class TestSparseDataFrameAnalytics(tm.TestCase):
def setUp(self):
self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
'C': np.arange(10),
'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
self.dates = bdate_range('1/1/2011', periods=10)
self.frame = SparseDataFrame(self.data, index=self.dates)
def test_cumsum(self):
result = self.frame.cumsum()
expected = SparseDataFrame(self.frame.to_dense().cumsum())
tm.assert_sp_frame_equal(result, expected)
def test_numpy_cumsum(self):
result = np.cumsum(self.frame, axis=0)
expected = SparseDataFrame(self.frame.to_dense().cumsum())
tm.assert_sp_frame_equal(result, expected)
msg = "the 'dtype' parameter is not supported"
tm.assertRaisesRegexp(ValueError, msg, np.cumsum,
self.frame, dtype=np.int64)
msg = "the 'out' parameter is not supported"
tm.assertRaisesRegexp(ValueError, msg, np.cumsum,
self.frame, out=result)
class TestSparseDataFrameAnalytics(tm.TestCase):
def setUp(self):
self.data = {
'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
'C': np.arange(10, dtype=float),
'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]
}
self.dates = bdate_range('1/1/2011', periods=10)
self.frame = SparseDataFrame(self.data, index=self.dates)
def test_cumsum(self):
expected = SparseDataFrame(self.frame.to_dense().cumsum())
result = self.frame.cumsum()
tm.assert_sp_frame_equal(result, expected)
result = self.frame.cumsum(axis=None)
tm.assert_sp_frame_equal(result, expected)
result = self.frame.cumsum(axis=0)
tm.assert_sp_frame_equal(result, expected)
def test_numpy_cumsum(self):
result = np.cumsum(self.frame)
expected = SparseDataFrame(self.frame.to_dense().cumsum())
tm.assert_sp_frame_equal(result, expected)
msg = "the 'dtype' parameter is not supported"
tm.assertRaisesRegexp(ValueError,
msg,
np.cumsum,
self.frame,
dtype=np.int64)
msg = "the 'out' parameter is not supported"
tm.assertRaisesRegexp(ValueError,
msg,
np.cumsum,
self.frame,
out=result)
def test_numpy_func_call(self):
# no exception should be raised even though
# numpy passes in 'axis=None' or `axis=-1'
funcs = [
'sum', 'cumsum', 'var', 'mean', 'prod', 'cumprod', 'std', 'min',
'max'
]
for func in funcs:
getattr(np, func)(self.frame)
class TestSparseDataFrameAnalytics(tm.TestCase):
def setUp(self):
self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
'C': np.arange(10, dtype=float),
'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
self.dates = bdate_range('1/1/2011', periods=10)
self.frame = SparseDataFrame(self.data, index=self.dates)
def test_cumsum(self):
expected = SparseDataFrame(self.frame.to_dense().cumsum())
result = self.frame.cumsum()
tm.assert_sp_frame_equal(result, expected)
result = self.frame.cumsum(axis=None)
tm.assert_sp_frame_equal(result, expected)
result = self.frame.cumsum(axis=0)
tm.assert_sp_frame_equal(result, expected)
def test_numpy_cumsum(self):
result = np.cumsum(self.frame)
expected = SparseDataFrame(self.frame.to_dense().cumsum())
tm.assert_sp_frame_equal(result, expected)
msg = "the 'dtype' parameter is not supported"
tm.assertRaisesRegexp(ValueError, msg, np.cumsum,
self.frame, dtype=np.int64)
msg = "the 'out' parameter is not supported"
tm.assertRaisesRegexp(ValueError, msg, np.cumsum,
self.frame, out=result)
def test_numpy_func_call(self):
# no exception should be raised even though
# numpy passes in 'axis=None' or `axis=-1'
funcs = ['sum', 'cumsum', 'var',
'mean', 'prod', 'cumprod',
'std', 'min', 'max']
for func in funcs:
getattr(np, func)(self.frame)
class TestSparseDataFrame(tm.TestCase, SharedWithSparse):
klass = SparseDataFrame
_multiprocess_can_split_ = True
def setUp(self):
self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
'C': np.arange(10, dtype=np.float64),
'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
self.dates = bdate_range('1/1/2011', periods=10)
self.orig = pd.DataFrame(self.data, index=self.dates)
self.iorig = pd.DataFrame(self.data, index=self.dates)
self.frame = SparseDataFrame(self.data, index=self.dates)
self.iframe = SparseDataFrame(self.data, index=self.dates,
default_kind='integer')
values = self.frame.values.copy()
values[np.isnan(values)] = 0
self.zorig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'],
index=self.dates)
self.zframe = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
default_fill_value=0, index=self.dates)
values = self.frame.values.copy()
values[np.isnan(values)] = 2
self.fill_orig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'],
index=self.dates)
self.fill_frame = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
default_fill_value=2,
index=self.dates)
self.empty = SparseDataFrame()
def test_fill_value_when_combine_const(self):
# GH12723
dat = np.array([0, 1, np.nan, 3, 4, 5], dtype='float')
df = SparseDataFrame({'foo': dat}, index=range(6))
exp = df.fillna(0).add(2)
res = df.add(2, fill_value=0)
tm.assert_sp_frame_equal(res, exp)
def test_as_matrix(self):
empty = self.empty.as_matrix()
self.assertEqual(empty.shape, (0, 0))
no_cols = SparseDataFrame(index=np.arange(10))
mat = no_cols.as_matrix()
self.assertEqual(mat.shape, (10, 0))
no_index = SparseDataFrame(columns=np.arange(10))
mat = no_index.as_matrix()
self.assertEqual(mat.shape, (0, 10))
def test_copy(self):
cp = self.frame.copy()
tm.assertIsInstance(cp, SparseDataFrame)
tm.assert_sp_frame_equal(cp, self.frame)
# as of v0.15.0
# this is now identical (but not is_a )
self.assertTrue(cp.index.identical(self.frame.index))
def test_constructor(self):
for col, series in compat.iteritems(self.frame):
tm.assertIsInstance(series, SparseSeries)
tm.assertIsInstance(self.iframe['A'].sp_index, IntIndex)
# constructed zframe from matrix above
self.assertEqual(self.zframe['A'].fill_value, 0)
tm.assert_numpy_array_equal(pd.SparseArray([1., 2., 3., 4., 5., 6.]),
self.zframe['A'].values)
tm.assert_numpy_array_equal(np.array([0., 0., 0., 0., 1., 2.,
3., 4., 5., 6.]),
self.zframe['A'].to_dense().values)
# construct no data
sdf = SparseDataFrame(columns=np.arange(10), index=np.arange(10))
for col, series in compat.iteritems(sdf):
tm.assertIsInstance(series, SparseSeries)
# construct from nested dict
data = {}
for c, s in compat.iteritems(self.frame):
data[c] = s.to_dict()
sdf = SparseDataFrame(data)
tm.assert_sp_frame_equal(sdf, self.frame)
# TODO: test data is copied from inputs
# init dict with different index
idx = self.frame.index[:5]
cons = SparseDataFrame(
self.frame, index=idx, columns=self.frame.columns,
default_fill_value=self.frame.default_fill_value,
default_kind=self.frame.default_kind, copy=True)
reindexed = self.frame.reindex(idx)
tm.assert_sp_frame_equal(cons, reindexed, exact_indices=False)
# assert level parameter breaks reindex
with tm.assertRaises(TypeError):
self.frame.reindex(idx, level=0)
repr(self.frame)
def test_constructor_ndarray(self):
# no index or columns
sp = SparseDataFrame(self.frame.values)
