COMPAT: Support pandas 019 (#83)

.travis.yml

@@ -1,11 +1,11 @@
 language: python
 
 env:
-  - PYTHON=2.7 PANDAS=0.17.1 SKLEARN=0.17.1
-  - PYTHON=2.7 PANDAS=0.18.1 SKLEARN=0.18.0 IMBALANCE=true
-  - PYTHON=3.4 PANDAS=0.16.1 SKLEARN=0.16.1
-  - PYTHON=3.4 PANDAS=0.17.1 SKLEARN=0.17.1
-  - PYTHON=3.5 PANDAS=0.18.1 SKLEARN=0.18.0 IMBALANCE=true COVERAGE=true
+  - PYTHON=2.7 PANDAS=0.18.1 SKLEARN=0.17.1
+  - PYTHON=2.7 PANDAS=0.19.0 SKLEARN=0.18.0 IMBALANCE=true
+  - PYTHON=3.4 PANDAS=0.17.1 SKLEARN=0.16.1
+  - PYTHON=3.4 PANDAS=0.18.1 SKLEARN=0.17.1
+  - PYTHON=3.5 PANDAS=0.19.0 SKLEARN=0.18.0 IMBALANCE=true COVERAGE=true
 
 addons:
   apt:

pandas_ml/compat.py

@@ -2,6 +2,17 @@
 
 from distutils.version import LooseVersion
 
+import pandas as pd
+
+PANDAS_VERSION = LooseVersion(pd.__version__)
+if PANDAS_VERSION >= LooseVersion('0.19'):
+    _PANDAS_ge_019 = True
+    from pandas.api.types import is_list_like, is_integer_dtype         # noqa
+
+else:
+    _PANDAS_ge_019 = False
+    from pandas.core.common import is_list_like, is_integer_dtype       # noqa
+
 try:
     import sklearn
     _SKLEARN_INSTALLED = True

pandas_ml/core/frame.py

@@ -4,10 +4,10 @@
 
 import numpy as np
 import pandas as pd
-import pandas.core.common as com
 import pandas.compat as compat
 from pandas.util.decorators import Appender, cache_readonly
 
+from pandas_ml.compat import is_list_like
 from pandas_ml.core.generic import ModelPredictor, _shared_docs
 from pandas_ml.core.series import ModelSeries
 from pandas_ml.core.accessor import _AccessorMethods
@@ -58,7 +58,7 @@ def __init__(self, data, target=None,
         if data is None and target is None:
             msg = '{0} must have either data or target'
             raise ValueError(msg.format(self.__class__.__name__))
-        elif data is None and not com.is_list_like(target):
+        elif data is None and not is_list_like(target):
             msg = 'target must be list-like when data is None'
             raise ValueError(msg)
 
@@ -71,7 +71,7 @@ def __init__(self, data, target=None,
 
         data, target = self._maybe_convert_data(data, target, *args, **kwargs)
 
-        if target is not None and not com.is_list_like(target):
+        if target is not None and not is_list_like(target):
             if target in data.columns:
                 target_name = target
                 df = data
@@ -127,7 +127,7 @@ def _maybe_convert_target(data, target, index=None):
             if data is not None:
                 data = pd.DataFrame(data, *args, **kwargs)
 
-            if com.is_list_like(target):
+            if is_list_like(target):
                 target = _maybe_convert_target(data, target)
 
         elif not init_df:
@@ -136,7 +136,7 @@ def _maybe_convert_target(data, target, index=None):
                 data = pd.DataFrame(data, index=index, *args, **kwargs)
 
         elif not init_target:
-            if com.is_list_like(target):
+            if is_list_like(target):
                 target = _maybe_convert_target(data, target)
 
         else:
@@ -167,6 +167,8 @@ def _concat_target(self, data, target):
 
         def _add_meta_columns(df, meta_name):
             df = df.copy()
+            if not is_list_like(meta_name):
+                meta_name = [meta_name]
             df.columns = pd.MultiIndex.from_product([meta_name, df.columns])
             return df
 
@@ -326,7 +328,7 @@ def target(self, target):
                 # DataFrame.columns should have values
                 self.target_name = target.columns
 
-        if not com.is_list_like(target):
+        if not is_list_like(target):
             if target in self.columns:
                 self.target_name = target
             else:

pandas_ml/skaccessors/preprocessing.py

@@ -1,10 +1,10 @@
 #!/usr/bin/env python
 
 import numpy as np
-import pandas as pd
 
 from pandas_ml.core.accessor import _AccessorMethods, _attach_methods
-from pandas_ml.compat import _SKLEARN_INSTALLED, _SKLEARN_ge_017
+from pandas_ml.compat import (_SKLEARN_INSTALLED, _SKLEARN_ge_017,
+                              is_integer_dtype)
 
 
 if _SKLEARN_INSTALLED:
@@ -81,7 +81,7 @@ def f(self, *args, **kwargs):
         if isinstance(self._df, ModelFrame):
             values = self._data.values
 
-            if pd.core.common.is_integer_dtype(values):
+            if is_integer_dtype(values):
                 # integer raises an error in normalize
                 values = values.astype(np.float)
 
@@ -91,7 +91,7 @@ def f(self, *args, **kwargs):
         else:
             # ModelSeries
             values = np.atleast_2d(self._df.values)
-            if pd.core.common.is_integer_dtype(values):
+            if is_integer_dtype(values):
                 values = values.astype(np.float)
 
             result = func(values, *args, **kwargs)

pandas_ml/skaccessors/test/test_linear_model.py

@@ -62,7 +62,8 @@ def test_lars_path(self):
 
         self.assertEqual(len(result), 3)
         self.assert_numpy_array_equal(result[0], expected[0])
-        self.assert_numpy_array_equal(result[1], expected[1])
+        self.assertEqual(result[1], expected[1])
+        self.assertIsInstance(result[1], list)
         self.assertIsInstance(result[2], pdml.ModelFrame)
         self.assert_index_equal(result[2].index, df.data.columns)
         self.assert_numpy_array_equal(result[2].values, expected[2])