API/DOC: in HDFStore, change fmt -> format, change Storer format to Fixed format (just a name change);

         option for format is now fixed(f) or table(t)

Author: jreback

doc/source/io.rst

@@ -1794,27 +1794,31 @@ similar to how ``read_csv`` and ``to_csv`` work. (new in 0.11.0)
 
    os.remove('store_tl.h5')
 
-.. _io.hdf5-storer:
+.. _io.hdf5-fixed:
 
-Storer Format
-~~~~~~~~~~~~~
+Fixed Format
+~~~~~~~~~~~~
+
+.. note::
+
+   This was prior to 0.13.0 the ``Storer`` format.
 
 The examples above show storing using ``put``, which write the HDF5 to ``PyTables`` in a fixed array format, called
-the ``storer`` format. These types of stores are are **not** appendable once written (though you can simply
+the ``fixed`` format. These types of stores are are **not** appendable once written (though you can simply
 remove them and rewrite). Nor are they **queryable**; they must be
 retrieved in their entirety. These offer very fast writing and slightly faster reading than ``table`` stores.
-This format is specified by default when using ``put`` or by ``fmt='s'``
+This format is specified by default when using ``put`` or ``to_hdf`` or by ``format='fixed'`` or ``format='f'``
 
 .. warning::
 
-   A ``storer`` format will raise a ``TypeError`` if you try to retrieve using a ``where`` .
+   A ``fixed`` format will raise a ``TypeError`` if you try to retrieve using a ``where`` .
 
    .. code-block:: python
 
-       DataFrame(randn(10,2)).to_hdf('test_storer.h5','df')
+       DataFrame(randn(10,2)).to_hdf('test_fixed.h5','df')
 
-       pd.read_hdf('test_storer.h5','df',where='index>5')
-       TypeError: cannot pass a where specification when reading a non-table
+       pd.read_hdf('test_fixed.h5','df',where='index>5')
+       TypeError: cannot pass a where specification when reading a fixed format.
                   this store must be selected in its entirety
 
 
@@ -1827,7 +1831,7 @@ Table Format
 format. Conceptually a ``table`` is shaped very much like a DataFrame,
 with rows and columns. A ``table`` may be appended to in the same or
 other sessions.  In addition, delete & query type operations are
-supported. This format is specified by ``fmt='t'`` to ``append`` or ``put``.
+supported. This format is specified by ``format='table'`` or ``format='t'`` to ``append`` or ``put`` or ``to_hdf``
 
 .. ipython:: python
    :suppress:
@@ -1854,7 +1858,7 @@ supported. This format is specified by ``fmt='t'`` to ``append`` or ``put``.
 
 .. note::
 
-   You can also create a ``table`` by passing ``fmt='t'`` to a ``put`` operation.
+   You can also create a ``table`` by passing ``format='table'`` or ``format='t'`` to a ``put`` operation.
 
 .. _io.hdf5-keys:
 
@@ -2363,7 +2367,7 @@ Starting in 0.11, passing a ``min_itemsize`` dict will cause all passed columns
 External Compatibility
 ~~~~~~~~~~~~~~~~~~~~~~
 
-``HDFStore`` write storer objects in specific formats suitable for
+``HDFStore`` write ``table`` format objects in specific formats suitable for
 producing loss-less roundtrips to pandas objects. For external
 compatibility, ``HDFStore`` can read native ``PyTables`` format
 tables. It is possible to write an ``HDFStore`` object that can easily

doc/source/release.rst

@@ -108,10 +108,11 @@ pandas 0.13
     - removed the ``warn`` argument from ``open``. Instead a ``PossibleDataLossError`` exception will
       be raised if you try to use ``mode='w'`` with an OPEN file handle (:issue:`4367`)
     - allow a passed locations array or mask as a ``where`` condition (:issue:`4467`)
-    - the ``fmt`` keyword now replaces the ``table`` keyword; allowed values are ``s|t``
     - add the keyword ``dropna=True`` to ``append`` to change whether ALL nan rows are not written
       to the store (default is ``True``, ALL nan rows are NOT written), also settable
       via the option ``io.hdf.dropna_table`` (:issue:`4625`)
+    - the ``format`` keyword now replaces the ``table`` keyword; allowed values are ``fixed(f)|table(t)``
+      the ``Storer`` format has been renamed to ``Fixed``
   - ``JSON``
 
     - added ``date_unit`` parameter to specify resolution of timestamps. Options

doc/source/v0.13.0.txt

@@ -79,17 +79,17 @@ API changes
     - allow a passed locations array or mask as a ``where`` condition (:issue:`4467`).
       See :ref:`here<io.hdf5-where_mask>` for an example.
 
-    - the ``fmt`` keyword now replaces the ``table`` keyword; allowed values are ``s|t``
-      the same defaults as prior < 0.13.0 remain, e.g. ``put`` implies 's' (Storer) format
-      and ``append`` imples 't' (Table) format
+    - the ``format`` keyword now replaces the ``table`` keyword; allowed values are ``fixed(f)`` or ``table(t)``
+      the same defaults as prior < 0.13.0 remain, e.g. ``put`` implies 'fixed` or 'f' (Fixed) format
+      and ``append`` imples 'table' or 't' (Table) format
 
       .. ipython:: python
 
          path = 'test.h5'
          df = DataFrame(randn(10,2))
-         df.to_hdf(path,'df_table',fmt='t')
+         df.to_hdf(path,'df_table',format='table')
          df.to_hdf(path,'df_table2',append=True)
-         df.to_hdf(path,'df_storer')
+         df.to_hdf(path,'df_fixed')
          with get_store(path) as store:
             print store
 

pandas/core/generic.py

@@ -678,6 +678,15 @@ def to_hdf(self, path_or_buf, key, **kwargs):
               and if the file does not exist it is created.
           ``'r+'``
               It is similar to ``'a'``, but the file must already exist.
+        format   : 'fixed(f)|table(t)', default is 'fixed'
+            fixed(f) : Fixed format
+                       Fast writing/reading. Not-appendable, nor searchable
+            table(t) : Table format
+                       Write as a PyTables Table structure which may perform worse but
+                       allow more flexible operations like searching / selecting subsets
+                       of the data
+        append   : boolean, default False
+            For Table formats, append the input data to the existing
         complevel : int, 1-9, default 0
             If a complib is specified compression will be applied
             where possible