Duplicate Labels — pandas 2.3.0 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:5164, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance) 5147 @doc( 5148 NDFrame.reindex, # type: ignore[has-type] 5149 klass=_shared_doc_kwargs["klass"], (...) 5162 tolerance=None, 5163 ) -> Series: -> 5164 return super().reindex( 5165 index=index, 5166 method=method, 5167 copy=copy, 5168 level=level, 5169 fill_value=fill_value, 5170 limit=limit, 5171 tolerance=tolerance, 5172 )

File ~/work/pandas/pandas/pandas/core/generic.py:5629, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance) 5626 return self._reindex_multi(axes, copy, fill_value) 5628 # perform the reindex on the axes -> 5629 return self._reindex_axes( 5630 axes, level, limit, tolerance, method, fill_value, copy 5631 ).finalize(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5652, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy) 5649 continue 5651 ax = self._get_axis(a) -> 5652 new_index, indexer = ax.reindex( 5653 labels, level=level, limit=limit, tolerance=tolerance, method=method 5654 ) 5656 axis = self._get_axis_number(a) 5657 obj = obj._reindex_with_indexers( 5658 {axis: [new_index, indexer]}, 5659 fill_value=fill_value, 5660 copy=copy, 5661 allow_dups=False, 5662 )

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4436, in Index.reindex(self, target, method, level, limit, tolerance) 4433 raise ValueError("cannot handle a non-unique multi-index!") 4434 elif not self.is_unique: 4435 # GH#42568 -> 4436 raise ValueError("cannot reindex on an axis with duplicate labels") 4437 else: 4438 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]: 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:508, in NDFrame.set_flags(self, copy, allows_duplicate_labels) 506 df = self.copy(deep=copy and not using_copy_on_write()) 507 if allows_duplicate_labels is not None: --> 508 df.flags["allows_duplicate_labels"] = allows_duplicate_labels 509 return df

File ~/work/pandas/pandas/pandas/core/flags.py:109, in Flags.setitem(self, key, value) 107 if key not in self._keys: 108 raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}") --> 109 setattr(self, key, value)

File ~/work/pandas/pandas/pandas/core/flags.py:96, in Flags.allows_duplicate_labels(self, value) 94 if not value: 95 for ax in obj.axes: ---> 96 ax._maybe_check_unique() 98 self._allows_duplicate_labels = value

File ~/work/pandas/pandas/pandas/core/indexes/base.py:716, in Index._maybe_check_unique(self) 713 duplicates = self._format_duplicate_message() 714 msg += f"\n{duplicates}" --> 716 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:5774, in DataFrame.rename(self, mapper, index, columns, axis, copy, inplace, level, errors) 5643 def rename( 5644 self, 5645 mapper: Renamer | None = None, (...) 5653 errors: IgnoreRaise = "ignore", 5654 ) -> DataFrame | None: 5655 """ 5656 Rename columns or index labels. 5657 (...) 5772 4 3 6 5773 """ -> 5774 return super()._rename( 5775 mapper=mapper, 5776 index=index, 5777 columns=columns, 5778 axis=axis, 5779 copy=copy, 5780 inplace=inplace, 5781 level=level, 5782 errors=errors, 5783 )

File ~/work/pandas/pandas/pandas/core/generic.py:1140, in NDFrame._rename(self, mapper, index, columns, axis, copy, inplace, level, errors) 1138 return None 1139 else: -> 1140 return result.finalize(self, method="rename")

File ~/work/pandas/pandas/pandas/core/generic.py:6281, in NDFrame.finalize(self, other, method, **kwargs) 6274 if other.attrs: 6275 # We want attrs propagation to have minimal performance 6276 # impact if attrs are not used; i.e. attrs is an empty dict. 6277 # One could make the deepcopy unconditionally, but a deepcopy 6278 # of an empty dict is 50x more expensive than the empty check. 6279 self.attrs = deepcopy(other.attrs) -> 6281 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels 6282 # For subclasses using _metadata. 6283 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:5101, in Series.rename(self, index, axis, copy, inplace, level, errors) 5094 axis = self._get_axis_number(axis) 5096 if callable(index) or is_dict_like(index): 5097 # error: Argument 1 to "_rename" of "NDFrame" has incompatible 5098 # type "Union[Union[Mapping[Any, Hashable], Callable[[Any], 5099 # Hashable]], Hashable, None]"; expected "Union[Mapping[Any, 5100 # Hashable], Callable[[Any], Hashable], None]" -> 5101 return super()._rename( 5102 index, # type: ignore[arg-type] 5103 copy=copy, 5104 inplace=inplace, 5105 level=level, 5106 errors=errors, 5107 ) 5108 else: 5109 return self._set_name(index, inplace=inplace, deep=copy)

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.