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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. see ref:`here <whatsnew_0190.api.perioddtype>`
.. 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
~~~~~~~~~~~
- ``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`)
.. _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.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.perioddtype:
``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
.. _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.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.
- Sparse data structure now can preserve ``dtype`` after arithmetic ops (:issue:`13848`)
.. ipython:: python
s = pd.SparseSeries([0, 2, 0, 1], fill_value=0, dtype=np.int64)
s.dtype
s + 1
- Sparse data structure now support ``astype`` to convert internal ``dtype`` (:issue:`13900`)
.. ipython:: python
s = pd.SparseSeries([1., 0., 2., 0.], fill_value=0)
s
s.astype(np.int64)
``astype`` fails if data contains values which cannot be converted to specified ``dtype``.
Note that the limitation is applied to ``fill_value`` which default is ``np.nan``.
.. code-block:: ipython
In [7]: pd.SparseSeries([1., np.nan, 2., np.nan], fill_value=np.nan).astype(np.int64)
Out[7]:
ValueError: unable to coerce current fill_value nan to int64 dtype
- Subclassed ``SparseDataFrame`` and ``SparseSeries`` now preserve class types when slicing or transposing. (:issue:`13787`)
- ``SparseArray`` with ``bool`` dtype now supports logical (bool) operators (:issue:`14000`)
- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing may raise ``IndexError`` (:issue:`13144`)
- Bug in ``SparseSeries`` with ``MultiIndex`` ``[]`` indexing result may have normal ``Index`` (:issue:`13144`)
- Bug in ``SparseDataFrame`` in which ``axis=None`` did not default to ``axis=0`` (:issue:`13048`)
- Bug in ``SparseSeries`` and ``SparseDataFrame`` creation with ``object`` dtype may raise ``TypeError`` (:issue:`11633`)
- Bug in ``SparseDataFrame`` doesn't respect passed ``SparseArray`` or ``SparseSeries`` 's dtype and ``fill_value`` (:issue:`13866`)
- Bug in ``SparseArray`` and ``SparseSeries`` don't apply ufunc to ``fill_value`` (:issue:`13853`)
- Bug in ``SparseSeries.abs`` incorrectly keeps negative ``fill_value`` (:issue:`13853`)
- Bug in single row slicing on multi-type ``SparseDataFrame``s, types were previously forced to float (:issue:`13917`)
- Bug in sparse indexing using ``SparseArray`` with ``bool`` dtype may return incorrect result (:issue:`13985`)
- Bug in ``SparseArray`` created from ``SparseSeries`` may lose ``dtype`` (:issue:`13999`)
- Bug in ``SparseSeries`` comparison with dense returns normal ``Series`` rather than ``SparseSeries`` (:issue:`13999`)
.. _whatsnew_0190.indexer_dtype:
Indexer dtype Changes
^^^^^^^^^^^^^^^^^^^^^
.. note::
This change only affects 64 bit python running on Windows, and only affects relatively advanced
indexing operations
Methods such as ``Index.get_indexer`` that return an indexer array, coerce that array to a "platform int", so that it can be
directly used in 3rd party library operations like ``numpy.take``. Previously, a platform int was defined as ``np.int_``
which corresponds to a C integer, but the correct type, and what is being used now, is ``np.intp``, which corresponds
to the C integer size that can hold a pointer. (:issue:`3033`, :issue:`13972`)
These types are the same on many platform, but for 64 bit python on Windows,
``np.int_`` is 32 bits, and ``np.intp`` is 64 bits. Changing this behavior improves performance for many
operations on that platform.
Previous behaviour:
.. code-block:: ipython
In [1]: i = pd.Index(['a', 'b', 'c'])
In [2]: i.get_indexer(['b', 'b', 'c']).dtype
Out[2]: dtype('int32')
New behaviour:
.. code-block:: ipython
In [1]: i = pd.Index(['a', 'b', 'c'])
In [2]: i.get_indexer(['b', 'b', 'c']).dtype
Out[2]: dtype('int64')
.. _whatsnew_0190.deprecations:
Deprecations
~~~~~~~~~~~~
- ``Categorical.reshape`` has been deprecated and will be removed in a subsequent release (:issue:`12882`)
- ``Series.reshape`` has been deprecated and will be removed in a subsequent release (:issue:`12882`)
- ``SparseList`` has been deprecated and will be removed in a future version (:issue:`13784`)
- ``DataFrame.to_html()`` and ``DataFrame.to_latex()`` have dropped the ``colSpace`` parameter in favor of ``col_space`` (:issue:`13857`)
- ``DataFrame.to_sql()`` has deprecated the ``flavor`` parameter, as it is superfluous when SQLAlchemy is not installed (:issue:`13611`)
- ``compact_ints`` and ``use_unsigned`` have been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13320`)
- ``buffer_lines`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13360`)
- ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`)
- ``skip_footer`` has been deprecated in ``pd.read_csv()`` in favor of ``skipfooter`` and will be removed in a future version (:issue:`13349`)
- top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`)
- ``Timestamp.offset`` property (and named arg in the constructor), has been deprecated in favor of ``freq`` (:issue:`12160`)
- ``pd.tseries.util.pivot_annual`` is deprecated. Use ``pivot_table`` as alternative, an example is :ref:`here <cookbook.pivot>` (:issue:`736`)
- ``pd.tseries.util.isleapyear`` has been deprecated and will be removed in a subsequent release. Datetime-likes now have a ``.is_leap_year`` property. (:issue:`13727`)
- ``Panel4D`` and ``PanelND`` constructors are deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the `xarray package <http://xarray.pydata.org/en/stable/>`__. Pandas provides a :meth:`~Panel4D.to_xarray` method to automate this conversion. (:issue:`13564`)
- ``pandas.tseries.frequencies.get_standard_freq`` is deprecated. Use ``pandas.tseries.frequencies.to_offset(freq).rule_code`` instead. (:issue:`13874`)
- ``pandas.tseries.frequencies.to_offset``'s ``freqstr`` keyword is deprecated in favor of ``freq``. (:issue:`13874`)
.. _whatsnew_0190.prior_deprecations:
Removal of prior version deprecations/changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- The ``SparsePanel`` class has been removed (:issue:`13778`)
- The ``pd.sandbox`` module has been removed in favor of the external library ``pandas-qt`` (:issue:`13670`)
- The ``pandas.io.data`` and ``pandas.io.wb`` modules are removed in favor of
the `pandas-datareader package <https://github.com/pydata/pandas-datareader>`__ (:issue:`13724`).
