python-pandas/python-pandas.changes

-------------------------------------------------------------------
Wed Jun 24 01:52:29 UTC 2020 - Todd R <toddrme2178@gmail.com>

- Update to version 1.0.5
  * Fixed regressions
    + Fix regression in read_parquet() when reading from file-like objects (GH34467).
    + Fix regression in reading from public S3 buckets (GH34626).
      Note this disables the ability to read Parquet files from
      directories on S3 again (GH26388, GH34632), which was added
      in the 1.0.4 release, but is now targeted for pandas 1.1.0.
    + Fixed regression in replace() raising an AssertionError when replacing values in an extension dtype with values of a different dtype (GH34530)
  * Bug fixes
    + Fixed building from source with Python 3.8 fetching the wrong version of NumPy

-------------------------------------------------------------------
Sat May 30 23:39:38 UTC 2020 - Arun Persaud <arun@gmx.de>

- update to version 1.0.4:
  * Fixed regressions
    + Fix regression where :meth:`Series.isna` and
      :meth:`DataFrame.isna` would raise for categorical dtype when
      pandas.options.mode.use_inf_as_na was set to True
      (:issue:`33594`)
    + Fix regression in :meth:`GroupBy.first` and :meth:`GroupBy.last`
      where None is not preserved in object dtype (:issue:`32800`)
    + Fix regression in DataFrame reductions using numeric_only=True
      and ExtensionArrays (:issue:`33256`).
    + Fix performance regression in memory_usage(deep=True) for object
      dtype (:issue:`33012`)
    + Fix regression where :meth:`Categorical.replace` would replace
      with NaN whenever the new value and replacement value were equal
      (:issue:`33288`)
    + Fix regression where an ordered :class:`Categorical` containing
      only NaN values would raise rather than returning NaN when
      taking the minimum or maximum (:issue:`33450`)
    + Fix regression in :meth:`DataFrameGroupBy.agg` with dictionary
      input losing ExtensionArray dtypes (:issue:`32194`)
    + Fix to preserve the ability to index with the "nearest" method
      with xarray's CFTimeIndex, an :class:`Index` subclass
      (pydata/xarray#3751, :issue:`32905`).
    + Fix regression in :meth:`DataFrame.describe` raising TypeError:
      unhashable type: 'dict' (:issue:`32409`)
    + Fix regression in :meth:`DataFrame.replace` casts columns to
      object dtype if items in to_replace not in values
      (:issue:`32988`)
    + Fix regression in :meth:`Series.groupby` would raise ValueError
      when grouping by :class:`PeriodIndex` level (:issue:`34010`)
    + Fix regression in :meth:`GroupBy.rolling.apply` ignores args and
      kwargs parameters (:issue:`33433`)
    + Fix regression in error message with np.min or np.max on
      unordered :class:`Categorical` (:issue:`33115`)
    + Fix regression in :meth:`DataFrame.loc` and :meth:`Series.loc`
      throwing an error when a datetime64[ns, tz] value is provided
      (:issue:`32395`)
  * Bug fixes
    + Bug in :meth:`SeriesGroupBy.first`, :meth:`SeriesGroupBy.last`,
      :meth:`SeriesGroupBy.min`, and :meth:`SeriesGroupBy.max`
      returning floats when applied to nullable Booleans
      (:issue:`33071`)
    + Bug in :meth:`Rolling.min` and :meth:`Rolling.max`: Growing
      memory usage after multiple calls when using a fixed window
      (:issue:`30726`)
    + Bug in :meth:`~DataFrame.to_parquet` was not raising
      PermissionError when writing to a private s3 bucket with invalid
      creds. (:issue:`27679`)
    + Bug in :meth:`~DataFrame.to_csv` was silently failing when
      writing to an invalid s3 bucket. (:issue:`32486`)
    + Bug in :meth:`read_parquet` was raising a FileNotFoundError when
      passed an s3 directory path. (:issue:`26388`)
    + Bug in :meth:`~DataFrame.to_parquet` was throwing an
      AttributeError when writing a partitioned parquet file to s3
      (:issue:`27596`)
    + Bug in :meth:`GroupBy.quantile` causes the quantiles to be
      shifted when the by axis contains NaN (:issue:`33200`,
      :issue:`33569`)

-------------------------------------------------------------------
Mon May 25 20:21:59 UTC 2020 - Martin Liška <mliska@suse.cz>

- Add gcc10-skip-one-test.patch in order to fix a failing test-case
  on i586.

-------------------------------------------------------------------
Sat Mar 28 16:42:49 UTC 2020 - Arun Persaud <arun@gmx.de>

- update to 1.0.3:
  * Fixed regressions
    + Fixed regression in resample.agg when the underlying data is
      non-writeable (GH31710)
    + Fixed regression in DataFrame exponentiation with reindexing
      (GH32685)
- Increase memory _constraints to 8GB RAM.

-------------------------------------------------------------------
Mon Mar 16 07:12:34 UTC 2020 - Tomáš Chvátal <tchvatal@suse.com>

- Skip i586 failing tests with upstream ticket

-------------------------------------------------------------------
Fri Mar 13 00:13:11 UTC 2020 - Hans-Peter Jansen <hpj@urpla.net>

- Update to 1.0.2:
  * see https://pandas.pydata.org/pandas-docs/stable/whatsnew/v1.0.2.html
- Add pyperclip and Jinja2 as test dependencies

-------------------------------------------------------------------
Mon Mar  9 15:19:33 UTC 2020 - Dirk Mueller <dmueller@suse.com>

- Update to 1.0.1:
  * see https://pandas.pydata.org/pandas-docs/stable/whatsnew/v1.0.1.html
  * see https://pandas.pydata.org/pandas-docs/stable/whatsnew/v1.0.0.html

-------------------------------------------------------------------
Tue Jan 14 12:28:49 UTC 2020 - Tomáš Chvátal <tchvatal@suse.com>

- Skip one test that fails on 32bit: test_encode_non_c_locale

-------------------------------------------------------------------
Mon Nov 11 01:59:25 UTC 2019 - Steve Kowalik <steven.kowalik@suse.com>

- Update to version 0.25.3
  + Support Python 3.8
  + Bug fixes
    > Indexing
      * Fix regression in DataFrame.reindex() not following the limit argument
      * Fix regression in RangeIndex.get_indexer() for decreasing RangeIndex
        where target values may be improperly identified as missing/present
    > I/O
      * Fix regression in notebook display where <th> tags were missing for
        DataFrame.index values
      * Regression in to_csv() where writing a Series or DataFrame indexed by
        an IntervalIndex would incorrectly raise a TypeError
      * Fix to_csv() with ExtensionArray with list-like values
    > Groupby/resample/rolling
      * Bug incorrectly raising an IndexError when passing a list of quantiles
        to pandas.core.groupby.DataFrameGroupBy.quantile()
      * Bug in pandas.core.groupby.GroupBy.shift(),
        pandas.core.groupby.GroupBy.bfill() and
        pandas.core.groupby.GroupBy.ffill() where timezone information would
        be dropped
      * Bug in DataFrameGroupBy.quantile() where NA values in the grouping
        could cause segfaults or incorrect results

-------------------------------------------------------------------
Fri Sep 20 09:40:08 UTC 2019 - Tomáš Chvátal <tchvatal@suse.com>

- Use xdist to run tests in threads, it takes ages otherwise

-------------------------------------------------------------------
Wed Aug 28 15:32:47 UTC 2019 - Todd R <toddrme2178@gmail.com>

- Update to version 0.25.1
  + Bug fixes
    > Categorical
      * Bug in :meth:`Categorical.fillna` that would replace all values, not just those that are ``NaN``
    > Datetimelike
      * Bug in :func:`to_datetime` where passing a timezone-naive :class:`DatetimeArray` or :class:`DatetimeIndex` and ``utc=True`` would incorrectly return a timezone-naive result
      * Bug in :meth:`Period.to_timestamp` where a :class:`Period` outside the :class:`Timestamp` implementation bounds (roughly 1677-09-21 to 2262-04-11) would return an incorrect :class:`Timestamp` instead of raising ``OutOfBoundsDatetime``
      * Bug in iterating over :class:`DatetimeIndex` when the underlying data is read-only
    > Timezones
      * Bug in :class:`Index` where a numpy object array with a timezone aware :class:`Timestamp` and ``np.nan`` would not return a :class:`DatetimeIndex`
    > Numeric
      * Bug in :meth:`Series.interpolate` when using a timezone aware :class:`DatetimeIndex`
      * Bug when printing negative floating point complex numbers would raise an ``IndexError``
      * Bug where :class:`DataFrame` arithmetic operators such as :meth:`DataFrame.mul` with a :class:`Series` with axis=1 would raise an ``AttributeError`` on :class:`DataFrame` larger than the minimum threshold to invoke numexpr
      * Bug in :class:`DataFrame` arithmetic where missing values in results were incorrectly masked with ``NaN`` instead of ``Inf``
    > Conversion
      * Improved the warnings for the deprecated methods :meth:`Series.real` and :meth:`Series.imag`
    > Interval
      * Bug in :class:`IntervalIndex` where `dir(obj)` would raise ``ValueError``
    > Indexing
      * Bug in partial-string indexing returning a NumPy array rather than a ``Series`` when indexing with a scalar like ``.loc['2015']``
      * Break reference cycle involving :class:`Index` and other index classes to allow garbage collection of index objects without running the GC.
      * Fix regression in assigning values to a single column of a DataFrame with a ``MultiIndex`` columns.
      * Fix regression in ``.ix`` fallback with an ``IntervalIndex``.
    > Missing
      * Bug in :func:`pandas.isnull` or :func:`pandas.isna` when the input is a type e.g. ``type(pandas.Series())``
    > I/O
      * Avoid calling ``S3File.s3`` when reading parquet, as this was removed in s3fs version 0.3.0
      * Better error message when a negative header is passed in :func:`pandas.read_csv`
      * Follow the ``min_rows`` display option (introduced in v0.25.0) correctly in the HTML repr in the notebook.
    > Plotting
      * Added a ``pandas_plotting_backends`` entrypoint group for registering plot backends. See :ref:`extending.plotting-backends` for more.
      * Fixed the re-instatement of Matplotlib datetime converters after calling
        :meth:`pandas.plotting.deregister_matplotlib_converters`.
      * Fix compatibility issue with matplotlib when passing a pandas ``Index`` to a plot call.
    > Groupby/resample/rolling
      * Fixed regression in :meth:`pands.core.groupby.DataFrameGroupBy.quantile` raising when multiple quantiles are given
      * Bug in :meth:`pandas.core.groupby.DataFrameGroupBy.transform` where applying a timezone conversion lambda function would drop timezone information
      * Bug in :meth:`pandas.core.groupby.GroupBy.nth` where ``observed=False`` was being ignored for Categorical groupers
      * Bug in windowing over read-only arrays
      * Fixed segfault in `pandas.core.groupby.DataFrameGroupBy.quantile` when an invalid quantile was passed
    > Reshaping
      * A ``KeyError`` is now raised if ``.unstack()`` is called on a :class:`Series` or :class:`DataFrame` with a flat :class:`Index` passing a name which is not the correct one
      * Bug :meth:`merge_asof` could not merge :class:`Timedelta` objects when passing `tolerance` kwarg
      * Bug in :meth:`DataFrame.crosstab` when ``margins`` set to ``True`` and ``normalize`` is not ``False``, an error is raised.
      * :meth:`DataFrame.join` now suppresses the ``FutureWarning`` when the sort parameter is specified
      * Bug in :meth:`DataFrame.join` raising with readonly arrays
    > Sparse
      * Bug in reductions for :class:`Series` with Sparse dtypes
    > Other
      * Bug in :meth:`Series.replace` and :meth:`DataFrame.replace` when replacing timezone-aware timestamps using a dict-like replacer
      * Bug in :meth:`Series.rename` when using a custom type indexer. Now any value that isn't callable or dict-like is treated as a scalar.

-------------------------------------------------------------------
Mon Jul 22 15:36:34 UTC 2019 - Todd R <toddrme2178@gmail.com>

