doc/source/whatsnew/v0.13.1.rst
.. _whatsnew_0131:
{{ header }}
This is a minor release from 0.13.0 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.
Highlights include:
infer_datetime_format keyword to read_csv/to_datetime to allow speedups for homogeneously formatted datetimes.~pandas.Panel.apply method.Tutorials<tutorials> section.ecosystem page <https://pandas.pydata.org/community/ecosystem.html>_ section.Contributing<contributing> section has been added.ScatterCI <http://scatterci.github.io/pydata/pandas>__... warning::
0.13.1 fixes a bug that was caused by a combination of having numpy < 1.8, and doing chained assignment on a string-like array. Chained indexing can have unexpected results and should generally be avoided.
This would previously segfault:
.. code-block:: python
df = pd.DataFrame({"A": np.array(["foo", "bar", "bah", "foo", "bar"])})
df["A"].iloc[0] = np.nan
The recommended way to do this type of assignment is:
.. ipython:: python
df = pd.DataFrame({"A": np.array(["foo", "bar", "bah", "foo", "bar"])})
df.loc[0, "A"] = np.nan
df
Output formatting enhancements
- df.info() view now display dtype info per column (:issue:`5682`)
- df.info() now honors the option ``max_info_rows``, to disable null counts for large frames (:issue:`5974`)
.. ipython:: python
max_info_rows = pd.get_option("max_info_rows")
df = pd.DataFrame(
{
"A": np.random.randn(10),
"B": np.random.randn(10),
"C": pd.date_range("20130101", periods=10),
}
)
df.iloc[3:6, [0, 2]] = np.nan
.. ipython:: python
# set to not display the null counts
pd.set_option("max_info_rows", 0)
df.info()
.. ipython:: python
# this is the default (same as in 0.13.0)
pd.set_option("max_info_rows", max_info_rows)
df.info()
- Add ``show_dimensions`` display option for the new DataFrame repr to control whether the dimensions print.
.. ipython:: python
df = pd.DataFrame([[1, 2], [3, 4]])
pd.set_option("show_dimensions", False)
df
pd.set_option("show_dimensions", True)
df
- The ``ArrayFormatter`` for ``datetime`` and ``timedelta64`` now intelligently
limit precision based on the values in the array (:issue:`3401`)
Previously output might look like:
.. code-block:: text
age today diff
0 2001-01-01 00:00:00 2013-04-19 00:00:00 4491 days, 00:00:00
1 2004-06-01 00:00:00 2013-04-19 00:00:00 3244 days, 00:00:00
Now the output looks like:
.. ipython:: python
df = pd.DataFrame(
[pd.Timestamp("20010101"), pd.Timestamp("20040601")], columns=["age"]
)
df["today"] = pd.Timestamp("20130419")
df["diff"] = df["today"] - df["age"]
df
API changes
~~~~~~~~~~~
- Add ``-NaN`` and ``-nan`` to the default set of NA values (:issue:`5952`).
See :ref:`NA Values <io.na_values>`.
- Added ``Series.str.get_dummies`` vectorized string method (:issue:`6021`), to extract
dummy/indicator variables for separated string columns:
.. ipython:: python
s = pd.Series(["a", "a|b", np.nan, "a|c"])
s.str.get_dummies(sep="|")
- Added the ``NDFrame.equals()`` method to compare if two NDFrames are
equal have equal axes, dtypes, and values. Added the
``array_equivalent`` function to compare if two ndarrays are
equal. NaNs in identical locations are treated as
equal. (:issue:`5283`) See also :ref:`the docs<basics.equals>` for a motivating example.
.. code-block:: python
df = pd.DataFrame({"col": ["foo", 0, np.nan]})
df2 = pd.DataFrame({"col": [np.nan, 0, "foo"]}, index=[2, 1, 0])
df.equals(df2)
df.equals(df2.sort_index())
- ``DataFrame.apply`` will use the ``reduce`` argument to determine whether a
``Series`` or a ``DataFrame`` should be returned when the ``DataFrame`` is
empty (:issue:`6007`).
