# v0.10.1 (January 22, 2013)

This is a minor release from 0.10.0 and includes new features, enhancements, and bug fixes. In particular, there is substantial new HDFStore functionality contributed by Jeff Reback.

An undesired API breakage with functions taking the `inplace` option has been reverted and deprecation warnings added.

## API changes

- Functions taking an `inplace` option return the calling object as before. A deprecation message has been added
- Groupby aggregations Max/Min no longer exclude non-numeric data (GH2700)
- Resampling an empty DataFrame now returns an empty DataFrame instead of raising an exception (GH2640)
- The file reader will now raise an exception when NA values are found in an explicitly specified integer column instead of converting the column to float (GH2631)
- DatetimeIndex.unique now returns a DatetimeIndex with the same name and
- timezone instead of an array (GH2563)

## New features

- MySQL support for database (contribution from Dan Allan)

## HDFStore

You may need to upgrade your existing data files. Please visit the compatibility section in the main docs.

You can designate (and index) certain columns that you want to be able to perform queries on a table, by passing a list to `data_columns`

    In [1]: store = pd.HDFStore('store.h5')

    In [2]: df = pd.DataFrame(np.random.randn(8, 3),
       ...:                   index=pd.date_range('1/1/2000', periods=8),
       ...:                   columns=['A', 'B', 'C'])
       ...:

    In [3]: df['string'] = 'foo'

    In [4]: df.loc[df.index[4:6], 'string'] = np.nan

    In [5]: df.loc[df.index[7:9], 'string'] = 'bar'

    In [6]: df['string2'] = 'cool'

    In [7]: df
    Out[7]:
                       A         B         C string string2
    2000-01-01  0.469112 -0.282863 -1.509059    foo    cool
    2000-01-02 -1.135632  1.212112 -0.173215    foo    cool
    2000-01-03  0.119209 -1.044236 -0.861849    foo    cool
    2000-01-04 -2.104569 -0.494929  1.071804    foo    cool
    2000-01-05  0.721555 -0.706771 -1.039575    NaN    cool
    2000-01-06  0.271860 -0.424972  0.567020    NaN    cool
    2000-01-07  0.276232 -1.087401 -0.673690    foo    cool
    2000-01-08  0.113648 -1.478427  0.524988    bar    cool

    # on-disk operations
    In [8]: store.append('df', df, data_columns=['B', 'C', 'string', 'string2'])

    In [9]: store.select('df', "B>0 and string=='foo'")
    Out[9]:
                       A         B         C string string2
    2000-01-02 -1.135632  1.212112 -0.173215    foo    cool

    # this is in-memory version of this type of selection
    In [10]: df[(df.B > 0) & (df.string == 'foo')]
    Out[10]:
                       A         B         C string string2
    2000-01-02 -1.135632  1.212112 -0.173215    foo    cool

Retrieving unique values in an indexable or data column.

    # note that this is deprecated as of 0.14.0
    # can be replicated by: store.select_column('df','index').unique()
    store.unique('df', 'index')
    store.unique('df', 'string')

You can now store `datetime64` in data columns

    In [11]: df_mixed = df.copy()

    In [12]: df_mixed['datetime64'] = pd.Timestamp('20010102')

    In [13]: df_mixed.loc[df_mixed.index[3:4], ['A', 'B']] = np.nan

    In [14]: store.append('df_mixed', df_mixed)

    In [15]: df_mixed1 = store.select('df_mixed')

    In [16]: df_mixed1
    Out[16]:
                       A         B         C string string2 datetime64
    2000-01-01  0.469112 -0.282863 -1.509059    foo    cool 2001-01-02
    2000-01-02 -1.135632  1.212112 -0.173215    foo    cool 2001-01-02
    2000-01-03  0.119209 -1.044236 -0.861849    foo    cool 2001-01-02
    2000-01-04       NaN       NaN  1.071804    foo    cool 2001-01-02
    2000-01-05  0.721555 -0.706771 -1.039575    NaN    cool 2001-01-02
    2000-01-06  0.271860 -0.424972  0.567020    NaN    cool 2001-01-02
    2000-01-07  0.276232 -1.087401 -0.673690    foo    cool 2001-01-02
    2000-01-08  0.113648 -1.478427  0.524988    bar    cool 2001-01-02

