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v0.19.0.txt
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.. _whatsnew_0190:
v0.19.0 (August ??, 2016)
-------------------------
This is a major release from 0.18.1 and includes a small number of API changes, several new features,
enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
users upgrade to this version.
.. warning::
pandas >= 0.19.0 will no longer silence numpy ufunc warnings upon import, see :ref:`here <whatsnew_0190.errstate>`.
Highlights include:
- :func:`merge_asof` for asof-style time-series joining, see :ref:`here <whatsnew_0190.enhancements.asof_merge>`
- ``.rolling()`` are now time-series aware, see :ref:`here <whatsnew_0190.enhancements.rolling_ts>`
- :func:`read_csv` now supports parsing ``Categorical`` data, see :ref:`here <whatsnew_0190.enhancements.read_csv_categorical>`
- A function :func:`union_categorical` has been added for combining categoricals, see :ref:`here <whatsnew_0190.enhancements.union_categoricals>`
- pandas development api, see :ref:`here <whatsnew_0190.dev_api>`
- ``PeriodIndex`` now has its own ``period`` dtype, and changed to be more consistent with other ``Index`` classes. See :ref:`here <whatsnew_0190.api.period>`
- Sparse data structures now gained enhanced support of ``int`` and ``bool`` dtypes, see :ref:`here <whatsnew_0190.sparse>`
.. contents:: What's new in v0.19.0
:local:
:backlinks: none
.. _whatsnew_0190.new_features:
New features
~~~~~~~~~~~~
.. _whatsnew_0190.dev_api:
pandas development API
^^^^^^^^^^^^^^^^^^^^^^
As part of making pandas APi more uniform and accessible in the future, we have created a standard
sub-package of pandas, ``pandas.api`` to hold public API's. We are starting by exposing type
introspection functions in ``pandas.api.types``. More sub-packages and officially sanctioned API's
will be published in future versions of pandas (:issue:`13147`, :issue:`13634`)
The following are now part of this API:
.. ipython:: python
import pprint
from pandas.api import types
funcs = [ f for f in dir(types) if not f.startswith('_') ]
pprint.pprint(funcs)
.. note::
Calling these functions from the internal module ``pandas.core.common`` will now show a ``DeprecationWarning`` (:issue:`13990`)
.. _whatsnew_0190.enhancements.asof_merge:
``merge_asof`` for asof-style time-series joining
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A long-time requested feature has been added through the :func:`merge_asof` function, to
support asof style joining of time-series. (:issue:`1870`, :issue:`13695`, :issue:`13709`, :issue:`13902`). Full documentation is
:ref:`here <merging.merge_asof>`
The :func:`merge_asof` performs an asof merge, which is similar to a left-join
except that we match on nearest key rather than equal keys.
.. ipython:: python
left = pd.DataFrame({'a': [1, 5, 10],
'left_val': ['a', 'b', 'c']})
right = pd.DataFrame({'a': [1, 2, 3, 6, 7],
'right_val': [1, 2, 3, 6, 7]})
left
right
We typically want to match exactly when possible, and use the most
recent value otherwise.
.. ipython:: python
pd.merge_asof(left, right, on='a')
We can also match rows ONLY with prior data, and not an exact match.
.. ipython:: python
pd.merge_asof(left, right, on='a', allow_exact_matches=False)
In a typical time-series example, we have ``trades`` and ``quotes`` and we want to ``asof-join`` them.
This also illustrates using the ``by`` parameter to group data before merging.
.. ipython:: python
trades = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.038',
'20160525 13:30:00.048',
'20160525 13:30:00.048',
'20160525 13:30:00.048']),
'ticker': ['MSFT', 'MSFT',
'GOOG', 'GOOG', 'AAPL'],
'price': [51.95, 51.95,
720.77, 720.92, 98.00],
'quantity': [75, 155,
100, 100, 100]},
columns=['time', 'ticker', 'price', 'quantity'])
quotes = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.030',
'20160525 13:30:00.041',
'20160525 13:30:00.048',
'20160525 13:30:00.049',
'20160525 13:30:00.072',
'20160525 13:30:00.075']),
'ticker': ['GOOG', 'MSFT', 'MSFT',
'MSFT', 'GOOG', 'AAPL', 'GOOG',
'MSFT'],
'bid': [720.50, 51.95, 51.97, 51.99,
720.50, 97.99, 720.50, 52.01],
'ask': [720.93, 51.96, 51.98, 52.00,
720.93, 98.01, 720.88, 52.03]},
columns=['time', 'ticker', 'bid', 'ask'])
.. ipython:: python
trades
quotes
An asof merge joins on the ``on``, typically a datetimelike field, which is ordered, and
in this case we are using a grouper in the ``by`` field. This is like a left-outer join, except
that forward filling happens automatically taking the most recent non-NaN value.
