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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 ~/work/pandas/pandas/pandas/core/series.py:5701, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
       5475 def reindex(  # type: ignore[override]
       5476     self,
       5477     index=None,
       (...)   5485     tolerance=None,
       5486 ) -> Series:
       5487     """
       5488     Conform Series to new index with optional filling logic.
       5489
       (...)   5699     See the :ref:`user guide <basics.reindexing>` for more.
       5700     """
    -> 5701     return super().reindex(
       5702         index=index,
       5703         method=method,
       5704         level=level,
       5705         fill_value=fill_value,
       5706         limit=limit,
       5707         tolerance=tolerance,
       5708         copy=copy,
       5709     )

    File ~/work/pandas/pandas/pandas/core/generic.py:5488, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
       5485     return self._reindex_multi(axes, fill_value)
       5487 # perform the reindex on the axes
    -> 5488 return self._reindex_axes(
       5489     axes, level, limit, tolerance, method, fill_value
       5490 ).__finalize__(self, method="reindex")

    File ~/work/pandas/pandas/pandas/core/generic.py:5510, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
       5507     continue
       5509 ax = self._get_axis(a)
    -> 5510 new_index, indexer = ax.reindex(
       5511     labels, level=level, limit=limit, tolerance=tolerance, method=method
       5512 )
       5514 axis = self._get_axis_number(a)
       5515 obj = obj._reindex_with_indexers(
       5516     {axis: [new_index, indexer]},
       5517     fill_value=fill_value,
       5518     allow_dups=False,
       5519 )

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:4246, in Index.reindex(self, target, method, level, limit, tolerance)
       4243     raise ValueError("cannot handle a non-unique multi-index!")
       4244 elif not self.is_unique:
       4245     # GH#42568
    -> 4246     raise ValueError("cannot reindex on an axis with duplicate labels")
       4247 else:
       4248     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

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

    File ~/work/pandas/pandas/pandas/core/flags.py:121, in Flags.__setitem__(self, key, value)
        119 if key not in self._keys:
        120     raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
    --> 121 setattr(self, key, value)

    File ~/work/pandas/pandas/pandas/core/flags.py:108, in Flags.allows_duplicate_labels(self, value)
        106 if not value:
        107     for ax in obj.axes:
    --> 108         ax._maybe_check_unique()
        110 self._allows_duplicate_labels = value

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:711, in Index._maybe_check_unique(self)
        708 duplicates = self._format_duplicate_message()
        709 msg += f"\n{duplicates}"
    --> 711 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:6327, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       6208 """
       6209 Rename columns or index labels.
       6210
       (...)   6324 4  3  6
       6325 """
       6326 self._check_copy_deprecation(copy)
    -> 6327 return super()._rename(
       6328     mapper=mapper,
       6329     index=index,
       6330     columns=columns,
       6331     axis=axis,
       6332     inplace=inplace,
       6333     level=level,
       6334     errors=errors,
       6335 )

    File ~/work/pandas/pandas/pandas/core/generic.py:1055, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
       1053     return None
       1054 else:
    -> 1055     return result.__finalize__(self, method="rename")

    File ~/work/pandas/pandas/pandas/core/generic.py:6183, in NDFrame.__finalize__(self, other, method, **kwargs)
       6177 if other.attrs:
       6178     # We want attrs propagation to have minimal performance
       6179     # impact if attrs are not used; i.e. attrs is an empty dict.
       6180     # One could make the deepcopy unconditionally, but a deepcopy
       6181     # of an empty dict is 50x more expensive than the empty check.
       6182     self.attrs = deepcopy(other.attrs)
    -> 6183 self.flags.allows_duplicate_labels = (
       6184     self.flags.allows_duplicate_labels
       6185     and other.flags.allows_duplicate_labels
       6186 )
       6187 # For subclasses using _metadata.
       6188 for name in set(self._metadata) & set(other._metadata):

    File ~/work/pandas/pandas/pandas/core/flags.py:108, in Flags.allows_duplicate_labels(self, value)
        106 if not value:
        107     for ax in obj.axes:
    --> 108         ax._maybe_check_unique()
        110 self._allows_duplicate_labels = value

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:711, in Index._maybe_check_unique(self)
        708 duplicates = self._format_duplicate_message()
        709 msg += f"\n{duplicates}"
    --> 711 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:5407, in Series.rename(self, index, axis, copy, inplace, level, errors)
       5400     axis = self._get_axis_number(axis)
       5402 if callable(index) or is_dict_like(index):
       5403     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
       5404     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
       5405     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
       5406     # Hashable], Callable[[Any], Hashable], None]"
    -> 5407     return super()._rename(
       5408         index,  # type: ignore[arg-type]
       5409         inplace=inplace,
       5410         level=level,
       5411         errors=errors,
       5412     )
       5413 else:
       5414     return self._set_name(index, inplace=inplace)

    File ~/work/pandas/pandas/pandas/core/generic.py:1055, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
       1053     return None
       1054 else:
    -> 1055     return result.__finalize__(self, method="rename")

    File ~/work/pandas/pandas/pandas/core/generic.py:6183, in NDFrame.__finalize__(self, other, method, **kwargs)
       6177 if other.attrs:
       6178     # We want attrs propagation to have minimal performance
       6179     # impact if attrs are not used; i.e. attrs is an empty dict.
       6180     # One could make the deepcopy unconditionally, but a deepcopy
       6181     # of an empty dict is 50x more expensive than the empty check.
       6182     self.attrs = deepcopy(other.attrs)
    -> 6183 self.flags.allows_duplicate_labels = (
       6184     self.flags.allows_duplicate_labels
       6185     and other.flags.allows_duplicate_labels
       6186 )
       6187 # For subclasses using _metadata.
       6188 for name in set(self._metadata) & set(other._metadata):

    File ~/work/pandas/pandas/pandas/core/flags.py:108, in Flags.allows_duplicate_labels(self, value)
        106 if not value:
        107     for ax in obj.axes:
    --> 108         ax._maybe_check_unique()
        110 self._allows_duplicate_labels = value

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:711, in Index._maybe_check_unique(self)
        708 duplicates = self._format_duplicate_message()
        709 msg += f"\n{duplicates}"
    --> 711 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`.