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:4830, in Series.reindex(self, *args, **kwargs)
   4826         raise TypeError(
   4827             "'index' passed as both positional and keyword argument"
   4828         )
   4829     kwargs.update({"index": index})
-> 4830 return super().reindex(**kwargs)

File ~/work/pandas/pandas/pandas/core/generic.py:5207, in NDFrame.reindex(self, *args, **kwargs)
   5204     return self._reindex_multi(axes, copy, fill_value)
   5206 # perform the reindex on the axes
-> 5207 return self._reindex_axes(
   5208     axes, level, limit, tolerance, method, fill_value, copy
   5209 ).__finalize__(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5222, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   5219     continue
   5221 ax = self._get_axis(a)
-> 5222 new_index, indexer = ax.reindex(
   5223     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5224 )
   5226 axis = self._get_axis_number(a)
   5227 obj = obj._reindex_with_indexers(
   5228     {axis: [new_index, indexer]},
   5229     fill_value=fill_value,
   5230     copy=copy,
   5231     allow_dups=False,
   5232 )

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4149, in Index.reindex(self, target, method, level, limit, tolerance)
   4145             indexer, _ = self.get_indexer_non_unique(target)
   4147         if not self.is_unique:
   4148             # GH#42568
-> 4149             raise ValueError("cannot reindex on an axis with duplicate labels")
   4151 target = self._wrap_reindex_result(target, indexer, preserve_names)
   4152 return target, indexer

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:444, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
    442 df = self.copy(deep=copy)
    443 if allows_duplicate_labels is not None:
--> 444     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    445 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:662, in Index._maybe_check_unique(self)
    659 duplicates = self._format_duplicate_message()
    660 msg += f"\n{duplicates}"
--> 662 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:5476, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5357 def rename(
   5358     self,
   5359     mapper: Renamer | None = None,
   (...)
   5367     errors: IgnoreRaise = "ignore",
   5368 ) -> DataFrame | None:
   5369     """
   5370     Alter axes labels.
   5371 
   (...)
   5474     4  3  6
   5475     """
-> 5476     return super()._rename(
   5477         mapper=mapper,
   5478         index=index,
   5479         columns=columns,
   5480         axis=axis,
   5481         copy=copy,
   5482         inplace=inplace,
   5483         level=level,
   5484         errors=errors,
   5485     )

File ~/work/pandas/pandas/pandas/core/generic.py:1043, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1041     return None
   1042 else:
-> 1043     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:5794, in NDFrame.__finalize__(self, other, method, **kwargs)
   5791 for name in other.attrs:
   5792     self.attrs[name] = other.attrs[name]
-> 5794 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5795 # For subclasses using _metadata.
   5796 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:662, in Index._maybe_check_unique(self)
    659 duplicates = self._format_duplicate_message()
    660 msg += f"\n{duplicates}"
--> 662 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:4768, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4761     axis = self._get_axis_number(axis)
   4763 if callable(index) or is_dict_like(index):
   4764     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
   4765     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
   4766     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
   4767     # Hashable], Callable[[Any], Hashable], None]"
-> 4768     return super()._rename(
   4769         index,  # type: ignore[arg-type]
   4770         copy=copy,
   4771         inplace=inplace,
   4772         level=level,
   4773         errors=errors,
   4774     )
   4775 else:
   4776     return self._set_name(index, inplace=inplace)

File ~/work/pandas/pandas/pandas/core/generic.py:1043, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1041     return None
   1042 else:
-> 1043     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:5794, in NDFrame.__finalize__(self, other, method, **kwargs)
   5791 for name in other.attrs:
   5792     self.attrs[name] = other.attrs[name]
-> 5794 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5795 # For subclasses using _metadata.
   5796 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:662, in Index._maybe_check_unique(self)
    659 duplicates = self._format_duplicate_message()
    660 msg += f"\n{duplicates}"
--> 662 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.