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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:5430, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
       5204 def reindex(  # type: ignore[override]
       5205     self,
       5206     index=None,
       (...)   5214     tolerance=None,
       5215 ) -> Series:
       5216     """
       5217     Conform Series to new index with optional filling logic.
       5218
       (...)   5428     See the :ref:`user guide <basics.reindexing>` for more.
       5429     """
    -> 5430     return super().reindex(
       5431         index=index,
       5432         method=method,
       5433         level=level,
       5434         fill_value=fill_value,
       5435         limit=limit,
       5436         tolerance=tolerance,
       5437         copy=copy,
       5438     )

    File ~/work/pandas/pandas/pandas/core/generic.py:5476, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
       5473     return self._reindex_multi(axes, fill_value)
       5475 # perform the reindex on the axes
    -> 5476 return self._reindex_axes(
       5477     axes, level, limit, tolerance, method, fill_value
       5478 ).__finalize__(self, method="reindex")

    File ~/work/pandas/pandas/pandas/core/generic.py:5498, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
       5495     continue
       5497 ax = self._get_axis(a)
    -> 5498 new_index, indexer = ax.reindex(
       5499     labels, level=level, limit=limit, tolerance=tolerance, method=method
       5500 )
       5502 axis = self._get_axis_number(a)
       5503 obj = obj._reindex_with_indexers(
       5504     {axis: [new_index, indexer]},
       5505     fill_value=fill_value,
       5506     allow_dups=False,
       5507 )

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:4255, in Index.reindex(self, target, method, level, limit, tolerance)
       4252     raise ValueError("cannot handle a non-unique multi-index!")
       4253 elif not self.is_unique:
       4254     # GH#42568
    -> 4255     raise ValueError("cannot reindex on an axis with duplicate labels")
       4256 else:
       4257     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:469, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
        467 df = self.copy(deep=False)
        468 if allows_duplicate_labels is not None:
    --> 469     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
        470 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:723, in Index._maybe_check_unique(self)
        720 duplicates = self._format_duplicate_message()
        721 msg += f"\n{duplicates}"
    --> 723 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:6334, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       6215 """
       6216 Rename columns or index labels.
       6217
       (...)   6331 4  3  6
       6332 """
       6333 self._check_copy_deprecation(copy)
    -> 6334 return super()._rename(
       6335     mapper=mapper,
       6336     index=index,
       6337     columns=columns,
       6338     axis=axis,
       6339     inplace=inplace,
       6340     level=level,
       6341     errors=errors,
       6342 )

    File ~/work/pandas/pandas/pandas/core/generic.py:1072, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
       1070     return None
       1071 else:
    -> 1072     return result.__finalize__(self, method="rename")

    File ~/work/pandas/pandas/pandas/core/generic.py:6167, in NDFrame.__finalize__(self, other, method, **kwargs)
       6161 if other.attrs:
       6162     # We want attrs propagation to have minimal performance
       6163     # impact if attrs are not used; i.e. attrs is an empty dict.
       6164     # One could make the deepcopy unconditionally, but a deepcopy
       6165     # of an empty dict is 50x more expensive than the empty check.
       6166     self.attrs = deepcopy(other.attrs)
    -> 6167 self.flags.allows_duplicate_labels = (
       6168     self.flags.allows_duplicate_labels
       6169     and other.flags.allows_duplicate_labels
       6170 )
       6171 # For subclasses using _metadata.
       6172 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:723, in Index._maybe_check_unique(self)
        720 duplicates = self._format_duplicate_message()
        721 msg += f"\n{duplicates}"
    --> 723 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:5158, in Series.rename(self, index, axis, copy, inplace, level, errors)
       5151     axis = self._get_axis_number(axis)
       5153 if callable(index) or is_dict_like(index):
       5154     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
       5155     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
       5156     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
       5157     # Hashable], Callable[[Any], Hashable], None]"
    -> 5158     return super()._rename(
       5159         index,  # type: ignore[arg-type]
       5160         inplace=inplace,
       5161         level=level,
       5162         errors=errors,
       5163     )
       5164 else:
       5165     return self._set_name(index, inplace=inplace)

    File ~/work/pandas/pandas/pandas/core/generic.py:1072, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
       1070     return None
       1071 else:
    -> 1072     return result.__finalize__(self, method="rename")

    File ~/work/pandas/pandas/pandas/core/generic.py:6167, in NDFrame.__finalize__(self, other, method, **kwargs)
       6161 if other.attrs:
       6162     # We want attrs propagation to have minimal performance
       6163     # impact if attrs are not used; i.e. attrs is an empty dict.
       6164     # One could make the deepcopy unconditionally, but a deepcopy
       6165     # of an empty dict is 50x more expensive than the empty check.
       6166     self.attrs = deepcopy(other.attrs)
    -> 6167 self.flags.allows_duplicate_labels = (
       6168     self.flags.allows_duplicate_labels
       6169     and other.flags.allows_duplicate_labels
       6170 )
       6171 # For subclasses using _metadata.
       6172 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:723, in Index._maybe_check_unique(self)
        720 duplicates = self._format_duplicate_message()
        721 msg += f"\n{duplicates}"
    --> 723 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`.