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:4918, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
   4901 @doc(
   4902     NDFrame.reindex,  # type: ignore[has-type]
   4903     klass=_shared_doc_kwargs["klass"],
   (...)
   4916     tolerance=None,
   4917 ) -> Series:
-> 4918     return super().reindex(
   4919         index=index,
   4920         method=method,
   4921         copy=copy,
   4922         level=level,
   4923         fill_value=fill_value,
   4924         limit=limit,
   4925         tolerance=tolerance,
   4926     )

File ~/work/pandas/pandas/pandas/core/generic.py:5360, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5357     return self._reindex_multi(axes, copy, fill_value)
   5359 # perform the reindex on the axes
-> 5360 return self._reindex_axes(
   5361     axes, level, limit, tolerance, method, fill_value, copy
   5362 ).__finalize__(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5375, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   5372     continue
   5374 ax = self._get_axis(a)
-> 5375 new_index, indexer = ax.reindex(
   5376     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5377 )
   5379 axis = self._get_axis_number(a)
   5380 obj = obj._reindex_with_indexers(
   5381     {axis: [new_index, indexer]},
   5382     fill_value=fill_value,
   5383     copy=copy,
   5384     allow_dups=False,
   5385 )

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4275, in Index.reindex(self, target, method, level, limit, tolerance)
   4272     raise ValueError("cannot handle a non-unique multi-index!")
   4273 elif not self.is_unique:
   4274     # GH#42568
-> 4275     raise ValueError("cannot reindex on an axis with duplicate labels")
   4276 else:
   4277     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]: 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 ~/work/pandas/pandas/pandas/core/generic.py:450, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
    448 df = self.copy(deep=copy and not using_copy_on_write())
    449 if allows_duplicate_labels is not None:
--> 450     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    451 return df

File ~/work/pandas/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 ~/work/pandas/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 ~/work/pandas/pandas/pandas/core/indexes/base.py:706, in Index._maybe_check_unique(self)
    703 duplicates = self._format_duplicate_message()
    704 msg += f"\n{duplicates}"
--> 706 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 ~/work/pandas/pandas/pandas/core/frame.py:5432, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5313 def rename(
   5314     self,
   5315     mapper: Renamer | None = None,
   (...)
   5323     errors: IgnoreRaise = "ignore",
   5324 ) -> DataFrame | None:
   5325     """
   5326     Rename columns or index labels.
   5327 
   (...)
   5430     4  3  6
   5431     """
-> 5432     return super()._rename(
   5433         mapper=mapper,
   5434         index=index,
   5435         columns=columns,
   5436         axis=axis,
   5437         copy=copy,
   5438         inplace=inplace,
   5439         level=level,
   5440         errors=errors,
   5441     )

File ~/work/pandas/pandas/pandas/core/generic.py:1042, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1040     return None
   1041 else:
-> 1042     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:5955, in NDFrame.__finalize__(self, other, method, **kwargs)
   5952 for name in other.attrs:
   5953     self.attrs[name] = other.attrs[name]
-> 5955 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5956 # For subclasses using _metadata.
   5957 for name in set(self._metadata) & set(other._metadata):

File ~/work/pandas/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 ~/work/pandas/pandas/pandas/core/indexes/base.py:706, in Index._maybe_check_unique(self)
    703 duplicates = self._format_duplicate_message()
    704 msg += f"\n{duplicates}"
--> 706 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 ~/work/pandas/pandas/pandas/core/series.py:4856, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4849     axis = self._get_axis_number(axis)
   4851 if callable(index) or is_dict_like(index):
   4852     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
   4853     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
   4854     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
   4855     # Hashable], Callable[[Any], Hashable], None]"
-> 4856     return super()._rename(
   4857         index,  # type: ignore[arg-type]
   4858         copy=copy,
   4859         inplace=inplace,
   4860         level=level,
   4861         errors=errors,
   4862     )
   4863 else:
   4864     return self._set_name(index, inplace=inplace, deep=copy)

File ~/work/pandas/pandas/pandas/core/generic.py:1042, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1040     return None
   1041 else:
-> 1042     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:5955, in NDFrame.__finalize__(self, other, method, **kwargs)
   5952 for name in other.attrs:
   5953     self.attrs[name] = other.attrs[name]
-> 5955 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5956 # For subclasses using _metadata.
   5957 for name in set(self._metadata) & set(other._metadata):

File ~/work/pandas/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 ~/work/pandas/pandas/pandas/core/indexes/base.py:706, in Index._maybe_check_unique(self)
    703 duplicates = self._format_duplicate_message()
    704 msg += f"\n{duplicates}"
--> 706 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.