.. ipython:: python
pd.merge_asof(trades, quotes,
on='time',
by='ticker')
This returns a merged DataFrame with the entries in the same order as the original left
passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged.
.. _whatsnew_0190.enhancements.rolling_ts:
``.rolling()`` are now time-series aware
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
``.rolling()`` objects are now time-series aware and can accept a time-series offset (or convertible) for the ``window`` argument (:issue:`13327`, :issue:`12995`)
See the full documentation :ref:`here <stats.moments.ts>`.
.. ipython:: python
dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
index=pd.date_range('20130101 09:00:00', periods=5, freq='s'))
dft
This is a regular frequency index. Using an integer window parameter works to roll along the window frequency.
.. ipython:: python
dft.rolling(2).sum()
dft.rolling(2, min_periods=1).sum()
Specifying an offset allows a more intuitive specification of the rolling frequency.
.. ipython:: python
dft.rolling('2s').sum()
Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation.
.. ipython:: python
dft = DataFrame({'B': [0, 1, 2, np.nan, 4]},
index = pd.Index([pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:00:02'),
pd.Timestamp('20130101 09:00:03'),
pd.Timestamp('20130101 09:00:05'),
pd.Timestamp('20130101 09:00:06')],
name='foo'))
dft
dft.rolling(2).sum()
Using the time-specification generates variable windows for this sparse data.
.. ipython:: python
dft.rolling('2s').sum()
Furthermore, we now allow an optional ``on`` parameter to specify a column (rather than the
default of the index) in a DataFrame.
.. ipython:: python
dft = dft.reset_index()
dft
dft.rolling('2s', on='foo').sum()
.. _whatsnew_0190.enhancements.read_csv_dupe_col_names_support:
``read_csv`` has improved support for duplicate column names
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. ipython:: python
:suppress:
from pandas.compat import StringIO
:ref:`Duplicate column names <io.dupe_names>` are now supported in :func:`read_csv` whether
they are in the file or passed in as the ``names`` parameter (:issue:`7160`, :issue:`9424`)
.. ipython :: python
data = '0,1,2\n3,4,5'
names = ['a', 'b', 'a']
Previous Behavior:
.. code-block:: ipython
In [2]: pd.read_csv(StringIO(data), names=names)
Out[2]:
a b a
0 2 1 2
1 5 4 5
The first ``a`` column contains the same data as the second ``a`` column, when it should have
contained the values ``[0, 3]``.
New Behavior:
.. ipython :: python
In [2]: pd.read_csv(StringIO(data), names=names)
.. _whatsnew_0190.enhancements.read_csv_categorical:
:func:`read_csv` supports parsing ``Categorical`` directly
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The :func:`read_csv` function now supports parsing a ``Categorical`` column when
specified as a dtype (:issue:`10153`). Depending on the structure of the data,
this can result in a faster parse time and lower memory usage compared to
converting to ``Categorical`` after parsing. See the io :ref:`docs here <io.categorical>`
.. ipython:: python
data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3'
pd.read_csv(StringIO(data))
pd.read_csv(StringIO(data)).dtypes
pd.read_csv(StringIO(data), dtype='category').dtypes
Individual columns can be parsed as a ``Categorical`` using a dict specification
.. ipython:: python
pd.read_csv(StringIO(data), dtype={'col1': 'category'}).dtypes
.. note::
The resulting categories will always be parsed as strings (object dtype).
If the categories are numeric they can be converted using the
:func:`to_numeric` function, or as appropriate, another converter
such as :func:`to_datetime`.
