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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:5164, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
       5147 @doc(
       5148     NDFrame.reindex,  # type: ignore[has-type]
       5149     klass=_shared_doc_kwargs["klass"],
       (...)
       5162     tolerance=None,
       5163 ) -> Series:
    -> 5164     return super().reindex(
       5165         index=index,
       5166         method=method,
       5167         copy=copy,
       5168         level=level,
       5169         fill_value=fill_value,
       5170         limit=limit,
       5171         tolerance=tolerance,
       5172     )

    File ~/work/pandas/pandas/pandas/core/generic.py:5629, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
       5626     return self._reindex_multi(axes, copy, fill_value)
       5628 # perform the reindex on the axes
    -> 5629 return self._reindex_axes(
       5630     axes, level, limit, tolerance, method, fill_value, copy
       5631 ).__finalize__(self, method="reindex")

    File ~/work/pandas/pandas/pandas/core/generic.py:5652, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
       5649     continue
       5651 ax = self._get_axis(a)
    -> 5652 new_index, indexer = ax.reindex(
       5653     labels, level=level, limit=limit, tolerance=tolerance, method=method
       5654 )
       5656 axis = self._get_axis_number(a)
       5657 obj = obj._reindex_with_indexers(
       5658     {axis: [new_index, indexer]},
       5659     fill_value=fill_value,
       5660     copy=copy,
       5661     allow_dups=False,
       5662 )

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:4436, in Index.reindex(self, target, method, level, limit, tolerance)
       4433     raise ValueError("cannot handle a non-unique multi-index!")
       4434 elif not self.is_unique:
       4435     # GH#42568
    -> 4436     raise ValueError("cannot reindex on an axis with duplicate labels")
       4437 else:
       4438     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

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 ~/work/pandas/pandas/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 ~/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:716, in Index._maybe_check_unique(self)
        713 duplicates = self._format_duplicate_message()
        714 msg += f"\n{duplicates}"
    --> 716 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:5774, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       5643 def rename(
       5644     self,
       5645     mapper: Renamer | None = None,
       (...)
       5653     errors: IgnoreRaise = "ignore",
       5654 ) -> DataFrame | None:
       5655     """
       5656     Rename columns or index labels.
       5657
       (...)
       5772     4  3  6
       5773     """
    -> 5774     return super()._rename(
       5775         mapper=mapper,
       5776         index=index,
       5777         columns=columns,
       5778         axis=axis,
       5779         copy=copy,
       5780         inplace=inplace,
       5781         level=level,
       5782         errors=errors,
       5783     )

    File ~/work/pandas/pandas/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 ~/work/pandas/pandas/pandas/core/generic.py:6281, in NDFrame.__finalize__(self, other, method, **kwargs)
       6274 if other.attrs:
       6275     # We want attrs propagation to have minimal performance
       6276     # impact if attrs are not used; i.e. attrs is an empty dict.
       6277     # One could make the deepcopy unconditionally, but a deepcopy
       6278     # of an empty dict is 50x more expensive than the empty check.
       6279     self.attrs = deepcopy(other.attrs)
    -> 6281 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
       6282 # For subclasses using _metadata.
       6283 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:716, in Index._maybe_check_unique(self)
        713 duplicates = self._format_duplicate_message()
        714 msg += f"\n{duplicates}"
    --> 716 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:5101, in Series.rename(self, index, axis, copy, inplace, level, errors)
       5094     axis = self._get_axis_number(axis)
       5096 if callable(index) or is_dict_like(index):
       5097     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
       5098     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
       5099     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
       5100     # Hashable], Callable[[Any], Hashable], None]"
    -> 5101     return super()._rename(
       5102         index,  # type: ignore[arg-type]
       5103         copy=copy,
       5104         inplace=inplace,
       5105         level=level,
       5106         errors=errors,
       5107     )
       5108 else:
       5109     return self._set_name(index, inplace=inplace, deep=copy)

    File ~/work/pandas/pandas/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 ~/work/pandas/pandas/pandas/core/generic.py:6281, in NDFrame.__finalize__(self, other, method, **kwargs)
       6274 if other.attrs:
       6275     # We want attrs propagation to have minimal performance
       6276     # impact if attrs are not used; i.e. attrs is an empty dict.
       6277     # One could make the deepcopy unconditionally, but a deepcopy
       6278     # of an empty dict is 50x more expensive than the empty check.
       6279     self.attrs = deepcopy(other.attrs)
    -> 6281 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
       6282 # For subclasses using _metadata.
       6283 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:716, in Index._maybe_check_unique(self)
        713 duplicates = self._format_duplicate_message()
        714 msg += f"\n{duplicates}"
    --> 716 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`.