pd.lib.infer_dtype infers bytes in Python3

doc/source/whatsnew/v0.16.1.txt

@@ -67,6 +67,8 @@ Enhancements
 
 - ``DataFrame`` and ``Series`` now have ``_constructor_expanddim`` property as overridable constructor for one higher dimensionality data. This should be used only when it is really needed, see :ref:`here <ref-subclassing-pandas>`
 
+- ``pd.lib.infer_dtype`` now returns ``'bytes'`` in Python 3 where appropriate :issue:`10032`.
+
 .. _whatsnew_0161.enhancements.categoricalindex:
 
 CategoricalIndex

pandas/compat/__init__.py

@@ -37,6 +37,8 @@
 
 PY3 = (sys.version_info[0] >= 3)
 PY3_2 = sys.version_info[:2] == (3, 2)
+PY2 = sys.version_info[0] == 2
+
 
 try:
     import __builtin__ as builtins

pandas/src/inference.pyx

@@ -1,8 +1,11 @@
+import sys
 cimport util
 from tslib import NaT
 from datetime import datetime, timedelta
 iNaT = util.get_nat()
 
+cdef bint PY2 = sys.version_info[0] == 2
+
 # core.common import for fast inference checks
 def is_float(object obj):
     return util.is_float_object(obj)
@@ -38,10 +41,10 @@ _TYPE_MAP = {
     'f' : 'floating',
     'complex128': 'complex',
     'c' : 'complex',
-    'string': 'string',
-    'S' : 'string',
-    'unicode': 'unicode',
-    'U' : 'unicode',
+    'string': 'string' if PY2 else 'bytes',
+    'S' : 'string' if PY2 else 'bytes',
+    'unicode': 'unicode' if PY2 else 'string',
+    'U' : 'unicode' if PY2 else 'string',
     'bool': 'boolean',
     'b' : 'boolean',
     'datetime64[ns]' : 'datetime64',
@@ -181,6 +184,10 @@ def infer_dtype(object _values):
         if is_unicode_array(values):
             return 'unicode'
 
+    elif PyBytes_Check(val):
+        if is_bytes_array(values):
+            return 'bytes'
+
     elif is_timedelta(val):
         if is_timedelta_or_timedelta64_array(values):
             return 'timedelta'
@@ -196,11 +203,6 @@ def infer_dtype(object _values):
 
     return 'mixed'
 
-def infer_dtype_list(list values):
-    cdef:
-        Py_ssize_t i, n = len(values)
-    pass
-
 
 def is_possible_datetimelike_array(object arr):
     # determine if we have a possible datetimelike (or null-like) array
@@ -253,7 +255,6 @@ def is_bool_array(ndarray values):
     cdef:
         Py_ssize_t i, n = len(values)
         ndarray[object] objbuf
-        object obj
 
     if issubclass(values.dtype.type, np.bool_):
         return True
@@ -277,7 +278,6 @@ def is_integer_array(ndarray values):
     cdef:
         Py_ssize_t i, n = len(values)
         ndarray[object] objbuf
-        object obj
 
     if issubclass(values.dtype.type, np.integer):
         return True
@@ -298,7 +298,6 @@ def is_integer_float_array(ndarray values):
     cdef:
         Py_ssize_t i, n = len(values)
         ndarray[object] objbuf
-        object obj
 
     if issubclass(values.dtype.type, np.integer):
         return True
@@ -321,7 +320,6 @@ def is_float_array(ndarray values):
     cdef:
         Py_ssize_t i, n = len(values)
         ndarray[object] objbuf
-        object obj
 
     if issubclass(values.dtype.type, np.floating):
         return True
@@ -342,9 +340,9 @@ def is_string_array(ndarray values):
     cdef:
         Py_ssize_t i, n = len(values)
         ndarray[object] objbuf
-        object obj
 
-    if issubclass(values.dtype.type, (np.string_, np.unicode_)):
+    if ((PY2 and issubclass(values.dtype.type, np.string_)) or
+        not PY2 and issubclass(values.dtype.type, np.unicode_)):
         return True
     elif values.dtype == np.object_:
         objbuf = values
@@ -363,7 +361,6 @@ def is_unicode_array(ndarray values):
     cdef:
         Py_ssize_t i, n = len(values)
         ndarray[object] objbuf
-        object obj
 
     if issubclass(values.dtype.type, np.unicode_):
         return True
@@ -381,8 +378,29 @@ def is_unicode_array(ndarray values):
         return False
 