.. ipython:: python
df = pd.read_csv(StringIO(data), dtype='category')
df.dtypes
df['col3']
df['col3'].cat.categories = pd.to_numeric(df['col3'].cat.categories)
df['col3']
.. _whatsnew_0190.enhancements.union_categoricals:
Categorical Concatenation
^^^^^^^^^^^^^^^^^^^^^^^^^
- A function :func:`union_categoricals` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`, :issue:`:13763`, issue:`13846`)
.. ipython:: python
from pandas.types.concat import union_categoricals
a = pd.Categorical(["b", "c"])
b = pd.Categorical(["a", "b"])
union_categoricals([a, b])
- ``concat`` and ``append`` now can concat ``category`` dtypes wifht different
``categories`` as ``object`` dtype (:issue:`13524`)
Previous Behavior:
.. code-block:: ipython
In [1]: s1 = pd.Series(['a', 'b'], dtype='category')
In [2]: s2 = pd.Series(['b', 'c'], dtype='category')
In [3]: pd.concat([s1, s2])
ValueError: incompatible categories in categorical concat
New Behavior:
.. ipython:: python
pd.concat([s1, s2])
.. _whatsnew_0190.enhancements.semi_month_offsets:
Semi-Month Offsets
^^^^^^^^^^^^^^^^^^
Pandas has gained new frequency offsets, ``SemiMonthEnd`` ('SM') and ``SemiMonthBegin`` ('SMS').
These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively.
(:issue:`1543`)
.. ipython:: python
from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin
SemiMonthEnd:
.. ipython:: python
Timestamp('2016-01-01') + SemiMonthEnd()
pd.date_range('2015-01-01', freq='SM', periods=4)
SemiMonthBegin:
.. ipython:: python
Timestamp('2016-01-01') + SemiMonthBegin()
pd.date_range('2015-01-01', freq='SMS', periods=4)
Using the anchoring suffix, you can also specify the day of month to use instead of the 15th.
.. ipython:: python
pd.date_range('2015-01-01', freq='SMS-16', periods=4)
pd.date_range('2015-01-01', freq='SM-14', periods=4)
.. _whatsnew_0190.enhancements.index:
New Index methods
^^^^^^^^^^^^^^^^^
The following methods and options are added to ``Index``, to be more consistent with the ``Series`` and ``DataFrame`` API.
``Index`` now supports the ``.where()`` function for same shape indexing (:issue:`13170`)
.. ipython:: python
idx = pd.Index(['a', 'b', 'c'])
idx.where([True, False, True])
``Index`` now supports ``.dropna()`` to exclude missing values (:issue:`6194`)
.. ipython:: python
idx = pd.Index([1, 2, np.nan, 4])
idx.dropna()
For ``MultiIndex``, values are dropped if any level is missing by default. Specifying
``how='all'`` only drops values where all levels are missing.
.. ipython:: python
midx = pd.MultiIndex.from_arrays([[1, 2, np.nan, 4],
[1, 2, np.nan, np.nan]])
midx
midx.dropna()
midx.dropna(how='all')
``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, see the :ref:`docs here <text.extractall>` (:issue:`10008`, :issue:`13156`)
.. ipython:: python
idx = pd.Index(["a1a2", "b1", "c1"])
idx.str.extractall("[ab](?P<digit>\d)")
``Index.astype()`` now accepts an optional boolean argument ``copy``, which allows optional copying if the requirements on dtype are satisfied (:issue:`13209`)
.. _whatsnew_0190.gbq:
Google BigQuery Enhancements
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The :func:`pandas.io.gbq.read_gbq` method has gained the ``dialect`` argument to allow users to specify whether to use BigQuery's legacy SQL or BigQuery's standard SQL. See the :ref:`docs <io.bigquery_reader>` for more details (:issue:`13615`).
.. _whatsnew_0190.errstate:
Fine-grained numpy errstate
^^^^^^^^^^^^^^^^^^^^^^^^^^^
Previous versions of pandas would permanently silence numpy's ufunc error handling when ``pandas`` was imported. Pandas did this in order to silence the warnings that would arise from using numpy ufuncs on missing data, which are usually represented as ``NaN`` s. Unfortunately, this silenced legitimate warnings arising in non-pandas code in the application. Starting with 0.19.0, pandas will use the ``numpy.errstate`` context manager to silence these warnings in a more fine-grained manner, only around where these operations are actually used in the pandas codebase. (:issue:`13109`, :issue:`13145`)
After upgrading pandas, you may see *new* ``RuntimeWarnings`` being issued from your code. These are likely legitimate, and the underlying cause likely existed in the code when using previous versions of pandas that simply silenced the warning. Use `numpy.errstate <http://docs.scipy.org/doc/numpy/reference/generated/numpy.errstate.html>`__ around the source of the ``RuntimeWarning`` to control how these conditions are handled.
