self.store['df'] = df
recons = self.store['df']
self.assert_(recons._data.is_consolidated())
# empty
self.assertRaises(ValueError, self._check_roundtrip, df[:0],
tm.assert_frame_equal)
def test_can_serialize_dates(self):
rng = [x.date() for x in DateRange('1/1/2000', '1/30/2000')]
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
self._check_roundtrip(frame, tm.assert_frame_equal)
def test_store_hierarchical(self):
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo', 'bar'])
frame = DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
self._check_roundtrip(frame, tm.assert_frame_equal)
self._check_roundtrip(frame.T, tm.assert_frame_equal)
self._check_roundtrip(frame['A'], tm.assert_series_equal)
# check that the names are stored
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
assert(recons.index.names == ['foo', 'bar'])
finally:
store.close()
os.remove(self.scratchpath)
def test_store_index_name(self):
df = tm.makeDataFrame()
df.index.name = 'foo'
try:
store = HDFStore(self.scratchpath)
store['frame'] = df
recons = store['frame']
assert(recons.index.name == 'foo')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_series_name(self):
df = tm.makeDataFrame()
series = df['A']
try:
store = HDFStore(self.scratchpath)
store['series'] = series
recons = store['series']
assert(recons.name == 'A')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_mixed(self):
def _make_one():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one()
df2 = _make_one()
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
self.store['obj'] = df1
tm.assert_frame_equal(self.store['obj'], df1)
self.store['obj'] = df2
tm.assert_frame_equal(self.store['obj'], df2)
# storing in Table not yet supported
self.assertRaises(Exception, self.store.put, 'foo',
df1, table=True)
# check that can store Series of all of these types
self._check_roundtrip(df1['obj1'], tm.assert_series_equal)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal)
self._check_roundtrip(df1['int1'], tm.assert_series_equal)
# try with compression
self._check_roundtrip(df1['obj1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['int1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1, tm.assert_frame_equal,
compression=True)
def test_wide(self):
wp = tm.makePanel()
self._check_roundtrip(wp, tm.assert_panel_equal)
def test_wide_table(self):
wp = tm.makePanel()
self._check_roundtrip_table(wp, tm.assert_panel_equal)
def test_wide_table_dups(self):
wp = tm.makePanel()
try:
store = HDFStore(self.scratchpath)
store._quiet = True
store.put('panel', wp, table=True)
store.put('panel', wp, table=True, append=True)
recons = store['panel']
tm.assert_panel_equal(recons, wp)
finally:
store.close()
os.remove(self.scratchpath)
def test_long(self):
def _check(left, right):
tm.assert_panel_equal(left.to_panel(), right.to_panel())
wp = tm.makePanel()
self._check_roundtrip(wp.to_frame(), _check)
# empty
self.assertRaises(ValueError, self._check_roundtrip, wp.to_frame()[:0],
_check)
def test_longpanel(self):
pass
def test_overwrite_node(self):
self.store['a'] = tm.makeTimeDataFrame()
ts = tm.makeTimeSeries()
self.store['a'] = ts
tm.assert_series_equal(self.store['a'], ts)
def test_panel_select(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = {
'field' : 'index',
'op' : '>=',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
result = self.store.select('wp', [crit1, crit2])
expected = wp.truncate(before=date).reindex(minor=['A', 'D'])
tm.assert_panel_equal(result, expected)
def test_frame_select(self):
df = tm.makeTimeDataFrame()
self.store.put('frame', df, table=True)
date = df.index[len(df) // 2]
crit1 = {
'field' : 'index',
'op' : '>=',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
crit3 = {
'field' : 'column',
'value' : 'A'
}
result = self.store.select('frame', [crit1, crit2])
expected = df.ix[date:, ['A', 'D']]
tm.assert_frame_equal(result, expected)
result = self.store.select('frame', [crit3])
expected = df.ix[:, ['A']]
tm.assert_frame_equal(result, expected)
# can't select if not written as table
self.store['frame'] = df
self.assertRaises(Exception, self.store.select,
'frame', [crit1, crit2])
def test_select_filter_corner(self):
df = DataFrame(np.random.randn(50, 100))
df.index = ['%.3d' % c for c in df.index]
df.columns = ['%.3d' % c for c in df.columns]
self.store.put('frame', df, table=True)
crit = {
'field' : 'column',
'value' : df.columns[:75]
}
result = self.store.select('frame', [crit])
tm.assert_frame_equal(result, df.ix[:, df.columns[:75]])
def _check_roundtrip(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj)
finally:
store.close()
os.remove(self.scratchpath)
def _check_roundtrip_table(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
sorted_obj = _test_sort(obj)
comparator(retrieved, sorted_obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_legacy_read(self):
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy.h5'), 'r')
store['a']
store['b']
store['c']
store['d']
store.close()
def test_store_datetime_fractional_secs(self):
dt = datetime(2012, 1, 2, 3, 4, 5, 123456)
series = Series([0], [dt])
self.store['a'] = series
self.assertEquals(self.store['a'].index[0], dt)
text = file.read()
text = json.loads(text)
转换成了一个字符串
json_normalize(text,'books') 查看图书信息表格，读取以books作为键的元素的值
----5.8 HDF5格式 （二进制） =====分析大量数据======
HDF5库
from pandas.io.pytables import HDFStore
创建一个.h5文件，把 DataFrame对象存储
store = HDFStore('mydata.h5')
store['obj1'] = frame
还可以吧多种数据结构的数据存储到一个 HDF5文件中
两种存储模式 ；fixed和table 后者有点慢
store.put('obj2',frame,format='table')
store.select('obj2',where=['index>=10 and index <= 15'])
逆操作
store['obj2']
5.9 pickle python对象序列化======
序列化是指把对象的层级几个转换成字节流的过程
5.9.1 用 cPickle实现python对象序列化
import cpickle as pickle
dumps()函数
pickled_data = pickle.dumps(data)
class TestHDFStore(unittest.TestCase):
path = '__test__.h5'
scratchpath = '__scratch__.h5'
def setUp(self):
self.store = HDFStore(self.path)
def tearDown(self):
self.store.close()
os.remove(self.path)
def test_factory_fun(self):
try:
with get_store(self.scratchpath) as tbl:
raise ValueError('blah')
except ValueError:
pass
with get_store(self.scratchpath) as tbl:
tbl['a'] = tm.makeDataFrame()
with get_store(self.scratchpath) as tbl:
self.assertEquals(len(tbl), 1)
self.assertEquals(type(tbl['a']), DataFrame)
os.remove(self.scratchpath)
def test_len_keys(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.assertEquals(len(self.store), 4)
self.assert_(set(self.store.keys()) == set(['a', 'b', 'c', 'd']))
def test_repr(self):
repr(self.store)
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
repr(self.store)
def test_contains(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeDataFrame()
self.assert_('a' in self.store)
self.assert_('b' in self.store)
self.assert_('c' not in self.store)
def test_reopen_handle(self):
self.store['a'] = tm.makeTimeSeries()
self.store.open('w', warn=False)
self.assert_(self.store.handle.isopen)
self.assertEquals(len(self.store), 0)
def test_flush(self):
self.store['a'] = tm.makeTimeSeries()
self.store.flush()
def test_get(self):
self.store['a'] = tm.makeTimeSeries()
left = self.store.get('a')
right = self.store['a']
tm.assert_series_equal(left, right)
self.assertRaises(KeyError, self.store.get, 'b')
def test_put(self):
ts = tm.makeTimeSeries()
df = tm.makeTimeDataFrame()
self.store['a'] = ts
self.store['b'] = df[:10]
self.store.put('c', df[:10], table=True)
