.. _whatsnew_0101: Version 0.10.1 (January 22, 2013) --------------------------------- {{ header }} This is a minor release from 0.10.0 and includes new features, enhancements, and bug fixes. In particular, there is substantial new HDFStore functionality contributed by Jeff Reback. An undesired API breakage with functions taking the ``inplace`` option has been reverted and deprecation warnings added. API changes ~~~~~~~~~~~ - Functions taking an ``inplace`` option return the calling object as before. A deprecation message has been added - Groupby aggregations Max/Min no longer exclude non-numeric data (:issue:`2700`) - Resampling an empty DataFrame now returns an empty DataFrame instead of raising an exception (:issue:`2640`) - The file reader will now raise an exception when NA values are found in an explicitly specified integer column instead of converting the column to float (:issue:`2631`) - DatetimeIndex.unique now returns a DatetimeIndex with the same name and - timezone instead of an array (:issue:`2563`) New features ~~~~~~~~~~~~ - MySQL support for database (contribution from Dan Allan) HDFStore ~~~~~~~~ You may need to upgrade your existing data files. Please visit the **compatibility** section in the main docs. .. ipython:: python :suppress: :okexcept: import os os.remove("store.h5") You can designate (and index) certain columns that you want to be able to perform queries on a table, by passing a list to ``data_columns`` .. ipython:: python store = pd.HDFStore("store.h5") df = pd.DataFrame( np.random.randn(8, 3), index=pd.date_range("1/1/2000", periods=8), columns=["A", "B", "C"], ) df["string"] = "foo" df.loc[df.index[4:6], "string"] = np.nan df.loc[df.index[7:9], "string"] = "bar" df["string2"] = "cool" df # on-disk operations store.append("df", df, data_columns=["B", "C", "string", "string2"]) store.select("df", "B>0 and string=='foo'") # this is in-memory version of this type of selection df[(df.B > 0) & (df.string == "foo")] Retrieving unique values in an indexable or data column. .. code-block:: python # note that this is deprecated as of 0.14.0 # can be replicated by: store.select_column('df','index').unique() store.unique("df", "index") store.unique("df", "string") You can now store ``datetime64`` in data columns .. ipython:: python df_mixed = df.copy() df_mixed["datetime64"] = pd.Timestamp("20010102") df_mixed.loc[df_mixed.index[3:4], ["A", "B"]] = np.nan store.append("df_mixed", df_mixed) df_mixed1 = store.select("df_mixed") df_mixed1 df_mixed1.dtypes.value_counts() You can pass ``columns`` keyword to select to filter a list of the return columns, this is equivalent to passing a ``Term('columns',list_of_columns_to_filter)`` .. ipython:: python store.select("df", columns=["A", "B"]) ``HDFStore`` now serializes MultiIndex dataframes when appending tables. .. code-block:: ipython In [19]: index = pd.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ....: ['one', 'two', 'three']], ....: labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], ....: [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], ....: names=['foo', 'bar']) ....: In [20]: df = pd.DataFrame(np.random.randn(10, 3), index=index, ....: columns=['A', 'B', 'C']) ....: In [21]: df Out[21]: A B C foo bar foo one -0.116619 0.295575 -1.047704 two 1.640556 1.905836 2.772115 three 0.088787 -1.144197 -0.633372 bar one 0.925372 -0.006438 -0.820408 two -0.600874 -1.039266 0.824758 baz two -0.824095 -0.337730 -0.927764 three -0.840123 0.248505 -0.109250 qux one 0.431977 -0.460710 0.336505 two -3.207595 -1.535854 0.409769 three -0.673145 -0.741113 -0.110891 In [22]: store.append('mi', df) In [23]: store.select('mi') Out[23]: A B C foo bar foo one -0.116619 0.295575 -1.047704 two 1.640556 1.905836 2.772115 three 0.088787 -1.144197 -0.633372 bar one 0.925372 -0.006438 -0.820408 two -0.600874 -1.039266 0.824758 baz two -0.824095 -0.337730 -0.927764 three -0.840123 0.248505 -0.109250 qux one 0.431977 -0.460710 0.336505 two -3.207595 -1.535854 0.409769 three -0.673145 -0.741113 -0.110891 # the levels are automatically included as data columns In [24]: store.select('mi', "foo='bar'") Out[24]: A B C foo bar bar one 0.925372 -0.006438 -0.820408 two -0.600874 -1.039266 0.824758 