- Update to Version 0.25.0
  + Warning
    * Starting with the 0.25.x series of releases, pandas only supports Python 3.5.3 and higher.
    * The minimum supported Python version will be bumped to 3.6 in a future release.
    * Panel has been fully removed. For N-D labeled data structures, please
      use xarray
    * read_pickle read_msgpack are only guaranteed backwards compatible back to
      pandas version 0.20.3
  + Enhancements
    * Groupby aggregation with relabeling
      Pandas has added special groupby behavior, known as "named aggregation", for naming the
      output columns when applying multiple aggregation functions to specific columns.
    * Groupby Aggregation with multiple lambdas
      You can now provide multiple lambda functions to a list-like aggregation in
      pandas.core.groupby.GroupBy.agg.
    * Better repr for MultiIndex
      Printing of MultiIndex instances now shows tuples of each row and ensures
      that the tuple items are vertically aligned, so it's now easier to understand
      the structure of the MultiIndex.
    * Shorter truncated repr for Series and DataFrame
      Currently, the default display options of pandas ensure that when a Series
      or DataFrame has more than 60 rows, its repr gets truncated to this maximum
      of 60 rows (the display.max_rows option). However, this still gives
      a repr that takes up a large part of the vertical screen estate. Therefore,
      a new option display.min_rows is introduced with a default of 10 which
      determines the number of rows showed in the truncated repr:
    * Json normalize with max_level param support
      json_normalize normalizes the provided input dict to all
      nested levels. The new max_level parameter provides more control over
      which level to end normalization.
    * Series.explode to split list-like values to rows
      Series and DataFrame have gained the DataFrame.explode methods to transform
      list-likes to individual rows.
    * DataFrame.plot keywords logy, logx and loglog can now accept the value 'sym' for symlog scaling. 
    * Added support for ISO week year format ('%G-%V-%u') when parsing datetimes using to_datetime 
    * Indexing of DataFrame and Series now accepts zerodim np.ndarray 
    * Timestamp.replace now supports the fold argument to disambiguate DST transition times 
    * DataFrame.at_time and Series.at_time now support datetime.time objects with timezones 
    * DataFrame.pivot_table now accepts an observed parameter which is passed to underlying calls to DataFrame.groupby to speed up grouping categorical data. 
    * Series.str has gained Series.str.casefold method to removes all case distinctions present in a string 
    * DataFrame.set_index now works for instances of abc.Iterator, provided their output is of the same length as the calling frame 
    * DatetimeIndex.union now supports the sort argument. The behavior of the sort parameter matches that of Index.union 
    * RangeIndex.union now supports the sort argument. If sort=False an unsorted Int64Index is always returned. sort=None is the default and returns a monotonically increasing RangeIndex if possible or a sorted Int64Index if not 
    * TimedeltaIndex.intersection now also supports the sort keyword 
    * DataFrame.rename now supports the errors argument to raise errors when attempting to rename nonexistent keys 
    * Added api.frame.sparse for working with a DataFrame whose values are sparse 
    * RangeIndex has gained ~RangeIndex.start, ~RangeIndex.stop, and ~RangeIndex.step attributes 
    * datetime.timezone objects are now supported as arguments to timezone methods and constructors 
    * DataFrame.query and DataFrame.eval now supports quoting column names with backticks to refer to names with spaces 
    * merge_asof now gives a more clear error message when merge keys are categoricals that are not equal 
    * pandas.core.window.Rolling supports exponential (or Poisson) window type 
    * Error message for missing required imports now includes the original import error's text 
    * DatetimeIndex and TimedeltaIndex now have a mean method 
    * DataFrame.describe now formats integer percentiles without decimal point 
    * Added support for reading SPSS .sav files using read_spss 
    * Added new option plotting.backend to be able to select a plotting backend different than the existing matplotlib one. Use pandas.set_option('plotting.backend', '<backend-module>') where <backend-module is a library implementing the pandas plotting API 
    * pandas.offsets.BusinessHour supports multiple opening hours intervals 
    * read_excel can now use openpyxl to read Excel files via the engine='openpyxl' argument. This will become the default in a future release 
    * pandas.io.excel.read_excel supports reading OpenDocument tables. Specify engine='odf' to enable. Consult the IO User Guide <io.ods> for more details 
    * Interval, IntervalIndex, and ~arrays.IntervalArray have gained an ~Interval.is_empty attribute denoting if the given interval(s) are empty 
  + Backwards incompatible API changes
    * Indexing with date strings with UTC offsets
      Indexing a DataFrame or Series with a DatetimeIndex with a
      date string with a UTC offset would previously ignore the UTC offset. Now, the UTC offset
      is respected in indexing. 
    * MultiIndex constructed from levels and codes
      Constructing a MultiIndex with NaN levels or codes value < -1 was allowed previously.
      Now, construction with codes value < -1 is not allowed and NaN levels' corresponding codes
      would be reassigned as -1. 
    * Groupby.apply on DataFrame evaluates first group only once
      The implementation of DataFrameGroupBy.apply()
      previously evaluated the supplied function consistently twice on the first group
      to infer if it is safe to use a fast code path. Particularly for functions with
      side effects, this was an undesired behavior and may have led to surprises. 
    * Concatenating sparse values
      When passed DataFrames whose values are sparse, concat will now return a
      Series or DataFrame with sparse values, rather than a SparseDataFrame .
    * The .str-accessor performs stricter type checks
      Due to the lack of more fine-grained dtypes, Series.str so far only checked whether the data was
      of object dtype. Series.str will now infer the dtype data *within* the Series; in particular,
      'bytes'-only data will raise an exception (except for Series.str.decode, Series.str.get,
      Series.str.len, Series.str.slice).
    * Categorical dtypes are preserved during groupby
      Previously, columns that were categorical, but not the groupby key(s) would be converted to object dtype during groupby operations. Pandas now will preserve these dtypes. 
    * Incompatible Index type unions
      When performing Index.union operations between objects of incompatible dtypes,
      the result will be a base Index of dtype object. This behavior holds true for
      unions between Index objects that previously would have been prohibited. The dtype
      of empty Index objects will now be evaluated before performing union operations
      rather than simply returning the other Index object. Index.union can now be
      considered commutative, such that A.union(B) == B.union(A) .
    * DataFrame groupby ffill/bfill no longer return group labels
      The methods ffill, bfill, pad and backfill of
      DataFrameGroupBy <pandas.core.groupby.DataFrameGroupBy>
      previously included the group labels in the return value, which was
      inconsistent with other groupby transforms. Now only the filled values
      are returned. 
    * DataFrame describe on an empty categorical / object column will return top and freq
      When calling DataFrame.describe with an empty categorical / object
      column, the 'top' and 'freq' columns were previously omitted, which was inconsistent with
      the output for non-empty columns. Now the 'top' and 'freq' columns will always be included,
      with numpy.nan in the case of an empty DataFrame 
    * __str__ methods now call __repr__ rather than vice versa
      Pandas has until now mostly defined string representations in a Pandas objects's
      __str__/__unicode__/__bytes__ methods, and called __str__ from the __repr__
      method, if a specific __repr__ method is not found. This is not needed for Python3.
      In Pandas 0.25, the string representations of Pandas objects are now generally
      defined in __repr__, and calls to __str__ in general now pass the call on to
      the __repr__, if a specific __str__ method doesn't exist, as is standard for Python.
      This change is backward compatible for direct usage of Pandas, but if you subclass
      Pandas objects *and* give your subclasses specific __str__/__repr__ methods,
      you may have to adjust your __str__/__repr__ methods .
    * Indexing an IntervalIndex with Interval objects
      Indexing methods for IntervalIndex have been modified to require exact matches only for Interval queries.
      IntervalIndex methods previously matched on any overlapping Interval.  Behavior with scalar points, e.g. querying
      with an integer, is unchanged .
    * Binary ufuncs on Series now align
      Applying a binary ufunc like numpy.power now aligns the inputs
      when both are Series .
    * Categorical.argsort now places missing values at the end
      Categorical.argsort now places missing values at the end of the array, making it
      consistent with NumPy and the rest of pandas .
    * Column order is preserved when passing a list of dicts to DataFrame
      Starting with Python 3.7 the key-order of dict is guaranteed <https://mail.python.org/pipermail/python-dev/2017-December/151283.html>_. In practice, this has been true since
      Python 3.6. The DataFrame constructor now treats a list of dicts in the same way as
      it does a list of OrderedDict, i.e. preserving the order of the dicts.
      This change applies only when pandas is running on Python>=3.6 .
    * Increased minimum versions for dependencies
    * DatetimeTZDtype will now standardize pytz timezones to a common timezone instance 
    * Timestamp and Timedelta scalars now implement the to_numpy method as aliases to Timestamp.to_datetime64 and Timedelta.to_timedelta64, respectively. 
    * Timestamp.strptime will now rise a NotImplementedError 
    * Comparing Timestamp with unsupported objects now returns :pyNotImplemented instead of raising TypeError. This implies that unsupported rich comparisons are delegated to the other object, and are now consistent with Python 3 behavior for datetime objects 
    * Bug in DatetimeIndex.snap which didn't preserving the name of the input Index 
    * The arg argument in pandas.core.groupby.DataFrameGroupBy.agg has been renamed to func 
    * The arg argument in pandas.core.window._Window.aggregate has been renamed to func 
    * Most Pandas classes had a __bytes__ method, which was used for getting a python2-style bytestring representation of the object. This method has been removed as a part of dropping Python2 
    * The .str-accessor has been disabled for 1-level MultiIndex, use MultiIndex.to_flat_index if necessary 
    * Removed support of gtk package for clipboards 
    * Using an unsupported version of Beautiful Soup 4 will now raise an ImportError instead of a ValueError 
    * Series.to_excel and DataFrame.to_excel will now raise a ValueError when saving timezone aware data. 
    * ExtensionArray.argsort places NA values at the end of the sorted array. 
    * DataFrame.to_hdf and Series.to_hdf will now raise a NotImplementedError when saving a MultiIndex with extention data types for a fixed format. 
    * Passing duplicate names in read_csv will now raise a ValueError 
  + Deprecations
    * Sparse subclasses
      The SparseSeries and SparseDataFrame subclasses are deprecated. Their functionality is better-provided
      by a Series or DataFrame with sparse values.
    * msgpack format
      The msgpack format is deprecated as of 0.25 and will be removed in a future version. It is recommended to use pyarrow for on-the-wire transmission of pandas objects. 
    * The deprecated .ix[] indexer now raises a more visible FutureWarning instead of DeprecationWarning .
    * Deprecated the units=M (months) and units=Y (year) parameters for units of pandas.to_timedelta, pandas.Timedelta and pandas.TimedeltaIndex 
    * pandas.concat has deprecated the join_axes-keyword. Instead, use DataFrame.reindex or DataFrame.reindex_like on the result or on the inputs 
    * The SparseArray.values attribute is deprecated. You can use np.asarray(...) or
      the SparseArray.to_dense method instead .
    * The functions pandas.to_datetime and pandas.to_timedelta have deprecated the box keyword. Instead, use to_numpy or Timestamp.to_datetime64 or Timedelta.to_timedelta64. 
    * The DataFrame.compound and Series.compound methods are deprecated and will be removed in a future version .
    * The internal attributes _start, _stop and _step attributes of RangeIndex have been deprecated.
      Use the public attributes ~RangeIndex.start, ~RangeIndex.stop and ~RangeIndex.step instead .
    * The Series.ftype, Series.ftypes and DataFrame.ftypes methods are deprecated and will be removed in a future version.
      Instead, use Series.dtype and DataFrame.dtypes .
    * The Series.get_values, DataFrame.get_values, Index.get_values,
      SparseArray.get_values and Categorical.get_values methods are deprecated.
      One of np.asarray(..) or ~Series.to_numpy can be used instead .
    * The 'outer' method on NumPy ufuncs, e.g. np.subtract.outer has been deprecated on Series objects. Convert the input to an array with Series.array first 
    * Timedelta.resolution is deprecated and replaced with Timedelta.resolution_string.  In a future version, Timedelta.resolution will be changed to behave like the standard library datetime.timedelta.resolution 
    * read_table has been undeprecated. 
    * Index.dtype_str is deprecated. 
    * Series.imag and Series.real are deprecated. 
    * Series.put is deprecated. 
    * Index.item and Series.item is deprecated. 
    * The default value ordered=None in ~pandas.api.types.CategoricalDtype has been deprecated in favor of ordered=False. When converting between categorical types ordered=True must be explicitly passed in order to be preserved. 
    * Index.contains is deprecated. Use key in index (__contains__) instead .
    * DataFrame.get_dtype_counts is deprecated. 
    * Categorical.ravel will return a Categorical instead of a np.ndarray 
  + Removal of prior version deprecations/changes
    * Removed Panel 
    * Removed the previously deprecated sheetname keyword in read_excel 
    * Removed the previously deprecated TimeGrouper 
    * Removed the previously deprecated parse_cols keyword in read_excel 
    * Removed the previously deprecated pd.options.html.border 
    * Removed the previously deprecated convert_objects 
    * Removed the previously deprecated select method of DataFrame and Series 
    * Removed the previously deprecated behavior of Series treated as list-like in ~Series.cat.rename_categories 
    * Removed the previously deprecated DataFrame.reindex_axis and Series.reindex_axis 
    * Removed the previously deprecated behavior of altering column or index labels with Series.rename_axis or DataFrame.rename_axis 
    * Removed the previously deprecated tupleize_cols keyword argument in read_html, read_csv, and DataFrame.to_csv 
    * Removed the previously deprecated DataFrame.from.csv and Series.from_csv 
    * Removed the previously deprecated raise_on_error keyword argument in DataFrame.where and DataFrame.mask 
    * Removed the previously deprecated ordered and categories keyword arguments in astype 
    * Removed the previously deprecated cdate_range 
    * Removed the previously deprecated True option for the dropna keyword argument in SeriesGroupBy.nth 
    * Removed the previously deprecated convert keyword argument in Series.take and DataFrame.take 
  + Performance improvements
    * Significant speedup in SparseArray initialization that benefits most operations, fixing performance regression introduced in v0.20.0 
    * DataFrame.to_stata() is now faster when outputting data with any string or non-native endian columns 
    * Improved performance of Series.searchsorted. The speedup is especially large when the dtype is
      int8/int16/int32 and the searched key is within the integer bounds for the dtype 
    * Improved performance of pandas.core.groupby.GroupBy.quantile 
    * Improved performance of slicing and other selected operation on a RangeIndex 
    * RangeIndex now performs standard lookup without instantiating an actual hashtable, hence saving memory 
    * Improved performance of read_csv by faster tokenizing and faster parsing of small float numbers 
    * Improved performance of read_csv by faster parsing of N/A and boolean values 
    * Improved performance of IntervalIndex.is_monotonic, IntervalIndex.is_monotonic_increasing and IntervalIndex.is_monotonic_decreasing by removing conversion to MultiIndex 
    * Improved performance of DataFrame.to_csv when writing datetime dtypes 
    * Improved performance of read_csv by much faster parsing of MM/YYYY and DD/MM/YYYY datetime formats 
    * Improved performance of nanops for dtypes that cannot store NaNs. Speedup is particularly prominent for Series.all and Series.any 
    * Improved performance of Series.map for dictionary mappers on categorical series by mapping the categories instead of mapping all values 
    * Improved performance of IntervalIndex.intersection 
    * Improved performance of read_csv by faster concatenating date columns without extra conversion to string for integer/float zero and float NaN; by faster checking the string for the possibility of being a date 
    * Improved performance of IntervalIndex.is_unique by removing conversion to MultiIndex 
    * Restored performance of DatetimeIndex.__iter__ by re-enabling specialized code path 
    * Improved performance when building MultiIndex with at least one CategoricalIndex level 
    * Improved performance by removing the need for a garbage collect when checking for SettingWithCopyWarning 
    * For to_datetime changed default value of cache parameter to True 
    * Improved performance of DatetimeIndex and PeriodIndex slicing given non-unique, monotonic data .
    * Improved performance of pd.read_json for index-oriented data. 
    * Improved performance of MultiIndex.shape .
  + Bug fixes
    > Categorical
      * Bug in DataFrame.at and Series.at that would raise exception if the index was a CategoricalIndex 
      * Fixed bug in comparison of ordered Categorical that contained missing values with a scalar which sometimes incorrectly resulted in True 
      * Bug in DataFrame.dropna when the DataFrame has a CategoricalIndex containing Interval objects incorrectly raised a TypeError 
    > Datetimelike
      * Bug in to_datetime which would raise an (incorrect) ValueError when called with a date far into the future and the format argument specified instead of raising OutOfBoundsDatetime 
      * Bug in to_datetime which would raise InvalidIndexError: Reindexing only valid with uniquely valued Index objects when called with cache=True, with arg including at least two different elements from the set {None, numpy.nan, pandas.NaT} 
      * Bug in DataFrame and Series where timezone aware data with dtype='datetime64[ns] was not cast to naive 
      * Improved Timestamp type checking in various datetime functions to prevent exceptions when using a subclassed datetime 
      * Bug in Series and DataFrame repr where np.datetime64('NaT') and np.timedelta64('NaT') with dtype=object would be represented as NaN 
      * Bug in to_datetime which does not replace the invalid argument with NaT when error is set to coerce 
      * Bug in adding DateOffset with nonzero month to DatetimeIndex would raise ValueError 
      * Bug in to_datetime which raises unhandled OverflowError when called with mix of invalid dates and NaN values with format='%Y%m%d' and error='coerce' 
      * Bug in isin for datetimelike indexes; DatetimeIndex, TimedeltaIndex and PeriodIndex where the levels parameter was ignored. 
      * Bug in to_datetime which raises TypeError for format='%Y%m%d' when called for invalid integer dates with length >= 6 digits with errors='ignore'
      * Bug when comparing a PeriodIndex against a zero-dimensional numpy array 
      * Bug in constructing a Series or DataFrame from a numpy datetime64 array with a non-ns unit and out-of-bound timestamps generating rubbish data, which will now correctly raise an OutOfBoundsDatetime error .
      * Bug in date_range with unnecessary OverflowError being raised for very large or very small dates 
      * Bug where adding Timestamp to a np.timedelta64 object would raise instead of returning a Timestamp 
      * Bug where comparing a zero-dimensional numpy array containing a np.datetime64 object to a Timestamp would incorrect raise TypeError 
      * Bug in to_datetime which would raise ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True when called with cache=True, with arg including datetime strings with different offset 
    > Timedelta
      * Bug in TimedeltaIndex.intersection where for non-monotonic indices in some cases an empty Index was returned when in fact an intersection existed 
      * Bug with comparisons between Timedelta and NaT raising TypeError 
      * Bug when adding or subtracting a BusinessHour to a Timestamp with the resulting time landing in a following or prior day respectively 
      * Bug when comparing a TimedeltaIndex against a zero-dimensional numpy array 
    > Timezones
      * Bug in DatetimeIndex.to_frame where timezone aware data would be converted to timezone naive data 
      * Bug in to_datetime with utc=True and datetime strings that would apply previously parsed UTC offsets to subsequent arguments 
      * Bug in Timestamp.tz_localize and Timestamp.tz_convert does not propagate freq 
      * Bug in Series.at where setting Timestamp with timezone raises TypeError 
      * Bug in DataFrame.update when updating with timezone aware data would return timezone naive data 
      * Bug in to_datetime where an uninformative RuntimeError was raised when passing a naive Timestamp with datetime strings with mixed UTC offsets 
      * Bug in to_datetime with unit='ns' would drop timezone information from the parsed argument 
      * Bug in DataFrame.join where joining a timezone aware index with a timezone aware column would result in a column of NaN 
      * Bug in date_range where ambiguous or nonexistent start or end times were not handled by the ambiguous or nonexistent keywords respectively 
      * Bug in DatetimeIndex.union when combining a timezone aware and timezone unaware DatetimeIndex 
      * Bug when applying a numpy reduction function (e.g. numpy.minimum) to a timezone aware Series 
    > Numeric
      * Bug in to_numeric in which large negative numbers were being improperly handled 
      * Bug in to_numeric in which numbers were being coerced to float, even though errors was not coerce 
      * Bug in to_numeric in which invalid values for errors were being allowed 
      * Bug in format in which floating point complex numbers were not being formatted to proper display precision and trimming 
      * Bug in error messages in DataFrame.corr and Series.corr. Added the possibility of using a callable. 
      * Bug in Series.divmod and Series.rdivmod which would raise an (incorrect) ValueError rather than return a pair of Series objects as result 
      * Raises a helpful exception when a non-numeric index is sent to interpolate with methods which require numeric index. 
      * Bug in ~pandas.eval when comparing floats with scalar operators, for example: x < -0.1 
      * Fixed bug where casting all-boolean array to integer extension array failed 
      * Bug in divmod with a Series object containing zeros incorrectly raising AttributeError 
      * Inconsistency in Series floor-division (//) and divmod filling positive//zero with NaN instead of Inf 
    > Conversion
      * Bug in DataFrame.astype() when passing a dict of columns and types the errors parameter was ignored. 
    > Strings
      * Bug in the __name__ attribute of several methods of Series.str, which were set incorrectly 
      * Improved error message when passing Series of wrong dtype to Series.str.cat 
    > Interval
      * Construction of Interval is restricted to numeric, Timestamp and Timedelta endpoints 
      * Fixed bug in Series/DataFrame not displaying NaN in IntervalIndex with missing values 
      * Bug in IntervalIndex.get_loc where a KeyError would be incorrectly raised for a decreasing IntervalIndex 
      * Bug in Index constructor where passing mixed closed Interval objects would result in a ValueError instead of an object dtype Index 
    > Indexing
      * Improved exception message when calling DataFrame.iloc with a list of non-numeric objects .
      * Improved exception message when calling .iloc or .loc with a boolean indexer with different length .
      * Bug in KeyError exception message when indexing a MultiIndex with a non-existant key not displaying the original key .
      * Bug in .iloc and .loc with a boolean indexer not raising an IndexError when too few items are passed .
      * Bug in DataFrame.loc and Series.loc where KeyError was not raised for a MultiIndex when the key was less than or equal to the number of levels in the MultiIndex .
      * Bug in which DataFrame.append produced an erroneous warning indicating that a KeyError will be thrown in the future when the data to be appended contains new columns .
      * Bug in which DataFrame.to_csv caused a segfault for a reindexed data frame, when the indices were single-level MultiIndex .
      * Fixed bug where assigning a arrays.PandasArray to a pandas.core.frame.DataFrame would raise error 
      * Allow keyword arguments for callable local reference used in the DataFrame.query string 
      * Fixed a KeyError when indexing a MultiIndex` level with a list containing exactly one label, which is missing 
      * Bug which produced AttributeError on partial matching Timestamp in a MultiIndex  
      * Bug in Categorical and  CategoricalIndex with Interval values when using the in operator (__contains) with objects that are not comparable to the values in the Interval 
      * Bug in DataFrame.loc and DataFrame.iloc on a DataFrame with a single timezone-aware datetime64[ns] column incorrectly returning a scalar instead of a Series 
      * Bug in CategoricalIndex and Categorical incorrectly raising ValueError instead of TypeError when a list is passed using the in operator (__contains__) 
      * Bug in setting a new value in a Series with a Timedelta object incorrectly casting the value to an integer 
      * Bug in Series setting a new key (__setitem__) with a timezone-aware datetime incorrectly raising ValueError 
      * Bug in DataFrame.iloc when indexing with a read-only indexer 
      * Bug in Series setting an existing tuple key (__setitem__) with timezone-aware datetime values incorrectly raising TypeError 
    > Missing
      * Fixed misleading exception message in Series.interpolate if argument order is required, but omitted .
      * Fixed class type displayed in exception message in DataFrame.dropna if invalid axis parameter passed 
      * A ValueError will now be thrown by DataFrame.fillna when limit is not a positive integer 
    > MultiIndex
      * Bug in which incorrect exception raised by Timedelta when testing the membership of MultiIndex 
    > I/O
      * Bug in DataFrame.to_html() where values were truncated using display options instead of outputting the full content 
      * Fixed bug in missing text when using to_clipboard if copying utf-16 characters in Python 3 on Windows 
      * Bug in read_json for orient='table' when it tries to infer dtypes by default, which is not applicable as dtypes are already defined in the JSON schema 
      * Bug in read_json for orient='table' and float index, as it infers index dtype by default, which is not applicable because index dtype is already defined in the JSON schema 
      * Bug in read_json for orient='table' and string of float column names, as it makes a column name type conversion to Timestamp, which is not applicable because column names are already defined in the JSON schema 
      * Bug in json_normalize for errors='ignore' where missing values in the input data, were filled in resulting DataFrame with the string "nan" instead of numpy.nan 
      * DataFrame.to_html now raises TypeError when using an invalid type for the classes parameter instead of AssertionError 
      * Bug in DataFrame.to_string and DataFrame.to_latex that would lead to incorrect output when the header keyword is used 
      * Bug in read_csv not properly interpreting the UTF8 encoded filenames on Windows on Python 3.6+ 
      * Improved performance in pandas.read_stata and pandas.io.stata.StataReader when converting columns that have missing values 
      * Bug in DataFrame.to_html where header numbers would ignore display options when rounding 
      * Bug in read_hdf where reading a table from an HDF5 file written directly with PyTables fails with a ValueError when using a sub-selection via the start or stop arguments 
      * Bug in read_hdf not properly closing store after a KeyError is raised 
      * Improved the explanation for the failure when value labels are repeated in Stata dta files and suggested work-arounds 
      * Improved pandas.read_stata and pandas.io.stata.StataReader to read incorrectly formatted 118 format files saved by Stata 
      * Improved the col_space parameter in DataFrame.to_html to accept a string so CSS length values can be set correctly 
      * Fixed bug in loading objects from S3 that contain # characters in the URL 
      * Adds use_bqstorage_api parameter to read_gbq to speed up downloads of large data frames. This feature requires version 0.10.0 of the pandas-gbq library as well as the google-cloud-bigquery-storage and fastavro libraries. 
      * Fixed memory leak in DataFrame.to_json when dealing with numeric data 
      * Bug in read_json where date strings with Z were not converted to a UTC timezone 
      * Added cache_dates=True parameter to read_csv, which allows to cache unique dates when they are parsed 
      * DataFrame.to_excel now raises a ValueError when the caller's dimensions exceed the limitations of Excel 
      * Fixed bug in pandas.read_csv where a BOM would result in incorrect parsing using engine='python' 
      * read_excel now raises a ValueError when input is of type pandas.io.excel.ExcelFile and engine param is passed since pandas.io.excel.ExcelFile has an engine defined 
      * Bug while selecting from HDFStore with where='' specified .
      * Fixed bug in DataFrame.to_excel() where custom objects (i.e. PeriodIndex) inside merged cells were not being converted into types safe for the Excel writer 
      * Bug in read_hdf where reading a timezone aware DatetimeIndex would raise a TypeError 
      * Bug in to_msgpack and read_msgpack which would raise a ValueError rather than a FileNotFoundError for an invalid path 
      * Fixed bug in DataFrame.to_parquet which would raise a ValueError when the dataframe had no columns 
      * Allow parsing of PeriodDtype columns when using read_csv 
    > Plotting
      * Fixed bug where api.extensions.ExtensionArray could not be used in matplotlib plotting 
      * Bug in an error message in DataFrame.plot. Improved the error message if non-numerics are passed to DataFrame.plot 
      * Bug in incorrect ticklabel positions when plotting an index that are non-numeric / non-datetime 
      * Fixed bug causing plots of PeriodIndex timeseries to fail if the frequency is a multiple of the frequency rule code 
      * Fixed bug when plotting a DatetimeIndex with datetime.timezone.utc timezone 
    > Groupby/resample/rolling
      * Bug in pandas.core.resample.Resampler.agg with a timezone aware index where OverflowError would raise when passing a list of functions 
      * Bug in pandas.core.groupby.DataFrameGroupBy.nunique in which the names of column levels were lost 
      * Bug in pandas.core.groupby.GroupBy.agg when applying an aggregation function to timezone aware data 
      * Bug in pandas.core.groupby.GroupBy.first and pandas.core.groupby.GroupBy.last where timezone information would be dropped 
      * Bug in pandas.core.groupby.GroupBy.size when grouping only NA values 
      * Bug in Series.groupby where observed kwarg was previously ignored 
      * Bug in Series.groupby where using groupby with a MultiIndex Series with a list of labels equal to the length of the series caused incorrect grouping 
      * Ensured that ordering of outputs in groupby aggregation functions is consistent across all versions of Python 
      * Ensured that result group order is correct when grouping on an ordered Categorical and specifying observed=True 
      * Bug in pandas.core.window.Rolling.min and pandas.core.window.Rolling.max that caused a memory leak 
      * Bug in pandas.core.window.Rolling.count and pandas.core.window.Expanding.count was previously ignoring the axis keyword 
      * Bug in pandas.core.groupby.GroupBy.idxmax and pandas.core.groupby.GroupBy.idxmin with datetime column would return incorrect dtype 
      * Bug in pandas.core.groupby.GroupBy.cumsum, pandas.core.groupby.GroupBy.cumprod, pandas.core.groupby.GroupBy.cummin and pandas.core.groupby.GroupBy.cummax with categorical column having absent categories, would return incorrect result or segfault 
      * Bug in pandas.core.groupby.GroupBy.nth where NA values in the grouping would return incorrect results 
      * Bug in pandas.core.groupby.SeriesGroupBy.transform where transforming an empty group would raise a ValueError 
      * Bug in pandas.core.frame.DataFrame.groupby where passing a pandas.core.groupby.grouper.Grouper would return incorrect groups when using the .groups accessor 
      * Bug in pandas.core.groupby.GroupBy.agg where incorrect results are returned for uint64 columns. 
      * Bug in pandas.core.window.Rolling.median and pandas.core.window.Rolling.quantile where MemoryError is raised with empty window 
      * Bug in pandas.core.window.Rolling.median and pandas.core.window.Rolling.quantile where incorrect results are returned with closed='left' and closed='neither' 
      * Improved pandas.core.window.Rolling, pandas.core.window.Window and pandas.core.window.EWM functions to exclude nuisance columns from results instead of raising errors and raise a DataError only if all columns are nuisance 
      * Bug in pandas.core.window.Rolling.max and pandas.core.window.Rolling.min where incorrect results are returned with an empty variable window 
      * Raise a helpful exception when an unsupported weighted window function is used as an argument of pandas.core.window.Window.aggregate 
    > Reshaping
      * Bug in pandas.merge adds a string of None, if None is assigned in suffixes instead of remain the column name as-is .
      * Bug in merge when merging by index name would sometimes result in an incorrectly numbered index (missing index values are now assigned NA) 
      * to_records now accepts dtypes to its column_dtypes parameter 
      * Bug in concat where order of OrderedDict (and dict in Python 3.6+) is not respected, when passed in as  objs argument 
      * Bug in pivot_table where columns with NaN values are dropped even if dropna argument is False, when the aggfunc argument contains a list 
      * Bug in concat where the resulting freq of two DatetimeIndex with the same freq would be dropped .
      * Bug in merge where merging with equivalent Categorical dtypes was raising an error 
      * bug in DataFrame instantiating with a dict of iterators or generators (e.g. pd.DataFrame({'A': reversed(range(3))})) raised an error .
      * Bug in DataFrame instantiating with a range (e.g. pd.DataFrame(range(3))) raised an error .
      * Bug in DataFrame constructor when passing non-empty tuples would cause a segmentation fault 
      * Bug in Series.apply failed when the series is a timezone aware DatetimeIndex 
      * Bug in pandas.cut where large bins could incorrectly raise an error due to an integer overflow 
      * Bug in DataFrame.sort_index where an error is thrown when a multi-indexed DataFrame is sorted on all levels with the initial level sorted last 
      * Bug in Series.nlargest treats True as smaller than False 
      * Bug in DataFrame.pivot_table with a IntervalIndex as pivot index would raise TypeError 
      * Bug in which DataFrame.from_dict ignored order of OrderedDict when orient='index' .
      * Bug in DataFrame.transpose where transposing a DataFrame with a timezone-aware datetime column would incorrectly raise ValueError 
      * Bug in pivot_table when pivoting a timezone aware column as the values would remove timezone information 
      * Bug in merge_asof when specifying multiple by columns where one is datetime64[ns, tz] dtype 
    > Sparse
      * Significant speedup in SparseArray initialization that benefits most operations, fixing performance regression introduced in v0.20.0 
      * Bug in SparseFrame constructor where passing None as the data would cause default_fill_value to be ignored 
      * Bug in SparseDataFrame when adding a column in which the length of values does not match length of index, AssertionError is raised instead of raising ValueError 
      * Introduce a better error message in Series.sparse.from_coo so it returns a TypeError for inputs that are not coo matrices 
      * Bug in numpy.modf on a SparseArray. Now a tuple of SparseArray is returned .
    > Build Changes
      * Fix install error with PyPy on macOS 
    > ExtensionArray
      * Bug in factorize when passing an ExtensionArray with a custom na_sentinel .
      * Series.count miscounts NA values in ExtensionArrays 
      * Added Series.__array_ufunc__ to better handle NumPy ufuncs applied to Series backed by extension arrays .
      * Keyword argument deep has been removed from ExtensionArray.copy 
    > Other
      * Removed unused C functions from vendored UltraJSON implementation 
      * Allow Index and RangeIndex to be passed to numpy min and max functions 
      * Use actual class name in repr of empty objects of a Series subclass .
      * Bug in DataFrame where passing an object array of timezone-aware datetime objects would incorrectly raise ValueError 
- Remove upstream-included pandas-tests-memory.patch

