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# pylint: disable-msg=E1101,W0612
import operator
from datetime import datetime
import pytest
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, DataFrame, bdate_range,
isna, compat, _np_version_under1p12)
from pandas.tseries.offsets import BDay
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.compat import range, PY36
from pandas.core.reshape.util import cartesian_product
import pandas.core.sparse.frame as spf
from pandas._libs.sparse import BlockIndex, IntIndex
from pandas.core.sparse.api import SparseSeries
from pandas.tests.series.test_api import SharedWithSparse
from itertools import product
def _test_data1():
# nan-based
arr = np.arange(20, dtype=float)
index = np.arange(20)
arr[:2] = nan
arr[5:10] = nan
arr[-3:] = nan
return arr, index
def _test_data2():
# nan-based
arr = np.arange(15, dtype=float)
index = np.arange(15)
arr[7:12] = nan
arr[-1:] = nan
return arr, index
def _test_data1_zero():
# zero-based
arr, index = _test_data1()
arr[np.isnan(arr)] = 0
return arr, index
def _test_data2_zero():
# zero-based
arr, index = _test_data2()
arr[np.isnan(arr)] = 0
return arr, index
class TestSparseSeries(SharedWithSparse):
series_klass = SparseSeries
# SharedWithSparse tests use generic, series_klass-agnostic assertion
_assert_series_equal = staticmethod(tm.assert_sp_series_equal)
def setup_method(self, method):
arr, index = _test_data1()
date_index = bdate_range('1/1/2011', periods=len(index))
self.bseries = SparseSeries(arr, index=index, kind='block',
name='bseries')
self.ts = self.bseries
self.btseries = SparseSeries(arr, index=date_index, kind='block')
self.iseries = SparseSeries(arr, index=index, kind='integer',
name='iseries')
arr, index = _test_data2()
self.bseries2 = SparseSeries(arr, index=index, kind='block')
self.iseries2 = SparseSeries(arr, index=index, kind='integer')
arr, index = _test_data1_zero()
self.zbseries = SparseSeries(arr, index=index, kind='block',
fill_value=0, name='zbseries')
self.ziseries = SparseSeries(arr, index=index, kind='integer',
fill_value=0)
arr, index = _test_data2_zero()
self.zbseries2 = SparseSeries(arr, index=index, kind='block',
fill_value=0)
self.ziseries2 = SparseSeries(arr, index=index, kind='integer',
fill_value=0)
def test_constructor_dict_input(self):
# gh-16905
constructor_dict = {1: 1.}
index = [0, 1, 2]
# Series with index passed in
series = pd.Series(constructor_dict)
expected = SparseSeries(series, index=index)
result = SparseSeries(constructor_dict, index=index)
tm.assert_sp_series_equal(result, expected)
# Series with index and dictionary with no index
expected = SparseSeries(series)
result = SparseSeries(constructor_dict)
tm.assert_sp_series_equal(result, expected)
def test_constructor_dict_order(self):
# GH19018
# initialization ordering: by insertion order if python>= 3.6, else
# order by value
d = {'b': 1, 'a': 0, 'c': 2}
result = SparseSeries(d)
if PY36:
expected = SparseSeries([1, 0, 2], index=list('bac'))
else:
expected = SparseSeries([0, 1, 2], index=list('abc'))
tm.assert_sp_series_equal(result, expected)
def test_constructor_dtype(self):
arr = SparseSeries([np.nan, 1, 2, np.nan])
assert arr.dtype == np.float64
assert np.isnan(arr.fill_value)
arr = SparseSeries([np.nan, 1, 2, np.nan], fill_value=0)
assert arr.dtype == np.float64
assert arr.fill_value == 0
arr = SparseSeries([0, 1, 2, 4], dtype=np.int64, fill_value=np.nan)
assert arr.dtype == np.int64
assert np.isnan(arr.fill_value)
arr = SparseSeries([0, 1, 2, 4], dtype=np.int64)
assert arr.dtype == np.int64
assert arr.fill_value == 0
arr = SparseSeries([0, 1, 2, 4], fill_value=0, dtype=np.int64)
assert arr.dtype == np.int64
assert arr.fill_value == 0
def test_iteration_and_str(self):
[x for x in self.bseries]
str(self.bseries)
def test_construct_DataFrame_with_sp_series(self):
# it works!
df = DataFrame({'col': self.bseries})
# printing & access
df.iloc[:1]
df['col']
df.dtypes
str(df)
tm.assert_sp_series_equal(df['col'], self.bseries, check_names=False)
result = df.iloc[:, 0]
tm.assert_sp_series_equal(result, self.bseries, check_names=False)
# blocking
expected = Series({'col': 'float64:sparse'})
result = df.ftypes
tm.assert_series_equal(expected, result)
def test_constructor_preserve_attr(self):
arr = pd.SparseArray([1, 0, 3, 0], dtype=np.int64, fill_value=0)
assert arr.dtype == np.int64
assert arr.fill_value == 0
s = pd.SparseSeries(arr, name='x')
assert s.dtype == np.int64
assert s.fill_value == 0
def test_series_density(self):
# GH2803
ts = Series(np.random.randn(10))
ts[2:-2] = nan
sts = ts.to_sparse()
density = sts.density # don't die
assert density == 4 / 10.0
def test_sparse_to_dense(self):
arr, index = _test_data1()
series = self.bseries.to_dense()
tm.assert_series_equal(series, Series(arr, name='bseries'))
# see gh-14647
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
series = self.bseries.to_dense(sparse_only=True)
indexer = np.isfinite(arr)
exp = Series(arr[indexer], index=index[indexer], name='bseries')
tm.assert_series_equal(series, exp)
series = self.iseries.to_dense()
tm.assert_series_equal(series, Series(arr, name='iseries'))
arr, index = _test_data1_zero()
series = self.zbseries.to_dense()
tm.assert_series_equal(series, Series(arr, name='zbseries'))
series = self.ziseries.to_dense()
tm.assert_series_equal(series, Series(arr))
def test_to_dense_fill_value(self):
