本文整理匯總了Python中pandas._libs.lib.get_reverse_indexer方法的典型用法代碼示例。如果您正苦於以下問題：Python lib.get_reverse_indexer方法的具體用法？Python lib.get_reverse_indexer怎麽用？Python lib.get_reverse_indexer使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類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個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: combine
# 需要導入模塊: from pandas._libs import lib [as 別名]
# 或者: from pandas._libs.lib import get_reverse_indexer [as 別名]
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
# 需要導入模塊: from pandas._libs import lib [as 別名]
# 或者: from pandas._libs.lib import get_reverse_indexer [as 別名]
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
# 需要導入模塊: from pandas._libs import lib [as 別名]
# 或者: from pandas._libs.lib import get_reverse_indexer [as 別名]
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
# 需要導入模塊: from pandas._libs import lib [as 別名]
# 或者: from pandas._libs.lib import get_reverse_indexer [as 別名]
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
# 需要導入模塊: from pandas._libs import lib [as 別名]
# 或者: from pandas._libs.lib import get_reverse_indexer [as 別名]
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)