# 1d
sp = SparseDataFrame(self.data['A'], index=self.dates, columns=['A'])
tm.assert_sp_frame_equal(sp, self.frame.reindex(columns=['A']))
# raise on level argument
self.assertRaises(TypeError, self.frame.reindex, columns=['A'],
level=1)
# wrong length index / columns
with tm.assertRaisesRegexp(ValueError, "^Index length"):
SparseDataFrame(self.frame.values, index=self.frame.index[:-1])
with tm.assertRaisesRegexp(ValueError, "^Column length"):
SparseDataFrame(self.frame.values, columns=self.frame.columns[:-1])
# GH 9272
def test_constructor_empty(self):
sp = SparseDataFrame()
self.assertEqual(len(sp.index), 0)
self.assertEqual(len(sp.columns), 0)
def test_constructor_dataframe(self):
dense = self.frame.to_dense()
sp = SparseDataFrame(dense)
tm.assert_sp_frame_equal(sp, self.frame)
def test_constructor_convert_index_once(self):
arr = np.array([1.5, 2.5, 3.5])
sdf = SparseDataFrame(columns=lrange(4), index=arr)
self.assertTrue(sdf[0].index is sdf[1].index)
def test_constructor_from_series(self):
# GH 2873
x = Series(np.random.randn(10000), name='a')
x = x.to_sparse(fill_value=0)
tm.assertIsInstance(x, SparseSeries)
df = SparseDataFrame(x)
tm.assertIsInstance(df, SparseDataFrame)
x = Series(np.random.randn(10000), name='a')
y = Series(np.random.randn(10000), name='b')
x2 = x.astype(float)
x2.ix[:9998] = np.NaN
# TODO: x_sparse is unused...fix
x_sparse = x2.to_sparse(fill_value=np.NaN) # noqa
# Currently fails too with weird ufunc error
# df1 = SparseDataFrame([x_sparse, y])
y.ix[:9998] = 0
# TODO: y_sparse is unsused...fix
y_sparse = y.to_sparse(fill_value=0) # noqa
# without sparse value raises error
# df2 = SparseDataFrame([x2_sparse, y])
def test_constructor_preserve_attr(self):
# GH 13866
arr = pd.SparseArray([1, 0, 3, 0], dtype=np.int64, fill_value=0)
self.assertEqual(arr.dtype, np.int64)
self.assertEqual(arr.fill_value, 0)
df = pd.SparseDataFrame({'x': arr})
self.assertEqual(df['x'].dtype, np.int64)
self.assertEqual(df['x'].fill_value, 0)
s = pd.SparseSeries(arr, name='x')
self.assertEqual(s.dtype, np.int64)
self.assertEqual(s.fill_value, 0)
df = pd.SparseDataFrame(s)
self.assertEqual(df['x'].dtype, np.int64)
self.assertEqual(df['x'].fill_value, 0)
df = pd.SparseDataFrame({'x': s})
self.assertEqual(df['x'].dtype, np.int64)
self.assertEqual(df['x'].fill_value, 0)
def test_dtypes(self):
df = DataFrame(np.random.randn(10000, 4))
df.ix[:9998] = np.nan
sdf = df.to_sparse()
result = sdf.get_dtype_counts()
expected = Series({'float64': 4})
tm.assert_series_equal(result, expected)
def test_shape(self):
# GH 10452
self.assertEqual(self.frame.shape, (10, 4))
self.assertEqual(self.iframe.shape, (10, 4))
self.assertEqual(self.zframe.shape, (10, 4))
self.assertEqual(self.fill_frame.shape, (10, 4))
def test_str(self):
df = DataFrame(np.random.randn(10000, 4))
df.ix[:9998] = np.nan
sdf = df.to_sparse()
str(sdf)
def test_array_interface(self):
res = np.sqrt(self.frame)
dres = np.sqrt(self.frame.to_dense())
tm.assert_frame_equal(res.to_dense(), dres)
def test_pickle(self):
def _test_roundtrip(frame, orig):
result = self.round_trip_pickle(frame)
tm.assert_sp_frame_equal(frame, result)
tm.assert_frame_equal(result.to_dense(), orig, check_dtype=False)
_test_roundtrip(SparseDataFrame(), DataFrame())
self._check_all(_test_roundtrip)
def test_dense_to_sparse(self):
df = DataFrame({'A': [nan, nan, nan, 1, 2],
'B': [1, 2, nan, nan, nan]})
sdf = df.to_sparse()
tm.assertIsInstance(sdf, SparseDataFrame)
self.assertTrue(np.isnan(sdf.default_fill_value))
tm.assertIsInstance(sdf['A'].sp_index, BlockIndex)
tm.assert_frame_equal(sdf.to_dense(), df)
sdf = df.to_sparse(kind='integer')
tm.assertIsInstance(sdf['A'].sp_index, IntIndex)
df = DataFrame({'A': [0, 0, 0, 1, 2],
'B': [1, 2, 0, 0, 0]}, dtype=float)
sdf = df.to_sparse(fill_value=0)
self.assertEqual(sdf.default_fill_value, 0)
tm.assert_frame_equal(sdf.to_dense(), df)
def test_density(self):
df = SparseSeries([nan, nan, nan, 0, 1, 2, 3, 4, 5, 6])
self.assertEqual(df.density, 0.7)
df = SparseDataFrame({'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
'C': np.arange(10),
'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]})
self.assertEqual(df.density, 0.75)
def test_sparse_to_dense(self):
pass
def test_sparse_series_ops(self):
self._check_frame_ops(self.frame)
def test_sparse_series_ops_i(self):
self._check_frame_ops(self.iframe)
def test_sparse_series_ops_z(self):
self._check_frame_ops(self.zframe)
def test_sparse_series_ops_fill(self):
self._check_frame_ops(self.fill_frame)
def _check_frame_ops(self, frame):
def _compare_to_dense(a, b, da, db, op):
sparse_result = op(a, b)
dense_result = op(da, db)
fill = sparse_result.default_fill_value
dense_result = dense_result.to_sparse(fill_value=fill)
tm.assert_sp_frame_equal(sparse_result, dense_result,
exact_indices=False)
if isinstance(a, DataFrame) and isinstance(db, DataFrame):
mixed_result = op(a, db)
tm.assertIsInstance(mixed_result, SparseDataFrame)
tm.assert_sp_frame_equal(mixed_result, sparse_result,
exact_indices=False)
opnames = ['add', 'sub', 'mul', 'truediv', 'floordiv']
ops = [getattr(operator, name) for name in opnames]
fidx = frame.index
# time series operations
series = [frame['A'], frame['B'], frame['C'], frame['D'],
frame['A'].reindex(fidx[:7]), frame['A'].reindex(fidx[::2]),
SparseSeries(
[], index=[])]
for op in opnames:
_compare_to_dense(frame, frame[::2], frame.to_dense(),
frame[::2].to_dense(), getattr(operator, op))
# 2304, no auto-broadcasting
for i, s in enumerate(series):
f = lambda a, b: getattr(a, op)(b, axis='index')
_compare_to_dense(frame, s, frame.to_dense(), s.to_dense(), f)
# rops are not implemented
# _compare_to_dense(s, frame, s.to_dense(),
# frame.to_dense(), f)
# cross-sectional operations
series = [frame.xs(fidx[0]), frame.xs(fidx[3]), frame.xs(fidx[5]),
frame.xs(fidx[7]), frame.xs(fidx[5])[:2]]
for op in ops:
for s in series:
_compare_to_dense(frame, s, frame.to_dense(), s, op)
_compare_to_dense(s, frame, s, frame.to_dense(), op)
# it works!
result = self.frame + self.frame.ix[:, ['A', 'B']] # noqa
def test_op_corners(self):
empty = self.empty + self.empty
self.assertTrue(empty.empty)
foo = self.frame + self.empty
tm.assertIsInstance(foo.index, DatetimeIndex)
tm.assert_frame_equal(foo, self.frame * np.nan)
foo = self.empty + self.frame
tm.assert_frame_equal(foo, self.frame * np.nan)
def test_scalar_ops(self):
pass
def test_getitem(self):
# 1585 select multiple columns
sdf = SparseDataFrame(index=[0, 1, 2], columns=['a', 'b', 'c'])
result = sdf[['a', 'b']]
exp = sdf.reindex(columns=['a', 'b'])
tm.assert_sp_frame_equal(result, exp)
self.assertRaises(Exception, sdf.__getitem__, ['a', 'd'])
def test_icol(self):
# 10711 deprecated
# 2227
result = self.frame.iloc[:, 0]
self.assertTrue(isinstance(result, SparseSeries))
tm.assert_sp_series_equal(result, self.frame['A'])
# preserve sparse index type. #2251
data = {'A': [0, 1]}
iframe = SparseDataFrame(data, default_kind='integer')
self.assertEqual(type(iframe['A'].sp_index),
type(iframe.iloc[:, 0].sp_index))
def test_set_value(self):
# ok as the index gets conver to object
frame = self.frame.copy()
res = frame.set_value('foobar', 'B', 1.5)
self.assertEqual(res.index.dtype, 'object')
res = self.frame
res.index = res.index.astype(object)
res = self.frame.set_value('foobar', 'B', 1.5)
self.assertIsNot(res, self.frame)
self.assertEqual(res.index[-1], 'foobar')
self.assertEqual(res.get_value('foobar', 'B'), 1.5)
res2 = res.set_value('foobar', 'qux', 1.5)
self.assertIsNot(res2, res)
self.assert_index_equal(res2.columns,
pd.Index(list(self.frame.columns) + ['qux']))
self.assertEqual(res2.get_value('foobar', 'qux'), 1.5)
def test_fancy_index_misc(self):
# axis = 0
sliced = self.frame.ix[-2:, :]
expected = self.frame.reindex(index=self.frame.index[-2:])
tm.assert_sp_frame_equal(sliced, expected)
# axis = 1
sliced = self.frame.ix[:, -2:]
expected = self.frame.reindex(columns=self.frame.columns[-2:])
tm.assert_sp_frame_equal(sliced, expected)
def test_getitem_overload(self):
# slicing
sl = self.frame[:20]
tm.assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20]))
# boolean indexing
d = self.frame.index[5]
indexer = self.frame.index > d
subindex = self.frame.index[indexer]
subframe = self.frame[indexer]
self.assert_index_equal(subindex, subframe.index)
self.assertRaises(Exception, self.frame.__getitem__, indexer[:-1])
def test_setitem(self):
def _check_frame(frame, orig):
N = len(frame)
# insert SparseSeries
frame['E'] = frame['A']
tm.assertIsInstance(frame['E'], SparseSeries)
tm.assert_sp_series_equal(frame['E'], frame['A'],
check_names=False)
# insert SparseSeries differently-indexed
to_insert = frame['A'][::2]
frame['E'] = to_insert
expected = to_insert.to_dense().reindex(frame.index)
result = frame['E'].to_dense()
tm.assert_series_equal(result, expected, check_names=False)
self.assertEqual(result.name, 'E')
# insert Series
frame['F'] = frame['A'].to_dense()
tm.assertIsInstance(frame['F'], SparseSeries)
tm.assert_sp_series_equal(frame['F'], frame['A'],
check_names=False)
# insert Series differently-indexed
to_insert = frame['A'].to_dense()[::2]
frame['G'] = to_insert
expected = to_insert.reindex(frame.index)
expected.name = 'G'
tm.assert_series_equal(frame['G'].to_dense(), expected)
# insert ndarray
frame['H'] = np.random.randn(N)
tm.assertIsInstance(frame['H'], SparseSeries)
to_sparsify = np.random.randn(N)
to_sparsify[N // 2:] = frame.default_fill_value
frame['I'] = to_sparsify
self.assertEqual(len(frame['I'].sp_values), N // 2)
# insert ndarray wrong size
self.assertRaises(Exception, frame.__setitem__, 'foo',
np.random.randn(N - 1))
# scalar value
frame['J'] = 5
self.assertEqual(len(frame['J'].sp_values), N)
self.assertTrue((frame['J'].sp_values == 5).all())
frame['K'] = frame.default_fill_value
self.assertEqual(len(frame['K'].sp_values), 0)
self._check_all(_check_frame)
def test_setitem_corner(self):
self.frame['a'] = self.frame['B']
tm.assert_sp_series_equal(self.frame['a'], self.frame['B'],
check_names=False)
def test_setitem_array(self):
arr = self.frame['B']
self.frame['E'] = arr
tm.assert_sp_series_equal(self.frame['E'], self.frame['B'],
check_names=False)
self.frame['F'] = arr[:-1]
index = self.frame.index[:-1]
tm.assert_sp_series_equal(self.frame['E'].reindex(index),
self.frame['F'].reindex(index),
check_names=False)
def test_delitem(self):
A = self.frame['A']
C = self.frame['C']
del self.frame['B']
self.assertNotIn('B', self.frame)
tm.assert_sp_series_equal(self.frame['A'], A)
tm.assert_sp_series_equal(self.frame['C'], C)
del self.frame['D']
self.assertNotIn('D', self.frame)
del self.frame['A']
self.assertNotIn('A', self.frame)
def test_set_columns(self):
self.frame.columns = self.frame.columns
self.assertRaises(Exception, setattr, self.frame, 'columns',
self.frame.columns[:-1])
def test_set_index(self):
self.frame.index = self.frame.index
self.assertRaises(Exception, setattr, self.frame, 'index',
self.frame.index[:-1])
def test_append(self):
a = self.frame[:5]
b = self.frame[5:]
appended = a.append(b)
tm.assert_sp_frame_equal(appended, self.frame, exact_indices=False)
a = self.frame.ix[:5, :3]
b = self.frame.ix[5:]
appended = a.append(b)
tm.assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3],
exact_indices=False)
def test_apply(self):
applied = self.frame.apply(np.sqrt)
tm.assertIsInstance(applied, SparseDataFrame)
tm.assert_almost_equal(applied.values, np.sqrt(self.frame.values))
applied = self.fill_frame.apply(np.sqrt)
self.assertEqual(applied['A'].fill_value, np.sqrt(2))
# agg / broadcast
broadcasted = self.frame.apply(np.sum, broadcast=True)
tm.assertIsInstance(broadcasted, SparseDataFrame)
exp = self.frame.to_dense().apply(np.sum, broadcast=True)
tm.assert_frame_equal(broadcasted.to_dense(), exp)
self.assertIs(self.empty.apply(np.sqrt), self.empty)
from pandas.core import nanops
applied = self.frame.apply(np.sum)
tm.assert_series_equal(applied,
self.frame.to_dense().apply(nanops.nansum))
def test_apply_nonuq(self):
orig = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=['a', 'a', 'c'])
sparse = orig.to_sparse()
res = sparse.apply(lambda s: s[0], axis=1)
exp = orig.apply(lambda s: s[0], axis=1)
# dtype must be kept
self.assertEqual(res.dtype, np.int64)
# ToDo: apply must return subclassed dtype
self.assertIsInstance(res, pd.Series)
tm.assert_series_equal(res.to_dense(), exp)
# df.T breaks
sparse = orig.T.to_sparse()
res = sparse.apply(lambda s: s[0], axis=0) # noqa
exp = orig.T.apply(lambda s: s[0], axis=0)
# TODO: no non-unique columns supported in sparse yet
# tm.assert_series_equal(res.to_dense(), exp)
def test_applymap(self):
# just test that it works
result = self.frame.applymap(lambda x: x * 2)
tm.assertIsInstance(result, SparseDataFrame)
def test_astype(self):
sparse = pd.SparseDataFrame({'A': SparseArray([1, 2, 3, 4],
dtype=np.int64),
'B': SparseArray([4, 5, 6, 7],
dtype=np.int64)})
self.assertEqual(sparse['A'].dtype, np.int64)
self.assertEqual(sparse['B'].dtype, np.int64)
res = sparse.astype(np.float64)
exp = pd.SparseDataFrame({'A': SparseArray([1., 2., 3., 4.],
fill_value=0.),
'B': SparseArray([4., 5., 6., 7.],
fill_value=0.)},
default_fill_value=np.nan)
tm.assert_sp_frame_equal(res, exp)
self.assertEqual(res['A'].dtype, np.float64)
self.assertEqual(res['B'].dtype, np.float64)
sparse = pd.SparseDataFrame({'A': SparseArray([0, 2, 0, 4],
dtype=np.int64),
'B': SparseArray([0, 5, 0, 7],
dtype=np.int64)},
default_fill_value=0)
self.assertEqual(sparse['A'].dtype, np.int64)
self.assertEqual(sparse['B'].dtype, np.int64)
res = sparse.astype(np.float64)
exp = pd.SparseDataFrame({'A': SparseArray([0., 2., 0., 4.],
fill_value=0.),
'B': SparseArray([0., 5., 0., 7.],
fill_value=0.)},
default_fill_value=0.)
