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:5397, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
5171 def reindex( # type: ignore[override]
5172 self,
5173 index=None,
(...) 5181 tolerance=None,
5182 ) -> Series:
5183 """
5184 Conform Series to new index with optional filling logic.
5185
(...) 5395 See the :ref:`user guide <basics.reindexing>` for more.
5396 """
-> 5397 return super().reindex(
5398 index=index,
5399 method=method,
5400 level=level,
5401 fill_value=fill_value,
5402 limit=limit,
5403 tolerance=tolerance,
5404 copy=copy,
5405 )
File ~/work/pandas/pandas/pandas/core/generic.py:5432, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5429 return self._reindex_multi(axes, fill_value)
5431 # perform the reindex on the axes
-> 5432 return self._reindex_axes(
5433 axes, level, limit, tolerance, method, fill_value
5434 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5454, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
5451 continue
5453 ax = self._get_axis(a)
-> 5454 new_index, indexer = ax.reindex(
5455 labels, level=level, limit=limit, tolerance=tolerance, method=method
5456 )
5458 axis = self._get_axis_number(a)
5459 obj = obj._reindex_with_indexers(
5460 {axis: [new_index, indexer]},
5461 fill_value=fill_value,
5462 allow_dups=False,
5463 )
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4249, in Index.reindex(self, target, method, level, limit, tolerance)
4246 raise ValueError("cannot handle a non-unique multi-index!")
4247 elif not self.is_unique:
4248 # GH#42568
-> 4249 raise ValueError("cannot reindex on an axis with duplicate labels")
4250 else:
4251 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:470, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
468 df = self.copy(deep=False)
469 if allows_duplicate_labels is not None:
--> 470 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
471 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:713, in Index._maybe_check_unique(self)
710 duplicates = self._format_duplicate_message()
711 msg += f"\n{duplicates}"
--> 713 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:6037, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
5918 """
5919 Rename columns or index labels.
5920
(...) 6034 4 3 6
6035 """
6036 self._check_copy_deprecation(copy)
-> 6037 return super()._rename(
6038 mapper=mapper,
6039 index=index,
6040 columns=columns,
6041 axis=axis,
6042 inplace=inplace,
6043 level=level,
6044 errors=errors,
6045 )
File ~/work/pandas/pandas/pandas/core/generic.py:1075, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
1073 return None
1074 else:
-> 1075 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6124, in NDFrame.__finalize__(self, other, method, **kwargs)
6118 if other.attrs:
6119 # We want attrs propagation to have minimal performance
6120 # impact if attrs are not used; i.e. attrs is an empty dict.
6121 # One could make the deepcopy unconditionally, but a deepcopy
6122 # of an empty dict is 50x more expensive than the empty check.
6123 self.attrs = deepcopy(other.attrs)
-> 6124 self.flags.allows_duplicate_labels = (
6125 self.flags.allows_duplicate_labels
6126 and other.flags.allows_duplicate_labels
6127 )
6128 # For subclasses using _metadata.
6129 for name in set(self._metadata) & set(other._metadata):
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:713, in Index._maybe_check_unique(self)
710 duplicates = self._format_duplicate_message()
711 msg += f"\n{duplicates}"
--> 713 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:5125, in Series.rename(self, index, axis, copy, inplace, level, errors)
5118 axis = self._get_axis_number(axis)
5120 if callable(index) or is_dict_like(index):
5121 # error: Argument 1 to "_rename" of "NDFrame" has incompatible
5122 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
5123 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
5124 # Hashable], Callable[[Any], Hashable], None]"
-> 5125 return super()._rename(
5126 index, # type: ignore[arg-type]
5127 inplace=inplace,
5128 level=level,
5129 errors=errors,
5130 )
5131 else:
5132 return self._set_name(index, inplace=inplace)
File ~/work/pandas/pandas/pandas/core/generic.py:1075, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
1073 return None
1074 else:
-> 1075 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6124, in NDFrame.__finalize__(self, other, method, **kwargs)
6118 if other.attrs:
6119 # We want attrs propagation to have minimal performance
6120 # impact if attrs are not used; i.e. attrs is an empty dict.
6121 # One could make the deepcopy unconditionally, but a deepcopy
6122 # of an empty dict is 50x more expensive than the empty check.
6123 self.attrs = deepcopy(other.attrs)
-> 6124 self.flags.allows_duplicate_labels = (
6125 self.flags.allows_duplicate_labels
6126 and other.flags.allows_duplicate_labels
6127 )
6128 # For subclasses using _metadata.
6129 for name in set(self._metadata) & set(other._metadata):
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:713, in Index._maybe_check_unique(self)
710 duplicates = self._format_duplicate_message()
711 msg += f"\n{duplicates}"
--> 713 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.