pandas/v2.0.0.rst at main · pandas

These are the changes in pandas 2.0.0. See :ref:`release` for a full changelog including other versions of pandas.

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When installing pandas using pip, sets of optional dependencies can also be installed by specifying extras.

The available extras, found in the :ref:`installation guide`, are [all, performance, computation, fss, aws, gcp, excel, parquet, feather, hdf5, spss, postgresql, mysql, sql-other, html, xml, plot, output_formatting, clipboard, compression, test] (:issue:`39164`).

It is now possible to use any numpy numeric dtype in a :class:`Index` (:issue:`42717`).

Previously it was only possible to use int64, uint64 & float64 dtypes:

:class:`Int64Index`, :class:`UInt64Index` & :class:`Float64Index` were deprecated in pandas version 1.4 and have now been removed. Instead :class:`Index` should be used directly, and can it now take all numpy numeric dtypes, i.e. int8/ int16/int32/int64/uint8/uint16/uint32/uint64/float32/float64 dtypes:

The ability for :class:`Index` to hold the numpy numeric dtypes has meant some changes in Pandas functionality. In particular, operations that previously were forced to create 64-bit indexes, can now create indexes with lower bit sizes, e.g. 32-bit indexes.

Below is a possibly non-exhaustive list of changes:

Instantiating using a numpy numeric array now follows the dtype of the numpy array. Previously, all indexes created from numpy numeric arrays were forced to 64-bit. Now, for example, Index(np.array([1, 2, 3])) will be int32 on 32-bit systems, where it previously would have been int64 even on 32-bit systems. Instantiating :class:`Index` using a list of numbers will still return 64bit dtypes, e.g. Index([1, 2, 3]) will have a int64 dtype, which is the same as previously.

The various numeric datetime attributes of :class:`DatetimeIndex` (:attr:`~DatetimeIndex.day`, :attr:`~DatetimeIndex.month`, :attr:`~DatetimeIndex.year` etc.) were previously in of dtype int64, while they were int32 for :class:`arrays.DatetimeArray`. They are now int32 on :class:`DatetimeIndex` also:

Level dtypes on Indexes from :meth:`Series.sparse.from_coo` are now of dtype int32, the same as they are on the rows/cols on a scipy sparse matrix. Previously they were of dtype int64.

:class:`Index` cannot be instantiated using a float16 dtype. Previously instantiating an :class:`Index` using dtype float16 resulted in a :class:`Float64Index` with a float64 dtype. It now raises a NotImplementedError:

The following functions gained a new keyword dtype_backend (:issue:`36712`)

When this option is set to "numpy_nullable" it will return a :class:`DataFrame` that is backed by nullable dtypes.

When this keyword is set to "pyarrow", then these functions will return pyarrow-backed nullable :class:`ArrowDtype` DataFrames (:issue:`48957`, :issue:`49997`):

Copy-on-Write can be enabled through one of

Alternatively, copy on write can be enabled locally through:

These are bug fixes that might have notable behavior changes.

In previous versions we cast to float when applying cumsum and cumprod which lead to incorrect results even if the result could be hold by int64 dtype. Additionally, the aggregation overflows consistent with numpy and the regular :meth:`DataFrame.cumprod` and :meth:`DataFrame.cumsum` methods when the limit of int64 is reached (:issue:`37493`).

Old Behavior

We return incorrect results with the 6th value.

New Behavior

We overflow with the 7th value, but the 6th value is still correct.

In previous versions of pandas, :meth:`.DataFrameGroupBy.nth` and :meth:`.SeriesGroupBy.nth` acted as if they were aggregations. However, for most inputs n, they may return either zero or multiple rows per group. This means that they are filtrations, similar to e.g. :meth:`.DataFrameGroupBy.head`. pandas now treats them as filtrations (:issue:`13666`).

Old Behavior

New Behavior

In particular, the index of the result is derived from the input by selecting the appropriate rows. Also, when n is larger than the group, no rows instead of NaN is returned.