-------------------------------------------------------------------
Sat Mar 16 22:35:08 UTC 2019 - Arun Persaud <arun@gmx.de>

- specfile:
  * requier pytest-mock

- update to version 0.24.2:
  * Fixed Regressions
    + Fixed regression in DataFrame.all() and DataFrame.any() where
      bool_only=True was ignored (GH25101)
    + Fixed issue in DataFrame construction with passing a mixed list
      of mixed types could segfault. (GH25075)
    + Fixed regression in DataFrame.apply() causing RecursionError
      when dict-like classes were passed as argument. (GH25196)
    + Fixed regression in DataFrame.replace() where regex=True was
      only replacing patterns matching the start of the string
      (GH25259)
    + Fixed regression in DataFrame.duplicated(), where empty
      dataframe was not returning a boolean dtyped Series. (GH25184)
    + Fixed regression in Series.min() and Series.max() where
      numeric_only=True was ignored when the Series contained
      Categorical data (GH25299)
    + Fixed regression in subtraction between Series objects with
      datetime64[ns] dtype incorrectly raising OverflowError when the
      Series on the right contains null values (GH25317)
    + Fixed regression in TimedeltaIndex where np.sum(index)
      incorrectly returned a zero-dimensional object instead of a
      scalar (GH25282)
    + Fixed regression in IntervalDtype construction where passing an
      incorrect string with ‘Interval’ as a prefix could result in a
      RecursionError. (GH25338)
    + Fixed regression in creating a period-dtype array from a
      read-only NumPy array of period objects. (GH25403)
    + Fixed regression in Categorical, where constructing it from a
      categorical Series and an explicit categories= that differed
      from that in the Series created an invalid object which could
      trigger segfaults. (GH25318)
    + Fixed regression in to_timedelta() losing precision when
      converting floating data to Timedelta data (GH25077).
    + Fixed pip installing from source into an environment without
      NumPy (GH25193)
    + Fixed regression in DataFrame.replace() where large strings of
      numbers would be coerced into int64, causing an OverflowError
      (GH25616)
    + Fixed regression in factorize() when passing a custom
      na_sentinel value with sort=True (GH25409).
    + Fixed regression in DataFrame.to_csv() writing duplicate line
      endings with gzip compress (GH25311)
  * Bug Fixes
    + I/O
      o Better handling of terminal printing when the terminal
        dimensions are not known (GH25080)
      o Bug in reading a HDF5 table-format DataFrame created in Python
        2, in Python 3 (GH24925)
      o Bug in reading a JSON with orient='table' generated by
        DataFrame.to_json() with index=False (GH25170)
      o Bug where float indexes could have misaligned values when
        printing (GH25061)
    + Reshaping
      o Bug in transform() where applying a function to a timezone aware
        column would return a timezone naive result (GH24198)
      o Bug in DataFrame.join() when joining on a timezone aware
        DatetimeIndex (GH23931)
      o Visualization
      o Bug in Series.plot() where a secondary y axis could not be set
        to log scale (GH25545)
    + Other
      o Bug in Series.is_unique() where single occurrences of NaN were
        not considered unique (GH25180)
      o Bug in merge() when merging an empty DataFrame with an Int64
        column or a non-empty DataFrame with an Int64 column that is all
        NaN (GH25183)
      o Bug in IntervalTree where a RecursionError occurs upon
        construction due to an overflow when adding endpoints, which
        also causes IntervalIndex to crash during indexing operations
        (GH25485)
      o Bug in Series.size raising for some extension-array-backed
        Series, rather than returning the size (GH25580)
      o Bug in resampling raising for nullable integer-dtype columns
        (GH25580)

