BUG: Allow value labels to be read with iterator

All value labels to be read before the iterator has been used
Fix issue where categorical data was incorrectly reformatted when
write_index was False

closes #16923

Author: bashtage

doc/source/whatsnew/v0.21.0.txt

@@ -162,6 +162,7 @@ I/O
 
 - Bug in :func:`read_csv` in which non integer values for the header argument generated an unhelpful / unrelated error message (:issue:`16338`)
 
+- Bug in :func:`read_stata` where value labels could not be read when using an iterator (:issue:`16923`)
 
 Plotting
 ^^^^^^^^

pandas/io/stata.py

@@ -997,6 +997,7 @@ def __init__(self, path_or_buf, convert_dates=True,
             self.path_or_buf = BytesIO(contents)
 
         self._read_header()
+        self._setup_dtype()
 
     def __enter__(self):
         """ enter context manager """
@@ -1299,6 +1300,23 @@ def _read_old_header(self, first_char):
         # necessary data to continue parsing
         self.data_location = self.path_or_buf.tell()
 
+    def _setup_dtype(self):
+        # Setup the dtype.
+        if self._dtype is not None:
+            return self._dtype
+
+        dtype = []  # Convert struct data types to numpy data type
+        for i, typ in enumerate(self.typlist):
+            if typ in self.NUMPY_TYPE_MAP:
+                dtype.append(('s' + str(i), self.byteorder +
+                              self.NUMPY_TYPE_MAP[typ]))
+            else:
+                dtype.append(('s' + str(i), 'S' + str(typ)))
+        dtype = np.dtype(dtype)
+        self._dtype = dtype
+
+        return self._dtype
+
     def _calcsize(self, fmt):
         return (type(fmt) is int and fmt or
                 struct.calcsize(self.byteorder + fmt))
@@ -1472,24 +1490,12 @@ def read(self, nrows=None, convert_dates=None,
         if nrows is None:
             nrows = self.nobs
 
-        if (self.format_version >= 117) and (self._dtype is None):
+        if (self.format_version >= 117) and (not self._value_labels_read):
             self._can_read_value_labels = True
             self._read_strls()
 
-        # Setup the dtype.
-        if self._dtype is None:
-            dtype = []  # Convert struct data types to numpy data type
-            for i, typ in enumerate(self.typlist):
-                if typ in self.NUMPY_TYPE_MAP:
-                    dtype.append(('s' + str(i), self.byteorder +
-                                  self.NUMPY_TYPE_MAP[typ]))
-                else:
-                    dtype.append(('s' + str(i), 'S' + str(typ)))
-            dtype = np.dtype(dtype)
-            self._dtype = dtype
-
         # Read data
-        dtype = self._dtype
+        dtype = self._setup_dtype()
         max_read_len = (self.nobs - self._lines_read) * dtype.itemsize
         read_len = nrows * dtype.itemsize
         read_len = min(read_len, max_read_len)
@@ -1958,7 +1964,6 @@ def _prepare_categoricals(self, data):
             return data
 
         get_base_missing_value = StataMissingValue.get_base_missing_value
-        index = data.index
         data_formatted = []
         for col, col_is_cat in zip(data, is_cat):
             if col_is_cat:
@@ -1981,8 +1986,7 @@ def _prepare_categoricals(self, data):
 
                 # Replace missing values with Stata missing value for type
                 values[values == -1] = get_base_missing_value(dtype)
-                data_formatted.append((col, values, index))
-
+                data_formatted.append((col, values))
             else:
                 data_formatted.append((col, data[col]))
         return DataFrame.from_items(data_formatted)

pandas/tests/io/test_stata.py

@@ -9,18 +9,18 @@
 from datetime import datetime
 from distutils.version import LooseVersion
 
-import pytest
 import numpy as np
 import pandas as pd
 import pandas.util.testing as tm
+import pytest
 from pandas import compat
+from pandas._libs.tslib import NaT
 from pandas.compat import iterkeys
+from pandas.core.dtypes.common import is_categorical_dtype
 from pandas.core.frame import DataFrame, Series
 from pandas.io.parsers import read_csv
 from pandas.io.stata import (read_stata, StataReader, InvalidColumnName,
                              PossiblePrecisionLoss, StataMissingValue)
-from pandas._libs.tslib import NaT
-from pandas.core.dtypes.common import is_categorical_dtype
 
 
 class TestStata(object):
@@ -1297,3 +1297,20 @@ def test_pickle_path_localpath(self):
         reader = lambda x: read_stata(x).set_index('index')
         result = tm.round_trip_localpath(df.to_stata, reader)
         tm.assert_frame_equal(df, result)
+
+    def test_value_labels_iterator(self):
+        # GH 16923
+        d = {'A': ['B', 'E', 'C', 'A', 'E']}
+        df = pd.DataFrame(data=d)
+        df['A'] = df['A'].astype('category')
+        with tm.ensure_clean() as path:
+            df.to_stata(path, write_index=False)
+            dta_iter = pd.read_stata('test.dta', iterator=True)
+            value_labels = dta_iter.value_labels()
+        assert value_labels == {'A': {0: 'A', 1: 'B', 2: 'C', 3: 'E'}}
+
+        with tm.ensure_clean() as path:
+            df.to_stata(path)
+            dta_iter = pd.read_stata('test.dta', iterator=True)
+            value_labels = dta_iter.value_labels()
+        assert value_labels == {'A': {0: 'A', 1: 'B', 2: 'C', 3: 'E'}}