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 /pandas/pandas/core/series.py:4672, in Series.reindex(self, *args, **kwargs)
   4668         raise TypeError(
   4669             "'index' passed as both positional and keyword argument"
   4670         )
   4671     kwargs.update({"index": index})
-> 4672 return super().reindex(**kwargs)
File /pandas/pandas/core/generic.py:4966, in NDFrame.reindex(self, *args, **kwargs)
   4963     return self._reindex_multi(axes, copy, fill_value)
   4965 # perform the reindex on the axes
-> 4966 return self._reindex_axes(
   4967     axes, level, limit, tolerance, method, fill_value, copy
   4968 ).__finalize__(self, method="reindex")
File /pandas/pandas/core/generic.py:4986, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   4981 new_index, indexer = ax.reindex(
   4982     labels, level=level, limit=limit, tolerance=tolerance, method=method
   4983 )
   4985 axis = self._get_axis_number(a)
-> 4986 obj = obj._reindex_with_indexers(
   4987     {axis: [new_index, indexer]},
   4988     fill_value=fill_value,
   4989     copy=copy,
   4990     allow_dups=False,
   4991 )
   4992 # If we've made a copy once, no need to make another one
   4993 copy = False
File /pandas/pandas/core/generic.py:5032, in NDFrame._reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
   5029     indexer = ensure_platform_int(indexer)
   5031 # TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)
-> 5032 new_data = new_data.reindex_indexer(
   5033     index,
   5034     indexer,
   5035     axis=baxis,
   5036     fill_value=fill_value,
   5037     allow_dups=allow_dups,
   5038     copy=copy,
   5039 )
   5040 # If we've made a copy once, no need to make another one
   5041 copy = False
File /pandas/pandas/core/internals/managers.py:676, in BaseBlockManager.reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice, use_na_proxy)
    674 # some axes don't allow reindexing with dups
    675 if not allow_dups:
--> 676     self.axes[axis]._validate_can_reindex(indexer)
    678 if axis >= self.ndim:
    679     raise IndexError("Requested axis not found in manager")
File /pandas/pandas/core/indexes/base.py:4121, in Index._validate_can_reindex(self, indexer)
   4119 # trying to reindex on an axis with duplicates
   4120 if not self._index_as_unique and len(indexer):
-> 4121     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]: 
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]: 
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]: 
In [14]: df2.index.is_unique
Out[14]: False
In [15]: df2.columns.is_unique
Out[15]: True
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]: 
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  0.5
b  2.0
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 /pandas/pandas/core/generic.py:438, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
    436 df = self.copy(deep=copy)
    437 if allows_duplicate_labels is not None:
--> 438     df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    439 return df
File /pandas/pandas/core/flags.py:105, in Flags.__setitem__(self, key, value)
    103 if key not in self._keys:
    104     raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
--> 105 setattr(self, key, value)
File /pandas/pandas/core/flags.py:92, in Flags.allows_duplicate_labels(self, value)
     90 if not value:
     91     for ax in obj.axes:
---> 92         ax._maybe_check_unique()
     94 self._allows_duplicate_labels = value
File /pandas/pandas/core/indexes/base.py:715, in Index._maybe_check_unique(self)
    712 duplicates = self._format_duplicate_message()
    713 msg += f"\n{duplicates}"
--> 715 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]: 
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=True 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 /pandas/pandas/core/frame.py:5086, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   4967 def rename(
   4968     self,
   4969     mapper: Renamer | None = None,
   (...)
   4977     errors: str = "ignore",
   4978 ) -> DataFrame | None:
   4979     """
   4980     Alter axes labels.
   (...)
   5084     4  3  6
   5085     """
-> 5086     return super()._rename(
   5087         mapper=mapper,
   5088         index=index,
   5089         columns=columns,
   5090         axis=axis,
   5091         copy=copy,
   5092         inplace=inplace,
   5093         level=level,
   5094         errors=errors,
   5095     )
File /pandas/pandas/core/generic.py:1163, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1161     return None
   1162 else:
-> 1163     return result.__finalize__(self, method="rename")
File /pandas/pandas/core/generic.py:5541, in NDFrame.__finalize__(self, other, method, **kwargs)
   5538 for name in other.attrs:
   5539     self.attrs[name] = other.attrs[name]
-> 5541 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5542 # For subclasses using _metadata.
   5543 for name in set(self._metadata) & set(other._metadata):
File /pandas/pandas/core/flags.py:92, in Flags.allows_duplicate_labels(self, value)
     90 if not value:
     91     for ax in obj.axes:
---> 92         ax._maybe_check_unique()
     94 self._allows_duplicate_labels = value
File /pandas/pandas/core/indexes/base.py:715, in Index._maybe_check_unique(self)
    712 duplicates = self._format_duplicate_message()
    713 msg += f"\n{duplicates}"
--> 715 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 /pandas/pandas/core/series.py:4601, in Series.rename(self, index, axis, copy, inplace, level, errors)
   4598     axis = self._get_axis_number(axis)
   4600 if callable(index) or is_dict_like(index):
-> 4601     return super()._rename(
   4602         index, copy=copy, inplace=inplace, level=level, errors=errors
   4603     )
   4604 else:
   4605     return self._set_name(index, inplace=inplace)
File /pandas/pandas/core/generic.py:1163, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1161     return None
   1162 else:
-> 1163     return result.__finalize__(self, method="rename")
File /pandas/pandas/core/generic.py:5541, in NDFrame.__finalize__(self, other, method, **kwargs)
   5538 for name in other.attrs:
   5539     self.attrs[name] = other.attrs[name]
-> 5541 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5542 # For subclasses using _metadata.
   5543 for name in set(self._metadata) & set(other._metadata):
File /pandas/pandas/core/flags.py:92, in Flags.allows_duplicate_labels(self, value)
     90 if not value:
     91     for ax in obj.axes:
---> 92         ax._maybe_check_unique()
     94 self._allows_duplicate_labels = value
File /pandas/pandas/core/indexes/base.py:715, in Index._maybe_check_unique(self)
    712 duplicates = self._format_duplicate_message()
    713 msg += f"\n{duplicates}"
--> 715 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.