Duplicate Labels#
Index objects are not required to be unique; you can have duplicate row
or column labels. This may be a bit confusing at first. If you’re familiar with
SQL, you know that row labels are similar to a primary key on a table, and you
would never want duplicates in a SQL table. But one of pandas’ roles is to clean
messy, real-world data before it goes to some downstream system. And real-world
data has duplicates, even in fields that are supposed to be unique.
This section describes how duplicate labels change the behavior of certain operations, and how prevent duplicates from arising during operations, or to detect them if they do.
In [1]: import pandas as pd
In [2]: import numpy as np
Consequences of Duplicate Labels#
Some pandas methods (Series.reindex() for example) just don’t work with
duplicates present. The output can’t be determined, and so pandas raises.
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:5133, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
5116 @doc(
5117 NDFrame.reindex, # type: ignore[has-type]
5118 klass=_shared_doc_kwargs["klass"],
(...)
5131 tolerance=None,
5132 ) -> Series:
-> 5133 return super().reindex(
5134 index=index,
5135 method=method,
5136 copy=copy,
5137 level=level,
5138 fill_value=fill_value,
5139 limit=limit,
5140 tolerance=tolerance,
5141 )
File ~/work/pandas/pandas/pandas/core/generic.py:5604, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5601 return self._reindex_multi(axes, copy, fill_value)
5603 # perform the reindex on the axes
-> 5604 return self._reindex_axes(
5605 axes, level, limit, tolerance, method, fill_value, copy
5606 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5627, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
5624 continue
5626 ax = self._get_axis(a)
-> 5627 new_index, indexer = ax.reindex(
5628 labels, level=level, limit=limit, tolerance=tolerance, method=method
5629 )
5631 axis = self._get_axis_number(a)
5632 obj = obj._reindex_with_indexers(
5633 {axis: [new_index, indexer]},
5634 fill_value=fill_value,
5635 copy=copy,
5636 allow_dups=False,
5637 )
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4426, in Index.reindex(self, target, method, level, limit, tolerance)
4423 raise ValueError("cannot handle a non-unique multi-index!")
4424 elif not self.is_unique:
4425 # GH#42568
-> 4426 raise ValueError("cannot reindex on an axis with duplicate labels")
4427 else:
4428 indexer, _ = self.get_indexer_non_unique(target)
ValueError: cannot reindex on an axis with duplicate labels
Other methods, like indexing, can give very surprising results. Typically
indexing with a scalar will reduce dimensionality. Slicing a DataFrame
with a scalar will return a Series. Slicing a Series with a scalar will
return a scalar. But with duplicates, this isn’t the case.
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
We have duplicates in the columns. If we slice 'B', we get back a Series
In [7]: df1["B"] # a series
Out[7]:
0 2
1 5
Name: B, dtype: int64
But slicing 'A' returns a DataFrame
In [8]: df1["A"] # a DataFrame
Out[8]:
A A
0 0 1
1 3 4
This applies to row labels as well
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
Duplicate Label Detection#
You can check whether an Index (storing the row or column labels) is
unique with Index.is_unique:
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
Note
Checking whether an index is unique is somewhat expensive for large datasets. pandas does cache this result, so re-checking on the same index is very fast.
Index.duplicated() will return a boolean ndarray indicating whether a
label is repeated.
In [16]: df2.index.duplicated()
Out[16]: array([False, True, False])
Which can be used as a boolean filter to drop duplicate rows.
In [17]: df2.loc[~df2.index.duplicated(), :]
Out[17]:
A
a 0
b 2
If you need additional logic to handle duplicate labels, rather than just
dropping the repeats, using groupby() on the index is a common
trick. For example, we’ll resolve duplicates by taking the average of all rows
with the same label.
In [18]: df2.groupby(level=0).mean()
Out[18]:
A
a 0.5
b 2.0
Disallowing Duplicate Labels#
New in version 1.2.0.
As noted above, handling duplicates is an important feature when reading in raw
data. That said, you may want to avoid introducing duplicates as part of a data
processing pipeline (from methods like pandas.concat(),
rename(), etc.). Both Series and DataFrame
disallow duplicate labels by calling .set_flags(allows_duplicate_labels=False).
(the default is to allow them). If there are duplicate labels, an exception
will be raised.
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]
This applies to both row and column labels for a DataFrame
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
This attribute can be checked or set with allows_duplicate_labels,
which indicates whether that object can have duplicate labels.
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() can be used to return a new DataFrame with attributes
like allows_duplicate_labels set to some value
In [24]: df2 = df.set_flags(allows_duplicate_labels=True)
In [25]: df2.flags.allows_duplicate_labels
Out[25]: True
The new DataFrame returned is a view on the same data as the old DataFrame.
Or the property can just be set directly on the same object
In [26]: df2.flags.allows_duplicate_labels = False
In [27]: df2.flags.allows_duplicate_labels
Out[27]: False
When processing raw, messy data you might initially read in the messy data (which potentially has duplicate labels), deduplicate, and then disallow duplicates going forward, to ensure that your data pipeline doesn’t introduce duplicates.
>>> raw = pd.read_csv("...")
>>> deduplicated = raw.groupby(level=0).first() # remove duplicates
>>> deduplicated.flags.allows_duplicate_labels = False # disallow going forward
Setting allows_duplicate_labels=False on a Series or DataFrame with duplicate
labels or performing an operation that introduces duplicate labels on a Series or
DataFrame that disallows duplicates will raise an
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:6256, in NDFrame.__finalize__(self, other, method, **kwargs)
6249 if other.attrs:
6250 # We want attrs propagation to have minimal performance
6251 # impact if attrs are not used; i.e. attrs is an empty dict.
6252 # One could make the deepcopy unconditionally, but a deepcopy
6253 # of an empty dict is 50x more expensive than the empty check.
6254 self.attrs = deepcopy(other.attrs)
-> 6256 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
6257 # For subclasses using _metadata.
6258 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]
This error message contains the labels that are duplicated, and the numeric positions
of all the duplicates (including the “original”) in the Series or DataFrame
Duplicate Label Propagation#
In general, disallowing duplicates is “sticky”. It’s preserved through operations.
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:5070, in Series.rename(self, index, axis, copy, inplace, level, errors)
5063 axis = self._get_axis_number(axis)
5065 if callable(index) or is_dict_like(index):
5066 # error: Argument 1 to "_rename" of "NDFrame" has incompatible
5067 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
5068 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
5069 # Hashable], Callable[[Any], Hashable], None]"
-> 5070 return super()._rename(
5071 index, # type: ignore[arg-type]
5072 copy=copy,
5073 inplace=inplace,
5074 level=level,
5075 errors=errors,
5076 )
5077 else:
5078 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:6256, in NDFrame.__finalize__(self, other, method, **kwargs)
6249 if other.attrs:
6250 # We want attrs propagation to have minimal performance
6251 # impact if attrs are not used; i.e. attrs is an empty dict.
6252 # One could make the deepcopy unconditionally, but a deepcopy
6253 # of an empty dict is 50x more expensive than the empty check.
6254 self.attrs = deepcopy(other.attrs)
-> 6256 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
6257 # For subclasses using _metadata.
6258 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]
Warning
This is an experimental feature. Currently, many methods fail to
propagate the allows_duplicate_labels value. In future versions
it is expected that every method taking or returning one or more
DataFrame or Series objects will propagate allows_duplicate_labels.