本文整理汇总了Python中pandas.sparse.api.SparseSeries.cumsum方法的典型用法代码示例。如果您正苦于以下问题：Python SparseSeries.cumsum方法的具体用法？Python SparseSeries.cumsum怎么用？Python SparseSeries.cumsum使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pandas.sparse.api.SparseSeries的用法示例。
在下文中一共展示了SparseSeries.cumsum方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: TestSparseSeriesAnalytics
# 需要导入模块: from pandas.sparse.api import SparseSeries [as 别名]
# 或者: from pandas.sparse.api.SparseSeries import cumsum [as 别名]
class TestSparseSeriesAnalytics(tm.TestCase):
def setUp(self):
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)
# TODO: gh-12855 - return a SparseSeries here
result = self.zbseries.cumsum()
expected = self.zbseries.to_dense().cumsum()
self.assertNotIsInstance(result, SparseSeries)
tm.assert_series_equal(result, expected)
def test_numpy_cumsum(self):
result = np.cumsum(self.bseries)
expected = SparseSeries(self.bseries.to_dense().cumsum())
tm.assert_sp_series_equal(result, expected)
# TODO: gh-12855 - return a SparseSeries here
result = np.cumsum(self.zbseries)
expected = self.zbseries.to_dense().cumsum()
self.assertNotIsInstance(result, SparseSeries)
tm.assert_series_equal(result, expected)
msg = "the 'dtype' parameter is not supported"
tm.assertRaisesRegexp(ValueError, msg, np.cumsum,
self.bseries, dtype=np.int64)
msg = "the 'out' parameter is not supported"
tm.assertRaisesRegexp(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',
'argmin', 'argmax', 'min', 'max']
for func in funcs:
for series in ('bseries', 'zbseries'):
getattr(np, func)(getattr(self, series))示例2: TestSparseSeriesAnalytics
# 需要导入模块: from pandas.sparse.api import SparseSeries [as 别名]
# 或者: from pandas.sparse.api.SparseSeries import cumsum [as 别名]
class TestSparseSeriesAnalytics(tm.TestCase):
def setUp(self):
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)
# TODO: gh-12855 - return a SparseSeries here
result = self.zbseries.cumsum()
expected = self.zbseries.to_dense().cumsum()
self.assertNotIsInstance(result, SparseSeries)
tm.assert_series_equal(result, expected)
def test_numpy_cumsum(self):
result = np.cumsum(self.bseries)
expected = SparseSeries(self.bseries.to_dense().cumsum())
tm.assert_sp_series_equal(result, expected)
# TODO: gh-12855 - return a SparseSeries here
result = np.cumsum(self.zbseries)
expected = self.zbseries.to_dense().cumsum()
self.assertNotIsInstance(result, SparseSeries)
tm.assert_series_equal(result, expected)
msg = "the 'dtype' parameter is not supported"
tm.assertRaisesRegexp(ValueError, msg, np.cumsum,
self.bseries, dtype=np.int64)
msg = "the 'out' parameter is not supported"
tm.assertRaisesRegexp(ValueError, msg, np.cumsum,
self.zbseries, out=result)示例3: TestSparseSeries
# 需要导入模块: from pandas.sparse.api import SparseSeries [as 别名]
# 或者: from pandas.sparse.api.SparseSeries import cumsum [as 别名]
#.........这里部分代码省略.........
_dense_series_compare(series, f)
f = lambda s: s.shift(2, freq=datetools.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())
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)
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_cumsum(self):
result = self.bseries.cumsum()
expected = self.bseries.to_dense().cumsum()
tm.assertIsInstance(result, SparseSeries)
self.assertEqual(result.name, self.bseries.name)
tm.assert_series_equal(result.to_dense(), expected)
result = self.zbseries.cumsum()
expected = self.zbseries.to_dense().cumsum()
tm.assertIsInstance(result, Series)
tm.assert_series_equal(result, expected)
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)示例4: TestSparseSeries
# 需要导入模块: from pandas.sparse.api import SparseSeries [as 别名]
# 或者: from pandas.sparse.api.SparseSeries import cumsum [as 别名]
#.........这里部分代码省略.........
_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)
def test_dropna(self):
sp = SparseSeries([0, 0, 0, nan, nan, 5, 6], fill_value=0)
sp_valid = sp.valid()
assert_almost_equal(sp_valid.values, sp.to_dense().valid().values)
self.assert_(sp_valid.index.equals(sp.to_dense().valid().index))
self.assertEquals(len(sp_valid.sp_values), 2)
result = self.bseries.dropna()
expected = self.bseries.to_dense().dropna()
self.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)
homogenized = spf.homogenize(data)
for k, v in homogenized.iteritems():
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)}
nose.tools.assert_raises(Exception, spf.homogenize, data)
def test_fill_value_corner(self):
cop = self.zbseries.copy()
cop.fill_value = 0
result = self.bseries / cop
self.assert_(np.isnan(result.fill_value))
cop2 = self.zbseries.copy()
cop2.fill_value = 1
result = cop2 / cop
self.assert_(np.isnan(result.fill_value))
def test_shift(self):
series = SparseSeries([nan, 1.0, 2.0, 3.0, nan, nan], index=np.arange(6))
shifted = series.shift(0)
self.assert_(shifted is not series)
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, 1.0, 2.0, 3.0, nan, nan], index=DateRange("1/1/2000", periods=6))
f = lambda s: s.shift(2, timeRule="WEEKDAY")
_dense_series_compare(series, f)
f = lambda s: s.shift(2, offset=datetools.bday)
_dense_series_compare(series, f)
def test_cumsum(self):
result = self.bseries.cumsum()
expected = self.bseries.to_dense().cumsum()
self.assert_(isinstance(result, SparseSeries))
self.assertEquals(result.name, self.bseries.name)
assert_series_equal(result.to_dense(), expected)
result = self.zbseries.cumsum()
expected = self.zbseries.to_dense().cumsum()
self.assert_(isinstance(result, Series))
assert_series_equal(result, expected)
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)
assert_sp_series_equal(result, result2)
assert_sp_series_equal(result, expected)