-------------------------------------------------------------------
Fri Feb 22 10:22:38 UTC 2019 - Tomáš Chvátal <tchvatal@suse.com>

- Add patch to fix testrun on 32bit:
  https://github.com/pandas-dev/pandas/issues/25384
  * pandas-tests-memory.patch

-------------------------------------------------------------------
Thu Feb 21 10:45:17 UTC 2019 - Tomáš Chvátal <tchvatal@suse.com>

- Add requirement for at least 4 GB of physical memory

-------------------------------------------------------------------
Tue Feb 19 14:31:25 UTC 2019 - Tomáš Chvátal <tchvatal@suse.com>

- Do not delete tests, they are used even by other inheriting packages
  for their testing
- Execute tests

-------------------------------------------------------------------
Tue Feb  5 22:16:08 UTC 2019 - Todd R <toddrme2178@gmail.com>

- Update to 0.24.1
  * The default ``sort`` value for :meth:`Index.union` has changed from ``True`` to ``None`` (:issue:`24959`).
    The default *behavior*, however, remains the same
  * Fixed regression in :meth:`DataFrame.to_dict` with ``records`` orient raising an
    ``AttributeError`` when the ``DataFrame`` contained more than 255 columns, or
    wrongly converting column names that were not valid python identifiers (:issue:`24939`, :issue:`24940`).
  * Fixed regression in :func:`read_sql` when passing certain queries with MySQL/pymysql (:issue:`24988`).
  * Fixed regression in :class:`Index.intersection` incorrectly sorting the values by default (:issue:`24959`).
  * Fixed regression in :func:`merge` when merging an empty ``DataFrame`` with multiple timezone-aware columns on one of the timezone-aware columns (:issue:`25014`).
  * Fixed regression in :meth:`Series.rename_axis` and :meth:`DataFrame.rename_axis` where passing ``None`` failed to remove the axis name (:issue:`25034`)
  * Fixed regression in :func:`to_timedelta` with `box=False` incorrectly returning a ``datetime64`` object instead of a ``timedelta64`` object (:issue:`24961`)
  * Fixed regression where custom hashable types could not be used as column keys in :meth:`DataFrame.set_index` (:issue:`24969`)
  * Bug in :meth:`DataFrame.groupby` with :class:`Grouper` when there is a time change (DST) and grouping frequency is ``'1d'`` (:issue:`24972`)
  * Fixed the warning for implicitly registered matplotlib converters not showing. See :ref:`whatsnew_0211.converters` for more (:issue:`24963`).
  * Fixed AttributeError when printing a DataFrame's HTML repr after accessing the IPython config object (:issue:`25036`)

-------------------------------------------------------------------
Mon Jan 28 15:46:08 UTC 2019 - Todd R <toddrme2178@gmail.com>

- Update to 0.24.0
  Highlights include:
  * Optional Integer NA Support
  * New APIs for accessing the array backing a Series or Index
  * A new top-level method for creating arrays
  * Store Interval and Period data in a Series or DataFrame
  * Support for joining on two MultiIndexes

-------------------------------------------------------------------
Wed Aug  8 16:26:30 UTC 2018 - jengelh@inai.de

- Ensure neutrality of description. Remove future visions.
  Use noun phrase in summary.

-------------------------------------------------------------------
Sat Aug  4 19:07:22 UTC 2018 - toddrme2178@gmail.com

- Update to 0.23.4
  * Python 3.7 with Windows gave all missing values for rolling variance calculations (:issue:`21813`)
  * Bug where calling :func:`DataFrameGroupBy.agg` with a list of functions including ``ohlc`` as the non-initial element would raise a ``ValueError`` (:issue:`21716`)
  * Bug in ``roll_quantile`` caused a memory leak when calling ``.rolling(...).quantile(q)`` with ``q`` in (0,1) (:issue:`21965`)
  * Bug in :func:`Series.clip` and :func:`DataFrame.clip` cannot accept list-like threshold containing ``NaN`` (:issue:`19992`)

-------------------------------------------------------------------
Sat Jul 14 01:59:02 UTC 2018 - arun@gmx.de

- update to version 0.23.3:
  * This release fixes a build issue with the sdist for Python 3.7
    (GH21785) There are no other changes.

-------------------------------------------------------------------
Sat Jul  7 17:09:22 UTC 2018 - arun@gmx.de

- update to version 0.23.2:
  * Fixed Regressions
    + Fixed regression in to_csv() when handling file-like object
      incorrectly (GH21471)
    + Re-allowed duplicate level names of a MultiIndex. Accessing a
      level that has a duplicate name by name still raises an error
      (GH19029).
    + Bug in both DataFrame.first_valid_index() and
      Series.first_valid_index() raised for a row index having
      duplicate values (GH21441)
    + Fixed printing of DataFrames with hierarchical columns with long
      names (GH21180)
    + Fixed regression in reindex() and groupby() with a MultiIndex or
      multiple keys that contains categorical datetime-like values
      (GH21390).
    + Fixed regression in unary negative operations with object dtype
      (GH21380)
    + Bug in Timestamp.ceil() and Timestamp.floor() when timestamp is
      a multiple of the rounding frequency (GH21262)
    + Fixed regression in to_clipboard() that defaulted to copying
      dataframes with space delimited instead of tab delimited
      (GH21104)
  * Build Changes
    + The source and binary distributions no longer include test data
      files, resulting in smaller download sizes. Tests relying on
      these data files will be skipped when using
      pandas.test(). (GH19320)
  * Bug Fixes
  * Conversion
    + Bug in constructing Index with an iterator or generator
      (GH21470)
    + Bug in Series.nlargest() for signed and unsigned integer dtypes
      when the minimum value is present (GH21426)
  * Indexing
    + Bug in Index.get_indexer_non_unique() with categorical key
      (GH21448)
    + Bug in comparison operations for MultiIndex where error was
      raised on equality / inequality comparison involving a
      MultiIndex with nlevels == 1 (GH21149)
    + Bug in DataFrame.drop() behaviour is not consistent for unique
      and non-unique indexes (GH21494)
    + Bug in DataFrame.duplicated() with a large number of columns
      causing a ‘maximum recursion depth exceeded’ (GH21524).
  * I/O
    + Bug in read_csv() that caused it to incorrectly raise an error
      when nrows=0, low_memory=True, and index_col was not None
      (GH21141)
    + Bug in json_normalize() when formatting the record_prefix with
      integer columns (GH21536)
  * Categorical
    + Bug in rendering Series with Categorical dtype in rare
      conditions under Python 2.7 (GH21002)
  * Timezones
    + Bug in Timestamp and DatetimeIndex where passing a Timestamp
      localized after a DST transition would return a datetime before
      the DST transition (GH20854)
    + Bug in comparing DataFrame`s with tz-aware :class:`DatetimeIndex
      columns with a DST transition that raised a KeyError (GH19970)
  * Timedelta
    + Bug in Timedelta where non-zero timedeltas shorter than 1
      microsecond were considered False (GH21484)

-------------------------------------------------------------------
Wed Jun 13 17:45:54 UTC 2018 - toddrme2178@gmail.com

- Update to 0.23.1
  + Fixed Regressions
    * Reverted change to comparing a Series holding datetimes and a datetime.date object
    * Reverted the ability of to_sql() to perform multivalue inserts as this caused regression in certain cases (GH21103). In the future this will be made configurable.
    * Fixed regression in the DatetimeIndex.date and DatetimeIndex.time attributes in case of timezone-aware data: DatetimeIndex.time returned a tz-aware time instead of tz-naive (GH21267) and DatetimeIndex.date returned incorrect date when the input date has a non-UTC timezone (GH21230).
    * Fixed regression in pandas.io.json.json_normalize() when called with None values in nested levels in JSON, and to not drop keys with value as None (GH21158, GH21356).
    * Bug in to_csv() causes encoding error when compression and encoding are specified (GH21241, GH21118)
    * Bug preventing pandas from being importable with -OO optimization (GH21071)
    * Bug in Categorical.fillna() incorrectly raising a TypeError when value the individual categories are iterable and value is an iterable (GH21097, GH19788)
    * Fixed regression in constructors coercing NA values like None to strings when passing dtype=str (GH21083)
    * Regression in pivot_table() where an ordered Categorical with missing values for the pivot’s index would give a mis-aligned result (GH21133)
    * Fixed regression in merging on boolean index/columns (GH21119).
  + Performance Improvements
    * Improved performance of CategoricalIndex.is_monotonic_increasing(), CategoricalIndex.is_monotonic_decreasing() and CategoricalIndex.is_monotonic() (GH21025)
    * Improved performance of CategoricalIndex.is_unique() (GH21107)
  + Bug fixes
    * Groupby/Resample/Rolling
      > Bug in DataFrame.agg() where applying multiple aggregation functions to a DataFrame with duplicated column names would cause a stack overflow (GH21063)
      > Bug in pandas.core.groupby.GroupBy.ffill() and pandas.core.groupby.GroupBy.bfill() where the fill within a grouping would not always be applied as intended due to the implementations’ use of a non-stable sort (GH21207)
      > Bug in pandas.core.groupby.GroupBy.rank() where results did not scale to 100% when specifying method='dense' and pct=True
      > Bug in pandas.DataFrame.rolling() and pandas.Series.rolling() which incorrectly accepted a 0 window size rather than raising (GH21286)
    * Data-type specific
      > Bug in Series.str.replace() where the method throws TypeError on Python 3.5.2 (:issue: 21078)
      > Bug in Timedelta: where passing a float with a unit would prematurely round the float precision (:issue: 14156)
      > Bug in pandas.testing.assert_index_equal() which raised AssertionError incorrectly, when comparing two CategoricalIndex objects with param check_categorical=False (GH19776)
    * Sparse
      > Bug in SparseArray.shape which previously only returned the shape SparseArray.sp_values (GH21126)
    * Indexing
      > Bug in Series.reset_index() where appropriate error was not raised with an invalid level name (GH20925)
      > Bug in interval_range() when start/periods or end/periods are specified with float start or end (GH21161)
      > Bug in MultiIndex.set_names() where error raised for a MultiIndex with nlevels == 1 (GH21149)
      > Bug in IntervalIndex constructors where creating an IntervalIndex from categorical data was not fully supported (GH21243, issue:21253)
      > Bug in MultiIndex.sort_index() which was not guaranteed to sort correctly with level=1; this was also causing data misalignment in particular DataFrame.stack() operations (GH20994, GH20945, GH21052)
    * Plotting
      > New keywords (sharex, sharey) to turn on/off sharing of x/y-axis by subplots generated with pandas.DataFrame().groupby().boxplot() (:issue: 20968)
    * I/O
      > Bug in IO methods specifying compression='zip' which produced uncompressed zip archives (GH17778, GH21144)
      > Bug in DataFrame.to_stata() which prevented exporting DataFrames to buffers and most file-like objects (GH21041)
      > Bug in read_stata() and StataReader which did not correctly decode utf-8 strings on Python 3 from Stata 14 files (dta version 118) (GH21244)
      > Bug in IO JSON read_json() reading empty JSON schema with orient='table' back to DataFrame caused an error (GH21287)
    * Reshaping
      > Bug in concat() where error was raised in concatenating Series with numpy scalar and tuple names (GH21015)
      > Bug in concat() warning message providing the wrong guidance for future behavior (GH21101)
    * Other
      > Tab completion on Index in IPython no longer outputs deprecation warnings (GH21125)
      > Bug preventing pandas being used on Windows without C++ redistributable installed (GH21106)



-------------------------------------------------------------------
Mon May 21 17:50:23 UTC 2018 - toddrme2178@gmail.com

- Update dependencies

-------------------------------------------------------------------
Thu May 17 12:28:44 UTC 2018 - tchvatal@suse.com

- Update to 0.23.0:
  * Round-trippable JSON format with ‘table’ orient.
  * Instantiation from dicts respects order for Python 3.6+.
  * Dependent column arguments for assign.
  * Merging / sorting on a combination of columns and index levels.
  * Extending Pandas with custom types.
  * Excluding unobserved categories from groupby.
  * Changes to make output shape of DataFrame.apply consistent.

-------------------------------------------------------------------
Thu May 17 12:06:17 UTC 2018 - tchvatal@suse.com

- Do not bother generating pandas doc if it is already in both
  html and pdf provided by upstream, just point to the URL

-------------------------------------------------------------------
Thu Jan 11 11:18:48 UTC 2018 - tchvatal@suse.com

- Drop commented code to allow us py3 only build

-------------------------------------------------------------------
Wed Jan  3 22:41:40 UTC 2018 - arun@gmx.de

- specfile:
  * update copyright year

- update to version 0.22.0:
  * Pandas 0.22.0 changes the handling of empty and all-NA sums and
    products. The summary is that
    + The sum of an empty or all-NA Series is now 0
    + The product of an empty or all-NA Series is now 1
    + We’ve added a min_count parameter to .sum() and .prod()
      controlling the minimum number of valid values for the result to
      be valid. If fewer than min_count non-NA values are present, the
      result is NA. The default is 0. To return NaN, the 0.21
      behavior, use min_count=1.

-------------------------------------------------------------------
Sat Dec 16 23:04:54 UTC 2017 - arun@gmx.de

