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# Version 0.9.1 (November 14, 2012)

This is a bug fix release from 0.9.0 and includes several new features and enhancements along with a large number of bug fixes. The new features include by-column sort order for DataFrame and Series, improved NA handling for the rank method, masking functions for DataFrame, and intraday time-series filtering for DataFrame.

## New features

> - `Series.sort`, `DataFrame.sort`, and `DataFrame.sort_index` can now be specified in a per-column manner to support multiple sort orders (GH 928)

>       In [2]: df = pd.DataFrame(np.random.randint(0, 2, (6, 3)),
>          ...:                   columns=['A', 'B', 'C'])

>       In [3]: df.sort(['A', 'B'], ascending=[1, 0])

>       Out[3]:
>          A  B  C
>       3  0  1  1
>       4  0  1  1
>       2  0  0  1
>       0  1  0  0
>       1  1  0  0
>       5  1  0  0

> - `DataFrame.rank` now supports additional argument values for the `na_option` parameter so missing values can be assigned either the largest or the smallest rank (GH 1508, GH 2159)

>       In [1]: df = pd.DataFrame(np.random.randn(6, 3), columns=['A', 'B', 'C'])

>       In [2]: df.loc[2:4] = np.nan

>       In [3]: df.rank()
>       Out[3]:
>            A    B    C
>       0  3.0  2.0  1.0
>       1  1.0  3.0  2.0
>       2  NaN  NaN  NaN
>       3  NaN  NaN  NaN
>       4  NaN  NaN  NaN
>       5  2.0  1.0  3.0

>       [6 rows x 3 columns]

>       In [4]: df.rank(na_option='top')
>       Out[4]:
>            A    B    C
>       0  6.0  5.0  4.0
>       1  4.0  6.0  5.0
>       2  2.0  2.0  2.0
>       3  2.0  2.0  2.0
>       4  2.0  2.0  2.0
>       5  5.0  4.0  6.0

>       [6 rows x 3 columns]

>       In [5]: df.rank(na_option='bottom')
>       Out[5]:
>            A    B    C
>       0  3.0  2.0  1.0
>       1  1.0  3.0  2.0
>       2  5.0  5.0  5.0
>       3  5.0  5.0  5.0
>       4  5.0  5.0  5.0
>       5  2.0  1.0  3.0

>       [6 rows x 3 columns]

> - DataFrame has new `where` and `mask` methods to select values according to a given boolean mask (GH 2109, GH 2151)

>   > DataFrame currently supports slicing via a boolean vector the same length as the DataFrame (inside the `[]`). The returned DataFrame has the same number of columns as the original, but is sliced on its index.

>   >     In [6]: df = pd.DataFrame(np.random.randn(5, 3), columns=['A', 'B', 'C'])

>   >     In [7]: df
>   >     Out[7]:
>   >               A         B         C
>   >     0  0.276232 -1.087401 -0.673690
>   >     1  0.113648 -1.478427  0.524988
>   >     2  0.404705  0.577046 -1.715002
>   >     3 -1.039268 -0.370647 -1.157892
>   >     4 -1.344312  0.844885  1.075770

>   >     [5 rows x 3 columns]

>   >     In [8]: df[df['A'] > 0]
>   >     Out[8]:
>   >               A         B         C
>   >     0  0.276232 -1.087401 -0.673690
>   >     1  0.113648 -1.478427  0.524988
>   >     2  0.404705  0.577046 -1.715002

>   >     [3 rows x 3 columns]

>   > If a DataFrame is sliced with a DataFrame based boolean condition (with the same size as the original DataFrame), then a DataFrame the same size (index and columns) as the original is returned, with elements that do not meet the boolean condition as `NaN`. This is accomplished via the new method `DataFrame.where`. In addition, `where` takes an optional `other` argument for replacement.