# not OK, not a table
self.assertRaises(ValueError, self.store.put, 'b', df[10:], append=True)
# node does not currently exist, test _is_table_type returns False in
# this case
self.assertRaises(ValueError, self.store.put, 'f', df[10:], append=True)
# OK
self.store.put('c', df[10:], append=True)
# overwrite table
self.store.put('c', df[:10], table=True, append=False)
tm.assert_frame_equal(df[:10], self.store['c'])
def test_put_compression(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df, table=True, compression='zlib')
tm.assert_frame_equal(self.store['c'], df)
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='zlib')
def test_put_compression_blosc(self):
tm.skip_if_no_package('tables', '2.2', app='blosc support')
df = tm.makeTimeDataFrame()
# can't compress if table=False
self.assertRaises(ValueError, self.store.put, 'b', df,
table=False, compression='blosc')
self.store.put('c', df, table=True, compression='blosc')
tm.assert_frame_equal(self.store['c'], df)
def test_put_integer(self):
# non-date, non-string index
df = DataFrame(np.random.randn(50, 100))
self._check_roundtrip(df, tm.assert_frame_equal)
def test_append(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df[:10], table=True)
self.store.append('c', df[10:])
tm.assert_frame_equal(self.store['c'], df)
def test_append_diff_item_order(self):
wp = tm.makePanel()
wp1 = wp.ix[:, :10, :]
wp2 = wp.ix[['ItemC', 'ItemB', 'ItemA'], 10:, :]
self.store.put('panel', wp1, table=True)
self.assertRaises(Exception, self.store.put, 'panel', wp2,
append=True)
def test_remove(self):
ts = tm.makeTimeSeries()
df = tm.makeDataFrame()
self.store['a'] = ts
self.store['b'] = df
self.store.remove('a')
self.assertEquals(len(self.store), 1)
tm.assert_frame_equal(df, self.store['b'])
self.store.remove('b')
self.assertEquals(len(self.store), 0)
def test_remove_where_not_exist(self):
crit1 = {
'field' : 'index',
'op' : '>',
'value' : 'foo'
}
self.store.remove('a', where=[crit1])
def test_remove_crit(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = {
'field' : 'index',
'op' : '>',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
self.store.remove('wp', where=[crit1])
self.store.remove('wp', where=[crit2])
result = self.store['wp']
expected = wp.truncate(after=date).reindex(minor=['B', 'C'])
tm.assert_panel_equal(result, expected)
def test_series(self):
s = tm.makeStringSeries()
self._check_roundtrip(s, tm.assert_series_equal)
ts = tm.makeTimeSeries()
self._check_roundtrip(ts, tm.assert_series_equal)
ts2 = Series(ts.index, Index(ts.index, dtype=object))
self._check_roundtrip(ts2, tm.assert_series_equal)
ts3 = Series(ts.values, Index(np.asarray(ts.index, dtype=object),
dtype=object))
self._check_roundtrip(ts3, tm.assert_series_equal)
def test_sparse_series(self):
s = tm.makeStringSeries()
s[3:5] = np.nan
ss = s.to_sparse()
self._check_roundtrip(ss, tm.assert_series_equal,
check_series_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_roundtrip(ss2, tm.assert_series_equal,
check_series_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_roundtrip(ss3, tm.assert_series_equal,
check_series_type=True)
def test_sparse_frame(self):
s = tm.makeDataFrame()
s.ix[3:5, 1:3] = np.nan
s.ix[8:10, -2] = np.nan
ss = s.to_sparse()
self._check_double_roundtrip(ss, tm.assert_frame_equal,
check_frame_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_double_roundtrip(ss2, tm.assert_frame_equal,
check_frame_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_double_roundtrip(ss3, tm.assert_frame_equal,
check_frame_type=True)
def test_sparse_panel(self):
items = ['x', 'y', 'z']
p = Panel(dict((i, tm.makeDataFrame()) for i in items))
sp = p.to_sparse()
self._check_double_roundtrip(sp, tm.assert_panel_equal,
check_panel_type=True)
sp2 = p.to_sparse(kind='integer')
self._check_double_roundtrip(sp2, tm.assert_panel_equal,
check_panel_type=True)
sp3 = p.to_sparse(fill_value=0)
self._check_double_roundtrip(sp3, tm.assert_panel_equal,
check_panel_type=True)
def test_float_index(self):
# GH #454
index = np.random.randn(10)
s = Series(np.random.randn(10), index=index)
self._check_roundtrip(s, tm.assert_series_equal)
def test_tuple_index(self):
# GH #492
col = np.arange(10)
idx = [(0.,1.), (2., 3.), (4., 5.)]
data = np.random.randn(30).reshape((3, 10))
DF = DataFrame(data, index=idx, columns=col)
self._check_roundtrip(DF, tm.assert_frame_equal)
def test_index_types(self):
values = np.random.randn(2)
func = lambda l, r : tm.assert_series_equal(l, r, True, True, True)
ser = Series(values, [0, 'y'])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime.today(), 0])
self._check_roundtrip(ser, func)
ser = Series(values, ['y', 0])
self._check_roundtrip(ser, func)
from datetime import date
ser = Series(values, [date.today(), 'a'])
self._check_roundtrip(ser, func)
ser = Series(values, [1.23, 'b'])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 1.53])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 5])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime(2012, 1, 1), datetime(2012, 1, 2)])
self._check_roundtrip(ser, func)
def test_timeseries_preepoch(self):
if sys.version_info[0] == 2 and sys.version_info[1] < 7:
raise nose.SkipTest
dr = bdate_range('1/1/1940', '1/1/1960')
ts = Series(np.random.randn(len(dr)), index=dr)
try:
self._check_roundtrip(ts, tm.assert_series_equal)
except OverflowError:
raise nose.SkipTest('known failer on some windows platforms')
def test_frame(self):
df = tm.makeDataFrame()
# put in some random NAs
df.values[0, 0] = np.nan
df.values[5, 3] = np.nan
self._check_roundtrip_table(df, tm.assert_frame_equal)
self._check_roundtrip(df, tm.assert_frame_equal)
self._check_roundtrip_table(df, tm.assert_frame_equal,
compression=True)
self._check_roundtrip(df, tm.assert_frame_equal,
compression=True)
tdf = tm.makeTimeDataFrame()
self._check_roundtrip(tdf, tm.assert_frame_equal)
self._check_roundtrip(tdf, tm.assert_frame_equal,
compression=True)
# not consolidated
df['foo'] = np.random.randn(len(df))
self.store['df'] = df
recons = self.store['df']
self.assert_(recons._data.is_consolidated())
# empty
self._check_roundtrip(df[:0], tm.assert_frame_equal)
def test_empty_series_frame(self):
s0 = Series()
s1 = Series(name='myseries')
df0 = DataFrame()
df1 = DataFrame(index=['a', 'b', 'c'])
df2 = DataFrame(columns=['d', 'e', 'f'])
self._check_roundtrip(s0, tm.assert_series_equal)
self._check_roundtrip(s1, tm.assert_series_equal)
self._check_roundtrip(df0, tm.assert_frame_equal)
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
def test_can_serialize_dates(self):
rng = [x.date() for x in bdate_range('1/1/2000', '1/30/2000')]
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
self._check_roundtrip(frame, tm.assert_frame_equal)
def test_timezones(self):
rng = date_range('1/1/2000', '1/30/2000', tz='US/Eastern')
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
self.assert_(recons.index.equals(rng))
self.assertEquals(rng.tz, recons.index.tz)
finally:
store.close()
os.remove(self.scratchpath)
def test_store_hierarchical(self):
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo', 'bar'])
frame = DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
self._check_roundtrip(frame, tm.assert_frame_equal)
self._check_roundtrip(frame.T, tm.assert_frame_equal)
self._check_roundtrip(frame['A'], tm.assert_series_equal)
