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What's new in 1.2.0 (??)

These are the changes in pandas 1.2.0. See :ref:`release` for a full changelog including other versions of pandas.

{{ header }}

Enhancements

Passing arguments to fsspec backends

Many read/write functions have acquired the storage_options optional argument, to pass a dictionary of parameters to the storage backend. This allows, for example, for passing credentials to S3 and GCS storage. The details of what parameters can be passed to which backends can be found in the documentation of the individual storage backends (detailed from the fsspec docs for builtin implementations and linked to external ones). See Section :ref:`io.remote`.

:issue:`35655` added fsspec support (including storage_options) for reading excel files.

Support for binary file handles in to_csv

:meth:`to_csv` supports file handles in binary mode (:issue:`19827` and :issue:`35058`) with encoding (:issue:`13068` and :issue:`23854`) and compression (:issue:`22555`). mode has to contain a b for binary handles to be supported.

For example:

.. ipython:: python

   import io

   data = pd.DataFrame([0, 1, 2])
   buffer = io.BytesIO()
   data.to_csv(buffer, mode="w+b", encoding="utf-8", compression="gzip")

Other enhancements

Increased minimum version for Python

Pandas 1.2.0 supports Python 3.7.1 and higher (:issue:`35214`).

Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated (:issue:`35214`). If installed, we now require:

Package Minimum Version Required Changed
numpy 1.16.5 X X
pytz 2017.3 X X
python-dateutil 2.7.3 X  
bottleneck 1.2.1    
numexpr 2.6.8   X
pytest (dev) 5.0.1   X

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.

Package Minimum Version Changed
beautifulsoup4 4.6.0  
fastparquet 0.3.2  
fsspec 0.7.4  
gcsfs 0.6.0  
lxml 4.3.0 X
matplotlib 2.2.3 X
numba 0.46.0  
openpyxl 2.5.7  
pyarrow 0.15.0 X
pymysql 0.7.11 X
pytables 3.4.4 X
s3fs 0.4.0  
scipy 1.2.0  
sqlalchemy 1.2.8 X
xarray 0.12.0 X
xlrd 1.2.0 X
xlsxwriter 1.0.2 X
xlwt 1.3.0 X
pandas-gbq 0.12.0  

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

Deprecations

:meth:`~DataFrame.groupby` no longer ignores group_keys for transform-like apply

:meth:`~DataFrame.groupby` will no longer ignore the group_keys argument for functions passed to apply that return like-indexed outputs (:issue:`34809`, :issue:`31612`, :issue:`14927`, :issue:`13056`). Previous versions of pandas would add the group keys only when the result from the applied function had a different index to the input.

Previous behavior:

>>> # pandas 1.0.4
>>> df = pd.DataFrame({"A": [1, 2, 2], "B": [1, 2, 3]})
>>> df
   A  B
0  1  1
1  2  2
2  2  3
>>> df.groupby("A").apply(lambda x: x.rename(np.exp))  # Different index
            A  B
A
1 1.000000  1  1
2 2.718282  2  2
  7.389056  2  3

>>> df.groupby("A").apply(lambda x: x)  # Same index
   A  B
0  1  1
1  2  2
2  2  3

In this future this behavior will change to always respect group_keys, which defaults to True.

New behavior:

.. ipython:: python

   df = pd.DataFrame({"A": [1, 2, 2], "B": [1, 2, 3]})
   df.groupby("A", group_keys=True).apply(lambda x: x)
   df.groupby("A", group_keys=True).apply(lambda x: x.rename(np.exp))

A warning will be issued if the result would change from pandas 1.0.4

.. ipython:: python
   :okwarning:

   df.groupby("A").apply(lambda x: x)

Other Deprecations

Performance improvements

Bug fixes

Categorical

Datetimelike

Timedelta

Timezones

Numeric

Conversion

Strings

Interval

Indexing

Missing

MultiIndex

I/O

Plotting

Groupby/resample/rolling

Reshaping

Sparse

ExtensionArray

Other

Contributors