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Version:
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# pylint: disable-msg=E1101,W0612
import operator
import pytest
from warnings import catch_warnings
from numpy import nan
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, bdate_range, Panel
from pandas.core.dtypes.common import (
is_bool_dtype,
is_float_dtype,
is_object_dtype,
is_float)
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.tseries.offsets import BDay
from pandas.util import testing as tm
from pandas.compat import lrange
from pandas import compat
from pandas.core.sparse import frame as spf
from pandas._libs.sparse import BlockIndex, IntIndex
from pandas.core.sparse.api import SparseSeries, SparseDataFrame, SparseArray
from pandas.tests.frame.test_api import SharedWithSparse
class TestSparseDataFrame(SharedWithSparse):
klass = SparseDataFrame
# SharedWithSparse tests use generic, klass-agnostic assertion
_assert_frame_equal = staticmethod(tm.assert_sp_frame_equal)
_assert_series_equal = staticmethod(tm.assert_sp_series_equal)
def setup_method(self, method):
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')
self.mixed_frame = self.frame.copy(False)
self.mixed_frame['foo'] = pd.SparseArray(['bar'] * len(self.dates))
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()
assert empty.shape == (0, 0)
no_cols = SparseDataFrame(index=np.arange(10))
mat = no_cols.as_matrix()
assert mat.shape == (10, 0)
no_index = SparseDataFrame(columns=np.arange(10))
mat = no_index.as_matrix()
assert mat.shape == (0, 10)
def test_copy(self):
cp = self.frame.copy()
assert isinstance(cp, SparseDataFrame)
tm.assert_sp_frame_equal(cp, self.frame)
# as of v0.15.0
# this is now identical (but not is_a )
assert cp.index.identical(self.frame.index)
def test_constructor(self):
for col, series in compat.iteritems(self.frame):
assert isinstance(series, SparseSeries)
assert isinstance(self.iframe['A'].sp_index, IntIndex)
# constructed zframe from matrix above
assert 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):
assert isinstance(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 pytest.raises(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
pytest.raises(TypeError, self.frame.reindex, columns=['A'],
level=1)
# wrong length index / columns
with tm.assert_raises_regex(ValueError, "^Index length"):
SparseDataFrame(self.frame.values, index=self.frame.index[:-1])
with tm.assert_raises_regex(ValueError, "^Column length"):
SparseDataFrame(self.frame.values, columns=self.frame.columns[:-1])
# GH 9272
def test_constructor_empty(self):
sp = SparseDataFrame()
assert len(sp.index) == 0
assert 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)
assert 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)
assert isinstance(x, SparseSeries)
df = SparseDataFrame(x)
assert isinstance(df, SparseDataFrame)
x = Series(np.random.randn(10000), name='a')
y = Series(np.random.randn(10000), name='b')
x2 = x.astype(float)
x2.loc[: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.loc[: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)
assert arr.dtype == np.int64
assert arr.fill_value == 0
df = pd.SparseDataFrame({'x': arr})
assert df['x'].dtype == np.int64
assert df['x'].fill_value == 0
s = pd.SparseSeries(arr, name='x')
assert s.dtype == np.int64
assert s.fill_value == 0
df = pd.SparseDataFrame(s)
assert df['x'].dtype == np.int64
assert df['x'].fill_value == 0
df = pd.SparseDataFrame({'x': s})
assert df['x'].dtype == np.int64
assert df['x'].fill_value == 0
def test_constructor_nan_dataframe(self):
# GH 10079
trains = np.arange(100)
tresholds = [10, 20, 30, 40, 50, 60]
tuples = [(i, j) for i in trains for j in tresholds]
index = pd.MultiIndex.from_tuples(tuples,
names=['trains', 'tresholds'])
matrix = np.empty((len(index), len(trains)))
matrix.fill(np.nan)
df = pd.DataFrame(matrix, index=index, columns=trains, dtype=float)
result = df.to_sparse()
expected = pd.SparseDataFrame(matrix, index=index, columns=trains,
dtype=float)
tm.assert_sp_frame_equal(result, expected)
def test_type_coercion_at_construction(self):
# GH 15682
result = pd.SparseDataFrame(
{'a': [1, 0, 0], 'b': [0, 1, 0], 'c': [0, 0, 1]}, dtype='uint8',
default_fill_value=0)
expected = pd.SparseDataFrame(
{'a': pd.SparseSeries([1, 0, 0], dtype='uint8'),
'b': pd.SparseSeries([0, 1, 0], dtype='uint8'),
'c': pd.SparseSeries([0, 0, 1], dtype='uint8')},
default_fill_value=0)
tm.assert_sp_frame_equal(result, expected)
def test_dtypes(self):
df = DataFrame(np.random.randn(10000, 4))
df.loc[: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):
# see gh-10452
assert self.frame.shape == (10, 4)
assert self.iframe.shape == (10, 4)
assert self.zframe.shape == (10, 4)
assert self.fill_frame.shape == (10, 4)
def test_str(self):
df = DataFrame(np.random.randn(10000, 4))
df.loc[: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 = tm.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()
assert isinstance(sdf, SparseDataFrame)
assert np.isnan(sdf.default_fill_value)
assert isinstance(sdf['A'].sp_index, BlockIndex)
tm.assert_frame_equal(sdf.to_dense(), df)
sdf = df.to_sparse(kind='integer')
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)
assert 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])
assert 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]})
assert 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)
assert isinstance(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.loc[:, ['A', 'B']] # noqa
def test_op_corners(self):
empty = self.empty + self.empty
assert empty.empty
foo = self.frame + self.empty
assert isinstance(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)
pytest.raises(Exception, sdf.__getitem__, ['a', 'd'])
def test_iloc(self):
# 2227
result = self.frame.iloc[:, 0]
assert 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')
tm.assert_class_equal(iframe['A'].sp_index,
iframe.iloc[:, 0].sp_index)
def test_set_value(self):
# ok, as the index gets converted to object
frame = self.frame.copy()
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
res = frame.set_value('foobar', 'B', 1.5)
assert res.index.dtype == 'object'
res = self.frame
res.index = res.index.astype(object)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
res = self.frame.set_value('foobar', 'B', 1.5)
assert res is not self.frame
assert res.index[-1] == 'foobar'
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
assert res.get_value('foobar', 'B') == 1.5
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
res2 = res.set_value('foobar', 'qux', 1.5)
assert res2 is not res
tm.assert_index_equal(res2.columns,
pd.Index(list(self.frame.columns) + ['qux']))
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
assert res2.get_value('foobar', 'qux') == 1.5
def test_fancy_index_misc(self):
# axis = 0
sliced = self.frame.iloc[-2:, :]
expected = self.frame.reindex(index=self.frame.index[-2:])
tm.assert_sp_frame_equal(sliced, expected)
# axis = 1
sliced = self.frame.iloc[:, -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]
tm.assert_index_equal(subindex, subframe.index)
pytest.raises(Exception, self.frame.__getitem__, indexer[:-1])
def test_setitem(self):
def _check_frame(frame, orig):
N = len(frame)
# insert SparseSeries
frame['E'] = frame['A']
assert isinstance(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)
assert result.name == 'E'
# insert Series
frame['F'] = frame['A'].to_dense()
assert isinstance(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)
assert isinstance(frame['H'], SparseSeries)
to_sparsify = np.random.randn(N)
to_sparsify[N // 2:] = frame.default_fill_value
frame['I'] = to_sparsify
assert len(frame['I'].sp_values) == N // 2
# insert ndarray wrong size
pytest.raises(Exception, frame.__setitem__, 'foo',
np.random.randn(N - 1))
# scalar value
frame['J'] = 5
assert len(frame['J'].sp_values) == N
assert (frame['J'].sp_values == 5).all()
frame['K'] = frame.default_fill_value
assert 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']
assert 'B' not in self.frame
tm.assert_sp_series_equal(self.frame['A'], A)
tm.assert_sp_series_equal(self.frame['C'], C)
del self.frame['D']
assert 'D' not in self.frame
del self.frame['A']
assert 'A' not in self.frame
def test_set_columns(self):
self.frame.columns = self.frame.columns
pytest.raises(Exception, setattr, self.frame, 'columns',
self.frame.columns[:-1])
def test_set_index(self):
self.frame.index = self.frame.index
pytest.raises(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.iloc[:5, :3]
b = self.frame.iloc[5:]
appended = a.append(b)
tm.assert_sp_frame_equal(appended.iloc[:, :3], self.frame.iloc[:, :3],
exact_indices=False)
def test_apply(self):
applied = self.frame.apply(np.sqrt)
assert isinstance(applied, SparseDataFrame)
tm.assert_almost_equal(applied.values, np.sqrt(self.frame.values))
applied = self.fill_frame.apply(np.sqrt)
assert applied['A'].fill_value == np.sqrt(2)
# agg / broadcast
broadcasted = self.frame.apply(np.sum, broadcast=True)
assert isinstance(broadcasted, SparseDataFrame)
exp = self.frame.to_dense().apply(np.sum, broadcast=True)
tm.assert_frame_equal(broadcasted.to_dense(), exp)
assert self.empty.apply(np.sqrt) is 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
assert res.dtype == np.int64
# ToDo: apply must return subclassed dtype
assert isinstance(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)
assert isinstance(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)})
assert sparse['A'].dtype == np.int64
assert 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)
assert res['A'].dtype == np.float64
assert 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)
assert sparse['A'].dtype == np.int64
assert 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)
assert res['A'].dtype == np.float64
assert 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)
assert sparse['A'].dtype == np.int64
assert 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)
assert res['A'].dtype == np.bool
assert 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_sparse_frame_pad_backfill_limit(self):
index = np.arange(10)
df = DataFrame(np.random.randn(10, 4), index=index)
sdf = df.to_sparse()
result = sdf[:2].reindex(index, method='pad', limit=5)
expected = sdf[:2].reindex(index).fillna(method='pad')
expected = expected.to_dense()
expected.values[-3:] = np.nan
expected = expected.to_sparse()
tm.assert_frame_equal(result, expected)
result = sdf[-2:].reindex(index, method='backfill', limit=5)
expected = sdf[-2:].reindex(index).fillna(method='backfill')
expected = expected.to_dense()
expected.values[:3] = np.nan
expected = expected.to_sparse()
tm.assert_frame_equal(result, expected)
def test_sparse_frame_fillna_limit(self):
index = np.arange(10)
df = DataFrame(np.random.randn(10, 4), index=index)
sdf = df.to_sparse()
result = sdf[:2].reindex(index)
result = result.fillna(method='pad', limit=5)
expected = sdf[:2].reindex(index).fillna(method='pad')
expected = expected.to_dense()
expected.values[-3:] = np.nan
expected = expected.to_sparse()
tm.assert_frame_equal(result, expected)
result = sdf[-2:].reindex(index)
result = result.fillna(method='backfill', limit=5)
expected = sdf[-2:].reindex(index).fillna(method='backfill')
expected = expected.to_dense()
expected.values[:3] = np.nan
expected = expected.to_sparse()
tm.assert_frame_equal(result, expected)
def test_rename(self):
result = self.frame.rename(index=str)
expected = SparseDataFrame(self.data, index=self.dates.strftime(
"%Y-%m-%d %H:%M:%S"))
tm.assert_sp_frame_equal(result, expected)
result = self.frame.rename(columns=lambda x: '%s%d' % (x, len(x)))
data = {'A1': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
'B1': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
'C1': np.arange(10, dtype=np.float64),
'D1': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
expected = SparseDataFrame(data, index=self.dates)
tm.assert_sp_frame_equal(result, expected)
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.loc[:, ['A', 'B']]
right = self.frame.loc[:, ['C', 'D']]
joined = left.join(right)
tm.assert_sp_frame_equal(joined, self.frame, exact_indices=False)
right = self.frame.loc[:, ['B', 'D']]
pytest.raises(Exception, left.join, right)
with tm.assert_raises_regex(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([])
assert len(length_zero) == 0
assert len(length_zero.columns) == len(frame.columns)
assert len(length_zero['A']) == 0
# frame being reindexed has length zero
length_n = length_zero.reindex(index)
assert len(length_n) == len(frame)
assert len(length_n.columns) == len(frame.columns)
assert len(length_n['A']) == len(frame)
# reindex columns
reindexed = frame.reindex(columns=['A', 'B', 'Z'])
assert len(reindexed.columns) == 3
tm.assert_almost_equal(reindexed['Z'].fill_value,
frame.default_fill_value)
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']
assert 'F' in self.frame
reindexed = self.frame.reindex(self.frame.index)
reindexed['G'] = reindexed['A']
assert 'G' not in 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_reindex_method(self):
sparse = SparseDataFrame(data=[[11., 12., 14.],
[21., 22., 24.],
[41., 42., 44.]],
index=[1, 2, 4],
columns=[1, 2, 4],
dtype=float)
# Over indices
# default method
result = sparse.reindex(index=range(6))
expected = SparseDataFrame(data=[[nan, nan, nan],
[11., 12., 14.],
[21., 22., 24.],
[nan, nan, nan],
[41., 42., 44.],