s = pd.Series([1, np.nan, np.nan, 3, np.nan])
res = SparseSeries(s).to_dense()
tm.assert_series_equal(res, s)
res = SparseSeries(s, fill_value=0).to_dense()
tm.assert_series_equal(res, s)
s = pd.Series([1, np.nan, 0, 3, 0])
res = SparseSeries(s, fill_value=0).to_dense()
tm.assert_series_equal(res, s)
res = SparseSeries(s, fill_value=0).to_dense()
tm.assert_series_equal(res, s)
s = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan])
res = SparseSeries(s).to_dense()
tm.assert_series_equal(res, s)
s = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan])
res = SparseSeries(s, fill_value=0).to_dense()
tm.assert_series_equal(res, s)
def test_dense_to_sparse(self):
series = self.bseries.to_dense()
bseries = series.to_sparse(kind='block')
iseries = series.to_sparse(kind='integer')
tm.assert_sp_series_equal(bseries, self.bseries)
tm.assert_sp_series_equal(iseries, self.iseries, check_names=False)
assert iseries.name == self.bseries.name
assert len(series) == len(bseries)
assert len(series) == len(iseries)
assert series.shape == bseries.shape
assert series.shape == iseries.shape
# non-NaN fill value
series = self.zbseries.to_dense()
zbseries = series.to_sparse(kind='block', fill_value=0)
ziseries = series.to_sparse(kind='integer', fill_value=0)
tm.assert_sp_series_equal(zbseries, self.zbseries)
tm.assert_sp_series_equal(ziseries, self.ziseries, check_names=False)
assert ziseries.name == self.zbseries.name
assert len(series) == len(zbseries)
assert len(series) == len(ziseries)
assert series.shape == zbseries.shape
assert series.shape == ziseries.shape
def test_to_dense_preserve_name(self):
assert (self.bseries.name is not None)
result = self.bseries.to_dense()
assert result.name == self.bseries.name
def test_constructor(self):
# test setup guys
assert np.isnan(self.bseries.fill_value)
assert isinstance(self.bseries.sp_index, BlockIndex)
assert np.isnan(self.iseries.fill_value)
assert isinstance(self.iseries.sp_index, IntIndex)
assert self.zbseries.fill_value == 0
tm.assert_numpy_array_equal(self.zbseries.values.values,
self.bseries.to_dense().fillna(0).values)
# pass SparseSeries
def _check_const(sparse, name):
# use passed series name
result = SparseSeries(sparse)
tm.assert_sp_series_equal(result, sparse)
assert sparse.name == name
assert result.name == name
# use passed name
result = SparseSeries(sparse, name='x')
tm.assert_sp_series_equal(result, sparse, check_names=False)
assert result.name == 'x'
_check_const(self.bseries, 'bseries')
_check_const(self.iseries, 'iseries')
_check_const(self.zbseries, 'zbseries')
# Sparse time series works
date_index = bdate_range('1/1/2000', periods=len(self.bseries))
s5 = SparseSeries(self.bseries, index=date_index)
assert isinstance(s5, SparseSeries)
# pass Series
bseries2 = SparseSeries(self.bseries.to_dense())
tm.assert_numpy_array_equal(self.bseries.sp_values, bseries2.sp_values)
# pass dict?
# don't copy the data by default
values = np.ones(self.bseries.npoints)
sp = SparseSeries(values, sparse_index=self.bseries.sp_index)
sp.sp_values[:5] = 97
assert values[0] == 97
assert len(sp) == 20
assert sp.shape == (20, )
# but can make it copy!
sp = SparseSeries(values, sparse_index=self.bseries.sp_index,
copy=True)
sp.sp_values[:5] = 100
assert values[0] == 97
assert len(sp) == 20
assert sp.shape == (20, )
def test_constructor_scalar(self):
data = 5
sp = SparseSeries(data, np.arange(100))
sp = sp.reindex(np.arange(200))
assert (sp.loc[:99] == data).all()
assert isna(sp.loc[100:]).all()
data = np.nan
sp = SparseSeries(data, np.arange(100))
assert len(sp) == 100
assert sp.shape == (100, )
def test_constructor_ndarray(self):
pass
def test_constructor_nonnan(self):
arr = [0, 0, 0, nan, nan]
sp_series = SparseSeries(arr, fill_value=0)
tm.assert_numpy_array_equal(sp_series.values.values, np.array(arr))
assert len(sp_series) == 5
assert sp_series.shape == (5, )
def test_constructor_empty(self):
# see gh-9272
sp = SparseSeries()
assert len(sp.index) == 0
assert sp.shape == (0, )
def test_copy_astype(self):
cop = self.bseries.astype(np.float64)
assert cop is not self.bseries
assert cop.sp_index is self.bseries.sp_index
assert cop.dtype == np.float64
cop2 = self.iseries.copy()
tm.assert_sp_series_equal(cop, self.bseries)
tm.assert_sp_series_equal(cop2, self.iseries)
# test that data is copied
cop[:5] = 97
assert cop.sp_values[0] == 97
assert self.bseries.sp_values[0] != 97
# correct fill value
zbcop = self.zbseries.copy()
zicop = self.ziseries.copy()
tm.assert_sp_series_equal(zbcop, self.zbseries)
tm.assert_sp_series_equal(zicop, self.ziseries)
# no deep copy
view = self.bseries.copy(deep=False)
view.sp_values[:5] = 5
assert (self.bseries.sp_values[:5] == 5).all()
def test_shape(self):
# see gh-10452
assert self.bseries.shape == (20, )
assert self.btseries.shape == (20, )
assert self.iseries.shape == (20, )
assert self.bseries2.shape == (15, )
assert self.iseries2.shape == (15, )
assert self.zbseries2.shape == (15, )
assert self.ziseries2.shape == (15, )
def test_astype(self):
with pytest.raises(ValueError):
self.bseries.astype(np.int64)
def test_astype_all(self):
orig = pd.Series(np.array([1, 2, 3]))
s = SparseSeries(orig)
types = [np.float64, np.float32, np.int64,
np.int32, np.int16, np.int8]
for typ in types:
res = s.astype(typ)
assert res.dtype == typ
tm.assert_series_equal(res.to_dense(), orig.astype(typ))