- update to version 0.21.1:
  * Highlights include:
    + Temporarily restore matplotlib datetime plotting
      functionality. This should resolve issues for users who
      implicitly relied on pandas to plot datetimes with
      matplotlib. See here.
    + Improvements to the Parquet IO functions introduced in
      0.21.0. See here.
  * Improvements to the Parquet IO functionality
    + DataFrame.to_parquet() will now write non-default indexes when
      the underlying engine supports it. The indexes will be preserved
      when reading back in with read_parquet() (GH18581).
    + read_parquet() now allows to specify the columns to read from a
      parquet file (GH18154)
    + read_parquet() now allows to specify kwargs which are passed to
      the respective engine (GH18216)
  * Other Enhancements
    + Timestamp.timestamp() is now available in Python 2.7. (GH17329)
    + Grouper and TimeGrouper now have a friendly repr output
      (GH18203).
  * Deprecations
    + pandas.tseries.register has been renamed to
      pandas.plotting.register_matplotlib_converters`() (GH18301)
  * Performance Improvements
    + Improved performance of plotting large series/dataframes
      (GH18236).
  * Conversion
    + Bug in TimedeltaIndex subtraction could incorrectly overflow
      when NaT is present (GH17791)
    + Bug in DatetimeIndex subtracting datetimelike from DatetimeIndex
      could fail to overflow (GH18020)
    + Bug in IntervalIndex.copy() when copying and IntervalIndex with
      non-default closed (GH18339)
    + Bug in DataFrame.to_dict() where columns of datetime that are
      tz-aware were not converted to required arrays when used with
      orient='records', raising"TypeError` (GH18372)
    + Bug in DateTimeIndex and date_range() where mismatching tz-aware
      start and end timezones would not raise an err if end.tzinfo is
      None (GH18431)
    + Bug in Series.fillna() which raised when passed a long integer
      on Python 2 (GH18159).
  * Indexing
    + Bug in a boolean comparison of a datetime.datetime and a
      datetime64[ns] dtype Series (GH17965)
    + Bug where a MultiIndex with more than a million records was not
      raising AttributeError when trying to access a missing attribute
      (GH18165)
    + Bug in IntervalIndex constructor when a list of intervals is
      passed with non-default closed (GH18334)
    + Bug in Index.putmask when an invalid mask passed (GH18368)
    + Bug in masked assignment of a timedelta64[ns] dtype Series,
      incorrectly coerced to float (GH18493)
  * I/O
    + Bug in class:~pandas.io.stata.StataReader not converting
      date/time columns with display formatting addressed
      (GH17990). Previously columns with display formatting were
      normally left as ordinal numbers and not converted to datetime
      objects.
    + Bug in read_csv() when reading a compressed UTF-16 encoded file
      (GH18071)
    + Bug in read_csv() for handling null values in index columns when
      specifying na_filter=False (GH5239)
    + Bug in read_csv() when reading numeric category fields with high
      cardinality (GH18186)
    + Bug in DataFrame.to_csv() when the table had MultiIndex columns,
      and a list of strings was passed in for header (GH5539)
    + Bug in parsing integer datetime-like columns with specified
      format in read_sql (GH17855).
    + Bug in DataFrame.to_msgpack() when serializing data of the
      numpy.bool_ datatype (GH18390)
    + Bug in read_json() not decoding when reading line deliminted
      JSON from S3 (GH17200)
    + Bug in pandas.io.json.json_normalize() to avoid modification of
      meta (GH18610)
    + Bug in to_latex() where repeated multi-index values were not
      printed even though a higher level index differed from the
      previous row (GH14484)
    + Bug when reading NaN-only categorical columns in HDFStore
      (GH18413)
    + Bug in DataFrame.to_latex() with longtable=True where a latex
      multicolumn always spanned over three columns (GH17959)
  * Plotting
    + Bug in DataFrame.plot() and Series.plot() with DatetimeIndex
      where a figure generated by them is not pickleable in Python 3
      (GH18439)
  * Groupby/Resample/Rolling
    + Bug in DataFrame.resample(...).apply(...) when there is a
      callable that returns different columns (GH15169)
    + Bug in DataFrame.resample(...) when there is a time change (DST)
      and resampling frequecy is 12h or higher (GH15549)
    + Bug in pd.DataFrameGroupBy.count() when counting over a
      datetimelike column (GH13393)
    + Bug in rolling.var where calculation is inaccurate with a
      zero-valued array (GH18430)
  * Reshaping
    + Error message in pd.merge_asof() for key datatype mismatch now
      includes datatype of left and right key (GH18068)
    + Bug in pd.concat when empty and non-empty DataFrames or Series
      are concatenated (GH18178 GH18187)
    + Bug in DataFrame.filter(...) when unicode is passed as a
      condition in Python 2 (GH13101)
    + Bug when merging empty DataFrames when np.seterr(divide='raise')
      is set (GH17776)
  * Numeric
    + Bug in pd.Series.rolling.skew() and rolling.kurt() with all
      equal values has floating issue (GH18044)
    + Bug in TimedeltaIndex subtraction could incorrectly overflow
      when NaT is present (GH17791)
    + Bug in DatetimeIndex subtracting datetimelike from DatetimeIndex
      could fail to overflow (GH18020)
  * Categorical
    + Bug in DataFrame.astype() where casting to ‘category’ on an
      empty DataFrame causes a segmentation fault (GH18004)
    + Error messages in the testing module have been improved when
      items have different CategoricalDtype (GH18069)
    + CategoricalIndex can now correctly take a
      pd.api.types.CategoricalDtype as its dtype (GH18116)
    + Bug in Categorical.unique() returning read-only codes array when
      all categories were NaN (GH18051)
    + Bug in DataFrame.groupby(axis=1) with a CategoricalIndex
      (GH18432)
  * String
    + Series.str.split() will now propogate NaN values across all
      expanded columns instead of None (GH18450)

-------------------------------------------------------------------
Mon Oct 30 06:05:48 UTC 2017 - arun@gmx.de

- specfile:
  * updated minimum numpy version to 1.9.0 (see setup.py)

- update to version 0.21.0:
  * Highlights include:
    + Integration with Apache Parquet, including a new top-level
      read_parquet() function and DataFrame.to_parquet() method, see
      here.
    + New user-facing pandas.api.types.CategoricalDtype for specifying
      categoricals independent of the data, see here.
    + The behavior of sum and prod on all-NaN Series/DataFrames is now
      consistent and no longer depends on whether bottleneck is
      installed, see here.
    + Compatibility fixes for pypy, see here.
    + Additions to the drop, reindex and rename API to make them more
      consistent, see here.
    + Addition of the new methods DataFrame.infer_objects (see here)
      and GroupBy.pipe (see here).
    + Indexing with a list of labels, where one or more of the labels
      is missing, is deprecated and will raise a KeyError in a future
      version, see here.
  * full list at http://pandas.pydata.org/pandas-docs/stable/whatsnew.html

-------------------------------------------------------------------
Sat Sep 23 21:12:48 UTC 2017 - arun@gmx.de

- update to version 0.20.3:
  * bug fix release, see http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#v0-20-3-july-7-2017
    for complete changelog

- changes from version 0.20.2:
  * bug fix release, see http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#v0-20-2-june-4-2017
    for complete changelog

-------------------------------------------------------------------
Thu May 18 01:07:08 UTC 2017 - toddrme2178@gmail.com

- Update to version 0.20.1
  Highlights include:
  * New ``.agg()`` API for Series/DataFrame similar to the
    groupby-rolling-resample API's
  * Integration with the ``feather-format``, including a new
    top-level ``pd.read_feather()`` and ``DataFrame.to_feather()``
    method
  * The ``.ix`` indexer has been deprecated
  * ``Panel`` has been deprecated
  * Addition of an ``IntervalIndex`` and ``Interval`` scalar type
  * Improved user API when grouping by index levels in ``.groupby()``
  * Improved support for ``UInt64`` dtypes
  * A new orient for JSON serialization, ``orient='table'``, that
    uses the Table Schema spec and that gives the possibility for
    a more interactive repr in the Jupyter Notebook
  * Experimental support for exporting styled DataFrames
    (``DataFrame.style``) to Excel
  * Window binary corr/cov operations now return a MultiIndexed
    ``DataFrame`` rather than a ``Panel``, as ``Panel`` is now
    deprecated
  * Support for S3 handling now uses ``s3fs``
  * Google BigQuery support now uses the ``pandas-gbq`` library

-------------------------------------------------------------------
Mon May  8 03:37:27 UTC 2017 - toddrme2178@gmail.com

- Fix dateutil dependency

-------------------------------------------------------------------
Tue Apr 25 18:39:03 UTC 2017 - toddrme2178@gmail.com

- Implement single-spec version.

-------------------------------------------------------------------
Thu Mar 30 15:00:41 UTC 2017 - toddrme2178@gmail.com

- update to version 0.19.2:
  * Enhancements
    The pd.merge_asof(), added in 0.19.0, gained some improvements:
    + pd.merge_asof() gained left_index/right_index and
      left_by/right_by arguments (GH14253)
    + pd.merge_asof() can take multiple columns in by parameter and
      has specialized dtypes for better performace (GH13936)
  * Performance Improvements
    + Performance regression with PeriodIndex (GH14822)
    + Performance regression in indexing with getitem (GH14930)
    + Improved performance of .replace() (GH12745)
    + Improved performance Series creation with a datetime index and
      dictionary data (GH14894)
  * Bug Fixes
    + Compat with python 3.6 for pickling of some offsets (GH14685)
    + Compat with python 3.6 for some indexing exception types
      (GH14684, GH14689)
    + Compat with python 3.6 for deprecation warnings in the test
      suite (GH14681)
    + Compat with python 3.6 for Timestamp pickles (GH14689)
    + Compat with dateutil==2.6.0; segfault reported in the testing
      suite (GH14621)
    + Allow nanoseconds in Timestamp.replace as a kwarg (GH14621)
    + Bug in pd.read_csv in which aliasing was being done for
      na_values when passed in as a dictionary (GH14203)
    + Bug in pd.read_csv in which column indices for a dict-like
      na_values were not being respected (GH14203)
    + Bug in pd.read_csv where reading files fails, if the number of
      headers is equal to the number of lines in the file (GH14515)
    + Bug in pd.read_csv for the Python engine in which an unhelpful
      error message was being raised when multi-char delimiters were
      not being respected with quotes (GH14582)
    + Fix bugs (GH14734, GH13654) in pd.read_sas and
      pandas.io.sas.sas7bdat.SAS7BDATReader that caused problems when
      reading a SAS file incrementally.
    + Bug in pd.read_csv for the Python engine in which an unhelpful
      error message was being raised when skipfooter was not being
      respected by Python’s CSV library (GH13879)
    + Bug in .fillna() in which timezone aware datetime64 values were
      incorrectly rounded (GH14872)
    + Bug in .groupby(..., sort=True) of a non-lexsorted MultiIndex
      when grouping with multiple levels (GH14776)
    + Bug in pd.cut with negative values and a single bin (GH14652)
    + Bug in pd.to_numeric where a 0 was not unsigned on a
      downcast='unsigned' argument (GH14401)
    + Bug in plotting regular and irregular timeseries using shared
      axes (sharex=True or ax.twinx()) (GH13341, GH14322).
    + Bug in not propogating exceptions in parsing invalid datetimes,
      noted in python 3.6 (GH14561)
    + Bug in resampling a DatetimeIndex in local TZ, covering a DST
      change, which would raise AmbiguousTimeError (GH14682)
    + Bug in indexing that transformed RecursionError into KeyError or
      IndexingError (GH14554)
    + Bug in HDFStore when writing a MultiIndex when using
      data_columns=True (GH14435)
    + Bug in HDFStore.append() when writing a Series and passing a
      min_itemsize argument containing a value for the index (GH11412)
    + Bug when writing to a HDFStore in table format with a
      min_itemsize value for the index and without asking to append
      (GH10381)
    + Bug in Series.groupby.nunique() raising an IndexError for an
      empty Series (GH12553)
    + Bug in DataFrame.nlargest and DataFrame.nsmallest when the index
      had duplicate values (GH13412)
    + Bug in clipboard functions on linux with python2 with unicode
      and separators (GH13747)
    + Bug in clipboard functions on Windows 10 and python 3 (GH14362,
      GH12807)
    + Bug in .to_clipboard() and Excel compat (GH12529)
    + Bug in DataFrame.combine_first() for integer columns (GH14687).
    + Bug in pd.read_csv() in which the dtype parameter was not being
      respected for empty data (GH14712)
    + Bug in pd.read_csv() in which the nrows parameter was not being
      respected for large input when using the C engine for parsing
      (GH7626)
    + Bug in pd.merge_asof() could not handle timezone-aware
      DatetimeIndex when a tolerance was specified (GH14844)
    + Explicit check in to_stata and StataWriter for out-of-range
      values when writing doubles (GH14618)
    + Bug in .plot(kind='kde') which did not drop missing values to
      generate the KDE Plot, instead generating an empty
      plot. (GH14821)
    + Bug in unstack() if called with a list of column(s) as an
      argument, regardless of the dtypes of all columns, they get
      coerced to object (GH11847)
- update to version 0.19.1:
  * Performance Improvements
    + Fixed performance regression in factorization of Period data
      (GH14338)
    + Fixed performance regression in Series.asof(where) when where is
      a scalar (GH14461)
    + Improved performance in DataFrame.asof(where) when where is a
      scalar (GH14461)
    + Improved performance in .to_json() when lines=True (GH14408)
    + Improved performance in certain types of loc indexing with a
      MultiIndex (GH14551).
  * Bug Fixes
    + Source installs from PyPI will now again work without cython
      installed, as in previous versions (GH14204)
    + Compat with Cython 0.25 for building (GH14496)
    + Fixed regression where user-provided file handles were closed in
      read_csv (c engine) (GH14418).
    + Fixed regression in DataFrame.quantile when missing values where
      present in some columns (GH14357).
    + Fixed regression in Index.difference where the freq of a
      DatetimeIndex was incorrectly set (GH14323)
    + Added back pandas.core.common.array_equivalent with a
      deprecation warning (GH14555).
    + Bug in pd.read_csv for the C engine in which quotation marks
      were improperly parsed in skipped rows (GH14459)
    + Bug in pd.read_csv for Python 2.x in which Unicode quote
      characters were no longer being respected (GH14477)
    + Fixed regression in Index.append when categorical indices were
      appended (GH14545).
    + Fixed regression in pd.DataFrame where constructor fails when
      given dict with None value (GH14381)
    + Fixed regression in DatetimeIndex._maybe_cast_slice_bound when
      index is empty (GH14354).
    + Bug in localizing an ambiguous timezone when a boolean is passed
      (GH14402)
    + Bug in TimedeltaIndex addition with a Datetime-like object where
      addition overflow in the negative direction was not being caught
      (GH14068, GH14453)
    + Bug in string indexing against data with object Index may raise
      AttributeError (GH14424)
    + Corrrecly raise ValueError on empty input to pd.eval() and
      df.query() (GH13139)
    + Bug in RangeIndex.intersection when result is a empty set
      (GH14364).
    + Bug in groupby-transform broadcasting that could cause incorrect
      dtype coercion (GH14457)
    + Bug in Series.__setitem__ which allowed mutating read-only
      arrays (GH14359).
    + Bug in DataFrame.insert where multiple calls with duplicate
      columns can fail (GH14291)
    + pd.merge() will raise ValueError with non-boolean parameters in
      passed boolean type arguments (GH14434)
    + Bug in Timestamp where dates very near the minimum (1677-09)
      could underflow on creation (GH14415)
    + Bug in pd.concat where names of the keys were not propagated to
      the resulting MultiIndex (GH14252)
    + Bug in pd.concat where axis cannot take string parameters 'rows'
      or 'columns' (GH14369)
    + Bug in pd.concat with dataframes heterogeneous in length and
      tuple keys (GH14438)
    + Bug in MultiIndex.set_levels where illegal level values were
      still set after raising an error (GH13754)
    + Bug in DataFrame.to_json where lines=True and a value contained
      a } character (GH14391)
    + Bug in df.groupby causing an AttributeError when grouping a
      single index frame by a column and the index level
      (:issue`14327`)
    + Bug in df.groupby where TypeError raised when
      pd.Grouper(key=...) is passed in a list (GH14334)
    + Bug in pd.pivot_table may raise TypeError or ValueError when
      index or columns is not scalar and values is not specified
      (GH14380)

-------------------------------------------------------------------
Sun Oct 23 01:32:23 UTC 2016 - toddrme2178@gmail.com

- update to version 0.19.0:
  (long changelog, see http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#v0-19-0-october-2-2016)
  * Highlights include:
    + merge_asof() for asof-style time-series joining
    + .rolling() is now time-series aware
    + read_csv() now supports parsing Categorical data
    + A function union_categorical() has been added for combining
      categoricals
    + PeriodIndex now has its own period dtype, and changed to be more
      consistent with other Index classes
    + Sparse data structures gained enhanced support of int and bool
      dtypes
    + Comparison operations with Series no longer ignores the index,
      see here for an overview of the API changes.
    + Introduction of a pandas development API for utility functions
    + Deprecation of Panel4D and PanelND. We recommend to represent
      these types of n-dimensional data with the xarray package.
    + Removal of the previously deprecated modules pandas.io.data,
      pandas.io.wb, pandas.tools.rplot.
- specfile:
  * require python3-Cython
  * Split documentation into own subpackage to speed up build.
  * Remove buildrequires for optional dependencies to speed up build.
- Remove unneeded patches:
  * 0001_disable_experimental_msgpack_big_endian.patch ^
  * 0001_respect_byteorder_in_statareader.patch 

-------------------------------------------------------------------
Tue Jul 12 16:44:48 UTC 2016 - antoine.belvire@laposte.net

- Update to 0.8.1:
  * .groupby(...) has been enhanced to provide convenient syntax
    when working with .rolling(..), .expanding(..) and
    .resample(..) per group.
  * pd.to_datetime() has gained the ability to assemble dates
    from a DataFrame.
  * Method chaining improvements.
  * Custom business hour offset.
  * Many bug fixes in the handling of sparse.
  * Expanded the Tutorials section with a feature on modern pandas,
    courtesy of @TomAugsb (GH13045).
- Changes from 0.8.0:
  * Moving and expanding window functions are now methods on Series
    and DataFrame, similar to .groupby.
  * Adding support for a RangeIndex as a specialized form of the
    Int64Index for memory savings.
  * API breaking change to the .resample method to make it more
    .groupby like.
  * Removal of support for positional indexing with floats, which
    was deprecated since 0.14.0. This will now raise a TypeError.
  * The .to_xarray() function has been added for compatibility with
    the xarray package.
  * The read_sas function has been enhanced to read sas7bdat files.
  * Addition of the .str.extractall() method, and API changes to
    the .str.extract() method and .str.cat() method.
  * pd.test() top-level nose test runner is available (GH4327).