>   >     In [9]: df[df > 0]
>   >     Out[9]:
>   >               A         B         C
>   >     0  0.276232       NaN       NaN
>   >     1  0.113648       NaN  0.524988
>   >     2  0.404705  0.577046       NaN
>   >     3       NaN       NaN       NaN
>   >     4       NaN  0.844885  1.075770

>   >     [5 rows x 3 columns]

>   >     In [10]: df.where(df > 0)
>   >     Out[10]:
>   >               A         B         C
>   >     0  0.276232       NaN       NaN
>   >     1  0.113648       NaN  0.524988
>   >     2  0.404705  0.577046       NaN
>   >     3       NaN       NaN       NaN
>   >     4       NaN  0.844885  1.075770

>   >     [5 rows x 3 columns]

>   >     In [11]: df.where(df > 0, -df)
>   >     Out[11]:
>   >               A         B         C
>   >     0  0.276232  1.087401  0.673690
>   >     1  0.113648  1.478427  0.524988
>   >     2  0.404705  0.577046  1.715002
>   >     3  1.039268  0.370647  1.157892
>   >     4  1.344312  0.844885  1.075770

>   >     [5 rows x 3 columns]

>   > Furthermore, `where` now aligns the input boolean condition (ndarray or DataFrame), such that partial selection with setting is possible. This is analogous to partial setting via `.ix` (but on the contents rather than the axis labels)

>   >     In [12]: df2 = df.copy()

>   >     In [13]: df2[df2[1:4] > 0] = 3

>   >     In [14]: df2
>   >     Out[14]:
>   >               A         B         C
>   >     0  0.276232 -1.087401 -0.673690
>   >     1  3.000000 -1.478427  3.000000
>   >     2  3.000000  3.000000 -1.715002
>   >     3 -1.039268 -0.370647 -1.157892
>   >     4 -1.344312  0.844885  1.075770

>   >     [5 rows x 3 columns]

>   > `DataFrame.mask` is the inverse boolean operation of `where`.

>   >     In [15]: df.mask(df <= 0)
>   >     Out[15]:
>   >               A         B         C
>   >     0  0.276232       NaN       NaN
>   >     1  0.113648       NaN  0.524988
>   >     2  0.404705  0.577046       NaN
>   >     3       NaN       NaN       NaN
>   >     4       NaN  0.844885  1.075770

>   >     [5 rows x 3 columns]

> - Enable referencing of Excel columns by their column names (GH 1936)

>       In [1]: xl = pd.ExcelFile('data/test.xls')

>       In [2]: xl.parse('Sheet1', index_col=0, parse_dates=True,
>                        parse_cols='A:D')

> - Added option to disable pandas-style tick locators and formatters using `series.plot(x_compat=True)` or `pandas.plot_params['x_compat'] = True` (GH 2205)

> - Existing TimeSeries methods `at_time` and `between_time` were added to DataFrame (GH 2149)

> - DataFrame.dot can now accept ndarrays (GH 2042)

> - DataFrame.drop now supports non-unique indexes (GH 2101)

> - Panel.shift now supports negative periods (GH 2164)

> - DataFrame now support unary ~ operator (GH 2110)

## API changes

> - Upsampling data with a PeriodIndex will result in a higher frequency TimeSeries that spans the original time window

>       In [1]: prng = pd.period_range('2012Q1', periods=2, freq='Q')

>       In [2]: s = pd.Series(np.random.randn(len(prng)), prng)

>       In [4]: s.resample('M')
>       Out[4]:
>       2012-01   -1.471992
>       2012-02         NaN
>       2012-03         NaN
>       2012-04   -0.493593
>       2012-05         NaN
>       2012-06         NaN
>       Freq: M, dtype: float64

> - Period.end_time now returns the last nanosecond in the time interval (GH 2124, GH 2125, GH 1764)

>       In [16]: p = pd.Period('2012')

>       In [17]: p.end_time
>       Out[17]: Timestamp('2012-12-31 23:59:59.999999999')

> - File parsers no longer coerce to float or bool for columns that have custom converters specified (GH 2184)

>       In [18]: import io

>       In [19]: data = ('A,B,C\n'
>          ....:         '00001,001,5\n'
>          ....:         '00002,002,6')
>          ....:

>       In [20]: pd.read_csv(io.StringIO(data), converters={'A': lambda x: x.strip()})
>       Out[20]:
>              A  B  C
>       0  00001  1  5
>       1  00002  2  6

>       [2 rows x 3 columns]

See the full release notes or issue tracker on GitHub for a complete list.

## Contributors

For contributors, please see /usr/share/doc/contributors_list.txt or https://github.com/pandas-dev/pandas/graphs/contributors