def test_kind(self):
assert self.bseries.kind == 'block'
assert self.iseries.kind == 'integer'
def test_to_frame(self):
# GH 9850
s = pd.SparseSeries([1, 2, 0, nan, 4, nan, 0], name='x')
exp = pd.SparseDataFrame({'x': [1, 2, 0, nan, 4, nan, 0]})
tm.assert_sp_frame_equal(s.to_frame(), exp)
exp = pd.SparseDataFrame({'y': [1, 2, 0, nan, 4, nan, 0]})
tm.assert_sp_frame_equal(s.to_frame(name='y'), exp)
s = pd.SparseSeries([1, 2, 0, nan, 4, nan, 0], name='x', fill_value=0)
exp = pd.SparseDataFrame({'x': [1, 2, 0, nan, 4, nan, 0]},
default_fill_value=0)
tm.assert_sp_frame_equal(s.to_frame(), exp)
exp = pd.DataFrame({'y': [1, 2, 0, nan, 4, nan, 0]})
tm.assert_frame_equal(s.to_frame(name='y').to_dense(), exp)
def test_pickle(self):
def _test_roundtrip(series):
unpickled = tm.round_trip_pickle(series)
tm.assert_sp_series_equal(series, unpickled)
tm.assert_series_equal(series.to_dense(), unpickled.to_dense())
self._check_all(_test_roundtrip)
def _check_all(self, check_func):
check_func(self.bseries)
check_func(self.iseries)
check_func(self.zbseries)
check_func(self.ziseries)
def test_getitem(self):
def _check_getitem(sp, dense):
for idx, val in compat.iteritems(dense):
tm.assert_almost_equal(val, sp[idx])
for i in range(len(dense)):
tm.assert_almost_equal(sp[i], dense[i])
# j = np.float64(i)
# assert_almost_equal(sp[j], dense[j])
# API change 1/6/2012
# negative getitem works
# for i in xrange(len(dense)):
# assert_almost_equal(sp[-i], dense[-i])
_check_getitem(self.bseries, self.bseries.to_dense())
_check_getitem(self.btseries, self.btseries.to_dense())
_check_getitem(self.zbseries, self.zbseries.to_dense())
_check_getitem(self.iseries, self.iseries.to_dense())
_check_getitem(self.ziseries, self.ziseries.to_dense())
# exception handling
pytest.raises(Exception, self.bseries.__getitem__,
len(self.bseries) + 1)
# index not contained
pytest.raises(Exception, self.btseries.__getitem__,
self.btseries.index[-1] + BDay())
def test_get_get_value(self):
tm.assert_almost_equal(self.bseries.get(10), self.bseries[10])
assert self.bseries.get(len(self.bseries) + 1) is None
dt = self.btseries.index[10]
result = self.btseries.get(dt)
expected = self.btseries.to_dense()[dt]
tm.assert_almost_equal(result, expected)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
tm.assert_almost_equal(
self.bseries.get_value(10), self.bseries[10])
def test_set_value(self):
idx = self.btseries.index[7]
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
self.btseries.set_value(idx, 0)
assert self.btseries[idx] == 0
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
self.iseries.set_value('foobar', 0)
assert self.iseries.index[-1] == 'foobar'
assert self.iseries['foobar'] == 0
def test_getitem_slice(self):
idx = self.bseries.index
res = self.bseries[::2]
assert isinstance(res, SparseSeries)
expected = self.bseries.reindex(idx[::2])
tm.assert_sp_series_equal(res, expected)
res = self.bseries[:5]
assert isinstance(res, SparseSeries)
tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:5]))
res = self.bseries[5:]
tm.assert_sp_series_equal(res, self.bseries.reindex(idx[5:]))
# negative indices
res = self.bseries[:-3]
tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:-3]))
def test_take(self):
def _compare_with_dense(sp):
dense = sp.to_dense()
def _compare(idx):
dense_result = dense.take(idx).values
sparse_result = sp.take(idx)
assert isinstance(sparse_result, SparseSeries)
tm.assert_almost_equal(dense_result,
sparse_result.values.values)
_compare([1., 2., 3., 4., 5., 0.])
_compare([7, 2, 9, 0, 4])
_compare([3, 6, 3, 4, 7])
self._check_all(_compare_with_dense)
pytest.raises(Exception, self.bseries.take,
[0, len(self.bseries) + 1])
# Corner case
sp = SparseSeries(np.ones(10) * nan)
exp = pd.Series(np.repeat(nan, 5))
tm.assert_series_equal(sp.take([0, 1, 2, 3, 4]), exp)
with tm.assert_produces_warning(FutureWarning):
sp.take([1, 5], convert=True)
with tm.assert_produces_warning(FutureWarning):
sp.take([1, 5], convert=False)
def test_numpy_take(self):
sp = SparseSeries([1.0, 2.0, 3.0])
indices = [1, 2]
if not _np_version_under1p12:
tm.assert_series_equal(np.take(sp, indices, axis=0).to_dense(),
np.take(sp.to_dense(), indices, axis=0))
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.take,
sp, indices, out=np.empty(sp.shape))
msg = "the 'mode' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.take,
sp, indices, out=None, mode='clip')
def test_setitem(self):
self.bseries[5] = 7.
assert self.bseries[5] == 7.
def test_setslice(self):
self.bseries[5:10] = 7.
tm.assert_series_equal(self.bseries[5:10].to_dense(),
Series(7., index=range(5, 10),
name=self.bseries.name))
def test_operators(self):
def _check_op(a, b, op):
sp_result = op(a, b)
adense = a.to_dense() if isinstance(a, SparseSeries) else a
bdense = b.to_dense() if isinstance(b, SparseSeries) else b
dense_result = op(adense, bdense)
tm.assert_almost_equal(sp_result.to_dense(), dense_result)
def check(a, b):
_check_op(a, b, operator.add)
_check_op(a, b, operator.sub)
_check_op(a, b, operator.truediv)
_check_op(a, b, operator.floordiv)
_check_op(a, b, operator.mul)
_check_op(a, b, lambda x, y: operator.add(y, x))
_check_op(a, b, lambda x, y: operator.sub(y, x))
_check_op(a, b, lambda x, y: operator.truediv(y, x))
_check_op(a, b, lambda x, y: operator.floordiv(y, x))
_check_op(a, b, lambda x, y: operator.mul(y, x))