.. _whatsnew_0190.get_dummies_dtypes:
get_dummies dtypes
^^^^^^^^^^^^^^^^^^
The ``pd.get_dummies`` function now returns dummy-encoded columns as small integers, rather than floats (:issue:`8725`). This should provide an improved memory footprint.
Previous Behavior:
.. code-block:: ipython
In [1]: pd.get_dummies(['a', 'b', 'a', 'c']).dtypes
Out[1]:
a float64
b float64
c float64
dtype: object
New Behavior:
.. ipython:: python
pd.get_dummies(['a', 'b', 'a', 'c']).dtypes
.. _whatsnew_0190.enhancements.other:
Other enhancements
^^^^^^^^^^^^^^^^^^
- The ``.get_credentials()`` method of ``GbqConnector`` can now first try to fetch `the application default credentials <https://developers.google.com/identity/protocols/application-default-credentials>`__. See the :ref:`docs <io.bigquery_authentication>` for more details (:issue:`13577`).
- The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behavior remains to raising a ``NonExistentTimeError`` (:issue:`13057`)
- ``pd.to_numeric()`` now accepts a ``downcast`` parameter, which will downcast the data if possible to smallest specified numerical dtype (:issue:`13352`)
.. ipython:: python
s = ['1', 2, 3]
pd.to_numeric(s, downcast='unsigned')
pd.to_numeric(s, downcast='integer')
- ``.to_hdf/read_hdf()`` now accept path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path (:issue:`11773`)
- ``Timestamp`` can now accept positional and keyword parameters similar to :func:`datetime.datetime` (:issue:`10758`, :issue:`11630`)
.. ipython:: python
pd.Timestamp(2012, 1, 1)
pd.Timestamp(year=2012, month=1, day=1, hour=8, minute=30)
- the ``.resample()`` function now accepts a ``on=`` or ``level=`` parameter for resampling on a datetimelike column or ``MultiIndex`` level (:issue:`13500`)
.. ipython:: python
df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W', periods=5),
'a': np.arange(5)},
index=pd.MultiIndex.from_arrays([
[1,2,3,4,5],
pd.date_range('2015-01-01', freq='W', periods=5)],
names=['v','d']))
df
df.resample('M', on='date').sum()
df.resample('M', level='d').sum()
- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``decimal`` option (:issue:`12933`)
- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``na_filter`` option (:issue:`13321`)
- The ``pd.read_csv()`` with ``engine='python'`` has gained support for the ``memory_map`` option (:issue:`13381`)
- Consistent with the Python API, ``pd.read_csv()`` will now interpret ``+inf`` as positive infinity (:issue:`13274`)
- The ``pd.read_html()`` has gained support for the ``na_values``, ``converters``, ``keep_default_na`` options (:issue:`13461`)
- ``Categorical.astype()`` now accepts an optional boolean argument ``copy``, effective when dtype is categorical (:issue:`13209`)
- ``DataFrame`` has gained the ``.asof()`` method to return the last non-NaN values according to the selected subset (:issue:`13358`)
- The ``DataFrame`` constructor will now respect key ordering if a list of ``OrderedDict`` objects are passed in (:issue:`13304`)
- ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`)
- ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`)
- ``DataFrame.to_sql()`` now allows a single value as the SQL type for all columns (:issue:`11886`).
- ``Series.append`` now supports the ``ignore_index`` option (:issue:`13677`)
- ``.to_stata()`` and ``StataWriter`` can now write variable labels to Stata dta files using a dictionary to make column names to labels (:issue:`13535`, :issue:`13536`)
- ``.to_stata()`` and ``StataWriter`` will automatically convert ``datetime64[ns]`` columns to Stata format ``%tc``, rather than raising a ``ValueError`` (:issue:`12259`)
- ``read_stata()`` and ``StataReader`` raise with a more explicit error message when reading Stata files with repeated value labels when ``convert_categoricals=True`` (:issue:`13923`)
- ``DataFrame.style`` will now render sparsified MultiIndexes (:issue:`11655`)
- ``DataFrame.style`` will now show column level names (e.g. ``DataFrame.columns.names``) (:issue:`13775`)
- ``DataFrame`` has gained support to re-order the columns based on the values
in a row using ``df.sort_values(by='...', axis=1)`` (:issue:`10806`)
.. ipython:: python
df = pd.DataFrame({'A': [2, 7], 'B': [3, 5], 'C': [4, 8]},
index=['row1', 'row2'])
df
df.sort_values(by='row2', axis=1)
- Added documentation to :ref:`I/O<io.dtypes>` regarding the perils of reading in columns with mixed dtypes and how to handle it (:issue:`13746`)
- :meth:`~DataFrame.to_html` now has a ``border`` argument to control the value in the opening ``<table>`` tag. The default is the value of the ``html.border`` option, which defaults to 1. This also affects the notebook HTML repr, but since Jupyter's CSS includes a border-width attribute, the visual effect is the same. (:issue:`11563`).
