BUG: fixed old version compatibility warnings

RELEASE.rst

@@ -34,9 +34,9 @@ pandas 0.10.1
 **Improvements to existing features**
 
   - ``HDFStore``
+
     - enables storing of multi-index dataframes (closes GH1277_)
-    - support data column indexing and selection, via ``data_columns`` keyword
-      in append
+    - support data column indexing and selection, via ``data_columns`` keyword in append
     - support write chunking to reduce memory footprint, via ``chunksize``
       keyword to append
     - support automagic indexing via ``index`` keywork to append
@@ -50,18 +50,22 @@ pandas 0.10.1
       to do multiple-table append/selection
     - added support for datetime64 in columns
     - added method ``unique`` to select the unique values in an indexable or data column
+    - added method ``copy`` to copy an existing store (and possibly upgrade)
+    - show the shape of the data on disk for non-table stores when printing the store
   - Add ``logx`` option to DataFrame/Series.plot (GH2327_, #2565)
   - Support reading gzipped data from file-like object
   - ``pivot_table`` aggfunc can be anything used in GroupBy.aggregate (GH2643_)
 
 **Bug fixes**
 
   - ``HDFStore``
+
     - correctly handle ``nan`` elements in string columns; serialize via the
       ``nan_rep`` keyword to append
     - raise correctly on non-implemented column types (unicode/date)
     - handle correctly ``Term`` passed types (e.g. ``index<1000``, when index
       is ``Int64``), (closes GH512_)
+    - handle Timestamp correctly in data_columns (closes GH2637_)
   - Fix DataFrame.info bug with UTF8-encoded columns. (GH2576_)
   - Fix DatetimeIndex handling of FixedOffset tz (GH2604_)
   - More robust detection of being in IPython session for wide DataFrame
@@ -76,18 +80,21 @@ pandas 0.10.1
 **API Changes**
 
   - ``HDFStore``
+
+    - refactored HFDStore to deal with non-table stores as objects, will allow future enhancements
     - removed keyword ``compression`` from ``put`` (replaced by keyword
       ``complib`` to be consistent across library)
 
 .. _GH512: https://github.com/pydata/pandas/issues/512
 .. _GH1277: https://github.com/pydata/pandas/issues/1277
 .. _GH2327: https://github.com/pydata/pandas/issues/2327
-.. _GH2576: https://github.com/pydata/pandas/issues/2576
 .. _GH2585: https://github.com/pydata/pandas/issues/2585
 .. _GH2604: https://github.com/pydata/pandas/issues/2604
+.. _GH2576: https://github.com/pydata/pandas/issues/2576
 .. _GH2616: https://github.com/pydata/pandas/issues/2616
 .. _GH2625: https://github.com/pydata/pandas/issues/2625
 .. _GH2643: https://github.com/pydata/pandas/issues/2643
+.. _GH2637: https://github.com/pydata/pandas/issues/2637
 
 pandas 0.10.0
 =============

doc/source/io.rst

@@ -1106,7 +1106,7 @@ Storing Mixed Types in a Table
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Storing mixed-dtype data is supported. Strings are store as a fixed-width using the maximum size of the appended column. Subsequent appends will truncate strings at this length.
-Passing ``min_itemsize = { `values` : size }`` as a parameter to append will set a larger minimum for the string columns. Storing ``floats, strings, ints, bools, datetime64`` are currently supported. For string columns, passing ``nan_rep = 'my_nan_rep'`` to append will change the default nan representation on disk (which converts to/from `np.nan`), this defaults to `nan`.
+Passing ``min_itemsize = { `values` : size }`` as a parameter to append will set a larger minimum for the string columns. Storing ``floats, strings, ints, bools, datetime64`` are currently supported. For string columns, passing ``nan_rep = 'nan'`` to append will change the default nan representation on disk (which converts to/from `np.nan`), this defaults to `nan`.
 
 .. ipython:: python
 
@@ -1115,9 +1115,6 @@ Passing ``min_itemsize = { `values` : size }`` as a parameter to append will set
     df_mixed['int']      = 1
     df_mixed['bool']     = True
     df_mixed['datetime64'] = Timestamp('20010102')
-
-    # make sure that we have datetime64[ns] types
-    df_mixed = df_mixed.convert_objects()
     df_mixed.ix[3:5,['A','B','string','datetime64']] = np.nan
 
     store.append('df_mixed', df_mixed, min_itemsize = { 'values' : 50 })
@@ -1128,8 +1125,6 @@ Passing ``min_itemsize = { `values` : size }`` as a parameter to append will set
     # we have provided a minimum string column size
     store.root.df_mixed.table
 
-It is ok to store ``np.nan`` in a ``float or string``. Make sure to do a ``convert_objects()`` on the frame before storing a ``np.nan`` in a datetime64 column. Storing a column with a ``np.nan`` in a ``int, bool`` will currently throw an ``Exception`` as these columns will have converted to ``object`` type.
-
 Storing Multi-Index DataFrames
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -1268,11 +1263,11 @@ To retrieve the *unique* values of an indexable or data column, use the method `
 
 **Table Object**
 
-If you want to inspect the table object, retrieve via ``get_table``. You could use this progamatically to say get the number of rows in the table.
+If you want to inspect the stored object, retrieve via ``get_storer``. You could use this progamatically to say get the number of rows in an object.
 
 .. ipython:: python
 
-   store.get_table('df_dc').nrows
+   store.get_storer('df_dc').nrows
 
 Multiple Table Queries
 ~~~~~~~~~~~~~~~~~~~~~~
@@ -1348,7 +1343,7 @@ Or on-the-fly compression (this only applies to tables). You can turn off file c
 
    - ``ptrepack --chunkshape=auto --propindexes --complevel=9 --complib=blosc in.h5 out.h5``
 
-Furthermore ``ptrepack in.h5 out.h5`` will *repack* the file to allow you to reuse previously deleted space (alternatively, one can simply remove the file and write again).
+Furthermore ``ptrepack in.h5 out.h5`` will *repack* the file to allow you to reuse previously deleted space. Aalternatively, one can simply remove the file and write again, or use the ``copy`` method.
 
 Notes & Caveats
 ~~~~~~~~~~~~~~~
@@ -1372,10 +1367,28 @@ Notes & Caveats
 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. ``HDFStore`` will issue a warning if you try to use a prior-version format file. You must read in the entire
-file and write it out using the new format to take advantage of the updates. The group attribute ``pandas_version`` contains the version information.
+0.10.1 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. ``HDFStore`` will issue a warning if you try to use a prior-version format file. You must read in the entire file and write it out using the new format, using the method ``copy`` to take advantage of the updates. The group attribute ``pandas_version`` contains the version information. ``copy`` takes a number of options, please see the docstring.
+
+
+     .. ipython:: python
+
+        # a legacy store
+	import os
+        legacy_store = HDFStore('legacy_0.10.h5', 'r')
+        legacy_store
 
+        # copy (and return the new handle)
+	new_store = legacy_store.copy('store_new.h5')
+	new_store
+        new_store.close()
+
+     .. ipython:: python
+        :suppress:
+
+        legacy_store.close()
+        import os
+        os.remove('store_new.h5')
+
 
 Performance
 ~~~~~~~~~~~

doc/source/v0.10.1.txt

@@ -17,6 +17,9 @@ New features
 HDFStore
 ~~~~~~~~
 
+You may need to upgrade your existing data files. Please visit the **compatibility** section in the main docs.
+
+
 .. ipython:: python
    :suppress:
    :okexcept: