Use OrderedDict instead of dict in io/stata.py

Author: reidy-p

pandas/io/stata.py

@@ -13,6 +13,7 @@
 import datetime
 import struct
 import sys
+from collections import OrderedDict
 
 import numpy as np
 from dateutil.relativedelta import relativedelta
@@ -1571,8 +1572,8 @@ def read(self, nrows=None, convert_dates=None,
                 else:
                     data_formatted.append((col, data[col]))
         if requires_type_conversion:
-            data = DataFrame(dict(data_formatted),
-                             columns=dict(data_formatted).keys())
+            data = DataFrame(OrderedDict(data_formatted),
+                             columns=OrderedDict(data_formatted).keys())
         del data_formatted
 
         self._do_convert_missing(data, convert_missing)
@@ -1610,8 +1611,8 @@ def read(self, nrows=None, convert_dates=None,
                     convert = True
                 retyped_data.append((col, data[col].astype(dtype)))
             if convert:
-                data = DataFrame(dict(retyped_data),
-                                 columns=dict(retyped_data).keys())
+                data = DataFrame(OrderedDict(retyped_data),
+                                 columns=OrderedDict(retyped_data).keys())
 
         if index_col is not None:
             data = data.set_index(data.pop(index_col))
@@ -1724,8 +1725,8 @@ def _do_convert_categoricals(self, data, value_label_dict, lbllist,
                 cat_converted_data.append((col, cat_data))
             else:
                 cat_converted_data.append((col, data[col]))
-        data = DataFrame(dict(cat_converted_data),
-                         columns=dict(cat_converted_data).keys())
+        data = DataFrame(OrderedDict(cat_converted_data),
+                         columns=OrderedDict(cat_converted_data).keys())
         return data
 
     def data_label(self):
@@ -2000,8 +2001,8 @@ def _prepare_categoricals(self, data):
                 data_formatted.append((col, values))
             else:
                 data_formatted.append((col, data[col]))
-        return DataFrame(dict(data_formatted),
-                         columns=dict(data_formatted).keys())
+        return DataFrame(OrderedDict(data_formatted),
+                         columns=OrderedDict(data_formatted).keys())
 
     def _replace_nans(self, data):
         # return data