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:4997, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
4980 @doc(
4981 NDFrame.reindex, # type: ignore[has-type]
4982 klass=_shared_doc_kwargs["klass"],
(...)
4995 tolerance=None,
4996 ) -> Series:
-> 4997 return super().reindex(
4998 index=index,
4999 method=method,
5000 copy=copy,
5001 level=level,
5002 fill_value=fill_value,
5003 limit=limit,
5004 tolerance=tolerance,
5005 )
File ~/work/pandas/pandas/pandas/core/generic.py:5557, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5554 return self._reindex_multi(axes, copy, fill_value)
5556 # perform the reindex on the axes
-> 5557 return self._reindex_axes(
5558 axes, level, limit, tolerance, method, fill_value, copy
5559 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5580, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
5577 continue
5579 ax = self._get_axis(a)
-> 5580 new_index, indexer = ax.reindex(
5581 labels, level=level, limit=limit, tolerance=tolerance, method=method
5582 )
5584 axis = self._get_axis_number(a)
5585 obj = obj._reindex_with_indexers(
5586 {axis: [new_index, indexer]},
5587 fill_value=fill_value,
5588 copy=copy,
5589 allow_dups=False,
5590 )
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4383, in Index.reindex(self, target, method, level, limit, tolerance)
4380 raise ValueError("cannot handle a non-unique multi-index!")
4381 elif not self.is_unique:
4382 # GH#42568
-> 4383 raise ValueError("cannot reindex on an axis with duplicate labels")
4384 else:
4385 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:492, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
490 df = self.copy(deep=copy and not using_copy_on_write())
491 if allows_duplicate_labels is not None:
--> 492 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
493 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:692, in Index._maybe_check_unique(self)
689 duplicates = self._format_duplicate_message()
690 msg += f"\n{duplicates}"
--> 692 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:5626, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
5507 def rename(
5508 self,
5509 mapper: Renamer | None = None,
(...)
5517 errors: IgnoreRaise = "ignore",
5518 ) -> DataFrame | None:
5519 """
5520 Rename columns or index labels.
5521
(...)
5624 4 3 6
5625 """
-> 5626 return super()._rename(
5627 mapper=mapper,
5628 index=index,
5629 columns=columns,
5630 axis=axis,
5631 copy=copy,
5632 inplace=inplace,
5633 level=level,
5634 errors=errors,
5635 )
File ~/work/pandas/pandas/pandas/core/generic.py:1100, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1098 return None
1099 else:
-> 1100 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6209, in NDFrame.__finalize__(self, other, method, **kwargs)
6202 if other.attrs:
6203 # We want attrs propagation to have minimal performance
6204 # impact if attrs are not used; i.e. attrs is an empty dict.
6205 # One could make the deepcopy unconditionally, but a deepcopy
6206 # of an empty dict is 50x more expensive than the empty check.
6207 self.attrs = deepcopy(other.attrs)
-> 6209 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
6210 # For subclasses using _metadata.
6211 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:692, in Index._maybe_check_unique(self)
689 duplicates = self._format_duplicate_message()
690 msg += f"\n{duplicates}"
--> 692 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:4934, in Series.rename(self, index, axis, copy, inplace, level, errors)
4927 axis = self._get_axis_number(axis)
4929 if callable(index) or is_dict_like(index):
4930 # error: Argument 1 to "_rename" of "NDFrame" has incompatible
4931 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
4932 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
4933 # Hashable], Callable[[Any], Hashable], None]"
-> 4934 return super()._rename(
4935 index, # type: ignore[arg-type]
4936 copy=copy,
4937 inplace=inplace,
4938 level=level,
4939 errors=errors,
4940 )
4941 else:
4942 return self._set_name(index, inplace=inplace, deep=copy)
File ~/work/pandas/pandas/pandas/core/generic.py:1100, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1098 return None
1099 else:
-> 1100 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6209, in NDFrame.__finalize__(self, other, method, **kwargs)
6202 if other.attrs:
6203 # We want attrs propagation to have minimal performance
6204 # impact if attrs are not used; i.e. attrs is an empty dict.
6205 # One could make the deepcopy unconditionally, but a deepcopy
6206 # of an empty dict is 50x more expensive than the empty check.
6207 self.attrs = deepcopy(other.attrs)
-> 6209 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
6210 # For subclasses using _metadata.
6211 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:692, in Index._maybe_check_unique(self)
689 duplicates = self._format_duplicate_message()
690 msg += f"\n{duplicates}"
--> 692 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.