重复标签#
Index 对象不需要是唯一的；您可以拥有重复的行或列标签。这可能一开始有点令人困惑。如果您熟悉 SQL，您知道行标签类似于表上的主键，并且您永远不希望在 SQL 表中出现重复项。但 pandas 的作用之一是在数据进入某些下游系统之前清理混乱的现实世界数据。现实世界的数据有重复项，即使是在应该唯一的字段中也是如此。
本节介绍重复标签如何改变某些操作的行为，以及如何在操作过程中防止重复项出现，或者在重复项出现时检测它们。
In [1]: import pandas as pd
In [2]: import numpy as np
重复标签的后果#
某些 pandas 方法（例如 Series.reindex()）在存在重复项的情况下无法正常工作。输出无法确定，因此 pandas 会引发异常。
In [3]: s1 = pd.Series([0, 1, 2], index=["a", "b", "b"])
In [4]: s1.reindex(["a", "b", "c"])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[4], line 1
----> 1 s1.reindex(["a", "b", "c"])
File ~/work/pandas/pandas/pandas/core/series.py:5144, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
5127 @doc(
5128 NDFrame.reindex, # type: ignore[has-type]
5129 klass=_shared_doc_kwargs["klass"],
(...)
5142 tolerance=None,
5143 ) -> Series:
-> 5144 return super().reindex(
5145 index=index,
5146 method=method,
5147 copy=copy,
5148 level=level,
5149 fill_value=fill_value,
5150 limit=limit,
5151 tolerance=tolerance,
5152 )
File ~/work/pandas/pandas/pandas/core/generic.py:5607, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5604 return self._reindex_multi(axes, copy, fill_value)
5606 # perform the reindex on the axes
-> 5607 return self._reindex_axes(
5608 axes, level, limit, tolerance, method, fill_value, copy
5609 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5630, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
5627 continue
5629 ax = self._get_axis(a)
-> 5630 new_index, indexer = ax.reindex(
5631 labels, level=level, limit=limit, tolerance=tolerance, method=method
5632 )
5634 axis = self._get_axis_number(a)
5635 obj = obj._reindex_with_indexers(
5636 {axis: [new_index, indexer]},
5637 fill_value=fill_value,
5638 copy=copy,
5639 allow_dups=False,
5640 )
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4429, in Index.reindex(self, target, method, level, limit, tolerance)
4426 raise ValueError("cannot handle a non-unique multi-index!")
4427 elif not self.is_unique:
4428 # GH#42568
-> 4429 raise ValueError("cannot reindex on an axis with duplicate labels")
4430 else:
4431 indexer, _ = self.get_indexer_non_unique(target)
ValueError: cannot reindex on an axis with duplicate labels
其他方法，例如索引，可能会产生非常令人惊讶的结果。通常，用标量索引会降低维度。用标量切片DataFrame将返回一个Series。用标量切片Series将返回一个标量。但是，对于重复项，情况并非如此。
In [5]: df1 = pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["A", "A", "B"])
In [6]: df1
Out[6]:
A A B
0 0 1 2
1 3 4 5
我们在列中存在重复项。如果我们切片'B'，我们将得到一个Series
In [7]: df1["B"] # a series
Out[7]:
0 2
1 5
Name: B, dtype: int64
但是切片'A'会返回一个DataFrame
In [8]: df1["A"] # a DataFrame
Out[8]:
A A
0 0 1
1 3 4
这也适用于行标签
In [9]: df2 = pd.DataFrame({"A": [0, 1, 2]}, index=["a", "a", "b"])
In [10]: df2
Out[10]:
A
a 0
a 1
b 2
In [11]: df2.loc["b", "A"] # a scalar
Out[11]: 2
In [12]: df2.loc["a", "A"] # a Series
Out[12]:
a 0
a 1
Name: A, dtype: int64
重复标签检测#
您可以使用Index.is_unique检查Index（存储行或列标签）是否唯一
In [13]: df2
Out[13]:
A
a 0
a 1
b 2
In [14]: df2.index.is_unique
Out[14]: False
In [15]: df2.columns.is_unique
Out[15]: True
注意
检查索引是否唯一对于大型数据集来说成本较高。pandas 会缓存此结果，因此对同一索引进行重新检查非常快。
Index.duplicated()将返回一个布尔型 ndarray，指示标签是否重复。
In [16]: df2.index.duplicated()
Out[16]: array([False, True, False])
这可以用作布尔型过滤器来删除重复行。
In [17]: df2.loc[~df2.index.duplicated(), :]
Out[17]:
A
a 0
b 2
如果您需要额外的逻辑来处理重复标签，而不是仅仅删除重复项，那么对索引使用groupby()是一个常见的技巧。例如，我们将通过对具有相同标签的所有行的平均值来解决重复项。