- Raise ``ImportError`` in the sql functions when ``sqlalchemy`` is not installed and a connection string is used (:issue:`11920`).
- Compatibility with matplotlib 2.0. Older versions of pandas should also work with matplotlib 2.0 (:issue:`13333`)
.. _whatsnew_0190.api:
API changes
~~~~~~~~~~~
- ``Timestamp.to_pydatetime`` will issue a ``UserWarning`` when ``warn=True``, and the instance has a non-zero number of nanoseconds, previously this would print a message to stdout. (:issue:`14101`)
- Non-convertible dates in an excel date column will be returned without conversion and the column will be ``object`` dtype, rather than raising an exception (:issue:`10001`)
- ``Series.unique()`` with datetime and timezone now returns return array of ``Timestamp`` with timezone (:issue:`13565`)
- ``Timestamp``, ``Period``, ``DatetimeIndex``, ``PeriodIndex`` and ``.dt`` accessor have gained a ``.is_leap_year`` property to check whether the date belongs to a leap year. (:issue:`13727`)
- ``pd.Timedelta(None)`` is now accepted and will return ``NaT``, mirroring ``pd.Timestamp`` (:issue:`13687`)
- ``Panel.to_sparse()`` will raise a ``NotImplementedError`` exception when called (:issue:`13778`)
- ``Index.reshape()`` will raise a ``NotImplementedError`` exception when called (:issue:`12882`)
- ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`)
- ``eval``'s upcasting rules for ``float32`` types have been updated to be more consistent with NumPy's rules. New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`)
- An ``UnsupportedFunctionCall`` error is now raised if NumPy ufuncs like ``np.mean`` are called on groupby or resample objects (:issue:`12811`)
- ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`)
- Calls to ``.sample()`` will respect the random seed set via ``numpy.random.seed(n)`` (:issue:`13161`)
- ``Styler.apply`` is now more strict about the outputs your function must return. For ``axis=0`` or ``axis=1``, the output shape must be identical. For ``axis=None``, the output must be a DataFrame with identical columns and index labels. (:issue:`13222`)
- ``Float64Index.astype(int)`` will now raise ``ValueError`` if ``Float64Index`` contains ``NaN`` values (:issue:`13149`)
- ``TimedeltaIndex.astype(int)`` and ``DatetimeIndex.astype(int)`` will now return ``Int64Index`` instead of ``np.array`` (:issue:`13209`)
- Passing ``Period`` with multiple frequencies to normal ``Index`` now returns ``Index`` with ``object`` dtype (:issue:`13664`)
- ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`)
- ``PeriodIndex.fillna`` with ``Period`` has different freq now coerces to ``object`` dtype (:issue:`13664`)
- Faceted boxplots from ``DataFrame.boxplot(by=col)`` now return a ``Series`` when ``return_type`` is not None. Previously these returned an ``OrderedDict``. Note that when ``return_type=None``, the default, these still return a 2-D NumPy array. (:issue:`12216`, :issue:`7096`)
- ``astype()`` will now accept a dict of column name to data types mapping as the ``dtype`` argument. (:issue:`12086`)
- The ``pd.read_json`` and ``DataFrame.to_json`` has gained support for reading and writing json lines with ``lines`` option see :ref:`Line delimited json <io.jsonl>` (:issue:`9180`)
- ``pd.read_hdf`` will now raise a ``ValueError`` instead of ``KeyError``, if a mode other than ``r``, ``r+`` and ``a`` is supplied. (:issue:`13623`)
- ``pd.read_csv()``, ``pd.read_table()``, and ``pd.read_hdf()`` raise the builtin ``FileNotFoundError`` exception for Python 3.x when called on a nonexistent file; this is back-ported as ``IOError`` in Python 2.x (:issue:`14086`)
- More informative exceptions are passed through the csv parser. The exception type would now be the original exception type instead of ``CParserError``. (:issue:`13652`)
- ``pd.read_csv()`` in the C engine will now issue a ``ParserWarning`` or raise a ``ValueError`` when ``sep`` encoded is more than one character long (:issue:`14065`)
- ``DataFrame.values`` will now return ``float64`` with a ``DataFrame`` of mixed ``int64`` and ``uint64`` dtypes, conforming to ``np.find_common_type`` (:issue:`10364`, :issue:`13917`)
.. _whatsnew_0190.api.tolist:
``Series.tolist()`` will now return Python types
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
``Series.tolist()`` will now return Python types in the output, mimicking NumPy ``.tolist()`` behavior (:issue:`10904`)
.. ipython:: python
s = pd.Series([1,2,3])
type(s.tolist()[0])
Previous Behavior:
.. code-block:: ipython
In [7]: type(s.tolist()[0])
Out[7]:
<class 'numpy.int64'>
New Behavior:
.. ipython:: python
type(s.tolist()[0])
.. _whatsnew_0190.api.series_ops:
``Series`` operators for different indexes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Following ``Series`` operators has been changed to make all operators consistent,
including ``DataFrame`` (:issue:`1134`, :issue:`4581`, :issue:`13538`)
- ``Series`` comparison operators now raise ``ValueError`` when ``index`` are different.
