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)
<ipython-input-4-18a38f6978fe> in <module>
----> 1 s1.reindex(["a", "b", "c"])
/pandas/pandas/core/series.py in reindex(self, index, **kwargs)
4343 )
4344 def reindex(self, index=None, **kwargs):
-> 4345 return super().reindex(index=index, **kwargs)
4346
4347 def drop(
/pandas/pandas/core/generic.py in reindex(self, *args, **kwargs)
4809
4810 # perform the reindex on the axes
-> 4811 return self._reindex_axes(
4812 axes, level, limit, tolerance, method, fill_value, copy
4813 ).__finalize__(self, method="reindex")
/pandas/pandas/core/generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
4830
4831 axis = self._get_axis_number(a)
-> 4832 obj = obj._reindex_with_indexers(
4833 {axis: [new_index, indexer]},
4834 fill_value=fill_value,
/pandas/pandas/core/generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
4875
4876 # TODO: speed up on homogeneous DataFrame objects
-> 4877 new_data = new_data.reindex_indexer(
4878 index,
4879 indexer,
/pandas/pandas/core/internals/managers.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice)
1299 # some axes don't allow reindexing with dups
1300 if not allow_dups:
-> 1301 self.axes[axis]._can_reindex(indexer)
1302
1303 if axis >= self.ndim:
/pandas/pandas/core/indexes/base.py in _can_reindex(self, indexer)
3475 # trying to reindex on an axis with duplicates
3476 if not self._index_as_unique and len(indexer):
-> 3477 raise ValueError("cannot reindex from a duplicate axis")
3478
3479 def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
ValueError: cannot reindex from a duplicate axis
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]: 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¶
New 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)
<ipython-input-19-11af4ee9738e> in <module>
----> 1 pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False)
/pandas/pandas/core/generic.py in set_flags(self, copy, allows_duplicate_labels)
335 df = self.copy(deep=copy)
336 if allows_duplicate_labels is not None:
--> 337 df.flags["allows_duplicate_labels"] = allows_duplicate_labels
338 return df
339
/pandas/pandas/core/flags.py in __setitem__(self, key, value)
103 if key not in self._keys:
104 raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
--> 105 setattr(self, key, value)
106
107 def __repr__(self):
/pandas/pandas/core/flags.py in allows_duplicate_labels(self, value)
90 if not value:
91 for ax in obj.axes:
---> 92 ax._maybe_check_unique()
93
94 self._allows_duplicate_labels = value
/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
469 msg += f"\n{duplicates}"
470
--> 471 raise DuplicateLabelError(msg)
472
473 @final
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=True 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)
<ipython-input-28-17c8fb0b7c7f> in <module>
----> 1 df.rename(str.upper)
/pandas/pandas/util/_decorators.py in wrapper(*args, **kwargs)
310 @wraps(func)
311 def wrapper(*args, **kwargs) -> Callable[..., Any]:
--> 312 return func(*args, **kwargs)
313
314 kind = inspect.Parameter.POSITIONAL_OR_KEYWORD
/pandas/pandas/core/frame.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
4439 4 3 6
4440 """
-> 4441 return super().rename(
4442 mapper=mapper,
4443 index=index,
/pandas/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1063 return None
1064 else:
-> 1065 return result.__finalize__(self, method="rename")
1066
1067 @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])
/pandas/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)
5433 self.attrs[name] = other.attrs[name]
5434
-> 5435 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
5436 # For subclasses using _metadata.
5437 for name in set(self._metadata) & set(other._metadata):
/pandas/pandas/core/flags.py in allows_duplicate_labels(self, value)
90 if not value:
91 for ax in obj.axes:
---> 92 ax._maybe_check_unique()
93
94 self._allows_duplicate_labels = value
/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
469 msg += f"\n{duplicates}"
470
--> 471 raise DuplicateLabelError(msg)
472
473 @final
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)
<ipython-input-31-8f09bda3af1a> in <module>
----> 1 s1.head().rename({"a": "b"})
/pandas/pandas/core/series.py in rename(self, index, axis, copy, inplace, level, errors)
4301 """
4302 if callable(index) or is_dict_like(index):
-> 4303 return super().rename(
4304 index, copy=copy, inplace=inplace, level=level, errors=errors
4305 )
/pandas/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, inplace, level, errors)
1063 return None
1064 else:
-> 1065 return result.__finalize__(self, method="rename")
1066
1067 @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])
/pandas/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)
5433 self.attrs[name] = other.attrs[name]
5434
-> 5435 self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
5436 # For subclasses using _metadata.
5437 for name in set(self._metadata) & set(other._metadata):
/pandas/pandas/core/flags.py in allows_duplicate_labels(self, value)
90 if not value:
91 for ax in obj.axes:
---> 92 ax._maybe_check_unique()
93
94 self._allows_duplicate_labels = value
/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
469 msg += f"\n{duplicates}"
470
--> 471 raise DuplicateLabelError(msg)
472
473 @final
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.