In [18]: df2.groupby(level=0).mean()
Out[18]:
A
a 0.5
b 2.0
禁止重复标签#
版本 1.2.0 中的新增功能。
如上所述，处理重复项是在读取原始数据时的一项重要功能。也就是说，您可能希望避免在数据处理管道（来自pandas.concat()、rename()等方法）中引入重复项。Series和DataFrame通过调用.set_flags(allows_duplicate_labels=False)禁止重复标签。（默认情况下允许它们）。如果存在重复标签，将引发异常。
In [19]: pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False)
---------------------------------------------------------------------------
DuplicateLabelError Traceback (most recent call last)
Cell In[19], line 1
----> 1 pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False)
File ~/work/pandas/pandas/pandas/core/generic.py:507, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
505 df = self.copy(deep=copy and not using_copy_on_write())
506 if allows_duplicate_labels is not None:
--> 507 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
508 return df
File ~/work/pandas/pandas/pandas/core/flags.py:109, in Flags.__setitem__(self, key, value)
107 if key not in self._keys:
108 raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
--> 109 setattr(self, key, value)
File ~/work/pandas/pandas/pandas/core/flags.py:96, in Flags.allows_duplicate_labels(self, value)
94 if not value:
95 for ax in obj.axes:
---> 96 ax._maybe_check_unique()
98 self._allows_duplicate_labels = value
File ~/work/pandas/pandas/pandas/core/indexes/base.py:715, in Index._maybe_check_unique(self)
712 duplicates = self._format_duplicate_message()
713 msg += f"\n{duplicates}"
--> 715 raise DuplicateLabelError(msg)
DuplicateLabelError: Index has duplicates.
positions
label
b [1, 2]
这适用于 DataFrame 的行标签和列标签。
In [20]: pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["A", "B", "C"],).set_flags(
....: allows_duplicate_labels=False
....: )
....:
Out[20]:
A B C
0 0 1 2
1 3 4 5
此属性可以通过 allows_duplicate_labels 进行检查或设置，它指示该对象是否可以具有重复标签。
In [21]: df = pd.DataFrame({"A": [0, 1, 2, 3]}, index=["x", "y", "X", "Y"]).set_flags(
....: allows_duplicate_labels=False
....: )
....:
In [22]: df
Out[22]:
A
x 0
y 1
X 2
Y 3
In [23]: df.flags.allows_duplicate_labels
Out[23]: False
DataFrame.set_flags() 可用于返回一个新的 DataFrame，其属性（如 allows_duplicate_labels）设置为某个值。
In [24]: df2 = df.set_flags(allows_duplicate_labels=True)
In [25]: df2.flags.allows_duplicate_labels
Out[25]: True
返回的新 DataFrame 是对旧 DataFrame 中相同数据的视图。或者，可以直接在同一个对象上设置该属性。
In [26]: df2.flags.allows_duplicate_labels = False
In [27]: df2.flags.allows_duplicate_labels
Out[27]: False
在处理原始的、杂乱无章的数据时，您可能最初会读取杂乱无章的数据（可能包含重复标签），进行去重，然后禁止以后出现重复，以确保您的数据管道不会引入重复。
>>> raw = pd.read_csv("...")
>>> deduplicated = raw.groupby(level=0).first() # remove duplicates
>>> deduplicated.flags.allows_duplicate_labels = False # disallow going forward
在具有重复标签的 Series 或 DataFrame 上设置 allows_duplicate_labels=False，或者对禁止重复的 Series 或 DataFrame 执行引入重复标签的操作，将引发 errors.DuplicateLabelError。
In [28]: df.rename(str.upper)
---------------------------------------------------------------------------
DuplicateLabelError Traceback (most recent call last)
Cell In[28], line 1
----> 1 df.rename(str.upper)
File ~/work/pandas/pandas/pandas/core/frame.py:5754, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
5623 def rename(
5624 self,
5625 mapper: Renamer | None = None,
(...)
5633 errors: IgnoreRaise = "ignore",
5634 ) -> DataFrame | None:
5635 """
5636 Rename columns or index labels.
5637
(...)