Previously, calling ``DataFrame.apply`` an empty ``DataFrame`` would return
either a ``DataFrame`` if there were no columns, or the function being
applied would be called with an empty ``Series`` to guess whether a
``Series`` or ``DataFrame`` should be returned:
.. code-block:: ipython
In [32]: def applied_func(col):
....: print("Apply function being called with: ", col)
....: return col.sum()
....:
In [33]: empty = DataFrame(columns=['a', 'b'])
In [34]: empty.apply(applied_func)
Apply function being called with: Series([], Length: 0, dtype: float64)
Out[34]:
a NaN
b NaN
Length: 2, dtype: float64
Now, when ``apply`` is called on an empty ``DataFrame``: if the ``reduce``
argument is ``True`` a ``Series`` will returned, if it is ``False`` a
``DataFrame`` will be returned, and if it is ``None`` (the default) the
function being applied will be called with an empty series to try and guess
the return type.
.. code-block:: ipython
In [35]: empty.apply(applied_func, reduce=True)
Out[35]:
a NaN
b NaN
Length: 2, dtype: float64
In [36]: empty.apply(applied_func, reduce=False)
Out[36]:
Empty DataFrame
Columns: [a, b]
Index: []
[0 rows x 2 columns]
Prior version deprecations/changes
There are no announced changes in 0.13 or prior that are taking effect as of 0.13.1
Deprecations
There are no deprecations of prior behavior in 0.13.1
Enhancements
pd.read_csv and pd.to_datetime learned a new infer_datetime_format keyword which greatly
improves parsing perf in many cases. Thanks to @lexual for suggesting and @danbirken
for rapidly implementing. (:issue:5490, :issue:6021)
If parse_dates is enabled and this flag is set, pandas will attempt to
infer the format of the datetime strings in the columns, and if it can
be inferred, switch to a faster method of parsing them. In some cases
this can increase the parsing speed by ~5-10x.
.. code-block:: python
# Try to infer the format for the index column
df = pd.read_csv(
"foo.csv", index_col=0, parse_dates=True, infer_datetime_format=True
)
date_format and datetime_format keywords can now be specified when writing to excel
files (:issue:4133)
MultiIndex.from_product convenience function for creating a MultiIndex from
the cartesian product of a set of iterables (:issue:6055):
.. ipython:: python
shades = ["light", "dark"]
colors = ["red", "green", "blue"]
pd.MultiIndex.from_product([shades, colors], names=["shade", "color"])
Panel :meth:~pandas.Panel.apply will work on non-ufuncs. See :ref:the docs<basics.apply>.
.. code-block:: ipython
In [28]: import pandas._testing as tm
In [29]: panel = tm.makePanel(5)
In [30]: panel
Out[30]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D
In [31]: panel['ItemA']
Out[31]:
A B C D
2000-01-03 -0.673690 0.577046 -1.344312 -1.469388
2000-01-04 0.113648 -1.715002 0.844885 0.357021
2000-01-05 -1.478427 -1.039268 1.075770 -0.674600
2000-01-06 0.524988 -0.370647 -0.109050 -1.776904
2000-01-07 0.404705 -1.157892 1.643563 -0.968914
[5 rows x 4 columns]
Specifying an apply that operates on a Series (to return a single element)
.. code-block:: ipython
In [32]: panel.apply(lambda x: x.dtype, axis='items')
Out[32]:
A B C D
2000-01-03 float64 float64 float64 float64
2000-01-04 float64 float64 float64 float64
2000-01-05 float64 float64 float64 float64
2000-01-06 float64 float64 float64 float64
2000-01-07 float64 float64 float64 float64
[5 rows x 4 columns]
A similar reduction type operation
.. code-block:: ipython
In [33]: panel.apply(lambda x: x.sum(), axis='major_axis')
Out[33]:
ItemA ItemB ItemC
A -1.108775 -1.090118 -2.984435
B -3.705764 0.409204 1.866240
C 2.110856 2.960500 -0.974967
D -4.532785 0.303202 -3.685193
[4 rows x 3 columns]
This is equivalent to
.. code-block:: ipython
In [34]: panel.sum('major_axis')
Out[34]:
ItemA ItemB ItemC
A -1.108775 -1.090118 -2.984435
B -3.705764 0.409204 1.866240
C 2.110856 2.960500 -0.974967
D -4.532785 0.303202 -3.685193
[4 rows x 3 columns]
A transformation operation that returns a Panel, but is computing the z-score across the major_axis
.. code-block:: ipython
In [35]: result = panel.apply(lambda x: (x - x.mean()) / x.std(),
....: axis='major_axis')