- ``Series`` logical operators align both ``index``.
.. warning::
Until 0.18.1, comparing ``Series`` with the same length, would succeed even if
the ``.index`` are different (the result ignores ``.index``). As of 0.19.0, this will raises ``ValueError`` to be more strict. This section also describes how to keep previous behavior or align different indexes, using the flexible comparison methods like ``.eq``.
As a result, ``Series`` and ``DataFrame`` operators behave as below:
Arithmetic operators
""""""""""""""""""""
Arithmetic operators align both ``index`` (no changes).
.. ipython:: python
s1 = pd.Series([1, 2, 3], index=list('ABC'))
s2 = pd.Series([2, 2, 2], index=list('ABD'))
s1 + s2
df1 = pd.DataFrame([1, 2, 3], index=list('ABC'))
df2 = pd.DataFrame([2, 2, 2], index=list('ABD'))
df1 + df2
Comparison operators
""""""""""""""""""""
Comparison operators raise ``ValueError`` when ``.index`` are different.
Previous Behavior (``Series``):
``Series`` compares values ignoring ``.index`` as long as both lengthes are the same.
.. code-block:: ipython
In [1]: s1 == s2
Out[1]:
A False
B True
C False
dtype: bool
New Behavior (``Series``):
.. code-block:: ipython
In [2]: s1 == s2
Out[2]:
ValueError: Can only compare identically-labeled Series objects
.. note::
To achieve the same result as previous versions (compare values based on locations ignoring ``.index``), compare both ``.values``.
.. ipython:: python
s1.values == s2.values
If you want to compare ``Series`` aligning its ``.index``, see flexible comparison methods section below.
Current Behavior (``DataFrame``, no change):
.. code-block:: ipython
In [3]: df1 == df2
Out[3]:
ValueError: Can only compare identically-labeled DataFrame objects
Logical operators
"""""""""""""""""
Logical operators align both ``.index``.
Previous behavior (``Series``), only left hand side ``index`` is kept:
.. code-block:: ipython
In [4]: s1 = pd.Series([True, False, True], index=list('ABC'))
In [5]: s2 = pd.Series([True, True, True], index=list('ABD'))
In [6]: s1 & s2
Out[6]:
A True
B False
C False
dtype: bool
New Behavior (``Series``):
.. ipython:: python
s1 = pd.Series([True, False, True], index=list('ABC'))
s2 = pd.Series([True, True, True], index=list('ABD'))
s1 & s2
.. note::
``Series`` logical operators fill a ``NaN`` result with ``False``.
.. note::
To achieve the same result as previous versions (compare values based on locations ignoring ``.index``), compare both ``.values``.
.. ipython:: python
s1.values & s2.values
Current Behavior (``DataFrame``, no change):
.. ipython:: python
df1 = pd.DataFrame([True, False, True], index=list('ABC'))
df2 = pd.DataFrame([True, True, True], index=list('ABD'))
df1 & df2
Flexible comparison methods
"""""""""""""""""""""""""""
``Series`` flexible comparison methods like ``eq``, ``ne``, ``le``, ``lt``, ``ge`` and ``gt`` now align both ``index``. Use these operators if you want to compare two ``Series``
which has the different ``index``.