# check that the names are stored
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
assert(recons.index.names == ['foo', 'bar'])
finally:
store.close()
os.remove(self.scratchpath)
def test_store_index_name(self):
df = tm.makeDataFrame()
df.index.name = 'foo'
try:
store = HDFStore(self.scratchpath)
store['frame'] = df
recons = store['frame']
assert(recons.index.name == 'foo')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_series_name(self):
df = tm.makeDataFrame()
series = df['A']
try:
store = HDFStore(self.scratchpath)
store['series'] = series
recons = store['series']
assert(recons.name == 'A')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_mixed(self):
def _make_one():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one()
df2 = _make_one()
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
self.store['obj'] = df1
tm.assert_frame_equal(self.store['obj'], df1)
self.store['obj'] = df2
tm.assert_frame_equal(self.store['obj'], df2)
# storing in Table not yet supported
self.assertRaises(Exception, self.store.put, 'foo',
df1, table=True)
# check that can store Series of all of these types
self._check_roundtrip(df1['obj1'], tm.assert_series_equal)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal)
self._check_roundtrip(df1['int1'], tm.assert_series_equal)
# try with compression
self._check_roundtrip(df1['obj1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['int1'], tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1, tm.assert_frame_equal,
compression=True)
def test_wide(self):
wp = tm.makePanel()
self._check_roundtrip(wp, tm.assert_panel_equal)
def test_wide_table(self):
wp = tm.makePanel()
self._check_roundtrip_table(wp, tm.assert_panel_equal)
def test_wide_table_dups(self):
wp = tm.makePanel()
try:
store = HDFStore(self.scratchpath)
store._quiet = True
store.put('panel', wp, table=True)
store.put('panel', wp, table=True, append=True)
recons = store['panel']
tm.assert_panel_equal(recons, wp)
finally:
store.close()
os.remove(self.scratchpath)
def test_long(self):
def _check(left, right):
tm.assert_panel_equal(left.to_panel(), right.to_panel())
wp = tm.makePanel()
self._check_roundtrip(wp.to_frame(), _check)
# empty
# self._check_roundtrip(wp.to_frame()[:0], _check)
def test_longpanel(self):
pass
def test_overwrite_node(self):
self.store['a'] = tm.makeTimeDataFrame()
ts = tm.makeTimeSeries()
self.store['a'] = ts
tm.assert_series_equal(self.store['a'], ts)
def test_panel_select(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = {
'field' : 'index',
'op' : '>=',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
result = self.store.select('wp', [crit1, crit2])
expected = wp.truncate(before=date).reindex(minor=['A', 'D'])
tm.assert_panel_equal(result, expected)
def test_frame_select(self):
df = tm.makeTimeDataFrame()
self.store.put('frame', df, table=True)
date = df.index[len(df) // 2]
crit1 = {
'field' : 'index',
'op' : '>=',
'value' : date
}
crit2 = {
'field' : 'column',
'value' : ['A', 'D']
}
crit3 = {
'field' : 'column',
'value' : 'A'
}
result = self.store.select('frame', [crit1, crit2])
expected = df.ix[date:, ['A', 'D']]
tm.assert_frame_equal(result, expected)
result = self.store.select('frame', [crit3])
expected = df.ix[:, ['A']]
tm.assert_frame_equal(result, expected)
# can't select if not written as table
self.store['frame'] = df
self.assertRaises(Exception, self.store.select,
'frame', [crit1, crit2])
def test_select_filter_corner(self):
df = DataFrame(np.random.randn(50, 100))
df.index = ['%.3d' % c for c in df.index]
df.columns = ['%.3d' % c for c in df.columns]
self.store.put('frame', df, table=True)
crit = {
'field' : 'column',
'value' : df.columns[:75]
}
result = self.store.select('frame', [crit])
tm.assert_frame_equal(result, df.ix[:, df.columns[:75]])
def _check_roundtrip(self, obj, comparator, compression=False, **kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_double_roundtrip(self, obj, comparator, compression=False,
**kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
store['obj'] = retrieved
again = store['obj']
comparator(again, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_roundtrip_table(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
sorted_obj = _test_sort(obj)
comparator(retrieved, sorted_obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_legacy_read(self):
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy.h5'), 'r')
store['a']
store['b']
store['c']
store['d']
store.close()
def test_store_datetime_fractional_secs(self):
dt = datetime(2012, 1, 2, 3, 4, 5, 123456)
series = Series([0], [dt])
self.store['a'] = series
self.assertEquals(self.store['a'].index[0], dt)
def test_tseries_indices_series(self):
idx = tm.makeDateIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
idx = tm.makePeriodIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
def test_tseries_indices_frame(self):
idx = tm.makeDateIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
idx = tm.makePeriodIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
def test_unicode_index(self):
unicode_values = [u'\u03c3', u'\u03c3\u03c3']
s = Series(np.random.randn(len(unicode_values)), unicode_values)
self._check_roundtrip(s, tm.assert_series_equal)
def test_store_datetime_mixed(self):
df = DataFrame({'a': [1,2,3], 'b': [1.,2.,3.], 'c': ['a', 'b', 'c']})
ts = tm.makeTimeSeries()
df['d'] = ts.index[:3]
self._check_roundtrip(df, tm.assert_frame_equal)
def test_cant_write_multiindex_table(self):
# for now, #1848
df = DataFrame(np.random.randn(10, 4),
index=[np.arange(5).repeat(2),
np.tile(np.arange(2), 5)])
self.assertRaises(Exception, self.store.put, 'foo', df, table=True)
class TestHDFStore(unittest.TestCase):
path = '__test__.h5'
scratchpath = '__scratch__.h5'
def setUp(self):
self.store = HDFStore(self.path)
def tearDown(self):
self.store.close()
os.remove(self.path)
def test_factory_fun(self):
try:
with get_store(self.scratchpath) as tbl:
raise ValueError('blah')
except ValueError:
pass
with get_store(self.scratchpath) as tbl:
tbl['a'] = tm.makeDataFrame()
with get_store(self.scratchpath) as tbl:
self.assertEquals(len(tbl), 1)
self.assertEquals(type(tbl['a']), DataFrame)
os.remove(self.scratchpath)
def test_keys(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.store['foo/bar'] = tm.makePanel()
self.assertEquals(len(self.store), 5)
self.assert_(
set(self.store.keys()) == set(['/a', '/b', '/c', '/d', '/foo/bar'
]))
def test_repr(self):
repr(self.store)
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeStringSeries()
self.store['c'] = tm.makeDataFrame()
self.store['d'] = tm.makePanel()
self.store['foo/bar'] = tm.makePanel()
self.store.append('e', tm.makePanel())
repr(self.store)
str(self.store)
def test_contains(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeDataFrame()
self.store['foo/bar'] = tm.makeDataFrame()
self.assert_('a' in self.store)
self.assert_('b' in self.store)
self.assert_('c' not in self.store)
self.assert_('foo/bar' in self.store)
self.assert_('/foo/bar' in self.store)
self.assert_('/foo/b' not in self.store)
self.assert_('bar' not in self.store)
def test_versioning(self):
self.store['a'] = tm.makeTimeSeries()
self.store['b'] = tm.makeDataFrame()
df = tm.makeTimeDataFrame()
self.store.remove('df1')
self.store.append('df1', df[:10])
self.store.append('df1', df[10:])
self.assert_(self.store.root.a._v_attrs.pandas_version == '0.10')
