Support pandas>=0.18.1

.travis.yml

@@ -1,10 +1,20 @@
 language: python
 sudo: false
 
-python:
-  - "2.7"
-  - "3.5"
-  - "3.6"
+python: 
+  - 2.7
+  - 3.5
+  - 3.6
+
+env:
+  - PANDAS_VERSION=0.18.1
+  - PANDAS_VERSION=0.19.2
+  - PANDAS_VERSION=0.20.3
+
+matrix:
+  exclude:
+  - python: 3.6
+    env: PANDAS_VERSION=0.18.1
 
 before_install:
   # We do this conditionally because it saves us some downloading if the
@@ -24,7 +34,7 @@ before_install:
   - cp pyfolio/tests/matplotlibrc .
 
 install:
-  - conda create -q -n testenv --yes python=$TRAVIS_PYTHON_VERSION ipython pyzmq numpy scipy nose matplotlib pandas Cython patsy flake8 seaborn scikit-learn runipy pytables networkx pandas-datareader matplotlib-tests joblib
+  - conda create -q -n testenv --yes python=$TRAVIS_PYTHON_VERSION ipython pyzmq numpy scipy nose matplotlib pandas=$PANDAS_VERSION Cython patsy flake8 seaborn scikit-learn runipy pytables networkx pandas-datareader matplotlib-tests joblib
   - source activate testenv
   - pip install nose_parameterized
   #- pip install --no-deps git+https://github.com/quantopian/zipline

pyfolio/pos.py

@@ -134,6 +134,10 @@ def extract_pos(positions, cash):
 
     values = values.join(cash).fillna(0)
 
+    # NOTE: Set name of DataFrame.columns to sid, to match the behavior
+    # of DataFrame.join in earlier versions of pandas.
+    values.columns.name = 'sid'
+
     return values
 
 

pyfolio/risk.py

@@ -12,9 +12,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import numpy as np
-import matplotlib.pyplot as plt
 from collections import OrderedDict
+from functools import partial
+
+import matplotlib.pyplot as plt
+import numpy as np
+
 
 SECTORS = OrderedDict([
     (101, 'Basic Materials'),
@@ -432,9 +435,26 @@ def compute_volume_exposures(shares_held, volumes, percentile):
     shorted_frac = shares_shorted.divide(volumes)
     grossed_frac = shares_grossed.divide(volumes)
 
-    longed_threshold = 100*longed_frac.quantile(percentile, axis='columns')
-    shorted_threshold = 100*shorted_frac.quantile(percentile, axis='columns')
-    grossed_threshold = 100*grossed_frac.quantile(percentile, axis='columns')
+    # NOTE: To work around a bug in `quantile` with nan-handling in
+    #       pandas 0.18, use np.nanpercentile by applying to each row of
+    #       the dataframe. This is fixed in pandas 0.19.
+    #
+    # longed_threshold = 100*longed_frac.quantile(percentile, axis='columns')
+    # shorted_threshold = 100*shorted_frac.quantile(percentile, axis='columns')
+    # grossed_threshold = 100*grossed_frac.quantile(percentile, axis='columns')
+
+    longed_threshold = 100 * longed_frac.apply(
+        partial(np.nanpercentile, q=100 * percentile),
+        axis='columns',
+    )
+    shorted_threshold = 100 * shorted_frac.apply(
+        partial(np.nanpercentile, q=100 * percentile),
+        axis='columns',
+    )
+    grossed_threshold = 100 * grossed_frac.apply(
+        partial(np.nanpercentile, q=100 * percentile),
+        axis='columns',
+    )
 
     return longed_threshold, shorted_threshold, grossed_threshold
 
@@ -465,7 +485,7 @@ def plot_volume_exposures_longshort(longed_threshold, shorted_threshold,
     ax.axhline(0, color='k')
     ax.set(title='Long and short exposures to illiquidity',
            ylabel='{}th percentile of proportion of volume (%)'
-           .format(100*percentile))
+           .format(100 * percentile))
     ax.legend(frameon=True, framealpha=0.5)
 
     return ax
@@ -494,7 +514,7 @@ def plot_volume_exposures_gross(grossed_threshold, percentile, ax=None):
     ax.axhline(0, color='k')
     ax.set(title='Gross exposure to illiquidity',
            ylabel='{}th percentile of \n proportion of volume (%)'
-           .format(100*percentile))
+           .format(100 * percentile))
     ax.legend(frameon=True, framealpha=0.5)
 
     return ax

pyfolio/round_trips.py

@@ -128,7 +128,7 @@ def vwap(transaction):
         t['order_sign'] = t.amount > 0
         t['block_dir'] = (t.order_sign.shift(
             1) != t.order_sign).astype(int).cumsum()
-        t['block_time'] = ((t.dt - t.dt.shift(1)) >
+        t['block_time'] = ((t.dt.sub(t.dt.shift(1))) >
                            max_delta).astype(int).cumsum()
         grouped_price = (t.groupby(('block_dir',
                                    'block_time'))
@@ -252,7 +252,7 @@ def extract_round_trips(transactions,
 
     roundtrips = pd.DataFrame(roundtrips)
 
-    roundtrips['duration'] = roundtrips['close_dt'] - roundtrips['open_dt']
+    roundtrips['duration'] = roundtrips['close_dt'].sub(roundtrips['open_dt'])
 
     if portfolio_value is not None:
         # Need to normalize so that we can join

pyfolio/tests/test_pos.py

@@ -81,6 +81,7 @@ def test_extract_pos(self):
             index=index
         )
         expected.index.name = 'index'
+        expected.columns.name = 'sid'
 
         assert_frame_equal(result, expected)
 

setup.py

@@ -48,7 +48,7 @@
     'ipython>=3.2.3' if support_ipython_6 else 'ipython>=3.2.3, <6',
     'matplotlib>=1.4.0',
     'numpy>=1.9.1',
-    'pandas>=0.19.0',
+    'pandas>=0.18.1',
     'pytz>=2014.10',
     'scipy>=0.14.0',
     'scikit-learn>=0.18.2',