本文整理汇总了Python中pandas._libs.lib.get_reverse_indexer方法的典型用法代码示例。如果您正苦于以下问题：Python lib.get_reverse_indexer方法的具体用法？Python lib.get_reverse_indexer怎么用？Python lib.get_reverse_indexer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._libs.lib的用法示例。
在下文中一共展示了lib.get_reverse_indexer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: combine
# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import get_reverse_indexer [as 别名]
def combine(self, blocks, copy=True):
""" return a new manager with the blocks """
if len(blocks) == 0:
return self.make_empty()
# FIXME: optimization potential
indexer = np.sort(np.concatenate([b.mgr_locs.as_array
for b in blocks]))
inv_indexer = lib.get_reverse_indexer(indexer, self.shape[0])
new_blocks = []
for b in blocks:
b = b.copy(deep=copy)
b.mgr_locs = algos.take_1d(inv_indexer, b.mgr_locs.as_array,
axis=0, allow_fill=False)
new_blocks.append(b)
axes = list(self.axes)
axes[0] = self.items.take(indexer)
return self.__class__(new_blocks, axes, do_integrity_check=False) 示例2: test_get_reverse_indexer
# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import get_reverse_indexer [as 别名]
def test_get_reverse_indexer(self):
indexer = np.array([-1, -1, 1, 2, 0, -1, 3, 4], dtype=np.int64)
result = lib.get_reverse_indexer(indexer, 5)
expected = np.array([4, 2, 3, 6, 7], dtype=np.int64)
tm.assert_numpy_array_equal(result, expected) 示例3: test_get_reverse_indexer
# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import get_reverse_indexer [as 别名]
def test_get_reverse_indexer(self):
indexer = np.array([-1, -1, 1, 2, 0, -1, 3, 4], dtype=np.int64)
result = lib.get_reverse_indexer(indexer, 5)
expected = np.array([4, 2, 3, 6, 7], dtype=np.int64)
assert np.array_equal(result, expected) 示例4: _sort_levels_monotonic
# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import get_reverse_indexer [as 别名]
def _sort_levels_monotonic(self):
"""
.. versionadded:: 0.20.0
This is an *internal* function.
create a new MultiIndex from the current to monotonically sorted
items IN the levels. This does not actually make the entire MultiIndex
monotonic, JUST the levels.
The resulting MultiIndex will have the same outward
appearance, meaning the same .values and ordering. It will also
be .equals() to the original.
Returns
-------
MultiIndex
Examples
--------
>>> i = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
>>> i
MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
>>> i.sort_monotonic()
MultiIndex(levels=[['a', 'b'], ['aa', 'bb']],
labels=[[0, 0, 1, 1], [1, 0, 1, 0]])
"""
if self.is_lexsorted() and self.is_monotonic:
return self
new_levels = []
new_labels = []
for lev, lab in zip(self.levels, self.labels):
if not lev.is_monotonic:
try:
# indexer to reorder the levels
indexer = lev.argsort()
except TypeError:
pass
else:
lev = lev.take(indexer)
# indexer to reorder the labels
indexer = _ensure_int64(indexer)
ri = lib.get_reverse_indexer(indexer, len(indexer))
lab = algos.take_1d(ri, lab)
new_levels.append(lev)
new_labels.append(lab)
return MultiIndex(new_levels, new_labels,
names=self.names, sortorder=self.sortorder,
verify_integrity=False) 示例5: _sort_levels_monotonic
# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import get_reverse_indexer [as 别名]
def _sort_levels_monotonic(self):
"""
.. versionadded:: 0.20.0
This is an *internal* function.
create a new MultiIndex from the current to monotonically sorted
items IN the levels. This does not actually make the entire MultiIndex
monotonic, JUST the levels.
The resulting MultiIndex will have the same outward
appearance, meaning the same .values and ordering. It will also
be .equals() to the original.
Returns
-------
MultiIndex
Examples
--------
>>> i = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
>>> i
MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
>>> i.sort_monotonic()
MultiIndex(levels=[['a', 'b'], ['aa', 'bb']],
labels=[[0, 0, 1, 1], [1, 0, 1, 0]])
"""
if self.is_lexsorted() and self.is_monotonic:
return self
new_levels = []
new_labels = []
for lev, lab in zip(self.levels, self.labels):
if lev.is_monotonic:
new_levels.append(lev)
new_labels.append(lab)
continue
# indexer to reorder the levels
indexer = lev.argsort()
lev = lev.take(indexer)
# indexer to reorder the labels
indexer = _ensure_int64(indexer)
ri = lib.get_reverse_indexer(indexer, len(indexer))
lab = algos.take_1d(ri, lab)
new_levels.append(lev)
new_labels.append(lab)
return MultiIndex(new_levels, new_labels,
names=self.names, sortorder=self.sortorder,
verify_integrity=False)