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:4731, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
   4714 @doc(
   4715     NDFrame.reindex,  # type: ignore[has-type]
   4716     klass=_shared_doc_kwargs["klass"],
   (...)
   4729     tolerance=None,
   4730 ) -> Series:
-> 4731     return super().reindex(
   4732         index=index,
   4733         method=method,
   4734         level=level,
   4735         fill_value=fill_value,
   4736         limit=limit,
   4737         tolerance=tolerance,
   4738         copy=copy,
   4739     )

File ~/work/pandas/pandas/pandas/core/generic.py:5348, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5345     return self._reindex_multi(axes, fill_value)
   5347 # perform the reindex on the axes
-> 5348 return self._reindex_axes(
   5349     axes, level, limit, tolerance, method, fill_value
   5350 ).__finalize__(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5370, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
   5367     continue
   5369 ax = self._get_axis(a)
-> 5370 new_index, indexer = ax.reindex(
   5371     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5372 )
   5374 axis = self._get_axis_number(a)
   5375 obj = obj._reindex_with_indexers(
   5376     {axis: [new_index, indexer]},
   5377     fill_value=fill_value,
   5378     allow_dups=False,
   5379 )

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4185, in Index.reindex(self, target, method, level, limit, tolerance)
   4182     raise ValueError("cannot handle a non-unique multi-index!")
   4183 elif not self.is_unique:
   4184     # GH#42568
-> 4185     raise ValueError("cannot reindex on an axis with duplicate labels")
   4186 else:
   4187     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#

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:464, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
    462 df = self.copy(deep=False)
    463 if allows_duplicate_labels is not None:
--> 464     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    465 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:700, in Index._maybe_check_unique(self)
    697 duplicates = self._format_duplicate_message()
    698 msg += f"\n{duplicates}"
--> 700 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:5575, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5453 """
   5454 Rename columns or index labels.
   5455 
   (...)
   5572 4  3  6
   5573 """
   5574 self._check_copy_deprecation(copy)
-> 5575 return super()._rename(
   5576     mapper=mapper,
   5577     index=index,
   5578     columns=columns,
   5579     axis=axis,
   5580     inplace=inplace,
   5581     level=level,
   5582     errors=errors,
   5583 )

File ~/work/pandas/pandas/pandas/core/generic.py:1064, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
   1062     return None
   1063 else:
-> 1064     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:6031, in NDFrame.__finalize__(self, other, method, **kwargs)
   6024 if other.attrs:
   6025     # We want attrs propagation to have minimal performance
   6026     # impact if attrs are not used; i.e. attrs is an empty dict.
   6027     # One could make the deepcopy unconditionally, but a deepcopy
   6028     # of an empty dict is 50x more expensive than the empty check.
   6029     self.attrs = deepcopy(other.attrs)
-> 6031 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   6032 # For subclasses using _metadata.
   6033 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:700, in Index._maybe_check_unique(self)
    697 duplicates = self._format_duplicate_message()
    698 msg += f"\n{duplicates}"
--> 700 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:4669, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4662     axis = self._get_axis_number(axis)
   4664 if callable(index) or is_dict_like(index):
   4665     # error: Argument 1 to "_rename" of "NDFrame" has incompatible
   4666     # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
   4667     # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
   4668     # Hashable], Callable[[Any], Hashable], None]"
-> 4669     return super()._rename(
   4670         index,  # type: ignore[arg-type]
   4671         inplace=inplace,
   4672         level=level,
   4673         errors=errors,
   4674     )
   4675 else:
   4676     return self._set_name(index, inplace=inplace)

File ~/work/pandas/pandas/pandas/core/generic.py:1064, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
   1062     return None
   1063 else:
-> 1064     return result.__finalize__(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:6031, in NDFrame.__finalize__(self, other, method, **kwargs)
   6024 if other.attrs:
   6025     # We want attrs propagation to have minimal performance
   6026     # impact if attrs are not used; i.e. attrs is an empty dict.
   6027     # One could make the deepcopy unconditionally, but a deepcopy
   6028     # of an empty dict is 50x more expensive than the empty check.
   6029     self.attrs = deepcopy(other.attrs)
-> 6031 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   6032 # For subclasses using _metadata.
   6033 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:700, in Index._maybe_check_unique(self)
    697 duplicates = self._format_duplicate_message()
    698 msg += f"\n{duplicates}"
--> 700 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.