# NaN ** 0 = 1 in C?
# _check_op(a, b, operator.pow)
# _check_op(a, b, lambda x, y: operator.pow(y, x))
check(self.bseries, self.bseries)
check(self.iseries, self.iseries)
check(self.bseries, self.iseries)
check(self.bseries, self.bseries2)
check(self.bseries, self.iseries2)
check(self.iseries, self.iseries2)
# scalar value
check(self.bseries, 5)
# zero-based
check(self.zbseries, self.zbseries * 2)
check(self.zbseries, self.zbseries2)
check(self.ziseries, self.ziseries2)
# with dense
result = self.bseries + self.bseries.to_dense()
tm.assert_sp_series_equal(result, self.bseries + self.bseries)
def test_binary_operators(self):
# skipping for now #####
import pytest
pytest.skip("skipping sparse binary operators test")
def _check_inplace_op(iop, op):
tmp = self.bseries.copy()
expected = op(tmp, self.bseries)
iop(tmp, self.bseries)
tm.assert_sp_series_equal(tmp, expected)
inplace_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow']
for op in inplace_ops:
_check_inplace_op(getattr(operator, "i%s" % op),
getattr(operator, op))
def test_abs(self):
s = SparseSeries([1, 2, -3], name='x')
expected = SparseSeries([1, 2, 3], name='x')
result = s.abs()
tm.assert_sp_series_equal(result, expected)
assert result.name == 'x'
result = abs(s)
tm.assert_sp_series_equal(result, expected)
assert result.name == 'x'
result = np.abs(s)
tm.assert_sp_series_equal(result, expected)
assert result.name == 'x'
s = SparseSeries([1, -2, 2, -3], fill_value=-2, name='x')
expected = SparseSeries([1, 2, 3], sparse_index=s.sp_index,
fill_value=2, name='x')
result = s.abs()
tm.assert_sp_series_equal(result, expected)
assert result.name == 'x'
result = abs(s)
tm.assert_sp_series_equal(result, expected)
assert result.name == 'x'
result = np.abs(s)
tm.assert_sp_series_equal(result, expected)
assert result.name == 'x'
def test_reindex(self):
def _compare_with_series(sps, new_index):
spsre = sps.reindex(new_index)
series = sps.to_dense()
seriesre = series.reindex(new_index)
seriesre = seriesre.to_sparse(fill_value=sps.fill_value)
tm.assert_sp_series_equal(spsre, seriesre)
tm.assert_series_equal(spsre.to_dense(), seriesre.to_dense())
_compare_with_series(self.bseries, self.bseries.index[::2])
_compare_with_series(self.bseries, list(self.bseries.index[::2]))
_compare_with_series(self.bseries, self.bseries.index[:10])
_compare_with_series(self.bseries, self.bseries.index[5:])
_compare_with_series(self.zbseries, self.zbseries.index[::2])
_compare_with_series(self.zbseries, self.zbseries.index[:10])
_compare_with_series(self.zbseries, self.zbseries.index[5:])
# special cases
same_index = self.bseries.reindex(self.bseries.index)
tm.assert_sp_series_equal(self.bseries, same_index)
assert same_index is not self.bseries
# corner cases
sp = SparseSeries([], index=[])
# TODO: sp_zero is not used anywhere...remove?
sp_zero = SparseSeries([], index=[], fill_value=0) # noqa
_compare_with_series(sp, np.arange(10))
# with copy=False
reindexed = self.bseries.reindex(self.bseries.index, copy=True)
reindexed.sp_values[:] = 1.
assert (self.bseries.sp_values != 1.).all()
reindexed = self.bseries.reindex(self.bseries.index, copy=False)
reindexed.sp_values[:] = 1.
tm.assert_numpy_array_equal(self.bseries.sp_values, np.repeat(1., 10))
def test_sparse_reindex(self):
length = 10
def _check(values, index1, index2, fill_value):
first_series = SparseSeries(values, sparse_index=index1,
fill_value=fill_value)
reindexed = first_series.sparse_reindex(index2)
assert reindexed.sp_index is index2
int_indices1 = index1.to_int_index().indices
int_indices2 = index2.to_int_index().indices
expected = Series(values, index=int_indices1)
expected = expected.reindex(int_indices2).fillna(fill_value)
tm.assert_almost_equal(expected.values, reindexed.sp_values)
# make sure level argument asserts
# TODO: expected is not used anywhere...remove?
expected = expected.reindex(int_indices2).fillna(fill_value) # noqa
def _check_with_fill_value(values, first, second, fill_value=nan):
i_index1 = IntIndex(length, first)
i_index2 = IntIndex(length, second)
b_index1 = i_index1.to_block_index()
b_index2 = i_index2.to_block_index()
_check(values, i_index1, i_index2, fill_value)
_check(values, b_index1, b_index2, fill_value)
def _check_all(values, first, second):
_check_with_fill_value(values, first, second, fill_value=nan)
_check_with_fill_value(values, first, second, fill_value=0)
index1 = [2, 4, 5, 6, 8, 9]
values1 = np.arange(6.)
_check_all(values1, index1, [2, 4, 5])
_check_all(values1, index1, [2, 3, 4, 5, 6, 7, 8, 9])
_check_all(values1, index1, [0, 1])
_check_all(values1, index1, [0, 1, 7, 8, 9])
_check_all(values1, index1, [])
first_series = SparseSeries(values1,
sparse_index=IntIndex(length, index1),
fill_value=nan)
with tm.assert_raises_regex(TypeError,
'new index must be a SparseIndex'):
reindexed = first_series.sparse_reindex(0) # noqa
def test_repr(self):
# TODO: These aren't used
bsrepr = repr(self.bseries) # noqa
isrepr = repr(self.iseries) # noqa
def test_iter(self):
pass
def test_truncate(self):
pass
def test_fillna(self):
pass
def test_groupby(self):
pass
def test_reductions(self):
def _compare_with_dense(obj, op):
sparse_result = getattr(obj, op)()
series = obj.to_dense()
dense_result = getattr(series, op)()
assert sparse_result == dense_result
to_compare = ['count', 'sum', 'mean', 'std', 'var', 'skew']
def _compare_all(obj):
for op in to_compare:
_compare_with_dense(obj, op)
_compare_all(self.bseries)
self.bseries.sp_values[5:10] = np.NaN
_compare_all(self.bseries)
_compare_all(self.zbseries)
self.zbseries.sp_values[5:10] = np.NaN
_compare_all(self.zbseries)
