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:5094, in Series.reindex(self, *args, **kwargs)
5090 raise TypeError(
5091 "'index' passed as both positional and keyword argument"
5092 )
5093 kwargs.update({"index": index})
-> 5094 return super().reindex(**kwargs)
File /pandas/pandas/core/generic.py:5294, in NDFrame.reindex(self, *args, **kwargs)
5291 return self._reindex_multi(axes, copy, fill_value)
5293 # perform the reindex on the axes
-> 5294 return self._reindex_axes(
5295 axes, level, limit, tolerance, method, fill_value, copy
5296 ).__finalize__(self, method="reindex")
File /pandas/pandas/core/generic.py:5314, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
5309 new_index, indexer = ax.reindex(
5310 labels, level=level, limit=limit, tolerance=tolerance, method=method
5311 )
5313 axis = self._get_axis_number(a)
-> 5314 obj = obj._reindex_with_indexers(
5315 {axis: [new_index, indexer]},
5316 fill_value=fill_value,
5317 copy=copy,
5318 allow_dups=False,
5319 )
5320 # If we've made a copy once, no need to make another one
5321 copy = False
File /pandas/pandas/core/generic.py:5360, in NDFrame._reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
5357 indexer = ensure_platform_int(indexer)
5359 # TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)
-> 5360 new_data = new_data.reindex_indexer(
5361 index,
5362 indexer,
5363 axis=baxis,
5364 fill_value=fill_value,
5365 allow_dups=allow_dups,
5366 copy=copy,
5367 )
5368 # If we've made a copy once, no need to make another one
5369 copy = False
File /pandas/pandas/core/internals/managers.py:730, in BaseBlockManager.reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)
728 # some axes don't allow reindexing with dups
729 if not allow_dups:
--> 730 self.axes[axis]._validate_can_reindex(indexer)
732 if axis >= self.ndim:
733 raise IndexError("Requested axis not found in manager")
File /pandas/pandas/core/indexes/base.py:4350, in Index._validate_can_reindex(self, indexer)
4348 # trying to reindex on an axis with duplicates
4349 if not self._index_as_unique and len(indexer):
-> 4350 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]:
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)
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:443, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
441 df = self.copy(deep=copy)
442 if allows_duplicate_labels is not None:
--> 443 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
444 return df
File /pandas/pandas/core/flags.py:107, in Flags.__setitem__(self, key, value)
105 if key not in self._keys:
106 raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
--> 107 setattr(self, key, value)
File /pandas/pandas/core/flags.py:94, in Flags.allows_duplicate_labels(self, value)
92 if not value:
93 for ax in obj.axes:
---> 94 ax._maybe_check_unique()
96 self._allows_duplicate_labels = value
File /pandas/pandas/core/indexes/base.py:752, in Index._maybe_check_unique(self)
749 duplicates = self._format_duplicate_message()
750 msg += f"\n{duplicates}"
--> 752 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)
Input In [28], in <cell line: 1>()
----> 1 df.rename(str.upper)
File /pandas/pandas/core/frame.py:5565, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
5446 def rename(
5447 self,
5448 mapper: Renamer | None = None,
(...)
5456 errors: IgnoreRaise = "ignore",
5457 ) -> DataFrame | None:
5458 """
5459 Alter axes labels.
5460
(...)
5563 4 3 6
5564 """
-> 5565 return super()._rename(
5566 mapper=mapper,
5567 index=index,
5568 columns=columns,
5569 axis=axis,
5570 copy=copy,
5571 inplace=inplace,
5572 level=level,
5573 errors=errors,
5574 )
File /pandas/pandas/core/generic.py:1111, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1109 return None
1110 else:
-> 1111 return result.__finalize__(self, method="rename")
File /pandas/pandas/core/generic.py:5873, in NDFrame.__finalize__(self, other, method, **kwargs)
5870 for name in other.attrs:
5871 self.attrs[name] = other.attrs[name]
-> 5873 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
5874 # For subclasses using _metadata.
5875 for name in set(self._metadata) & set(other._metadata):
File /pandas/pandas/core/flags.py:94, in Flags.allows_duplicate_labels(self, value)
92 if not value:
93 for ax in obj.axes:
---> 94 ax._maybe_check_unique()
96 self._allows_duplicate_labels = value
File /pandas/pandas/core/indexes/base.py:752, in Index._maybe_check_unique(self)
749 duplicates = self._format_duplicate_message()
750 msg += f"\n{duplicates}"
--> 752 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:4997, in Series.rename(self, index, axis, copy, inplace, level, errors)
4990 axis = self._get_axis_number(axis)
4992 if callable(index) or is_dict_like(index):
4993 # error: Argument 1 to "_rename" of "NDFrame" has incompatible
4994 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
4995 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
4996 # Hashable], Callable[[Any], Hashable], None]"
-> 4997 return super()._rename(
4998 index, # type: ignore[arg-type]
4999 copy=copy,
5000 inplace=inplace,
5001 level=level,
5002 errors=errors,
5003 )
5004 else:
5005 return self._set_name(index, inplace=inplace)
File /pandas/pandas/core/generic.py:1111, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1109 return None
1110 else:
-> 1111 return result.__finalize__(self, method="rename")
File /pandas/pandas/core/generic.py:5873, in NDFrame.__finalize__(self, other, method, **kwargs)
5870 for name in other.attrs:
5871 self.attrs[name] = other.attrs[name]
-> 5873 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
5874 # For subclasses using _metadata.
5875 for name in set(self._metadata) & set(other._metadata):
File /pandas/pandas/core/flags.py:94, in Flags.allows_duplicate_labels(self, value)
92 if not value:
93 for ax in obj.axes:
---> 94 ax._maybe_check_unique()
96 self._allows_duplicate_labels = value
File /pandas/pandas/core/indexes/base.py:752, in Index._maybe_check_unique(self)
749 duplicates = self._format_duplicate_message()
750 msg += f"\n{duplicates}"
--> 752 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.