.. _copy_on_write:

{{ header }}

*******************
Copy-on-Write (CoW)
*******************

.. note::

    Copy-on-Write is now the default with pandas 3.0.

Copy-on-Write was first introduced in version 1.5.0. Starting from version 2.0 most of the
optimizations that become possible through CoW are implemented and supported. All possible
optimizations are supported starting from pandas 2.1.

CoW will lead to more predictable behavior since it is not possible to update more than
one object with one statement, e.g. indexing operations or methods won't have side-effects. Additionally, through
delaying copies as long as possible, the average performance and memory usage will improve.

Previous behavior
-----------------

pandas indexing behavior is tricky to understand. Some operations return views while
other return copies. Depending on the result of the operation, mutating one object
might accidentally mutate another:

.. code-block:: ipython

    In [1]: df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    In [2]: subset = df["foo"]
    In [3]: subset.iloc[0] = 100
    In [4]: df
    Out[4]:
       foo  bar
    0  100    4
    1    2    5
    2    3    6


Mutating ``subset``, e.g. updating its values, also updated ``df``. The exact behavior was
hard to predict. Copy-on-Write solves accidentally modifying more than one object,
it explicitly disallows this. ``df`` is unchanged:

.. ipython:: python

    df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    subset = df["foo"]
    subset.iloc[0] = 100
    df

The following sections will explain what this means and how it impacts existing
applications.

.. _copy_on_write.migration_guide:

Migrating to Copy-on-Write
--------------------------

Copy-on-Write is the default and only mode in pandas 3.0. This means that users
need to migrate their code to be compliant with CoW rules.

The default mode in pandas < 3.0 raises warnings for certain cases that will actively
change behavior and thus change user intended behavior.

pandas 2.2 has a warning mode

.. code-block:: python

    pd.options.mode.copy_on_write = "warn"

that will warn for every operation that will change behavior with CoW. We expect this mode
to be very noisy, since many cases that we don't expect that they will influence users will
also emit a warning. We recommend checking this mode and analyzing the warnings, but it is
not necessary to address all of these warning. The first two items of the following lists
are the only cases that need to be addressed to make existing code work with CoW.

The following few items describe the user visible changes:

**Chained assignment will never work**

``loc`` should be used as an alternative. Check the
:ref:`chained assignment section <copy_on_write_chained_assignment>` for more details.

**Accessing the underlying array of a pandas object will return a read-only view**

.. ipython:: python

    ser = pd.Series([1, 2, 3])
    ser.to_numpy()

This example returns a NumPy array that is a view of the Series object. This view can
be modified and thus also modify the pandas object. This is not compliant with CoW
rules. The returned array is set to non-writeable to protect against this behavior.
Creating a copy of this array allows modification. You can also make the array
writeable again if you don't care about the pandas object anymore.

See the section about :ref:`read-only NumPy arrays <copy_on_write_read_only_na>`
for more details.

**Only one pandas object is updated at once**

The following code snippet updated both ``df`` and ``subset`` without CoW:

.. code-block:: ipython

    In [1]: df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    In [2]: subset = df["foo"]
    In [3]: subset.iloc[0] = 100
    In [4]: df
    Out[4]:
       foo  bar
    0  100    4
    1    2    5
    2    3    6

This is not possible anymore with CoW, since the CoW rules explicitly forbid this.
This includes updating a single column as a :class:`Series` and relying on the change
propagating back to the parent :class:`DataFrame`.
This statement can be rewritten into a single statement with ``loc`` or ``iloc`` if
this behavior is necessary. :meth:`DataFrame.where` is another suitable alternative
for this case.

Updating a column selected from a :class:`DataFrame` with an inplace method will
also not work anymore.

.. ipython:: python
    :okwarning:

    df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    df["foo"].replace(1, 5, inplace=True)
    df

This is another form of chained assignment. This can generally be rewritten in 2
different forms:

.. ipython:: python

    df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    df.replace({"foo": {1: 5}}, inplace=True)
    df

A different alternative would be to not use ``inplace``:

.. ipython:: python

    df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    df["foo"] = df["foo"].replace(1, 5)
    df

**Constructors now copy NumPy arrays by default**

The Series and DataFrame constructors now copies a NumPy array by default when not
otherwise specified. This was changed to avoid mutating a pandas object when the
NumPy array is changed inplace outside of pandas. You can set ``copy=False`` to
avoid this copy.

Description
-----------

CoW means that any DataFrame or Series derived from another in any way always
behaves as a copy. As a consequence, we can only change the values of an object
through modifying the object itself. CoW disallows updating a DataFrame or a Series
that shares data with another DataFrame or Series object inplace.

This avoids side-effects when modifying values and hence, most methods can avoid
actually copying the data and only trigger a copy when necessary.

