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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:5908, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
       5904         original index entry) unfilled.
       5905
       5906         See the :ref:`user guide <basics.reindexing>` for more.
       5907         """
    -> 5908         return super().reindex(
       5909             index=index,
       5910             method=method,
       5911             level=level,

    File ~/work/pandas/pandas/pandas/core/generic.py:5610, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
       5606         if self._needs_reindex_multi(axes, method, level):
       5607             return self._reindex_multi(axes, fill_value)
       5608
       5609         # perform the reindex on the axes
    -> 5610         return self._reindex_axes(
       5611             axes, level, limit, tolerance, method, fill_value
       5612         ).__finalize__(self, method="reindex")

    File ~/work/pandas/pandas/pandas/core/generic.py:5632, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
       5628             if labels is None:
       5629                 continue
       5630
       5631             ax = self._get_axis(a)
    -> 5632             new_index, indexer = ax.reindex(
       5633                 labels, level=level, limit=limit, tolerance=tolerance, method=method
       5634             )
       5635

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:4426, in Index.reindex(self, target, method, level, limit, tolerance)
       4423     raise ValueError("cannot handle a non-unique multi-index!")
       4424 elif not self.is_unique:
       4425     # GH#42568
    -> 4426     raise ValueError("cannot reindex on an axis with duplicate labels")
       4427 else:
       4428     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

This approach keeps the first occurrence of each label. To keep the last occurrence instead, you can pass `keep="last"`:

    In [18]: df2.loc[~df2.index.duplicated(keep="last"), :]
    Out[18]:
       A
    a  1
    b  2

If you want to remove all occurrences of duplicated labels, you can use `keep=False`:

    In [19]: df2.loc[~df2.index.duplicated(keep=False), :]
    Out[19]:
       A
    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 [20]: df2.groupby(level=0).mean()
    Out[20]:
         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 [21]: pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False)
    ---------------------------------------------------------------------------
    DuplicateLabelError                       Traceback (most recent call last)
    Cell In[21], 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:464, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
        460         """
        461         self._check_copy_deprecation(copy)
        462         df = self.copy(deep=False)
        463         if allows_duplicate_labels is not None:
    --> 464             df.flags["allows_duplicate_labels"] = allows_duplicate_labels
        465         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:727, in Index._maybe_check_unique(self)
        724 duplicates = self._format_duplicate_message()
        725 msg += f"\n{duplicates}"
    --> 727 raise DuplicateLabelError(msg)

    DuplicateLabelError: Index has duplicates.
          positions
    label
    b        [1, 2]

This applies to both row and column labels for a `DataFrame`

    In [22]: pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["A", "B", "C"],).set_flags(
       ....:     allows_duplicate_labels=False
       ....: )
       ....:
    Out[22]:
       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 [23]: df = pd.DataFrame({"A": [0, 1, 2, 3]}, index=["x", "y", "X", "Y"]).set_flags(
       ....:     allows_duplicate_labels=False
       ....: )
       ....:

    In [24]: df
    Out[24]:
       A
    x  0
    y  1
    X  2
    Y  3

    In [25]: df.flags.allows_duplicate_labels
    Out[25]: False

`DataFrame.set_flags()` can be used to return a new `DataFrame` with attributes like `allows_duplicate_labels` set to some value

    In [26]: df2 = df.set_flags(allows_duplicate_labels=True)

    In [27]: df2.flags.allows_duplicate_labels
    Out[27]: 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 [28]: df2.flags.allows_duplicate_labels = False

    In [29]: df2.flags.allows_duplicate_labels
    Out[29]: 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 [30]: df.rename(str.upper)
    ---------------------------------------------------------------------------
    DuplicateLabelError                       Traceback (most recent call last)
    Cell In[30], line 1
    ----> 1 df.rename(str.upper)

    File ~/work/pandas/pandas/pandas/core/frame.py:6833, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       6829             inplace = False
       6830
       6831         self._check_copy_deprecation(copy)
       6832
    -> 6833         return super()._rename(
       6834             mapper=mapper,
       6835             index=index,
       6836             columns=columns,

    File ~/work/pandas/pandas/pandas/core/generic.py:1058, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
       1054         if inplace:
       1055             self._update_inplace(result)
       1056             return None
       1057         else:
    -> 1058             return result.__finalize__(self, method="rename")

    File ~/work/pandas/pandas/pandas/core/generic.py:6307, in NDFrame.__finalize__(self, other, method, **kwargs)
       6303                 self.attrs = deepcopy(other.attrs)
       6304             # Since new objects always start with allows_duplicate_labels=True,
       6305             # we only need to act when other has it set to False.
       6306             if not other._flags._allows_duplicate_labels:
    -> 6307                 self.flags.allows_duplicate_labels = False
       6308             # For subclasses using _metadata.
       6309             for name in set(self._metadata) & set(other._metadata):
       6310                 assert isinstance(name, str)

    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:727, in Index._maybe_check_unique(self)
        724 duplicates = self._format_duplicate_message()
        725 msg += f"\n{duplicates}"
    --> 727 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 [31]: s1 = pd.Series(0, index=["a", "b"]).set_flags(allows_duplicate_labels=False)

    In [32]: s1
    Out[32]:
    a    0
    b    0
    dtype: int64

    In [33]: s1.head().rename({"a": "b"})
    ---------------------------------------------------------------------------
    DuplicateLabelError                       Traceback (most recent call last)
    Cell In[33], line 1
    ----> 1 s1.head().rename({"a": "b"})

    File ~/work/pandas/pandas/pandas/core/series.py:5605, in Series.rename(self, index, axis, copy, inplace, level, errors)
       5601             # error: Argument 1 to "_rename" of "NDFrame" has incompatible
       5602             # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
       5603             # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
       5604             # Hashable], Callable[[Any], Hashable], None]"
    -> 5605             return super()._rename(
       5606                 index,  # type: ignore[arg-type]
       5607                 inplace=inplace,
       5608                 level=level,

    File ~/work/pandas/pandas/pandas/core/generic.py:1058, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
       1054         if inplace:
       1055             self._update_inplace(result)
       1056             return None
       1057         else:
    -> 1058             return result.__finalize__(self, method="rename")

    File ~/work/pandas/pandas/pandas/core/generic.py:6307, in NDFrame.__finalize__(self, other, method, **kwargs)
       6303                 self.attrs = deepcopy(other.attrs)
       6304             # Since new objects always start with allows_duplicate_labels=True,
       6305             # we only need to act when other has it set to False.
       6306             if not other._flags._allows_duplicate_labels:
    -> 6307                 self.flags.allows_duplicate_labels = False
       6308             # For subclasses using _metadata.
       6309             for name in set(self._metadata) & set(other._metadata):
       6310                 assert isinstance(name, str)

    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:727, in Index._maybe_check_unique(self)
        724 duplicates = self._format_duplicate_message()
        725 msg += f"\n{duplicates}"
    --> 727 raise DuplicateLabelError(msg)

    DuplicateLabelError: Index has duplicates.
          positions
    label
    b        [0, 1]

Warning

Many methods do not yet propagate the `allows_duplicate_labels` value through to their result. The long-term goal is for every method that takes or returns a `DataFrame` or `Series` to preserve it.