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v0.9.1.txt
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.. _whatsnew_0901:
.. ipython:: python
:suppress:
from pandas.compat import StringIO
v0.9.1 (November 14, 2012)
--------------------------
This is a bugfix 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 (:issue:`928`)
.. ipython:: python
df = DataFrame(np.random.randint(0, 2, (6, 3)), columns=['A', 'B', 'C'])
df.sort(['A', 'B'], ascending=[1, 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 (:issue:`1508`, :issue:`2159`)
.. ipython:: python
df = DataFrame(np.random.randn(6, 3), columns=['A', 'B', 'C'])
df.ix[2:4] = np.nan
df.rank()
df.rank(na_option='top')
df.rank(na_option='bottom')
- DataFrame has new `where` and `mask` methods to select values according to a
given boolean mask (:issue:`2109`, :issue:`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.
.. ipython:: python
df = DataFrame(np.random.randn(5, 3), columns = ['A','B','C'])
df
df[df['A'] > 0]
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.
.. ipython:: python
df[df>0]
df.where(df>0)
df.where(df>0,-df)
Furthermore, `where` now aligns the input boolean condition (ndarray or DataFrame), such that partial selection
with setting is possible. This is analagous to partial setting via `.ix` (but on the contents rather than the axis labels)
.. ipython:: python
df2 = df.copy()
df2[ df2[1:4] > 0 ] = 3
df2
`DataFrame.mask` is the inverse boolean operation of `where`.
.. ipython:: python
df.mask(df<=0)
- Enable referencing of Excel columns by their column names (:issue:`1936`)
.. ipython:: python
xl = ExcelFile('data/test.xls')
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` (:issue:`2205`)
- Existing TimeSeries methods `at_time` and `between_time` were added to
DataFrame (:issue:`2149`)
- DataFrame.dot can now accept ndarrays (:issue:`2042`)
- DataFrame.drop now supports non-unique indexes (:issue:`2101`)
- Panel.shift now supports negative periods (:issue:`2164`)
- DataFrame now support unary ~ operator (:issue:`2110`)
API changes
~~~~~~~~~~~
- Upsampling data with a PeriodIndex will result in a higher frequency
TimeSeries that spans the original time window
.. ipython:: python
prng = period_range('2012Q1', periods=2, freq='Q')
s = Series(np.random.randn(len(prng)), prng)
s.resample('M')
- Period.end_time now returns the last nanosecond in the time interval
(:issue:`2124`, :issue:`2125`, :issue:`1764`)
.. ipython:: python
p = Period('2012')
p.end_time
- File parsers no longer coerce to float or bool for columns that have custom
converters specified (:issue:`2184`)
.. ipython:: python
data = 'A,B,C\n00001,001,5\n00002,002,6'
read_csv(StringIO(data), converters={'A' : lambda x: x.strip()})
See the :ref:`full release notes
<release>` or issue tracker
on GitHub for a complete list.