series = self.zbseries.copy()
series.fill_value = 2
_compare_all(series)
nonna = Series(np.random.randn(20)).to_sparse()
_compare_all(nonna)
nonna2 = Series(np.random.randn(20)).to_sparse(fill_value=0)
_compare_all(nonna2)
def test_dropna(self):
sp = SparseSeries([0, 0, 0, nan, nan, 5, 6], fill_value=0)
sp_valid = sp.dropna()
expected = sp.to_dense().dropna()
expected = expected[expected != 0]
exp_arr = pd.SparseArray(expected.values, fill_value=0, kind='block')
tm.assert_sp_array_equal(sp_valid.values, exp_arr)
tm.assert_index_equal(sp_valid.index, expected.index)
assert len(sp_valid.sp_values) == 2
result = self.bseries.dropna()
expected = self.bseries.to_dense().dropna()
assert not isinstance(result, SparseSeries)
tm.assert_series_equal(result, expected)
def test_homogenize(self):
def _check_matches(indices, expected):
data = {}
for i, idx in enumerate(indices):
data[i] = SparseSeries(idx.to_int_index().indices,
sparse_index=idx, fill_value=np.nan)
# homogenized is only valid with NaN fill values
homogenized = spf.homogenize(data)
for k, v in compat.iteritems(homogenized):
assert (v.sp_index.equals(expected))
indices1 = [BlockIndex(10, [2], [7]), BlockIndex(10, [1, 6], [3, 4]),
BlockIndex(10, [0], [10])]
expected1 = BlockIndex(10, [2, 6], [2, 3])
_check_matches(indices1, expected1)
indices2 = [BlockIndex(10, [2], [7]), BlockIndex(10, [2], [7])]
expected2 = indices2[0]
_check_matches(indices2, expected2)
# must have NaN fill value
data = {'a': SparseSeries(np.arange(7), sparse_index=expected2,
fill_value=0)}
with tm.assert_raises_regex(TypeError, "NaN fill value"):
spf.homogenize(data)
def test_fill_value_corner(self):
cop = self.zbseries.copy()
cop.fill_value = 0
result = self.bseries / cop
assert np.isnan(result.fill_value)
cop2 = self.zbseries.copy()
cop2.fill_value = 1
result = cop2 / cop
# 1 / 0 is inf
assert np.isinf(result.fill_value)
def test_fill_value_when_combine_const(self):
# GH12723
s = SparseSeries([0, 1, np.nan, 3, 4, 5], index=np.arange(6))
exp = s.fillna(0).add(2)
res = s.add(2, fill_value=0)
tm.assert_series_equal(res, exp)
def test_shift(self):
series = SparseSeries([nan, 1., 2., 3., nan, nan], index=np.arange(6))
shifted = series.shift(0)
assert shifted is not series
tm.assert_sp_series_equal(shifted, series)
f = lambda s: s.shift(1)
_dense_series_compare(series, f)
f = lambda s: s.shift(-2)
_dense_series_compare(series, f)
series = SparseSeries([nan, 1., 2., 3., nan, nan],
index=bdate_range('1/1/2000', periods=6))
f = lambda s: s.shift(2, freq='B')
_dense_series_compare(series, f)
f = lambda s: s.shift(2, freq=BDay())
_dense_series_compare(series, f)
def test_shift_nan(self):
# GH 12908
orig = pd.Series([np.nan, 2, np.nan, 4, 0, np.nan, 0])
sparse = orig.to_sparse()
tm.assert_sp_series_equal(sparse.shift(0), orig.shift(0).to_sparse())
tm.assert_sp_series_equal(sparse.shift(1), orig.shift(1).to_sparse())
tm.assert_sp_series_equal(sparse.shift(2), orig.shift(2).to_sparse())
tm.assert_sp_series_equal(sparse.shift(3), orig.shift(3).to_sparse())
tm.assert_sp_series_equal(sparse.shift(-1), orig.shift(-1).to_sparse())
tm.assert_sp_series_equal(sparse.shift(-2), orig.shift(-2).to_sparse())
tm.assert_sp_series_equal(sparse.shift(-3), orig.shift(-3).to_sparse())
tm.assert_sp_series_equal(sparse.shift(-4), orig.shift(-4).to_sparse())
sparse = orig.to_sparse(fill_value=0)
tm.assert_sp_series_equal(sparse.shift(0),
orig.shift(0).to_sparse(fill_value=0))
tm.assert_sp_series_equal(sparse.shift(1),
orig.shift(1).to_sparse(fill_value=0))
tm.assert_sp_series_equal(sparse.shift(2),
orig.shift(2).to_sparse(fill_value=0))
tm.assert_sp_series_equal(sparse.shift(3),
orig.shift(3).to_sparse(fill_value=0))
tm.assert_sp_series_equal(sparse.shift(-1),
orig.shift(-1).to_sparse(fill_value=0))
tm.assert_sp_series_equal(sparse.shift(-2),
orig.shift(-2).to_sparse(fill_value=0))
tm.assert_sp_series_equal(sparse.shift(-3),
orig.shift(-3).to_sparse(fill_value=0))
tm.assert_sp_series_equal(sparse.shift(-4),
orig.shift(-4).to_sparse(fill_value=0))
def test_shift_dtype(self):
# GH 12908
orig = pd.Series([1, 2, 3, 4], dtype=np.int64)
sparse = orig.to_sparse()
tm.assert_sp_series_equal(sparse.shift(0), orig.shift(0).to_sparse())
sparse = orig.to_sparse(fill_value=np.nan)
tm.assert_sp_series_equal(sparse.shift(0),
orig.shift(0).to_sparse(fill_value=np.nan))
# shift(1) or more span changes dtype to float64
tm.assert_sp_series_equal(sparse.shift(1), orig.shift(1).to_sparse())
tm.assert_sp_series_equal(sparse.shift(2), orig.shift(2).to_sparse())
tm.assert_sp_series_equal(sparse.shift(3), orig.shift(3).to_sparse())
tm.assert_sp_series_equal(sparse.shift(-1), orig.shift(-1).to_sparse())
tm.assert_sp_series_equal(sparse.shift(-2), orig.shift(-2).to_sparse())
tm.assert_sp_series_equal(sparse.shift(-3), orig.shift(-3).to_sparse())
tm.assert_sp_series_equal(sparse.shift(-4), orig.shift(-4).to_sparse())
def test_shift_dtype_fill_value(self):
# GH 12908
orig = pd.Series([1, 0, 0, 4], dtype=np.int64)
for v in [0, 1, np.nan]:
sparse = orig.to_sparse(fill_value=v)
tm.assert_sp_series_equal(sparse.shift(0),
orig.shift(0).to_sparse(fill_value=v))
tm.assert_sp_series_equal(sparse.shift(1),
orig.shift(1).to_sparse(fill_value=v))
tm.assert_sp_series_equal(sparse.shift(2),
orig.shift(2).to_sparse(fill_value=v))
tm.assert_sp_series_equal(sparse.shift(3),
orig.shift(3).to_sparse(fill_value=v))
tm.assert_sp_series_equal(sparse.shift(-1),
orig.shift(-1).to_sparse(fill_value=v))
tm.assert_sp_series_equal(sparse.shift(-2),
orig.shift(-2).to_sparse(fill_value=v))
tm.assert_sp_series_equal(sparse.shift(-3),