[nan, nan, nan]],
index=range(6),
columns=[1, 2, 4],
dtype=float)
tm.assert_sp_frame_equal(result, expected)
# method='bfill'
result = sparse.reindex(index=range(6), method='bfill')
expected = SparseDataFrame(data=[[11., 12., 14.],
[11., 12., 14.],
[21., 22., 24.],
[41., 42., 44.],
[41., 42., 44.],
[nan, nan, nan]],
index=range(6),
columns=[1, 2, 4],
dtype=float)
tm.assert_sp_frame_equal(result, expected)
# method='ffill'
result = sparse.reindex(index=range(6), method='ffill')
expected = SparseDataFrame(data=[[nan, nan, nan],
[11., 12., 14.],
[21., 22., 24.],
[21., 22., 24.],
[41., 42., 44.],
[41., 42., 44.]],
index=range(6),
columns=[1, 2, 4],
dtype=float)
tm.assert_sp_frame_equal(result, expected)
# Over columns
# default method
result = sparse.reindex(columns=range(6))
expected = SparseDataFrame(data=[[nan, 11., 12., nan, 14., nan],
[nan, 21., 22., nan, 24., nan],
[nan, 41., 42., nan, 44., nan]],
index=[1, 2, 4],
columns=range(6),
dtype=float)
tm.assert_sp_frame_equal(result, expected)
# method='bfill'
with pytest.raises(NotImplementedError):
sparse.reindex(columns=range(6), method='bfill')
# method='ffill'
with pytest.raises(NotImplementedError):
sparse.reindex(columns=range(6), method='ffill')
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):
with catch_warnings(record=True):
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)
tm.assert_numpy_array_equal(from_dense_lp.values,
from_sparse_lp.values)
_check(self.frame)
_check(self.iframe)
# for now
pytest.raises(Exception, _check, self.zframe)
pytest.raises(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,
kind=frame.default_kind)
tm.assert_frame_equal(shifted, exp)
shifted = frame.shift(2, freq=BDay())
exp = orig.shift(2, freq=BDay())
exp = exp.to_sparse(frame.default_fill_value,
kind=frame.default_kind)
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.assert_raises_regex(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']
assert 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')
# deprecated 0.21.0
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
df_blocks = df.blocks
assert list(df_blocks.keys()) == ['float64']
tm.assert_frame_equal(df_blocks['float64'], df)
@pytest.mark.xfail(reason='nan column names in _init_dict problematic '
'(GH 16894)')
def test_nan_columnname(self):
# GH 8822
nan_colname = DataFrame(Series(1.0, index=[0]), columns=[nan])
nan_colname_sparse = nan_colname.to_sparse()
assert np.isnan(nan_colname_sparse.columns[0])
def test_isna(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.isna()
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.isna()
assert isinstance(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_notna(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.notna()
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.notna()
assert isinstance(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)
@pytest.mark.parametrize('index', [None, list('abc')]) # noqa: F811
@pytest.mark.parametrize('columns', [None, list('def')])
@pytest.mark.parametrize('fill_value', [None, 0, np.nan])
@pytest.mark.parametrize('dtype', [bool, int, float, np.uint16])
def test_from_to_scipy(spmatrix, index, columns, fill_value, dtype):
# GH 4343
tm.skip_if_no_package('scipy')
# Make one ndarray and from it one sparse matrix, both to be used for
# constructing frames and comparing results
arr = np.eye(3, dtype=dtype)
# GH 16179
arr[0, 1] = dtype(2)
try:
spm = spmatrix(arr)
assert spm.dtype == arr.dtype
except (TypeError, AssertionError):
# If conversion to sparse fails for this spmatrix type and arr.dtype,
# then the combination is not currently supported in NumPy, so we
# can just skip testing it thoroughly
return
sdf = pd.SparseDataFrame(spm, index=index, columns=columns,
default_fill_value=fill_value)
# Expected result construction is kind of tricky for all
# dtype-fill_value combinations; easiest to cast to something generic
# and except later on
rarr = arr.astype(object)
rarr[arr == 0] = np.nan
expected = pd.SparseDataFrame(rarr, index=index, columns=columns).fillna(
fill_value if fill_value is not None else np.nan)
# Assert frame is as expected
sdf_obj = sdf.astype(object)
tm.assert_sp_frame_equal(sdf_obj, expected)
tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense())
# Assert spmatrices equal
assert dict(sdf.to_coo().todok()) == dict(spm.todok())
# Ensure dtype is preserved if possible
was_upcast = ((fill_value is None or is_float(fill_value)) and
