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:6042, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
6038 original index entry) unfilled.
6039
6040 See the :ref:`user guide <basics.reindexing>` for more.
6041 """
-> 6042 return super().reindex(
6043 index=index,
6044 method=method,
6045 level=level,
File ~/work/pandas/pandas/pandas/core/generic.py:5550, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5546 if self._needs_reindex_multi(axes, method, level):
5547 return self._reindex_multi(axes, fill_value)
5548
5549 # perform the reindex on the axes
-> 5550 return self._reindex_axes(
5551 axes, level, limit, tolerance, method, fill_value
5552 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5572, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
5568 if labels is None:
5569 continue
5570
5571 ax = self._get_axis(a)
-> 5572 new_index, indexer = ax.reindex(
5573 labels, level=level, limit=limit, tolerance=tolerance, method=method
5574 )
5575
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4440, in Index.reindex(self, target, method, level, limit, tolerance)
4437 raise ValueError("cannot handle a non-unique multi-index!")
4438 elif not self.is_unique:
4439 # GH#42568
-> 4440 raise ValueError("cannot reindex on an axis with duplicate labels")
4441 else:
4442 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:488, in NDFrame.set_flags(self, copy, allows_duplicate_labels)
484 """
485 self._check_copy_deprecation(copy)
486 df = self.copy(deep=False)
487 if allows_duplicate_labels is not None:
--> 488 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
489 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:728, in Index._maybe_check_unique(self)
725 duplicates = self._format_duplicate_message()
726 msg += f"\n{duplicates}"
--> 728 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", "B", "C"],).set_flags(
....: allows_duplicate_labels=False
....: )
....:
Out[22]:
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 [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:7167, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
7163 """
7164 inplace = self._check_inplace_deprecation(inplace, "rename")
7165 self._check_copy_deprecation(copy)
7166
-> 7167 return super()._rename(
7168 mapper=mapper,
7169 index=index,
7170 columns=columns,
File ~/work/pandas/pandas/pandas/core/generic.py:1082, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
1078 if inplace:
1079 self._update_inplace(result)
1080 return None
1081 else:
-> 1082 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6399, in NDFrame.__finalize__(self, other, method, **kwargs)
6395 self.attrs = deepcopy(other.attrs)
6396 # Since new objects always start with allows_duplicate_labels=True,
6397 # we only need to act when other has it set to False.
6398 if not other._flags._allows_duplicate_labels:
-> 6399 self.flags.allows_duplicate_labels = False
6400 # For subclasses using _metadata.
6401 for name in set(self._metadata) & set(other._metadata):
6402 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:728, in Index._maybe_check_unique(self)
725 duplicates = self._format_duplicate_message()
726 msg += f"\n{duplicates}"
--> 728 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:5738, in Series.rename(self, index, axis, copy, inplace, level, errors)
5734 # error: Argument 1 to "_rename" of "NDFrame" has incompatible
5735 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
5736 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
5737 # Hashable], Callable[[Any], Hashable], None]"
-> 5738 return super()._rename(
5739 index, # type: ignore[arg-type]
5740 inplace=inplace,
5741 level=level,
File ~/work/pandas/pandas/pandas/core/generic.py:1082, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors)
1078 if inplace:
1079 self._update_inplace(result)
1080 return None
1081 else:
-> 1082 return result.__finalize__(self, method="rename")
File ~/work/pandas/pandas/pandas/core/generic.py:6399, in NDFrame.__finalize__(self, other, method, **kwargs)
6395 self.attrs = deepcopy(other.attrs)
6396 # Since new objects always start with allows_duplicate_labels=True,
6397 # we only need to act when other has it set to False.
6398 if not other._flags._allows_duplicate_labels:
-> 6399 self.flags.allows_duplicate_labels = False
6400 # For subclasses using _metadata.
6401 for name in set(self._metadata) & set(other._metadata):
6402 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:728, in Index._maybe_check_unique(self)
725 duplicates = self._format_duplicate_message()
726 msg += f"\n{duplicates}"
--> 728 raise DuplicateLabelError(msg)
DuplicateLabelError: Index has duplicates.
positions
label
b [0, 1]