self.assert_(self.store.root.b._v_attrs.pandas_version == '0.10')
self.assert_(self.store.root.df1._v_attrs.pandas_version == '0.10')
# write a file and wipe its versioning
self.store.remove('df2')
self.store.append('df2', df)
self.store.get_node('df2')._v_attrs.pandas_version = None
self.store.select('df2')
self.store.select('df2', [Term('index', '>', df.index[2])])
def test_meta(self):
raise nose.SkipTest('no meta')
meta = {'foo': ['I love pandas ']}
s = tm.makeTimeSeries()
s.meta = meta
self.store['a'] = s
self.assert_(self.store['a'].meta == meta)
df = tm.makeDataFrame()
df.meta = meta
self.store['b'] = df
self.assert_(self.store['b'].meta == meta)
# this should work, but because slicing doesn't propgate meta it doesn
self.store.remove('df1')
self.store.append('df1', df[:10])
self.store.append('df1', df[10:])
results = self.store['df1']
#self.assert_(getattr(results,'meta',None) == meta)
# no meta
df = tm.makeDataFrame()
self.store['b'] = df
self.assert_(hasattr(self.store['b'], 'meta') == False)
def test_reopen_handle(self):
self.store['a'] = tm.makeTimeSeries()
self.store.open('w', warn=False)
self.assert_(self.store.handle.isopen)
self.assertEquals(len(self.store), 0)
def test_flush(self):
self.store['a'] = tm.makeTimeSeries()
self.store.flush()
def test_get(self):
self.store['a'] = tm.makeTimeSeries()
left = self.store.get('a')
right = self.store['a']
tm.assert_series_equal(left, right)
left = self.store.get('/a')
right = self.store['/a']
tm.assert_series_equal(left, right)
self.assertRaises(KeyError, self.store.get, 'b')
def test_put(self):
ts = tm.makeTimeSeries()
df = tm.makeTimeDataFrame()
self.store['a'] = ts
self.store['b'] = df[:10]
self.store['foo/bar/bah'] = df[:10]
self.store['foo'] = df[:10]
self.store['/foo'] = df[:10]
self.store.put('c', df[:10], table=True)
# not OK, not a table
self.assertRaises(ValueError,
self.store.put,
'b',
df[10:],
append=True)
# node does not currently exist, test _is_table_type returns False in
# this case
self.assertRaises(ValueError,
self.store.put,
'f',
df[10:],
append=True)
# OK
self.store.put('c', df[10:], append=True)
# overwrite table
self.store.put('c', df[:10], table=True, append=False)
tm.assert_frame_equal(df[:10], self.store['c'])
def test_put_string_index(self):
index = Index(
["I am a very long string index: %s" % i for i in range(20)])
s = Series(np.arange(20), index=index)
df = DataFrame({'A': s, 'B': s})
self.store['a'] = s
tm.assert_series_equal(self.store['a'], s)
self.store['b'] = df
tm.assert_frame_equal(self.store['b'], df)
# mixed length
index = Index(
['abcdefghijklmnopqrstuvwxyz1234567890'] +
["I am a very long string index: %s" % i for i in range(20)])
s = Series(np.arange(21), index=index)
df = DataFrame({'A': s, 'B': s})
self.store['a'] = s
tm.assert_series_equal(self.store['a'], s)
self.store['b'] = df
tm.assert_frame_equal(self.store['b'], df)
def test_put_compression(self):
df = tm.makeTimeDataFrame()
self.store.put('c', df, table=True, compression='zlib')
tm.assert_frame_equal(self.store['c'], df)
# can't compress if table=False
self.assertRaises(ValueError,
self.store.put,
'b',
df,
table=False,
compression='zlib')
def test_put_compression_blosc(self):
tm.skip_if_no_package('tables', '2.2', app='blosc support')
df = tm.makeTimeDataFrame()
# can't compress if table=False
self.assertRaises(ValueError,
self.store.put,
'b',
df,
table=False,
compression='blosc')
self.store.put('c', df, table=True, compression='blosc')
tm.assert_frame_equal(self.store['c'], df)
def test_put_integer(self):
# non-date, non-string index
df = DataFrame(np.random.randn(50, 100))
self._check_roundtrip(df, tm.assert_frame_equal)
def test_append(self):
df = tm.makeTimeDataFrame()
self.store.remove('df1')
self.store.append('df1', df[:10])
self.store.append('df1', df[10:])
tm.assert_frame_equal(self.store['df1'], df)
self.store.remove('df2')
self.store.put('df2', df[:10], table=True)
self.store.append('df2', df[10:])
tm.assert_frame_equal(self.store['df2'], df)
self.store.remove('df3')
self.store.append('/df3', df[:10])
self.store.append('/df3', df[10:])
tm.assert_frame_equal(self.store['df3'], df)
# this is allowed by almost always don't want to do it
warnings.filterwarnings('ignore', category=tables.NaturalNameWarning)
self.store.remove('/df3 foo')
self.store.append('/df3 foo', df[:10])
self.store.append('/df3 foo', df[10:])
tm.assert_frame_equal(self.store['df3 foo'], df)
warnings.filterwarnings('always', category=tables.NaturalNameWarning)
# panel
wp = tm.makePanel()
self.store.remove('wp1')
self.store.append('wp1', wp.ix[:, :10, :])
self.store.append('wp1', wp.ix[:, 10:, :])
tm.assert_panel_equal(self.store['wp1'], wp)
# ndim
p4d = tm.makePanel4D()
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:, :, :10, :])
self.store.append('p4d', p4d.ix[:, :, 10:, :])
tm.assert_panel4d_equal(self.store['p4d'], p4d)
# test using axis labels
self.store.remove('p4d')
self.store.append('p4d',
p4d.ix[:, :, :10, :],
axes=['items', 'major_axis', 'minor_axis'])
self.store.append('p4d',
p4d.ix[:, :, 10:, :],
axes=['items', 'major_axis', 'minor_axis'])
tm.assert_panel4d_equal(self.store['p4d'], p4d)
# test using differnt number of items on each axis
p4d2 = p4d.copy()
p4d2['l4'] = p4d['l1']
p4d2['l5'] = p4d['l1']
self.store.remove('p4d2')
self.store.append('p4d2',
p4d2,
axes=['items', 'major_axis', 'minor_axis'])
tm.assert_panel4d_equal(self.store['p4d2'], p4d2)
# test using differt order of items on the non-index axes
self.store.remove('wp1')
wp_append1 = wp.ix[:, :10, :]
self.store.append('wp1', wp_append1)
wp_append2 = wp.ix[:, 10:, :].reindex(items=wp.items[::-1])
self.store.append('wp1', wp_append2)
tm.assert_panel_equal(self.store['wp1'], wp)
def test_append_frame_column_oriented(self):
# column oriented
df = tm.makeTimeDataFrame()
self.store.remove('df1')
self.store.append('df1', df.ix[:, :2], axes=['columns'])
self.store.append('df1', df.ix[:, 2:])
tm.assert_frame_equal(self.store['df1'], df)
result = self.store.select('df1', 'columns=A')
expected = df.reindex(columns=['A'])
tm.assert_frame_equal(expected, result)
# this isn't supported
self.assertRaises(Exception, self.store.select, 'df1',
('columns=A', Term('index', '>', df.index[4])))
# selection on the non-indexable
result = self.store.select(
'df1', ('columns=A', Term('index', '=', df.index[0:4])))
expected = df.reindex(columns=['A'], index=df.index[0:4])
tm.assert_frame_equal(expected, result)
def test_ndim_indexables(self):
""" test using ndim tables in new ways"""
p4d = tm.makePanel4D()
def check_indexers(key, indexers):
for i, idx in enumerate(indexers):
self.assert_(
getattr(
getattr(self.store.root, key).table.description,
idx)._v_pos == i)
# append then change (will take existing schema)
indexers = ['items', 'major_axis', 'minor_axis']
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:, :, :10, :], axes=indexers)
self.store.append('p4d', p4d.ix[:, :, 10:, :])
tm.assert_panel4d_equal(self.store.select('p4d'), p4d)
check_indexers('p4d', indexers)
# same as above, but try to append with differnt axes
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:, :, :10, :], axes=indexers)
self.store.append('p4d',
p4d.ix[:, :, 10:, :],
axes=['labels', 'items', 'major_axis'])
tm.assert_panel4d_equal(self.store.select('p4d'), p4d)
check_indexers('p4d', indexers)
# pass incorrect number of axes
self.store.remove('p4d')
