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"])
/pandas/pandas/core/series.py in reindex(self, index, **kwargs)
4578 )
4579 def reindex(self, index=None, **kwargs):
-> 4580 return super().reindex(index=index, **kwargs)
4581
4582 @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
/pandas/pandas/core/generic.py in reindex(self, *args, **kwargs)
4816
4817 # perform the reindex on the axes
-> 4818 return self._reindex_axes(
4819 axes, level, limit, tolerance, method, fill_value, copy
4820 ).__finalize__(self, method="reindex")
/pandas/pandas/core/generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
4837
4838 axis = self._get_axis_number(a)
-> 4839 obj = obj._reindex_with_indexers(
4840 {axis: [new_index, indexer]},
4841 fill_value=fill_value,
/pandas/pandas/core/generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
4881
4882 # TODO: speed up on homogeneous DataFrame objects
-> 4883 new_data = new_data.reindex_indexer(
4884 index,
4885 indexer,
/pandas/pandas/core/internals/managers.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice)
668 # some axes don't allow reindexing with dups
669 if not allow_dups:
--> 670 self.axes[axis]._validate_can_reindex(indexer)
671
672 if axis >= self.ndim:
/pandas/pandas/core/indexes/base.py in _validate_can_reindex(self, indexer)
3783 # trying to reindex on an axis with duplicates
3784 if not self._index_as_unique and len(indexer):
-> 3785 raise ValueError("cannot reindex from a duplicate axis")
3786
3787 def reindex(
ValueError: cannot reindex from a duplicate axis
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)
/pandas/pandas/core/generic.py in set_flags(self, copy, allows_duplicate_labels)
432 df = self.copy(deep=copy)
433 if allows_duplicate_labels is not None:
--> 434 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
435 return df
436
/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):
/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
/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
649 msg += f"\n{duplicates}"
650
--> 651 raise DuplicateLabelError(msg)
652
653 @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)
/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
/pandas/pandas/core/frame.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
5037 4 3 6
5038 """
-> 5039 return super().rename(
5040 mapper=mapper,
5041 index=index,
/pandas/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1162 return None
1163 else:
-> 1164 return result.__finalize__(self, method="rename")
1165
1166 @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])
/pandas/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)
5457 self.attrs[name] = other.attrs[name]
5458
-> 5459 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
5460 # For subclasses using _metadata.
5461 for name in set(self._metadata) & set(other._metadata):
/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
/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
649 msg += f"\n{duplicates}"
650
--> 651 raise DuplicateLabelError(msg)
652
653 @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"})
/pandas/pandas/core/series.py in rename(self, index, axis, copy, inplace, level, errors)
4516 """
4517 if callable(index) or is_dict_like(index):
-> 4518 return super().rename(
4519 index, copy=copy, inplace=inplace, level=level, errors=errors
4520 )
/pandas/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1162 return None
1163 else:
-> 1164 return result.__finalize__(self, method="rename")
1165
1166 @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])
/pandas/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)
5457 self.attrs[name] = other.attrs[name]
5458
-> 5459 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
5460 # For subclasses using _metadata.
5461 for name in set(self._metadata) & set(other._metadata):
/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
/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
649 msg += f"\n{duplicates}"
650
--> 651 raise DuplicateLabelError(msg)
652
653 @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.
© 2008–2021, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
Licensed under the 3-clause BSD License.
https://pandas.pydata.org/pandas-docs/version/1.3.4/user_guide/duplicates.html