MAINT: Remove outdated compat code.

tests/pipeline/test_engine.py

@@ -78,12 +78,6 @@
 )
 from zipline.utils.memoize import lazyval
 from zipline.utils.numpy_utils import bool_dtype
-from zipline.utils.pandas_utils import (
-    ewma,
-    ewmstd,
-    rolling_apply,
-    rolling_mean,
-)
 
 
 class RollingSumDifference(CustomFactor):
@@ -961,16 +955,16 @@ def test_SMA(self):
         # Shift back the raw inputs by a trading day because we expect our
         # computed results to be computed using values anchored on the
         # **previous** day's data.
-        expected_raw = rolling_mean(
-            DataFrame(
-                expected_bar_values_2d(
-                    dates - self.trading_calendar.day,
-                    self.equity_info,
-                    'close',
-                ),
+        expected_raw = DataFrame(
+            expected_bar_values_2d(
+                dates - self.trading_calendar.day,
+                self.equity_info,
+                'close',
             ),
+        ).rolling(
             window_length,
             min_periods=1,
+        ).mean(
         ).values
 
         expected = DataFrame(
@@ -1082,10 +1076,11 @@ def expected_ewma(self, window_length, decay_rate):
         # Don't use it outside of testing.  We're using rolling-apply of an
         # ewma (which is itself a rolling-window function) because we only want
         # to look at ``window_length`` rows at a time.
-        return rolling_apply(
-            self.raw_data,
-            window_length,
-            lambda window: ewma(DataFrame(window), span=span).values[-1],
+        return self.raw_data.rolling(window_length).apply(
+            lambda subarray: (DataFrame(subarray)
+                              .ewm(span=span)
+                              .mean()
+                              .values[-1])
         )[window_length:]
 
     def expected_ewmstd(self, window_length, decay_rate):
@@ -1096,10 +1091,11 @@ def expected_ewmstd(self, window_length, decay_rate):
         # EWMSTD.  Don't use it outside of testing.  We're using rolling-apply
         # of an ewma (which is itself a rolling-window function) because we
         # only want to look at ``window_length`` rows at a time.
-        return rolling_apply(
-            self.raw_data,
-            window_length,
-            lambda window: ewmstd(DataFrame(window), span=span).values[-1],
+        return self.raw_data.rolling(window_length).apply(
+            lambda subarray: (DataFrame(subarray)
+                              .ewm(span=span)
+                              .std()
+                              .values[-1])
         )[window_length:]
 
     @parameterized.expand([
@@ -1224,7 +1220,7 @@ def test_dollar_volume(self):
         expected_1 = (self.raw_data[5:] ** 2) * 2
         assert_frame_equal(results['dv1'].unstack(), expected_1)
 
-        expected_5 = rolling_mean((self.raw_data ** 2) * 2, window=5)[5:]
+        expected_5 = ((self.raw_data ** 2) * 2).rolling(5).mean()[5:]
         assert_frame_equal(results['dv5'].unstack(), expected_5)
 
         # The following two use USEquityPricing.open and .volume as inputs.
@@ -1234,9 +1230,11 @@ def test_dollar_volume(self):
                           * self.raw_data[5:] * 2).fillna(0)
         assert_frame_equal(results['dv1_nan'].unstack(), expected_1_nan)
 
-        expected_5_nan = rolling_mean((self.raw_data_with_nans
-                                       * self.raw_data * 2).fillna(0),
-                                      window=5)[5:]
+        expected_5_nan = ((self.raw_data_with_nans * self.raw_data * 2)
+                          .fillna(0)
+                          .rolling(5).mean()
+                          [5:])
+
         assert_frame_equal(results['dv5_nan'].unstack(), expected_5_nan)
 
 

zipline/pipeline/loaders/blaze/core.py

@@ -187,7 +187,6 @@
     optionally,
 )
 from zipline.utils.numpy_utils import bool_dtype, categorical_dtype
-from zipline.utils.pandas_utils import sort_values
 from zipline.utils.pool import SequentialPool
 from zipline.utils.preprocess import preprocess
 