orig.shift(-3).to_sparse(fill_value=v))
tm.assert_sp_series_equal(sparse.shift(-4),
orig.shift(-4).to_sparse(fill_value=v))
def test_combine_first(self):
s = self.bseries
result = s[::2].combine_first(s)
result2 = s[::2].combine_first(s.to_dense())
expected = s[::2].to_dense().combine_first(s.to_dense())
expected = expected.to_sparse(fill_value=s.fill_value)
tm.assert_sp_series_equal(result, result2)
tm.assert_sp_series_equal(result, expected)
@pytest.mark.parametrize('deep,fill_values', [([True, False],
[0, 1, np.nan, None])])
def test_memory_usage_deep(self, deep, fill_values):
for deep, fill_value in product(deep, fill_values):
sparse_series = SparseSeries(fill_values, fill_value=fill_value)
dense_series = Series(fill_values)
sparse_usage = sparse_series.memory_usage(deep=deep)
dense_usage = dense_series.memory_usage(deep=deep)
assert sparse_usage < dense_usage
class TestSparseHandlingMultiIndexes(object):
def setup_method(self, method):
miindex = pd.MultiIndex.from_product(
[["x", "y"], ["10", "20"]], names=['row-foo', 'row-bar'])
micol = pd.MultiIndex.from_product(
[['a', 'b', 'c'], ["1", "2"]], names=['col-foo', 'col-bar'])
dense_multiindex_frame = pd.DataFrame(
index=miindex, columns=micol).sort_index().sort_index(axis=1)
self.dense_multiindex_frame = dense_multiindex_frame.fillna(value=3.14)
def test_to_sparse_preserve_multiindex_names_columns(self):
sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse()
sparse_multiindex_frame = sparse_multiindex_frame.copy()
tm.assert_index_equal(sparse_multiindex_frame.columns,
self.dense_multiindex_frame.columns)
def test_round_trip_preserve_multiindex_names(self):
sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse()
round_trip_multiindex_frame = sparse_multiindex_frame.to_dense()
tm.assert_frame_equal(self.dense_multiindex_frame,
round_trip_multiindex_frame,
check_column_type=True,
check_names=True)
@td.skip_if_no_scipy
class TestSparseSeriesScipyInteraction(object):
# Issue 8048: add SparseSeries coo methods
def setup_method(self, method):
import scipy.sparse
# SparseSeries inputs used in tests, the tests rely on the order
self.sparse_series = []
s = pd.Series([3.0, nan, 1.0, 2.0, nan, nan])
s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
(1, 2, 'a', 1),
(1, 1, 'b', 0),
(1, 1, 'b', 1),
(2, 1, 'b', 0),
(2, 1, 'b', 1)],
names=['A', 'B', 'C', 'D'])
self.sparse_series.append(s.to_sparse())
ss = self.sparse_series[0].copy()
ss.index.names = [3, 0, 1, 2]
self.sparse_series.append(ss)
ss = pd.Series([
nan
] * 12, index=cartesian_product((range(3), range(4)))).to_sparse()
for k, v in zip([(0, 0), (1, 2), (1, 3)], [3.0, 1.0, 2.0]):
ss[k] = v
self.sparse_series.append(ss)
# results used in tests
self.coo_matrices = []
self.coo_matrices.append(scipy.sparse.coo_matrix(
([3.0, 1.0, 2.0], ([0, 1, 1], [0, 2, 3])), shape=(3, 4)))
self.coo_matrices.append(scipy.sparse.coo_matrix(
([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)))
self.coo_matrices.append(scipy.sparse.coo_matrix(
([3.0, 1.0, 2.0], ([0, 1, 1], [0, 0, 1])), shape=(3, 2)))
self.ils = [[(1, 2), (1, 1), (2, 1)], [(1, 1), (1, 2), (2, 1)],
[(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]]
self.jls = [[('a', 0), ('a', 1), ('b', 0), ('b', 1)], [0, 1]]
def test_to_coo_text_names_integer_row_levels_nosort(self):
ss = self.sparse_series[0]
kwargs = {'row_levels': [0, 1], 'column_levels': [2, 3]}
result = (self.coo_matrices[0], self.ils[0], self.jls[0])
self._run_test(ss, kwargs, result)
def test_to_coo_text_names_integer_row_levels_sort(self):
ss = self.sparse_series[0]
kwargs = {'row_levels': [0, 1],
'column_levels': [2, 3],
'sort_labels': True}
result = (self.coo_matrices[1], self.ils[1], self.jls[0])
self._run_test(ss, kwargs, result)
def test_to_coo_text_names_text_row_levels_nosort_col_level_single(self):
ss = self.sparse_series[0]
kwargs = {'row_levels': ['A', 'B', 'C'],
'column_levels': ['D'],
'sort_labels': False}
result = (self.coo_matrices[2], self.ils[2], self.jls[1])
self._run_test(ss, kwargs, result)
def test_to_coo_integer_names_integer_row_levels_nosort(self):
ss = self.sparse_series[1]
kwargs = {'row_levels': [3, 0], 'column_levels': [1, 2]}
result = (self.coo_matrices[0], self.ils[0], self.jls[0])
self._run_test(ss, kwargs, result)
def test_to_coo_text_names_text_row_levels_nosort(self):
ss = self.sparse_series[0]
kwargs = {'row_levels': ['A', 'B'], 'column_levels': ['C', 'D']}
result = (self.coo_matrices[0], self.ils[0], self.jls[0])
self._run_test(ss, kwargs, result)
def test_to_coo_bad_partition_nonnull_intersection(self):
ss = self.sparse_series[0]
pytest.raises(ValueError, ss.to_coo, ['A', 'B', 'C'], ['C', 'D'])
def test_to_coo_bad_partition_small_union(self):
ss = self.sparse_series[0]
pytest.raises(ValueError, ss.to_coo, ['A'], ['C', 'D'])
def test_to_coo_nlevels_less_than_two(self):
ss = self.sparse_series[0]
ss.index = np.arange(len(ss.index))
pytest.raises(ValueError, ss.to_coo)
def test_to_coo_bad_ilevel(self):
ss = self.sparse_series[0]
pytest.raises(KeyError, ss.to_coo, ['A', 'B'], ['C', 'D', 'E'])
def test_to_coo_duplicate_index_entries(self):
ss = pd.concat([self.sparse_series[0],
self.sparse_series[0]]).to_sparse()
pytest.raises(ValueError, ss.to_coo, ['A', 'B'], ['C', 'D'])
def test_from_coo_dense_index(self):
ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=True)
check = self.sparse_series[2]
tm.assert_sp_series_equal(ss, check)
def test_from_coo_nodense_index(self):
ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=False)
check = self.sparse_series[2]
check = check.dropna().to_sparse()
tm.assert_sp_series_equal(ss, check)