self.assertRaises(Exception,
self.store.append,
'p4d',
p4d.ix[:, :, :10, :],
axes=['major_axis', 'minor_axis'])
# different than default indexables #1
indexers = ['labels', 'major_axis', 'minor_axis']
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:, :, :10, :], axes=indexers)
self.store.append('p4d', p4d.ix[:, :, 10:, :])
tm.assert_panel4d_equal(self.store['p4d'], p4d)
check_indexers('p4d', indexers)
# different than default indexables #2
indexers = ['major_axis', 'labels', 'minor_axis']
self.store.remove('p4d')
self.store.append('p4d', p4d.ix[:, :, :10, :], axes=indexers)
self.store.append('p4d', p4d.ix[:, :, 10:, :])
tm.assert_panel4d_equal(self.store['p4d'], p4d)
check_indexers('p4d', indexers)
# partial selection
result = self.store.select('p4d', ['labels=l1'])
expected = p4d.reindex(labels=['l1'])
tm.assert_panel4d_equal(result, expected)
# partial selection2
result = self.store.select(
'p4d',
[Term('labels=l1'),
Term('items=ItemA'),
Term('minor_axis=B')])
expected = p4d.reindex(labels=['l1'],
items=['ItemA'],
minor_axis=['B'])
tm.assert_panel4d_equal(result, expected)
# non-existant partial selection
result = self.store.select(
'p4d',
[Term('labels=l1'),
Term('items=Item1'),
Term('minor_axis=B')])
expected = p4d.reindex(labels=['l1'], items=[], minor_axis=['B'])
tm.assert_panel4d_equal(result, expected)
def test_append_with_strings(self):
wp = tm.makePanel()
wp2 = wp.rename_axis(dict([(x, "%s_extra" % x)
for x in wp.minor_axis]),
axis=2)
self.store.append('s1', wp, min_itemsize=20)
self.store.append('s1', wp2)
expected = concat([wp, wp2], axis=2)
expected = expected.reindex(minor_axis=sorted(expected.minor_axis))
tm.assert_panel_equal(self.store['s1'], expected)
# test dict format
self.store.append('s2', wp, min_itemsize={'minor_axis': 20})
self.store.append('s2', wp2)
expected = concat([wp, wp2], axis=2)
expected = expected.reindex(minor_axis=sorted(expected.minor_axis))
tm.assert_panel_equal(self.store['s2'], expected)
# apply the wrong field (similar to #1)
self.store.append('s3', wp, min_itemsize={'major_axis': 20})
self.assertRaises(Exception, self.store.append, 's3')
# test truncation of bigger strings
self.store.append('s4', wp)
self.assertRaises(Exception, self.store.append, 's4', wp2)
# avoid truncation on elements
df = DataFrame([[123, 'asdqwerty'], [345, 'dggnhebbsdfbdfb']])
self.store.append('df_big', df, min_itemsize={'values': 1024})
tm.assert_frame_equal(self.store.select('df_big'), df)
# appending smaller string ok
df2 = DataFrame([[124, 'asdqy'], [346, 'dggnhefbdfb']])
self.store.append('df_big', df2)
expected = concat([df, df2])
tm.assert_frame_equal(self.store.select('df_big'), expected)
# avoid truncation on elements
df = DataFrame([[123, 'asdqwerty'], [345, 'dggnhebbsdfbdfb']])
self.store.append('df_big2', df, min_itemsize={'values': 10})
tm.assert_frame_equal(self.store.select('df_big2'), df)
# bigger string on next append
self.store.append('df_new', df, min_itemsize={'values': 16})
df_new = DataFrame([[124, 'abcdefqhij'],
[346, 'abcdefghijklmnopqrtsuvwxyz']])
self.assertRaises(Exception, self.store.append, 'df_new', df_new)
def test_create_table_index(self):
wp = tm.makePanel()
self.store.append('p5', wp)
self.store.create_table_index('p5')
assert (
self.store.handle.root.p5.table.cols.major_axis.is_indexed == True)
assert (self.store.handle.root.p5.table.cols.minor_axis.is_indexed ==
False)
# default optlevels
assert (self.store.handle.root.p5.table.cols.major_axis.index.optlevel
== 6)
assert (self.store.handle.root.p5.table.cols.major_axis.index.kind ==
'medium')
# let's change the indexing scheme
self.store.create_table_index('p5')
assert (self.store.handle.root.p5.table.cols.major_axis.index.optlevel
== 6)
assert (self.store.handle.root.p5.table.cols.major_axis.index.kind ==
'medium')
self.store.create_table_index('p5', optlevel=9)
assert (self.store.handle.root.p5.table.cols.major_axis.index.optlevel
== 9)
assert (self.store.handle.root.p5.table.cols.major_axis.index.kind ==
'medium')
self.store.create_table_index('p5', kind='full')
assert (self.store.handle.root.p5.table.cols.major_axis.index.optlevel
== 9)
assert (self.store.handle.root.p5.table.cols.major_axis.index.kind ==
'full')
self.store.create_table_index('p5', optlevel=1, kind='light')
assert (self.store.handle.root.p5.table.cols.major_axis.index.optlevel
== 1)
assert (self.store.handle.root.p5.table.cols.major_axis.index.kind ==
'light')
df = tm.makeTimeDataFrame()
self.store.append('f', df[:10])
self.store.append('f', df[10:])
self.store.create_table_index('f')
# try to index a non-table
self.store.put('f2', df)
self.assertRaises(Exception, self.store.create_table_index, 'f2')
# try to change the version supports flag
from pandas.io import pytables
pytables._table_supports_index = False
self.assertRaises(Exception, self.store.create_table_index, 'f')
# test out some versions
original = tables.__version__
for v in ['2.2', '2.2b']:
pytables._table_mod = None
pytables._table_supports_index = False
tables.__version__ = v
self.assertRaises(Exception, self.store.create_table_index, 'f')
for v in ['2.3.1', '2.3.1b', '2.4dev', '2.4', original]:
pytables._table_mod = None
pytables._table_supports_index = False
tables.__version__ = v
self.store.create_table_index('f')
pytables._table_mod = None
pytables._table_supports_index = False
tables.__version__ = original
def test_big_table(self):
raise nose.SkipTest('no big table')
# create and write a big table
wp = Panel(np.random.randn(20, 1000, 1000),
items=['Item%s' % i for i in xrange(20)],
major_axis=date_range('1/1/2000', periods=1000),
minor_axis=['E%s' % i for i in xrange(1000)])
wp.ix[:, 100:200, 300:400] = np.nan
try:
store = HDFStore(self.scratchpath)
store._debug_memory = True
store.append('wp', wp)
recons = store.select('wp')
finally:
store.close()
os.remove(self.scratchpath)
def test_append_diff_item_order(self):
raise nose.SkipTest('append diff item order')
wp = tm.makePanel()
wp1 = wp.ix[:, :10, :]
wp2 = wp.ix[['ItemC', 'ItemB', 'ItemA'], 10:, :]
self.store.put('panel', wp1, table=True)
self.assertRaises(Exception, self.store.put, 'panel', wp2, append=True)
def test_table_index_incompatible_dtypes(self):
df1 = DataFrame({'a': [1, 2, 3]})
df2 = DataFrame({'a': [4, 5, 6]},
index=date_range('1/1/2000', periods=3))
self.store.put('frame', df1, table=True)
self.assertRaises(Exception,
self.store.put,
'frame',
df2,
table=True,
append=True)
def test_table_values_dtypes_roundtrip(self):
df1 = DataFrame({'a': [1, 2, 3]}, dtype='f8')
self.store.append('df1', df1)
assert df1.dtypes == self.store['df1'].dtypes
df2 = DataFrame({'a': [1, 2, 3]}, dtype='i8')
self.store.append('df2', df2)
assert df2.dtypes == self.store['df2'].dtypes
# incompatible dtype
self.assertRaises(Exception, self.store.append, 'df2', df1)
def test_table_mixed_dtypes(self):
# frame
def _make_one_df():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['bool3'] = True
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one_df()
self.store.append('df1_mixed', df1)
tm.assert_frame_equal(self.store.select('df1_mixed'), df1)
# panel
def _make_one_panel():
wp = tm.makePanel()
wp['obj1'] = 'foo'
wp['obj2'] = 'bar'
wp['bool1'] = wp['ItemA'] > 0
wp['bool2'] = wp['ItemB'] > 0
wp['int1'] = 1
wp['int2'] = 2
return wp.consolidate()
p1 = _make_one_panel()
self.store.append('p1_mixed', p1)
