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)
Input In [4], in <cell line: 1>()
----> 1 s1.reindex(["a", "b", "c"])

File ~/work/pandas/pandas/pandas/core/series.py:4849, in Series.reindex(self, *args, **kwargs)
   4845         raise TypeError(
   4846             "'index' passed as both positional and keyword argument"
   4847         )
   4848     kwargs.update({"index": index})
-> 4849 return super().reindex(**kwargs)

File ~/work/pandas/pandas/pandas/core/generic.py:4987, in NDFrame.reindex(self, *args, **kwargs)
   4984     return self._reindex_multi(axes, copy, fill_value)
   4986 # perform the reindex on the axes
-> 4987 return self._reindex_axes(
   4988     axes, level, limit, tolerance, method, fill_value, copy
   4989 ).__finalize__(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5007, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   5002 new_index, indexer = ax.reindex(
   5003     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5004 )
   5006 axis = self._get_axis_number(a)
-> 5007 obj = obj._reindex_with_indexers(
   5008     {axis: [new_index, indexer]},
   5009     fill_value=fill_value,
   5010     copy=copy,
   5011     allow_dups=False,
   5012 )
   5013 # If we've made a copy once, no need to make another one
   5014 copy = False

File ~/work/pandas/pandas/pandas/core/generic.py:5053, in NDFrame._reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
   5050     indexer = ensure_platform_int(indexer)
   5052 # TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)
-> 5053 new_data = new_data.reindex_indexer(
   5054     index,
   5055     indexer,
   5056     axis=baxis,
   5057     fill_value=fill_value,
   5058     allow_dups=allow_dups,
   5059     copy=copy,
   5060 )
   5061 # If we've made a copy once, no need to make another one
   5062 copy = False

File ~/work/pandas/pandas/pandas/core/internals/managers.py:636, in BaseBlockManager.reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)
    634 # some axes don't allow reindexing with dups
    635 if not allow_dups:
--> 636     self.axes[axis]._validate_can_reindex(indexer)
    638 if axis >= self.ndim:
    639     raise IndexError("Requested axis not found in manager")

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4326, in Index._validate_can_reindex(self, indexer)
   4324 # trying to reindex on an axis with duplicates
   4325 if not self._index_as_unique and len(indexer):
-> 4326     raise ValueError("cannot reindex on an axis with duplicate labels")

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)
Input In [19], in <cell 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:428, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
    426 df = self.copy(deep=copy)
    427 if allows_duplicate_labels is not None:
--> 428     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    429 return df

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

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

File ~/work/pandas/pandas/pandas/core/indexes/base.py:741, in Index._maybe_check_unique(self)
    738 duplicates = self._format_duplicate_message()
    739 msg += f"\n{duplicates}"
--> 741 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)
Input In [28], in <cell line: 1>()
----> 1 df.rename(str.upper)

File ~/work/pandas/pandas/pandas/core/frame.py:5237, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5118 def rename(
   5119     self,
   5120     mapper: Renamer | None = None,
   (...)
   5128     errors: IgnoreRaise = "ignore",
   5129 ) -> DataFrame | None:
   5130     """
   5131     Alter axes labels.
   5132 
   (...)
   5235     4  3  6
   5236     """
-> 5237     return super()._rename(
   5238         mapper=mapper,
   5239         index=index,
   5240         columns=columns,
   5241         axis=axis,
   5242         copy=copy,
   5243         inplace=inplace,
   5244         level=level,
   5245         errors=errors,
   5246     )

File ~/work/pandas/pandas/pandas/core/generic.py:1044, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1042     return None
   1043 else:
-> 1044     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:5566, in NDFrame.__finalize__(self, other, method, **kwargs)
   5563 for name in other.attrs:
   5564     self.attrs[name] = other.attrs[name]
-> 5566 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5567 # For subclasses using _metadata.
   5568 for name in set(self._metadata) & set(other._metadata):

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

File ~/work/pandas/pandas/pandas/core/indexes/base.py:741, in Index._maybe_check_unique(self)
    738 duplicates = self._format_duplicate_message()
    739 msg += f"\n{duplicates}"
--> 741 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)
Input In [31], in <cell line: 1>()
----> 1 s1.head().rename({"a": "b"})

File ~/work/pandas/pandas/pandas/core/series.py:4774, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4767     axis = self._get_axis_number(axis)
   4769 if callable(index) or is_dict_like(index):
   4770     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
   4771     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
   4772     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
   4773     # Hashable], Callable[[Any], Hashable], None]"
-> 4774     return super()._rename(
   4775         index,  # type: ignore[arg-type]
   4776         copy=copy,
   4777         inplace=inplace,
   4778         level=level,
   4779         errors=errors,
   4780     )
   4781 else:
   4782     return self._set_name(index, inplace=inplace)

File ~/work/pandas/pandas/pandas/core/generic.py:1044, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1042     return None
   1043 else:
-> 1044     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:5566, in NDFrame.__finalize__(self, other, method, **kwargs)
   5563 for name in other.attrs:
   5564     self.attrs[name] = other.attrs[name]
-> 5566 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5567 # For subclasses using _metadata.
   5568 for name in set(self._metadata) & set(other._metadata):

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

File ~/work/pandas/pandas/pandas/core/indexes/base.py:741, in Index._maybe_check_unique(self)
    738 duplicates = self._format_duplicate_message()
    739 msg += f"\n{duplicates}"
--> 741 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.