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# Version 0.10.0 (December 17, 2012)

This is a major release from 0.9.1 and includes many new features and enhancements along with a large number of bug fixes. There are also a number of important API changes that long-time pandas users should pay close attention to.

## File parsing new features

The delimited file parsing engine (the guts of `read_csv` and `read_table`) has been rewritten from the ground up and now uses a fraction the amount of memory while parsing, while being 40% or more faster in most use cases (in some cases much faster).

There are also many new features:

- Much-improved Unicode handling via the `encoding` option.

- Column filtering (`usecols`)

- Dtype specification (`dtype` argument)

- Ability to specify strings to be recognized as True/False

- Ability to yield NumPy record arrays (`as_recarray`)

- High performance `delim_whitespace` option

- Decimal format (e.g. European format) specification

- Easier CSV dialect options: `escapechar`, `lineterminator`, `quotechar`, etc.

- More robust handling of many exceptional kinds of files observed in the wild

## API changes

Deprecated DataFrame BINOP TimeSeries special case behavior

The default behavior of binary operations between a DataFrame and a Series has always been to align on the DataFrame’s columns and broadcast down the rows, except in the special case that the DataFrame contains time series. Since there are now method for each binary operator enabling you to specify how you want to broadcast, we are phasing out this special case (Zen of Python: Special cases aren’t special enough to break the rules). Here’s what I’m talking about:

    In [1]: import pandas as pd

    In [2]: df = pd.DataFrame(np.random.randn(6, 4), index=pd.date_range("1/1/2000", periods=6))

    In [3]: df
    Out[3]:
                       0         1         2         3
    2000-01-01  0.469112 -0.282863 -1.509059 -1.135632
    2000-01-02  1.212112 -0.173215  0.119209 -1.044236
    2000-01-03 -0.861849 -2.104569 -0.494929  1.071804
    2000-01-04  0.721555 -0.706771 -1.039575  0.271860
    2000-01-05 -0.424972  0.567020  0.276232 -1.087401
    2000-01-06 -0.673690  0.113648 -1.478427  0.524988

    # deprecated now
    In [4]: df - df[0]
    Out[4]:
                 0   1  ...  2000-01-05 00:00:00  2000-01-06 00:00:00
    2000-01-01 NaN NaN  ...                  NaN                  NaN
    2000-01-02 NaN NaN  ...                  NaN                  NaN
    2000-01-03 NaN NaN  ...                  NaN                  NaN
    2000-01-04 NaN NaN  ...                  NaN                  NaN
    2000-01-05 NaN NaN  ...                  NaN                  NaN
    2000-01-06 NaN NaN  ...                  NaN                  NaN

    [6 rows x 10 columns]

    # Change your code to
    In [5]: df.sub(df[0], axis=0)  # align on axis 0 (rows)
    Out[5]:
                  0         1         2         3
    2000-01-01  0.0 -0.751976 -1.978171 -1.604745
    2000-01-02  0.0 -1.385327 -1.092903 -2.256348
    2000-01-03  0.0 -1.242720  0.366920  1.933653
    2000-01-04  0.0 -1.428326 -1.761130 -0.449695
    2000-01-05  0.0  0.991993  0.701204 -0.662428
    2000-01-06  0.0  0.787338 -0.804737  1.198677

You will get a deprecation warning in the 0.10.x series, and the deprecated functionality will be removed in 0.11 or later.

Altered resample default behavior

The default time series `resample` binning behavior of daily `D` and higher frequencies has been changed to `closed='left', label='left'`. Lower nfrequencies are unaffected. The prior defaults were causing a great deal of confusion for users, especially resampling data to daily frequency (which labeled the aggregated group with the end of the interval: the next day).

    In [1]: dates = pd.date_range('1/1/2000', '1/5/2000', freq='4h')

    In [2]: series = pd.Series(np.arange(len(dates)), index=dates)

    In [3]: series
    Out[3]:
    2000-01-01 00:00:00     0
    2000-01-01 04:00:00     1
    2000-01-01 08:00:00     2
    2000-01-01 12:00:00     3
    2000-01-01 16:00:00     4
    2000-01-01 20:00:00     5
    2000-01-02 00:00:00     6
    2000-01-02 04:00:00     7
    2000-01-02 08:00:00     8
    2000-01-02 12:00:00     9
    2000-01-02 16:00:00    10
    2000-01-02 20:00:00    11
    2000-01-03 00:00:00    12
    2000-01-03 04:00:00    13
    2000-01-03 08:00:00    14
    2000-01-03 12:00:00    15
    2000-01-03 16:00:00    16
    2000-01-03 20:00:00    17
    2000-01-04 00:00:00    18
    2000-01-04 04:00:00    19
    2000-01-04 08:00:00    20
    2000-01-04 12:00:00    21
    2000-01-04 16:00:00    22
    2000-01-04 20:00:00    23
    2000-01-05 00:00:00    24
    Freq: 4H, dtype: int64

    In [4]: series.resample('D', how='sum')
    Out[4]:
    2000-01-01     15
    2000-01-02     51
    2000-01-03     87
    2000-01-04    123
    2000-01-05     24
    Freq: D, dtype: int64

    In [5]: # old behavior
    In [6]: series.resample('D', how='sum', closed='right', label='right')
    Out[6]:
    2000-01-01      0
    2000-01-02     21
    2000-01-03     57
    2000-01-04     93
    2000-01-05    129
    Freq: D, dtype: int64

- Infinity and negative infinity are no longer treated as NA by `isnull` and `notnull`. That they ever were was a relic of early pandas. This behavior can be re-enabled globally by the `mode.use_inf_as_null` option:

    In [6]: s = pd.Series([1.5, np.inf, 3.4, -np.inf])

    In [7]: pd.isnull(s)
    Out[7]:
    0    False
    1    False
    2    False
    3    False
    Length: 4, dtype: bool

    In [8]: s.fillna(0)
    Out[8]:
    0    1.500000
    1         inf
    2    3.400000
    3        -inf
    Length: 4, dtype: float64

    In [9]: pd.set_option('use_inf_as_null', True)

    In [10]: pd.isnull(s)
    Out[10]:
    0    False
    1     True
    2    False
    3     True
    Length: 4, dtype: bool

    In [11]: s.fillna(0)
    Out[11]:
    0    1.5
    1    0.0
    2    3.4
    3    0.0
    Length: 4, dtype: float64

    In [12]: pd.reset_option('use_inf_as_null')

- Methods with the `inplace` option now all return `None` instead of the calling object. E.g. code written like `df = df.fillna(0, inplace=True)` may stop working. To fix, simply delete the unnecessary variable assignment.

- `pandas.merge` no longer sorts the group keys (`sort=False`) by default. This was done for performance reasons: the group-key sorting is often one of the more expensive parts of the computation and is often unnecessary.

- The default column names for a file with no header have been changed to the integers `0` through `N - 1`. This is to create consistency with the DataFrame constructor with no columns specified. The v0.9.0 behavior (names `X0`, `X1`, …) can be reproduced by specifying `prefix='X'`:

    In [6]: import io

    In [7]: data = """
      ...: a,b,c
      ...: 1,Yes,2
      ...: 3,No,4
      ...: """
      ...:

    In [8]: print(data)

        a,b,c
        1,Yes,2
        3,No,4

    In [9]: pd.read_csv(io.StringIO(data), header=None)
    Out[9]:
           0    1  2
    0      a    b  c
    1      1  Yes  2
    2      3   No  4

    In [10]: pd.read_csv(io.StringIO(data), header=None, prefix="X")
    Out[10]:
            X0   X1 X2
    0       a    b  c
    1       1  Yes  2
    2       3   No  4

- Values like `'Yes'` and `'No'` are not interpreted as boolean by default, though this can be controlled by new `true_values` and `false_values` arguments:

    In [4]: print(data)

        a,b,c
        1,Yes,2
        3,No,4

    In [5]: pd.read_csv(io.StringIO(data))
    Out[5]:
           a    b  c
    0      1  Yes  2
    1      3   No  4

    In [6]: pd.read_csv(io.StringIO(data), true_values=["Yes"], false_values=["No"])
    Out[6]:
           a      b  c
    0      1   True  2
    1      3  False  4

- The file parsers will not recognize non-string values arising from a converter function as NA if passed in the `na_values` argument. It’s better to do post-processing using the `replace` function instead.

- Calling `fillna` on Series or DataFrame with no arguments is no longer valid code. You must either specify a fill value or an interpolation method:

    In [6]: s = pd.Series([np.nan, 1.0, 2.0, np.nan, 4])

    In [7]: s
    Out[7]:
    0    NaN
    1    1.0
    2    2.0
    3    NaN
    4    4.0
    dtype: float64

    In [8]: s.fillna(0)
    Out[8]:
    0    0.0
    1    1.0
    2    2.0
    3    0.0
    4    4.0
    dtype: float64

    In [9]: s.fillna(method="pad")
    Out[9]:
    0    NaN
    1    1.0
    2    2.0
    3    2.0
    4    4.0
    dtype: float64

Convenience methods `ffill` and `bfill` have been added:

    In [10]: s.ffill()
    Out[10]:
    0    NaN
    1    1.0
    2    2.0
    3    2.0
    4    4.0
    dtype: float64

- `Series.apply` will now operate on a returned value from the applied function, that is itself a series, and possibly upcast the result to a DataFrame

      In [11]: def f(x):
         ....:     return pd.Series([x, x ** 2], index=["x", "x^2"])
         ....:

      In [12]: s = pd.Series(np.random.rand(5))

      In [13]: s
      Out[13]:
      0    0.340445
      1    0.984729
      2    0.919540
      3    0.037772
      4    0.861549
      dtype: float64

      In [14]: s.apply(f)
      Out[14]:
                x       x^2
      0  0.340445  0.115903
      1  0.984729  0.969691
      2  0.919540  0.845555
      3  0.037772  0.001427
      4  0.861549  0.742267

- New API functions for working with pandas options (GH 2097):

  - `get_option` / `set_option` - get/set the value of an option. Partial names are accepted. - `reset_option` - reset one or more options to their default value. Partial names are accepted. - `describe_option` - print a description of one or more options. When called with no arguments. print all registered options.

  Note: `set_printoptions`/ `reset_printoptions` are now deprecated (but functioning), the print options now live under “display.XYZ”. For example:

      In [15]: pd.get_option("display.max_rows")
      Out[15]: 15

- to_string() methods now always return unicode strings (GH 2224).

## New features

## Wide DataFrame printing

Instead of printing the summary information, pandas now splits the string representation across multiple rows by default:

    In [16]: wide_frame = pd.DataFrame(np.random.randn(5, 16))

    In [17]: wide_frame
    Out[17]:
             0         1         2   ...        13        14        15
    0 -0.548702  1.467327 -1.015962  ...  1.669052  1.037882 -1.705775
    1 -0.919854 -0.042379  1.247642  ...  1.956030  0.017587 -0.016692
    2 -0.575247  0.254161 -1.143704  ...  1.211526  0.268520  0.024580
    3 -1.577585  0.396823 -0.105381  ...  0.593616  0.884345  1.591431
    4  0.141809  0.220390  0.435589  ... -0.392670  0.007207  1.928123

    [5 rows x 16 columns]

The old behavior of printing out summary information can be achieved via the ‘expand_frame_repr’ print option:

    In [18]: pd.set_option("expand_frame_repr", False)

    In [19]: wide_frame
    Out[19]:
             0         1         2         3         4         5         6         7         8         9         10        11        12        13        14        15
    0 -0.548702  1.467327 -1.015962 -0.483075  1.637550 -1.217659 -0.291519 -1.745505 -0.263952  0.991460 -0.919069  0.266046 -0.709661  1.669052  1.037882 -1.705775
    1 -0.919854 -0.042379  1.247642 -0.009920  0.290213  0.495767  0.362949  1.548106 -1.131345 -0.089329  0.337863 -0.945867 -0.932132  1.956030  0.017587 -0.016692
    2 -0.575247  0.254161 -1.143704  0.215897  1.193555 -0.077118 -0.408530 -0.862495  1.346061  1.511763  1.627081 -0.990582 -0.441652  1.211526  0.268520  0.024580
    3 -1.577585  0.396823 -0.105381 -0.532532  1.453749  1.208843 -0.080952 -0.264610 -0.727965 -0.589346  0.339969 -0.693205 -0.339355  0.593616  0.884345  1.591431
    4  0.141809  0.220390  0.435589  0.192451 -0.096701  0.803351  1.715071 -0.708758 -1.202872 -1.814470  1.018601 -0.595447  1.395433 -0.392670  0.007207  1.928123

The width of each line can be changed via ‘line_width’ (80 by default):

    pd.set_option("line_width", 40)

    wide_frame

## Updated PyTables support

Docs for PyTables `Table` format & several enhancements to the api. Here is a taste of what to expect.

    In [41]: store = pd.HDFStore('store.h5')

    In [42]: df = pd.DataFrame(np.random.randn(8, 3),
       ....:                   index=pd.date_range('1/1/2000', periods=8),
       ....:                   columns=['A', 'B', 'C'])

    In [43]: df
    Out[43]:
                       A         B         C
    2000-01-01 -2.036047  0.000830 -0.955697
    2000-01-02 -0.898872 -0.725411  0.059904
    2000-01-03 -0.449644  1.082900 -1.221265
    2000-01-04  0.361078  1.330704  0.855932
    2000-01-05 -1.216718  1.488887  0.018993
    2000-01-06 -0.877046  0.045976  0.437274
    2000-01-07 -0.567182 -0.888657 -0.556383
    2000-01-08  0.655457  1.117949 -2.782376

    [8 rows x 3 columns]

    # appending data frames
    In [44]: df1 = df[0:4]

    In [45]: df2 = df[4:]

    In [46]: store.append('df', df1)

    In [47]: store.append('df', df2)

    In [48]: store
    Out[48]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /df            frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])

    # selecting the entire store
    In [49]: store.select('df')
    Out[49]:
                       A         B         C
    2000-01-01 -2.036047  0.000830 -0.955697
    2000-01-02 -0.898872 -0.725411  0.059904
    2000-01-03 -0.449644  1.082900 -1.221265
    2000-01-04  0.361078  1.330704  0.855932
    2000-01-05 -1.216718  1.488887  0.018993
    2000-01-06 -0.877046  0.045976  0.437274
    2000-01-07 -0.567182 -0.888657 -0.556383
    2000-01-08  0.655457  1.117949 -2.782376

    [8 rows x 3 columns]

    In [50]: wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
       ....:               major_axis=pd.date_range('1/1/2000', periods=5),
       ....:               minor_axis=['A', 'B', 'C', 'D'])

    In [51]: wp
    Out[51]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 5 (major_axis) x 4 (minor_axis)
    Items axis: Item1 to Item2
    Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
    Minor_axis axis: A to D

    # storing a panel
    In [52]: store.append('wp', wp)

    # selecting via A QUERY
    In [53]: store.select('wp', [pd.Term('major_axis>20000102'),
       ....:                     pd.Term('minor_axis', '=', ['A', 'B'])])
       ....:
    Out[53]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 3 (major_axis) x 2 (minor_axis)
    Items axis: Item1 to Item2
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
    Minor_axis axis: A to B

    # removing data from tables
    In [54]: store.remove('wp', pd.Term('major_axis>20000103'))
    Out[54]: 8

    In [55]: store.select('wp')
    Out[55]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 3 (major_axis) x 4 (minor_axis)
    Items axis: Item1 to Item2
    Major_axis axis: 2000-01-01 00:00:00 to 2000-01-03 00:00:00
    Minor_axis axis: A to D

    # deleting a store
    In [56]: del store['df']

    In [57]: store
    Out[57]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5
    /wp            wide_table   (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])

Enhancements

- added ability to hierarchical keys

  >     In [58]: store.put('foo/bar/bah', df)

  >     In [59]: store.append('food/orange', df)

  >     In [60]: store.append('food/apple', df)

  >     In [61]: store
  >     Out[61]:
  >     <class 'pandas.io.pytables.HDFStore'>
  >     File path: store.h5
  >     /foo/bar/bah            frame        (shape->[8,3])
  >     /food/apple             frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
  >     /food/orange            frame_table  (typ->appendable,nrows->8,ncols->3,indexers->[index])
  >     /wp                     wide_table   (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])

  >     # remove all nodes under this level
  >     In [62]: store.remove('food')

  >     In [63]: store
  >     Out[63]:
  >     <class 'pandas.io.pytables.HDFStore'>
  >     File path: store.h5
  >     /foo/bar/bah            frame        (shape->[8,3])
  >     /wp                     wide_table   (typ->appendable,nrows->12,ncols->2,indexers->[major_axis,minor_axis])

- added mixed-dtype support!

  >     In [64]: df['string'] = 'string'

  >     In [65]: df['int'] = 1

  >     In [66]: store.append('df', df)

  >     In [67]: df1 = store.select('df')

  >     In [68]: df1
  >     Out[68]:
  >                        A         B         C  string  int
  >     2000-01-01 -2.036047  0.000830 -0.955697  string    1
  >     2000-01-02 -0.898872 -0.725411  0.059904  string    1
  >     2000-01-03 -0.449644  1.082900 -1.221265  string    1
  >     2000-01-04  0.361078  1.330704  0.855932  string    1
  >     2000-01-05 -1.216718  1.488887  0.018993  string    1
  >     2000-01-06 -0.877046  0.045976  0.437274  string    1
  >     2000-01-07 -0.567182 -0.888657 -0.556383  string    1
  >     2000-01-08  0.655457  1.117949 -2.782376  string    1

  >     [8 rows x 5 columns]

  >     In [69]: df1.get_dtype_counts()
  >     Out[69]:
  >     float64    3
  >     int64      1
  >     object     1
  >     dtype: int64

- performance improvements on table writing

- support for arbitrarily indexed dimensions

- `SparseSeries` now has a `density` property (GH 2384)

- enable `Series.str.strip/lstrip/rstrip` methods to take an input argument to strip arbitrary characters (GH 2411)

- implement `value_vars` in `melt` to limit values to certain columns and add `melt` to pandas namespace (GH 2412)

Bug Fixes

- added `Term` method of specifying where conditions (GH 1996).

- `del store['df']` now call `store.remove('df')` for store deletion

- deleting of consecutive rows is much faster than before

- `min_itemsize` parameter can be specified in table creation to force a minimum size for indexing columns (the previous implementation would set the column size based on the first append)

- indexing support via `create_table_index` (requires PyTables >= 2.3) (GH 698).

- appending on a store would fail if the table was not first created via `put`

- fixed issue with missing attributes after loading a pickled dataframe (GH2431)

- minor change to select and remove: require a table ONLY if where is also provided (and not None)

Compatibility

0.10 of `HDFStore` is backwards compatible for reading tables created in a prior version of pandas, however, query terms using the prior (undocumented) methodology are unsupported. You must read in the entire file and write it out using the new format to take advantage of the updates.

## N dimensional panels (experimental)

Adding experimental support for Panel4D and factory functions to create n-dimensional named panels. Here is a taste of what to expect.

    In [58]: p4d = Panel4D(np.random.randn(2, 2, 5, 4),
      ....:       labels=['Label1','Label2'],
      ....:       items=['Item1', 'Item2'],
      ....:       major_axis=date_range('1/1/2000', periods=5),
      ....:       minor_axis=['A', 'B', 'C', 'D'])
      ....:

    In [59]: p4d
    Out[59]:
    <class 'pandas.core.panelnd.Panel4D'>
    Dimensions: 2 (labels) x 2 (items) x 5 (major_axis) x 4 (minor_axis)
    Labels axis: Label1 to Label2
    Items axis: Item1 to Item2
    Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
    Minor_axis axis: A to D

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