tm.assert_panel_equal(self.store.select('p1_mixed'), p1)
# ndim
def _make_one_p4d():
wp = tm.makePanel4D()
wp['obj1'] = 'foo'
wp['obj2'] = 'bar'
wp['bool1'] = wp['l1'] > 0
wp['bool2'] = wp['l2'] > 0
wp['int1'] = 1
wp['int2'] = 2
return wp.consolidate()
p4d = _make_one_p4d()
self.store.append('p4d_mixed', p4d)
tm.assert_panel4d_equal(self.store.select('p4d_mixed'), p4d)
def test_remove(self):
ts = tm.makeTimeSeries()
df = tm.makeDataFrame()
self.store['a'] = ts
self.store['b'] = df
self.store.remove('a')
self.assertEquals(len(self.store), 1)
tm.assert_frame_equal(df, self.store['b'])
self.store.remove('b')
self.assertEquals(len(self.store), 0)
# pathing
self.store['a'] = ts
self.store['b/foo'] = df
self.store.remove('foo')
self.store.remove('b/foo')
self.assertEquals(len(self.store), 1)
self.store['a'] = ts
self.store['b/foo'] = df
self.store.remove('b')
self.assertEquals(len(self.store), 1)
# __delitem__
self.store['a'] = ts
self.store['b'] = df
del self.store['a']
del self.store['b']
self.assertEquals(len(self.store), 0)
def test_remove_where(self):
# non-existance
crit1 = Term('index', '>', 'foo')
self.store.remove('a', where=[crit1])
# try to remove non-table (with crit)
# non-table ok (where = None)
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
self.store.remove('wp', [('minor_axis', ['A', 'D'])])
rs = self.store.select('wp')
expected = wp.reindex(minor_axis=['B', 'C'])
tm.assert_panel_equal(rs, expected)
# empty where
self.store.remove('wp')
self.store.put('wp', wp, table=True)
# deleted number (entire table)
n = self.store.remove('wp', [])
assert (n == 120)
# non - empty where
self.store.remove('wp')
self.store.put('wp', wp, table=True)
self.assertRaises(Exception, self.store.remove, 'wp', ['foo'])
# selectin non-table with a where
#self.store.put('wp2', wp, table=False)
#self.assertRaises(Exception, self.store.remove,
# 'wp2', [('column', ['A', 'D'])])
def test_remove_crit(self):
wp = tm.makePanel()
# group row removal
date4 = wp.major_axis.take([0, 1, 2, 4, 5, 6, 8, 9, 10])
crit4 = Term('major_axis', date4)
self.store.put('wp3', wp, table=True)
n = self.store.remove('wp3', where=[crit4])
assert (n == 36)
result = self.store.select('wp3')
expected = wp.reindex(major_axis=wp.major_axis - date4)
tm.assert_panel_equal(result, expected)
# upper half
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = Term('major_axis', '>', date)
crit2 = Term('minor_axis', ['A', 'D'])
n = self.store.remove('wp', where=[crit1])
assert (n == 56)
n = self.store.remove('wp', where=[crit2])
assert (n == 32)
result = self.store['wp']
expected = wp.truncate(after=date).reindex(minor=['B', 'C'])
tm.assert_panel_equal(result, expected)
# individual row elements
self.store.put('wp2', wp, table=True)
date1 = wp.major_axis[1:3]
crit1 = Term('major_axis', date1)
self.store.remove('wp2', where=[crit1])
result = self.store.select('wp2')
expected = wp.reindex(major_axis=wp.major_axis - date1)
tm.assert_panel_equal(result, expected)
date2 = wp.major_axis[5]
crit2 = Term('major_axis', date2)
self.store.remove('wp2', where=[crit2])
result = self.store['wp2']
expected = wp.reindex(major_axis=wp.major_axis - date1 -
Index([date2]))
tm.assert_panel_equal(result, expected)
date3 = [wp.major_axis[7], wp.major_axis[9]]
crit3 = Term('major_axis', date3)
self.store.remove('wp2', where=[crit3])
result = self.store['wp2']
expected = wp.reindex(major_axis=wp.major_axis - date1 -
Index([date2]) - Index(date3))
tm.assert_panel_equal(result, expected)
# corners
self.store.put('wp4', wp, table=True)
n = self.store.remove(
'wp4', where=[Term('major_axis', '>', wp.major_axis[-1])])
result = self.store.select('wp4')
tm.assert_panel_equal(result, wp)
def test_terms(self):
wp = tm.makePanel()
p4d = tm.makePanel4D()
self.store.put('wp', wp, table=True)
self.store.put('p4d', p4d, table=True)
# some invalid terms
terms = [
['minor', ['A', 'B']],
['index', ['20121114']],
['index', ['20121114', '20121114']],
]
for t in terms:
self.assertRaises(Exception, self.store.select, 'wp', t)
self.assertRaises(Exception, Term.__init__)
self.assertRaises(Exception, Term.__init__, 'blah')
self.assertRaises(Exception, Term.__init__, 'index')
self.assertRaises(Exception, Term.__init__, 'index', '==')
self.assertRaises(Exception, Term.__init__, 'index', '>', 5)
# panel
result = self.store.select(
'wp',
[Term('major_axis<20000108'),
Term('minor_axis', '=', ['A', 'B'])])
expected = wp.truncate(after='20000108').reindex(minor=['A', 'B'])
tm.assert_panel_equal(result, expected)
# p4d
result = self.store.select('p4d', [
Term('major_axis<20000108'),
Term('minor_axis', '=', ['A', 'B']),
Term('items', '=', ['ItemA', 'ItemB'])
])
expected = p4d.truncate(after='20000108').reindex(
minor=['A', 'B'], items=['ItemA', 'ItemB'])
tm.assert_panel4d_equal(result, expected)
# valid terms
terms = [
dict(field='major_axis', op='>', value='20121114'),
('major_axis', '20121114'),
('major_axis', '>', '20121114'),
(('major_axis', ['20121114', '20121114']), ),
('major_axis', datetime(2012, 11, 14)),
'major_axis>20121114',
'major_axis>20121114',
'major_axis>20121114',
(('minor_axis', ['A', 'B']), ),
(('minor_axis', ['A', 'B']), ),
((('minor_axis', ['A', 'B']), ), ),
(('items', ['ItemA', 'ItemB']), ),
('items=ItemA'),
]
for t in terms:
self.store.select('wp', t)
self.store.select('p4d', t)
# valid for p4d only
terms = [
(('labels', '=', ['l1', 'l2']), ),
Term('labels', '=', ['l1', 'l2']),
]
for t in terms:
self.store.select('p4d', t)
def test_series(self):
s = tm.makeStringSeries()
self._check_roundtrip(s, tm.assert_series_equal)
ts = tm.makeTimeSeries()
self._check_roundtrip(ts, tm.assert_series_equal)
ts2 = Series(ts.index, Index(ts.index, dtype=object))
self._check_roundtrip(ts2, tm.assert_series_equal)
ts3 = Series(ts.values,
Index(np.asarray(ts.index, dtype=object), dtype=object))
self._check_roundtrip(ts3, tm.assert_series_equal)
def test_sparse_series(self):
s = tm.makeStringSeries()
s[3:5] = np.nan
ss = s.to_sparse()
self._check_roundtrip(ss,
tm.assert_series_equal,
check_series_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_roundtrip(ss2,
tm.assert_series_equal,
check_series_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_roundtrip(ss3,
tm.assert_series_equal,
check_series_type=True)
def test_sparse_frame(self):
s = tm.makeDataFrame()
s.ix[3:5, 1:3] = np.nan
s.ix[8:10, -2] = np.nan
ss = s.to_sparse()
self._check_double_roundtrip(ss,
tm.assert_frame_equal,
check_frame_type=True)
ss2 = s.to_sparse(kind='integer')
self._check_double_roundtrip(ss2,
tm.assert_frame_equal,
check_frame_type=True)
ss3 = s.to_sparse(fill_value=0)
self._check_double_roundtrip(ss3,
tm.assert_frame_equal,
check_frame_type=True)
def test_sparse_panel(self):
items = ['x', 'y', 'z']
p = Panel(dict((i, tm.makeDataFrame().ix[:2, :2]) for i in items))
sp = p.to_sparse()
self._check_double_roundtrip(sp,
tm.assert_panel_equal,
check_panel_type=True)
sp2 = p.to_sparse(kind='integer')
self._check_double_roundtrip(sp2,
tm.assert_panel_equal,
check_panel_type=True)
sp3 = p.to_sparse(fill_value=0)
self._check_double_roundtrip(sp3,
tm.assert_panel_equal,
check_panel_type=True)
def test_float_index(self):
# GH #454
index = np.random.randn(10)
s = Series(np.random.randn(10), index=index)
self._check_roundtrip(s, tm.assert_series_equal)
def test_tuple_index(self):
# GH #492
col = np.arange(10)
idx = [(0., 1.), (2., 3.), (4., 5.)]