tm.assert_sp_frame_equal(res, exp)
self.assertEqual(res['A'].dtype, np.float64)
self.assertEqual(res['B'].dtype, np.float64)
def test_astype_bool(self):
sparse = pd.SparseDataFrame({'A': SparseArray([0, 2, 0, 4],
fill_value=0,
dtype=np.int64),
'B': SparseArray([0, 5, 0, 7],
fill_value=0,
dtype=np.int64)},
default_fill_value=0)
self.assertEqual(sparse['A'].dtype, np.int64)
self.assertEqual(sparse['B'].dtype, np.int64)
res = sparse.astype(bool)
exp = pd.SparseDataFrame({'A': SparseArray([False, True, False, True],
dtype=np.bool,
fill_value=False),
'B': SparseArray([False, True, False, True],
dtype=np.bool,
fill_value=False)},
default_fill_value=False)
tm.assert_sp_frame_equal(res, exp)
self.assertEqual(res['A'].dtype, np.bool)
self.assertEqual(res['B'].dtype, np.bool)
def test_fillna(self):
df = self.zframe.reindex(lrange(5))
dense = self.zorig.reindex(lrange(5))
result = df.fillna(0)
expected = dense.fillna(0)
tm.assert_sp_frame_equal(result, expected.to_sparse(fill_value=0),
exact_indices=False)
tm.assert_frame_equal(result.to_dense(), expected)
result = df.copy()
result.fillna(0, inplace=True)
expected = dense.fillna(0)
tm.assert_sp_frame_equal(result, expected.to_sparse(fill_value=0),
exact_indices=False)
tm.assert_frame_equal(result.to_dense(), expected)
result = df.copy()
result = df['A']
result.fillna(0, inplace=True)
expected = dense['A'].fillna(0)
# this changes internal SparseArray repr
# tm.assert_sp_series_equal(result, expected.to_sparse(fill_value=0))
tm.assert_series_equal(result.to_dense(), expected)
def test_fillna_fill_value(self):
df = pd.DataFrame({'A': [1, 0, 0], 'B': [np.nan, np.nan, 4]})
sparse = pd.SparseDataFrame(df)
tm.assert_frame_equal(sparse.fillna(-1).to_dense(),
df.fillna(-1), check_dtype=False)
sparse = pd.SparseDataFrame(df, default_fill_value=0)
tm.assert_frame_equal(sparse.fillna(-1).to_dense(),
df.fillna(-1), check_dtype=False)
def test_rename(self):
# just check this works
renamed = self.frame.rename(index=str) # noqa
renamed = self.frame.rename(columns=lambda x: '%s%d' % (x, len(x))) # noqa
def test_corr(self):
res = self.frame.corr()
tm.assert_frame_equal(res, self.frame.to_dense().corr())
def test_describe(self):
self.frame['foo'] = np.nan
self.frame.get_dtype_counts()
str(self.frame)
desc = self.frame.describe() # noqa
def test_join(self):
left = self.frame.ix[:, ['A', 'B']]
right = self.frame.ix[:, ['C', 'D']]
joined = left.join(right)
tm.assert_sp_frame_equal(joined, self.frame, exact_indices=False)
right = self.frame.ix[:, ['B', 'D']]
self.assertRaises(Exception, left.join, right)
with tm.assertRaisesRegexp(ValueError,
'Other Series must have a name'):
self.frame.join(Series(
np.random.randn(len(self.frame)), index=self.frame.index))
def test_reindex(self):
def _check_frame(frame):
index = frame.index
sidx = index[::2]
sidx2 = index[:5] # noqa
sparse_result = frame.reindex(sidx)
dense_result = frame.to_dense().reindex(sidx)
tm.assert_frame_equal(sparse_result.to_dense(), dense_result)
tm.assert_frame_equal(frame.reindex(list(sidx)).to_dense(),
dense_result)
sparse_result2 = sparse_result.reindex(index)
dense_result2 = dense_result.reindex(index)
tm.assert_frame_equal(sparse_result2.to_dense(), dense_result2)
# propagate CORRECT fill value
tm.assert_almost_equal(sparse_result.default_fill_value,
frame.default_fill_value)
tm.assert_almost_equal(sparse_result['A'].fill_value,
frame['A'].fill_value)
# length zero
length_zero = frame.reindex([])
self.assertEqual(len(length_zero), 0)
self.assertEqual(len(length_zero.columns), len(frame.columns))
self.assertEqual(len(length_zero['A']), 0)
# frame being reindexed has length zero
length_n = length_zero.reindex(index)
self.assertEqual(len(length_n), len(frame))
self.assertEqual(len(length_n.columns), len(frame.columns))
self.assertEqual(len(length_n['A']), len(frame))
# reindex columns
reindexed = frame.reindex(columns=['A', 'B', 'Z'])
self.assertEqual(len(reindexed.columns), 3)
tm.assert_almost_equal(reindexed['Z'].fill_value,
frame.default_fill_value)
self.assertTrue(np.isnan(reindexed['Z'].sp_values).all())
_check_frame(self.frame)
_check_frame(self.iframe)
_check_frame(self.zframe)
_check_frame(self.fill_frame)
# with copy=False
reindexed = self.frame.reindex(self.frame.index, copy=False)
reindexed['F'] = reindexed['A']
self.assertIn('F', self.frame)
reindexed = self.frame.reindex(self.frame.index)
reindexed['G'] = reindexed['A']
self.assertNotIn('G', self.frame)
def test_reindex_fill_value(self):
rng = bdate_range('20110110', periods=20)
result = self.zframe.reindex(rng, fill_value=0)
exp = self.zorig.reindex(rng, fill_value=0)
exp = exp.to_sparse(self.zframe.default_fill_value)
tm.assert_sp_frame_equal(result, exp)
def test_take(self):
result = self.frame.take([1, 0, 2], axis=1)
expected = self.frame.reindex(columns=['B', 'A', 'C'])
tm.assert_sp_frame_equal(result, expected)
def test_to_dense(self):
def _check(frame, orig):
dense_dm = frame.to_dense()
tm.assert_frame_equal(frame, dense_dm)
tm.assert_frame_equal(dense_dm, orig, check_dtype=False)
self._check_all(_check)
def test_stack_sparse_frame(self):
def _check(frame):
dense_frame = frame.to_dense() # noqa
wp = Panel.from_dict({'foo': frame})
from_dense_lp = wp.to_frame()
from_sparse_lp = spf.stack_sparse_frame(frame)
self.assert_numpy_array_equal(from_dense_lp.values,
from_sparse_lp.values)
_check(self.frame)
_check(self.iframe)
# for now
self.assertRaises(Exception, _check, self.zframe)
self.assertRaises(Exception, _check, self.fill_frame)
def test_transpose(self):
def _check(frame, orig):
transposed = frame.T
untransposed = transposed.T
tm.assert_sp_frame_equal(frame, untransposed)
tm.assert_frame_equal(frame.T.to_dense(), orig.T)
tm.assert_frame_equal(frame.T.T.to_dense(), orig.T.T)
tm.assert_sp_frame_equal(frame, frame.T.T, exact_indices=False)
self._check_all(_check)
def test_shift(self):
def _check(frame, orig):
shifted = frame.shift(0)
exp = orig.shift(0)
tm.assert_frame_equal(shifted.to_dense(), exp)
shifted = frame.shift(1)
exp = orig.shift(1)
tm.assert_frame_equal(shifted, exp)
shifted = frame.shift(-2)
exp = orig.shift(-2)
tm.assert_frame_equal(shifted, exp)
shifted = frame.shift(2, freq='B')
exp = orig.shift(2, freq='B')
exp = exp.to_sparse(frame.default_fill_value)
tm.assert_frame_equal(shifted, exp)
shifted = frame.shift(2, freq=datetools.bday)
exp = orig.shift(2, freq=datetools.bday)
exp = exp.to_sparse(frame.default_fill_value)
tm.assert_frame_equal(shifted, exp)
self._check_all(_check)
def test_count(self):
dense_result = self.frame.to_dense().count()
result = self.frame.count()
tm.assert_series_equal(result, dense_result)
result = self.frame.count(axis=None)
tm.assert_series_equal(result, dense_result)
result = self.frame.count(axis=0)
tm.assert_series_equal(result, dense_result)
result = self.frame.count(axis=1)
dense_result = self.frame.to_dense().count(axis=1)
# win32 don't check dtype
tm.assert_series_equal(result, dense_result, check_dtype=False)
def _check_all(self, check_func):
check_func(self.frame, self.orig)
check_func(self.iframe, self.iorig)
check_func(self.zframe, self.zorig)
check_func(self.fill_frame, self.fill_orig)
def test_numpy_transpose(self):
sdf = SparseDataFrame([1, 2, 3], index=[1, 2, 3], columns=['a'])
result = np.transpose(np.transpose(sdf))
tm.assert_sp_frame_equal(result, sdf)
msg = "the 'axes' parameter is not supported"
tm.assertRaisesRegexp(ValueError, msg, np.transpose, sdf, axes=1)
def test_combine_first(self):
df = self.frame
result = df[::2].combine_first(df)
result2 = df[::2].combine_first(df.to_dense())
expected = df[::2].to_dense().combine_first(df.to_dense())
expected = expected.to_sparse(fill_value=df.default_fill_value)
tm.assert_sp_frame_equal(result, result2)
tm.assert_sp_frame_equal(result, expected)
def test_combine_add(self):
df = self.frame.to_dense()
df2 = df.copy()
df2['C'][:3] = np.nan
df['A'][:3] = 5.7
result = df.to_sparse().add(df2.to_sparse(), fill_value=0)
expected = df.add(df2, fill_value=0).to_sparse()
tm.assert_sp_frame_equal(result, expected)
def test_isin(self):
sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.)
xp = sparse_df[sparse_df.flag == 1.]
rs = sparse_df[sparse_df.flag.isin([1.])]
tm.assert_frame_equal(xp, rs)
def test_sparse_pow_issue(self):
# 2220
df = SparseDataFrame({'A': [1.1, 3.3], 'B': [2.5, -3.9]})
# note : no error without nan
df = SparseDataFrame({'A': [nan, 0, 1]})
# note that 2 ** df works fine, also df ** 1
result = 1**df
r1 = result.take([0], 1)['A']
r2 = result['A']
self.assertEqual(len(r2.sp_values), len(r1.sp_values))
def test_as_blocks(self):
df = SparseDataFrame({'A': [1.1, 3.3], 'B': [nan, -3.9]},
dtype='float64')
df_blocks = df.blocks
self.assertEqual(list(df_blocks.keys()), ['float64'])
tm.assert_frame_equal(df_blocks['float64'], df)
def test_nan_columnname(self):
# GH 8822
nan_colname = DataFrame(Series(1.0, index=[0]), columns=[nan])
nan_colname_sparse = nan_colname.to_sparse()
self.assertTrue(np.isnan(nan_colname_sparse.columns[0]))
def test_isnull(self):
# GH 8276
df = pd.SparseDataFrame({'A': [np.nan, np.nan, 1, 2, np.nan],
'B': [0, np.nan, np.nan, 2, np.nan]})
res = df.isnull()
exp = pd.SparseDataFrame({'A': [True, True, False, False, True],
'B': [False, True, True, False, True]},
default_fill_value=True)
exp._default_fill_value = np.nan
tm.assert_sp_frame_equal(res, exp)
# if fill_value is not nan, True can be included in sp_values
df = pd.SparseDataFrame({'A': [0, 0, 1, 2, np.nan],
'B': [0, np.nan, 0, 2, np.nan]},
default_fill_value=0.)
