# 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)
    <ipython-input-4-18a38f6978fe> in <module>
    ----> 1 s1.reindex(["a", "b", "c"])

    /usr/lib/python3/dist-packages/pandas/core/series.py in reindex(self, index, **kwargs)
       4578     )
       4579     def reindex(self, index=None, **kwargs):
    -> 4580         return super().reindex(index=index, **kwargs)
       4581
       4582     @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])

    /usr/lib/python3/dist-packages/pandas/core/generic.py in reindex(self, *args, **kwargs)
       4816
       4817         # perform the reindex on the axes
    -> 4818         return self._reindex_axes(
       4819             axes, level, limit, tolerance, method, fill_value, copy
       4820         ).__finalize__(self, method="reindex")

    /usr/lib/python3/dist-packages/pandas/core/generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
       4837
       4838             axis = self._get_axis_number(a)
    -> 4839             obj = obj._reindex_with_indexers(
       4840                 {axis: [new_index, indexer]},
       4841                 fill_value=fill_value,

    /usr/lib/python3/dist-packages/pandas/core/generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
       4881
       4882             # TODO: speed up on homogeneous DataFrame objects
    -> 4883             new_data = new_data.reindex_indexer(
       4884                 index,
       4885                 indexer,

    /usr/lib/python3/dist-packages/pandas/core/internals/managers.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice)
        668         # some axes don't allow reindexing with dups
        669         if not allow_dups:
    --> 670             self.axes[axis]._validate_can_reindex(indexer)
        671
        672         if axis >= self.ndim:

    /usr/lib/python3/dist-packages/pandas/core/indexes/base.py in _validate_can_reindex(self, indexer)
       3783         # trying to reindex on an axis with duplicates
       3784         if not self._index_as_unique and len(indexer):
    -> 3785             raise ValueError("cannot reindex from a duplicate axis")
       3786
       3787     def reindex(

    ValueError: cannot reindex from a duplicate axis

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

New 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)
    <ipython-input-19-11af4ee9738e> in <module>
    ----> 1 pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False)

    /usr/lib/python3/dist-packages/pandas/core/generic.py in set_flags(self, copy, allows_duplicate_labels)
        432         df = self.copy(deep=copy)
        433         if allows_duplicate_labels is not None:
    --> 434             df.flags["allows_duplicate_labels"] = allows_duplicate_labels
        435         return df
        436

    /usr/lib/python3/dist-packages/pandas/core/flags.py in __setitem__(self, key, value)
        103         if key not in self._keys:
        104             raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
    --> 105         setattr(self, key, value)
        106
        107     def __repr__(self):

    /usr/lib/python3/dist-packages/pandas/core/flags.py in allows_duplicate_labels(self, value)
         90         if not value:
         91             for ax in obj.axes:
    ---> 92                 ax._maybe_check_unique()
         93
         94         self._allows_duplicate_labels = value

    /usr/lib/python3/dist-packages/pandas/core/indexes/base.py in _maybe_check_unique(self)
        649             msg += f"\n{duplicates}"
        650
    --> 651             raise DuplicateLabelError(msg)
        652
        653     @final

    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=True` 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)
    <ipython-input-28-17c8fb0b7c7f> in <module>
    ----> 1 df.rename(str.upper)

    /usr/lib/python3/dist-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
        322         @wraps(func)
        323         def wrapper(*args, **kwargs) -> Callable[..., Any]:
    --> 324             return func(*args, **kwargs)
        325
        326         kind = inspect.Parameter.POSITIONAL_OR_KEYWORD

    /usr/lib/python3/dist-packages/pandas/core/frame.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       5037         4  3  6
       5038         """
    -> 5039         return super().rename(
       5040             mapper=mapper,
       5041             index=index,

    /usr/lib/python3/dist-packages/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       1162             return None
       1163         else:
    -> 1164             return result.__finalize__(self, method="rename")
       1165
       1166     @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])

    /usr/lib/python3/dist-packages/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)
       5457                 self.attrs[name] = other.attrs[name]
       5458
    -> 5459             self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
       5460             # For subclasses using _metadata.
       5461             for name in set(self._metadata) & set(other._metadata):

    /usr/lib/python3/dist-packages/pandas/core/flags.py in allows_duplicate_labels(self, value)
         90         if not value:
         91             for ax in obj.axes:
    ---> 92                 ax._maybe_check_unique()
         93
         94         self._allows_duplicate_labels = value

    /usr/lib/python3/dist-packages/pandas/core/indexes/base.py in _maybe_check_unique(self)
        649             msg += f"\n{duplicates}"
        650
    --> 651             raise DuplicateLabelError(msg)
        652
        653     @final

    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)
    <ipython-input-31-8f09bda3af1a> in <module>
    ----> 1 s1.head().rename({"a": "b"})

    /usr/lib/python3/dist-packages/pandas/core/series.py in rename(self, index, axis, copy, inplace, level, errors)
       4516         """
       4517         if callable(index) or is_dict_like(index):
    -> 4518             return super().rename(
       4519                 index, copy=copy, inplace=inplace, level=level, errors=errors
       4520             )

    /usr/lib/python3/dist-packages/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
       1162             return None
       1163         else:
    -> 1164             return result.__finalize__(self, method="rename")
       1165
       1166     @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])

    /usr/lib/python3/dist-packages/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)
       5457                 self.attrs[name] = other.attrs[name]
       5458
    -> 5459             self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
       5460             # For subclasses using _metadata.
       5461             for name in set(self._metadata) & set(other._metadata):

    /usr/lib/python3/dist-packages/pandas/core/flags.py in allows_duplicate_labels(self, value)
         90         if not value:
         91             for ax in obj.axes:
    ---> 92                 ax._maybe_check_unique()
         93
         94         self._allows_duplicate_labels = value

    /usr/lib/python3/dist-packages/pandas/core/indexes/base.py in _maybe_check_unique(self)
        649             msg += f"\n{duplicates}"
        650
    --> 651             raise DuplicateLabelError(msg)
        652
        653     @final

    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`.