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"])

~/work/pandas/pandas/pandas/core/series.py in reindex(self, index, **kwargs)
   4586     )
   4587     def reindex(self, index=None, **kwargs):
-> 4588         return super().reindex(index=index, **kwargs)
   4589 
   4590     @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])

~/work/pandas/pandas/pandas/core/generic.py in reindex(self, *args, **kwargs)
   4846 
   4847         # perform the reindex on the axes
-> 4848         return self._reindex_axes(
   4849             axes, level, limit, tolerance, method, fill_value, copy
   4850         ).__finalize__(self, method="reindex")

~/work/pandas/pandas/pandas/core/generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   4866 
   4867             axis = self._get_axis_number(a)
-> 4868             obj = obj._reindex_with_indexers(
   4869                 {axis: [new_index, indexer]},
   4870                 fill_value=fill_value,

~/work/pandas/pandas/pandas/core/generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
   4912 
   4913             # TODO: speed up on homogeneous DataFrame objects
-> 4914             new_data = new_data.reindex_indexer(
   4915                 index,
   4916                 indexer,

~/work/pandas/pandas/pandas/core/internals/managers.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice, use_na_proxy)
    652         # some axes don't allow reindexing with dups
    653         if not allow_dups:
--> 654             self.axes[axis]._validate_can_reindex(indexer)
    655 
    656         if axis >= self.ndim:

~/work/pandas/pandas/pandas/core/indexes/base.py in _validate_can_reindex(self, indexer)
   3920         # trying to reindex on an axis with duplicates
   3921         if not self._index_as_unique and len(indexer):
-> 3922             raise ValueError("cannot reindex on an axis with duplicate labels")
   3923 
   3924     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)

~/work/pandas/pandas/pandas/core/generic.py in set_flags(self, copy, allows_duplicate_labels)
    435         df = self.copy(deep=copy)
    436         if allows_duplicate_labels is not None:
--> 437             df.flags["allows_duplicate_labels"] = allows_duplicate_labels
    438         return df
    439 

~/work/pandas/pandas/pandas/core/flags.py in __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)
    106 
    107     def __repr__(self):

~/work/pandas/pandas/pandas/core/flags.py in allows_duplicate_labels(self, value)
     90         if not value:
     91             for ax in obj.axes:
---> 92                 ax._maybe_check_unique()
     93 
     94         self._allows_duplicate_labels = value

~/work/pandas/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
    692             msg += f"\n{duplicates}"
    693 
--> 694             raise DuplicateLabelError(msg)
    695 
    696     @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=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)
<ipython-input-28-17c8fb0b7c7f> in <module>
----> 1 df.rename(str.upper)

~/work/pandas/pandas/pandas/util/_decorators.py in wrapper(*args, **kwargs)
    322         @wraps(func)
    323         def wrapper(*args, **kwargs) -> Callable[..., Any]:
--> 324             return func(*args, **kwargs)
    325 
    326         kind = inspect.Parameter.POSITIONAL_OR_KEYWORD

~/work/pandas/pandas/pandas/core/frame.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   5074         4  3  6
   5075         """
-> 5076         return super().rename(
   5077             mapper=mapper,
   5078             index=index,

~/work/pandas/pandas/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1165             return None
   1166         else:
-> 1167             return result.__finalize__(self, method="rename")
   1168 
   1169     @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])

~/work/pandas/pandas/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)
   5422                 self.attrs[name] = other.attrs[name]
   5423 
-> 5424             self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5425             # For subclasses using _metadata.
   5426             for name in set(self._metadata) & set(other._metadata):

~/work/pandas/pandas/pandas/core/flags.py in allows_duplicate_labels(self, value)
     90         if not value:
     91             for ax in obj.axes:
---> 92                 ax._maybe_check_unique()
     93 
     94         self._allows_duplicate_labels = value

~/work/pandas/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
    692             msg += f"\n{duplicates}"
    693 
--> 694             raise DuplicateLabelError(msg)
    695 
    696     @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"})

~/work/pandas/pandas/pandas/core/series.py in rename(self, index, axis, copy, inplace, level, errors)
   4524         """
   4525         if callable(index) or is_dict_like(index):
-> 4526             return super().rename(
   4527                 index, copy=copy, inplace=inplace, level=level, errors=errors
   4528             )

~/work/pandas/pandas/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
   1165             return None
   1166         else:
-> 1167             return result.__finalize__(self, method="rename")
   1168 
   1169     @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])

~/work/pandas/pandas/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)
   5422                 self.attrs[name] = other.attrs[name]
   5423 
-> 5424             self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
   5425             # For subclasses using _metadata.
   5426             for name in set(self._metadata) & set(other._metadata):

~/work/pandas/pandas/pandas/core/flags.py in allows_duplicate_labels(self, value)
     90         if not value:
     91             for ax in obj.axes:
---> 92                 ax._maybe_check_unique()
     93 
     94         self._allows_duplicate_labels = value

~/work/pandas/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
    692             msg += f"\n{duplicates}"
    693 
--> 694             raise DuplicateLabelError(msg)
    695 
    696     @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.