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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>`
- 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 behaviour:
.. 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 array ``[0, 3]``.
New behaviour:
.. 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.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
^^^^^^^^^^^^^^^^^
Following methods and options are added to ``Index`` to be more consistent with ``Series`` and ``DataFrame``.
``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``, the see :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.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 behaviour 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 ``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`)
- 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`)
- Consistent with the Python API, ``pd.read_csv()`` will now interpret ``+inf`` as positive infinity (:issue:`13274`)
- 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`)
- A function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`, :issue:`:13763`, issue:`13846`)
- ``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.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`)
- 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 (:issue:`14101`)
- ``Panel.to_sparse`` will raise a ``NotImplementedError`` exception when called (:issue:`13778`)
- ``Index.reshape`` will raise a ``NotImplementedError`` exception when called (:issue:`12882`)
- 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`)
- ``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`)
- 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`)
- ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`)
- ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`)
- ``__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`)
- Passing ``Period`` with multiple frequencies to normal ``Index`` now returns ``Index`` with ``object`` dtype (:issue:`13664`)
- ``PeriodIndex.fillna`` with ``Period`` has different freq now coerces to ``object`` dtype (:issue:`13664`)
- More informative exceptions are passed through the csv parser. The exception type would now be the original exception type instead of ``CParserError``. (:issue:`13652`)
- ``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.Timedelta(None)`` is now accepted and will return ``NaT``, mirroring ``pd.Timestamp`` (:issue:`13687`)
- ``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.read_hdf`` will now raise a ``ValueError`` instead of ``KeyError``, if a mode other than ``r``, ``r+`` and ``a`` is supplied. (:issue:`13623`)
- ``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`)
- ``Series.unique()`` with datetime and timezone now returns return array of ``Timestamp`` with timezone (:issue:`13565`)
.. _whatsnew_0190.api.tolist:
``Series.tolist()`` will now return Python types
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
``Series.tolist()`` will now return Python types in the output, mimicking NumPy ``.tolist()`` behaviour (: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 has been succeeded even if
these ``index`` are different (the result ignores ``index``). As of 0.19.0, it raises ``ValueError`` to be more strict. This section also describes how to keep previous behaviour or align different indexes using 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 ``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, it worked as 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 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:`13845`)
.. _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`)
.. code-block:: ipython
In [6]: pi = pd.PeriodIndex(['2011-01', '2011-02'], freq='M')
In [7]: pi.values
array([492, 493])
.. ipython:: python
pi = pd.PeriodIndex(['2011-01', '2011-02'], freq='M')
pi.values
.. _whatsnew_0190.api.difference:
``Index.difference`` and ``.symmetric_difference`` changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
``Index.difference`` and ``Index.symmetric_difference`` will now, more consistently, treat ``NaN`` values as any other values. (:issue:`13514`)
.. ipython:: python
idx1 = pd.Index([1, 2, 3, np.nan])
idx2 = pd.Index([0, 1, np.nan])
Previous Behavior:
.. code-block:: ipython
In [3]: idx1.difference(idx2)
Out[3]: Float64Index([nan, 2.0, 3.0], dtype='float64')
In [4]: idx1.symmetric_difference(idx2)
Out[4]: Float64Index([0.0, nan, 2.0, 3.0], dtype='float64')
New Behavior:
.. ipython:: python
idx1.difference(idx2)
idx1.symmetric_difference(idx2)
.. _whatsnew_0190.api.unique_index:
``Index.unique`` consistently returns ``Index``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
``Index.unique()`` now returns unique values as an
``Index`` of the appropriate ``dtype``. (:issue:`13395`)
Previously, most ``Index`` classes returned ``np.ndarray``, and ``DatetimeIndex``,
``TimedeltaIndex`` and ``PeriodIndex`` returned ``Index`` to keep metadata like timezone.
Previous Behavior:
.. code-block:: ipython
In [1]: pd.Index([1, 2, 3]).unique()
Out[1]: array([1, 2, 3])
In [2]: pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], tz='Asia/Tokyo').unique()
Out[2]: DatetimeIndex(['2011-01-01 00:00:00+09:00', '2011-01-02 00:00:00+09:00',
'2011-01-03 00:00:00+09:00'],
dtype='datetime64[ns, Asia/Tokyo]', freq=None)
New Behavior:
.. ipython:: python
pd.Index([1, 2, 3]).unique()
pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], tz='Asia/Tokyo').unique()
.. _whatsnew_0190.api.autogenerated_chunksize_index:
``read_csv`` will progressively enumerate chunks
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
When :func:`read_csv` is called with ``chunksize='n'`` and without specifying an index,
each chunk used to have an independently generated index from `0`` to ``n-1``.
They are now given instead a progressive index, starting from ``0`` for the first chunk,
from ``n`` for the second, and so on, so that, when concatenated, they are identical to
the result of calling :func:`read_csv` without the ``chunksize=`` argument.
(:issue:`12185`)
.. ipython :: python
data = 'A,B\n0,1\n2,3\n4,5\n6,7'
Previous behaviour:
.. code-block:: ipython
In [2]: pd.concat(pd.read_csv(StringIO(data), chunksize=2))
Out[2]:
A B
0 0 1
1 2 3
0 4 5
1 6 7
New behaviour:
.. ipython :: python
pd.concat(pd.read_csv(StringIO(data), chunksize=2))
.. _whatsnew_0190.sparse:
Sparse Changes
^^^^^^^^^^^^^^
These changes allow pandas to handle sparse data with more dtypes, and for work to make a smoother experience with data handling.
``int64`` and ``bool`` support enhancements
"""""""""""""""""""""""""""""""""""""""""""
Sparse data structures now gained enhanced support of ``int64`` and ``bool`` ``dtype`` (:issue:`667`, :issue:`13849`)
Previously, sparse data were ``float64`` dtype by default, even if all inputs were ``int`` or ``bool`` dtype. You had to specify ``dtype`` explicitly to create sparse data with ``int64`` dtype. Also, ``fill_value`` had to be specified explicitly becuase it's default was ``np.nan`` which doesn't appear in ``int64`` or ``bool`` data.
.. code-block:: ipython
In [1]: pd.SparseArray([1, 2, 0, 0])
Out[1]:
[1.0, 2.0, 0.0, 0.0]
Fill: nan
IntIndex
Indices: array([0, 1, 2, 3], dtype=int32)
# specifying int64 dtype, but all values are stored in sp_values because
# fill_value default is np.nan
In [2]: pd.SparseArray([1, 2, 0, 0], dtype=np.int64)
Out[2]:
[1, 2, 0, 0]