Version 0.7.0 (February 9, 2012)

New features

  • New unified merge function for efficiently performing full gamut of database / relational-algebra operations. Refactored existing join methods to use the new infrastructure, resulting in substantial performance gains (GH220, GH249, GH267)

  • New unified concatenation function for concatenating Series, DataFrame or Panel objects along an axis. Can form union or intersection of the other axes. Improves performance of Series.append and DataFrame.append (GH468, GH479, GH273)

  • Can pass multiple DataFrames to DataFrame.append to concatenate (stack) and multiple Series to Series.append too

  • Can pass list of dicts (e.g., a list of JSON objects) to DataFrame constructor (GH526)

  • You can now set multiple columns in a DataFrame via __getitem__, useful for transformation (GH342)

  • Handle differently-indexed output values in DataFrame.apply (GH498)

In [1]: df = pd.DataFrame(np.random.randn(10, 4))

In [2]: df.apply(lambda x: x.describe())
               0          1          2          3
count  10.000000  10.000000  10.000000  10.000000
mean    0.190912  -0.395125  -0.731920  -0.403130
std     0.730951   0.813266   1.112016   0.961912
min    -0.861849  -2.104569  -1.776904  -1.469388
25%    -0.411391  -0.698728  -1.501401  -1.076610
50%     0.380863  -0.228039  -1.191943  -1.004091
75%     0.658444   0.057974  -0.034326   0.461706
max     1.212112   0.577046   1.643563   1.071804
  • Add reorder_levels method to Series and DataFrame (GH534)

  • Add dict-like get function to DataFrame and Panel (GH521)

  • Add DataFrame.iterrows method for efficiently iterating through the rows of a DataFrame

  • Add DataFrame.to_panel with code adapted from LongPanel.to_long

  • Add reindex_axis method added to DataFrame

  • Add level option to binary arithmetic functions on DataFrame and Series

  • Add level option to the reindex and align methods on Series and DataFrame for broadcasting values across a level (GH542, GH552, others)

  • Add attribute-based item access to Panel and add IPython completion (GH563)

  • Add logy option to Series.plot for log-scaling on the Y axis

  • Add index and header options to DataFrame.to_string

  • Can pass multiple DataFrames to DataFrame.join to join on index (GH115)

  • Can pass multiple Panels to Panel.join (GH115)

  • Added justify argument to DataFrame.to_string to allow different alignment of column headers

  • Add sort option to GroupBy to allow disabling sorting of the group keys for potential speedups (GH595)

  • Can pass MaskedArray to Series constructor (GH563)

  • Add Panel item access via attributes and IPython completion (GH554)

  • Implement DataFrame.lookup, fancy-indexing analogue for retrieving values given a sequence of row and column labels (GH338)

  • Can pass a list of functions to aggregate with groupby on a DataFrame, yielding an aggregated result with hierarchical columns (GH166)

  • Can call cummin and cummax on Series and DataFrame to get cumulative minimum and maximum, respectively (GH647)

  • value_range added as utility function to get min and max of a dataframe (GH288)

  • Added encoding argument to read_csv, read_table, to_csv and from_csv for non-ascii text (GH717)

  • Added abs method to pandas objects

  • Added crosstab function for easily computing frequency tables

  • Added isin method to index objects

  • Added level argument to xs method of DataFrame.

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:

In [3]: s = pd.Series(np.random.randn(10), index=range(0, 20, 2))

In [4]: s
0    -1.294524
2     0.413738
4     0.276662
6    -0.472035
8    -0.013960
10   -0.362543
12   -0.006154
14   -0.923061
16    0.895717
18    0.805244
dtype: float64

In [5]: s[0]
Out[5]: -1.2945235902555294

In [6]: s[2]
Out[6]: 0.41373810535784006

In [7]: s[4]
Out[7]: 0.2766617129497566

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:




Retrieve value stored at location i


Alias for iget_value


Retrieve the i-th row


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
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']
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
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']
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:

In [8]: s = pd.Series(np.random.randn(6), index=list('acegkm'))

In [9]: s
a   -1.206412
c    2.565646
e    1.431256
g    1.340309
k   -1.170299
m   -0.226169
dtype: float64

In [10]: s[['m', 'a', 'c', 'e']]
m   -0.226169
a   -1.206412
c    2.565646
e    1.431256
dtype: float64

In [11]: s['b':'l']
c    2.565646
e    1.431256
g    1.340309
k   -1.170299
dtype: float64

In [12]: s['c':'k']
c    2.565646
e    1.431256
g    1.340309
k   -1.170299
dtype: float64

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

In [13]: s = pd.Series(np.random.randn(6), index=range(0, 12, 2))

In [14]: s[[4, 0, 2]]
4    0.132003
0    0.410835
2    0.813850
dtype: float64

In [15]: s[1:5]
2    0.813850
4    0.132003
6   -0.827317
8   -0.076467
dtype: float64

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() (GH316)

  • Miscellaneous renames and deprecations which will (harmlessly) raise FutureWarning

  • drop added as an optional parameter to DataFrame.reset_index (GH699)

Performance improvements

  • Cythonized GroupBy aggregations no longer presort the data, thus achieving a significant speedup (GH93). GroupBy aggregations with Python functions significantly sped up by clever manipulation of the ndarray data type in Cython (GH496).

  • Better error message in DataFrame constructor when passed column labels don’t match data (GH497)

  • Substantially improve performance of multi-GroupBy aggregation when a Python function is passed, reuse ndarray object in Cython (GH496)

  • Can store objects indexed by tuples and floats in HDFStore (GH492)

  • Don’t print length by default in Series.to_string, add length option (GH489)

  • Improve Cython code for multi-groupby to aggregate without having to sort the data (GH93)

  • 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 if NumPy not installed

  • Use common set of NA-handling operations (sum, mean, etc.) in Panel class also (GH536)

  • Default name assignment when calling reset_index on DataFrame with a regular (non-hierarchical) index (GH476)

  • Use Cythonized groupers when possible in Series/DataFrame stat ops with level parameter passed (GH545)

  • Ported skiplist data structure to C to speed up rolling_median by about 5-10x in most typical use cases (GH374)


A total of 18 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

  • Adam Klein

  • Bayle Shanks +

  • Chris Billington +

  • Dieter Vandenbussche

  • Fabrizio Pollastri +

  • Graham Taylor +

  • Gregg Lind +

  • Josh Klein +

  • Luca Beltrame

  • Olivier Grisel +

  • Skipper Seabold

  • Thomas Kluyver

  • Thomas Wiecki +

  • Wes McKinney

  • Wouter Overmeire

  • Yaroslav Halchenko

  • fabriziop +

  • theandygross +