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:4977, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
4960 @doc(
4961 NDFrame.reindex, # type: ignore[has-type]
4962 klass=_shared_doc_kwargs["klass"],
(...)
4975 tolerance=None,
4976 ) -> Series:
-> 4977 return super().reindex(
4978 index=index,
4979 method=method,
4980 copy=copy,
4981 level=level,
4982 fill_value=fill_value,
4983 limit=limit,
4984 tolerance=tolerance,
4985 )
File ~/work/pandas/pandas/pandas/core/generic.py:5521, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5518 return self._reindex_multi(axes, copy, fill_value)
5520 # perform the reindex on the axes
-> 5521 return self._reindex_axes(
5522 axes, level, limit, tolerance, method, fill_value, copy
5523 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5544, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
5541 continue
5543 ax = self._get_axis(a)
-> 5544 new_index, indexer = ax.reindex(
5545 labels, level=level, limit=limit, tolerance=tolerance, method=method
5546 )
5548 axis = self._get_axis_number(a)
5549 obj = obj._reindex_with_indexers(
5550 {axis: [new_index, indexer]},
5551 fill_value=fill_value,
5552 copy=copy,
5553 allow_dups=False,
5554 )
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4433, in Index.reindex(self, target, method, level, limit, tolerance)
4430 raise ValueError("cannot handle a non-unique multi-index!")
4431 elif not self.is_unique:
4432 # GH#42568
-> 4433 raise ValueError("cannot reindex on an axis with duplicate labels")
4434 else:
4435 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:484, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
482 df = self.copy(deep=copy and not using_copy_on_write())
483 if allows_duplicate_labels is not None:
--> 484 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
485 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: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:5518, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
5399 def rename(
5400 self,
5401 mapper: Renamer | None = None,
(...)
5409 errors: IgnoreRaise = "ignore",
5410 ) -> DataFrame | None:
5411 """
5412 Rename columns or index labels.
5413
(...)
5516 4 3 6
5517 """
-> 5518 return super()._rename(
5519 mapper=mapper,
5520 index=index,
5521 columns=columns,
5522 axis=axis,
5523 copy=copy,
5524 inplace=inplace,
5525 level=level,
5526 errors=errors,
5527 )
File ~/work/pandas/pandas/pandas/core/generic.py:1094, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1092 return None
1093 else:
-> 1094 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6169, in NDFrame.__finalize__(self, other, method, **kwargs)
6166 for name in other.attrs:
6167 self.attrs[name] = other.attrs[name]
-> 6169 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
6170 # For subclasses using _metadata.
6171 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: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:4914, in Series.rename(self, index, axis, copy, inplace, level, errors)
4907 axis = self._get_axis_number(axis)
4909 if callable(index) or is_dict_like(index):
4910 # error: Argument 1 to "_rename" of "NDFrame" has incompatible
4911 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
4912 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
4913 # Hashable], Callable[[Any], Hashable], None]"
-> 4914 return super()._rename(
4915 index, # type: ignore[arg-type]
4916 copy=copy,
4917 inplace=inplace,
4918 level=level,
4919 errors=errors,
4920 )
4921 else:
4922 return self._set_name(index, inplace=inplace, deep=copy)
File ~/work/pandas/pandas/pandas/core/generic.py:1094, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1092 return None
1093 else:
-> 1094 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6169, in NDFrame.__finalize__(self, other, method, **kwargs)
6166 for name in other.attrs:
6167 self.attrs[name] = other.attrs[name]
-> 6169 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
6170 # For subclasses using _metadata.
6171 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: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.