....:
In [36]: result
Out[36]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D
In [37]: result['ItemA'] # noqa E999
Out[37]:
A B C D
2000-01-03 -0.535778 1.500802 -1.506416 -0.681456
2000-01-04 0.397628 -1.108752 0.360481 1.529895
2000-01-05 -1.489811 -0.339412 0.557374 0.280845
2000-01-06 0.885279 0.421830 -0.453013 -1.053785
2000-01-07 0.742682 -0.474468 1.041575 -0.075499
[5 rows x 4 columns]
Panel :meth:~pandas.Panel.apply operating on cross-sectional slabs. (:issue:1148)
.. code-block:: ipython
In [38]: def f(x):
....: return ((x.T - x.mean(1)) / x.std(1)).T
....:
In [39]: result = panel.apply(f, axis=['items', 'major_axis'])
In [40]: result
Out[40]:
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
Items axis: A to D
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: ItemA to ItemC
In [41]: result.loc[:, :, 'ItemA']
Out[41]:
A B C D
2000-01-03 0.012922 -0.030874 -0.629546 -0.757034
2000-01-04 0.392053 -1.071665 0.163228 0.548188
2000-01-05 -1.093650 -0.640898 0.385734 -1.154310
2000-01-06 1.005446 -1.154593 -0.595615 -0.809185
2000-01-07 0.783051 -0.198053 0.919339 -1.052721
[5 rows x 4 columns]
This is equivalent to the following
.. code-block:: ipython
In [42]: result = pd.Panel({ax: f(panel.loc[:, :, ax]) for ax in panel.minor_axis})
In [43]: result
Out[43]:
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
Items axis: A to D
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: ItemA to ItemC
In [44]: result.loc[:, :, 'ItemA']
Out[44]:
A B C D
2000-01-03 0.012922 -0.030874 -0.629546 -0.757034
2000-01-04 0.392053 -1.071665 0.163228 0.548188
2000-01-05 -1.093650 -0.640898 0.385734 -1.154310
2000-01-06 1.005446 -1.154593 -0.595615 -0.809185
2000-01-07 0.783051 -0.198053 0.919339 -1.052721
[5 rows x 4 columns]
Performance
Performance improvements for 0.13.1
- Series datetime/timedelta binary operations (:issue:`5801`)
- DataFrame ``count/dropna`` for ``axis=1``
- Series.str.contains now has a ``regex=False`` keyword which can be faster for plain (non-regex) string patterns. (:issue:`5879`)
- Series.str.extract (:issue:`5944`)
- ``dtypes/ftypes`` methods (:issue:`5968`)
- indexing with object dtypes (:issue:`5968`)
- ``DataFrame.apply`` (:issue:`6013`)
- Regression in JSON IO (:issue:`5765`)
- Index construction from Series (:issue:`6150`)
Experimental
There are no experimental changes in 0.13.1
.. _release.bug_fixes-0.13.1:
Bug fixes
- Bug in ``io.wb.get_countries`` not including all countries (:issue:`6008`)
- Bug in Series replace with timestamp dict (:issue:`5797`)
- read_csv/read_table now respects the ``prefix`` kwarg (:issue:`5732`).
- Bug in selection with missing values via ``.ix`` from a duplicate indexed DataFrame failing (:issue:`5835`)
- Fix issue of boolean comparison on empty DataFrames (:issue:`5808`)
- Bug in isnull handling ``NaT`` in an object array (:issue:`5443`)
- Bug in ``to_datetime`` when passed a ``np.nan`` or integer datelike and a format string (:issue:`5863`)
- Bug in groupby dtype conversion with datetimelike (:issue:`5869`)
- Regression in handling of empty Series as indexers to Series (:issue:`5877`)
- Bug in internal caching, related to (:issue:`5727`)
- Testing bug in reading JSON/msgpack from a non-filepath on windows under py3 (:issue:`5874`)
- Bug when assigning to .ix[tuple(...)] (:issue:`5896`)
- Bug in fully reindexing a Panel (:issue:`5905`)
- Bug in idxmin/max with object dtypes (:issue:`5914`)
- Bug in ``BusinessDay`` when adding n days to a date not on offset when n>5 and n%5==0 (:issue:`5890`)
- Bug in assigning to chained series with a series via ix (:issue:`5928`)
- Bug in creating an empty DataFrame, copying, then assigning (:issue:`5932`)
- Bug in DataFrame.tail with empty frame (:issue:`5846`)
- Bug in propagating metadata on ``resample`` (:issue:`5862`)
- Fixed string-representation of ``NaT`` to be "NaT" (:issue:`5708`)
- Fixed string-representation for Timestamp to show nanoseconds if present (:issue:`5912`)
- ``pd.match`` not returning passed sentinel
- ``Panel.to_frame()`` no longer fails when ``major_axis`` is a
``MultiIndex`` (:issue:`5402`).