res = df.isnull()
tm.assertIsInstance(res, pd.SparseDataFrame)
exp = pd.DataFrame({'A': [False, False, False, False, True],
'B': [False, True, False, False, True]})
tm.assert_frame_equal(res.to_dense(), exp)
def test_isnotnull(self):
# GH 8276
df = pd.SparseDataFrame({'A': [np.nan, np.nan, 1, 2, np.nan],
'B': [0, np.nan, np.nan, 2, np.nan]})
res = df.isnotnull()
exp = pd.SparseDataFrame({'A': [False, False, True, True, False],
'B': [True, False, False, True, False]},
default_fill_value=False)
exp._default_fill_value = np.nan
tm.assert_sp_frame_equal(res, exp)
# if fill_value is not nan, True can be included in sp_values
df = pd.SparseDataFrame({'A': [0, 0, 1, 2, np.nan],
'B': [0, np.nan, 0, 2, np.nan]},
default_fill_value=0.)
res = df.isnotnull()
tm.assertIsInstance(res, pd.SparseDataFrame)
exp = pd.DataFrame({'A': [True, True, True, True, False],
'B': [True, False, True, True, False]})
tm.assert_frame_equal(res.to_dense(), exp)
class TestSparseDataFrame(TestCase, test_frame.SafeForSparse):
klass = SparseDataFrame
def setUp(self):
self.data = {
"A": [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
"B": [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
"C": np.arange(10),
"D": [0, 1, 2, 3, 4, 5, nan, nan, nan, nan],
}
self.dates = DateRange("1/1/2011", periods=10)
self.frame = SparseDataFrame(self.data, index=self.dates)
self.iframe = SparseDataFrame(self.data, index=self.dates, default_kind="integer")
values = self.frame.values.copy()
values[np.isnan(values)] = 0
self.zframe = SparseDataFrame(values, columns=["A", "B", "C", "D"], default_fill_value=0, index=self.dates)
values = self.frame.values.copy()
values[np.isnan(values)] = 2
self.fill_frame = SparseDataFrame(values, columns=["A", "B", "C", "D"], default_fill_value=2, index=self.dates)
self.empty = SparseDataFrame()
def test_as_matrix(self):
empty = self.empty.as_matrix()
self.assert_(empty.shape == (0, 0))
no_cols = SparseDataFrame(index=np.arange(10))
mat = no_cols.as_matrix()
self.assert_(mat.shape == (10, 0))
no_index = SparseDataFrame(columns=np.arange(10))
mat = no_index.as_matrix()
self.assert_(mat.shape == (0, 10))
def test_copy(self):
cp = self.frame.copy()
self.assert_(isinstance(cp, SparseDataFrame))
assert_sp_frame_equal(cp, self.frame)
self.assert_(cp.index is self.frame.index)
def test_constructor(self):
for col, series in self.frame.iteritems():
self.assert_(isinstance(series, SparseSeries))
self.assert_(isinstance(self.iframe["A"].sp_index, IntIndex))
# constructed zframe from matrix above
self.assertEquals(self.zframe["A"].fill_value, 0)
assert_almost_equal([0, 0, 0, 0, 1, 2, 3, 4, 5, 6], self.zframe["A"].values)
# construct from nested dict
data = {}
for c, s in self.frame.iteritems():
data[c] = s.to_dict()
sdf = SparseDataFrame(data)
assert_sp_frame_equal(sdf, self.frame)
# TODO: test data is copied from inputs
# init dict with different index
idx = self.frame.index[:5]
cons = SparseDataFrame(
self.frame._series,
index=idx,
columns=self.frame.columns,
default_fill_value=self.frame.default_fill_value,
default_kind=self.frame.default_kind,
)
reindexed = self.frame.reindex(idx)
assert_sp_frame_equal(cons, reindexed)
# assert level parameter breaks reindex
self.assertRaises(Exception, self.frame.reindex, idx, level=0)
def test_constructor_ndarray(self):
# no index or columns
sp = SparseDataFrame(self.frame.values)
# 1d
sp = SparseDataFrame(self.data["A"], index=self.dates, columns=["A"])
assert_sp_frame_equal(sp, self.frame.reindex(columns=["A"]))
# raise on level argument
self.assertRaises(Exception, self.frame.reindex, columns=["A"], level=1)
# wrong length index / columns
self.assertRaises(Exception, SparseDataFrame, self.frame.values, index=self.frame.index[:-1])
self.assertRaises(Exception, SparseDataFrame, self.frame.values, columns=self.frame.columns[:-1])
def test_constructor_empty(self):
sp = SparseDataFrame()
self.assert_(len(sp.index) == 0)
self.assert_(len(sp.columns) == 0)
def test_constructor_dataframe(self):
dense = self.frame.to_dense()
sp = SparseDataFrame(dense)
assert_sp_frame_equal(sp, self.frame)
def test_array_interface(self):
res = np.sqrt(self.frame)
dres = np.sqrt(self.frame.to_dense())
assert_frame_equal(res.to_dense(), dres)
def test_pickle(self):
def _test_roundtrip(frame):
pickled = pickle.dumps(frame, protocol=pickle.HIGHEST_PROTOCOL)
unpickled = pickle.loads(pickled)
assert_sp_frame_equal(frame, unpickled)
_test_roundtrip(SparseDataFrame())
self._check_all(_test_roundtrip)
def test_dense_to_sparse(self):
df = DataFrame({"A": [nan, nan, nan, 1, 2], "B": [1, 2, nan, nan, nan]})
sdf = df.to_sparse()
self.assert_(isinstance(sdf, SparseDataFrame))
self.assert_(np.isnan(sdf.default_fill_value))
self.assert_(isinstance(sdf["A"].sp_index, BlockIndex))
tm.assert_frame_equal(sdf.to_dense(), df)
sdf = df.to_sparse(kind="integer")
self.assert_(isinstance(sdf["A"].sp_index, IntIndex))
df = DataFrame({"A": [0, 0, 0, 1, 2], "B": [1, 2, 0, 0, 0]}, dtype=float)
sdf = df.to_sparse(fill_value=0)
self.assertEquals(sdf.default_fill_value, 0)
tm.assert_frame_equal(sdf.to_dense(), df)
def test_sparse_to_dense(self):
pass
def test_sparse_series_ops(self):
self._check_all(self._check_frame_ops)
def _check_frame_ops(self, frame):
fill = frame.default_fill_value
def _compare_to_dense(a, b, da, db, op):
sparse_result = op(a, b)
dense_result = op(da, db)
dense_result = dense_result.to_sparse(fill_value=fill)
assert_sp_frame_equal(sparse_result, dense_result, exact_indices=False)
if isinstance(a, DataFrame) and isinstance(db, DataFrame):
mixed_result = op(a, db)
self.assert_(isinstance(mixed_result, SparseDataFrame))
assert_sp_frame_equal(mixed_result, sparse_result, exact_indices=False)
opnames = ["add", "sub", "mul", "truediv", "floordiv"]
ops = [getattr(operator, name) for name in opnames]
fidx = frame.index
# time series operations
series = [
frame["A"],
frame["B"],
frame["C"],
frame["D"],
frame["A"].reindex(fidx[:7]),
frame["A"].reindex(fidx[::2]),
SparseSeries([], index=[]),
]
for op in ops:
_compare_to_dense(frame, frame[::2], frame.to_dense(), frame[::2].to_dense(), op)
for s in series:
_compare_to_dense(frame, s, frame.to_dense(), s.to_dense(), op)
_compare_to_dense(s, frame, s.to_dense(), frame.to_dense(), op)
# cross-sectional operations
series = [frame.xs(fidx[0]), frame.xs(fidx[3]), frame.xs(fidx[5]), frame.xs(fidx[7]), frame.xs(fidx[5])[:2]]
for op in ops:
for s in series:
_compare_to_dense(frame, s, frame.to_dense(), s, op)
_compare_to_dense(s, frame, s, frame.to_dense(), op)
