Merge pull request #27 from BertrandBordage/master

Simplifications

Author: chrisdev

django_pandas/io.py

@@ -3,15 +3,13 @@
 
 
 def to_fields(qs, fieldnames):
-    fields = []
     for fieldname in fieldnames:
         model = qs.model
         for fieldname_part in fieldname.split('__'):
             field = model._meta.get_field(fieldname_part)
             if field.get_internal_type() == 'ForeignKey':
                 model = field.rel.to
-        fields.append(field)
-    return fields
+        yield field
 
 
 def read_frame(qs, fieldnames=(), index_col=None, coerce_float=False,

django_pandas/managers.py

@@ -125,22 +125,17 @@ def to_timeseries(self, fieldnames=(), verbose=True,
                   defined in the ``__unicode__`` or ``__str__``
                   methods of the related class definition
         """
-        if index is None:
-            raise AssertionError('You must supply an index field')
-        if storage not in ('wide', 'long'):
-            raise AssertionError('storage must be wide or long')
+        assert index is not None, 'You must supply an index field'
+        assert storage in ('wide', 'long'), 'storage must be wide or long'
         if rs_kwargs is None:
             rs_kwargs = {}
 
         if storage == 'wide':
             df = self.to_dataframe(fieldnames, verbose=verbose, index=index)
         else:
             df = self.to_dataframe(fieldnames, verbose=verbose)
-            if values is None:
-                raise AssertionError('You must specify a values field')
-
-            if pivot_columns is None:
-                raise AssertionError('You must specify pivot_columns')
+            assert values is not None, 'You must specify a values field'
+            assert pivot_columns is not None, 'You must specify pivot_columns'
 
             if isinstance(pivot_columns, (tuple, list)):
                 df['combined_keys'] = ''
@@ -190,11 +185,8 @@ def to_dataframe(self, fieldnames=(), verbose=True, index=None,
 
         """
 
-        df = read_frame(self, fieldnames=fieldnames, verbose=verbose,
-                        index_col=index,
-                        coerce_float=coerce_float)
-
-        return df
+        return read_frame(self, fieldnames=fieldnames, verbose=verbose,
+                          index_col=index, coerce_float=coerce_float)
 
 
 class DataFrameManager(PassThroughManager):

django_pandas/utils.py

@@ -36,41 +36,29 @@ def inner(pk_list):
                       for pk in pk_list]
         out_dict = cache.get_many(frozenset(cache_keys))
         try:
-            out_list = [None if k is None else out_dict[k]
-                        for k in cache_keys]
+            return [None if k is None else out_dict[k] for k in cache_keys]
         except KeyError:
             out_dict = {
                 base_cache_key % obj.pk: force_text(obj)
                 for obj in model.objects.filter(pk__in={pk for pk in pk_list
                                                         if not isnan(pk)})}
             cache.set_many(out_dict)
-            out_list = list(map(out_dict.get, cache_keys))
-        return out_list
+            return list(map(out_dict.get, cache_keys))
 
     return inner
 
 
 def build_update_functions(fieldnames, fields):
-    update_functions = []
-
     for fieldname, field in zip(fieldnames, fields):
         if field.choices:
             choices = {k: force_text(v)
                        for k, v in field.flatchoices}
-            update_functions.append((fieldname,
-                                     replace_from_choices(choices)))
+            yield fieldname, replace_from_choices(choices)
 
         elif field.get_internal_type() == 'ForeignKey':
-            update_functions.append((fieldname, replace_pk(field.rel.to)))
-
-    return update_functions
+            yield fieldname, replace_pk(field.rel.to)
 
 
 def update_with_verbose(df, fieldnames, fields):
-    update_functions = build_update_functions(fieldnames, fields)
-
-    if not update_functions:
-        return
-
-    for fieldname, function in update_functions:
+    for fieldname, function in build_update_functions(fieldnames, fields):
         df[fieldname] = function(df[fieldname])