def test_from_coo_long_repr(self):
# GH 13114
# test it doesn't raise error. Formatting is tested in test_format
import scipy.sparse
sparse = SparseSeries.from_coo(scipy.sparse.rand(350, 18))
repr(sparse)
def _run_test(self, ss, kwargs, check):
results = ss.to_coo(**kwargs)
self._check_results_to_coo(results, check)
# for every test, also test symmetry property (transpose), switch
# row_levels and column_levels
d = kwargs.copy()
d['row_levels'] = kwargs['column_levels']
d['column_levels'] = kwargs['row_levels']
results = ss.to_coo(**d)
results = (results[0].T, results[2], results[1])
self._check_results_to_coo(results, check)
def _check_results_to_coo(self, results, check):
(A, il, jl) = results
(A_result, il_result, jl_result) = check
# convert to dense and compare
tm.assert_numpy_array_equal(A.todense(), A_result.todense())
# or compare directly as difference of sparse
# assert(abs(A - A_result).max() < 1e-12) # max is failing in python
# 2.6
assert il == il_result
assert jl == jl_result
def test_concat(self):
val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
val2 = np.array([3, np.nan, 4, 0, 0])
for kind in ['integer', 'block']:
sparse1 = pd.SparseSeries(val1, name='x', kind=kind)
sparse2 = pd.SparseSeries(val2, name='y', kind=kind)
res = pd.concat([sparse1, sparse2])
exp = pd.concat([pd.Series(val1), pd.Series(val2)])
exp = pd.SparseSeries(exp, kind=kind)
tm.assert_sp_series_equal(res, exp)
sparse1 = pd.SparseSeries(val1, fill_value=0, name='x', kind=kind)
sparse2 = pd.SparseSeries(val2, fill_value=0, name='y', kind=kind)
res = pd.concat([sparse1, sparse2])
exp = pd.concat([pd.Series(val1), pd.Series(val2)])
exp = pd.SparseSeries(exp, fill_value=0, kind=kind)
tm.assert_sp_series_equal(res, exp)
def test_concat_axis1(self):
val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
val2 = np.array([3, np.nan, 4, 0, 0])
sparse1 = pd.SparseSeries(val1, name='x')
sparse2 = pd.SparseSeries(val2, name='y')
res = pd.concat([sparse1, sparse2], axis=1)
exp = pd.concat([pd.Series(val1, name='x'),
pd.Series(val2, name='y')], axis=1)
exp = pd.SparseDataFrame(exp)
tm.assert_sp_frame_equal(res, exp)
def test_concat_different_fill(self):
val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
val2 = np.array([3, np.nan, 4, 0, 0])
for kind in ['integer', 'block']:
sparse1 = pd.SparseSeries(val1, name='x', kind=kind)
sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0)
res = pd.concat([sparse1, sparse2])
exp = pd.concat([pd.Series(val1), pd.Series(val2)])
exp = pd.SparseSeries(exp, kind=kind)
tm.assert_sp_series_equal(res, exp)
res = pd.concat([sparse2, sparse1])
exp = pd.concat([pd.Series(val2), pd.Series(val1)])
exp = pd.SparseSeries(exp, kind=kind, fill_value=0)
tm.assert_sp_series_equal(res, exp)
def test_concat_axis1_different_fill(self):
val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
val2 = np.array([3, np.nan, 4, 0, 0])
sparse1 = pd.SparseSeries(val1, name='x')
sparse2 = pd.SparseSeries(val2, name='y', fill_value=0)
res = pd.concat([sparse1, sparse2], axis=1)
exp = pd.concat([pd.Series(val1, name='x'),
pd.Series(val2, name='y')], axis=1)
assert isinstance(res, pd.SparseDataFrame)
tm.assert_frame_equal(res.to_dense(), exp)
def test_concat_different_kind(self):
val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
val2 = np.array([3, np.nan, 4, 0, 0])
sparse1 = pd.SparseSeries(val1, name='x', kind='integer')
sparse2 = pd.SparseSeries(val2, name='y', kind='block', fill_value=0)
res = pd.concat([sparse1, sparse2])
exp = pd.concat([pd.Series(val1), pd.Series(val2)])
exp = pd.SparseSeries(exp, kind='integer')
tm.assert_sp_series_equal(res, exp)
res = pd.concat([sparse2, sparse1])
exp = pd.concat([pd.Series(val2), pd.Series(val1)])
exp = pd.SparseSeries(exp, kind='block', fill_value=0)
tm.assert_sp_series_equal(res, exp)
def test_concat_sparse_dense(self):
# use first input's fill_value
val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
val2 = np.array([3, np.nan, 4, 0, 0])
for kind in ['integer', 'block']:
sparse = pd.SparseSeries(val1, name='x', kind=kind)
dense = pd.Series(val2, name='y')
res = pd.concat([sparse, dense])
exp = pd.concat([pd.Series(val1), dense])
exp = pd.SparseSeries(exp, kind=kind)
tm.assert_sp_series_equal(res, exp)
res = pd.concat([dense, sparse, dense])
exp = pd.concat([dense, pd.Series(val1), dense])
exp = pd.SparseSeries(exp, kind=kind)
tm.assert_sp_series_equal(res, exp)
sparse = pd.SparseSeries(val1, name='x', kind=kind, fill_value=0)
dense = pd.Series(val2, name='y')
res = pd.concat([sparse, dense])
exp = pd.concat([pd.Series(val1), dense])
exp = pd.SparseSeries(exp, kind=kind, fill_value=0)
tm.assert_sp_series_equal(res, exp)
res = pd.concat([dense, sparse, dense])
exp = pd.concat([dense, pd.Series(val1), dense])
exp = pd.SparseSeries(exp, kind=kind, fill_value=0)
tm.assert_sp_series_equal(res, exp)
def test_value_counts(self):
vals = [1, 2, nan, 0, nan, 1, 2, nan, nan, 1, 2, 0, 1, 1]
dense = pd.Series(vals, name='xx')
sparse = pd.SparseSeries(vals, name='xx')
tm.assert_series_equal(sparse.value_counts(),
dense.value_counts())
tm.assert_series_equal(sparse.value_counts(dropna=False),
dense.value_counts(dropna=False))
sparse = pd.SparseSeries(vals, name='xx', fill_value=0)
tm.assert_series_equal(sparse.value_counts(),
dense.value_counts())
tm.assert_series_equal(sparse.value_counts(dropna=False),
dense.value_counts(dropna=False))
def test_value_counts_dup(self):
vals = [1, 2, nan, 0, nan, 1, 2, nan, nan, 1, 2, 0, 1, 1]
# numeric op may cause sp_values to include the same value as
# fill_value
dense = pd.Series(vals, name='xx') / 0.