-------------------------------------------------------------------
Fri Feb 26 13:13:58 UTC 2016 - tbechtold@suse.com

- Require python-python-dateutil. package was renamed

-------------------------------------------------------------------
Tue Feb  9 17:01:02 UTC 2016 - aplanas@suse.com

- Add 0001_respect_byteorder_in_statareader.patch
  Fix StataReader in big endian architectures
  https://github.com/pydata/pandas/issues/11282
- Add 0001_disable_experimental_msgpack_big_endian.patch 
  Skip experimental msgpack test in big endian systems

-------------------------------------------------------------------
Wed Feb  3 15:27:31 UTC 2016 - aplanas@suse.com

- Remove non-needed BuildRequires
- Update Requires from documentation
- Update Recommends from documentation
- Add tests in %check section

-------------------------------------------------------------------
Mon Nov 30 09:56:31 UTC 2015 - toddrme2178@gmail.com

- update to version 0.17.1:
  (for full changelog see http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#v0-17-1-november-21-2015)
  Highlights include:
  * Support for Conditional HTML Formatting, see here
  * Releasing the GIL on the csv reader & other ops, see here
  * Fixed regression in DataFrame.drop_duplicates from 0.16.2, causing
    incorrect results on integer values (GH11376)

-------------------------------------------------------------------
Mon Oct 12 09:28:25 UTC 2015 - toddrme2178@gmail.com

- update to version 0.17.0:
  (for full changelog see http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#v0-17-0-october-9-2015)
  Highlights:
  * Release the Global Interpreter Lock (GIL) on some cython
    operations, see here
  * Plotting methods are now available as attributes of the .plot
    accessor, see here
  * The sorting API has been revamped to remove some long-time
    inconsistencies, see here
  * Support for a datetime64[ns] with timezones as a first-class
    dtype, see here
  * The default for to_datetime will now be to raise when presented
    with unparseable formats, previously this would return the
    original input.  Also, date parse functions now return consistent
    results. See here
  * The default for dropna in HDFStore has changed to False, to store
    by default all rows even if they are all NaN, see here
  * Datetime accessor (dt) now supports Series.dt.strftime to generate
    formatted strings for datetime-likes, and Series.dt.total_seconds
    to ge nerate each duration of the timedelta in seconds. See here
  * Period and PeriodIndex can handle multiplied freq like 3D, which
    corresponding to 3 days span. See here
  * Development installed versions of pandas will now have PEP440
    compliant version strings (GH9518)
  * Development support for benchmarking with the Air Speed Velocity
    library (GH8361)
  * Support for reading SAS xport files, see here
  * Documentation comparing SAS to pandas, see here
  * Removal of the automatic TimeSeries broadcasting, deprecated since
    0.8.0, see here
  * Display format with plain text can optionally align with Unicode
    East Asian Width, see here
  * Compatibility with Python 3.5 (GH11097)
  * Compatibility with matplotlib 1.5.0 (GH11111)

-------------------------------------------------------------------
Mon Jun 29 11:06:30 UTC 2015 - toddrme2178@gmail.com

- update to version 0.16.2:
  (see http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#v0-16-2-june-12-2015)
  * Highlights
    + A new pipe method
    + Documentation on how to use numba with pandas
  * Enhancements
    + Added rsplit to Index/Series StringMethods (GH10303)
    + Removed the hard-coded size limits on the DataFrame HTML
      representation in the IPython notebook, and leave this to
      IPython itself (only for IPython v3.0 or greater). This
      eliminates the duplicate scroll bars that appeared in the
      notebook with large frames (GH10231).

      Note that the notebook has a toggle output scrolling feature to
      limit the display of very large frames (by clicking left of the
      output). You can also configure the way DataFrames are displayed
      using the pandas options, see here here.
    + axis parameter of DataFrame.quantile now accepts also index and
      column. (GH9543)
  * API Changes
    + Holiday now raises NotImplementedError if both offset and
      observance are used in the constructor instead of returning an
      incorrect result (GH10217).
  * Performance Improvements
    + Improved Series.resample performance with dtype=datetime64[ns]
      (GH7754)
    + Increase performance of str.split when expand=True (GH10081)
  * Bug Fixes
    + Bug in Series.hist raises an error when a one row Series was
      given (GH10214)
    + Bug where HDFStore.select modifies the passed columns list
      (GH7212)
    + Bug in Categorical repr with display.width of None in Python 3
      (GH10087)
    + Bug in to_json with certain orients and a CategoricalIndex would
      segfault (GH10317)
    + Bug where some of the nan funcs do not have consistent return
      dtypes (GH10251)
    + Bug in DataFrame.quantile on checking that a valid axis was
      passed (GH9543)
    + Bug in groupby.apply aggregation for Categorical not preserving
      categories (GH10138)
    + Bug in to_csv where date_format is ignored if the datetime is
      fractional (GH10209)
    + Bug in DataFrame.to_json with mixed data types (GH10289)
    + Bug in cache updating when consolidating (GH10264)
    + Bug in mean() where integer dtypes can overflow (GH10172)
    + Bug where Panel.from_dict does not set dtype when specified
      (GH10058)
    + Bug in Index.union raises AttributeError when passing
      array-likes. (GH10149)
    + Bug in Timestamp‘s’ microsecond, quarter, dayofyear, week and
      daysinmonth properties return np.int type, not built-in
      int. (GH10050)
    + Bug in NaT raises AttributeError when accessing to daysinmonth,
      dayofweek properties. (GH10096)
    + Bug in Index repr when using the max_seq_items=None setting
      (GH10182).
    + Bug in getting timezone data with dateutil on various platforms
      ( GH9059, GH8639, GH9663, GH10121)
    + Bug in displaying datetimes with mixed frequencies; display ‘ms’
      datetimes to the proper precision. (GH10170)
    + Bug in setitem where type promotion is applied to the entire
      block (GH10280)
    + Bug in Series arithmetic methods may incorrectly hold names
      (GH10068)
    + Bug in GroupBy.get_group when grouping on multiple keys, one of
      which is categorical. (GH10132)
    + Bug in DatetimeIndex and TimedeltaIndex names are lost after
      timedelta arithmetics ( GH9926)
    + Bug in DataFrame construction from nested dict with datetime64
      (GH10160)
    + Bug in Series construction from dict with datetime64 keys
      (GH9456)
    + Bug in Series.plot(label="LABEL") not correctly setting the
      label (GH10119)
    + Bug in plot not defaulting to matplotlib axes.grid setting
      (GH9792)
    + Bug causing strings containing an exponent, but no decimal to be
      parsed as int instead of float in engine='python' for the read_csv
      parser (GH9565)
    + Bug in Series.align resets name when fill_value is specified
      (GH10067)
    + Bug in read_csv causing index name not to be set on an empty
      DataFrame (GH10184)
    + Bug in SparseSeries.abs resets name (GH10241)
    + Bug in TimedeltaIndex slicing may reset freq (GH10292)
    + Bug in GroupBy.get_group raises ValueError when group key
      contains NaT (GH6992)
    + Bug in SparseSeries constructor ignores input data name
      (GH10258)
    + Bug in Categorical.remove_categories causing a ValueError when
      removing the NaN category if underlying dtype is floating-point
      (GH10156)
    + Bug where infer_freq infers timerule (WOM-5XXX) unsupported by
      to_offset (GH9425)
    + Bug in DataFrame.to_hdf() where table format would raise a
      seemingly unrelated error for invalid (non-string) column
      names. This is now explicitly forbidden. (GH9057)
    + Bug to handle masking empty DataFrame (GH10126).
    + Bug where MySQL interface could not handle numeric table/column
      names (GH10255)
    + Bug in read_csv with a date_parser that returned a datetime64
      array of other time resolution than [ns] (GH10245)
    + Bug in Panel.apply when the result has ndim=0 (GH10332)
    + Bug in read_hdf where auto_close could not be passed (GH9327).
    + Bug in read_hdf where open stores could not be used (GH10330).
    + Bug in adding empty DataFrame``s, now results in a ``DataFrame
      that .equals an empty DataFrame (GH10181).
    + Bug in to_hdf and HDFStore which did not check that complib
      choices were valid (GH4582, GH8874).

-------------------------------------------------------------------
Tue May 19 09:18:50 UTC 2015 - toddrme2178@gmail.com

- Update to version 0.16.1
  * Highlights
    - Support for a ``CategoricalIndex``, a category based index
    - New section on how-to-contribute to pandas
    - Revised "Merge, join, and concatenate" documentation, 
      including graphical examples to make it easier to understand
      each operations
    - New method sample for drawing random samples from Series,
      DataFrames and Panels.
    - The default Index printing has changed to a more uniform 
      format
    - BusinessHour datetime-offset is now supported
  * Enhancements
    - BusinessHour`offset is now supported, which represents 
      business hours starting from 09:00 - 17:00 on BusinessDay by 
      default.
    - DataFrame.diff now takes an axis parameter that determines the
      direction of differencing
    - Allow clip, clip_lower, and clip_upper to accept array-like 
      arguments as thresholds (This is a regression from 0.11.0).
      These methods now have an axis parameter which determines 
      how the Series or DataFrame will be aligned with the 
      threshold(s).
    - DataFrame.mask() and Series.mask() now support same keywords 
      as where
    - drop function can now accept errors keyword to suppress 
      ValueError raised when any of label does not exist in the 
      target data.
    - Allow conversion of values with dtype datetime64 or timedelta64
      to strings using astype(str)
    - get_dummies function now accepts sparse keyword.  If set to
      True, the return DataFrame is sparse, e.g. SparseDataFrame.
    - Period now accepts datetime64 as value input.
    - Allow timedelta string conversion when leading zero is 
      missing from time definition, ie 0:00:00 vs 00:00:00.
    - Allow Panel.shift with axis='items'
    - Trying to write an excel file now raises NotImplementedError 
      if the DataFrame has a MultiIndex instead of writing a broken
      Excel file.
    - Allow Categorical.add_categories to accept Series or np.array.
    - Add/delete str/dt/cat accessors dynamically from __dir__.
    - Add normalize as a dt accessor method.
    - DataFrame and Series now have _constructor_expanddim property
      as overridable constructor for one higher dimensionality 
      data. This should be used only when it is really needed
    - pd.lib.infer_dtype now returns 'bytes' in Python 3 where 
      appropriate.
    - We introduce a CategoricalIndex, a new type of index object
      that is useful for supporting indexing with duplicates. This
      is a container around a Categorical (introduced in v0.15.0)
      and allows efficient indexing and storage of an index with a
      large number of duplicated elements. Prior to 0.16.1, 
      setting the index of a DataFrame/Series with a category 
      dtype would convert this to regular object-based Index.
    - Series, DataFrames, and Panels now have a new method: 
      pandas.DataFrame.sample. The method accepts a specific number
      of rows or columns to return, or a fraction of the total
      number or rows or columns. It also has options for sampling
      with or without replacement, for passing in a column for
      weights for non-uniform sampling, and for setting seed values
      to facilitate replication.
    - The following new methods are accesible via .str accessor to
      apply the function to each values.
      + capitalize()
      + swapcase()
      + normalize()
      + partition()
      + rpartition()
      + index()
      + rindex()
      + translate()
    - Added StringMethods (.str accessor) to Index
    - split now takes expand keyword to specify whether to expand
      dimensionality. return_type is deprecated.
  * API changes
    - When passing in an ax to df.plot( ..., ax=ax), the sharex 
      kwarg will now default to False.
    - Add support for separating years and quarters using dashes, 
      for example 2014-Q1.
    - pandas.DataFrame.assign now inserts new columns in 
      alphabetical order. Previously the order was arbitrary.
    - By default, read_csv and read_table will now try to infer
      the compression type based on the file extension. Set 
      compression=None to restore the previous behavior 
      (no decompression).
    - The string representation of Index and its sub-classes have 
      now been unified. These will show a single-line display if 
      there are few values; a wrapped multi-line display for a lot
      of values (but less than display.max_seq_items; if lots of 
      items > display.max_seq_items) will show a truncated display 
      (the head and tail of the data). The formatting for 
      MultiIndex is unchanges (a multi-line wrapped display). The
      display width responds to the option display.max_seq_items,
      which is defaulted to 100.
  * Deprecations
    - Series.str.split's return_type keyword was removed in favor 
      of expand
  * Performance Improvements
    - Improved csv write performance with mixed dtypes, including 
      datetimes by up to 5x
    - Improved csv write performance generally by 2x
    - Improved the performance of pd.lib.max_len_string_array 
      by 5-7x
  * Bug Fixes
    - Bug where labels did not appear properly in the legend of 
      DataFrame.plot(), passing label= arguments works, and Series 
      indices are no longer mutated.
    - Bug in json serialization causing a segfault when a frame had 
      zero length.
    - Bug in read_csv where missing trailing delimiters would cause 
      segfault.
    - Bug in retaining index name on appending
    - Bug in scatter_matrix draws unexpected axis ticklabels
    - Fixed bug in StataWriter resulting in changes to input 
      DataFrame upon save.
    - Bug in transform causing length mismatch when null entries 
      were present and a fast aggregator was being used
    - Bug in equals causing false negatives when block order 
      differed
    - Bug in grouping with multiple pd.Grouper where one is 
      non-time based
    - Bug in read_sql_table error when reading postgres table with 
      timezone
    - Bug in DataFrame slicing may not retain metadata
    - Bug where TimdeltaIndex were not properly serialized in fixed 
      HDFStore
    - Bug with TimedeltaIndex constructor ignoring name when given 
      another TimedeltaIndex as data.
    - Bug in DataFrameFormatter._get_formatted_index with not 
      applying max_colwidth to the DataFrame index
    - Bug in .loc with a read-only ndarray data source
    - Bug in groupby.apply() that would raise if a passed user 
      defined function either returned only None (for all input).
    - Always use temporary files in pytables tests
    - Bug in plotting continuously using secondary_y may not show 
      legend properly.
    - Bug in DataFrame.plot(kind="hist") results in TypeError when 
      DataFrame contains non-numeric columns
    - Bug where repeated plotting of DataFrame with a DatetimeIndex 
      may raise TypeError
    - Bug in setup.py that would allow an incompat cython version 
      to build
    - Bug in plotting secondary_y incorrectly attaches right_ax 
      property to secondary axes specifying itself recursively.
    - Bug in Series.quantile on empty Series of type Datetime or 
      Timedelta
    - Bug in where causing incorrect results when upcasting was 
      required
    - Bug in FloatArrayFormatter where decision boundary for 
      displaying "small" floats in decimal format is off by one 
      order of magnitude for a given display.precision
    - Fixed bug where DataFrame.plot() raised an error when both 
      color and style keywords were passed and there was no color 
      symbol in the style strings
    - Not showing a DeprecationWarning on combining list-likes with 
      an Index
    - Bug in read_csv and read_table when using skip_rows parameter 
      if blank lines are present.
    - Bug in read_csv() interprets index_col=True as 1
    - Bug in index equality comparisons using == failing on 
      Index/MultiIndex type incompatibility
    - Bug in which SparseDataFrame could not take nan as a column 
      name
    - Bug in to_msgpack and read_msgpack zlib and blosc compression 
      support
    - Bug GroupBy.size doesn't attach index name properly if 
      grouped by TimeGrouper
    - Bug causing an exception in slice assignments because 
      length_of_indexer returns wrong results
    - Bug in csv parser causing lines with initial whitespace plus 
      one non-space character to be skipped.
    - Bug in C csv parser causing spurious NaNs when data started 
      with newline followed by whitespace.
    - Bug causing elements with a null group to spill into the 
      final group when grouping by a Categorical
    - Bug where .iloc and .loc behavior is not consistent on empty 
      dataframes
    - Bug in invalid attribute access on a TimedeltaIndex 
      incorrectly raised ValueError instead of AttributeError
    - Bug in unequal comparisons between categorical data and a 
      scalar, which was not in the categories (e.g. 
      Series(Categorical(list("abc"), ordered=True)) > "d". This 
      returned False for all elements, but now raises a TypeError. 
      Equality comparisons also now return False for == and True 
      for !=.
    - Bug in DataFrame __setitem__ when right hand side is a 
      dictionary
    - Bug in where when dtype is datetime64/timedelta64, but dtype 
      of other is not
    - Bug in MultiIndex.sortlevel() results in unicode level name 
      breaks
    - Bug in which groupby.transform incorrectly enforced output 
      dtypes to match input dtypes.
    - Bug in DataFrame constructor when columns parameter is set, 
      and data is an empty list
    - Bug in bar plot with log=True raises TypeError if all values 
      are less than 1
    - Bug in horizontal bar plot ignores log=True
    - Bug in PyTables queries that did not return proper results 
      using the index
    - Bug where dividing a dataframe containing values of type 
      Decimal by another Decimal would raise.
    - Bug where using DataFrames asfreq would remove the name of 
      the index.
    - Bug causing extra index point when resample BM/BQ
    - Changed caching in AbstractHolidayCalendar to be at the 
      instance level rather than at the class level as the latter 
      can result in  unexpected behaviour.
    - Fixed latex output for multi-indexed dataframes
    - Bug causing an exception when setting an empty range using 
      DataFrame.loc
    - Bug in hiding ticklabels with subplots and shared axes when 
      adding a new plot to an existing grid of axes
    - Bug in transform and filter when grouping on a categorical 
      variable
    - Bug in transform when groups are equal in number and dtype to 
      the input index
    - Google BigQuery connector now imports dependencies on a 
      per-method basis.
    - Updated BigQuery connector to no longer use deprecated 
      oauth2client.tools.run()
    - Bug in subclassed DataFrame. It may not return the correct 
      class, when slicing or subsetting it.
    - Bug in .median() where non-float null values are not handled 
      correctly
    - Bug in Series.fillna() where it raises if a numerically 
      convertible string is given