5752 4 3 6
5753 """
-> 5754 return super()._rename(
5755 mapper=mapper,
5756 index=index,
5757 columns=columns,
5758 axis=axis,
5759 copy=copy,
5760 inplace=inplace,
5761 level=level,
5762 errors=errors,
5763 )
File ~/work/pandas/pandas/pandas/core/generic.py:1139, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1137 return None
1138 else:
-> 1139 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6259, in NDFrame.__finalize__(self, other, method, **kwargs)
6252 if other.attrs:
6253 # We want attrs propagation to have minimal performance
6254 # impact if attrs are not used; i.e. attrs is an empty dict.
6255 # One could make the deepcopy unconditionally, but a deepcopy
6256 # of an empty dict is 50x more expensive than the empty check.
6257 self.attrs = deepcopy(other.attrs)
-> 6259 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
6260 # For subclasses using _metadata.
6261 for name in set(self._metadata) & set(other._metadata):
File ~/work/pandas/pandas/pandas/core/flags.py:96, in Flags.allows_duplicate_labels(self, value)
94 if not value:
95 for ax in obj.axes:
---> 96 ax._maybe_check_unique()
98 self._allows_duplicate_labels = value
File ~/work/pandas/pandas/pandas/core/indexes/base.py:715, in Index._maybe_check_unique(self)
712 duplicates = self._format_duplicate_message()
713 msg += f"\n{duplicates}"
--> 715 raise DuplicateLabelError(msg)
DuplicateLabelError: Index has duplicates.
positions
label
X [0, 2]
Y [1, 3]
此错误消息包含重复的标签，以及 Series 或 DataFrame 中所有重复项（包括“原始”项）的数字位置。
重复标签传播#
通常，禁止重复是“粘性”的。它在操作中得以保留。
In [29]: s1 = pd.Series(0, index=["a", "b"]).set_flags(allows_duplicate_labels=False)
In [30]: s1
Out[30]:
a 0
b 0
dtype: int64
In [31]: s1.head().rename({"a": "b"})
---------------------------------------------------------------------------
DuplicateLabelError Traceback (most recent call last)
Cell In[31], line 1
----> 1 s1.head().rename({"a": "b"})
File ~/work/pandas/pandas/pandas/core/series.py:5081, in Series.rename(self, index, axis, copy, inplace, level, errors)
5074 axis = self._get_axis_number(axis)
5076 if callable(index) or is_dict_like(index):
5077 # error: Argument 1 to "_rename" of "NDFrame" has incompatible
5078 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
5079 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
5080 # Hashable], Callable[[Any], Hashable], None]"
-> 5081 return super()._rename(
5082 index, # type: ignore[arg-type]
5083 copy=copy,
5084 inplace=inplace,
5085 level=level,
5086 errors=errors,
5087 )
5088 else:
5089 return self._set_name(index, inplace=inplace, deep=copy)
File ~/work/pandas/pandas/pandas/core/generic.py:1139, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1137 return None
1138 else:
-> 1139 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6259, in NDFrame.__finalize__(self, other, method, **kwargs)
6252 if other.attrs:
6253 # We want attrs propagation to have minimal performance
6254 # impact if attrs are not used; i.e. attrs is an empty dict.
6255 # One could make the deepcopy unconditionally, but a deepcopy
6256 # of an empty dict is 50x more expensive than the empty check.
6257 self.attrs = deepcopy(other.attrs)
-> 6259 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
6260 # For subclasses using _metadata.
6261 for name in set(self._metadata) & set(other._metadata):
File ~/work/pandas/pandas/pandas/core/flags.py:96, in Flags.allows_duplicate_labels(self, value)
94 if not value:
95 for ax in obj.axes:
---> 96 ax._maybe_check_unique()
98 self._allows_duplicate_labels = value
File ~/work/pandas/pandas/pandas/core/indexes/base.py:715, in Index._maybe_check_unique(self)
712 duplicates = self._format_duplicate_message()
713 msg += f"\n{duplicates}"
--> 715 raise DuplicateLabelError(msg)
DuplicateLabelError: Index has duplicates.
positions
label
b [0, 1]
警告
这是一个实验性功能。目前，许多方法无法传播 allows_duplicate_labels 值。在未来的版本中，预计每个接受或返回一个或多个 DataFrame 或 Series 对象的方法都将传播 allows_duplicate_labels。