.. ipython:: python
s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s2 = pd.Series([2, 2, 2], index=['b', 'c', 'd'])
s1.eq(s2)
s1.ge(s2)
Previously, this worked the same as comparison operators (see above).
.. _whatsnew_0190.api.promote:
``Series`` type promotion on assignment
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A ``Series`` will now correctly promote its dtype for assignment with incompat values to the current dtype (:issue:`13234`)
.. ipython:: python
s = pd.Series()
Previous Behavior:
.. code-block:: ipython
In [2]: s["a"] = pd.Timestamp("2016-01-01")
In [3]: s["b"] = 3.0
TypeError: invalid type promotion
New Behavior:
.. ipython:: python
s["a"] = pd.Timestamp("2016-01-01")
s["b"] = 3.0
s
s.dtype
.. _whatsnew_0190.api.to_datetime_coerce:
``.to_datetime()`` changes
^^^^^^^^^^^^^^^^^^^^^^^^^^
Previously if ``.to_datetime()`` encountered mixed integers/floats and strings, but no datetimes with ``errors='coerce'`` it would convert all to ``NaT``.
Previous Behavior:
.. code-block:: ipython
In [2]: pd.to_datetime([1, 'foo'], errors='coerce')
Out[2]: DatetimeIndex(['NaT', 'NaT'], dtype='datetime64[ns]', freq=None)
This will now convert integers/floats with the default unit of ``ns``.
.. ipython:: python
pd.to_datetime([1, 'foo'], errors='coerce')
Bug fixes related to ``.to_datetime()``:
- Bug in ``pd.to_datetime()`` when passing integers or floats, and no ``unit`` and ``errors='coerce'`` (:issue:`13180`).
- Bug in ``pd.to_datetime()`` when passing invalid datatypes (e.g. bool); will now respect the ``errors`` keyword (:issue:`13176`)
- Bug in ``pd.to_datetime()`` which overflowed on ``int8``, and ``int16`` dtypes (:issue:`13451`)
- Bug in ``pd.to_datetime()`` raise ``AttributeError`` with ``NaN`` and the other string is not valid when ``errors='ignore'`` (:issue:`12424`)
- Bug in ``pd.to_datetime()`` did not cast floats correctly when ``unit`` was specified, resulting in truncated datetime (:issue:`13834`)
.. _whatsnew_0190.api.merging:
Merging changes
^^^^^^^^^^^^^^^
Merging will now preserve the dtype of the join keys (:issue:`8596`)
.. ipython:: python
df1 = pd.DataFrame({'key': [1], 'v1': [10]})
df1
df2 = pd.DataFrame({'key': [1, 2], 'v1': [20, 30]})
df2
Previous Behavior:
.. code-block:: ipython
In [5]: pd.merge(df1, df2, how='outer')
Out[5]:
key v1
0 1.0 10.0
1 1.0 20.0
2 2.0 30.0
In [6]: pd.merge(df1, df2, how='outer').dtypes
Out[6]:
key float64
v1 float64
dtype: object
New Behavior:
We are able to preserve the join keys
.. ipython:: python
pd.merge(df1, df2, how='outer')
pd.merge(df1, df2, how='outer').dtypes
Of course if you have missing values that are introduced, then the
resulting dtype will be upcast, which is unchanged from previous.
.. ipython:: python
pd.merge(df1, df2, how='outer', on='key')
pd.merge(df1, df2, how='outer', on='key').dtypes
.. _whatsnew_0190.api.describe:
``.describe()`` changes
^^^^^^^^^^^^^^^^^^^^^^^
Percentile identifiers in the index of a ``.describe()`` output will now be rounded to the least precision that keeps them distinct (:issue:`13104`)
.. ipython:: python
s = pd.Series([0, 1, 2, 3, 4])
df = pd.DataFrame([0, 1, 2, 3, 4])
Previous Behavior:
The percentiles were rounded to at most one decimal place, which could raise ``ValueError`` for a data frame if the percentiles were duplicated.
.. code-block:: ipython
In [3]: s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999])
Out[3]:
count 5.000000
mean 2.000000
std 1.581139
min 0.000000
0.0% 0.000400
0.1% 0.002000
0.1% 0.004000
50% 2.000000
99.9% 3.996000
100.0% 3.998000
100.0% 3.999600
max 4.000000
dtype: float64
In [4]: df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999])
Out[4]:
...