-------------------------------------------------------------------
Tue Mar 24 12:44:20 UTC 2015 - toddrme2178@gmail.com
   
- update to version 0.16.0:
  * Highlights:
    - DataFrame.assign method
    - Series.to_coo/from_coo methods to interact with scipy.sparse
    - Backwards incompatible change to Timedelta to conform the .seconds 
      attribute with datetime.timedelta
    - Changes to the .loc slicing API to conform with the behavior of .ix
    - Changes to the default for ordering in the Categorical constructor
    - Enhancement to the .str accessor to make string operations easier
    - The pandas.tools.rplot, pandas.sandbox.qtpandas and pandas.rpy 
      modules are deprecated.  We refer users to external packages like 
      seaborn, pandas-qt and rpy2 for similar or equivalent functionality
  * New features
    - Inspired by dplyr's mutate verb, DataFrame has a new assign method.
    - Added SparseSeries.to_coo and SparseSeries.from_coo methods for 
      converting to and from scipy.sparse.coo_matrix instances.
    - Following new methods are accesible via .str accessor to apply the 
      function to each values. This is intended to make it more consistent with
      standard methods on strings: isalnum(), isalpha(), isdigit(), isdigit(),
      isspace(), islower(), isupper(), istitle(), isnumeric(), isdecimal(),
      find(), rfind(), ljust(), rjust(), zfill()
    - Reindex now supports method='nearest' for frames or series with a 
      monotonic increasing or decreasing index.
    - The read_excel() function's sheetname argument now accepts a list and 
      None, to get multiple or all sheets respectively. If more than one sheet 
      is specified, a dictionary is returned.
    - Allow Stata files to be read incrementally with an iterator; support for 
      long strings in Stata files.
    - Paths beginning with ~ will now be expanded to begin with the user's home
      directory.
    - Added time interval selection in get_data_yahoo.
    - Added Timestamp.to_datetime64() to complement Timedelta.to_timedelta64().
    - tseries.frequencies.to_offset() now accepts Timedelta as input.
    - Lag parameter was added to the autocorrelation method of Series, defaults
      to lag-1 autocorrelation.
    - Timedelta will now accept nanoseconds keyword in constructor.
    - SQL code now safely escapes table and column names.
    - Added auto-complete for Series.str.<tab>, Series.dt.<tab> and 
      Series.cat.<tab>.
    - Index.get_indexer now supports method='pad' and method='backfill' even 
      for any target array, not just monotonic targets.
    - Index.asof now works on all index types.
    - A verbose argument has been augmented in io.read_excel(), defaults to 
      False. Set to True to print sheet names as they are parsed.
    - Added days_in_month (compatibility alias daysinmonth) property to 
      Timestamp, DatetimeIndex, Period, PeriodIndex, and Series.dt.
    - Added decimal option in to_csv to provide formatting for non-'.' decimal 
      separators
    - Added normalize option for Timestamp to normalized to midnight
    - Added example for DataFrame import to R using HDF5 file and rhdf5
      library.
  * Backwards incompatible API changes
    - In v0.16.0, we are restoring the API to match that of datetime.timedelta.
      Further, the component values are still available through the .components
      accessor. This affects the .seconds and .microseconds accessors, and 
      removes the .hours, .minutes, .milliseconds accessors. These changes 
      affect TimedeltaIndex and the Series .dt accessor as well.
    - The behavior of a small sub-set of edge cases for using .loc have 
      changed. Furthermore we have improved the content of the error messages 
      that are raised:
      + Slicing with .loc where the start and/or stop bound is not found in 
        the index is now allowed; this previously would raise a KeyError. This 
        makes the behavior the same as .ix in this case. This change is only 
        for slicing, not when indexing with a single label.
      + Allow slicing with float-like values on an integer index for .ix. 
        Previously this was only enabled for .loc:
      + Provide a useful exception for indexing with an invalid type for that 
        index when using .loc. For example trying to use .loc on an index of 
        type DatetimeIndex or PeriodIndex or TimedeltaIndex, with an integer 
        (or a float).
    - In prior versions, Categoricals that had an unspecified ordering 
      (meaning no ordered keyword was passed) were defaulted as ordered 
      Categoricals. Going forward, the ordered keyword in the Categorical 
      constructor will default to False. Ordering must now be explicit.
      Furthermore, previously you *could* change the ordered attribute of a 
      Categorical by just setting the attribute, e.g. cat.ordered=True; This is
      now deprecated and you should use cat.as_ordered() or cat.as_unordered().
      These will by default return a **new** object and not modify the 
      existing object.
    - Index.duplicated now returns np.array(dtype=bool) rather than 
      Index(dtype=object) containing bool values.
    - DataFrame.to_json now returns accurate type serialisation for each column 
      for frames of mixed dtype
    - DatetimeIndex, PeriodIndex and TimedeltaIndex.summary now output the same 
      format.
    - TimedeltaIndex.freqstr now output the same string format as 
      DatetimeIndex.
    - Bar and horizontal bar plots no longer add a dashed line along the info 
      axis. The prior style can be achieved with matplotlib's axhline or 
      axvline methods.
    - Series accessors .dt, .cat and .str now raise AttributeError instead of 
      TypeError if the series does not contain the appropriate type of data. 
      This  follows Python's built-in exception hierarchy more closely and 
      ensures that  tests like hasattr(s, 'cat') are consistent on both Python 
      2 and 3.
    - Series now supports bitwise operation for integral types. Previously even 
      if the input dtypes were integral, the output dtype was coerced to bool.
    - During division involving a Series or DataFrame, 0/0 and 0//0 now give 
      np.nan instead of np.inf.
    - Series.values_counts and Series.describe for categorical data will now 
      put NaN entries at the end.
    - Series.describe for categorical data will now give counts and frequencies 
      of 0, not NaN, for unused categories
    - Due to a bug fix, looking up a partial string label with 
      DatetimeIndex.asof now includes values that match the string, even if 
      they are after the start of the partial string label. Old behavior:
  * Deprecations
    - The rplot trellis plotting interface is deprecated and will be removed
      in a future version. We refer to external packages like
      seaborn for similar but more refined functionality.
    - The pandas.sandbox.qtpandas interface is deprecated and will be removed 
      in a future version.
      We refer users to the external package pandas-qt.
    - The pandas.rpy interface is deprecated and will be removed in a future 
      version.
      Similar functionaility can be accessed thru the rpy2 project
    - Adding DatetimeIndex/PeriodIndex to another DatetimeIndex/PeriodIndex is 
      being deprecated as a set-operation. This will be changed to a TypeError 
      in a future version. .union() should be used for the union set operation.
    - Subtracting DatetimeIndex/PeriodIndex from another 
      DatetimeIndex/PeriodIndex is being deprecated as a set-operation. This 
      will be  changed to an actual numeric subtraction yielding a 
      TimeDeltaIndex in a future  version. .difference() should be used for 
      the differencing set operation.
  * Removal of prior version deprecations/changes
    - DataFrame.pivot_table and crosstab's rows and cols keyword arguments were 
      removed in favor
      of index and columns
    - DataFrame.to_excel and DataFrame.to_csv cols keyword argument was removed 
      in favor of columns
    - Removed convert_dummies in favor of get_dummies
    - Removed value_range in favor of describe
  * Performance Improvements
    - Fixed a performance regression for .loc indexing with an array or 
      list-like.
    - DataFrame.to_json 30x performance improvement for mixed dtype frames.
    - Performance improvements in MultiIndex.duplicated by working with labels 
      instead of values
    - Improved the speed of nunique by calling unique instead of value_counts
    - Performance improvement of up to 10x in DataFrame.count and 
      DataFrame.dropna by taking advantage of homogeneous/heterogeneous dtypes 
      appropriately
    - Performance improvement of up to 20x in DataFrame.count when using a 
      MultiIndex and the level keyword argument
    - Performance and memory usage improvements in merge when key space exceeds 
      int64 bounds
    - Performance improvements in multi-key groupby
    - Performance improvements in MultiIndex.sortlevel
    - Performance and memory usage improvements in DataFrame.duplicated
    - Cythonized Period
    - Decreased memory usage on to_hdf
  * Bug Fixes
    - Changed .to_html to remove leading/trailing spaces in table body
    - Fixed issue using read_csv on s3 with Python 3
    - Fixed compatibility issue in DatetimeIndex affecting architectures where 
      numpy.int_ defaults to numpy.int32
    - Bug in Panel indexing with an object-like
    - Bug in the returned Series.dt.components index was reset to the default 
      index
    - Bug in Categorical.__getitem__/__setitem__ with listlike input getting 
      incorrect results from indexer coercion
    - Bug in partial setting with a DatetimeIndex
    - Bug in groupby for integer and datetime64 columns when applying an 
      aggregator that caused the value to be
      changed when the number was sufficiently large
    - Fixed bug in to_sql when mapping a Timestamp object column (datetime
      column with timezone info) to the appropriate sqlalchemy type.
    - Fixed bug in to_sql dtype argument not accepting an instantiated
      SQLAlchemy type.
    - Bug in .loc partial setting with a np.datetime64
    - Incorrect dtypes inferred on datetimelike looking Series & on .xs slices
    - Items in Categorical.unique() (and s.unique() if s is of dtype category) 
      now appear in the order in which they are originally found, not in sorted 
      order. This is now consistent with the behavior for other dtypes in pandas.
    - Fixed bug on big endian platforms which produced incorrect results in 
      StataReader.
    - Bug in MultiIndex.has_duplicates when having many levels causes an 
      indexer overflow
    - Bug in pivot and unstack where nan values would break index alignment
    - Bug in left join on multi-index with sort=True or null values.
    - Bug in MultiIndex where inserting new keys would fail.
    - Bug in groupby when key space exceeds int64 bounds.
    - Bug in unstack with TimedeltaIndex or DatetimeIndex and nulls.
    - Bug in rank where comparing floats with tolerance will cause inconsistent 
      behaviour.
    - Fixed character encoding bug in read_stata and StataReader when loading 
      data from a URL.
    - Bug in adding offsets.Nano to other offets raises TypeError
    - Bug in DatetimeIndex iteration, related to, fixed in
    - Bugs in resample around DST transitions. This required fixing offset 
      classes so they behave correctly on DST transitions.
    - Bug in binary operator method (eg .mul()) alignment with integer levels.
    - Bug in boxplot, scatter and hexbin plot may show an unnecessary warning
    - Bug in subplot with layout kw may show unnecessary warning
    - Bug in using grouper functions that need passed thru arguments (e.g. 
      axis), when using wrapped function (e.g. fillna),
    - DataFrame now properly supports simultaneous copy and dtype arguments in 
      constructor
    - Bug in read_csv when using skiprows on a file with CR line endings with 
      the c engine.
    - isnull now detects NaT in PeriodIndex
    - Bug in groupby .nth() with a multiple column groupby
    - Bug in DataFrame.where and Series.where coerce numerics to string 
      incorrectly
    - Bug in DataFrame.where and Series.where raise ValueError when string 
      list-like is passed.
    - Accessing Series.str methods on with non-string values now raises 
      TypeError instead of producing incorrect results
    - Bug in DatetimeIndex.__contains__ when index has duplicates and is not 
      monotonic increasing
    - Fixed division by zero error for Series.kurt() when all values are equal
    - Fixed issue in the xlsxwriter engine where it added a default 'General' 
      format to cells if no other format wass applied. This prevented other 
      row or column formatting being applied.
    - Fixes issue with index_col=False when usecols is also specified in 
      read_csv.
    - Bug where wide_to_long would modify the input stubnames list
    - Bug in to_sql not storing float64 values using double precision.
    - SparseSeries and SparsePanel now accept zero argument constructors (same 
      as their non-sparse counterparts).
    - Regression in merging Categorical and object dtypes
    - Bug in read_csv with buffer overflows with certain malformed input files
    - Bug in groupby MultiIndex with missing pair
    - Fixed bug in Series.groupby where grouping on MultiIndex levels would 
      ignore the sort argument
    - Fix bug in DataFrame.Groupby where sort=False is ignored in the case of 
      Categorical columns.
    - Fixed bug with reading CSV files from Amazon S3 on python 3 raising a 
      TypeError
    - Bug in the Google BigQuery reader where the 'jobComplete' key may be 
      present but False in the query results
    - Bug in Series.values_counts with excluding NaN for categorical type 
      Series with dropna=True
    - Fixed mising numeric_only option for DataFrame.std/var/sem
    - Support constructing Panel or Panel4D with scalar data
    - Series text representation disconnected from `max_rows`/`max_columns`.
    - Series number formatting inconsistent when truncated.
    - A Spurious SettingWithCopy Warning was generated when setting a new item 
      in a frame in some cases


-------------------------------------------------------------------
Mon Jan 12 13:46:26 UTC 2015 - toddrme2178@gmail.com