# it works!
result = self.frame + self.frame.ix[:, ["A", "B"]]
def test_op_corners(self):
empty = self.empty + self.empty
self.assert_(not empty)
foo = self.frame + self.empty
assert_frame_equal(foo, self.frame * np.nan)
foo = self.empty + self.frame
assert_frame_equal(foo, self.frame * np.nan)
def test_scalar_ops(self):
pass
def test_getitem(self):
pass
def test_set_value(self):
res = self.frame.set_value("foobar", "B", 1.5)
self.assert_(res is not self.frame)
self.assert_(res.index[-1] == "foobar")
self.assertEqual(res.get_value("foobar", "B"), 1.5)
res2 = res.set_value("foobar", "qux", 1.5)
self.assert_(res2 is not res)
self.assert_(np.array_equal(res2.columns, list(self.frame.columns) + ["qux"]))
self.assertEqual(res2.get_value("foobar", "qux"), 1.5)
def test_fancy_index_misc(self):
# axis = 0
sliced = self.frame.ix[-2:, :]
expected = self.frame.reindex(index=self.frame.index[-2:])
assert_sp_frame_equal(sliced, expected)
# axis = 1
sliced = self.frame.ix[:, -2:]
expected = self.frame.reindex(columns=self.frame.columns[-2:])
assert_sp_frame_equal(sliced, expected)
def test_getitem_overload(self):
# slicing
sl = self.frame[:20]
assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20]))
# boolean indexing
d = self.frame.index[5]
indexer = self.frame.index > d
subindex = self.frame.index[indexer]
subframe = self.frame[indexer]
self.assert_(np.array_equal(subindex, subframe.index))
self.assertRaises(Exception, self.frame.__getitem__, indexer[:-1])
def test_setitem(self):
def _check_frame(frame):
N = len(frame)
# insert SparseSeries
frame["E"] = frame["A"]
self.assert_(isinstance(frame["E"], SparseSeries))
assert_sp_series_equal(frame["E"], frame["A"])
# insert SparseSeries differently-indexed
to_insert = frame["A"][::2]
frame["E"] = to_insert
assert_series_equal(frame["E"].to_dense(), to_insert.to_dense().reindex(frame.index))
# insert Series
frame["F"] = frame["A"].to_dense()
self.assert_(isinstance(frame["F"], SparseSeries))
assert_sp_series_equal(frame["F"], frame["A"])
# insert Series differently-indexed
to_insert = frame["A"].to_dense()[::2]
frame["G"] = to_insert
assert_series_equal(frame["G"].to_dense(), to_insert.reindex(frame.index))
# insert ndarray
frame["H"] = np.random.randn(N)
self.assert_(isinstance(frame["H"], SparseSeries))
to_sparsify = np.random.randn(N)
to_sparsify[N // 2 :] = frame.default_fill_value
frame["I"] = to_sparsify
self.assertEquals(len(frame["I"].sp_values), N // 2)
# insert ndarray wrong size
self.assertRaises(Exception, frame.__setitem__, "foo", np.random.randn(N - 1))
# scalar value
frame["J"] = 5
self.assertEquals(len(frame["J"].sp_values), N)
self.assert_((frame["J"].sp_values == 5).all())
frame["K"] = frame.default_fill_value
self.assertEquals(len(frame["K"].sp_values), 0)
self._check_all(_check_frame)
def test_setitem_corner(self):
self.frame["a"] = self.frame["B"]
assert_sp_series_equal(self.frame["a"], self.frame["B"])
def test_setitem_array(self):
arr = self.frame["B"].view(SparseArray)
self.frame["E"] = arr
assert_sp_series_equal(self.frame["E"], self.frame["B"])
self.assertRaises(Exception, self.frame.__setitem__, "F", arr[:-1])
def test_delitem(self):
A = self.frame["A"]
C = self.frame["C"]
del self.frame["B"]
self.assert_("B" not in self.frame)
assert_sp_series_equal(self.frame["A"], A)
assert_sp_series_equal(self.frame["C"], C)
del self.frame["D"]
self.assert_("D" not in self.frame)
del self.frame["A"]
self.assert_("A" not in self.frame)
def test_set_columns(self):
self.frame.columns = self.frame.columns
self.assertRaises(Exception, setattr, self.frame, "columns", self.frame.columns[:-1])
def test_set_index(self):
self.frame.index = self.frame.index
self.assertRaises(Exception, setattr, self.frame, "index", self.frame.index[:-1])
def test_append(self):
a = self.frame[:5]
b = self.frame[5:]
appended = a.append(b)
assert_sp_frame_equal(appended, self.frame)
a = self.frame.ix[:5, :3]
b = self.frame.ix[5:]
appended = a.append(b)
assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3])
def test_apply(self):
applied = self.frame.apply(np.sqrt)
self.assert_(isinstance(applied, SparseDataFrame))
assert_almost_equal(applied.values, np.sqrt(self.frame.values))
applied = self.fill_frame.apply(np.sqrt)
self.assert_(applied["A"].fill_value == np.sqrt(2))
# agg / broadcast
applied = self.frame.apply(np.sum)
assert_series_equal(applied, self.frame.to_dense().apply(np.sum))
broadcasted = self.frame.apply(np.sum, broadcast=True)
self.assert_(isinstance(broadcasted, SparseDataFrame))
assert_frame_equal(broadcasted.to_dense(), self.frame.to_dense().apply(np.sum, broadcast=True))
self.assert_(self.empty.apply(np.sqrt) is self.empty)
def test_applymap(self):
# just test that it works
result = self.frame.applymap(lambda x: x * 2)
self.assert_(isinstance(result, SparseDataFrame))
def test_astype(self):
self.assertRaises(Exception, self.frame.astype, np.int64)
def test_fillna(self):
self.assertRaises(NotImplementedError, self.frame.fillna, 0)
def test_rename(self):
# just check this works
renamed = self.frame.rename(index=str)
renamed = self.frame.rename(columns=lambda x: "%s%d" % (x, len(x)))
def test_corr(self):
res = self.frame.corr()
assert_frame_equal(res, self.frame.to_dense().corr())
def test_describe(self):
self.frame["foo"] = np.nan
desc = self.frame.describe()
def test_join(self):
left = self.frame.ix[:, ["A", "B"]]
right = self.frame.ix[:, ["C", "D"]]
joined = left.join(right)
assert_sp_frame_equal(joined, self.frame)
right = self.frame.ix[:, ["B", "D"]]
self.assertRaises(Exception, left.join, right)
def test_reindex(self):
def _check_frame(frame):
index = frame.index
sidx = index[::2]
sidx2 = index[:5]
sparse_result = frame.reindex(sidx)
dense_result = frame.to_dense().reindex(sidx)
assert_frame_equal(sparse_result.to_dense(), dense_result)
assert_frame_equal(frame.reindex(list(sidx)).to_dense(), dense_result)
sparse_result2 = sparse_result.reindex(index)
dense_result2 = dense_result.reindex(index)
assert_frame_equal(sparse_result2.to_dense(), dense_result2)
# propagate CORRECT fill value
assert_almost_equal(sparse_result.default_fill_value, frame.default_fill_value)
assert_almost_equal(sparse_result["A"].fill_value, frame["A"].fill_value)
# length zero
length_zero = frame.reindex([])
self.assertEquals(len(length_zero), 0)
self.assertEquals(len(length_zero.columns), len(frame.columns))
self.assertEquals(len(length_zero["A"]), 0)
# frame being reindexed has length zero
length_n = length_zero.reindex(index)
self.assertEquals(len(length_n), len(frame))
self.assertEquals(len(length_n.columns), len(frame.columns))
self.assertEquals(len(length_n["A"]), len(frame))
# reindex columns
reindexed = frame.reindex(columns=["A", "B", "Z"])
self.assertEquals(len(reindexed.columns), 3)
assert_almost_equal(reindexed["Z"].fill_value, frame.default_fill_value)
self.assert_(np.isnan(reindexed["Z"].sp_values).all())
_check_frame(self.frame)
_check_frame(self.iframe)
_check_frame(self.zframe)
_check_frame(self.fill_frame)
# with copy=False
reindexed = self.frame.reindex(self.frame.index, copy=False)
reindexed["F"] = reindexed["A"]
self.assert_("F" in self.frame)
reindexed = self.frame.reindex(self.frame.index)
reindexed["G"] = reindexed["A"]
self.assert_("G" not in self.frame)
def test_take(self):
result = self.frame.take([1, 0, 2], axis=1)
expected = self.frame.reindex(columns=["B", "A", "C"])
assert_sp_frame_equal(result, expected)
def test_density(self):
df = SparseDataFrame(
{
"A": [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
"B": [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
"C": np.arange(10),
"D": [0, 1, 2, 3, 4, 5, nan, nan, nan, nan],
}
)
self.assertEquals(df.density, 0.75)
def test_to_dense(self):
def _check(frame):
dense_dm = frame.to_dense()
assert_frame_equal(frame, dense_dm)
self._check_all(_check)
def test_stack_sparse_frame(self):
def _check(frame):
dense_frame = frame.to_dense()
wp = Panel.from_dict({"foo": frame})
from_dense_lp = wp.to_frame()
from_sparse_lp = spf.stack_sparse_frame(frame)
self.assert_(np.array_equal(from_dense_lp.values, from_sparse_lp.values))
_check(self.frame)
_check(self.iframe)
