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:4580, in Series.reindex(self, index, **kwargs)
   4572 @doc(
   4573     NDFrame.reindex,  # type: ignore[has-type]
   4574     klass=_shared_doc_kwargs["klass"],
   (...)
   4578 )
   4579 def reindex(self, index=None, **kwargs):
-> 4580     return super().reindex(index=index, **kwargs)

File /usr/lib/python3/dist-packages/pandas/core/generic.py:4818, in NDFrame.reindex(self, *args, **kwargs)
   4815     return self._reindex_multi(axes, copy, fill_value)
   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")

File /usr/lib/python3/dist-packages/pandas/core/generic.py:4839, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   4834     new_index, indexer = ax.reindex(
   4835         labels, level=level, limit=limit, tolerance=tolerance, method=method
   4836     )
   4838     axis = self._get_axis_number(a)
-> 4839     obj = obj._reindex_with_indexers(
   4840         {axis: [new_index, indexer]},
   4841         fill_value=fill_value,
   4842         copy=copy,
   4843         allow_dups=False,
   4844     )
   4846 return obj

File /usr/lib/python3/dist-packages/pandas/core/generic.py:4883, in NDFrame._reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
   4880     indexer = ensure_platform_int(indexer)
   4882 # TODO: speed up on homogeneous DataFrame objects
-> 4883 new_data = new_data.reindex_indexer(
   4884     index,
   4885     indexer,
   4886     axis=baxis,
   4887     fill_value=fill_value,
   4888     allow_dups=allow_dups,
   4889     copy=copy,
   4890 )
   4891 # If we've made a copy once, no need to make another one
   4892 copy = False

File /usr/lib/python3/dist-packages/pandas/core/internals/managers.py:670, in BaseBlockManager.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)
    672 if axis >= self.ndim:
    673     raise IndexError("Requested axis not found in manager")

File /usr/lib/python3/dist-packages/pandas/core/indexes/base.py:3785, in Index._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")

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)
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:434, in NDFrame.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

File /usr/lib/python3/dist-packages/pandas/core/flags.py:105, in Flags.__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)

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

File /usr/lib/python3/dist-packages/pandas/core/indexes/base.py:651, in Index._maybe_check_unique(self)
    648 duplicates = self._format_duplicate_message()
    649 msg += f"\n{duplicates}"
--> 651 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=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)
Cell In [28], line 1
----> 1 df.rename(str.upper)

File /usr/lib/python3/dist-packages/pandas/util/_decorators.py:324, in rewrite_axis_style_signature.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    322 @wraps(func)
    323 def wrapper(*args, **kwargs) -> Callable[..., Any]:
--> 324     return func(*args, **kwargs)

File /usr/lib/python3/dist-packages/pandas/core/frame.py:5039, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   4916 @rewrite_axis_style_signature(
   4917     "mapper",
   4918     [("copy", True), ("inplace", False), ("level", None), ("errors", "ignore")],
   (...)
   4930     errors: str = "ignore",
   4931 ) -> DataFrame | None:
   4932     """
   4933     Alter axes labels.
   4934 
   (...)
   5037     4  3  6
   5038     """
-> 5039     return super().rename(
   5040         mapper=mapper,
   5041         index=index,
   5042         columns=columns,
   5043         axis=axis,
   5044         copy=copy,
   5045         inplace=inplace,
   5046         level=level,
   5047         errors=errors,
   5048     )

File /usr/lib/python3/dist-packages/pandas/core/generic.py:1164, in NDFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1162     return None
   1163 else:
-> 1164     return result.__finalize__(self, method="rename")

File /usr/lib/python3/dist-packages/pandas/core/generic.py:5459, in NDFrame.__finalize__(self, other, method, **kwargs)
   5456 for name in other.attrs:
   5457     self.attrs[name] = other.attrs[name]
-> 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):

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

File /usr/lib/python3/dist-packages/pandas/core/indexes/base.py:651, in Index._maybe_check_unique(self)
    648 duplicates = self._format_duplicate_message()
    649 msg += f"\n{duplicates}"
--> 651 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:4518, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4458 """
   4459 Alter Series index labels or name.
   4460 
   (...)
   4515 dtype: int64
   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     )
   4521 else:
   4522     return self._set_name(index, inplace=inplace)

File /usr/lib/python3/dist-packages/pandas/core/generic.py:1164, in NDFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1162     return None
   1163 else:
-> 1164     return result.__finalize__(self, method="rename")

File /usr/lib/python3/dist-packages/pandas/core/generic.py:5459, in NDFrame.__finalize__(self, other, method, **kwargs)
   5456 for name in other.attrs:
   5457     self.attrs[name] = other.attrs[name]
-> 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):

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

File /usr/lib/python3/dist-packages/pandas/core/indexes/base.py:651, in Index._maybe_check_unique(self)
    648 duplicates = self._format_duplicate_message()
    649 msg += f"\n{duplicates}"
--> 651 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.