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:4977, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
   4960 @doc(
   4961     NDFrame.reindex,  # type: ignore[has-type]
   4962     klass=_shared_doc_kwargs["klass"],
   (...)
   4975     tolerance=None,
   4976 ) -> Series:
-> 4977     return super().reindex(
   4978         index=index,
   4979         method=method,
   4980         copy=copy,
   4981         level=level,
   4982         fill_value=fill_value,
   4983         limit=limit,
   4984         tolerance=tolerance,
   4985     )

File ~/work/pandas/pandas/pandas/core/generic.py:5521, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5518     return self._reindex_multi(axes, copy, fill_value)
   5520 # perform the reindex on the axes
-> 5521 return self._reindex_axes(
   5522     axes, level, limit, tolerance, method, fill_value, copy
   5523 ).__finalize__(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5544, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   5541     continue
   5543 ax = self._get_axis(a)
-> 5544 new_index, indexer = ax.reindex(
   5545     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5546 )
   5548 axis = self._get_axis_number(a)
   5549 obj = obj._reindex_with_indexers(
   5550     {axis: [new_index, indexer]},
   5551     fill_value=fill_value,
   5552     copy=copy,
   5553     allow_dups=False,
   5554 )

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4433, in Index.reindex(self, target, method, level, limit, tolerance)
   4430     raise ValueError("cannot handle a non-unique multi-index!")
   4431 elif not self.is_unique:
   4432     # GH#42568
-> 4433     raise ValueError("cannot reindex on an axis with duplicate labels")
   4434 else:
   4435     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:484, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
    482 df = self.copy(deep=copy and not using_copy_on_write())
    483 if allows_duplicate_labels is not None:
--> 484     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    485 return df

File ~/work/pandas/pandas/pandas/core/flags.py:111, in Flags.__setitem__(self, key, value)
    109 if key not in self._keys:
    110     raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
--> 111 setattr(self, key, value)

File ~/work/pandas/pandas/pandas/core/flags.py:98, in Flags.allows_duplicate_labels(self, value)
     96 if not value:
     97     for ax in obj.axes:
---> 98         ax._maybe_check_unique()
    100 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:714, in Index._maybe_check_unique(self)
    711 duplicates = self._format_duplicate_message()
    712 msg += f"\n{duplicates}"
--> 714 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:5518, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5399 def rename(
   5400     self,
   5401     mapper: Renamer | None = None,
   (...)
   5409     errors: IgnoreRaise = "ignore",
   5410 ) -> DataFrame | None:
   5411     """
   5412     Rename columns or index labels.
   5413 
   (...)
   5516     4  3  6
   5517     """
-> 5518     return super()._rename(
   5519         mapper=mapper,
   5520         index=index,
   5521         columns=columns,
   5522         axis=axis,
   5523         copy=copy,
   5524         inplace=inplace,
   5525         level=level,
   5526         errors=errors,
   5527     )

File ~/work/pandas/pandas/pandas/core/generic.py:1094, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1092     return None
   1093 else:
-> 1094     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:6169, in NDFrame.__finalize__(self, other, method, **kwargs)
   6166 for name in other.attrs:
   6167     self.attrs[name] = other.attrs[name]
-> 6169 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   6170 # For subclasses using _metadata.
   6171 for name in set(self._metadata) & set(other._metadata):

File ~/work/pandas/pandas/pandas/core/flags.py:98, in Flags.allows_duplicate_labels(self, value)
     96 if not value:
     97     for ax in obj.axes:
---> 98         ax._maybe_check_unique()
    100 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:714, in Index._maybe_check_unique(self)
    711 duplicates = self._format_duplicate_message()
    712 msg += f"\n{duplicates}"
--> 714 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:4914, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4907     axis = self._get_axis_number(axis)
   4909 if callable(index) or is_dict_like(index):
   4910     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
   4911     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
   4912     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
   4913     # Hashable], Callable[[Any], Hashable], None]"
-> 4914     return super()._rename(
   4915         index,  # type: ignore[arg-type]
   4916         copy=copy,
   4917         inplace=inplace,
   4918         level=level,
   4919         errors=errors,
   4920     )
   4921 else:
   4922     return self._set_name(index, inplace=inplace, deep=copy)

File ~/work/pandas/pandas/pandas/core/generic.py:1094, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1092     return None
   1093 else:
-> 1094     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:6169, in NDFrame.__finalize__(self, other, method, **kwargs)
   6166 for name in other.attrs:
   6167     self.attrs[name] = other.attrs[name]
-> 6169 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   6170 # For subclasses using _metadata.
   6171 for name in set(self._metadata) & set(other._metadata):

File ~/work/pandas/pandas/pandas/core/flags.py:98, in Flags.allows_duplicate_labels(self, value)
     96 if not value:
     97     for ax in obj.axes:
---> 98         ax._maybe_check_unique()
    100 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:714, in Index._maybe_check_unique(self)
    711 duplicates = self._format_duplicate_message()
    712 msg += f"\n{duplicates}"
--> 714 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.