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:5729, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
5725 in the original DataFrame, use the ``fillna()`` method.
5726
5727 See the :ref:`user guide <basics.reindexing>` for more.
5728 """
-> 5729 return super().reindex(
5730 index=index,
5731 method=method,
5732 level=level,
File ~/work/pandas/pandas/pandas/core/generic.py:5508, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5504 if self._needs_reindex_multi(axes, method, level):
5505 return self._reindex_multi(axes, fill_value)
5506
5507 # perform the reindex on the axes
-> 5508 return self._reindex_axes(
5509 axes, level, limit, tolerance, method, fill_value
5510 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5530, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
5526 if labels is None:
5527 continue
5528
5529 ax = self._get_axis(a)
-> 5530 new_index, indexer = ax.reindex(
5531 labels, level=level, limit=limit, tolerance=tolerance, method=method
5532 )
5533
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4414, in Index.reindex(self, target, method, level, limit, tolerance)
4411 raise ValueError("cannot handle a non-unique multi-index!")
4412 elif not self.is_unique:
4413 # GH#42568
-> 4414 raise ValueError("cannot reindex on an axis with duplicate labels")
4415 else:
4416 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:455, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
451 """
452 self._check_copy_deprecation(copy)
453 df = self.copy(deep=False)
454 if allows_duplicate_labels is not None:
--> 455 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
456 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:723, in Index._maybe_check_unique(self)
720 duplicates = self._format_duplicate_message()
721 msg += f"\n{duplicates}"
--> 723 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:6431, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
6427 inplace = False
6428
6429 self._check_copy_deprecation(copy)
6430
-> 6431 return super()._rename(
6432 mapper=mapper,
6433 index=index,
6434 columns=columns,
File ~/work/pandas/pandas/pandas/core/generic.py:1061, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
1057 if inplace:
1058 self._update_inplace(result)
1059 return None
1060 else:
-> 1061 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6206, in NDFrame.__finalize__(self, other, method, **kwargs)
6202 self.attrs = deepcopy(other.attrs)
6203 # Since new objects always start with allows_duplicate_labels=True,
6204 # we only need to act when other has it set to False.
6205 if not other._flags._allows_duplicate_labels:
-> 6206 self.flags.allows_duplicate_labels = False
6207 # For subclasses using _metadata.
6208 for name in set(self._metadata) & set(other._metadata):
6209 assert isinstance(name, str)
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:723, in Index._maybe_check_unique(self)
720 duplicates = self._format_duplicate_message()
721 msg += f"\n{duplicates}"
--> 723 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:5443, in Series.rename(self, index, axis, copy, inplace, level, errors)
5439 # error: Argument 1 to "_rename" of "NDFrame" has incompatible
5440 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
5441 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
5442 # Hashable], Callable[[Any], Hashable], None]"
-> 5443 return super()._rename(
5444 index, # type: ignore[arg-type]
5445 inplace=inplace,
5446 level=level,
File ~/work/pandas/pandas/pandas/core/generic.py:1061, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
1057 if inplace:
1058 self._update_inplace(result)
1059 return None
1060 else:
-> 1061 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6206, in NDFrame.__finalize__(self, other, method, **kwargs)
6202 self.attrs = deepcopy(other.attrs)
6203 # Since new objects always start with allows_duplicate_labels=True,
6204 # we only need to act when other has it set to False.
6205 if not other._flags._allows_duplicate_labels:
-> 6206 self.flags.allows_duplicate_labels = False
6207 # For subclasses using _metadata.
6208 for name in set(self._metadata) & set(other._metadata):
6209 assert isinstance(name, str)
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:723, in Index._maybe_check_unique(self)
720 duplicates = self._format_duplicate_message()
721 msg += f"\n{duplicates}"
--> 723 raise DuplicateLabelError(msg)
DuplicateLabelError: Index has duplicates.
positions
label
b [0, 1]
Warning
Many methods do not yet propagate the allows_duplicate_labels
value through to their result. The long-term goal is for every
method that takes or returns a DataFrame or Series
to preserve it.