本文整理汇总了Python中pandas.core.dtypes.common.is_sparse方法的典型用法代码示例。如果您正苦于以下问题：Python common.is_sparse方法的具体用法？Python common.is_sparse怎么用？Python common.is_sparse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.core.dtypes.common的用法示例。
在下文中一共展示了common.is_sparse方法的5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: __init__
# 需要导入模块: from pandas.core.dtypes import common [as 别名]
# 或者: from pandas.core.dtypes.common import is_sparse [as 别名]
def __init__(self, blocks, axes, do_integrity_check=True):
self.axes = [ensure_index(ax) for ax in axes]
self.blocks = tuple(blocks)
for block in blocks:
if block.is_sparse:
if len(block.mgr_locs) != 1:
raise AssertionError("Sparse block refers to multiple "
"items")
else:
if self.ndim != block.ndim:
raise AssertionError(
'Number of Block dimensions ({block}) must equal '
'number of axes ({self})'.format(block=block.ndim,
self=self.ndim))
if do_integrity_check:
self._verify_integrity()
self._consolidate_check()
self._rebuild_blknos_and_blklocs() 示例2: test_concat_sparse_dense_cols
# 需要导入模块: from pandas.core.dtypes import common [as 别名]
# 或者: from pandas.core.dtypes.common import is_sparse [as 别名]
def test_concat_sparse_dense_cols(self, fill_value, sparse_idx, dense_idx):
# See GH16874, GH18914 and #18686 for why this should be a DataFrame
from pandas.core.dtypes.common import is_sparse
frames = [self.dense1, self.dense3]
sparse_frame = [frames[dense_idx],
frames[sparse_idx].to_sparse(fill_value=fill_value)]
dense_frame = [frames[dense_idx], frames[sparse_idx]]
# This will try both directions sparse + dense and dense + sparse
for _ in range(2):
res = pd.concat(sparse_frame, axis=1)
exp = pd.concat(dense_frame, axis=1)
cols = [i for (i, x) in enumerate(res.dtypes) if is_sparse(x)]
for col in cols:
exp.iloc[:, col] = exp.iloc[:, col].astype("Sparse")
for column in frames[dense_idx].columns:
if dense_idx == sparse_idx:
tm.assert_frame_equal(res[column], exp[column])
else:
tm.assert_series_equal(res[column], exp[column])
tm.assert_frame_equal(res, exp)
sparse_frame = sparse_frame[::-1]
dense_frame = dense_frame[::-1] 示例3: test_is_sparse
# 需要导入模块: from pandas.core.dtypes import common [as 别名]
# 或者: from pandas.core.dtypes.common import is_sparse [as 别名]
def test_is_sparse(check_scipy):
assert com.is_sparse(pd.SparseArray([1, 2, 3]))
assert com.is_sparse(pd.SparseSeries([1, 2, 3]))
assert not com.is_sparse(np.array([1, 2, 3]))
if check_scipy:
import scipy.sparse
assert not com.is_sparse(scipy.sparse.bsr_matrix([1, 2, 3])) 示例4: _interleave
# 需要导入模块: from pandas.core.dtypes import common [as 别名]
# 或者: from pandas.core.dtypes.common import is_sparse [as 别名]
def _interleave(self):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
from pandas.core.dtypes.common import is_sparse
dtype = _interleaved_dtype(self.blocks)
# TODO: https://github.com/pandas-dev/pandas/issues/22791
# Give EAs some input on what happens here. Sparse needs this.
if is_sparse(dtype):
dtype = dtype.subtype
elif is_extension_array_dtype(dtype):
dtype = 'object'
result = np.empty(self.shape, dtype=dtype)
itemmask = np.zeros(self.shape[0])
for blk in self.blocks:
rl = blk.mgr_locs
result[rl.indexer] = blk.get_values(dtype)
itemmask[rl.indexer] = 1
if not itemmask.all():
raise AssertionError('Some items were not contained in blocks')
return result 示例5: test_is_sparse
# 需要导入模块: from pandas.core.dtypes import common [as 别名]
# 或者: from pandas.core.dtypes.common import is_sparse [as 别名]
def test_is_sparse():
assert com.is_sparse(pd.SparseArray([1, 2, 3]))
assert com.is_sparse(pd.SparseSeries([1, 2, 3]))
assert not com.is_sparse(np.array([1, 2, 3]))
# This test will only skip if the previous assertions
# pass AND scipy is not installed.
sparse = pytest.importorskip("scipy.sparse")
assert not com.is_sparse(sparse.bsr_matrix([1, 2, 3]))