Old Behavior

New Behavior

In past versions, when constructing a :class:`Series` or :class:`DataFrame` and passing a "datetime64" or "timedelta64" dtype with unsupported resolution (i.e. anything other than "ns"), pandas would silently replace the given dtype with its nanosecond analogue:

Previous behavior:

In pandas 2.0 we support resolutions "s", "ms", "us", and "ns". When passing a supported dtype (e.g. "datetime64[s]"), the result now has exactly the requested dtype:

New behavior:

With an un-supported dtype, pandas now raises instead of silently swapping in a supported dtype:

New behavior:

In past versions, when running :meth:`Series.value_counts`, the result would inherit the original object's name, and the result index would be nameless. This would cause confusion when resetting the index, and the column names would not correspond with the column values. Now, the result name will be 'count' (or 'proportion' if normalize=True was passed), and the index will be named after the original object (:issue:`49497`).

Previous behavior:

New behavior:

Likewise for other value_counts methods (for example, :meth:`DataFrame.value_counts`).

In previous versions, converting a :class:`Series` or :class:`DataFrame` from datetime64[ns] to a different datetime64[X] dtype would return with datetime64[ns] dtype instead of the requested dtype. In pandas 2.0, support is added for "datetime64[s]", "datetime64[ms]", and "datetime64[us]" dtypes, so converting to those dtypes gives exactly the requested dtype:

Previous behavior:

Previous behavior:

With the new behavior, we get exactly the requested dtype:

New behavior:

For non-supported resolutions e.g. "datetime64[D]", we raise instead of silently ignoring the requested dtype:

New behavior:

For conversion from timedelta64[ns] dtypes, the old behavior converted to a floating point format.

Previous behavior:

Previous behavior:

The new behavior, as for datetime64, either gives exactly the requested dtype or raises:

New behavior:

In previous versions, the default tzinfo object used to represent UTC was pytz.UTC. In pandas 2.0, we default to datetime.timezone.utc instead. Similarly, for timezones represent fixed UTC offsets, we use datetime.timezone objects instead of pytz.FixedOffset objects. See (:issue:`34916`)

Previous behavior:

New behavior:

For timezones that are neither UTC nor fixed offsets, e.g. "US/Pacific", we continue to default to pytz objects.

Before, constructing an empty (where data is None or an empty list-like argument) :class:`Series` or :class:`DataFrame` without specifying the axes (index=None, columns=None) would return the axes as empty :class:`Index` with object dtype.

Now, the axes return an empty :class:`RangeIndex` (:issue:`49572`).

Previous behavior:

New behavior:

The existing :meth:`DataFrame.to_latex` has been restructured to utilise the extended implementation previously available under :meth:`.Styler.to_latex`. The arguments signature is similar, albeit col_space has been removed since it is ignored by LaTeX engines. This render engine also requires jinja2 as a dependency which needs to be installed, since rendering is based upon jinja2 templates.

The pandas latex options below are no longer used and have been removed. The generic max rows and columns arguments remain but for this functionality should be replaced by the Styler equivalents. The alternative options giving similar functionality are indicated below:

Note that due to this change some defaults have also changed:

Note that the behaviour of _repr_latex_ is also changed. Previously setting display.latex.repr would generate LaTeX only when using nbconvert for a JupyterNotebook, and not when the user is running the notebook. Now the styler.render.repr option allows control of the specific output within JupyterNotebooks for operations (not just on nbconvert). See :issue:`39911`.

Some minimum supported versions of dependencies were updated. If installed, we now require:

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported.

See :ref:`install.dependencies` and :ref:`install.optional_dependencies` for more.

In the past, :func:`to_datetime` guessed the format for each element independently. This was appropriate for some cases where elements had mixed date formats - however, it would regularly cause problems when users expected a consistent format but the function would switch formats between elements. As of version 2.0.0, parsing will use a consistent format, determined by the first non-NA value (unless the user specifies a format, in which case that is used).

Old behavior:

New behavior:

Note that this affects :func:`read_csv` as well.

If you still need to parse dates with inconsistent formats, you can use format='mixed' (possibly alongside dayfirst)

or, if your formats are all ISO8601 (but possibly not identically-formatted)

Note

A current PDEP proposes the deprecation and removal of the keywords inplace and copy for all but a small subset of methods from the pandas API. The current discussion takes place at here. The keywords won't be necessary anymore in the context of Copy-on-Write. If this proposal is accepted, both keywords would be deprecated in the next release of pandas and removed in pandas 3.0.