Removed references to future versions

doc/source/io.rst

@@ -138,8 +138,7 @@ usecols : array-like or callable, default ``None``
   Using this parameter results in much faster parsing time and lower memory usage.
 as_recarray : boolean, default ``False``
   .. deprecated:: 0.18.2
-     This argument will be removed in a future version. Please call
-     ``pd.read_csv(...).to_records()`` instead.
+     Please call ``pd.read_csv(...).to_records()`` instead.
 
   Return a NumPy recarray instead of a DataFrame after parsing the data. If
   set to ``True``, this option takes precedence over the ``squeeze`` parameter.
@@ -204,19 +203,18 @@ low_memory : boolean, default ``True``
   use the ``chunksize`` or ``iterator`` parameter to return the data in chunks.
   (Only valid with C parser)
 buffer_lines : int, default None
-  .. deprecated:: 0.18.2
-     This argument will be removed in a future version because its
-     value is not respected by the parser
+  .. deprecated:: 0.19.0
+     Argument removed because its value is not respected by the parser
 compact_ints : boolean, default False
-  .. deprecated:: 0.18.2
-  DEPRECATED: this argument will be removed in a future version
+  .. deprecated:: 0.19.0
+     Argument moved to ``pd.to_numeric``
 
   If ``compact_ints`` is ``True``, then for any column that is of integer dtype, the
   parser will attempt to cast it as the smallest integer ``dtype`` possible, either
   signed or unsigned depending on the specification from the ``use_unsigned`` parameter.
 use_unsigned : boolean, default False
   .. deprecated:: 0.18.2
-     This argument will be removed in a future version
+     Argument moved to ``pd.to_numeric``
 
   If integer columns are being compacted (i.e. ``compact_ints=True``), specify whether
   the column should be compacted to the smallest signed or unsigned integer dtype.

pandas/core/categorical.py

@@ -423,7 +423,7 @@ def base(self):
     @classmethod
     def from_array(cls, data, **kwargs):
         """
-        .. deprecated::
+        .. deprecated:: 0.19.0
            Use ``Categorical`` instead.
 
         Make a Categorical type from a single array-like object.

pandas/core/common.py

@@ -568,7 +568,7 @@ def in_ipnb():
     check if we're inside an IPython Notebook
 
     .. deprecated:: 0.14.1
-       This is no longer used in pandas, and won't work in IPython 3 and above.
+       This is no longer needed, or working, in IPython 3 and above.
     """
     try:
         ip = get_ipython()  # noqa

pandas/core/generic.py

@@ -1323,8 +1323,7 @@ def to_sql(self, name, con, flavor=None, schema=None, if_exists='fail',
             library. If a DBAPI2 object, only sqlite3 is supported.
         flavor : 'sqlite', default None
             .. deprecated:: 0.19.0
-               This parameter will be removed in a future version,
-               as 'sqlite' is the only supported option if SQLAlchemy is not
+               'sqlite' is the only supported option if SQLAlchemy is not
                installed.
         schema : string, default None
             Specify the schema (if database flavor supports this). If None, use

pandas/core/panel4d.py

@@ -20,7 +20,6 @@
     N-Dimensional named containers.
 
     .. deprecated:: 0.19.0
-       Panel4D is deprecated and will be removed in a future version.
        The recommended way to represent these types of n-dimensional data
        are with the `xarray package <http://xarray.pydata.org/en/stable/>`__.
        Pandas provides a `.to_xarray()` method to automate this conversion.

pandas/core/panelnd.py

@@ -10,7 +10,6 @@ def create_nd_panel_factory(klass_name, orders, slices, slicer, aliases=None,
     """ manufacture a n-d class:
 
     .. deprecated:: 0.19.0
-       Panelnd is deprecated and will be removed in a future version.
        The recommended way to represent these types of n-dimensional data
        are with the `xarray package <http://xarray.pydata.org/en/stable/>`__.
        Pandas provides a `.to_xarray()` method to automate this conversion.

pandas/core/series.py

@@ -851,8 +851,8 @@ def repeat(self, repeats, *args, **kwargs):
     def reshape(self, *args, **kwargs):
         """
         .. deprecated:: 0.19.0
-           Calling this method will raise an error in a future release.
-           Please call ``.values.reshape(...)`` instead.
+           Calling this method will raise an error. Please call
+           ``.values.reshape(...)`` instead.
 
         return an ndarray with the values shape
         if the specified shape matches exactly the current shape, then

pandas/core/sparse/array.py

@@ -392,8 +392,7 @@ def to_dense(self, fill=None):
         ----------
         fill: float, default None
             .. deprecated:: 0.20.0
-               This argument will be removed in a future version
-               because it is not respected by this function.
+               This argument is not respected by this function.
 
         Returns
         -------

pandas/io/parsers.py

@@ -106,8 +106,7 @@
     parsing time and lower memory usage.
 as_recarray : boolean, default False
     .. deprecated:: 0.19.0
-       This argument will be removed in a future version. Please call
-       `pd.read_csv(...).to_records()` instead.
+       Please call `pd.read_csv(...).to_records()` instead.
 
     Return a NumPy recarray instead of a DataFrame after parsing the data.
     If set to True, this option takes precedence over the `squeeze` parameter.
@@ -279,19 +278,18 @@
     (Only valid with C parser)
 buffer_lines : int, default None
     .. deprecated:: 0.19.0
-       This argument will be removed in a future version because its
-       value is not respected by the parser
+       This argument is not respected by the parser
 compact_ints : boolean, default False
     .. deprecated:: 0.19.0
-       This argument will be removed in a future version
+       Argument moved to ``pd.to_numeric``
 
     If compact_ints is True, then for any column that is of integer dtype,
     the parser will attempt to cast it as the smallest integer dtype possible,
     either signed or unsigned depending on the specification from the
     `use_unsigned` parameter.
 use_unsigned : boolean, default False
     .. deprecated:: 0.19.0
-       This argument will be removed in a future version
+       Argument moved to ``pd.to_numeric``
 
     If integer columns are being compacted (i.e. `compact_ints=True`), specify
     whether the column should be compacted to the smallest signed or unsigned

pandas/io/sql.py

@@ -433,7 +433,8 @@ def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail',
         If a DBAPI2 object, only sqlite3 is supported.
     flavor : 'sqlite', default None
         .. deprecated:: 0.19.0
-           This parameter will be removed in a future version
+           'sqlite' is the only supported option if SQLAlchemy is not
+           installed.
     schema : string, default None
         Name of SQL schema in database to write to (if database flavor
         supports this). If None, use default schema (default).
@@ -486,7 +487,8 @@ def has_table(table_name, con, flavor=None, schema=None):
         If a DBAPI2 object, only sqlite3 is supported.
     flavor : 'sqlite', default None
         .. deprecated:: 0.19.0
-           This parameter will be removed in a future version
+           'sqlite' is the only supported option if SQLAlchemy is not
+           installed.
     schema : string, default None
         Name of SQL schema in database to write to (if database flavor supports
         this). If None, use default schema (default).
@@ -1548,7 +1550,8 @@ def get_schema(frame, name, flavor=None, keys=None, con=None, dtype=None):
         If a DBAPI2 object, only sqlite3 is supported.
     flavor : 'sqlite', default None
         .. deprecated:: 0.19.0
-           This parameter will be removed in a future version
+           'sqlite' is the only supported option if SQLAlchemy is not
+           installed.
     dtype : dict of column name to SQL type, default None
         Optional specifying the datatype for columns. The SQL type should
         be a SQLAlchemy type, or a string for sqlite3 fallback connection.