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:4907, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
   4890 @doc(
   4891     NDFrame.reindex,  # type: ignore[has-type]
   4892     klass=_shared_doc_kwargs["klass"],
   (...)   4905     tolerance=None,
   4906 ) -> Series:
-> 4907     return super().reindex(
   4908         index=index,
   4909         method=method,
   4910         level=level,
   4911         fill_value=fill_value,
   4912         limit=limit,
   4913         tolerance=tolerance,
   4914         copy=copy,
   4915     )

File ~/work/pandas/pandas/pandas/core/generic.py:5437, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5434     return self._reindex_multi(axes, fill_value)
   5436 # perform the reindex on the axes
-> 5437 return self._reindex_axes(
   5438     axes, level, limit, tolerance, method, fill_value
   5439 ).__finalize__(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5459, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
   5456     continue
   5458 ax = self._get_axis(a)
-> 5459 new_index, indexer = ax.reindex(
   5460     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5461 )
   5463 axis = self._get_axis_number(a)
   5464 obj = obj._reindex_with_indexers(
   5465     {axis: [new_index, indexer]},
   5466     fill_value=fill_value,
   5467     allow_dups=False,
   5468 )

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4211, in Index.reindex(self, target, method, level, limit, tolerance)
   4208     raise ValueError("cannot handle a non-unique multi-index!")
   4209 elif not self.is_unique:
   4210     # GH#42568
-> 4211     raise ValueError("cannot reindex on an axis with duplicate labels")
   4212 else:
   4213     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]: np.int64(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#

Added 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:474, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
    472 df = self.copy(deep=False)
    473 if allows_duplicate_labels is not None:
--> 474     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    475 return df

File ~/work/pandas/pandas/pandas/core/flags.py:118, in Flags.__setitem__(self, key, value)
    116 if key not in self._keys:
    117     raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
--> 118 setattr(self, key, value)

File ~/work/pandas/pandas/pandas/core/flags.py:105, in Flags.allows_duplicate_labels(self, value)
    103 if not value:
    104     for ax in obj.axes:
--> 105         ax._maybe_check_unique()
    107 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:712, in Index._maybe_check_unique(self)
    709 duplicates = self._format_duplicate_message()
    710 msg += f"\n{duplicates}"
--> 712 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:5917, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5795 """
   5796 Rename columns or index labels.
   5797 
   (...)   5914 4  3  6
   5915 """
   5916 self._check_copy_deprecation(copy)
-> 5917 return super()._rename(
   5918     mapper=mapper,
   5919     index=index,
   5920     columns=columns,
   5921     axis=axis,
   5922     inplace=inplace,
   5923     level=level,
   5924     errors=errors,
   5925 )

File ~/work/pandas/pandas/pandas/core/generic.py:1079, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
   1077     return None
   1078 else:
-> 1079     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:6125, in NDFrame.__finalize__(self, other, method, **kwargs)
   6119 if other.attrs:
   6120     # We want attrs propagation to have minimal performance
   6121     # impact if attrs are not used; i.e. attrs is an empty dict.
   6122     # One could make the deepcopy unconditionally, but a deepcopy
   6123     # of an empty dict is 50x more expensive than the empty check.
   6124     self.attrs = deepcopy(other.attrs)
-> 6125 self.flags.allows_duplicate_labels = (
   6126     self.flags.allows_duplicate_labels
   6127     and other.flags.allows_duplicate_labels
   6128 )
   6129 # For subclasses using _metadata.
   6130 for name in set(self._metadata) & set(other._metadata):

File ~/work/pandas/pandas/pandas/core/flags.py:105, in Flags.allows_duplicate_labels(self, value)
    103 if not value:
    104     for ax in obj.axes:
--> 105         ax._maybe_check_unique()
    107 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:712, in Index._maybe_check_unique(self)
    709 duplicates = self._format_duplicate_message()
    710 msg += f"\n{duplicates}"
--> 712 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:4845, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4838     axis = self._get_axis_number(axis)
   4840 if callable(index) or is_dict_like(index):
   4841     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
   4842     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
   4843     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
   4844     # Hashable], Callable[[Any], Hashable], None]"
-> 4845     return super()._rename(
   4846         index,  # type: ignore[arg-type]
   4847         inplace=inplace,
   4848         level=level,
   4849         errors=errors,
   4850     )
   4851 else:
   4852     return self._set_name(index, inplace=inplace)

File ~/work/pandas/pandas/pandas/core/generic.py:1079, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
   1077     return None
   1078 else:
-> 1079     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:6125, in NDFrame.__finalize__(self, other, method, **kwargs)
   6119 if other.attrs:
   6120     # We want attrs propagation to have minimal performance
   6121     # impact if attrs are not used; i.e. attrs is an empty dict.
   6122     # One could make the deepcopy unconditionally, but a deepcopy
   6123     # of an empty dict is 50x more expensive than the empty check.
   6124     self.attrs = deepcopy(other.attrs)
-> 6125 self.flags.allows_duplicate_labels = (
   6126     self.flags.allows_duplicate_labels
   6127     and other.flags.allows_duplicate_labels
   6128 )
   6129 # For subclasses using _metadata.
   6130 for name in set(self._metadata) & set(other._metadata):

File ~/work/pandas/pandas/pandas/core/flags.py:105, in Flags.allows_duplicate_labels(self, value)
    103 if not value:
    104     for ax in obj.axes:
--> 105         ax._maybe_check_unique()
    107 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:712, in Index._maybe_check_unique(self)
    709 duplicates = self._format_duplicate_message()
    710 msg += f"\n{duplicates}"
--> 712 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.