not is_object_dtype(dtype) and
not is_float_dtype(dtype))
res_dtype = (bool if is_bool_dtype(dtype) else
float if was_upcast else
dtype)
tm.assert_contains_all(sdf.dtypes, {np.dtype(res_dtype)})
assert sdf.to_coo().dtype == res_dtype
# However, adding a str column results in an upcast to object
sdf['strings'] = np.arange(len(sdf)).astype(str)
assert sdf.to_coo().dtype == np.object_
@pytest.mark.parametrize('fill_value', [None, 0, np.nan]) # noqa: F811
def test_from_to_scipy_object(spmatrix, fill_value):
# GH 4343
dtype = object
columns = list('cd')
index = list('ab')
tm.skip_if_no_package('scipy', max_version='0.19.0')
# Make one ndarray and from it one sparse matrix, both to be used for
# constructing frames and comparing results
arr = np.eye(2, dtype=dtype)
try:
spm = spmatrix(arr)
assert spm.dtype == arr.dtype
except (TypeError, AssertionError):
# If conversion to sparse fails for this spmatrix type and arr.dtype,
# then the combination is not currently supported in NumPy, so we
# can just skip testing it thoroughly
return
sdf = pd.SparseDataFrame(spm, index=index, columns=columns,
default_fill_value=fill_value)
# Expected result construction is kind of tricky for all
# dtype-fill_value combinations; easiest to cast to something generic
# and except later on
rarr = arr.astype(object)
rarr[arr == 0] = np.nan
expected = pd.SparseDataFrame(rarr, index=index, columns=columns).fillna(
fill_value if fill_value is not None else np.nan)
# Assert frame is as expected
sdf_obj = sdf.astype(object)
tm.assert_sp_frame_equal(sdf_obj, expected)
tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense())
# Assert spmatrices equal
assert dict(sdf.to_coo().todok()) == dict(spm.todok())
# Ensure dtype is preserved if possible
res_dtype = object
tm.assert_contains_all(sdf.dtypes, {np.dtype(res_dtype)})
assert sdf.to_coo().dtype == res_dtype
def test_from_scipy_correct_ordering(spmatrix):
# GH 16179
tm.skip_if_no_package('scipy')
arr = np.arange(1, 5).reshape(2, 2)
try:
spm = spmatrix(arr)
assert spm.dtype == arr.dtype
except (TypeError, AssertionError):
# If conversion to sparse fails for this spmatrix type and arr.dtype,
# then the combination is not currently supported in NumPy, so we
# can just skip testing it thoroughly
return
sdf = pd.SparseDataFrame(spm)
expected = pd.SparseDataFrame(arr)
tm.assert_sp_frame_equal(sdf, expected)
tm.assert_frame_equal(sdf.to_dense(), expected.to_dense())
def test_from_scipy_fillna(spmatrix):
# GH 16112
tm.skip_if_no_package('scipy')
arr = np.eye(3)
arr[1:, 0] = np.nan
try:
spm = spmatrix(arr)
assert spm.dtype == arr.dtype
except (TypeError, AssertionError):
# If conversion to sparse fails for this spmatrix type and arr.dtype,
# then the combination is not currently supported in NumPy, so we
# can just skip testing it thoroughly
return
sdf = pd.SparseDataFrame(spm).fillna(-1.0)
# Returning frame should fill all nan values with -1.0
expected = pd.SparseDataFrame({
0: pd.SparseSeries([1., -1, -1]),
1: pd.SparseSeries([np.nan, 1, np.nan]),
2: pd.SparseSeries([np.nan, np.nan, 1]),
}, default_fill_value=-1)
# fill_value is expected to be what .fillna() above was called with
# We don't use -1 as initial fill_value in expected SparseSeries
# construction because this way we obtain "compressed" SparseArrays,
# avoiding having to construct them ourselves
for col in expected:
expected[col].fill_value = -1
tm.assert_sp_frame_equal(sdf, expected)
class TestSparseDataFrameArithmetic(object):
def test_numeric_op_scalar(self):
df = pd.DataFrame({'A': [nan, nan, 0, 1, ],
'B': [0, 1, 2, nan],
'C': [1., 2., 3., 4.],
'D': [nan, nan, nan, nan]})
sparse = df.to_sparse()
tm.assert_sp_frame_equal(sparse + 1, (df + 1).to_sparse())
def test_comparison_op_scalar(self):
# GH 13001
df = pd.DataFrame({'A': [nan, nan, 0, 1, ],
'B': [0, 1, 2, nan],
'C': [1., 2., 3., 4.],
'D': [nan, nan, nan, nan]})
sparse = df.to_sparse()
# comparison changes internal repr, compare with dense
res = sparse > 1
assert isinstance(res, pd.SparseDataFrame)
tm.assert_frame_equal(res.to_dense(), df > 1)
res = sparse != 0
assert isinstance(res, pd.SparseDataFrame)
tm.assert_frame_equal(res.to_dense(), df != 0)
class TestSparseDataFrameAnalytics(object):
def setup_method(self, method):
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.assert_raises_regex(ValueError, msg, np.cumsum,
self.frame, dtype=np.int64)
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(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)