Fixes GH16624

doc/source/whatsnew/v0.20.3.txt

@@ -66,7 +66,7 @@ Plotting
 Groupby/Resample/Rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
-
+- Bug in ``infer_freq`` causing indices with 2-day gaps during the working week to be wrongly inferred as business daily (:issue:`16624`)
 
 Sparse
 ^^^^^^

pandas/tests/indexes/timedeltas/test_timedelta.py

@@ -567,7 +567,7 @@ class TestSlicing(object):
 
     def test_timedelta(self):
         # this is valid too
-        index = date_range('1/1/2000', periods=50, freq='B')
+        index = date_range('1/1/2000', periods=50, freq='D')
         shifted = index + timedelta(1)
         back = shifted + timedelta(-1)
         assert tm.equalContents(index, back)

pandas/tests/tseries/test_frequencies.py

@@ -504,9 +504,14 @@ def test_raise_if_too_few(self):
         pytest.raises(ValueError, frequencies.infer_freq, index)
 
     def test_business_daily(self):
-        index = _dti(['12/31/1998', '1/3/1999', '1/4/1999'])
+        index = _dti(['01/01/1999', '1/4/1999', '1/5/1999'])
         assert frequencies.infer_freq(index) == 'B'
 
+    def test_business_daily_look_alike(self):
+        # 'weekend' (2-day gap) in wrong place
+        index = _dti(['12/31/1998', '1/3/1999', '1/4/1999'])
+        assert frequencies.infer_freq(index) is None
+
     def test_day(self):
         self._check_tick(timedelta(1), 'D')
 

pandas/tseries/frequencies.py

@@ -975,8 +975,7 @@ def _infer_daily_rule(self):
             else:
                 return _maybe_add_count('D', days)
 
-        # Business daily. Maybe
-        if self.day_deltas == [1, 3]:
+        if self._is_business_daily():
             return 'B'
 
         wom_rule = self._get_wom_rule()
@@ -1012,6 +1011,17 @@ def _get_monthly_rule(self):
         return {'cs': 'MS', 'bs': 'BMS',
                 'ce': 'M', 'be': 'BM'}.get(pos_check)
 
+    def _is_business_daily(self):
+        if self.day_deltas != [1, 3]:  # quick check: cannot be business daily
+            return False
+        # probably business daily, but need to confirm
+        first_weekday = self.index[0].weekday()
+        shifts = np.diff(np.asarray(self.index).view('i8'))
+        shifts = np.floor_divide(shifts, _ONE_DAY)
+        weekdays = np.mod(first_weekday + np.cumsum(shifts), 7)
+        return np.all(((weekdays == 0) & (shifts == 3)) |
+                      ((weekdays > 0) & (weekdays <= 4) & (shifts == 1)))
+
     def _get_wom_rule(self):
         #         wdiffs = unique(np.diff(self.index.week))
         # We also need -47, -49, -48 to catch index spanning year boundary