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Optionally disallow duplicate labels (#28394)
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Original file line number | Diff line number | Diff line change |
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.. _duplicates: | ||
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**************** | ||
Duplicate Labels | ||
**************** | ||
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: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. | ||
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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. | ||
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.. ipython:: python | ||
import pandas as pd | ||
import numpy as np | ||
Consequences of Duplicate Labels | ||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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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. | ||
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.. 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. | ||
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.. 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`` | ||
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.. ipython:: python | ||
df1['B'] # a series | ||
But slicing ``'A'`` returns a ``DataFrame`` | ||
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.. ipython:: python | ||
df1['A'] # a DataFrame | ||
This applies to row labels as well | ||
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.. 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 | ||
~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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You can check whether an :class:`Index` (storing the row or column labels) is | ||
unique with :attr:`Index.is_unique`: | ||
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.. ipython:: python | ||
df2 | ||
df2.index.is_unique | ||
df2.columns.is_unique | ||
.. note:: | ||
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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. | ||
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:meth:`Index.duplicated` will return a boolean ndarray indicating whether a | ||
label is repeated. | ||
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.. ipython:: python | ||
df2.index.duplicated() | ||
Which can be used as a boolean filter to drop duplicate rows. | ||
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.. 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. | ||
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.. ipython:: python | ||
df2.groupby(level=0).mean() | ||
.. _duplicates.disallow: | ||
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Disallowing Duplicate Labels | ||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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.. versionadded:: 1.2.0 | ||
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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` | ||
*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. | ||
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.. ipython:: python | ||
:okexcept: | ||
pd.Series( | ||
[0, 1, 2], | ||
index=['a', 'b', 'b'] | ||
).set_flags(allows_duplicate_labels=False) | ||
This applies to both row and column labels for a :class:`DataFrame` | ||
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.. ipython:: python | ||
:okexcept: | ||
pd.DataFrame( | ||
[[0, 1, 2], [3, 4, 5]], columns=["A", "B", "C"], | ||
).set_flags(allows_duplicate_labels=False) | ||
This attribute can be checked or set with :attr:`~DataFrame.flags.allows_duplicate_labels`, | ||
which indicates whether that object can have duplicate labels. | ||
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.. ipython:: python | ||
df = ( | ||
pd.DataFrame({"A": [0, 1, 2, 3]}, | ||
index=['x', 'y', 'X', 'Y']) | ||
.set_flags(allows_duplicate_labels=False) | ||
) | ||
df | ||
df.flags.allows_duplicate_labels | ||
:meth:`DataFrame.set_flags` can be used to return a new ``DataFrame`` with attributes | ||
like ``allows_duplicate_labels`` set to some value | ||
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.. ipython:: python | ||
df2 = df.set_flags(allows_duplicate_labels=True) | ||
df2.flags.allows_duplicate_labels | ||
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 | ||
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.. ipython:: python | ||
df2.flags.allows_duplicate_labels = False | ||
df2.flags.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. | ||
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.. code-block:: python | ||
>>> 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 | ||
:class:`errors.DuplicateLabelError`. | ||
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.. 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`` | ||
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Duplicate Label Propagation | ||
^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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In general, disallowing duplicates is "sticky". It's preserved through | ||
operations. | ||
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.. ipython:: python | ||
:okexcept: | ||
s1 = pd.Series(0, index=['a', 'b']).set_flags(allows_duplicate_labels=False) | ||
s1 | ||
s1.head().rename({"a": "b"}) | ||
.. warning:: | ||
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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``. |
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to_datetime, | ||
to_timedelta, | ||
# misc | ||
Flags, | ||
Grouper, | ||
factorize, | ||
unique, | ||
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