- Bug in ``pd.read_msgpack`` with inferring a ``DateTimeIndex`` frequency
incorrectly (:issue:`5947`)
- Fixed ``to_datetime`` for array with both Tz-aware datetimes and ``NaT``'s (:issue:`5961`)
- Bug in rolling skew/kurtosis when passed a Series with bad data (:issue:`5749`)
- Bug in scipy ``interpolate`` methods with a datetime index (:issue:`5975`)
- Bug in NaT comparison if a mixed datetime/np.datetime64 with NaT were passed (:issue:`5968`)
- Fixed bug with ``pd.concat`` losing dtype information if all inputs are empty (:issue:`5742`)
- Recent changes in IPython cause warnings to be emitted when using previous versions
of pandas in QTConsole, now fixed. If you're using an older version and
need to suppress the warnings, see (:issue:`5922`).
- Bug in merging ``timedelta`` dtypes (:issue:`5695`)
- Bug in plotting.scatter_matrix function. Wrong alignment among diagonal
and off-diagonal plots, see (:issue:`5497`).
- Regression in Series with a MultiIndex via ix (:issue:`6018`)
- Bug in Series.xs with a MultiIndex (:issue:`6018`)
- Bug in Series construction of mixed type with datelike and an integer (which should result in
object type and not automatic conversion) (:issue:`6028`)
- Possible segfault when chained indexing with an object array under NumPy 1.7.1 (:issue:`6026`, :issue:`6056`)
- Bug in setting using fancy indexing a single element with a non-scalar (e.g. a list),
(:issue:`6043`)
- ``to_sql`` did not respect ``if_exists`` (:issue:`4110` :issue:`4304`)
- Regression in ``.get(None)`` indexing from 0.12 (:issue:`5652`)
- Subtle ``iloc`` indexing bug, surfaced in (:issue:`6059`)
- Bug with insert of strings into DatetimeIndex (:issue:`5818`)
- Fixed unicode bug in to_html/HTML repr (:issue:`6098`)
- Fixed missing arg validation in get_options_data (:issue:`6105`)
- Bug in assignment with duplicate columns in a frame where the locations
are a slice (e.g. next to each other) (:issue:`6120`)
- Bug in propagating _ref_locs during construction of a DataFrame with dups
index/columns (:issue:`6121`)
- Bug in ``DataFrame.apply`` when using mixed datelike reductions (:issue:`6125`)
- Bug in ``DataFrame.append`` when appending a row with different columns (:issue:`6129`)
- Bug in DataFrame construction with recarray and non-ns datetime dtype (:issue:`6140`)
- Bug in ``.loc`` setitem indexing with a dataframe on rhs, multiple item setting, and
a datetimelike (:issue:`6152`)
- Fixed a bug in ``query``/``eval`` during lexicographic string comparisons (:issue:`6155`).
- Fixed a bug in ``query`` where the index of a single-element ``Series`` was
being thrown away (:issue:`6148`).
- Bug in ``HDFStore`` on appending a dataframe with MultiIndexed columns to
an existing table (:issue:`6167`)
- Consistency with dtypes in setting an empty DataFrame (:issue:`6171`)
- Bug in selecting on a MultiIndex ``HDFStore`` even in the presence of under
specified column spec (:issue:`6169`)
- Bug in ``nanops.var`` with ``ddof=1`` and 1 elements would sometimes return ``inf``
rather than ``nan`` on some platforms (:issue:`6136`)
- Bug in Series and DataFrame bar plots ignoring the ``use_index`` keyword (:issue:`6209`)
- Bug in groupby with mixed str/int under python3 fixed; ``argsort`` was failing (:issue:`6212`)
.. _whatsnew_0.13.1.contributors:
Contributors
.. contributors:: v0.13.0..v0.13.1