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)
Input In [4], in <cell line: 1>()
----> 1 s1.reindex(["a", "b", "c"])

File /pandas/pandas/core/series.py:5094, in Series.reindex(self, *args, **kwargs)
   5090         raise TypeError(
   5091             "'index' passed as both positional and keyword argument"
   5092         )
   5093     kwargs.update({"index": index})
-> 5094 return super().reindex(**kwargs)

File /pandas/pandas/core/generic.py:5294, in NDFrame.reindex(self, *args, **kwargs)
   5291     return self._reindex_multi(axes, copy, fill_value)
   5293 # perform the reindex on the axes
-> 5294 return self._reindex_axes(
   5295     axes, level, limit, tolerance, method, fill_value, copy
   5296 ).__finalize__(self, method="reindex")

File /pandas/pandas/core/generic.py:5314, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   5309 new_index, indexer = ax.reindex(
   5310     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5311 )
   5313 axis = self._get_axis_number(a)
-> 5314 obj = obj._reindex_with_indexers(
   5315     {axis: [new_index, indexer]},
   5316     fill_value=fill_value,
   5317     copy=copy,
   5318     allow_dups=False,
   5319 )
   5320 # If we've made a copy once, no need to make another one
   5321 copy = False

File /pandas/pandas/core/generic.py:5360, in NDFrame._reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
   5357     indexer = ensure_platform_int(indexer)
   5359 # TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)
-> 5360 new_data = new_data.reindex_indexer(
   5361     index,
   5362     indexer,
   5363     axis=baxis,
   5364     fill_value=fill_value,
   5365     allow_dups=allow_dups,
   5366     copy=copy,
   5367 )
   5368 # If we've made a copy once, no need to make another one
   5369 copy = False

File /pandas/pandas/core/internals/managers.py:730, in BaseBlockManager.reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)
    728 # some axes don't allow reindexing with dups
    729 if not allow_dups:
--> 730     self.axes[axis]._validate_can_reindex(indexer)
    732 if axis >= self.ndim:
    733     raise IndexError("Requested axis not found in manager")

File /pandas/pandas/core/indexes/base.py:4350, in Index._validate_can_reindex(self, indexer)
   4348 # trying to reindex on an axis with duplicates
   4349 if not self._index_as_unique and len(indexer):
-> 4350     raise ValueError("cannot reindex on an axis with duplicate labels")

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]: 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)
Input In [19], in <cell line: 1>()
----> 1 pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False)

File /pandas/pandas/core/generic.py:443, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
    441 df = self.copy(deep=copy)
    442 if allows_duplicate_labels is not None:
--> 443     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    444 return df

File /pandas/pandas/core/flags.py:107, in Flags.__setitem__(self, key, value)
    105 if key not in self._keys:
    106     raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
--> 107 setattr(self, key, value)

File /pandas/pandas/core/flags.py:94, in Flags.allows_duplicate_labels(self, value)
     92 if not value:
     93     for ax in obj.axes:
---> 94         ax._maybe_check_unique()
     96 self._allows_duplicate_labels = value

File /pandas/pandas/core/indexes/base.py:752, in Index._maybe_check_unique(self)
    749 duplicates = self._format_duplicate_message()
    750 msg += f"\n{duplicates}"
--> 752 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)
Input In [28], in <cell line: 1>()
----> 1 df.rename(str.upper)

File /pandas/pandas/core/frame.py:5565, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5446 def rename(
   5447     self,
   5448     mapper: Renamer | None = None,
   (...)
   5456     errors: IgnoreRaise = "ignore",
   5457 ) -> DataFrame | None:
   5458     """
   5459     Alter axes labels.
   5460 
   (...)
   5563     4  3  6
   5564     """
-> 5565     return super()._rename(
   5566         mapper=mapper,
   5567         index=index,
   5568         columns=columns,
   5569         axis=axis,
   5570         copy=copy,
   5571         inplace=inplace,
   5572         level=level,
   5573         errors=errors,
   5574     )

File /pandas/pandas/core/generic.py:1111, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1109     return None
   1110 else:
-> 1111     return result.__finalize__(self, method="rename")

File /pandas/pandas/core/generic.py:5873, in NDFrame.__finalize__(self, other, method, **kwargs)
   5870 for name in other.attrs:
   5871     self.attrs[name] = other.attrs[name]
-> 5873 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5874 # For subclasses using _metadata.
   5875 for name in set(self._metadata) & set(other._metadata):

File /pandas/pandas/core/flags.py:94, in Flags.allows_duplicate_labels(self, value)
     92 if not value:
     93     for ax in obj.axes:
---> 94         ax._maybe_check_unique()
     96 self._allows_duplicate_labels = value

File /pandas/pandas/core/indexes/base.py:752, in Index._maybe_check_unique(self)
    749 duplicates = self._format_duplicate_message()
    750 msg += f"\n{duplicates}"
--> 752 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)
Input In [31], in <cell line: 1>()
----> 1 s1.head().rename({"a": "b"})

File /pandas/pandas/core/series.py:4997, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4990     axis = self._get_axis_number(axis)
   4992 if callable(index) or is_dict_like(index):
   4993     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
   4994     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
   4995     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
   4996     # Hashable], Callable[[Any], Hashable], None]"
-> 4997     return super()._rename(
   4998         index,  # type: ignore[arg-type]
   4999         copy=copy,
   5000         inplace=inplace,
   5001         level=level,
   5002         errors=errors,
   5003     )
   5004 else:
   5005     return self._set_name(index, inplace=inplace)

File /pandas/pandas/core/generic.py:1111, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1109     return None
   1110 else:
-> 1111     return result.__finalize__(self, method="rename")

File /pandas/pandas/core/generic.py:5873, in NDFrame.__finalize__(self, other, method, **kwargs)
   5870 for name in other.attrs:
   5871     self.attrs[name] = other.attrs[name]
-> 5873 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5874 # For subclasses using _metadata.
   5875 for name in set(self._metadata) & set(other._metadata):

File /pandas/pandas/core/flags.py:94, in Flags.allows_duplicate_labels(self, value)
     92 if not value:
     93     for ax in obj.axes:
---> 94         ax._maybe_check_unique()
     96 self._allows_duplicate_labels = value

File /pandas/pandas/core/indexes/base.py:752, in Index._maybe_check_unique(self)
    749 duplicates = self._format_duplicate_message()
    750 msg += f"\n{duplicates}"
--> 752 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.