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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:5172, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
       5155 @doc(
       5156     NDFrame.reindex,  # type: ignore[has-type]
       5157     klass=_shared_doc_kwargs["klass"],
       (...)   5170     tolerance=None,
       5171 ) -> Series:
    -> 5172     return super().reindex(
       5173         index=index,
       5174         method=method,
       5175         copy=copy,
       5176         level=level,
       5177         fill_value=fill_value,
       5178         limit=limit,
       5179         tolerance=tolerance,
       5180     )

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:5632, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
       5629     return self._reindex_multi(axes, copy, fill_value)
       5631 # perform the reindex on the axes
    -> 5632 return self._reindex_axes(
       5633     axes, level, limit, tolerance, method, fill_value, copy
       5634 ).__finalize__(self, method="reindex")

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:5655, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
       5652     continue
       5654 ax = self._get_axis(a)
    -> 5655 new_index, indexer = ax.reindex(
       5656     labels, level=level, limit=limit, tolerance=tolerance, method=method
       5657 )
       5659 axis = self._get_axis_number(a)
       5660 obj = obj._reindex_with_indexers(
       5661     {axis: [new_index, indexer]},
       5662     fill_value=fill_value,
       5663     copy=copy,
       5664     allow_dups=False,
       5665 )

    File /usr/lib/python3/dist-packages/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]: 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:511, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
        509 df = self.copy(deep=copy and not using_copy_on_write())
        510 if allows_duplicate_labels is not None:
    --> 511     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
        512 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: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 /usr/lib/python3/dist-packages/pandas/core/frame.py:5789, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       5658 def rename(
       5659     self,
       5660     mapper: Renamer | None = None,
       (...)   5668     errors: IgnoreRaise = "ignore",
       5669 ) -> DataFrame | None:
       5670     """
       5671     Rename columns or index labels.
       5672
       (...)   5787     4  3  6
       5788     """
    -> 5789     return super()._rename(
       5790         mapper=mapper,
       5791         index=index,
       5792         columns=columns,
       5793         axis=axis,
       5794         copy=copy,
       5795         inplace=inplace,
       5796         level=level,
       5797         errors=errors,
       5798     )

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:1143, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       1141     return None
       1142 else:
    -> 1143     return result.__finalize__(self, method="rename")

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:6284, in NDFrame.__finalize__(self, other, method, **kwargs)
       6277 if other.attrs:
       6278     # We want attrs propagation to have minimal performance
       6279     # impact if attrs are not used; i.e. attrs is an empty dict.
       6280     # One could make the deepcopy unconditionally, but a deepcopy
       6281     # of an empty dict is 50x more expensive than the empty check.
       6282     self.attrs = deepcopy(other.attrs)
    -> 6284 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
       6285 # For subclasses using _metadata.
       6286 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: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 /usr/lib/python3/dist-packages/pandas/core/series.py:5109, in Series.rename(self, index, axis, copy, inplace, level, errors)
       5102     axis = self._get_axis_number(axis)
       5104 if callable(index) or is_dict_like(index):
       5105     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
       5106     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
       5107     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
       5108     # Hashable], Callable[[Any], Hashable], None]"
    -> 5109     return super()._rename(
       5110         index,  # type: ignore[arg-type]
       5111         copy=copy,
       5112         inplace=inplace,
       5113         level=level,
       5114         errors=errors,
       5115     )
       5116 else:
       5117     return self._set_name(index, inplace=inplace, deep=copy)

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:1143, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       1141     return None
       1142 else:
    -> 1143     return result.__finalize__(self, method="rename")

    File /usr/lib/python3/dist-packages/pandas/core/generic.py:6284, in NDFrame.__finalize__(self, other, method, **kwargs)
       6277 if other.attrs:
       6278     # We want attrs propagation to have minimal performance
       6279     # impact if attrs are not used; i.e. attrs is an empty dict.
       6280     # One could make the deepcopy unconditionally, but a deepcopy
       6281     # of an empty dict is 50x more expensive than the empty check.
       6282     self.attrs = deepcopy(other.attrs)
    -> 6284 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
       6285 # For subclasses using _metadata.
       6286 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: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`.