Version 0.11.0 (April 22, 2013)¶

This is a major release from 0.10.1 and includes many new features and enhancements along with a large number of bug fixes. The methods of Selecting Data have had quite a number of additions, and Dtype support is now full-fledged. There are also a number of important API changes that long-time pandas users should pay close attention to.

There is a new section in the documentation, 10 Minutes to Pandas, primarily geared to new users.

There is a new section in the documentation, Cookbook, a collection of useful recipes in pandas (and that we want contributions!).

There are several libraries that are now Recommended Dependencies

Selection choices¶

Starting in 0.11.0, object selection has had a number of user-requested additions in order to support more explicit location based indexing. Pandas now supports three types of multi-axis indexing.

.loc is strictly label based, will raise KeyError when the items are not found, allowed inputs are:
- A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index. This use is not an integer position along the index)
- A list or array of labels ['a', 'b', 'c']
- A slice object with labels 'a':'f', (note that contrary to usual python slices, both the start and the stop are included!)
- A boolean array
See more at Selection by Label
.iloc is strictly integer position based (from 0 to length-1 of the axis), will raise IndexError when the requested indices are out of bounds. Allowed inputs are:
- An integer e.g. 5
- A list or array of integers [4, 3, 0]
- A slice object with ints 1:7
- A boolean array
See more at Selection by Position
.ix supports mixed integer and label based access. It is primarily label based, but will fallback to integer positional access. .ix is the most general and will support any of the inputs to .loc and .iloc, as well as support for floating point label schemes. .ix is especially useful when dealing with mixed positional and label based hierarchical indexes.

As using integer slices with .ix have different behavior depending on whether the slice is interpreted as position based or label based, it’s usually better to be explicit and use .iloc or .loc.

See more at Advanced Indexing and Advanced Hierarchical.

Selection deprecations¶

Starting in version 0.11.0, these methods may be deprecated in future versions.

irow
icol
iget_value

See the section Selection by Position for substitutes.

Dtypes¶

Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the dtype keyword, a passed ndarray, or a passed Series, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will NOT be combined. The following example will give you a taste.

In [1]: df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')

In [2]: df1
Out[2]: 
          A
0  0.469112
1 -0.282863
2 -1.509058
3 -1.135632
4  1.212112
5 -0.173215
6  0.119209
7 -1.044236

In [3]: df1.dtypes
Out[3]: 
A    float32
dtype: object

In [4]: df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float16'),
   ...:                     'B': pd.Series(np.random.randn(8)),
   ...:                     'C': pd.Series(range(8), dtype='uint8')})
   ...: 

In [5]: df2
Out[5]: 
          A         B  C
0 -0.861816 -0.424972  0
1 -2.105469  0.567020  1
2 -0.494873  0.276232  2
3  1.072266 -1.087401  3
4  0.721680 -0.673690  4
5 -0.706543  0.113648  5
6 -1.040039 -1.478427  6
7  0.271973  0.524988  7

In [6]: df2.dtypes
Out[6]: 
A    float16
B    float64
C      uint8
dtype: object

# here you get some upcasting
In [7]: df3 = df1.reindex_like(df2).fillna(value=0.0) + df2

In [8]: df3
Out[8]: 
          A         B    C
0 -0.392704 -0.424972  0.0
1 -2.388332  0.567020  1.0
2 -2.003932  0.276232  2.0
3 -0.063367 -1.087401  3.0
4  1.933792 -0.673690  4.0
5 -0.879758  0.113648  5.0
6 -0.920830 -1.478427  6.0
7 -0.772263  0.524988  7.0

In [9]: df3.dtypes
Out[9]: 
A    float32
B    float64
C    float64
dtype: object

Dtype conversion¶

This is lower-common-denominator upcasting, meaning you get the dtype which can accommodate all of the types

In [10]: df3.values.dtype
Out[10]: dtype('float64')

Conversion

In [11]: df3.astype('float32').dtypes
Out[11]: 
A    float32
B    float32
C    float32
dtype: object

Mixed conversion

In [12]: df3['D'] = '1.'

