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)
<ipython-input-4-18a38f6978fe> in <module>
----> 1 s1.reindex(["a", "b", "c"])

/usr/lib/python3/dist-packages/pandas/core/series.py in reindex(self, *args, **kwargs)
   5092                 )
   5093             kwargs.update({"index": index})
-> 5094         return super().reindex(**kwargs)
   5095 
   5096     @overload

/usr/lib/python3/dist-packages/pandas/core/generic.py in reindex(self, *args, **kwargs)
   5287 
   5288         # perform the reindex on the axes
-> 5289         return self._reindex_axes(
   5290             axes, level, limit, tolerance, method, fill_value, copy
   5291         ).__finalize__(self, method="reindex")

/usr/lib/python3/dist-packages/pandas/core/generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   5307 
   5308             axis = self._get_axis_number(a)
-> 5309             obj = obj._reindex_with_indexers(
   5310                 {axis: [new_index, indexer]},
   5311                 fill_value=fill_value,

/usr/lib/python3/dist-packages/pandas/core/generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
   5353 
   5354             # TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)
-> 5355             new_data = new_data.reindex_indexer(
   5356                 index,
   5357                 indexer,

/usr/lib/python3/dist-packages/pandas/core/internals/managers.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)
    735         # some axes don't allow reindexing with dups
    736         if not allow_dups:
--> 737             self.axes[axis]._validate_can_reindex(indexer)
    738 
    739         if axis >= self.ndim:

/usr/lib/python3/dist-packages/pandas/core/indexes/base.py in _validate_can_reindex(self, indexer)
   4314         # trying to reindex on an axis with duplicates
   4315         if not self._index_as_unique and len(indexer):
-> 4316             raise ValueError("cannot reindex on an axis with duplicate labels")
   4317 
   4318     def reindex(

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)
<ipython-input-19-11af4ee9738e> in <module>
----> 1 pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False)

/usr/lib/python3/dist-packages/pandas/core/generic.py in set_flags(self, copy, allows_duplicate_labels)
    440         df = self.copy(deep=copy)
    441         if allows_duplicate_labels is not None:
--> 442             df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    443         return df
    444 

/usr/lib/python3/dist-packages/pandas/core/flags.py in __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)
    108 
    109     def __repr__(self) -> str:

/usr/lib/python3/dist-packages/pandas/core/flags.py in allows_duplicate_labels(self, value)
     92         if not value:
     93             for ax in obj.axes:
---> 94                 ax._maybe_check_unique()
     95 
     96         self._allows_duplicate_labels = value

/usr/lib/python3/dist-packages/pandas/core/indexes/base.py in _maybe_check_unique(self)
    749             msg += f"\n{duplicates}"
    750 
--> 751             raise DuplicateLabelError(msg)
    752 
    753     @final

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)
<ipython-input-28-17c8fb0b7c7f> in <module>
----> 1 df.rename(str.upper)

/usr/lib/python3/dist-packages/pandas/core/frame.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5571         4  3  6
   5572         """
-> 5573         return super()._rename(
   5574             mapper=mapper,
   5575             index=index,

/usr/lib/python3/dist-packages/pandas/core/generic.py in _rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1110             return None
   1111         else:
-> 1112             return result.__finalize__(self, method="rename")
   1113 
   1114     @overload

/usr/lib/python3/dist-packages/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)
   5866                 self.attrs[name] = other.attrs[name]
   5867 
-> 5868             self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5869             # For subclasses using _metadata.
   5870             for name in set(self._metadata) & set(other._metadata):

/usr/lib/python3/dist-packages/pandas/core/flags.py in allows_duplicate_labels(self, value)
     92         if not value:
     93             for ax in obj.axes:
---> 94                 ax._maybe_check_unique()
     95 
     96         self._allows_duplicate_labels = value

/usr/lib/python3/dist-packages/pandas/core/indexes/base.py in _maybe_check_unique(self)
    749             msg += f"\n{duplicates}"
    750 
--> 751             raise DuplicateLabelError(msg)
    752 
    753     @final

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)
<ipython-input-31-8f09bda3af1a> in <module>
----> 1 s1.head().rename({"a": "b"})

/usr/lib/python3/dist-packages/pandas/core/series.py in rename(self, index, axis, copy, inplace, level, errors)
   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,

/usr/lib/python3/dist-packages/pandas/core/generic.py in _rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1110             return None
   1111         else:
-> 1112             return result.__finalize__(self, method="rename")
   1113 
   1114     @overload

/usr/lib/python3/dist-packages/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)
   5866                 self.attrs[name] = other.attrs[name]
   5867 
-> 5868             self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5869             # For subclasses using _metadata.
   5870             for name in set(self._metadata) & set(other._metadata):

/usr/lib/python3/dist-packages/pandas/core/flags.py in allows_duplicate_labels(self, value)
     92         if not value:
     93             for ax in obj.axes:
---> 94                 ax._maybe_check_unique()
     95 
     96         self._allows_duplicate_labels = value

/usr/lib/python3/dist-packages/pandas/core/indexes/base.py in _maybe_check_unique(self)
    749             msg += f"\n{duplicates}"
    750 
--> 751             raise DuplicateLabelError(msg)
    752 
    753     @final

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.