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Most pandas operations return copies of the ``Series``/``DataFrame``. To make the changes "stick",
you'll need to either assign to a new variable:
.. code-block:: python
sorted_df = df.sort_values("col1")
or overwrite the original one:
.. code-block:: python
df = df.sort_values("col1")
.. note::
You will see an ``inplace=True`` or ``copy=False`` keyword argument available for
some methods:
.. code-block:: python
df.replace(5, inplace=True)
There is an active discussion about deprecating and removing ``inplace`` and ``copy`` for
most methods (e.g. ``dropna``) except for a very small subset of methods
(including ``replace``). Both keywords won't be
necessary anymore in the context of Copy-on-Write. The proposal can be found
`here <https://github.com/pandas-dev/pandas/pull/51466>`_.
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