In [13]: df3['E'] = '1'

In [14]: df3.convert_objects(convert_numeric=True).dtypes
Out[14]:
A    float32
B    float64
C    float64
D    float64
E      int64
dtype: object

# same, but specific dtype conversion
In [15]: df3['D'] = df3['D'].astype('float16')

In [16]: df3['E'] = df3['E'].astype('int32')

In [17]: df3.dtypes
Out[17]:
A    float32
B    float64
C    float64
D    float16
E      int32
dtype: object

Forcing date coercion (and setting NaT when not datelike)

In [18]: import datetime

In [19]: s = pd.Series([datetime.datetime(2001, 1, 1, 0, 0), 'foo', 1.0, 1,
   ....:                pd.Timestamp('20010104'), '20010105'], dtype='O')
   ....:

In [20]: s.convert_objects(convert_dates='coerce')
Out[20]:
0   2001-01-01
1          NaT
2          NaT
3          NaT
4   2001-01-04
5   2001-01-05
dtype: datetime64[ns]

Dtype gotchas¶

Platform gotchas

Starting in 0.11.0, construction of DataFrame/Series will use default dtypes of int64 and float64, regardless of platform. This is not an apparent change from earlier versions of pandas. If you specify dtypes, they WILL be respected, however (GH2837)

The following will all result in int64 dtypes

In [21]: pd.DataFrame([1, 2], columns=['a']).dtypes
Out[21]:
a    int64
dtype: object

In [22]: pd.DataFrame({'a': [1, 2]}).dtypes
Out[22]:
a    int64
dtype: object

In [23]: pd.DataFrame({'a': 1}, index=range(2)).dtypes
Out[23]:
a    int64
dtype: object

Keep in mind that DataFrame(np.array([1,2])) WILL result in int32 on 32-bit platforms!

Upcasting gotchas

Performing indexing operations on integer type data can easily upcast the data. The dtype of the input data will be preserved in cases where nans are not introduced.

In [24]: dfi = df3.astype('int32')

In [25]: dfi['D'] = dfi['D'].astype('int64')

In [26]: dfi
Out[26]:
  A  B  C  D  E
0  0  0  0  1  1
1 -2  0  1  1  1
2 -2  0  2  1  1
3  0 -1  3  1  1
4  1  0  4  1  1
5  0  0  5  1  1
6  0 -1  6  1  1
7  0  0  7  1  1

In [27]: dfi.dtypes
Out[27]:
A    int32
B    int32
C    int32
D    int64
E    int32
dtype: object

In [28]: casted = dfi[dfi > 0]

In [29]: casted
Out[29]:
    A   B    C  D  E
0  NaN NaN  NaN  1  1
1  NaN NaN  1.0  1  1
2  NaN NaN  2.0  1  1
3  NaN NaN  3.0  1  1
4  1.0 NaN  4.0  1  1
5  NaN NaN  5.0  1  1
6  NaN NaN  6.0  1  1
7  NaN NaN  7.0  1  1

In [30]: casted.dtypes
Out[30]:
A    float64
B    float64
C    float64
D      int64
E      int32
dtype: object

While float dtypes are unchanged.

In [31]: df4 = df3.copy()

In [32]: df4['A'] = df4['A'].astype('float32')

In [33]: df4.dtypes
Out[33]:
A    float32
B    float64
C    float64
D    float16
E      int32
dtype: object

In [34]: casted = df4[df4 > 0]

In [35]: casted
Out[35]:
          A         B    C    D  E
0       NaN       NaN  NaN  1.0  1
1       NaN  0.567020  1.0  1.0  1
2       NaN  0.276232  2.0  1.0  1
3       NaN       NaN  3.0  1.0  1
4  1.933792       NaN  4.0  1.0  1
5       NaN  0.113648  5.0  1.0  1
6       NaN       NaN  6.0  1.0  1
7       NaN  0.524988  7.0  1.0  1

In [36]: casted.dtypes
Out[36]:
A    float32
B    float64
C    float64
D    float16
E      int32
dtype: object