 
+def is_bytes_array(ndarray values):
+    cdef:
+        Py_ssize_t i, n = len(values)
+        ndarray[object] objbuf
+
+    if issubclass(values.dtype.type, np.bytes_):
+        return True
+    elif values.dtype == np.object_:
+        objbuf = values
+
+        if n == 0:
+            return False
+
+        for i in range(n):
+            if not PyBytes_Check(objbuf[i]):
+                return False
+        return True
+    else:
+        return False
+
+
 def is_datetime_array(ndarray[object] values):
-    cdef int i, null_count = 0, n = len(values)
+    cdef Py_ssize_t i, null_count = 0, n = len(values)
     cdef object v
     if n == 0:
         return False
@@ -399,7 +417,7 @@ def is_datetime_array(ndarray[object] values):
     return null_count != n
 
 def is_datetime64_array(ndarray values):
-    cdef int i, null_count = 0, n = len(values)
+    cdef Py_ssize_t i, null_count = 0, n = len(values)
     cdef object v
     if n == 0:
         return False
@@ -416,7 +434,7 @@ def is_datetime64_array(ndarray values):
     return null_count != n
 
 def is_timedelta_array(ndarray values):
-    cdef int i, null_count = 0, n = len(values)
+    cdef Py_ssize_t i, null_count = 0, n = len(values)
     cdef object v
     if n == 0:
         return False
@@ -431,7 +449,7 @@ def is_timedelta_array(ndarray values):
     return null_count != n
 
 def is_timedelta64_array(ndarray values):
-    cdef int i, null_count = 0, n = len(values)
+    cdef Py_ssize_t i, null_count = 0, n = len(values)
     cdef object v
     if n == 0:
         return False
@@ -447,7 +465,7 @@ def is_timedelta64_array(ndarray values):
 
 def is_timedelta_or_timedelta64_array(ndarray values):
     """ infer with timedeltas and/or nat/none """
-    cdef int i, null_count = 0, n = len(values)
+    cdef Py_ssize_t i, null_count = 0, n = len(values)
     cdef object v
     if n == 0:
         return False
@@ -462,7 +480,7 @@ def is_timedelta_or_timedelta64_array(ndarray values):
     return null_count != n
 
 def is_date_array(ndarray[object] values):
-    cdef int i, n = len(values)
+    cdef Py_ssize_t i, n = len(values)
     if n == 0:
         return False
     for i in range(n):
@@ -471,7 +489,7 @@ def is_date_array(ndarray[object] values):
     return True
 
 def is_time_array(ndarray[object] values):
-    cdef int i, n = len(values)
+    cdef Py_ssize_t i, n = len(values)
     if n == 0:
         return False
     for i in range(n):
@@ -484,7 +502,7 @@ def is_period(object o):
     return isinstance(o,Period)
 
 def is_period_array(ndarray[object] values):
-    cdef int i, n = len(values)
+    cdef Py_ssize_t i, n = len(values)
     from pandas.tseries.period import Period
 
     if n == 0:

pandas/tests/test_lib.py

@@ -6,7 +6,7 @@
 import pandas as pd
 from pandas.lib import isscalar, item_from_zerodim, max_len_string_array
 import pandas.util.testing as tm
-from pandas.compat import u
+from pandas.compat import u, PY2
 
 
 class TestMisc(tm.TestCase):
@@ -28,6 +28,17 @@ def test_max_len_string_array(self):
         tm.assertRaises(TypeError,
                         lambda: max_len_string_array(arr.astype('U')))
 
+    def test_infer_dtype_bytes(self):
+        compare = 'string' if PY2 else 'bytes'
+
+        # string array of bytes
+        arr = np.array(list('abc'), dtype='S1')
+        self.assertEqual(pd.lib.infer_dtype(arr), compare)
+
+        # object array of bytes
+        arr = arr.astype(object)
+        self.assertEqual(pd.lib.infer_dtype(arr), compare)
+
 
 class TestIsscalar(tm.TestCase):
 

pandas/tests/test_strings.py

@@ -1802,6 +1802,17 @@ def test_index_str_accessor_visibility(self):
         with self.assertRaisesRegexp(AttributeError, message):
             idx.str
 
+    def test_method_on_bytes(self):
+        lhs = Series(np.array(list('abc'), 'S1').astype(object))
+        rhs = Series(np.array(list('def'), 'S1').astype(object))
+        if compat.PY3:
+            self.assertRaises(TypeError, lhs.str.cat, rhs)
+        else:
+            result = lhs.str.cat(rhs)
+            expected = Series(np.array(['ad', 'be', 'cf'],
+                                       'S2').astype(object))
+            tm.assert_series_equal(result, expected)
+
 
 if __name__ == '__main__':
     nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],