ENH: Add type conversion to read_stata and StataReader

Added upcasting of numeric types which will default numeric data to either
int64 or float64.  This option has been enabled by default.

closes pandas-dev#8527

Author: bashtage

doc/source/io.rst

@@ -3654,10 +3654,15 @@ missing values are represented as ``np.nan``.  If ``True``, missing values are
 represented using ``StataMissingValue`` objects, and columns containing missing
 values will have ``dtype`` set to ``object``.
 
-
 The StataReader supports .dta Formats 104, 105, 108, 113-115 and 117.
 Alternatively, the function :func:`~pandas.io.stata.read_stata` can be used
 
+.. note::
+
+   Setting ``preserve_dtypes=False`` will upcast all integer data types to
+   ``int64`` and all floating point data types to ``float64``.  By default,
+   the Stata data types are preserved when importing.
+
 .. ipython:: python
    :suppress:
 

doc/source/v0.15.0.txt

@@ -864,6 +864,7 @@ Enhancements
 - Added support for writing datetime64 columns with ``to_sql`` for all database flavors (:issue:`7103`).
 
 - Added support for bool, uint8, uint16 and uint32 datatypes in ``to_stata`` (:issue:`7097`, :issue:`7365`)
+- Added conversion option when importing Stata files (:issue:`8527`)
 
 - Added ``layout`` keyword to ``DataFrame.plot``. You can pass a tuple of ``(rows, columns)``, one of which can be ``-1`` to automatically infer (:issue:`6667`, :issue:`8071`).
 - Allow to pass multiple axes to ``DataFrame.plot``, ``hist`` and ``boxplot`` (:issue:`5353`, :issue:`6970`, :issue:`7069`)

pandas/io/stata.py

@@ -29,7 +29,7 @@
 
 def read_stata(filepath_or_buffer, convert_dates=True,
                convert_categoricals=True, encoding=None, index=None,
-               convert_missing=False):
+               convert_missing=False, preserve_dtypes=True):
     """
     Read Stata file into DataFrame
 
@@ -52,13 +52,14 @@ def read_stata(filepath_or_buffer, convert_dates=True,
         If True, columns containing missing values are returned with
         object data types and missing values are represented by
         StataMissingValue objects.
+    preserve_dtypes : boolean, defaults to True
+        Preserve Stata datatypes. If False, numeric data are upcast to pandas
+        default types for foreign data (float64 or int64)
     """
     reader = StataReader(filepath_or_buffer, encoding)
 
-    return reader.data(convert_dates,
-                       convert_categoricals,
-                       index,
-                       convert_missing)
+    return reader.data(convert_dates, convert_categoricals, index,
+                       convert_missing, preserve_dtypes)
 
 _date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"]
 
@@ -976,7 +977,7 @@ def _read_strls(self):
             self.path_or_buf.read(1)  # zero-termination
 
     def data(self, convert_dates=True, convert_categoricals=True, index=None,
-             convert_missing=False):
+             convert_missing=False, preserve_dtypes=True):
         """
         Reads observations from Stata file, converting them into a dataframe
 
@@ -995,7 +996,9 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None,
             nans.  If True, columns containing missing values are returned with
             object data types and missing values are represented by
             StataMissingValue objects.
-
+        preserve_dtypes : boolean, defaults to True
+            Preserve Stata datatypes. If False, numeric data are upcast to
+            pandas default types for foreign data (float64 or int64)
         Returns
         -------
         y : DataFrame instance
@@ -1107,6 +1110,21 @@ def data(self, convert_dates=True, convert_categoricals=True, index=None,
                     labeled_data[(data[col] == k).values] = v
                 data[col] = Categorical.from_array(labeled_data)
 
+        if not preserve_dtypes:
+            retyped_data = []
+            convert = False
+            for col in data:
+                dtype = data[col].dtype
+                if dtype in (np.float16, np.float32):
+                    dtype = np.float64
+                    convert = True
+                elif dtype in (np.int8, np.int16, np.int32):
+                    dtype = np.int64
+                    convert = True
+                retyped_data.append((col, data[col].astype(dtype)))
+            if convert:
+                data = DataFrame.from_items(retyped_data)
+
         return data
 
     def data_label(self):

pandas/io/tests/test_stata.py

@@ -83,6 +83,7 @@ def setUp(self):
 
 
     def read_dta(self, file):
+        # Legacy default reader configuration
         return read_stata(file, convert_dates=True)
 
     def read_csv(self, file):
@@ -694,6 +695,35 @@ def test_big_dates(self):
             tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                   expected)
 
+    def test_dtype_conversion(self):
+        expected = self.read_csv(self.csv15)
+        expected['byte_'] = expected['byte_'].astype(np.int8)
+        expected['int_'] = expected['int_'].astype(np.int16)
+        expected['long_'] = expected['long_'].astype(np.int32)
+        expected['float_'] = expected['float_'].astype(np.float32)
+        expected['double_'] = expected['double_'].astype(np.float64)
+        expected['date_td'] = expected['date_td'].apply(datetime.strptime,
+                                                        args=('%Y-%m-%d',))
+
+        no_conversion = read_stata(self.dta15_117,
+                                   convert_dates=True)
+        tm.assert_frame_equal(expected, no_conversion)
+
+        conversion = read_stata(self.dta15_117,
+                                convert_dates=True,
+                                preserve_dtypes=False)
+
+        # read_csv types are the same
+        expected = self.read_csv(self.csv15)
+        expected['date_td'] = expected['date_td'].apply(datetime.strptime,
+                                                        args=('%Y-%m-%d',))
+
+        tm.assert_frame_equal(expected, conversion)
+
+
+
+
+