- The ``pandas.tools.rplot`` module has been removed in favor of
the `seaborn package <https://github.com/mwaskom/seaborn>`__ (:issue:`13855`)
- ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`)
- ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`)
- ``pd.Categorical`` has dropped setting of the ``ordered`` attribute directly in favor of the ``set_ordered`` method (:issue:`13671`)
- ``pd.Categorical`` has dropped the ``levels`` attribute in favour of ``categories`` (:issue:`8376`)
- ``DataFrame.to_sql()`` has dropped the ``mysql`` option for the ``flavor`` parameter (:issue:`13611`)
- ``Panel.shift()`` has dropped the ``lags`` parameter in favour of ``periods`` (:issue:`14041`)
- ``pd.Index`` has dropped the ``diff`` method in favour of ``difference`` (:issue:`13669`)
- ``pd.DataFrame`` has dropped the ``to_wide`` method in favour of ``to_panel`` (:issue:`14039`)
- ``Series.to_csv`` has dropped the ``nanRep`` parameter in favor of ``na_rep`` (:issue:`13804`)
- ``Series.xs``, ``DataFrame.xs``, ``Panel.xs``, ``Panel.major_xs``, and ``Panel.minor_xs`` have dropped the ``copy`` parameter (:issue:`13781`)
- ``str.split`` has dropped the ``return_type`` parameter in favor of ``expand`` (:issue:`13701`)
- Removal of the legacy time rules (offset aliases), deprecated since 0.17.0 (this has been alias since 0.8.0) (:issue:`13590`, :issue:`13868`)
Previous Behavior:
.. code-block:: ipython
In [2]: pd.date_range('2016-07-01', freq='W@MON', periods=3)
pandas/tseries/frequencies.py:465: FutureWarning: Freq "W@MON" is deprecated, use "W-MON" as alternative.
Out[2]: DatetimeIndex(['2016-07-04', '2016-07-11', '2016-07-18'], dtype='datetime64[ns]', freq='W-MON')
Now legacy time rules raises ``ValueError``. For the list of currently supported offsets, see :ref:`here <timeseries.offset_aliases>`
- The ``tquery`` and ``uquery`` functions in the ``pandas.io.sql`` module are removed (:issue:`5950`).