data = np.random.randn(30).reshape((3, 10))
DF = DataFrame(data, index=idx, columns=col)
self._check_roundtrip(DF, tm.assert_frame_equal)
def test_index_types(self):
values = np.random.randn(2)
func = lambda l, r: tm.assert_series_equal(l, r, True, True, True)
ser = Series(values, [0, 'y'])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime.today(), 0])
self._check_roundtrip(ser, func)
ser = Series(values, ['y', 0])
self._check_roundtrip(ser, func)
from datetime import date
ser = Series(values, [date.today(), 'a'])
self._check_roundtrip(ser, func)
ser = Series(values, [1.23, 'b'])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 1.53])
self._check_roundtrip(ser, func)
ser = Series(values, [1, 5])
self._check_roundtrip(ser, func)
ser = Series(values, [datetime(2012, 1, 1), datetime(2012, 1, 2)])
self._check_roundtrip(ser, func)
def test_timeseries_preepoch(self):
if sys.version_info[0] == 2 and sys.version_info[1] < 7:
raise nose.SkipTest
dr = bdate_range('1/1/1940', '1/1/1960')
ts = Series(np.random.randn(len(dr)), index=dr)
try:
self._check_roundtrip(ts, tm.assert_series_equal)
except OverflowError:
raise nose.SkipTest('known failer on some windows platforms')
def test_frame(self):
df = tm.makeDataFrame()
# put in some random NAs
df.values[0, 0] = np.nan
df.values[5, 3] = np.nan
self._check_roundtrip_table(df, tm.assert_frame_equal)
self._check_roundtrip(df, tm.assert_frame_equal)
self._check_roundtrip_table(df,
tm.assert_frame_equal,
compression=True)
self._check_roundtrip(df, tm.assert_frame_equal, compression=True)
tdf = tm.makeTimeDataFrame()
self._check_roundtrip(tdf, tm.assert_frame_equal)
self._check_roundtrip(tdf, tm.assert_frame_equal, compression=True)
# not consolidated
df['foo'] = np.random.randn(len(df))
self.store['df'] = df
recons = self.store['df']
self.assert_(recons._data.is_consolidated())
# empty
self._check_roundtrip(df[:0], tm.assert_frame_equal)
def test_empty_series_frame(self):
s0 = Series()
s1 = Series(name='myseries')
df0 = DataFrame()
df1 = DataFrame(index=['a', 'b', 'c'])
df2 = DataFrame(columns=['d', 'e', 'f'])
self._check_roundtrip(s0, tm.assert_series_equal)
self._check_roundtrip(s1, tm.assert_series_equal)
self._check_roundtrip(df0, tm.assert_frame_equal)
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
def test_can_serialize_dates(self):
rng = [x.date() for x in bdate_range('1/1/2000', '1/30/2000')]
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
self._check_roundtrip(frame, tm.assert_frame_equal)
def test_timezones(self):
rng = date_range('1/1/2000', '1/30/2000', tz='US/Eastern')
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
self.assert_(recons.index.equals(rng))
self.assertEquals(rng.tz, recons.index.tz)
finally:
store.close()
os.remove(self.scratchpath)
def test_fixed_offset_tz(self):
rng = date_range('1/1/2000 00:00:00-07:00', '1/30/2000 00:00:00-07:00')
frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
self.assert_(recons.index.equals(rng))
self.assertEquals(rng.tz, recons.index.tz)
finally:
store.close()
os.remove(self.scratchpath)
def test_store_hierarchical(self):
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['foo', 'bar'])
frame = DataFrame(np.random.randn(10, 3),
index=index,
columns=['A', 'B', 'C'])
self._check_roundtrip(frame, tm.assert_frame_equal)
self._check_roundtrip(frame.T, tm.assert_frame_equal)
self._check_roundtrip(frame['A'], tm.assert_series_equal)
# check that the names are stored
try:
store = HDFStore(self.scratchpath)
store['frame'] = frame
recons = store['frame']
assert (recons.index.names == ['foo', 'bar'])
finally:
store.close()
os.remove(self.scratchpath)
def test_store_index_name(self):
df = tm.makeDataFrame()
df.index.name = 'foo'
try:
store = HDFStore(self.scratchpath)
store['frame'] = df
recons = store['frame']
assert (recons.index.name == 'foo')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_series_name(self):
df = tm.makeDataFrame()
series = df['A']
try:
store = HDFStore(self.scratchpath)
store['series'] = series
recons = store['series']
assert (recons.name == 'A')
finally:
store.close()
os.remove(self.scratchpath)
def test_store_mixed(self):
def _make_one():
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['int1'] = 1
df['int2'] = 2
return df.consolidate()
df1 = _make_one()
df2 = _make_one()
self._check_roundtrip(df1, tm.assert_frame_equal)
self._check_roundtrip(df2, tm.assert_frame_equal)
self.store['obj'] = df1
tm.assert_frame_equal(self.store['obj'], df1)
self.store['obj'] = df2
tm.assert_frame_equal(self.store['obj'], df2)
# check that can store Series of all of these types
self._check_roundtrip(df1['obj1'], tm.assert_series_equal)
self._check_roundtrip(df1['bool1'], tm.assert_series_equal)
self._check_roundtrip(df1['int1'], tm.assert_series_equal)
# try with compression
self._check_roundtrip(df1['obj1'],
tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['bool1'],
tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1['int1'],
tm.assert_series_equal,
compression=True)
self._check_roundtrip(df1, tm.assert_frame_equal, compression=True)
def test_wide(self):
wp = tm.makePanel()
self._check_roundtrip(wp, tm.assert_panel_equal)
def test_wide_table(self):
wp = tm.makePanel()
self._check_roundtrip_table(wp, tm.assert_panel_equal)
def test_wide_table_dups(self):
wp = tm.makePanel()
try:
store = HDFStore(self.scratchpath)
store._quiet = True
store.put('panel', wp, table=True)
store.put('panel', wp, table=True, append=True)
recons = store['panel']
tm.assert_panel_equal(recons, wp)
finally:
store.close()
os.remove(self.scratchpath)
def test_long(self):
def _check(left, right):
tm.assert_panel_equal(left.to_panel(), right.to_panel())
wp = tm.makePanel()
self._check_roundtrip(wp.to_frame(), _check)
# empty
# self._check_roundtrip(wp.to_frame()[:0], _check)
def test_longpanel(self):
pass
def test_overwrite_node(self):
self.store['a'] = tm.makeTimeDataFrame()
ts = tm.makeTimeSeries()
self.store['a'] = ts
tm.assert_series_equal(self.store['a'], ts)
def test_select(self):
wp = tm.makePanel()
# put/select ok
self.store.remove('wp')
