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v0.7.0.rst

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v.0.7.0 (February 9, 2012)

{{ header }}

New features

.. ipython:: python

   df = pd.DataFrame(np.random.randn(10, 4))
   df.apply(lambda x: x.describe())

API Changes to integer indexing

One of the potentially riskiest API changes in 0.7.0, but also one of the most important, was a complete review of how integer indexes are handled with regard to label-based indexing. Here is an example:

.. ipython:: python

    s = pd.Series(np.random.randn(10), index=range(0, 20, 2))
    s
    s[0]
    s[2]
    s[4]

This is all exactly identical to the behavior before. However, if you ask for a key not contained in the Series, in versions 0.6.1 and prior, Series would fall back on a location-based lookup. This now raises a KeyError:

In [2]: s[1]
KeyError: 1

This change also has the same impact on DataFrame:

In [3]: df = pd.DataFrame(np.random.randn(8, 4), index=range(0, 16, 2))

In [4]: df
    0        1       2       3
0   0.88427  0.3363 -0.1787  0.03162
2   0.14451 -0.1415  0.2504  0.58374
4  -1.44779 -0.9186 -1.4996  0.27163
6  -0.26598 -2.4184 -0.2658  0.11503
8  -0.58776  0.3144 -0.8566  0.61941
10  0.10940 -0.7175 -1.0108  0.47990
12 -1.16919 -0.3087 -0.6049 -0.43544
14 -0.07337  0.3410  0.0424 -0.16037

In [5]: df.ix[3]
KeyError: 3

In order to support purely integer-based indexing, the following methods have been added:

Method Description
Series.iget_value(i) Retrieve value stored at location i
Series.iget(i) Alias for iget_value
DataFrame.irow(i) Retrieve the i-th row
DataFrame.icol(j) Retrieve the j-th column
DataFrame.iget_value(i, j) Retrieve the value at row i and column j

API tweaks regarding label-based slicing

Label-based slicing using ix now requires that the index be sorted (monotonic) unless both the start and endpoint are contained in the index:

In [1]: s = pd.Series(np.random.randn(6), index=list('gmkaec'))

In [2]: s
Out[2]:
g   -1.182230
m   -0.276183
k   -0.243550
a    1.628992
e    0.073308
c   -0.539890
dtype: float64

Then this is OK:

In [3]: s.ix['k':'e']
Out[3]:
k   -0.243550
a    1.628992
e    0.073308
dtype: float64

But this is not:

In [12]: s.ix['b':'h']
KeyError 'b'

If the index had been sorted, the "range selection" would have been possible:

In [4]: s2 = s.sort_index()

In [5]: s2
Out[5]:
a    1.628992
c   -0.539890
e    0.073308
g   -1.182230
k   -0.243550
m   -0.276183
dtype: float64

In [6]: s2.ix['b':'h']
Out[6]:
c   -0.539890
e    0.073308
g   -1.182230
dtype: float64

Changes to Series [] operator

As as notational convenience, you can pass a sequence of labels or a label slice to a Series when getting and setting values via [] (i.e. the __getitem__ and __setitem__ methods). The behavior will be the same as passing similar input to ix except in the case of integer indexing:

.. ipython:: python

   s = pd.Series(np.random.randn(6), index=list('acegkm'))
   s
   s[['m', 'a', 'c', 'e']]
   s['b':'l']
   s['c':'k']

In the case of integer indexes, the behavior will be exactly as before (shadowing ndarray):

.. ipython:: python

   s = pd.Series(np.random.randn(6), index=range(0, 12, 2))
   s[[4, 0, 2]]
   s[1:5]

If you wish to do indexing with sequences and slicing on an integer index with label semantics, use ix.

Other API Changes

  • The deprecated LongPanel class has been completely removed
  • If Series.sort is called on a column of a DataFrame, an exception will now be raised. Before it was possible to accidentally mutate a DataFrame's column by doing df[col].sort() instead of the side-effect free method df[col].order() (:issue:`316`)
  • Miscellaneous renames and deprecations which will (harmlessly) raise FutureWarning
  • drop added as an optional parameter to DataFrame.reset_index (:issue:`699`)

Performance improvements

  • :ref:`Cythonized GroupBy aggregations <groupby.aggregate.cython>` no longer presort the data, thus achieving a significant speedup (:issue:`93`). GroupBy aggregations with Python functions significantly sped up by clever manipulation of the ndarray data type in Cython (:issue:`496`).
  • Better error message in DataFrame constructor when passed column labels don't match data (:issue:`497`)
  • Substantially improve performance of multi-GroupBy aggregation when a Python function is passed, reuse ndarray object in Cython (:issue:`496`)
  • Can store objects indexed by tuples and floats in HDFStore (:issue:`492`)
  • Don't print length by default in Series.to_string, add length option (:issue:`489`)
  • Improve Cython code for multi-groupby to aggregate without having to sort the data (:issue:`93`)
  • Improve MultiIndex reindexing speed by storing tuples in the MultiIndex, test for backwards unpickling compatibility
  • Improve column reindexing performance by using specialized Cython take function
  • Further performance tweaking of Series.__getitem__ for standard use cases
  • Avoid Index dict creation in some cases (i.e. when getting slices, etc.), regression from prior versions
  • Friendlier error message in setup.py if NumPy not installed
  • Use common set of NA-handling operations (sum, mean, etc.) in Panel class also (:issue:`536`)
  • Default name assignment when calling reset_index on DataFrame with a regular (non-hierarchical) index (:issue:`476`)
  • Use Cythonized groupers when possible in Series/DataFrame stat ops with level parameter passed (:issue:`545`)
  • Ported skiplist data structure to C to speed up rolling_median by about 5-10x in most typical use cases (:issue:`374`)

Contributors

.. contributors:: v0.6.1..v0.7.0