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:4871, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
   4854 @doc(
   4855     NDFrame.reindex,  # type: ignore[has-type]
   4856     klass=_shared_doc_kwargs["klass"],
   (...)
   4869     tolerance=None,
   4870 ) -> Series:
-> 4871     return super().reindex(
   4872         index=index,
   4873         method=method,
   4874         copy=copy,
   4875         level=level,
   4876         fill_value=fill_value,
   4877         limit=limit,
   4878         tolerance=tolerance,
   4879     )

File ~/work/pandas/pandas/pandas/core/generic.py:5322, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5319     return self._reindex_multi(axes, copy, fill_value)
   5321 # perform the reindex on the axes
-> 5322 return self._reindex_axes(
   5323     axes, level, limit, tolerance, method, fill_value, copy
   5324 ).__finalize__(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5337, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   5334     continue
   5336 ax = self._get_axis(a)
-> 5337 new_index, indexer = ax.reindex(
   5338     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5339 )
   5341 axis = self._get_axis_number(a)
   5342 obj = obj._reindex_with_indexers(
   5343     {axis: [new_index, indexer]},
   5344     fill_value=fill_value,
   5345     copy=copy,
   5346     allow_dups=False,
   5347 )

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4231, in Index.reindex(self, target, method, level, limit, tolerance)
   4227             indexer, _ = self.get_indexer_non_unique(target)
   4229         if not self.is_unique:
   4230             # GH#42568
-> 4231             raise ValueError("cannot reindex on an axis with duplicate labels")
   4233 target = self._wrap_reindex_result(target, indexer, preserve_names)
   4234 return target, indexer

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:450, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
    448 df = self.copy(deep=copy)
    449 if allows_duplicate_labels is not None:
--> 450     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    451 return df

File ~/work/pandas/pandas/pandas/core/flags.py:107, in Flags.__setitem__(self, key, value)
    105 if key not in self._keys:
    106     raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
--> 107 setattr(self, key, value)

File ~/work/pandas/pandas/pandas/core/flags.py:94, in Flags.allows_duplicate_labels(self, value)
     92 if not value:
     93     for ax in obj.axes:
---> 94         ax._maybe_check_unique()
     96 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:680, in Index._maybe_check_unique(self)
    677 duplicates = self._format_duplicate_message()
    678 msg += f"\n{duplicates}"
--> 680 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:5404, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5285 def rename(
   5286     self,
   5287     mapper: Renamer | None = None,
   (...)
   5295     errors: IgnoreRaise = "ignore",
   5296 ) -> DataFrame | None:
   5297     """
   5298     Rename columns or index labels.
   5299 
   (...)
   5402     4  3  6
   5403     """
-> 5404     return super()._rename(
   5405         mapper=mapper,
   5406         index=index,
   5407         columns=columns,
   5408         axis=axis,
   5409         copy=copy,
   5410         inplace=inplace,
   5411         level=level,
   5412         errors=errors,
   5413     )

File ~/work/pandas/pandas/pandas/core/generic.py:1038, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1036     return None
   1037 else:
-> 1038     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:5913, in NDFrame.__finalize__(self, other, method, **kwargs)
   5910 for name in other.attrs:
   5911     self.attrs[name] = other.attrs[name]
-> 5913 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5914 # For subclasses using _metadata.
   5915 for name in set(self._metadata) & set(other._metadata):

File ~/work/pandas/pandas/pandas/core/flags.py:94, in Flags.allows_duplicate_labels(self, value)
     92 if not value:
     93     for ax in obj.axes:
---> 94         ax._maybe_check_unique()
     96 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:680, in Index._maybe_check_unique(self)
    677 duplicates = self._format_duplicate_message()
    678 msg += f"\n{duplicates}"
--> 680 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:4809, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4802     axis = self._get_axis_number(axis)
   4804 if callable(index) or is_dict_like(index):
   4805     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
   4806     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
   4807     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
   4808     # Hashable], Callable[[Any], Hashable], None]"
-> 4809     return super()._rename(
   4810         index,  # type: ignore[arg-type]
   4811         copy=copy,
   4812         inplace=inplace,
   4813         level=level,
   4814         errors=errors,
   4815     )
   4816 else:
   4817     return self._set_name(index, inplace=inplace)

File ~/work/pandas/pandas/pandas/core/generic.py:1038, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1036     return None
   1037 else:
-> 1038     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:5913, in NDFrame.__finalize__(self, other, method, **kwargs)
   5910 for name in other.attrs:
   5911     self.attrs[name] = other.attrs[name]
-> 5913 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5914 # For subclasses using _metadata.
   5915 for name in set(self._metadata) & set(other._metadata):

File ~/work/pandas/pandas/pandas/core/flags.py:94, in Flags.allows_duplicate_labels(self, value)
     92 if not value:
     93     for ax in obj.axes:
---> 94         ax._maybe_check_unique()
     96 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:680, in Index._maybe_check_unique(self)
    677 duplicates = self._format_duplicate_message()
    678 msg += f"\n{duplicates}"
--> 680 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.