.. _whatsnew_0190.performance:
Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~
- Improved performance of sparse ``IntIndex.intersect`` (:issue:`13082`)
- Improved performance of sparse arithmetic with ``BlockIndex`` when the number of blocks are large, though recommended to use ``IntIndex`` in such cases (:issue:`13082`)
- increased performance of ``DataFrame.quantile()`` as it now operates per-block (:issue:`11623`)
- Improved performance of float64 hash table operations, fixing some very slow indexing and groupby operations in python 3 (:issue:`13166`, :issue:`13334`)
- Improved performance of ``DataFrameGroupBy.transform`` (:issue:`12737`)
- Improved performance of ``Index`` and ``Series`` ``.duplicated`` (:issue:`10235`)
- Improved performance of ``Index.difference`` (:issue:`12044`)
- Improved performance of ``RangeIndex.is_monotonic_increasing`` and ``is_monotonic_decreasing`` (:issue:`13749`)
- Improved performance of datetime string parsing in ``DatetimeIndex`` (:issue:`13692`)
- Improved performance of hashing ``Period`` (:issue:`12817`)
- Improved performance of ``factorize`` of datetime with timezone (:issue:`13750`)
.. _whatsnew_0190.bug_fixes:
Bug Fixes
~~~~~~~~~
- Bug in ``groupby().shift()``, which could cause a segfault or corruption in rare circumstances when grouping by columns with missing values (:issue:`13813`)
- Bug in ``groupby().cumsum()`` calculating ``cumprod`` when ``axis=1``. (:issue:`13994`)
- Bug in ``pd.read_csv()``, which may cause a segfault or corruption when iterating in large chunks over a stream/file under rare circumstances (:issue:`13703`)
- Bug in ``pd.read_csv()``, which caused errors to be raised when a dictionary containing scalars is passed in for ``na_values`` (:issue:`12224`)
- Bug in ``pd.read_csv()``, which caused BOM files to be incorrectly parsed by not ignoring the BOM (:issue:`4793`)
- Bug in ``pd.read_csv()`` with ``engine='python'`` which raised errors when a numpy array was passed in for ``usecols`` (:issue:`12546`)
- Bug in ``pd.to_timedelta()`` in which the ``errors`` parameter was not being respected (:issue:`13613`)
- Bug in ``io.json.json_normalize()``, where non-ascii keys raised an exception (:issue:`13213`)
- Bug when passing a not-default-indexed ``Series`` as ``xerr`` or ``yerr`` in ``.plot()`` (:issue:`11858`)
- Bug in area plot draws legend incorrectly if subplot is enabled or legend is moved after plot (matplotlib 1.5.0 is required to draw area plot legend properly) (issue:`9161`, :issue:`13544`)
- Bug in ``DataFrame`` assignment with an object-dtyped ``Index`` where the resultant column is mutable to the original object. (:issue:`13522`)
- Bug in matplotlib ``AutoDataFormatter``; this restores the second scaled formatting and re-adds micro-second scaled formatting (:issue:`13131`)
- Bug in selection from a ``HDFStore`` with a fixed format and ``start`` and/or ``stop`` specified will now return the selected range (:issue:`8287`)
- Bug in ``Categorical.from_codes()`` where an unhelpful error was raised when an invalid ``ordered`` parameter was passed in (:issue:`14058`)
- Bug in ``Series`` construction from a tuple of integers on windows not returning default dtype (int64) (:issue:`13646`)
- Bug in ``.groupby(..).resample(..)`` when the same object is called multiple times (:issue:`13174`)
- Bug in ``.to_records()`` when index name is a unicode string (:issue:`13172`)
- Bug in calling ``.memory_usage()`` on object which doesn't implement (:issue:`12924`)
- Regression in ``Series.quantile`` with nans (also shows up in ``.median()`` and ``.describe()`` ); furthermore now names the ``Series`` with the quantile (:issue:`13098`, :issue:`13146`)
- Bug in ``SeriesGroupBy.transform`` with datetime values and missing groups (:issue:`13191`)
- Bug in ``Series.str.extractall()`` with ``str`` index raises ``ValueError`` (:issue:`13156`)
- Bug in ``Series.str.extractall()`` with single group and quantifier (:issue:`13382`)
- Bug in ``DatetimeIndex`` and ``Period`` subtraction raises ``ValueError`` or ``AttributeError`` rather than ``TypeError`` (:issue:`13078`)
- Bug in ``Index`` and ``Series`` created with ``NaN`` and ``NaT`` mixed data may not have ``datetime64`` dtype (:issue:`13324`)
- Bug in ``Index`` and ``Series`` may ignore ``np.datetime64('nat')`` and ``np.timdelta64('nat')`` to infer dtype (:issue:`13324`)
- Bug in ``PeriodIndex`` and ``Period`` subtraction raises ``AttributeError`` (:issue:`13071`)
- Bug in ``PeriodIndex`` construction returning a ``float64`` index in some circumstances (:issue:`13067`)
- Bug in ``.resample(..)`` with a ``PeriodIndex`` not changing its ``freq`` appropriately when empty (:issue:`13067`)
- Bug in ``.resample(..)`` with a ``PeriodIndex`` not retaining its type or name with an empty ``DataFrame`` appropriately when empty (:issue:`13212`)
- Bug in ``groupby(..).apply(..)`` when the passed function returns scalar values per group (:issue:`13468`).
- Bug in ``groupby(..).resample(..)`` where passing some keywords would raise an exception (:issue:`13235`)
- Bug in ``.tz_convert`` on a tz-aware ``DateTimeIndex`` that relied on index being sorted for correct results (:issue:`13306`)
- Bug in ``.tz_localize`` with ``dateutil.tz.tzlocal`` may return incorrect result (:issue:`13583`)
- Bug in ``DatetimeTZDtype`` dtype with ``dateutil.tz.tzlocal`` cannot be regarded as valid dtype (:issue:`13583`)
- Bug in ``pd.read_hdf()`` where attempting to load an HDF file with a single dataset, that had one or more categorical columns, failed unless the key argument was set to the name of the dataset. (:issue:`13231`)
- Bug in ``.rolling()`` that allowed a negative integer window in contruction of the ``Rolling()`` object, but would later fail on aggregation (:issue:`13383`)
- Bug in printing ``pd.DataFrame`` where unusual elements with the ``object`` dtype were causing segfaults (:issue:`13717`)
- Bug in ranking ``Series`` which could result in segfaults (:issue:`13445`)