sparse = pd.SparseSeries(vals, name='xx') / 0.
tm.assert_series_equal(sparse.value_counts(),
dense.value_counts())
tm.assert_series_equal(sparse.value_counts(dropna=False),
dense.value_counts(dropna=False))
vals = [1, 2, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 1, 1]
dense = pd.Series(vals, name='xx') * 0.
sparse = pd.SparseSeries(vals, name='xx') * 0.
tm.assert_series_equal(sparse.value_counts(),
dense.value_counts())
tm.assert_series_equal(sparse.value_counts(dropna=False),
dense.value_counts(dropna=False))
def test_value_counts_int(self):
vals = [1, 2, 0, 1, 2, 1, 2, 0, 1, 1]
dense = pd.Series(vals, name='xx')
# fill_value is np.nan, but should not be included in the result
sparse = pd.SparseSeries(vals, name='xx')
tm.assert_series_equal(sparse.value_counts(),
dense.value_counts())
tm.assert_series_equal(sparse.value_counts(dropna=False),
dense.value_counts(dropna=False))
sparse = pd.SparseSeries(vals, name='xx', fill_value=0)
tm.assert_series_equal(sparse.value_counts(),
dense.value_counts())
tm.assert_series_equal(sparse.value_counts(dropna=False),
dense.value_counts(dropna=False))
def test_isna(self):
# GH 8276
s = pd.SparseSeries([np.nan, np.nan, 1, 2, np.nan], name='xxx')
res = s.isna()
exp = pd.SparseSeries([True, True, False, False, True], name='xxx',
fill_value=True)
tm.assert_sp_series_equal(res, exp)
# if fill_value is not nan, True can be included in sp_values
s = pd.SparseSeries([np.nan, 0., 1., 2., 0.], name='xxx',
fill_value=0.)
res = s.isna()
assert isinstance(res, pd.SparseSeries)
exp = pd.Series([True, False, False, False, False], name='xxx')
tm.assert_series_equal(res.to_dense(), exp)
def test_notna(self):
# GH 8276
s = pd.SparseSeries([np.nan, np.nan, 1, 2, np.nan], name='xxx')
res = s.notna()
exp = pd.SparseSeries([False, False, True, True, False], name='xxx',
fill_value=False)
tm.assert_sp_series_equal(res, exp)
# if fill_value is not nan, True can be included in sp_values
s = pd.SparseSeries([np.nan, 0., 1., 2., 0.], name='xxx',
fill_value=0.)
res = s.notna()
assert isinstance(res, pd.SparseSeries)
exp = pd.Series([False, True, True, True, True], name='xxx')
tm.assert_series_equal(res.to_dense(), exp)
def _dense_series_compare(s, f):
result = f(s)
assert (isinstance(result, SparseSeries))
dense_result = f(s.to_dense())
tm.assert_series_equal(result.to_dense(), dense_result)
class TestSparseSeriesAnalytics(object):
def setup_method(self, method):
arr, index = _test_data1()
self.bseries = SparseSeries(arr, index=index, kind='block',
name='bseries')
arr, index = _test_data1_zero()
self.zbseries = SparseSeries(arr, index=index, kind='block',
fill_value=0, name='zbseries')
def test_cumsum(self):
result = self.bseries.cumsum()
expected = SparseSeries(self.bseries.to_dense().cumsum())
tm.assert_sp_series_equal(result, expected)
result = self.zbseries.cumsum()
expected = self.zbseries.to_dense().cumsum()
tm.assert_series_equal(result, expected)
axis = 1 # Series is 1-D, so only axis = 0 is valid.
msg = "No axis named {axis}".format(axis=axis)
with tm.assert_raises_regex(ValueError, msg):
self.bseries.cumsum(axis=axis)
def test_numpy_cumsum(self):
result = np.cumsum(self.bseries)
expected = SparseSeries(self.bseries.to_dense().cumsum())
tm.assert_sp_series_equal(result, expected)
result = np.cumsum(self.zbseries)
expected = self.zbseries.to_dense().cumsum()
tm.assert_series_equal(result, expected)
msg = "the 'dtype' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.cumsum,
self.bseries, dtype=np.int64)
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.cumsum,
self.zbseries, 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', 'argsort',
'min', 'max']
for func in funcs:
for series in ('bseries', 'zbseries'):
getattr(np, func)(getattr(self, series))
def test_deprecated_numpy_func_call(self):
# NOTE: These should be add to the 'test_numpy_func_call' test above
# once the behavior of argmin/argmax is corrected.
funcs = ['argmin', 'argmax']
for func in funcs:
for series in ('bseries', 'zbseries'):
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
getattr(np, func)(getattr(self, series))
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
getattr(getattr(self, series), func)()
def test_deprecated_reindex_axis(self):
# https://github.com/pandas-dev/pandas/issues/17833
with tm.assert_produces_warning(FutureWarning) as m:
self.bseries.reindex_axis([0, 1, 2])
assert 'reindex' in str(m[0].message)
@pytest.mark.parametrize(
'datetime_type', (np.datetime64,
pd.Timestamp,
lambda x: datetime.strptime(x, '%Y-%m-%d')))
def test_constructor_dict_datetime64_index(datetime_type):
# GH 9456
dates = ['1984-02-19', '1988-11-06', '1989-12-03', '1990-03-15']
values = [42544017.198965244, 1234565, 40512335.181958228, -1]
result = SparseSeries(dict(zip(map(datetime_type, dates), values)))
expected = SparseSeries(values, map(pd.Timestamp, dates))
tm.assert_sp_series_equal(result, expected)