Duplicate Labels — pandas 3.0.0.dev0+2097.gcdc5b7418e documentation (original) (raw)

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:4847, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance) 4830 @doc( 4831 NDFrame.reindex, # type: ignore[has-type] 4832 klass=_shared_doc_kwargs["klass"], (...) 4845 tolerance=None, 4846 ) -> Series: -> 4847 return super().reindex( 4848 index=index, 4849 method=method, 4850 level=level, 4851 fill_value=fill_value, 4852 limit=limit, 4853 tolerance=tolerance, 4854 copy=copy, 4855 )

File ~/work/pandas/pandas/pandas/core/generic.py:5407, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance) 5404 return self._reindex_multi(axes, fill_value) 5406 # perform the reindex on the axes -> 5407 return self._reindex_axes( 5408 axes, level, limit, tolerance, method, fill_value 5409 ).finalize(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5429, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value) 5426 continue 5428 ax = self._get_axis(a) -> 5429 new_index, indexer = ax.reindex( 5430 labels, level=level, limit=limit, tolerance=tolerance, method=method 5431 ) 5433 axis = self._get_axis_number(a) 5434 obj = obj._reindex_with_indexers( 5435 {axis: [new_index, indexer]}, 5436 fill_value=fill_value, 5437 allow_dups=False, 5438 )

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4202, in Index.reindex(self, target, method, level, limit, tolerance) 4199 raise ValueError("cannot handle a non-unique multi-index!") 4200 elif not self.is_unique: 4201 # GH#42568 -> 4202 raise ValueError("cannot reindex on an axis with duplicate labels") 4203 else: 4204 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 DataFramewith 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#

Added 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: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:118, in Flags.setitem(self, key, value) 116 if key not in self._keys: 117 raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}") --> 118 setattr(self, key, value)

File ~/work/pandas/pandas/pandas/core/flags.py:105, in Flags.allows_duplicate_labels(self, value) 103 if not value: 104 for ax in obj.axes: --> 105 ax._maybe_check_unique() 107 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:709, in Index._maybe_check_unique(self) 706 duplicates = self._format_duplicate_message() 707 msg += f"\n{duplicates}" --> 709 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 orDataFrame that disallows duplicates will raise anerrors.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:5618, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors) 5496 """ 5497 Rename columns or index labels. 5498 (...) 5615 4 3 6 5616 """ 5617 self._check_copy_deprecation(copy) -> 5618 return super()._rename( 5619 mapper=mapper, 5620 index=index, 5621 columns=columns, 5622 axis=axis, 5623 inplace=inplace, 5624 level=level, 5625 errors=errors, 5626 )

File ~/work/pandas/pandas/pandas/core/generic.py:1070, in NDFrame._rename(self, mapper, index, columns, axis, inplace, level, errors) 1068 return None 1069 else: -> 1070 return result.finalize(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:6088, in NDFrame.finalize(self, other, method, **kwargs) 6082 if other.attrs: 6083 # We want attrs propagation to have minimal performance 6084 # impact if attrs are not used; i.e. attrs is an empty dict. 6085 # One could make the deepcopy unconditionally, but a deepcopy 6086 # of an empty dict is 50x more expensive than the empty check. 6087 self.attrs = deepcopy(other.attrs) -> 6088 self.flags.allows_duplicate_labels = ( 6089 self.flags.allows_duplicate_labels 6090 and other.flags.allows_duplicate_labels 6091 ) 6092 # For subclasses using _metadata. 6093 for name in set(self._metadata) & set(other._metadata):

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:4785, in Series.rename(self, index, axis, copy, inplace, level, errors) 4778 axis = self._get_axis_number(axis) 4780 if callable(index) or is_dict_like(index): 4781 # error: Argument 1 to "_rename" of "NDFrame" has incompatible 4782 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any], 4783 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any, 4784 # Hashable], Callable[[Any], Hashable], None]" -> 4785 return super()._rename( 4786 index, # type: ignore[arg-type] 4787 inplace=inplace, 4788 level=level, 4789 errors=errors, 4790 ) 4791 else: 4792 return self._set_name(index, inplace=inplace)

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.