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 ~/work/pandas/pandas/pandas/core/series.py:5002, in Series.reindex(self, *args, **kwargs)
   4998         raise TypeError(
   4999             "'index' passed as both positional and keyword argument"
   5000         )
   5001     kwargs.update({"index": index})
-> 5002 return super().reindex(**kwargs)

File ~/work/pandas/pandas/pandas/core/generic.py:5241, in NDFrame.reindex(self, *args, **kwargs)
   5238     return self._reindex_multi(axes, copy, fill_value)
   5240 # perform the reindex on the axes
-> 5241 return self._reindex_axes(
   5242     axes, level, limit, tolerance, method, fill_value, copy
   5243 ).__finalize__(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5261, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   5256 new_index, indexer = ax.reindex(
   5257     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5258 )
   5260 axis = self._get_axis_number(a)
-> 5261 obj = obj._reindex_with_indexers(
   5262     {axis: [new_index, indexer]},
   5263     fill_value=fill_value,
   5264     copy=copy,
   5265     allow_dups=False,
   5266 )
   5267 # If we've made a copy once, no need to make another one
   5268 copy = False

File ~/work/pandas/pandas/pandas/core/generic.py:5307, in NDFrame._reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
   5304     indexer = ensure_platform_int(indexer)
   5306 # TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)
-> 5307 new_data = new_data.reindex_indexer(
   5308     index,
   5309     indexer,
   5310     axis=baxis,
   5311     fill_value=fill_value,
   5312     allow_dups=allow_dups,
   5313     copy=copy,
   5314 )
   5315 # If we've made a copy once, no need to make another one
   5316 copy = False

File ~/work/pandas/pandas/pandas/core/internals/managers.py:641, in BaseBlockManager.reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)
    639 # some axes don't allow reindexing with dups
    640 if not allow_dups:
--> 641     self.axes[axis]._validate_can_reindex(indexer)
    643 if axis >= self.ndim:
    644     raise IndexError("Requested axis not found in manager")

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4332, in Index._validate_can_reindex(self, indexer)
   4330 # trying to reindex on an axis with duplicates
   4331 if not self._index_as_unique and len(indexer):
-> 4332     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 ~/work/pandas/pandas/pandas/core/generic.py:441, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
    439 df = self.copy(deep=copy)
    440 if allows_duplicate_labels is not None:
--> 441     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    442 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: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 ~/work/pandas/pandas/pandas/core/frame.py:5457, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5338 def rename(
   5339     self,
   5340     mapper: Renamer | None = None,
   (...)
   5348     errors: IgnoreRaise = "ignore",
   5349 ) -> DataFrame | None:
   5350     """
   5351     Alter axes labels.
   5352 
   (...)
   5455     4  3  6
   5456     """
-> 5457     return super()._rename(
   5458         mapper=mapper,
   5459         index=index,
   5460         columns=columns,
   5461         axis=axis,
   5462         copy=copy,
   5463         inplace=inplace,
   5464         level=level,
   5465         errors=errors,
   5466     )

File ~/work/pandas/pandas/pandas/core/generic.py:1061, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1059     return None
   1060 else:
-> 1061     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:5820, in NDFrame.__finalize__(self, other, method, **kwargs)
   5817 for name in other.attrs:
   5818     self.attrs[name] = other.attrs[name]
-> 5820 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5821 # For subclasses using _metadata.
   5822 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: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 ~/work/pandas/pandas/pandas/core/series.py:4926, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4919     axis = self._get_axis_number(axis)
   4921 if callable(index) or is_dict_like(index):
   4922     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
   4923     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
   4924     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
   4925     # Hashable], Callable[[Any], Hashable], None]"
-> 4926     return super()._rename(
   4927         index,  # type: ignore[arg-type]
   4928         copy=copy,
   4929         inplace=inplace,
   4930         level=level,
   4931         errors=errors,
   4932     )
   4933 else:
   4934     return self._set_name(index, inplace=inplace)

File ~/work/pandas/pandas/pandas/core/generic.py:1061, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1059     return None
   1060 else:
-> 1061     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:5820, in NDFrame.__finalize__(self, other, method, **kwargs)
   5817 for name in other.attrs:
   5818     self.attrs[name] = other.attrs[name]
-> 5820 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5821 # For subclasses using _metadata.
   5822 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: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.