    In [17]: df_mixed1.dtypes.value_counts()
    Out[17]:
    float64           3
    object            2
    datetime64[ns]    1
    dtype: int64

You can pass `columns` keyword to select to filter a list of the return columns, this is equivalent to passing a `Term('columns',list_of_columns_to_filter)`

    In [18]: store.select('df', columns=['A', 'B'])
    Out[18]:
                       A         B
    2000-01-01  0.469112 -0.282863
    2000-01-02 -1.135632  1.212112
    2000-01-03  0.119209 -1.044236
    2000-01-04 -2.104569 -0.494929
    2000-01-05  0.721555 -0.706771
    2000-01-06  0.271860 -0.424972
    2000-01-07  0.276232 -1.087401
    2000-01-08  0.113648 -1.478427

`HDFStore` now serializes MultiIndex dataframes when appending tables.

    In [19]: index = pd.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
       ....:                               ['one', 'two', 'three']],
       ....:                       labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
       ....:                               [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
       ....:                       names=['foo', 'bar'])
       ....:

    In [20]: df = pd.DataFrame(np.random.randn(10, 3), index=index,
       ....:                   columns=['A', 'B', 'C'])
       ....:

    In [21]: df
    Out[21]:
                      A         B         C
    foo bar
    foo one   -0.116619  0.295575 -1.047704
        two    1.640556  1.905836  2.772115
        three  0.088787 -1.144197 -0.633372
    bar one    0.925372 -0.006438 -0.820408
        two   -0.600874 -1.039266  0.824758
    baz two   -0.824095 -0.337730 -0.927764
        three -0.840123  0.248505 -0.109250
    qux one    0.431977 -0.460710  0.336505
        two   -3.207595 -1.535854  0.409769
        three -0.673145 -0.741113 -0.110891

    In [22]: store.append('mi', df)

    In [23]: store.select('mi')
    Out[23]:
                      A         B         C
    foo bar
    foo one   -0.116619  0.295575 -1.047704
        two    1.640556  1.905836  2.772115
        three  0.088787 -1.144197 -0.633372
    bar one    0.925372 -0.006438 -0.820408
        two   -0.600874 -1.039266  0.824758
    baz two   -0.824095 -0.337730 -0.927764
        three -0.840123  0.248505 -0.109250
    qux one    0.431977 -0.460710  0.336505
        two   -3.207595 -1.535854  0.409769
        three -0.673145 -0.741113 -0.110891

    # the levels are automatically included as data columns
    In [24]: store.select('mi', "foo='bar'")
    Out[24]:
                    A         B         C
    foo bar
    bar one  0.925372 -0.006438 -0.820408
        two -0.600874 -1.039266  0.824758

Multi-table creation via `append_to_multiple` and selection via `select_as_multiple` can create/select from multiple tables and return a combined result, by using `where` on a selector table.

    In [19]: df_mt = pd.DataFrame(np.random.randn(8, 6),
       ....:                      index=pd.date_range('1/1/2000', periods=8),
       ....:                      columns=['A', 'B', 'C', 'D', 'E', 'F'])
       ....:

    In [20]: df_mt['foo'] = 'bar'

    # you can also create the tables individually
    In [21]: store.append_to_multiple({'df1_mt': ['A', 'B'], 'df2_mt': None},
       ....:                          df_mt, selector='df1_mt')
       ....:

    In [22]: store
    Out[22]:
    <class 'pandas.io.pytables.HDFStore'>
    File path: store.h5

    # individual tables were created
    In [23]: store.select('df1_mt')
    Out[23]:
                       A         B
    2000-01-01  0.404705  0.577046
    2000-01-02 -1.344312  0.844885
    2000-01-03  0.357021 -0.674600
    2000-01-04  0.276662 -0.472035
    2000-01-05  0.895717  0.805244
    2000-01-06 -1.170299 -0.226169
    2000-01-07 -0.076467 -1.187678
    2000-01-08  1.024180  0.569605

