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:5693, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
5475 def reindex( # type: ignore[override]
5476 self,
5477 index=None,
(...) 5485 tolerance=None,
5486 ) -> Series:
5487 """
5488 Conform Series to new index with optional filling logic.
5489
(...) 5691 See the :ref:`user guide <basics.reindexing>` for more.
5692 """
-> 5693 return super().reindex(
5694 index=index,
5695 method=method,
5696 level=level,
5697 fill_value=fill_value,
5698 limit=limit,
5699 tolerance=tolerance,
5700 copy=copy,
5701 )
File ~/work/pandas/pandas/pandas/core/generic.py:5492, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5489 return self._reindex_multi(axes, fill_value)
5491 # perform the reindex on the axes
-> 5492 return self._reindex_axes(
5493 axes, level, limit, tolerance, method, fill_value
5494 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5514, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
5511 continue
5513 ax = self._get_axis(a)
-> 5514 new_index, indexer = ax.reindex(
5515 labels, level=level, limit=limit, tolerance=tolerance, method=method
5516 )
5518 axis = self._get_axis_number(a)
5519 obj = obj._reindex_with_indexers(
5520 {axis: [new_index, indexer]},
5521 fill_value=fill_value,
5522 allow_dups=False,
5523 )
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4347, in Index.reindex(self, target, method, level, limit, tolerance)
4344 raise ValueError("cannot handle a non-unique multi-index!")
4345 elif not self.is_unique:
4346 # GH#42568
-> 4347 raise ValueError("cannot reindex on an axis with duplicate labels")
4348 else:
4349 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#
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:450, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
448 df = self.copy(deep=False)
449 if allows_duplicate_labels is not None:
--> 450 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
451 return df
File ~/work/pandas/pandas/pandas/core/flags.py:121, in Flags.__setitem__(self, key, value)
119 if key not in self._keys:
120 raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
--> 121 setattr(self, key, value)
File ~/work/pandas/pandas/pandas/core/flags.py:108, in Flags.allows_duplicate_labels(self, value)
106 if not value:
107 for ax in obj.axes:
--> 108 ax._maybe_check_unique()
110 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:6348, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
6229 """
6230 Rename columns or index labels.
6231
(...) 6345 4 3 6
6346 """
6347 self._check_copy_deprecation(copy)
-> 6348 return super()._rename(
6349 mapper=mapper,
6350 index=index,
6351 columns=columns,
6352 axis=axis,
6353 inplace=inplace,
6354 level=level,
6355 errors=errors,
6356 )
File ~/work/pandas/pandas/pandas/core/generic.py:1056, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
1054 return None
1055 else:
-> 1056 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6190, in NDFrame.__finalize__(self, other, method, **kwargs)
6187 # Since new objects always start with allows_duplicate_labels=True,
6188 # we only need to act when other has it set to False.
6189 if not other._flags._allows_duplicate_labels:
-> 6190 self.flags.allows_duplicate_labels = False
6191 # For subclasses using _metadata.
6192 for name in set(self._metadata) & set(other._metadata):
File ~/work/pandas/pandas/pandas/core/flags.py:108, in Flags.allows_duplicate_labels(self, value)
106 if not value:
107 for ax in obj.axes:
--> 108 ax._maybe_check_unique()
110 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:5407, in Series.rename(self, index, axis, copy, inplace, level, errors)
5400 axis = self._get_axis_number(axis)
5402 if callable(index) or is_dict_like(index):
5403 # error: Argument 1 to "_rename" of "NDFrame" has incompatible
5404 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
5405 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
5406 # Hashable], Callable[[Any], Hashable], None]"
-> 5407 return super()._rename(
5408 index, # type: ignore[arg-type]
5409 inplace=inplace,
5410 level=level,
5411 errors=errors,
5412 )
5413 else:
5414 return self._set_name(index, inplace=inplace)
File ~/work/pandas/pandas/pandas/core/generic.py:1056, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
1054 return None
1055 else:
-> 1056 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6190, in NDFrame.__finalize__(self, other, method, **kwargs)
6187 # Since new objects always start with allows_duplicate_labels=True,
6188 # we only need to act when other has it set to False.
6189 if not other._flags._allows_duplicate_labels:
-> 6190 self.flags.allows_duplicate_labels = False
6191 # For subclasses using _metadata.
6192 for name in set(self._metadata) & set(other._metadata):
File ~/work/pandas/pandas/pandas/core/flags.py:108, in Flags.allows_duplicate_labels(self, value)
106 if not value:
107 for ax in obj.axes:
--> 108 ax._maybe_check_unique()
110 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.