BUG: Set index when reading stata file

doc/source/whatsnew/v0.21.0.txt

@@ -293,6 +293,7 @@ Other API Changes
 - :func:`Series.argmin` and :func:`Series.argmax` will now raise a ``TypeError`` when used with ``object`` dtypes, instead of a ``ValueError`` (:issue:`13595`)
 - :class:`Period` is now immutable, and will now raise an ``AttributeError`` when a user tries to assign a new value to the ``ordinal`` or ``freq`` attributes (:issue:`17116`).
 - :func:`to_datetime` when passed a tz-aware ``origin=`` kwarg will now raise a more informative ``ValueError`` rather than a ``TypeError`` (:issue:`16842`)
+- Renamed non-functional ``index`` to ``index_col`` in :func:`read_stata` to improve API consistency (:issue:`16342`)
 
 
 .. _whatsnew_0210.deprecations:
@@ -370,6 +371,7 @@ I/O
 - Bug in :func:`read_csv` when called with ``low_memory=False`` in which a CSV with at least one column > 2GB in size would incorrectly raise a ``MemoryError`` (:issue:`16798`).
 - Bug in :func:`read_csv` when called with a single-element list ``header`` would return a ``DataFrame`` of all NaN values (:issue:`7757`)
 - Bug in :func:`read_stata` where value labels could not be read when using an iterator (:issue:`16923`)
+- Bug in :func:`read_stata` where the index was not set (:issue:`16342`)
 - Bug in :func:`read_html` where import check fails when run in multiple threads (:issue:`16928`)
 
 Plotting

pandas/io/stata.py

@@ -9,31 +9,30 @@
 You can find more information on http://presbrey.mit.edu/PyDTA and
 http://www.statsmodels.org/devel/
 """
-import numpy as np
 
-import sys
+import datetime
 import struct
-from dateutil.relativedelta import relativedelta
+import sys
 
-from pandas.core.dtypes.common import (
-    is_categorical_dtype, is_datetime64_dtype,
-    _ensure_object)
+import numpy as np
+from dateutil.relativedelta import relativedelta
+from pandas._libs.lib import max_len_string_array, infer_dtype
+from pandas._libs.tslib import NaT, Timestamp
 
+import pandas as pd
+from pandas import compat, to_timedelta, to_datetime, isna, DatetimeIndex
+from pandas.compat import (lrange, lmap, lzip, text_type, string_types, range,
+                           zip, BytesIO)
 from pandas.core.base import StringMixin
 from pandas.core.categorical import Categorical
+from pandas.core.dtypes.common import (is_categorical_dtype, _ensure_object,
+                                       is_datetime64_dtype)
 from pandas.core.frame import DataFrame
 from pandas.core.series import Series
-import datetime
-from pandas import compat, to_timedelta, to_datetime, isna, DatetimeIndex
-from pandas.compat import lrange, lmap, lzip, text_type, string_types, range, \
-    zip, BytesIO
-from pandas.util._decorators import Appender
-import pandas as pd
-
 from pandas.io.common import (get_filepath_or_buffer, BaseIterator,
                               _stringify_path)
-from pandas._libs.lib import max_len_string_array, infer_dtype
-from pandas._libs.tslib import NaT, Timestamp
+from pandas.util._decorators import Appender
+from pandas.util._decorators import deprecate_kwarg
 
 VALID_ENCODINGS = ('ascii', 'us-ascii', 'latin-1', 'latin_1', 'iso-8859-1',
                    'iso8859-1', '8859', 'cp819', 'latin', 'latin1', 'L1')
@@ -53,8 +52,8 @@
     Encoding used to parse the files. None defaults to latin-1."""
 
 _statafile_processing_params2 = """\
-index : identifier of index column
-    identifier of column that should be used as index of the DataFrame
+index_col : string, optional, default: None
+    Column to set as index
 convert_missing : boolean, defaults to False
     Flag indicating whether to convert missing values to their Stata
     representations.  If False, missing values are replaced with nans.
@@ -159,15 +158,16 @@
 
 
 @Appender(_read_stata_doc)
+@deprecate_kwarg(old_arg_name='index', new_arg_name='index_col')
 def read_stata(filepath_or_buffer, convert_dates=True,
-               convert_categoricals=True, encoding=None, index=None,
+               convert_categoricals=True, encoding=None, index_col=None,
                convert_missing=False, preserve_dtypes=True, columns=None,
                order_categoricals=True, chunksize=None, iterator=False):
 
     reader = StataReader(filepath_or_buffer,
                          convert_dates=convert_dates,
                          convert_categoricals=convert_categoricals,
-                         index=index, convert_missing=convert_missing,
+                         index_col=index_col, convert_missing=convert_missing,
                          preserve_dtypes=preserve_dtypes,
                          columns=columns,
                          order_categoricals=order_categoricals,
@@ -944,8 +944,9 @@ def __init__(self, encoding):
 class StataReader(StataParser, BaseIterator):
     __doc__ = _stata_reader_doc
 
+    @deprecate_kwarg(old_arg_name='index', new_arg_name='index_col')
     def __init__(self, path_or_buf, convert_dates=True,
-                 convert_categoricals=True, index=None,
+                 convert_categoricals=True, index_col=None,
                  convert_missing=False, preserve_dtypes=True,
                  columns=None, order_categoricals=True,
                  encoding='latin-1', chunksize=None):
@@ -956,7 +957,7 @@ def __init__(self, path_or_buf, convert_dates=True,
         # calls to read).
         self._convert_dates = convert_dates
         self._convert_categoricals = convert_categoricals
-        self._index = index
+        self._index_col = index_col
         self._convert_missing = convert_missing
         self._preserve_dtypes = preserve_dtypes
         self._columns = columns
@@ -1460,8 +1461,9 @@ def get_chunk(self, size=None):
         return self.read(nrows=size)
 
     @Appender(_read_method_doc)
+    @deprecate_kwarg(old_arg_name='index', new_arg_name='index_col')
     def read(self, nrows=None, convert_dates=None,
-             convert_categoricals=None, index=None,
+             convert_categoricals=None, index_col=None,
              convert_missing=None, preserve_dtypes=None,
              columns=None, order_categoricals=None):
         # Handle empty file or chunk.  If reading incrementally raise
@@ -1486,6 +1488,8 @@ def read(self, nrows=None, convert_dates=None,
             columns = self._columns
         if order_categoricals is None:
             order_categoricals = self._order_categoricals
+        if index_col is None:
+            index_col = self._index_col
 
         if nrows is None:
             nrows = self.nobs
@@ -1524,14 +1528,14 @@ def read(self, nrows=None, convert_dates=None,
             self._read_value_labels()
 
         if len(data) == 0:
-            data = DataFrame(columns=self.varlist, index=index)
+            data = DataFrame(columns=self.varlist)
         else:
-            data = DataFrame.from_records(data, index=index)
+            data = DataFrame.from_records(data)
             data.columns = self.varlist
 
         # If index is not specified, use actual row number rather than
         # restarting at 0 for each chunk.
-        if index is None:
+        if index_col is None:
             ix = np.arange(self._lines_read - read_lines, self._lines_read)
             data = data.set_index(ix)
 
@@ -1553,7 +1557,7 @@ def read(self, nrows=None, convert_dates=None,
         cols_ = np.where(self.dtyplist)[0]
 
         # Convert columns (if needed) to match input type
-        index = data.index
+        ix = data.index
         requires_type_conversion = False
         data_formatted = []
         for i in cols_:
@@ -1563,7 +1567,7 @@ def read(self, nrows=None, convert_dates=None,
                 if dtype != np.dtype(object) and dtype != self.dtyplist[i]:
                     requires_type_conversion = True
                     data_formatted.append(
-                        (col, Series(data[col], index, self.dtyplist[i])))
+                        (col, Series(data[col], ix, self.dtyplist[i])))
                 else:
                     data_formatted.append((col, data[col]))
         if requires_type_conversion:
@@ -1606,6 +1610,9 @@ def read(self, nrows=None, convert_dates=None,
             if convert:
                 data = DataFrame.from_items(retyped_data)
 
+        if index_col is not None:
+            data = data.set_index(data.pop(index_col))
+
         return data
 
     def _do_convert_missing(self, data, convert_missing):

pandas/tests/io/test_stata.py

@@ -476,7 +476,7 @@ def test_read_write_reread_dta15(self):
         tm.assert_frame_equal(parsed_114, parsed_117)
 
     def test_timestamp_and_label(self):
-        original = DataFrame([(1,)], columns=['var'])
+        original = DataFrame([(1,)], columns=['variable'])
         time_stamp = datetime(2000, 2, 29, 14, 21)
         data_label = 'This is a data file.'
         with tm.ensure_clean() as path:
@@ -1309,3 +1309,12 @@ def test_value_labels_iterator(self, write_index):
             dta_iter = pd.read_stata(path, iterator=True)
             value_labels = dta_iter.value_labels()
         assert value_labels == {'A': {0: 'A', 1: 'B', 2: 'C', 3: 'E'}}
+
+    def test_set_index(self):
+        # GH 17328
+        df = tm.makeDataFrame()
+        df.index.name = 'index'
+        with tm.ensure_clean() as path:
+            df.to_stata(path)
+            reread = pd.read_stata(path, index_col='index')
+        tm.assert_frame_equal(df, reread)