CLN/DEPS: Clean up post numpy bump to 1.12 (#23796)

Author: h-vetinari

README.md

@@ -171,7 +171,7 @@ pip install pandas
 ```
 
 ## Dependencies
-- [NumPy](https://www.numpy.org): 1.9.0 or higher
+- [NumPy](https://www.numpy.org): 1.12.0 or higher
 - [python-dateutil](https://labix.org/python-dateutil): 2.5.0 or higher
 - [pytz](https://pythonhosted.org/pytz): 2011k or higher
 

ci/azure/linux.yml

@@ -9,7 +9,7 @@ jobs:
   strategy:
     maxParallel: 11
     matrix:
-      py27_np_19:
+      py27_np_120:
         ENV_FILE: ci/deps/azure-27-compat.yaml
         CONDA_PY: "27"
         TEST_ARGS: "--skip-slow --skip-network"

pandas/_libs/algos_rank_helper.pxi.in

@@ -102,15 +102,7 @@ def rank_1d_{{dtype}}(object in_arr, ties_method='average',
     ranks = np.empty(n, dtype='f8')
 
     {{if dtype == 'object'}}
-
-    try:
-        _as = np.lexsort(keys=order)
-    except TypeError:
-        # lexsort on object array will raise TypeError for numpy version
-        # earlier than 1.11.0. Use argsort with order argument instead.
-        _dt = [('values', 'O'), ('mask', '?')]
-        _values = np.asarray(list(zip(order[0], order[1])), dtype=_dt)
-        _as = np.argsort(_values, kind='mergesort', order=('mask', 'values'))
+    _as = np.lexsort(keys=order)
     {{else}}
     if tiebreak == TIEBREAK_FIRST:
         # need to use a stable sort here

pandas/_libs/lib.pyx

@@ -518,9 +518,7 @@ def astype_intsafe(ndarray[object] arr, new_dtype):
         bint is_datelike
         ndarray result
 
-    # on 32-bit, 1.6.2 numpy M8[ns] is a subdtype of integer, which is weird
-    is_datelike = new_dtype in ['M8[ns]', 'm8[ns]']
-
+    is_datelike = new_dtype == 'm8[ns]'
     result = np.empty(n, dtype=new_dtype)
     for i in range(n):
         val = arr[i]

pandas/_libs/sparse.pyx

@@ -8,14 +8,6 @@ from numpy cimport (ndarray, uint8_t, int64_t, int32_t, int16_t, int8_t,
 cnp.import_array()
 
 
-from distutils.version import LooseVersion
-
-# numpy versioning
-_np_version = np.version.short_version
-_np_version_under1p10 = LooseVersion(_np_version) < LooseVersion('1.10')
-_np_version_under1p11 = LooseVersion(_np_version) < LooseVersion('1.11')
-
-
 # -----------------------------------------------------------------------------
 # Preamble stuff
 

pandas/_libs/sparse_op_helper.pxi.in

@@ -42,13 +42,6 @@ cdef inline sparse_t __mod__(sparse_t a, sparse_t b):
 cdef inline sparse_t __floordiv__(sparse_t a, sparse_t b):
     if b == 0:
         if sparse_t is float64_t:
-            # numpy >= 1.11 returns NaN
-            # for a // 0, rather than +-inf
-            if _np_version_under1p11:
-                if a > 0:
-                    return INF
-                elif a < 0:
-                    return -INF
             return NaN
         else:
             return 0

pandas/core/arrays/categorical.py

@@ -98,7 +98,7 @@ def f(self, other):
                 ret[na_mask] = False
             return ret
 
-        # Numpy-1.9 and earlier may convert a scalar to a zerodim array during
+        # Numpy < 1.13 may convert a scalar to a zerodim array during
         # comparison operation when second arg has higher priority, e.g.
         #
         #     cat[0] < cat
@@ -2038,15 +2038,7 @@ def __setitem__(self, key, value):
         elif isinstance(key, slice):
             pass
 
-        # Array of True/False in Series or Categorical
-        else:
-            # There is a bug in numpy, which does not accept a Series as a
-            # indexer
-            # https://github.com/pandas-dev/pandas/issues/6168
-            # https://github.com/numpy/numpy/issues/4240 -> fixed in numpy 1.9
-            # FIXME: remove when numpy 1.9 is the lowest numpy version pandas
-            # accepts...
-            key = np.asarray(key)
+        # else: array of True/False in Series or Categorical
 
         lindexer = self.categories.get_indexer(rvalue)
         lindexer = self._maybe_coerce_indexer(lindexer)

pandas/core/arrays/datetimelike.py

@@ -835,8 +835,7 @@ def __isub__(self, other):
     def _evaluate_compare(self, other, op):
         """
         We have been called because a comparison between
-        8 aware arrays. numpy >= 1.11 will
-        now warn about NaT comparisons
+        8 aware arrays. numpy will warn about NaT comparisons
         """
         # Called by comparison methods when comparing datetimelike
         # with datetimelike

pandas/core/arrays/sparse.py

@@ -1015,9 +1015,6 @@ def __getitem__(self, key):
                     key = np.asarray(key)
 
             if com.is_bool_indexer(key) and len(self) == len(key):
-                # TODO(numpy 1.11): Remove this asarray.
-                # Old NumPy didn't treat array-like as boolean masks.
-                key = np.asarray(key)
                 return self.take(np.arange(len(key), dtype=np.int32)[key])
             elif hasattr(key, '__len__'):
                 return self.take(key)

pandas/core/dtypes/cast.py

@@ -263,28 +263,10 @@ def maybe_promote(dtype, fill_value=np.nan):
             fill_value = np.nan
 
     # returns tuple of (dtype, fill_value)
-    if issubclass(dtype.type, (np.datetime64, np.timedelta64)):
-        # for now: refuse to upcast datetime64
-        # (this is because datetime64 will not implicitly upconvert
-        #  to object correctly as of numpy 1.6.1)
-        if isna(fill_value):
-            fill_value = iNaT
-        else:
-            if issubclass(dtype.type, np.datetime64):
-                try:
-                    fill_value = tslibs.Timestamp(fill_value).value
-                except Exception:
-                    # the proper thing to do here would probably be to upcast
-                    # to object (but numpy 1.6.1 doesn't do this properly)
-                    fill_value = iNaT
-            elif issubclass(dtype.type, np.timedelta64):
-                try:
-                    fill_value = tslibs.Timedelta(fill_value).value
-                except Exception:
-                    # as for datetimes, cannot upcast to object
-                    fill_value = iNaT
-            else:
-                fill_value = iNaT
+    if issubclass(dtype.type, np.datetime64):
+        fill_value = tslibs.Timestamp(fill_value).value
+    elif issubclass(dtype.type, np.timedelta64):
+        fill_value = tslibs.Timedelta(fill_value).value
     elif is_datetimetz(dtype):
         if isna(fill_value):
             fill_value = iNaT