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:6095, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
6091 original index entry) unfilled.
6092
6093 See the :ref:`user guide <basics.reindexing>` for more.
6094 """
-> 6095 return super().reindex(
6096 index=index,
6097 method=method,
6098 level=level,
File ~/work/pandas/pandas/pandas/core/generic.py:5594, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5590 if self._needs_reindex_multi(axes, method, level):
5591 return self._reindex_multi(axes, fill_value)
5592
5593 # perform the reindex on the axes
-> 5594 return self._reindex_axes(
5595 axes, level, limit, tolerance, method, fill_value
5596 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5616, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
5612 if labels is None:
5613 continue
5614
5615 ax = self._get_axis(a)
-> 5616 new_index, indexer = ax.reindex(
5617 labels, level=level, limit=limit, tolerance=tolerance, method=method
5618 )
5619
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4447, in Index.reindex(self, target, method, level, limit, tolerance)
4444 raise ValueError("cannot handle a non-unique multi-index!")
4445 elif not self.is_unique:
4446 # GH#42568
-> 4447 raise ValueError("cannot reindex on an axis with duplicate labels")
4448 else:
4449 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
This approach keeps the first occurrence of each label. To keep the last occurrence instead, you can pass keep="last":
In [18]: df2.loc[~df2.index.duplicated(keep="last"), :]
Out[18]:
A
a 1
b 2
If you want to remove all occurrences of duplicated labels, you can use keep=False:
In [19]: df2.loc[~df2.index.duplicated(keep=False), :]
Out[19]:
A
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 [20]: df2.groupby(level=0).mean()
Out[20]:
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 [21]: pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False)
---------------------------------------------------------------------------
DuplicateLabelError Traceback (most recent call last)
Cell In[21], 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:520, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
516 """
517 self._check_copy_deprecation(copy)
518 df = self.copy(deep=False)
519 if allows_duplicate_labels is not None:
--> 520 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
521 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:743, in Index._maybe_check_unique(self)
740 duplicates = self._format_duplicate_message()
741 msg += f"\n{duplicates}"
--> 743 raise DuplicateLabelError(msg)
DuplicateLabelError: Index has duplicates.
positions
label
b [1, 2]
This applies to both row and column labels for a DataFrame
In [22]: pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["A", "A", "B"],).set_flags(
....: allows_duplicate_labels=False
....: )
....:
---------------------------------------------------------------------------
DuplicateLabelError Traceback (most recent call last)
Cell In[22], line 1
----> 1 pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["A", "A", "B"],).set_flags(
2 allows_duplicate_labels=False
3 )
File ~/work/pandas/pandas/pandas/core/generic.py:520, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
516 """
517 self._check_copy_deprecation(copy)
518 df = self.copy(deep=False)
519 if allows_duplicate_labels is not None:
--> 520 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
521 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:743, in Index._maybe_check_unique(self)
740 duplicates = self._format_duplicate_message()
741 msg += f"\n{duplicates}"
--> 743 raise DuplicateLabelError(msg)
DuplicateLabelError: Index has duplicates.
positions
label
A [0, 1]
This attribute can be checked or set with allows_duplicate_labels,
which indicates whether that object can have duplicate labels.
In [23]: df = pd.DataFrame({"A": [0, 1, 2, 3]}, index=["x", "y", "X", "Y"]).set_flags(
....: allows_duplicate_labels=False
....: )
....:
In [24]: df
Out[24]:
A
x 0
y 1
X 2
Y 3
In [25]: df.flags.allows_duplicate_labels
Out[25]: False
DataFrame.set_flags() can be used to return a new DataFrame with attributes
like allows_duplicate_labels set to some value
In [26]: df2 = df.set_flags(allows_duplicate_labels=True)
In [27]: df2.flags.allows_duplicate_labels
Out[27]: 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 [28]: df2.flags.allows_duplicate_labels = False
In [29]: df2.flags.allows_duplicate_labels
Out[29]: 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 [30]: df.rename(str.upper)
---------------------------------------------------------------------------
DuplicateLabelError Traceback (most recent call last)
Cell In[30], line 1
----> 1 df.rename(str.upper)
File ~/work/pandas/pandas/pandas/core/frame.py:7228, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
7224 """
7225 inplace = self._check_inplace_deprecation(inplace, "rename")
7226 self._check_copy_deprecation(copy)
7227
-> 7228 return super()._rename(
7229 mapper=mapper,
7230 index=index,
7231 columns=columns,
File ~/work/pandas/pandas/pandas/core/generic.py:1117, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
1113 if inplace:
1114 self._update_inplace(result)
1115 return None
1116 else:
-> 1117 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6448, in NDFrame.__finalize__(self, other, method, **kwargs)
6444 self.attrs = deepcopy(other.attrs)
6445 # Since new objects always start with allows_duplicate_labels=True,
6446 # we only need to act when other has it set to False.
6447 if not other._flags._allows_duplicate_labels:
-> 6448 self.flags.allows_duplicate_labels = False
6449 # For subclasses using _metadata.
6450 for name in set(self._metadata) & set(other._metadata):
6451 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:743, in Index._maybe_check_unique(self)
740 duplicates = self._format_duplicate_message()
741 msg += f"\n{duplicates}"
--> 743 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 [31]: s1 = pd.Series(0, index=["a", "b"]).set_flags(allows_duplicate_labels=False)
In [32]: s1
Out[32]:
a 0
b 0
dtype: int64
In [33]: s1.head().rename({"a": "b"})
---------------------------------------------------------------------------
DuplicateLabelError Traceback (most recent call last)
Cell In[33], line 1
----> 1 s1.head().rename({"a": "b"})
File ~/work/pandas/pandas/pandas/core/series.py:5791, in Series.rename(self, index, axis, copy, inplace, level, errors)
5787 # error: Argument 1 to "_rename" of "NDFrame" has incompatible
5788 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
5789 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
5790 # Hashable], Callable[[Any], Hashable], None]"
-> 5791 return super()._rename(
5792 index, # type: ignore[arg-type]
5793 inplace=inplace,
5794 level=level,
File ~/work/pandas/pandas/pandas/core/generic.py:1117, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
1113 if inplace:
1114 self._update_inplace(result)
1115 return None
1116 else:
-> 1117 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6448, in NDFrame.__finalize__(self, other, method, **kwargs)
6444 self.attrs = deepcopy(other.attrs)
6445 # Since new objects always start with allows_duplicate_labels=True,
6446 # we only need to act when other has it set to False.
6447 if not other._flags._allows_duplicate_labels:
-> 6448 self.flags.allows_duplicate_labels = False
6449 # For subclasses using _metadata.
6450 for name in set(self._metadata) & set(other._metadata):
6451 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:743, in Index._maybe_check_unique(self)
740 duplicates = self._format_duplicate_message()
741 msg += f"\n{duplicates}"
--> 743 raise DuplicateLabelError(msg)
DuplicateLabelError: Index has duplicates.
positions
label
b [0, 1]