ValueError: cannot reindex from a duplicate axis
New Behavior:
.. ipython:: python
s.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999])
df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995, 0.9999])
Furthermore:
- Passing duplicated ``percentiles`` will now raise a ``ValueError``.
- Bug in ``.describe()`` on a DataFrame with a mixed-dtype column index, which would previously raise a ``TypeError`` (:issue:`13288`)
.. _whatsnew_0190.api.period:
``Period`` changes
^^^^^^^^^^^^^^^^^^
``PeriodIndex`` now has ``period`` dtype
""""""""""""""""""""""""""""""""""""""""
``PeriodIndex`` now has its own ``period`` dtype. The ``period`` dtype is a
pandas extension dtype like ``category`` or :ref:`timezone aware dtype <timeseries.timezone_series>` (``datetime64[ns, tz]``). (:issue:`13941`).
As a consequence of this change, ``PeriodIndex`` no longer has an integer dtype:
Previous Behavior:
.. code-block:: ipython
In [1]: pi = pd.PeriodIndex(['2016-08-01'], freq='D')
In [2]: pi
Out[2]: PeriodIndex(['2016-08-01'], dtype='int64', freq='D')
In [3]: pd.api.types.is_integer_dtype(pi)
Out[3]: True
In [4]: pi.dtype
Out[4]: dtype('int64')
New Behavior:
.. ipython:: python
pi = pd.PeriodIndex(['2016-08-01'], freq='D')
pi
pd.api.types.is_integer_dtype(pi)
pd.api.types.is_period_dtype(pi)
pi.dtype
type(pi.dtype)
.. _whatsnew_0190.api.periodnat:
``Period('NaT')`` now returns ``pd.NaT``
""""""""""""""""""""""""""""""""""""""""
Previously, ``Period`` has its own ``Period('NaT')`` representation different from ``pd.NaT``. Now ``Period('NaT')`` has been changed to return ``pd.NaT``. (:issue:`12759`, :issue:`13582`)
Previous Behavior:
.. code-block:: ipython
In [5]: pd.Period('NaT', freq='D')
Out[5]: Period('NaT', 'D')
New Behavior:
These result in ``pd.NaT`` without providing ``freq`` option.
.. ipython:: python
pd.Period('NaT')
pd.Period(None)
To be compat with ``Period`` addition and subtraction, ``pd.NaT`` now supports addition and subtraction with ``int``. Previously it raises ``ValueError``.
Previous Behavior:
.. code-block:: ipython
In [5]: pd.NaT + 1
...
ValueError: Cannot add integral value to Timestamp without freq.
New Behavior:
.. ipython:: python
pd.NaT + 1
pd.NaT - 1
``PeriodIndex.values`` now returns array of ``Period`` object
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
``.values`` is changed to return array of ``Period`` object, rather than array
of ``int64`` (:issue:`13988`)
Previous Behavior:
.. code-block:: ipython
In [6]: pi = pd.PeriodIndex(['2011-01', '2011-02'], freq='M')
In [7]: pi.values
array([492, 493])
New Behavior:
.. ipython:: python
pi = pd.PeriodIndex(['2011-01', '2011-02'], freq='M')
pi.values
.. _whatsnew_0190.api.setops:
Index ``+`` / ``-`` no longer used for set operations
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Addition and subtraction of the base Index type (not the numeric subclasses)
previously performed set operations (set union and difference). This
behavior was already deprecated since 0.15.0 (in favor using the specific
``.union()`` and ``.difference()`` methods), and is now disabled. When
possible, ``+`` and ``-`` are now used for element-wise operations, for
example for concatenating strings (:issue:`8227`, :issue:`14127`).
Previous Behavior:
.. code-block:: ipython
In [1]: pd.Index(['a', 'b']) + pd.Index(['a', 'c'])
FutureWarning: using '+' to provide set union with Indexes is deprecated, use '|' or .union()
Out[1]: Index(['a', 'b', 'c'], dtype='object')
The same operation will now perform element-wise addition:
.. ipython:: python
pd.Index(['a', 'b']) + pd.Index(['a', 'c'])
Note that numeric Index objects already performed element-wise operations.
For example, the behavior of adding two integer Indexes:
.. ipython:: python
pd.Index([1, 2, 3]) + pd.Index([2, 3, 4])
is unchanged. The base ``Index`` is now made consistent with this behavior.
.. _whatsnew_0190.api.difference: