duplicates.rst

Duplicate Labels

:class:`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.

.. ipython:: python

   import pandas as pd
   import numpy as np

Consequences of Duplicate Labels

Some pandas methods (:meth:`Series.reindex` for example) just don't work with duplicates present. The output can't be determined, and so pandas raises.

.. ipython:: python
   :okexcept:

   s1 = pd.Series([0, 1, 2], index=['a', 'b', 'b'])
   s1.reindex(['a', 'b', 'c'])

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.

.. ipython:: python

   df1 = pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=['A', 'A', 'B'])
   df1

We have duplicates in the columns. If we slice 'B', we get back a Series

.. ipython:: python

   df1['B']  # a series

But slicing 'A' returns a DataFrame

.. ipython:: python

   df1['A']  # a DataFrame

This applies to row labels as well

.. ipython:: python

   df2 = pd.DataFrame({"A": [0, 1, 2]}, index=['a', 'a', 'b'])
   df2
   df2.loc['b', 'A']  # a scalar
   df2.loc['a', 'A']  # a Series

Duplicate Label Detection

You can check whether an :class:`Index` (storing the row or column labels) is unique with :attr:`Index.is_unique`:

.. ipython:: python

   df2
   df2.index.is_unique
   df2.columns.is_unique

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.

:meth:`Index.duplicated` will return a boolean ndarray indicating whether a label is repeated.

.. ipython:: python

   df2.index.duplicated()

Which can be used as a boolean filter to drop duplicate rows.

.. ipython:: python

   df2.loc[~df2.index.duplicated(), :]

If you need additional logic to handle duplicate labels, rather than just dropping the repeats, using :meth:`~DataFrame.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.

.. ipython:: python

   df2.groupby(level=0).mean()

Disallowing Duplicate Labels

.. versionadded:: 1.1.0

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 :meth:`pandas.concat`, :meth:`~DataFrame.rename`, etc.). Both :class:`Series` and :class:`DataFrame` can be created with the argument allows_duplicate_labels=False to disallow duplicate labels (the default is to allow them). If there are duplicate labels, an exception will be raised.

.. ipython:: python
   :okexcept:

   pd.Series([0, 1, 2], index=['a', 'b', 'b'], allows_duplicate_labels=False)

This applies to both row and column labels for a :class:`DataFrame`

.. ipython:: python
   :okexcept:

   pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["A", "B", "C"],
                allows_duplicate_labels=False)

This attribute can be checked or set with :attr:`~DataFrame.allows_duplicate_labels`, which indicates whether that object can have duplicate labels.

.. ipython:: python

   df = pd.DataFrame({"A": [0, 1, 2, 3]},
                     index=['x', 'y', 'X', 'Y'],
                     allows_duplicate_labels=False)
   df
   df.allows_duplicate_labels

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.allows_duplicate_labels = False  # disallow going forward

Performing an operation that introduces duplicate labels on a Series or DataFrame that disallows duplicates will raise an :class:`errors.DuplicateLabelError`.

.. ipython:: python
   :okexcept:

   df.rename(str.upper)

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.

.. ipython:: python
   :okexcept:

   s1 = pd.Series(0, index=['a', 'b'], allows_duplicate_labels=False)
   s1
   s1.head().rename({"a": "b"})

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.