Default values for dropna to "False" (issue 9382)

doc/source/whatsnew/v0.16.0.txt

@@ -130,6 +130,54 @@ methods (:issue:`9088`).
     d    7
     dtype: int64
 
+- default behavior for HDF write functions with "table" format is now to keep rows that are all missing except for index. (:issue:`9382`)
+
+ Previously,
+
+  .. code-block:: python
+    In [1]: 
+     df_with_missing = pd.DataFrame({'col1':[0, np.nan, 2], 'col2':[1, np.nan, 3]})
+     df_with_missing.to_hdf('file.h5', 'df_with_missing', format = 't')
+
+     df_without_missing = pd.DataFrame({'col1':[0, -1, 2], 'col2':[1, -1, 3]})
+     df_without_missing.to_hdf('file.h5', 'df_without_missing')
+
+     print(pd.read_hdf('file.h5', 'df_with_missing'))
+     print(pd.read_hdf('file.h5', 'df_without_missing'))
+
+   Out [1]: 
+         col1  col2
+     0     0     1
+     2     2     3
+        col1  col2
+     0     0     1
+     1    -1    -1
+     2     2     3
+
+
+
+New behavior: do 
+
+  .. code-block:: python
+    In [1]: 
+     df_with_missing = pd.DataFrame({'col1':[0, np.nan, 2], 'col2':[1, np.nan, 3]})
+     df_with_missing.to_hdf('file.h5', 'df_with_missing', format = 't')
+
+     df_without_missing = pd.DataFrame({'col1':[0, -1, 2], 'col2':[1, -1, 3]})
+     df_without_missing.to_hdf('file.h5', 'df_without_missing')
+
+     print(pd.read_hdf('file.h5', 'df_with_missing'))
+     print(pd.read_hdf('file.h5', 'df_without_missing'))
+
+   Out [2]: 
+          col1  col2
+     0     0     1
+     1   NaN   NaN
+     2     2     3
+         col1  col2
+     0     0     1
+     1    -1    -1
+     2     2     3
 
 
 Deprecations

pandas/io/pytables.py

@@ -219,7 +219,7 @@ class DuplicateWarning(Warning):
 """
 
 with config.config_prefix('io.hdf'):
-    config.register_option('dropna_table', True, dropna_doc,
+    config.register_option('dropna_table', False, dropna_doc,
                            validator=config.is_bool)
     config.register_option(
         'default_format', None, format_doc,
@@ -801,8 +801,8 @@ def put(self, key, value, format=None, append=False, **kwargs):
             This will force Table format, append the input data to the
             existing.
         encoding : default None, provide an encoding for strings
-        dropna   : boolean, default True, do not write an ALL nan row to
-            the store settable by the option 'io.hdf.dropna_table'
+        dropna   : boolean, default False. if True do not write an ALL nan 
+            row to the store. Settable by the option 'io.hdf.dropna_table'
         """
         if format is None:
             format = get_option("io.hdf.default_format") or 'fixed'
@@ -883,8 +883,8 @@ def append(self, key, value, format=None, append=True, columns=None,
         chunksize    : size to chunk the writing
         expectedrows : expected TOTAL row size of this table
         encoding     : default None, provide an encoding for strings
-        dropna       : boolean, default True, do not write an ALL nan row to
-            the store settable by the option 'io.hdf.dropna_table'
+        dropna       : boolean, default False. If true, do not write an ALL nan 
+            row to the store. settable by the option 'io.hdf.dropna_table'
         Notes
         -----
         Does *not* check if data being appended overlaps with existing
@@ -903,7 +903,7 @@ def append(self, key, value, format=None, append=True, columns=None,
                              **kwargs)
 
     def append_to_multiple(self, d, value, selector, data_columns=None,
-                           axes=None, dropna=True, **kwargs):
+                           axes=None, dropna=False, **kwargs):
         """
         Append to multiple tables
 
@@ -918,7 +918,7 @@ def append_to_multiple(self, d, value, selector, data_columns=None,
         data_columns : list of columns to create as data columns, or True to
             use all columns
         dropna : if evaluates to True, drop rows from all tables if any single
-                 row in each table has all NaN
+                 row in each table has all NaN. Default False. 
 
         Notes
         -----
@@ -3740,7 +3740,7 @@ class AppendableTable(LegacyTable):
 
     def write(self, obj, axes=None, append=False, complib=None,
               complevel=None, fletcher32=None, min_itemsize=None,
-              chunksize=None, expectedrows=None, dropna=True, **kwargs):
+              chunksize=None, expectedrows=None, dropna=False, **kwargs):
 
         if not append and self.is_exists:
             self._handle.remove_node(self.group, 'table')
@@ -3777,7 +3777,7 @@ def write(self, obj, axes=None, append=False, complib=None,
         # add the rows
         self.write_data(chunksize, dropna=dropna)
 
-    def write_data(self, chunksize, dropna=True):
+    def write_data(self, chunksize, dropna=False):
         """ we form the data into a 2-d including indexes,values,mask
             write chunk-by-chunk """
 

pandas/io/tests/test_pytables.py

@@ -4678,6 +4678,13 @@ def test_duplicate_column_name(self):
             other = read_hdf(path, 'df')
             tm.assert_frame_equal(df, other)
 
+    def test_all_missing_values(self):
+        df_with_missing = DataFrame({'col1':[0, np.nan, 2], 'col2':[1, np.nan, np.nan]})
+
+        with ensure_clean_path(self.path) as path:
+            df_with_missing.to_hdf(path, 'df_with_missing', format = 't')
+            reloaded = read_hdf(path, 'df_with_missing')
+            tm.assert_frame_equal(df_with_missing, reloaded)
 
 def _test_sort(obj):
     if isinstance(obj, DataFrame):