    In [24]: store.select('df2_mt')
    Out[24]:
                       C         D         E         F  foo
    2000-01-01 -1.715002 -1.039268 -0.370647 -1.157892  bar
    2000-01-02  1.075770 -0.109050  1.643563 -1.469388  bar
    2000-01-03 -1.776904 -0.968914 -1.294524  0.413738  bar
    2000-01-04 -0.013960 -0.362543 -0.006154 -0.923061  bar
    2000-01-05 -1.206412  2.565646  1.431256  1.340309  bar
    2000-01-06  0.410835  0.813850  0.132003 -0.827317  bar
    2000-01-07  1.130127 -1.436737 -1.413681  1.607920  bar
    2000-01-08  0.875906 -2.211372  0.974466 -2.006747  bar

    # as a multiple
    In [25]: store.select_as_multiple(['df1_mt', 'df2_mt'], where=['A>0', 'B>0'],
       ....:                          selector='df1_mt')
       ....:
    Out[25]:
                       A         B         C         D         E         F  foo
    2000-01-01  0.404705  0.577046 -1.715002 -1.039268 -0.370647 -1.157892  bar
    2000-01-05  0.895717  0.805244 -1.206412  2.565646  1.431256  1.340309  bar
    2000-01-08  1.024180  0.569605  0.875906 -2.211372  0.974466 -2.006747  bar

Enhancements

- `HDFStore` now can read native PyTables table format tables
- You can pass `nan_rep = 'my_nan_rep'` to append, to change the default nan representation on disk (which converts to/from np.nan), this defaults to nan.
- You can pass `index` to `append`. This defaults to `True`. This will automagically create indices on the indexables and data columns of the table
- You can pass `chunksize=an integer` to `append`, to change the writing chunksize (default is 50000). This will significantly lower your memory usage on writing.
- You can pass `expectedrows=an integer` to the first `append`, to set the TOTAL number of expected rows that `PyTables` will expected. This will optimize read/write performance.
- `Select` now supports passing `start` and `stop` to provide selection space limiting in selection.
- Greatly improved ISO8601 (e.g., yyyy-mm-dd) date parsing for file parsers (GH2698)
- Allow `DataFrame.merge` to handle combinatorial sizes too large for 64-bit integer (GH2690)
- Series now has unary negation (-series) and inversion (~series) operators (GH2686)
- DataFrame.plot now includes a `logx` parameter to change the x-axis to log scale (GH2327)
- Series arithmetic operators can now handle constant and ndarray input (GH2574)
- ExcelFile now takes a `kind` argument to specify the file type (GH2613)
- A faster implementation for Series.str methods (GH2602)

Bug Fixes

- `HDFStore` tables can now store `float32` types correctly (cannot be mixed with `float64` however)
- Fixed Google Analytics prefix when specifying request segment (GH2713).
- Function to reset Google Analytics token store so users can recover from improperly setup client secrets (GH2687).
- Fixed groupby bug resulting in segfault when passing in MultiIndex (GH2706)
- Fixed bug where passing a Series with datetime64 values into to_datetime results in bogus output values (GH2699)
- Fixed bug in `pattern in HDFStore` expressions when pattern is not a valid regex (GH2694)
- Fixed performance issues while aggregating boolean data (GH2692)
- When given a boolean mask key and a Series of new values, Series __setitem__ will now align the incoming values with the original Series (GH2686)
- Fixed MemoryError caused by performing counting sort on sorting MultiIndex levels with a very large number of combinatorial values (GH2684)
- Fixed bug that causes plotting to fail when the index is a DatetimeIndex with a fixed-offset timezone (GH2683)
- Corrected business day subtraction logic when the offset is more than 5 bdays and the starting date is on a weekend (GH2680)
- Fixed C file parser behavior when the file has more columns than data (GH2668)
- Fixed file reader bug that misaligned columns with data in the presence of an implicit column and a specified usecols value
- DataFrames with numerical or datetime indices are now sorted prior to plotting (GH2609)
- Fixed DataFrame.from_records error when passed columns, index, but empty records (GH2633)
- Several bug fixed for Series operations when dtype is datetime64 (GH2689, GH2629, GH2626)

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
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