- update to version 0.15.2:
  * API changes:
    - Indexing in MultiIndex beyond lex-sort depth is now supported,
      though a lexically sorted index will have a better
      performance. (GH2646)
    - Bug in unique of Series with category dtype, which returned all
      categories regardless whether they were "used" or not (see
      GH8559 for the discussion). Previous behaviour was to return all
      categories.
    - Series.all and Series.any now support the level and skipna
      parameters. Series.all, Series.any, Index.all, and Index.any no
      longer support the out and keepdims parameters, which existed
      for compatibility with ndarray. Various index types no longer
      support the all and any aggregation functions and will now raise
      TypeError. (GH8302).
    - Allow equality comparisons of Series with a categorical dtype
      and object dtype; previously these would raise TypeError
      (GH8938)
    - Bug in NDFrame: conflicting attribute/column names now behave
      consistently between getting and setting. Previously, when both
      a column and attribute named y existed, data.y would return the
      attribute, while data.y = z would update the column (GH8994)
    - Timestamp('now') is now equivalent to Timestamp.now() in that it
      returns the local time rather than UTC. Also, Timestamp('today')
      is now equivalent to Timestamp.today() and both have tz as a
      possible argument. (GH9000)
    - Fix negative step support for label-based slices (GH8753)
  * Enhancements:
    - Added ability to export Categorical data to Stata (GH8633). See
      here for limitations of categorical variables exported to Stata
      data files.
    - Added flag order_categoricals to StataReader and read_stata to
      select whether to order imported categorical data (GH8836). See
      here for more information on importing categorical variables
      from Stata data files.
    - Added ability to export Categorical data to to/from HDF5
      (GH7621). Queries work the same as if it was an object
      array. However, the category dtyped data is stored in a more
      efficient manner. See here for an example and caveats
      w.r.t. prior versions of pandas.
    - Added support for searchsorted() on Categorical class (GH8420).
    - Added the ability to specify the SQL type of columns when
      writing a DataFrame to a database (GH8778). For example,
      specifying to use the sqlalchemy String type instead of the
      default Text type for string columns.
    - Series.all and Series.any now support the level and skipna
      parameters (GH8302).
    - Panel now supports the all and any aggregation
      functions. (GH8302).
    - Added support for utcfromtimestamp(), fromtimestamp(), and
      combine() on Timestamp class (GH5351).
    - Added Google Analytics (pandas.io.ga) basic documentation
      (GH8835).
    - Timedelta arithmetic returns NotImplemented in unknown cases,
      allowing extensions by custom classes (GH8813).
    - Timedelta now supports arithemtic with numpy.ndarray objects of
      the appropriate dtype (numpy 1.8 or newer only) (GH8884).
    - Added Timedelta.to_timedelta64() method to the public API
      (GH8884).
    - Added gbq.generate_bq_schema() function to the gbq module
      (GH8325).
    - Series now works with map objects the same way as generators
      (GH8909).
    - Added context manager to HDFStore for automatic closing
      (GH8791).
    - to_datetime gains an exact keyword to allow for a format to not
      require an exact match for a provided format string (if its
      False). exact defaults to True (meaning that exact matching is
      still the default) (GH8904)
    - Added axvlines boolean option to parallel_coordinates plot
      function, determines whether vertical lines will be printed,
      default is True
    - Added ability to read table footers to read_html (GH8552).
    - to_sql now infers datatypes of non-NA values for columns that
      contain NA values and have dtype object (GH8778).
  * Performance:
    - Reduce memory usage when skiprows is an integer in read_csv
      (GH8681)
    - Performance boost for to_datetime conversions with a passed
      format=, and the exact=False (GH8904)
  * Bug fixes:
    - Bug in concat of Series with category dtype which were coercing
      to object. (GH8641)
    - Bug in Timestamp-Timestamp not returning a Timedelta type and
      datelike-datelike ops with timezones (GH8865)
    - Made consistent a timezone mismatch exception (either tz
      operated with None or incompatible timezone), will now return
      TypeError rather than ValueError (a couple of edge cases only),
      (GH8865)
    - Bug in using a pd.Grouper(key=...) with no level/axis or level
      only (GH8795, GH8866)
    - Report a TypeError when invalid/no paramaters are passed in a
      groupby (GH8015)
    - Bug in packaging pandas with py2app/cx_Freeze (GH8602, GH8831)
    - Bug in groupby signatures that didn’t include *args or **kwargs
      (GH8733).
    - io.data.Options now raises RemoteDataError when no expiry dates
      are available from Yahoo and when it receives no data from Yahoo
      (GH8761), (GH8783).
    - Unclear error message in csv parsing when passing dtype and
      names and the parsed data is a different data type (GH8833)
    - Bug in slicing a multi-index with an empty list and at least one
      boolean indexer (GH8781)
    - io.data.Options now raises RemoteDataError when no expiry dates
      are available from Yahoo (GH8761).
    - Timedelta kwargs may now be numpy ints and floats (GH8757).
    - Fixed several outstanding bugs for Timedelta arithmetic and
      comparisons (GH8813, GH5963, GH5436).
    - sql_schema now generates dialect appropriate CREATE TABLE
      statements (GH8697)
    - slice string method now takes step into account (GH8754)
    - Bug in BlockManager where setting values with different type
      would break block integrity (GH8850)
    - Bug in DatetimeIndex when using time object as key (GH8667)
    - Bug in merge where how='left' and sort=False would not preserve
      left frame order (GH7331)
    - Bug in MultiIndex.reindex where reindexing at level would not
      reorder labels (GH4088)
    - Bug in certain operations with dateutil timezones, manifesting
      with dateutil 2.3 (GH8639)
    - Regression in DatetimeIndex iteration with a Fixed/Local offset
      timezone (GH8890)
    - Bug in to_datetime when parsing a nanoseconds using the %f
      format (GH8989)
    - io.data.Options now raises RemoteDataError when no expiry dates
      are available from Yahoo and when it receives no data from Yahoo
      (GH8761), (GH8783).
    - Fix: The font size was only set on x axis if vertical or the y
      axis if horizontal. (GH8765)
    - Fixed division by 0 when reading big csv files in python 3
      (GH8621)
    - Bug in outputing a Multindex with to_html,index=False which
      would add an extra column (GH8452)
    - Imported categorical variables from Stata files retain the
      ordinal information in the underlying data (GH8836).
    - Defined .size attribute across NDFrame objects to provide compat
      with numpy >= 1.9.1; buggy with np.array_split (GH8846)
    - Skip testing of histogram plots for matplotlib <= 1.2 (GH8648).
    - Bug where get_data_google returned object dtypes (GH3995)
    - Bug in DataFrame.stack(..., dropna=False) when the DataFrame’s
      columns is a MultiIndex whose labels do not reference all its
      levels. (GH8844)
    - Bug in that Option context applied on __enter__ (GH8514)
    - Bug in resample that causes a ValueError when resampling across
      multiple days and the last offset is not calculated from the
      start of the range (GH8683)
    - Bug where DataFrame.plot(kind='scatter') fails when checking if
      an np.array is in the DataFrame (GH8852)
    - Bug in pd.infer_freq/DataFrame.inferred_freq that prevented
      proper sub-daily frequency inference when the index contained
      DST days (GH8772).
    - Bug where index name was still used when plotting a series with
      use_index=False (GH8558).
    - Bugs when trying to stack multiple columns, when some (or all)
      of the level names are numbers (GH8584).
    - Bug in MultiIndex where __contains__ returns wrong result if
      index is not lexically sorted or unique (GH7724)
    - BUG CSV: fix problem with trailing whitespace in skipped rows,
      (GH8679), (GH8661), (GH8983)
    - Regression in Timestamp does not parse ‘Z’ zone designator for
      UTC (GH8771)
    - Bug in StataWriter the produces writes strings with 244
      characters irrespective of actual size (GH8969)
    - Fixed ValueError raised by cummin/cummax when datetime64 Series
      contains NaT. (GH8965)
    - Bug in Datareader returns object dtype if there are missing
      values (GH8980)
    - Bug in plotting if sharex was enabled and index was a
      timeseries, would show labels on multiple axes (GH3964).
    - Bug where passing a unit to the TimedeltaIndex constructor
      applied the to nano-second conversion twice. (GH9011).
    - Bug in plotting of a period-like array (GH9012)
- Update copyright year

-------------------------------------------------------------------
Sun Nov  9 15:40:36 UTC 2014 - toddrme2178@gmail.com

- Updated to version 0.15.1:
  + API changes
    - Represent ``MultiIndex`` labels with a dtype that utilizes memory based 
      on the level size.
    - ``groupby`` with ``as_index=False`` will not add erroneous extra columns 
      to result (:issue:`8582`):
    - ``groupby`` will not erroneously exclude columns if the column name 
      conflics with the grouper name (:issue:`8112`):
    - ``concat`` permits a wider variety of iterables of pandas objects to be
      passed as the first parameter (:issue:`8645`):
    - ``s.dt.hour`` and other ``.dt`` accessors will now return ``np.nan`` for 
      missing values (rather than previously -1), (:issue:`8689`)
    - support for slicing with monotonic decreasing indexes, even if ``start`` 
      or ``stop`` is not found in the index (:issue:`7860`):
    - added Index properties `is_monotonic_increasing` and 
      `is_monotonic_decreasing` (:issue:`8680`).
    - pandas now also registers the ``datetime64`` dtype in matplotlib's units 
      registry to plot such values as datetimes. 
  + Enhancements
    - Added option to select columns when importing Stata files (:issue:`7935`)
    - Qualify memory usage in ``DataFrame.info()`` by adding ``+`` if it is a 
      lower bound (:issue:`8578`)
    - Raise errors in certain aggregation cases where an argument such as 
      ``numeric_only`` is not handled (:issue:`8592`).
    - Added support for 3-character ISO and non-standard country codes in 
      :func:``io.wb.download()`` (:issue:`8482`)
    - :ref:`World Bank data requests <remote_data.wb>` now will warn/raise 
      based on an ``errors`` argument, as well as a list of hard-coded country 
      codes and the World Bank's JSON response.
    - Added option to ``Series.str.split()`` to return a ``DataFrame`` rather 
      than a ``Series`` (:issue:`8428`)
    - Added option to ``df.info(null_counts=None|True|False)`` to override the 
      default display options and force showing of the null-counts 
      (:issue:`8701`)
  + Bug Fixes
    - Bug in unpickling  of a ``CustomBusinessDay`` object (:issue:`8591`)
    - Bug in coercing ``Categorical`` to a records array, e.g. 
      ``df.to_records()`` (:issue:`8626`)
    - Bug in ``Categorical`` not created properly with ``Series.to_frame()`` 
      (:issue:`8626`)
    - Bug in coercing in astype of a ``Categorical`` of a passed 
      ``pd.Categorical`` (this now raises ``TypeError`` correctly), 
      (:issue:`8626`)
    - Bug in ``cut``/``qcut`` when using ``Series`` and ``retbins=True`` 
      (:issue:`8589`)
    - Bug in writing Categorical columns to an SQL database with ``to_sql`` 
      (:issue:`8624`).
    - Bug in comparing ``Categorical`` of datetime raising when being compared 
      to a scalar datetime (:issue:`8687`)
    - Bug in selecting from a ``Categorical`` with ``.iloc`` (:issue:`8623`)
    - Bug in groupby-transform with a Categorical (:issue:`8623`)
    - Bug in duplicated/drop_duplicates with a Categorical (:issue:`8623`)
    - Bug in ``Categorical`` reflected comparison operator raising if the first 
      argument was a numpy array scalar (e.g. np.int64) (:issue:`8658`)
    - Bug in Panel indexing with a list-like (:issue:`8710`)
    - Compat issue is ``DataFrame.dtypes`` when 
      ``options.mode.use_inf_as_null`` is True (:issue:`8722`)
    - Bug in ``read_csv``, ``dialect`` parameter would not take a string 
      (:issue: `8703`)
    - Bug in slicing a multi-index level with an empty-list (:issue:`8737`)
    - Bug in numeric index operations of add/sub with Float/Index Index with 
      numpy arrays (:issue:`8608`)
    - Bug in setitem with empty indexer and unwanted coercion of dtypes 
      (:issue:`8669`)
    - Bug in ix/loc block splitting on setitem (manifests with integer-like 
      dtypes, e.g. datetime64) (:issue:`8607`)
    - Bug when doing label based indexing with integers not found in the index 
      for non-unique but monotonic indexes (:issue:`8680`).
    - Bug when indexing a Float64Index with ``np.nan`` on numpy 1.7 
      (:issue:`8980`).
    - Fix ``shape`` attribute for ``MultiIndex`` (:issue:`8609`)
    - Bug in ``GroupBy`` where a name conflict between the grouper and columns
      would break ``groupby`` operations (:issue:`7115`, :issue:`8112`)
    - Fixed a bug where plotting a column ``y`` and specifying a label would 
      mutate the index name of the original DataFrame (:issue:`8494`)
    - Fix regression in plotting of a DatetimeIndex directly with matplotlib 
      (:issue:`8614`).
    - Bug in ``date_range`` where partially-specified dates would incorporate 
      current date (:issue:`6961`)
    - Bug in Setting by indexer to a scalar value with a mixed-dtype `Panel4d` 
      was failing (:issue:`8702`)
    - Bug where ``DataReader``'s would fail if one of the symbols passed was 
      invalid.  Now returns data for valid symbols and np.nan for invalid 
      (:issue:`8494`)
    - Bug in ``get_quote_yahoo`` that wouldn't allow non-float return values 
      (:issue:`5229`).

-------------------------------------------------------------------
Mon Oct 20 10:42:30 UTC 2014 - toddrme2178@gmail.com

- Update to 0.15.0, highlights:  
  - Drop support for numpy < 1.7.0
  - The Categorical type was integrated as a first-class 
    pandas type
  - New scalar type Timedelta, and a new index type TimedeltaIndex
  - New DataFrame default display for df.info() to 
    include memory usage
  - New datetimelike properties accessor .dt for Series
  - Split indexing documentation into Indexing and Selecting Data and 
    MultiIndex / Advanced Indexing
  - Split out string methods documentation into Working with Text Data
  - read_csv will now by default ignore blank lines when parsing
  - API change in using Indexes in set operations
  - Internal refactoring of the Index class to no longer 
    sub-class ndarray
  - dropping support for PyTables less than version 3.0.0, 
    and numexpr less than version 2.1
- Update minimum dependency versions of 
  python-numpy, python-tables, and python-numexpr

-------------------------------------------------------------------
Tue Jul 15 12:31:13 UTC 2014 - toddrme2178@gmail.com

- Update to 0.14.1, highlights:  
  - New methods :meth:`~pandas.DataFrame.select_dtypes` to select columns
    based on the dtype and :meth:`~pandas.Series.sem` to calculate the
    standard error of the mean.
  - Support for dateutil timezones (see :ref:`docs <timeseries.timezone>`).
  - Support for ignoring full line comments in the :func:`~pandas.read_csv`
    text parser.
  - New documentation section on :ref:`Options and Settings <options>`.
  - Lots of bug fixes.

-------------------------------------------------------------------
Sun Jun  1 07:41:11 UTC 2014 - toddrme2178@gmail.com

- Update to 0.14.0, highlights:  
  * Officially support Python 3.4
  * SQL interfaces updated to use sqlalchemy
  * Display interface changes
  * MultiIndexing Using Slicers
  * Ability to join a singly-indexed DataFrame with a multi-indexed DataFrame
  * More consistency in groupby results and more flexible groupby specifications
  * Holiday calendars are now supported in CustomBusinessDay
  * Several improvements in plotting functions, including: hexbin, area and pie plots
  * Performance doc section on I/O operations, See Here
- Added python-SQLAlchemy dependency

-------------------------------------------------------------------
Fri Mar  7 04:11:36 UTC 2014 - arun@gmx.de

-  updated to 0.13.1

  500 lines worth of Changelog entries, so too long:) For a complete
  list see: http://pandas.pydata.org/pandas-docs/dev/release.html

-------------------------------------------------------------------
Mon Oct 21 21:59:47 UTC 2013 - toddrme2178@gmail.com

- Update to 0.12.0
  * Integrated JSON reading and writing with the read_json
    functions and methods like DataFrame.to_json.
  * New HTML table reading function read_html which will use either
    lxml or BeautifulSoup under the hood.
  * Support for reading and writing STATA format files.
- Add all optional dependencies as Recommends
- Build and install documentation

-------------------------------------------------------------------
Mon May  6 06:01:46 UTC 2013 - highwaystar.ru@gmail.com

- added Recommends: python-tables
- update to 0.11.0
 * New precision indexing fields loc, iloc, at, and iat, to reduce 
 occasional ambiguity in the catch-all hitherto ix method.
 * Expanded support for NumPy data types in DataFrame
 * NumExpr integration to accelerate various operator evaluation
 * New Cookbook and 10 minutes to pandas pages in the documentation 
 by Jeff Reback
 * Improved DataFrame to CSV exporting performance 

-------------------------------------------------------------------
Tue Jun 19 20:29:31 UTC 2012 - scorot@free.fr

- remove unneeded python-Pygments and python-Sphinx from build
  requirements

-------------------------------------------------------------------
Tue Jun 19 20:23:50 UTC 2012 - scorot@free.fr

- remove duplicates
- fix bytecode inconsistent mtime 

-------------------------------------------------------------------
Wed Jun 13 20:45:39 UTC 2012 - scorot@free.fr

- use proper commands instead of deprecated macro
- remove unneeded -01 and --skip-build flags from the install
  command line
- set install prefix with %%{_prefix} instead of hard coded path

-------------------------------------------------------------------
Wed Jun 13 18:41:46 UTC 2012 - scorot@free.fr

- add %%py_compile macro in order to fix byte code mtime
  inconsistency 

-------------------------------------------------------------------
Tue Jun 12 21:03:07 UTC 2012 - scorot@free.fr

- spec file reformating 

-------------------------------------------------------------------
Tue Jun 12 20:46:31 UTC 2012 - scorot@free.fr

- first package