本文整理汇总了Python中pandas.sparse.api.SparseSeries.dropna方法的典型用法代码示例。如果您正苦于以下问题：Python SparseSeries.dropna方法的具体用法？Python SparseSeries.dropna怎么用？Python SparseSeries.dropna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.dropna方法的2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: TestSparseSeries
# 需要导入模块: from pandas.sparse.api import SparseSeries [as 别名]
# 或者: from pandas.sparse.api.SparseSeries import dropna [as 别名]
#.........这里部分代码省略.........
def test_reductions(self):
def _compare_with_dense(obj, op):
sparse_result = getattr(obj, op)()
series = obj.to_dense()
dense_result = getattr(series, op)()
self.assertEqual(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.valid()
expected = sp.to_dense().valid()
expected = expected[expected != 0]
tm.assert_almost_equal(sp_valid.values, expected.values)
self.assertTrue(sp_valid.index.equals(expected.index))
self.assertEqual(len(sp_valid.sp_values), 2)
result = self.bseries.dropna()
expected = self.bseries.to_dense().dropna()
self.assertNotIsInstance(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 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)
示例2: TestSparseSeries
# 需要导入模块: from pandas.sparse.api import SparseSeries [as 别名]
# 或者: from pandas.sparse.api.SparseSeries import dropna [as 别名]
#.........这里部分代码省略.........
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)()
self.assertEquals(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)
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)}