The following example will operate inplace:

.. ipython:: python

    df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    df.iloc[0, 0] = 100
    df

The object ``df`` does not share any data with any other object and hence no
copy is triggered when updating the values. In contrast, the following operation
triggers a copy of the data under CoW:


.. ipython:: python

    df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    df2 = df.reset_index(drop=True)
    df2.iloc[0, 0] = 100

    df
    df2

``reset_index`` returns a lazy copy with CoW while it copies the data without CoW.
Since both objects, ``df`` and ``df2`` share the same data, a copy is triggered
when modifying ``df2``. The object ``df`` still has the same values as initially
while ``df2`` was modified.

If the object ``df`` isn't needed anymore after performing the ``reset_index`` operation,
you can emulate an inplace-like operation through assigning the output of ``reset_index``
to the same variable:

.. ipython:: python

    df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    df = df.reset_index(drop=True)
    df.iloc[0, 0] = 100
    df

The initial object gets out of scope as soon as the result of ``reset_index`` is
reassigned and hence ``df`` does not share data with any other object. No copy
is necessary when modifying the object. This is generally true for all methods
listed in :ref:`Copy-on-Write optimizations <copy_on_write.optimizations>`.

Previously, when operating on views, the view and the parent object was modified:

.. code-block:: ipython

    In [1]: df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    In [2]: subset = df["foo"]
    In [3]: subset.iloc[0] = 100
    In [4]: df
    Out[4]:
       foo  bar
    0  100    4
    1    2    5
    2    3    6

CoW triggers a copy when ``df`` is changed to avoid mutating ``view`` as well:

.. ipython:: python

    df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    view = df[:]
    df.iloc[0, 0] = 100

    df
    view

.. _copy_on_write_chained_assignment:

Chained Assignment
------------------

Chained assignment references a technique where an object is updated through
two subsequent indexing operations, e.g.

.. code-block:: ipython

    In [1]: df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    In [2]: df["foo"][df["bar"] > 5] = 100
    In [3]: df
    Out[3]:
       foo  bar
    0    1    4
    1    2    5
    2  100    6

The column ``foo`` was updated where the column ``bar`` is greater than 5.
This violated the CoW principles though, because it would have to modify the
view ``df["foo"]`` and ``df`` in one step. Hence, chained assignment will
consistently never work and raise a ``ChainedAssignmentError`` warning
with CoW enabled:

.. ipython:: python
    :okwarning:

    df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
    df["foo"][df["bar"] > 5] = 100

With copy on write this can be done by using ``loc``.

.. ipython:: python

    df.loc[df["bar"] > 5, "foo"] = 100

.. _copy_on_write_read_only_na:

Read-only NumPy arrays
----------------------

Accessing the underlying NumPy array of a DataFrame will return a read-only array if the array
shares data with the initial DataFrame:

The array is a copy if the initial DataFrame consists of more than one array:

.. ipython:: python

    df = pd.DataFrame({"a": [1, 2], "b": [1.5, 2.5]})
    df.to_numpy()

The array shares data with the DataFrame if the DataFrame consists of only one NumPy array:

.. ipython:: python

    df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
    df.to_numpy()

This array is read-only, which means that it can't be modified inplace:

.. ipython:: python
    :okexcept:

    arr = df.to_numpy()
    arr[0, 0] = 100

The same holds true for a Series, since a Series always consists of a single array.

There are two potential solutions to this:

- Trigger a copy manually if you want to avoid updating DataFrames that share memory with your array.
- Make the array writeable. This is a more performant solution but circumvents Copy-on-Write rules, so
  it should be used with caution.

.. ipython:: python

    arr = df.to_numpy()
    arr.flags.writeable = True
    arr[0, 0] = 100
    arr

Patterns to avoid
-----------------

No defensive copy will be performed if two objects share the same data while
you are modifying one object inplace.

.. ipython:: python

    df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
    df2 = df.reset_index(drop=True)
    df2.iloc[0, 0] = 100

This creates two objects that share data and thus the setitem operation will trigger a
copy. This is not necessary if the initial object ``df`` isn't needed anymore.
Simply reassigning to the same variable will invalidate the reference that is
held by the object.

.. ipython:: python

    df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
    df = df.reset_index(drop=True)
    df.iloc[0, 0] = 100

No copy is necessary in this example.
Creating multiple references keeps unnecessary references alive
and thus will hurt performance with Copy-on-Write.

.. _copy_on_write.optimizations:

Copy-on-Write optimizations
---------------------------

A new lazy copy mechanism that defers the copy until the object in question is modified
and only if this object shares data with another object. This mechanism was added to
methods that don't require a copy of the underlying data. Popular examples are :meth:`DataFrame.drop` for ``axis=1``
and :meth:`DataFrame.rename`.

These methods return views when Copy-on-Write is enabled, which provides a significant
performance improvement compared to the regular execution.