@@ -754,7 +753,7 @@ def overwrite_novel_deltas(baseline, deltas, dates):
         ignore_index=True,
         copy=False,
     )
-    sort_values(cat, TS_FIELD_NAME, inplace=True)
+    cat.sort_values(TS_FIELD_NAME, inplace=True)
     return cat, non_novel_deltas
 
 

zipline/pipeline/loaders/frame.py

@@ -15,7 +15,6 @@
 )
 from zipline.lib.adjusted_array import AdjustedArray
 from zipline.lib.adjustment import make_adjustment_from_labels
-from zipline.utils.pandas_utils import sort_values
 from .base import PipelineLoader
 
 ADJUSTMENT_COLUMNS = Index([
@@ -72,7 +71,7 @@ def __init__(self, column, baseline, adjustments=None):
         else:
             # Ensure that columns are in the correct order.
             adjustments = adjustments.reindex_axis(ADJUSTMENT_COLUMNS, axis=1)
-            sort_values(adjustments, ['apply_date', 'sid'], inplace=True)
+            adjustments.sort_values(['apply_date', 'sid'], inplace=True)
 
         self.adjustments = adjustments
         self.adjustment_apply_dates = DatetimeIndex(adjustments.apply_date)

zipline/utils/pandas_utils.py

@@ -12,6 +12,10 @@
 pandas_version = StrictVersion(pd.__version__)
 
 
+def july_5th_holiday_observance(datetime_index):
+    return datetime_index[datetime_index.year != 2013]
+
+
 def explode(df):
     """
     Take a DataFrame and return a triple of
@@ -21,19 +25,6 @@ def explode(df):
     return df.index, df.columns, df.values
 
 
-try:
-    # This branch is hit in pandas 17
-    sort_values = pd.DataFrame.sort_values
-except AttributeError:
-    # This branch is hit in pandas 16
-    sort_values = pd.DataFrame.sort
-
-if pandas_version >= StrictVersion('0.17.1'):
-    july_5th_holiday_observance = lambda dtix: dtix[dtix.year != 2013]
-else:
-    july_5th_holiday_observance = lambda dt: None if dt.year == 2013 else dt
-
-
 def _time_to_micros(time):
     """Convert a time into microseconds since midnight.
     Parameters
@@ -125,85 +116,3 @@ def ignore_pandas_nan_categorical_warning():
             category=FutureWarning,
         )
         yield
-
-
-# Remove when we drop support for 0.17
-if pandas_version >= StrictVersion('0.18'):
-    def rolling_mean(arg,
-                     window,
-                     min_periods=None,
-                     freq=None,
-                     center=False,
-                     **kwargs):
-        return arg.rolling(
-            window,
-            min_periods=min_periods,
-            freq=freq,
-            center=center,
-            **kwargs
-        ).mean()
-
-    def rolling_apply(arg,
-                      window,
-                      func,
-                      min_periods=None,
-                      freq=None,
-                      center=False,
-                      **kwargs):
-        return arg.rolling(
-            window,
-            min_periods=min_periods,
-            freq=freq,
-            center=center,
-            **kwargs
-        ).apply(func)
-
-    def ewma(arg,
-             com=None,
-             span=None,
-             halflife=None,
-             alpha=None,
-             min_periods=0,
-             freq=None,
-             adjust=True,
-             how=None,
-             ignore_na=False):
-
-        return arg.ewm(
-            com=com,
-            span=span,
-            halflife=halflife,
-            alpha=alpha,
-            min_periods=min_periods,
-            freq=freq,
-            adjust=adjust,
-            ignore_na=ignore_na,
-        ).mean()
-
-    def ewmstd(arg,
-               com=None,
-               span=None,
-               halflife=None,
-               alpha=None,
-               min_periods=0,
-               freq=None,
-               adjust=True,
-               how=None,
-               ignore_na=False):
-
-        return arg.ewm(
-            com=com,
-            span=span,
-            halflife=halflife,
-            alpha=alpha,
-            min_periods=min_periods,
-            freq=freq,
-            adjust=adjust,
-            ignore_na=ignore_na,
-        ).std()
-
-else:
-    rolling_mean = pd.rolling_mean
-    rolling_apply = pd.rolling_apply
-    ewma = pd.ewma
-    ewmstd = pd.ewmstd