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# 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 /usr/lib/python3/dist-packages/pandas/core/series.py:5153, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
       5136 @doc(
       5137     NDFrame.reindex,  # type: ignore[has-type]
       5138     klass=_shared_doc_kwargs["klass"],
       (...)
       5151     tolerance=None,
       5152 ) -> Series:
    -> 5153     return super().reindex(
       5154         index=index,
       5155         method=method,
       5156         copy=copy,
       5157         level=level,
       5158         fill_value=fill_value,
       5159         limit=limit,
       5160         tolerance=tolerance,
       5161     )

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:5610, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
       5607     return self._reindex_multi(axes, copy, fill_value)
       5609 # perform the reindex on the axes
    -> 5610 return self._reindex_axes(
       5611     axes, level, limit, tolerance, method, fill_value, copy
       5612 ).__finalize__(self, method="reindex")

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:5633, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
       5630     continue
       5632 ax = self._get_axis(a)
    -> 5633 new_index, indexer = ax.reindex(
       5634     labels, level=level, limit=limit, tolerance=tolerance, method=method
       5635 )
       5637 axis = self._get_axis_number(a)
       5638 obj = obj._reindex_with_indexers(
       5639     {axis: [new_index, indexer]},
       5640     fill_value=fill_value,
       5641     copy=copy,
       5642     allow_dups=False,
       5643 )

    File /usr/lib/python3/dist-packages/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

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]: np.int64(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

Added 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 /usr/lib/python3/dist-packages/pandas/core/generic.py:508, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
        506 df = self.copy(deep=copy and not using_copy_on_write())
        507 if allows_duplicate_labels is not None:
    --> 508     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
        509 return df

    File /usr/lib/python3/dist-packages/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 /usr/lib/python3/dist-packages/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 /usr/lib/python3/dist-packages/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 /usr/lib/python3/dist-packages/pandas/core/frame.py:5767, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       5636 def rename(
       5637     self,
       5638     mapper: Renamer | None = None,
       (...)
       5646     errors: IgnoreRaise = "ignore",
       5647 ) -> DataFrame | None:
       5648     """
       5649     Rename columns or index labels.
       5650
       (...)
       5765     4  3  6
       5766     """
    -> 5767     return super()._rename(
       5768         mapper=mapper,
       5769         index=index,
       5770         columns=columns,
       5771         axis=axis,
       5772         copy=copy,
       5773         inplace=inplace,
       5774         level=level,
       5775         errors=errors,
       5776     )

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:1140, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       1138     return None
       1139 else:
    -> 1140     return result.__finalize__(self, method="rename")

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:6262, in NDFrame.__finalize__(self, other, method, **kwargs)
       6255 if other.attrs:
       6256     # We want attrs propagation to have minimal performance
       6257     # impact if attrs are not used; i.e. attrs is an empty dict.
       6258     # One could make the deepcopy unconditionally, but a deepcopy
       6259     # of an empty dict is 50x more expensive than the empty check.
       6260     self.attrs = deepcopy(other.attrs)
    -> 6262 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
       6263 # For subclasses using _metadata.
       6264 for name in set(self._metadata) & set(other._metadata):

    File /usr/lib/python3/dist-packages/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 /usr/lib/python3/dist-packages/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 /usr/lib/python3/dist-packages/pandas/core/series.py:5090, in Series.rename(self, index, axis, copy, inplace, level, errors)
       5083     axis = self._get_axis_number(axis)
       5085 if callable(index) or is_dict_like(index):
       5086     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
       5087     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
       5088     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
       5089     # Hashable], Callable[[Any], Hashable], None]"
    -> 5090     return super()._rename(
       5091         index,  # type: ignore[arg-type]
       5092         copy=copy,
       5093         inplace=inplace,
       5094         level=level,
       5095         errors=errors,
       5096     )
       5097 else:
       5098     return self._set_name(index, inplace=inplace, deep=copy)

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:1140, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       1138     return None
       1139 else:
    -> 1140     return result.__finalize__(self, method="rename")

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:6262, in NDFrame.__finalize__(self, other, method, **kwargs)
       6255 if other.attrs:
       6256     # We want attrs propagation to have minimal performance
       6257     # impact if attrs are not used; i.e. attrs is an empty dict.
       6258     # One could make the deepcopy unconditionally, but a deepcopy
       6259     # of an empty dict is 50x more expensive than the empty check.
       6260     self.attrs = deepcopy(other.attrs)
    -> 6262 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
       6263 # For subclasses using _metadata.
       6264 for name in set(self._metadata) & set(other._metadata):

    File /usr/lib/python3/dist-packages/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 /usr/lib/python3/dist-packages/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`.