# for now
self.assertRaises(Exception, _check, self.zframe)
self.assertRaises(Exception, _check, self.fill_frame)
def test_transpose(self):
def _check(frame):
transposed = frame.T
untransposed = transposed.T
assert_sp_frame_equal(frame, untransposed)
self._check_all(_check)
def test_shift(self):
def _check(frame):
shifted = frame.shift(0)
self.assert_(shifted is not frame)
assert_sp_frame_equal(shifted, frame)
f = lambda s: s.shift(1)
_dense_frame_compare(frame, f)
f = lambda s: s.shift(-2)
_dense_frame_compare(frame, f)
f = lambda s: s.shift(2, timeRule="WEEKDAY")
_dense_frame_compare(frame, f)
f = lambda s: s.shift(2, offset=datetools.bday)
_dense_frame_compare(frame, f)
self._check_all(_check)
def test_count(self):
result = self.frame.count()
dense_result = self.frame.to_dense().count()
assert_series_equal(result, dense_result)
result = self.frame.count(1)
dense_result = self.frame.to_dense().count(1)
# win32 don't check dtype
assert_series_equal(result, dense_result, check_dtype=False)
def test_cumsum(self):
result = self.frame.cumsum()
expected = self.frame.to_dense().cumsum()
self.assert_(isinstance(result, SparseDataFrame))
assert_frame_equal(result.to_dense(), expected)
def _check_all(self, check_func):
check_func(self.frame)
check_func(self.iframe)
check_func(self.zframe)
check_func(self.fill_frame)
def test_combine_first(self):
df = self.frame
result = df[::2].combine_first(df)
result2 = df[::2].combine_first(df.to_dense())
expected = df[::2].to_dense().combine_first(df.to_dense())
expected = expected.to_sparse(fill_value=df.default_fill_value)
assert_sp_frame_equal(result, result2)
assert_sp_frame_equal(result, expected)
class TestSparseDataFrame(TestCase, test_frame.SafeForSparse):
klass = SparseDataFrame
def setUp(self):
self.data = {
'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
'C': np.arange(10),
'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]
}
self.dates = DateRange('1/1/2011', periods=10)
self.frame = SparseDataFrame(self.data, index=self.dates)
self.iframe = SparseDataFrame(self.data,
index=self.dates,
default_kind='integer')
values = self.frame.values.copy()
values[np.isnan(values)] = 0
self.zframe = SparseDataFrame(values,
columns=['A', 'B', 'C', 'D'],
default_fill_value=0,
index=self.dates)
values = self.frame.values.copy()
values[np.isnan(values)] = 2
self.fill_frame = SparseDataFrame(values,
columns=['A', 'B', 'C', 'D'],
default_fill_value=2,
index=self.dates)
self.empty = SparseDataFrame()
def test_as_matrix(self):
empty = self.empty.as_matrix()
self.assert_(empty.shape == (0, 0))
no_cols = SparseDataFrame(index=np.arange(10))
mat = no_cols.as_matrix()
self.assert_(mat.shape == (10, 0))
no_index = SparseDataFrame(columns=np.arange(10))
mat = no_index.as_matrix()
self.assert_(mat.shape == (0, 10))
def test_copy(self):
cp = self.frame.copy()
self.assert_(isinstance(cp, SparseDataFrame))
assert_sp_frame_equal(cp, self.frame)
self.assert_(cp.index is self.frame.index)
def test_constructor(self):
for col, series in self.frame.iteritems():
self.assert_(isinstance(series, SparseSeries))
self.assert_(isinstance(self.iframe['A'].sp_index, IntIndex))
# constructed zframe from matrix above
self.assertEquals(self.zframe['A'].fill_value, 0)
assert_almost_equal([0, 0, 0, 0, 1, 2, 3, 4, 5, 6],
self.zframe['A'].values)
# construct from nested dict
data = {}
for c, s in self.frame.iteritems():
data[c] = s.to_dict()
sdf = SparseDataFrame(data)
assert_sp_frame_equal(sdf, self.frame)
# TODO: test data is copied from inputs
# init dict with different index
idx = self.frame.index[:5]
cons = SparseDataFrame(
self.frame._series,
index=idx,
columns=self.frame.columns,
default_fill_value=self.frame.default_fill_value,
default_kind=self.frame.default_kind)
reindexed = self.frame.reindex(idx)
assert_sp_frame_equal(cons, reindexed)
# assert level parameter breaks reindex
self.assertRaises(Exception, self.frame.reindex, idx, level=0)
def test_constructor_ndarray(self):
# no index or columns
sp = SparseDataFrame(self.frame.values)
# 1d
sp = SparseDataFrame(self.data['A'], index=self.dates, columns=['A'])
assert_sp_frame_equal(sp, self.frame.reindex(columns=['A']))
# raise on level argument
self.assertRaises(Exception,
self.frame.reindex,
columns=['A'],
level=1)
# wrong length index / columns
self.assertRaises(Exception,
SparseDataFrame,
self.frame.values,
index=self.frame.index[:-1])
self.assertRaises(Exception,
SparseDataFrame,
self.frame.values,
columns=self.frame.columns[:-1])
def test_constructor_empty(self):
sp = SparseDataFrame()
self.assert_(len(sp.index) == 0)
self.assert_(len(sp.columns) == 0)
def test_constructor_dataframe(self):
dense = self.frame.to_dense()
sp = SparseDataFrame(dense)
assert_sp_frame_equal(sp, self.frame)
def test_array_interface(self):
res = np.sqrt(self.frame)
dres = np.sqrt(self.frame.to_dense())
assert_frame_equal(res.to_dense(), dres)
def test_pickle(self):
def _test_roundtrip(frame):
pickled = pickle.dumps(frame, protocol=pickle.HIGHEST_PROTOCOL)
unpickled = pickle.loads(pickled)
assert_sp_frame_equal(frame, unpickled)
self._check_all(_test_roundtrip)
def test_dense_to_sparse(self):
df = DataFrame({
'A': [nan, nan, nan, 1, 2],
'B': [1, 2, nan, nan, nan]
})
sdf = df.to_sparse()
self.assert_(isinstance(sdf, SparseDataFrame))
self.assert_(np.isnan(sdf.default_fill_value))
self.assert_(isinstance(sdf['A'].sp_index, BlockIndex))
testing.assert_frame_equal(sdf.to_dense(), df)
sdf = df.to_sparse(kind='integer')
self.assert_(isinstance(sdf['A'].sp_index, IntIndex))
df = DataFrame({
'A': [0, 0, 0, 1, 2],
'B': [1, 2, 0, 0, 0]
},
dtype=float)
sdf = df.to_sparse(fill_value=0)
self.assertEquals(sdf.default_fill_value, 0)
testing.assert_frame_equal(sdf.to_dense(), df)
def test_sparse_to_dense(self):
pass
def test_sparse_series_ops(self):
self._check_all(self._check_frame_ops)
def _check_frame_ops(self, frame):
fill = frame.default_fill_value
def _compare_to_dense(a, b, da, db, op):
sparse_result = op(a, b)
dense_result = op(da, db)
dense_result = dense_result.to_sparse(fill_value=fill)
assert_sp_frame_equal(sparse_result,
dense_result,
exact_indices=False)
if isinstance(a, DataFrame) and isinstance(db, DataFrame):
mixed_result = op(a, db)
self.assert_(isinstance(mixed_result, SparseDataFrame))
assert_sp_frame_equal(mixed_result,
sparse_result,
exact_indices=False)
opnames = ['add', 'sub', 'mul', 'truediv', 'floordiv']
ops = [getattr(operator, name) for name in opnames]
fidx = frame.index
# time series operations
series = [
frame['A'], frame['B'], frame['C'], frame['D'],
frame['A'].reindex(fidx[:7]), frame['A'].reindex(fidx[::2]),
SparseSeries([], index=[])
]
for op in ops:
_compare_to_dense(frame, frame[::2], frame.to_dense(),
frame[::2].to_dense(), op)
for s in series:
_compare_to_dense(frame, s, frame.to_dense(), s.to_dense(), op)
_compare_to_dense(s, frame, s.to_dense(), frame.to_dense(), op)
# cross-sectional operations
series = [
frame.xs(fidx[0]),
frame.xs(fidx[3]),
frame.xs(fidx[5]),
frame.xs(fidx[7]),
frame.xs(fidx[5])[:2]
]
for op in ops:
for s in series:
_compare_to_dense(frame, s, frame.to_dense(), s, op)
_compare_to_dense(s, frame, s, frame.to_dense(), op)
def test_op_corners(self):
empty = self.empty + self.empty
self.assert_(not empty)
foo = self.frame + self.empty
assert_sp_frame_equal(foo, self.frame * np.nan)
foo = self.empty + self.frame
assert_sp_frame_equal(foo, self.frame * np.nan)
def test_scalar_ops(self):
pass
def test_getitem(self):
pass
def test_set_value(self):
res = self.frame.set_value('foobar', 'B', 1.5)
self.assert_(res is not self.frame)
self.assert_(res.index[-1] == 'foobar')
self.assertEqual(res.get_value('foobar', 'B'), 1.5)
res2 = res.set_value('foobar', 'qux', 1.5)
self.assert_(res2 is not res)
self.assert_(
np.array_equal(res2.columns,
list(self.frame.columns) + ['qux']))
self.assertEqual(res2.get_value('foobar', 'qux'), 1.5)
def test_fancy_index_misc(self):
# axis = 0
sliced = self.frame.ix[-2:, :]
expected = self.frame.reindex(index=self.frame.index[-2:])
assert_sp_frame_equal(sliced, expected)
# axis = 1
sliced = self.frame.ix[:, -2:]
expected = self.frame.reindex(columns=self.frame.columns[-2:])
assert_sp_frame_equal(sliced, expected)
