.. _whatsnew_300.enhancements.copy_on_write:

Copy-on-Write

Consistent copy/view behaviour withCopy-on-Write

The new "copy-on-write" behaviour in pandas 3.0 brings changes in behavior in

how pandas operates with respect to copies and views. A summary of the changes:

confusing).

Because every single indexing step now behaves as a copy, this also means that

"chained assignment" (updating a DataFrame with multiple setitem steps) will

stop working. Because this now consistently never works, the

``SettingWithCopyWarning`` is removed.

**"chained assignment"** (updating a DataFrame with multiple setitem steps)

**will stop working**. Because this now consistently never works, the

``SettingWithCopyWarning`` is removed, and defensive ``.copy()`` calls to

silence the warning are no longer needed.

The new behavioral semantics are explained in more detail in the

:ref:`user guide about Copy-on-Write<copy_on_write>`.

.. _whatsnew_300.enhancements.col:

``pd.col`` syntaxcan now be used in:meth:`DataFrame.assign` and:meth:`DataFrame.loc`

Initial support for``pd.col()`` syntaxto create expressions

You can now use ``pd.col`` to create callables for use in dataframe methods which accept them. For example, if you have a dataframe

This release introduces:func:`col` to refer to a DataFrame column by name

and build up expressions.

This can be used as a simplified syntax to create callables for use in

methods such as:meth:`DataFrame.assign`. In practice, where you would

have to use a ``lambda`` function before, you can now use ``pd.col()``

instead.

For example, if you have a dataframe

..ipython::python

The expression object returned by:func:`col` supports all standard operators

(like ``+``, ``-``, ``*``, ``/``, etc.) and all Series methods and namespaces

(like ``pd.col("name").sum()``, ``pd.col("name").str.upper()``, etc.).

Currently, the ``pd.col()`` syntax can be used in any place which accepts a

callable that takes the calling DataFrame as first argument and returns a

Series, like ``lambda df: df[col_name]``.

This includes:meth:`DataFrame.assign`,:meth:`DataFrame.loc`, and getitem/setitem.

It is expected that the support for ``pd.col()`` will be expanded to more methods

in future releases.

New Deprecation Policy

pandas 3.0.0 introduces a new 3-stage deprecation policy: using ``DeprecationWarning`` initially, then switching to ``FutureWarning`` for broader visibility in the last minor version before the next major release, and then removal of the deprecated functionality in the major release. This was done to give downstream packages more time to adjust to pandas deprecations, which should reduce the amount of warnings that a user gets from code that isn't theirs. See `PDEP 17<https://pandas.pydata.org/pdeps/0017-backwards-compatibility-and-deprecation-policy.html>`_ for more details.

cls,

data=None,

dtype=None,

copy:bool=False,

copy:bool|None=None,

name=None,

tupleize_cols:bool=True,

)->Self:

ifnotcopyandisinstance(data, (ABCSeries,Index)):

refs=data._references

ifisinstance(data, (ExtensionArray,np.ndarray)):

ifcopyisnotFalse:

ifdtypeisNoneorastype_is_view(data.dtype,dtype):

data=data.copy()

ifisinstance(data, (range,RangeIndex)):

result=RangeIndex(start=data,copy=copy,name=name)

result=RangeIndex(start=data,copy=bool(copy),name=name)

ifdtypeisnotNone:

returnresult.astype(dtype,copy=False)

data=com.asarray_tuplesafe(data,dtype=_dtype_obj)

try:

arr=sanitize_array(data,None,dtype=dtype,copy=copy)

arr=sanitize_array(data,None,dtype=dtype,copy=bool(copy))

exceptValueErroraserr:

if"index must be specified when data is not list-like"instr(err):

raisecls._raise_scalar_data_error(data)fromerr

DataFrame,

Index,

Series,

array,

)

importpandas._testingastm

frompandas.tests.copy_view.utilimportget_array

idx=Index([1,2,3])

result=idx.values

assertresult.flags.writeableisFalse

deftest_constructor_copy_input_ndarray_default():

arr=np.array([0,1])

idx=Index(arr)

assertnotnp.shares_memory(arr,get_array(idx))

deftest_constructor_copy_input_ea_default():

arr=array([0,1],dtype="Int64")

idx=Index(arr)

assertnottm.shares_memory(arr,idx.array)

deftest_series_from_temporary_index_readonly_data():

arr=np.array([0,1],dtype=np.dtype(np.int8))

arr.flags.writeable=False

ser=Series(Index(arr))

assertnotnp.shares_memory(arr,get_array(ser))

assertser._mgr._has_no_reference(0)

ser[[False,True]]=np.array([0,2],dtype=np.dtype(np.int8))

expected=Series([0,2],dtype=np.dtype(np.int8))

tm.assert_series_equal(ser,expected)

pandas 3.0 introduces several major enhancements:

-**Dedicated string data type by default**:String columns are now inferred as

-**Dedicated string data type by default**:string columns are now inferred as

the new`str` dtype instead of`object`, providing better performance and type

safety

-**Consistent copy/view behaviour with Copy-on-Write (CoW)** (a.k.a. getting

rid of the SettingWithCopyWarning):More predictable and consistent behavior

rid of the SettingWithCopyWarning):more predictable and consistent behavior

for all operations, with improved performance through avoiding unnecessary

copies

-**New`pd.col` syntax**:Initial support for`pd.col()` as a simplified syntax

-**New`pd.col` syntax**:initial support for`pd.col()` as a simplified syntax

for creating callables in`DataFrame.assign`

Together witha lot of other improvements and bug fixes. You can find the

complete list of changes in our

Further, pandas 3.0 includesa lot of other improvements and bug fixes. You can

find thecomplete list of changes in our

[release notes](https://pandas.pydata.org/docs/dev/whatsnew/v3.0.0.html).