Merge pull request #6960 from jreback/warnings

COMPAT: fix numpy 1.9-dev deprecation warnings in test suite

Author: jreback

ci/script.sh

@@ -16,6 +16,13 @@ fi
 "$TRAVIS_BUILD_DIR"/ci/build_docs.sh 2>&1 > /tmp/doc.log &
 # doc build log will be shown after tests
 
+# export the testing mode
+if [ -n "$NUMPY_BUILD" ]; then
+
+    export PANDAS_TESTING_MODE="numpy_deprecate"
+
+fi
+
 echo nosetests --exe -w /tmp -A "$NOSE_ARGS" pandas --with-xunit --xunit-file=/tmp/nosetests.xml
 nosetests --exe -w /tmp -A "$NOSE_ARGS" pandas --with-xunit --xunit-file=/tmp/nosetests.xml
 

doc/source/release.rst

@@ -240,6 +240,9 @@ Prior Version Deprecations/Changes
 - Remove ``time_rule`` from several rolling-moment statistical functions, such
   as :func:`rolling_sum` (:issue:`1042`)
 
+- Removed neg (-) boolean operations on numpy arrays in favor of inv (~), as this is going to
+  be deprecated in numpy 1.9 (:issue:`6960`)
+
 Experimental Features
 ~~~~~~~~~~~~~~~~~~~~~
 

doc/source/v0.14.0.txt

@@ -368,6 +368,8 @@ There are prior version deprecations that are taking effect as of 0.14.0.
   ( `commit 3136390 <https://github.com/pydata/pandas/commit/3136390>`__ )
 - Remove ``time_rule`` from several rolling-moment statistical functions, such
   as :func:`rolling_sum` (:issue:`1042`)
+- Removed neg (-) boolean operations on numpy arrays in favor of inv (~), as this is going to
+  be deprecated in numpy 1.9 (:issue:`6960`)
 
 .. _whatsnew_0140.deprecations:
 

pandas/algos.pyx

@@ -510,7 +510,7 @@ def rank_1d_generic(object in_arr, bint retry=1, ties_method='average',
         if not retry:
             raise
 
-        valid_locs = (-mask).nonzero()[0]
+        valid_locs = (~mask).nonzero()[0]
         ranks.put(valid_locs, rank_1d_generic(values.take(valid_locs), 0,
                                               ties_method=ties_method,
                                               ascending=ascending))

pandas/computation/ops.py

@@ -42,9 +42,7 @@ class Term(StringMixin):
     def __new__(cls, name, env, side=None, encoding=None):
         klass = Constant if not isinstance(name, string_types) else cls
         supr_new = super(Term, klass).__new__
-        if PY3:
-            return supr_new(klass)
-        return supr_new(klass, name, env, side=side, encoding=encoding)
+        return supr_new(klass)
 
     def __init__(self, name, env, side=None, encoding=None):
         self._name = name

pandas/computation/pytables.py

@@ -33,9 +33,7 @@ class Term(ops.Term):
     def __new__(cls, name, env, side=None, encoding=None):
         klass = Constant if not isinstance(name, string_types) else cls
         supr_new = StringMixin.__new__
-        if PY3:
-            return supr_new(klass)
-        return supr_new(klass, name, env, side=side, encoding=encoding)
+        return supr_new(klass)
 
     def __init__(self, name, env, side=None, encoding=None):
         super(Term, self).__init__(name, env, side=side, encoding=encoding)

pandas/core/algorithms.py

@@ -329,7 +329,7 @@ def quantile(x, q, interpolation_method='fraction'):
     x = np.asarray(x)
     mask = com.isnull(x)
 
-    x = x[-mask]
+    x = x[~mask]
 
     values = np.sort(x)
 
@@ -339,7 +339,7 @@ def _get_score(at):
 
         idx = at * (len(values) - 1)
         if idx % 1 == 0:
-            score = values[idx]
+            score = values[int(idx)]
         else:
             if interpolation_method == 'fraction':
                 score = _interpolate(values[int(idx)], values[int(idx) + 1],

pandas/core/common.py

@@ -248,7 +248,7 @@ def _isnull_ndarraylike_old(obj):
         # this is the NaT pattern
         result = values.view('i8') == tslib.iNaT
     else:
-        result = -np.isfinite(values)
+        result = ~np.isfinite(values)
 
     # box
     if isinstance(obj, ABCSeries):
@@ -280,12 +280,22 @@ def notnull(obj):
     res = isnull(obj)
     if np.isscalar(res):
         return not res
-    return -res
+    return ~res
 
 def _is_null_datelike_scalar(other):
     """ test whether the object is a null datelike, e.g. Nat
     but guard against passing a non-scalar """
-    return (np.isscalar(other) and (isnull(other) or other == tslib.iNaT)) or other is pd.NaT or other is None
+    if other is pd.NaT or other is None:
+        return True
+    elif np.isscalar(other):
+
+        # a timedelta
+        if hasattr(other,'dtype'):
+            return other.view('i8') == tslib.iNaT
+        elif is_integer(other) and other == tslib.iNaT:
+            return True
+        return isnull(other)
+    return False
 
 def array_equivalent(left, right):
     """
@@ -363,7 +373,7 @@ def mask_missing(arr, values_to_mask):
         values_to_mask = np.array(values_to_mask, dtype=object)
 
     na_mask = isnull(values_to_mask)
-    nonna = values_to_mask[-na_mask]
+    nonna = values_to_mask[~na_mask]
 
     mask = None
     for x in nonna:

pandas/core/format.py

@@ -1861,7 +1861,7 @@ def _get_format_timedelta64(values):
     def impl(x):
         if x is None or lib.checknull(x):
             return 'NaT'
-        elif format_short and x == 0:
+        elif format_short and com.is_integer(x) and x.view('int64') == 0:
             return "0 days" if even_days else "00:00:00"
         else:
             return lib.repr_timedelta64(x, format=format)

pandas/core/frame.py

@@ -3041,7 +3041,7 @@ def update(self, other, join='left', overwrite=True, filter_func=None,
             this = self[col].values
             that = other[col].values
             if filter_func is not None:
-                mask = -filter_func(this) | isnull(that)
+                mask = ~filter_func(this) | isnull(that)
             else:
                 if raise_conflict:
                     mask_this = notnull(that)