Datetimes conversion¶

Datetime64[ns] columns in a DataFrame (or a Series) allow the use of np.nan to indicate a nan value, in addition to the traditional NaT, or not-a-time. This allows convenient nan setting in a generic way. Furthermore datetime64[ns] columns are created by default, when passed datetimelike objects (this change was introduced in 0.10.1) (GH2809, GH2810)

In [12]: df = pd.DataFrame(np.random.randn(6, 2), pd.date_range('20010102', periods=6),
   ....:                   columns=['A', ' B'])
   ....: 

In [13]: df['timestamp'] = pd.Timestamp('20010103')

In [14]: df
Out[14]: 
                   A         B  timestamp
2001-01-02  0.404705  0.577046 2001-01-03
2001-01-03 -1.715002 -1.039268 2001-01-03
2001-01-04 -0.370647 -1.157892 2001-01-03
2001-01-05 -1.344312  0.844885 2001-01-03
2001-01-06  1.075770 -0.109050 2001-01-03
2001-01-07  1.643563 -1.469388 2001-01-03

# datetime64[ns] out of the box
In [15]: df.dtypes.value_counts()
Out[15]: 
float64           2
datetime64[ns]    1
dtype: int64

# use the traditional nan, which is mapped to NaT internally
In [16]: df.loc[df.index[2:4], ['A', 'timestamp']] = np.nan

In [17]: df
Out[17]: 
                   A         B  timestamp
2001-01-02  0.404705  0.577046 2001-01-03
2001-01-03 -1.715002 -1.039268 2001-01-03
2001-01-04       NaN -1.157892        NaT
2001-01-05       NaN  0.844885        NaT
2001-01-06  1.075770 -0.109050 2001-01-03
2001-01-07  1.643563 -1.469388 2001-01-03

Astype conversion on datetime64[ns] to object, implicitly converts NaT to np.nan

In [18]: s = pd.Series([datetime.datetime(2001, 1, 2, 0, 0) for i in range(3)])

In [19]: s.dtype
Out[19]: dtype('<M8[ns]')

In [20]: s[1] = np.nan

In [21]: s
Out[21]: 
0   2001-01-02
1          NaT
2   2001-01-02
dtype: datetime64[ns]

In [22]: s.dtype
Out[22]: dtype('<M8[ns]')

In [23]: s = s.astype('O')

In [24]: s
Out[24]: 
0    2001-01-02 00:00:00
1                    NaT
2    2001-01-02 00:00:00
dtype: object

In [25]: s.dtype
Out[25]: dtype('O')

API changes¶

Added to_series() method to indices, to facilitate the creation of indexers (GH3275)

HDFStore

added the method select_column to select a single column from a table as a Series.

deprecated the unique method, can be replicated by select_column(key,column).unique()

min_itemsize parameter to append will now automatically create data_columns for passed keys

Enhancements¶

Improved performance of df.to_csv() by up to 10x in some cases. (GH3059)

Numexpr is now a Recommended Dependencies, to accelerate certain types of numerical and boolean operations

Bottleneck is now a Recommended Dependencies, to accelerate certain types of nan operations
HDFStore
support read_hdf/to_hdf API similar to read_csv/to_csv
In [26]: df = pd.DataFrame({'A': range(5), 'B': range(5)})

In [27]: df.to_hdf('store.h5', 'table', append=True)

In [28]: pd.read_hdf('store.h5', 'table', where=['index > 2'])
Out[28]: 
   A  B
3  3  3
4  4  4
provide dotted attribute access to get from stores, e.g. store.df == store['df']

new keywords iterator=boolean, and chunksize=number_in_a_chunk are provided to support iteration on select and select_as_multiple (GH3076)
You can now select timestamps from an unordered timeseries similarly to an ordered timeseries (GH2437)
You can now select with a string from a DataFrame with a datelike index, in a similar way to a Series (GH3070)
In [29]: idx = pd.date_range("2001-10-1", periods=5, freq='M')

In [30]: ts = pd.Series(np.random.rand(len(idx)), index=idx)

In [31]: ts['2001']
Out[31]: 
2001-10-31    0.117967
2001-11-30    0.702184
2001-12-31    0.414034
Freq: M, dtype: float64

In [32]: df = pd.DataFrame({'A': ts})

In [33]: df['2001']
Out[33]: 
                   A
2001-10-31  0.117967
2001-11-30  0.702184
2001-12-31  0.414034
Squeeze to possibly remove length 1 dimensions from an object.
>>> p = pd.Panel(np.random.randn(3, 4, 4), items=['ItemA', 'ItemB', 'ItemC'],
...              major_axis=pd.date_range('20010102', periods=4),
...              minor_axis=['A', 'B', 'C', 'D'])
>>> p
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 4 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2001-01-02 00:00:00 to 2001-01-05 00:00:00
Minor_axis axis: A to D

>>> p.reindex(items=['ItemA']).squeeze()
                   A         B         C         D
2001-01-02  0.926089 -2.026458  0.501277 -0.204683
2001-01-03 -0.076524  1.081161  1.141361  0.479243
2001-01-04  0.641817 -0.185352  1.824568  0.809152
2001-01-05  0.575237  0.669934  1.398014 -0.399338

>>> p.reindex(items=['ItemA'], minor=['B']).squeeze()
2001-01-02   -2.026458
2001-01-03    1.081161
2001-01-04   -0.185352
2001-01-05    0.669934
Freq: D, Name: B, dtype: float64
In pd.io.data.Options,

Fix bug when trying to fetch data for the current month when already past expiry.

Now using lxml to scrape html instead of BeautifulSoup (lxml was faster).

New instance variables for calls and puts are automatically created when a method that creates them is called. This works for current month where the instance variables are simply calls and puts. Also works for future expiry months and save the instance variable as callsMMYY or putsMMYY, where MMYY are, respectively, the month and year of the option’s expiry.

Options.get_near_stock_price now allows the user to specify the month for which to get relevant options data.

Options.get_forward_data now has optional kwargs near and above_below. This allows the user to specify if they would like to only return forward looking data for options near the current stock price. This just obtains the data from Options.get_near_stock_price instead of Options.get_xxx_data() (GH2758).

Cursor coordinate information is now displayed in time-series plots.

added option display.max_seq_items to control the number of elements printed per sequence pprinting it. (GH2979)

added option display.chop_threshold to control display of small numerical values. (GH2739)

added option display.max_info_rows to prevent verbose_info from being calculated for frames above 1M rows (configurable). (GH2807, GH2918)

value_counts() now accepts a “normalize” argument, for normalized histograms. (GH2710).

DataFrame.from_records now accepts not only dicts but any instance of the collections.Mapping ABC.

added option display.mpl_style providing a sleeker visual style for plots. Based on https://gist.github.com/huyng/816622 (GH3075).

Treat boolean values as integers (values 1 and 0) for numeric operations. (GH2641)

to_html() now accepts an optional “escape” argument to control reserved HTML character escaping (enabled by default) and escapes &, in addition to < and >. (GH2919)

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

Contributors¶

A total of 50 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Adam Greenhall +
Alvaro Tejero-Cantero +
Andy Hayden
Brad Buran +
Chang She
Chapman Siu +
Chris Withers +
Christian Geier +
Christopher Whelan
Damien Garaud
Dan Birken
Dan Davison +
Dieter Vandenbussche
Dražen Lučanin +
Dražen Lučanin +
Garrett Drapala
Illia Polosukhin +
James Casbon +
Jeff Reback
Jeremy Wagner +
Jonathan Chambers +
K.-Michael Aye
Karmel Allison +
Loïc Estève +
Nicholaus E. Halecky +
Peter Prettenhofer +
Phillip Cloud +
Robert Gieseke +
Skipper Seabold
Spencer Lyon
Stephen Lin +
Thierry Moisan +
Thomas Kluyver
Tim Akinbo +
Vytautas Jancauskas
Vytautas Jančauskas +
Wes McKinney
Will Furnass +
Wouter Overmeire
anomrake +
davidjameshumphreys +
dengemann +
dieterv77 +
jreback
lexual +
stephenwlin +
thauck +
vytas +
waitingkuo +
y-p

Version 0.12.0 (July 24, 2013) Version 0.10.1 (January 22, 2013)