self.store.put('wp', wp, table=True)
self.store.select('wp')
# non-table ok (where = None)
self.store.remove('wp')
self.store.put('wp2', wp, table=False)
self.store.select('wp2')
# selection on the non-indexable with a large number of columns
wp = Panel(np.random.randn(100, 100, 100),
items=['Item%03d' % i for i in xrange(100)],
major_axis=date_range('1/1/2000', periods=100),
minor_axis=['E%03d' % i for i in xrange(100)])
self.store.remove('wp')
self.store.append('wp', wp)
items = ['Item%03d' % i for i in xrange(80)]
result = self.store.select('wp', Term('items', items))
expected = wp.reindex(items=items)
tm.assert_panel_equal(expected, result)
# selectin non-table with a where
#self.assertRaises(Exception, self.store.select,
# 'wp2', ('column', ['A', 'D']))
def test_panel_select(self):
wp = tm.makePanel()
self.store.put('wp', wp, table=True)
date = wp.major_axis[len(wp.major_axis) // 2]
crit1 = ('major_axis', '>=', date)
crit2 = ('minor_axis', '=', ['A', 'D'])
result = self.store.select('wp', [crit1, crit2])
expected = wp.truncate(before=date).reindex(minor=['A', 'D'])
tm.assert_panel_equal(result, expected)
result = self.store.select(
'wp', ['major_axis>=20000124', ('minor_axis', '=', ['A', 'B'])])
expected = wp.truncate(before='20000124').reindex(minor=['A', 'B'])
tm.assert_panel_equal(result, expected)
def test_frame_select(self):
df = tm.makeTimeDataFrame()
self.store.put('frame', df, table=True)
date = df.index[len(df) // 2]
crit1 = ('index', '>=', date)
crit2 = ('columns', ['A', 'D'])
crit3 = ('columns', 'A')
result = self.store.select('frame', [crit1, crit2])
expected = df.ix[date:, ['A', 'D']]
tm.assert_frame_equal(result, expected)
result = self.store.select('frame', [crit3])
expected = df.ix[:, ['A']]
tm.assert_frame_equal(result, expected)
# other indicies for a frame
# integer
df = DataFrame(dict(A=np.random.rand(20), B=np.random.rand(20)))
self.store.append('df_int', df)
self.store.select(
'df_int',
[Term("index<10"), Term("columns", "=", ["A"])])
df = DataFrame(
dict(A=np.random.rand(20),
B=np.random.rand(20),
index=np.arange(20, dtype='f8')))
self.store.append('df_float', df)
self.store.select('df_float',
[Term("index<10.0"),
Term("columns", "=", ["A"])])
# can't select if not written as table
#self.store['frame'] = df
#self.assertRaises(Exception, self.store.select,
# 'frame', [crit1, crit2])
def test_select_filter_corner(self):
df = DataFrame(np.random.randn(50, 100))
df.index = ['%.3d' % c for c in df.index]
df.columns = ['%.3d' % c for c in df.columns]
self.store.put('frame', df, table=True)
crit = Term('columns', df.columns[:75])
result = self.store.select('frame', [crit])
tm.assert_frame_equal(result, df.ix[:, df.columns[:75]])
def _check_roundtrip(self, obj, comparator, compression=False, **kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_double_roundtrip(self,
obj,
comparator,
compression=False,
**kwargs):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store['obj'] = obj
retrieved = store['obj']
comparator(retrieved, obj, **kwargs)
store['obj'] = retrieved
again = store['obj']
comparator(again, obj, **kwargs)
finally:
store.close()
os.remove(self.scratchpath)
def _check_roundtrip_table(self, obj, comparator, compression=False):
options = {}
if compression:
options['complib'] = _default_compressor
store = HDFStore(self.scratchpath, 'w', **options)
try:
store.put('obj', obj, table=True)
retrieved = store['obj']
# sorted_obj = _test_sort(obj)
comparator(retrieved, obj)
finally:
store.close()
os.remove(self.scratchpath)
def test_legacy_read(self):
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy.h5'), 'r')
store['a']
store['b']
store['c']
store['d']
store.close()
def test_legacy_table_read(self):
# legacy table types
pth = curpath()
store = HDFStore(os.path.join(pth, 'legacy_table.h5'), 'r')
store.select('df1')
store.select('df2')
store.select('wp1')
# force the frame
store.select('df2', typ='legacy_frame')
# old version (this still throws an exception though)
import warnings
warnings.filterwarnings('ignore', category=IncompatibilityWarning)
self.assertRaises(Exception, store.select, 'wp1',
Term('minor_axis', '=', 'B'))
warnings.filterwarnings('always', category=IncompatibilityWarning)
store.close()
def test_legacy_table_write(self):
# legacy table types
pth = curpath()
df = tm.makeDataFrame()
wp = tm.makePanel()
store = HDFStore(os.path.join(pth, 'legacy_table.h5'), 'a')
self.assertRaises(Exception, store.append, 'df1', df)
self.assertRaises(Exception, store.append, 'wp1', wp)
store.close()
def test_store_datetime_fractional_secs(self):
dt = datetime(2012, 1, 2, 3, 4, 5, 123456)
series = Series([0], [dt])
self.store['a'] = series
self.assertEquals(self.store['a'].index[0], dt)
def test_tseries_indices_series(self):
idx = tm.makeDateIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
idx = tm.makePeriodIndex(10)
ser = Series(np.random.randn(len(idx)), idx)
self.store['a'] = ser
result = self.store['a']
assert_series_equal(result, ser)
self.assertEquals(type(result.index), type(ser.index))
self.assertEquals(result.index.freq, ser.index.freq)
def test_tseries_indices_frame(self):
idx = tm.makeDateIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
idx = tm.makePeriodIndex(10)
df = DataFrame(np.random.randn(len(idx), 3), idx)
self.store['a'] = df
result = self.store['a']
assert_frame_equal(result, df)
self.assertEquals(type(result.index), type(df.index))
self.assertEquals(result.index.freq, df.index.freq)
def test_unicode_index(self):
unicode_values = [u'\u03c3', u'\u03c3\u03c3']
s = Series(np.random.randn(len(unicode_values)), unicode_values)
self._check_roundtrip(s, tm.assert_series_equal)
def test_store_datetime_mixed(self):
df = DataFrame({
'a': [1, 2, 3],
'b': [1., 2., 3.],
'c': ['a', 'b', 'c']
})
ts = tm.makeTimeSeries()
df['d'] = ts.index[:3]
self._check_roundtrip(df, tm.assert_frame_equal)
def test_cant_write_multiindex_table(self):
# for now, #1848
df = DataFrame(
np.random.randn(10, 4),
index=[np.arange(5).repeat(2),
np.tile(np.arange(2), 5)])
self.assertRaises(Exception, self.store.put, 'foo', df, table=True)