def test_getitem_overload(self):
# slicing
sl = self.frame[:20]
assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20]))
# boolean indexing
d = self.frame.index[5]
indexer = self.frame.index > d
subindex = self.frame.index[indexer]
subframe = self.frame[indexer]
self.assert_(np.array_equal(subindex, subframe.index))
self.assertRaises(Exception, self.frame.__getitem__, indexer[:-1])
def test_setitem(self):
def _check_frame(frame):
N = len(frame)
# insert SparseSeries
frame['E'] = frame['A']
self.assert_(isinstance(frame['E'], SparseSeries))
assert_sp_series_equal(frame['E'], frame['A'])
# insert SparseSeries differently-indexed
to_insert = frame['A'][::2]
frame['E'] = to_insert
assert_series_equal(frame['E'].to_dense(),
to_insert.to_dense().reindex(frame.index))
# insert Series
frame['F'] = frame['A'].to_dense()
self.assert_(isinstance(frame['F'], SparseSeries))
assert_sp_series_equal(frame['F'], frame['A'])
# insert Series differently-indexed
to_insert = frame['A'].to_dense()[::2]
frame['G'] = to_insert
assert_series_equal(frame['G'].to_dense(),
to_insert.reindex(frame.index))
# insert ndarray
frame['H'] = np.random.randn(N)
self.assert_(isinstance(frame['H'], SparseSeries))
to_sparsify = np.random.randn(N)
to_sparsify[N // 2:] = frame.default_fill_value
frame['I'] = to_sparsify
self.assertEquals(len(frame['I'].sp_values), N // 2)
# insert ndarray wrong size
self.assertRaises(Exception, frame.__setitem__, 'foo',
np.random.randn(N - 1))
# scalar value
frame['J'] = 5
self.assertEquals(len(frame['J'].sp_values), N)
self.assert_((frame['J'].sp_values == 5).all())
frame['K'] = frame.default_fill_value
self.assertEquals(len(frame['K'].sp_values), 0)
self._check_all(_check_frame)
def test_setitem_corner(self):
self.frame['a'] = self.frame['B']
assert_sp_series_equal(self.frame['a'], self.frame['B'])
def test_setitem_array(self):
arr = self.frame['B'].view(SparseArray)
self.frame['E'] = arr
assert_sp_series_equal(self.frame['E'], self.frame['B'])
self.assertRaises(Exception, self.frame.__setitem__, 'F', arr[:-1])
def test_delitem(self):
A = self.frame['A']
C = self.frame['C']
del self.frame['B']
self.assert_('B' not in self.frame)
assert_sp_series_equal(self.frame['A'], A)
assert_sp_series_equal(self.frame['C'], C)
del self.frame['D']
self.assert_('D' not in self.frame)
del self.frame['A']
self.assert_('A' not in self.frame)
def test_set_columns(self):
self.frame.columns = self.frame.columns
self.assertRaises(Exception, setattr, self.frame, 'columns',
self.frame.columns[:-1])
def test_set_index(self):
self.frame.index = self.frame.index
self.assertRaises(Exception, setattr, self.frame, 'index',
self.frame.index[:-1])
def test_append(self):
a = self.frame[:5]
b = self.frame[5:]
appended = a.append(b)
assert_sp_frame_equal(appended, self.frame)
a = self.frame.ix[:5, :3]
b = self.frame.ix[5:]
appended = a.append(b)
assert_sp_frame_equal(appended.ix[:, :3], self.frame.ix[:, :3])
def test_apply(self):
applied = self.frame.apply(np.sqrt)
self.assert_(isinstance(applied, SparseDataFrame))
assert_almost_equal(applied.values, np.sqrt(self.frame.values))
applied = self.fill_frame.apply(np.sqrt)
self.assert_(applied['A'].fill_value == np.sqrt(2))
# agg / broadcast
applied = self.frame.apply(np.sum)
assert_series_equal(applied, self.frame.to_dense().apply(np.sum))
broadcasted = self.frame.apply(np.sum, broadcast=True)
self.assert_(isinstance(broadcasted, SparseDataFrame))
assert_frame_equal(broadcasted.to_dense(),
self.frame.to_dense().apply(np.sum, broadcast=True))
self.assert_(self.empty.apply(np.sqrt) is self.empty)
def test_applymap(self):
# just test that it works
result = self.frame.applymap(lambda x: x * 2)
self.assert_(isinstance(result, SparseDataFrame))
def test_astype(self):
self.assertRaises(Exception, self.frame.astype, np.int64)
def test_fillna(self):
self.assertRaises(NotImplementedError, self.frame.fillna, 0)
def test_rename(self):
# just check this works
renamed = self.frame.rename(index=str)
renamed = self.frame.rename(columns=lambda x: '%s%d' % (x, len(x)))
def test_corr(self):
res = self.frame.corr()
assert_frame_equal(res, self.frame.to_dense().corr())
def test_describe(self):
self.frame['foo'] = np.nan
desc = self.frame.describe()
def test_join(self):
left = self.frame.ix[:, ['A', 'B']]
right = self.frame.ix[:, ['C', 'D']]
joined = left.join(right)
assert_sp_frame_equal(joined, self.frame)
right = self.frame.ix[:, ['B', 'D']]
self.assertRaises(Exception, left.join, right)
def test_reindex(self):
def _check_frame(frame):
index = frame.index
sidx = index[::2]
sidx2 = index[:5]
sparse_result = frame.reindex(sidx)
dense_result = frame.to_dense().reindex(sidx)
assert_frame_equal(sparse_result.to_dense(), dense_result)
assert_frame_equal(
frame.reindex(list(sidx)).to_dense(), dense_result)
sparse_result2 = sparse_result.reindex(index)
dense_result2 = dense_result.reindex(index)
assert_frame_equal(sparse_result2.to_dense(), dense_result2)
# propagate CORRECT fill value
assert_almost_equal(sparse_result.default_fill_value,
frame.default_fill_value)
assert_almost_equal(sparse_result['A'].fill_value,
frame['A'].fill_value)
# length zero
length_zero = frame.reindex([])
self.assertEquals(len(length_zero), 0)
self.assertEquals(len(length_zero.columns), len(frame.columns))
self.assertEquals(len(length_zero['A']), 0)
# frame being reindexed has length zero
length_n = length_zero.reindex(index)
self.assertEquals(len(length_n), len(frame))
self.assertEquals(len(length_n.columns), len(frame.columns))
self.assertEquals(len(length_n['A']), len(frame))
# reindex columns
reindexed = frame.reindex(columns=['A', 'B', 'Z'])
self.assertEquals(len(reindexed.columns), 3)
assert_almost_equal(reindexed['Z'].fill_value,
frame.default_fill_value)
self.assert_(np.isnan(reindexed['Z'].sp_values).all())
_check_frame(self.frame)
_check_frame(self.iframe)
_check_frame(self.zframe)
_check_frame(self.fill_frame)
# with copy=False
reindexed = self.frame.reindex(self.frame.index, copy=False)
reindexed['F'] = reindexed['A']
self.assert_('F' in self.frame)
reindexed = self.frame.reindex(self.frame.index)
reindexed['G'] = reindexed['A']
self.assert_('G' not in self.frame)
def test_density(self):
df = SparseDataFrame({
'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
'C': np.arange(10),
'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]
})
self.assertEquals(df.density, 0.75)
def test_to_dense(self):
def _check(frame):
dense_dm = frame.to_dense()
assert_frame_equal(frame, dense_dm)
self._check_all(_check)
def test_stack_sparse_frame(self):
def _check(frame):
dense_frame = frame.to_dense()
wp = Panel.from_dict({'foo': frame})
from_dense_lp = wp.to_frame()
from_sparse_lp = spf.stack_sparse_frame(frame)
self.assert_(
np.array_equal(from_dense_lp.values, from_sparse_lp.values))
_check(self.frame)
_check(self.iframe)
# for now
self.assertRaises(Exception, _check, self.zframe)
self.assertRaises(Exception, _check, self.fill_frame)
def test_transpose(self):
def _check(frame):
transposed = frame.T
untransposed = transposed.T
assert_sp_frame_equal(frame, untransposed)
self._check_all(_check)
def test_shift(self):
def _check(frame):
shifted = frame.shift(0)
self.assert_(shifted is not frame)
assert_sp_frame_equal(shifted, frame)
f = lambda s: s.shift(1)
_dense_frame_compare(frame, f)
f = lambda s: s.shift(-2)
_dense_frame_compare(frame, f)
f = lambda s: s.shift(2, timeRule='WEEKDAY')
_dense_frame_compare(frame, f)
f = lambda s: s.shift(2, offset=datetools.bday)
_dense_frame_compare(frame, f)
self._check_all(_check)
def test_count(self):
result = self.frame.count()
dense_result = self.frame.to_dense().count()
assert_series_equal(result, dense_result)
result = self.frame.count(1)
dense_result = self.frame.to_dense().count(1)
# win32 don't check dtype
assert_series_equal(result, dense_result, check_dtype=False)
def test_cumsum(self):
result = self.frame.cumsum()
expected = self.frame.to_dense().cumsum()
self.assert_(isinstance(result, SparseDataFrame))
assert_frame_equal(result.to_dense(), expected)
def _check_all(self, check_func):
check_func(self.frame)
check_func(self.iframe)
check_func(self.zframe)
check_func(self.fill_frame)