Multi-table creation via ``append_to_multiple`` and selection via ``select_as_multiple`` can create/select from multiple tables and return a combined result, by using ``where`` on a selector table. .. ipython:: python df_mt = pd.DataFrame( np.random.randn(8, 6), index=pd.date_range("1/1/2000", periods=8), columns=["A", "B", "C", "D", "E", "F"], ) df_mt["foo"] = "bar" # you can also create the tables individually store.append_to_multiple( {"df1_mt": ["A", "B"], "df2_mt": None}, df_mt, selector="df1_mt" ) store # individual tables were created store.select("df1_mt") store.select("df2_mt") # as a multiple store.select_as_multiple( ["df1_mt", "df2_mt"], where=["A>0", "B>0"], selector="df1_mt" ) .. ipython:: python :suppress: store.close() os.remove("store.h5") **Enhancements** - ``HDFStore`` now can read native PyTables table format tables - You can pass ``nan_rep = 'my_nan_rep'`` to append, to change the default nan representation on disk (which converts to/from ``np.nan``), this defaults to ``nan``. - You can pass ``index`` to ``append``. This defaults to ``True``. This will automagically create indices on the *indexables* and *data columns* of the table - You can pass ``chunksize=an integer`` to ``append``, to change the writing chunksize (default is 50000). This will significantly lower your memory usage on writing. - You can pass ``expectedrows=an integer`` to the first ``append``, to set the TOTAL number of expected rows that ``PyTables`` will expected. This will optimize read/write performance. - ``Select`` now supports passing ``start`` and ``stop`` to provide selection space limiting in selection. - Greatly improved ISO8601 (e.g., yyyy-mm-dd) date parsing for file parsers (:issue:`2698`) - Allow ``DataFrame.merge`` to handle combinatorial sizes too large for 64-bit integer (:issue:`2690`) - Series now has unary negation (-series) and inversion (~series) operators (:issue:`2686`) - DataFrame.plot now includes a ``logx`` parameter to change the x-axis to log scale (:issue:`2327`) - Series arithmetic operators can now handle constant and ndarray input (:issue:`2574`) - ExcelFile now takes a ``kind`` argument to specify the file type (:issue:`2613`) - A faster implementation for Series.str methods (:issue:`2602`) **Bug Fixes** - ``HDFStore`` tables can now store ``float32`` types correctly (cannot be mixed with ``float64`` however) - Fixed Google Analytics prefix when specifying request segment (:issue:`2713`). - Function to reset Google Analytics token store so users can recover from improperly setup client secrets (:issue:`2687`). - Fixed groupby bug resulting in segfault when passing in MultiIndex (:issue:`2706`) - Fixed bug where passing a Series with datetime64 values into ``to_datetime`` results in bogus output values (:issue:`2699`) - Fixed bug in ``pattern in HDFStore`` expressions when pattern is not a valid regex (:issue:`2694`) - Fixed performance issues while aggregating boolean data (:issue:`2692`) - When given a boolean mask key and a Series of new values, Series __setitem__ will now align the incoming values with the original Series (:issue:`2686`) - Fixed MemoryError caused by performing counting sort on sorting MultiIndex levels with a very large number of combinatorial values (:issue:`2684`) - Fixed bug that causes plotting to fail when the index is a DatetimeIndex with a fixed-offset timezone (:issue:`2683`) - Corrected business day subtraction logic when the offset is more than 5 bdays and the starting date is on a weekend (:issue:`2680`) - Fixed C file parser behavior when the file has more columns than data (:issue:`2668`) - Fixed file reader bug that misaligned columns with data in the presence of an implicit column and a specified ``usecols`` value - DataFrames with numerical or datetime indices are now sorted prior to plotting (:issue:`2609`) - Fixed DataFrame.from_records error when passed columns, index, but empty records (:issue:`2633`) - Several bug fixed for Series operations when dtype is datetime64 (:issue:`2689`, :issue:`2629`, :issue:`2626`) See the :ref:`full release notes ` or issue tracker on GitHub for a complete list. .. _whatsnew_0.10.1.contributors: Contributors ~~~~~~~~~~~~ .. contributors:: v0.10.0..v0.10.1