ENH: add method use_inf_as_null to core.common (GH pandas-dev#1919)

pandas/core/common.py

@@ -65,6 +65,58 @@ def isnull(obj):
         return _isnull_ndarraylike(obj)
     else:
         return obj is None
+isnull_new = isnull
+
+def isnull_old(obj):
+    '''
+    Replacement for numpy.isnan / -numpy.isfinite which is suitable
+    for use on object arrays.  Treat None, NaN, INF, -INF as null.
+
+    Parameters
+    ----------
+    arr: ndarray or object value
+
+    Returns
+    -------
+    boolean ndarray or boolean
+    '''
+    if lib.isscalar(obj):
+        return lib.checknull_old(obj)
+
+    from pandas.core.generic import PandasObject
+    if isinstance(obj, np.ndarray):
+        return _isnull_ndarraylike_old(obj)
+    elif isinstance(obj, PandasObject):
+        # TODO: optimize for DataFrame, etc.
+        return obj.apply(isnull_old)
+    elif isinstance(obj, list) or hasattr(obj, '__array__'):
+        return _isnull_ndarraylike_old(obj)
+    else:
+        return obj is None
+
+def use_inf_as_null(flag):
+    '''
+    Choose which replacement for numpy.isnan / -numpy.isfinite is used.
+
+    Parameters
+    ----------
+    flag: bool
+        True means treat None, NaN, INF, -INF as null (old way),
+        False means None and NaN are null, but INF, -INF are not null
+        (new way).
+
+    Notes
+    -----
+    This approach to setting global module values is discussed and
+    approved here:
+
+    * http://stackoverflow.com/questions/4859217/programmatically-creating-variables-in-python/4859312#4859312
+    '''
+    if flag == True:
+        globals()['isnull'] = isnull_old
+    else:
+        globals()['isnull'] = isnull_new
+
 
 def _isnull_ndarraylike(obj):
     from pandas import Series
@@ -90,6 +142,30 @@ def _isnull_ndarraylike(obj):
         result = -np.isfinite(obj)
     return result
 
+def _isnull_ndarraylike_old(obj):
+    from pandas import Series
+    values = np.asarray(obj)
+
+    if values.dtype.kind in ('O', 'S', 'U'):
+        # Working around NumPy ticket 1542
+        shape = values.shape
+
+        if values.dtype.kind in ('S', 'U'):
+            result = np.zeros(values.shape, dtype=bool)
+        else:
+            result = np.empty(shape, dtype=bool)
+            vec = lib.isnullobj_old(values.ravel())
+            result[:] = vec.reshape(shape)
+
+        if isinstance(obj, Series):
+            result = Series(result, index=obj.index, copy=False)
+    elif values.dtype == np.dtype('M8[ns]'):
+        # this is the NaT pattern
+        result = values.view('i8') == lib.iNaT
+    else:
+        result = -np.isfinite(obj)
+    return result
+
 def notnull(obj):
     '''
     Replacement for numpy.isfinite / -numpy.isnan which is suitable

pandas/src/tseries.pyx

@@ -189,6 +189,18 @@ cpdef checknull(object val):
     else:
         return util._checknull(val)
 
+cpdef checknull_old(object val):
+    if util.is_float_object(val) or util.is_complex_object(val):
+        return val != val or val == INF or val == NEGINF
+    elif util.is_datetime64_object(val):
+        return get_datetime64_value(val) == NPY_NAT
+    elif isinstance(val, _NaT):
+        return True
+    elif is_array(val):
+        return False
+    else:
+        return util._checknull(val)
+
 
 def isscalar(object val):
     return np.isscalar(val) or val is None or isinstance(val, _Timestamp)
@@ -207,6 +219,19 @@ def isnullobj(ndarray[object] arr):
         result[i] = util._checknull(arr[i])
     return result.view(np.bool_)
 
+@cython.wraparound(False)
+@cython.boundscheck(False)
+def isnullobj_old(ndarray[object] arr):
+    cdef Py_ssize_t i, n
+    cdef object val
+    cdef ndarray[uint8_t] result
+
+    n = len(arr)
+    result = np.zeros(n, dtype=np.uint8)
+    for i from 0 <= i < n:
+        result[i] = util._checknull_old(arr[i])
+    return result.view(np.bool_)
+
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
@@ -224,6 +249,22 @@ def isnullobj2d(ndarray[object, ndim=2] arr):
                 result[i, j] = 1
     return result.view(np.bool_)
 
+@cython.wraparound(False)
+@cython.boundscheck(False)
+def isnullobj2d_old(ndarray[object, ndim=2] arr):
+    cdef Py_ssize_t i, j, n, m
+    cdef object val
+    cdef ndarray[uint8_t, ndim=2] result
+
+    n, m = (<object> arr).shape
+    result = np.zeros((n, m), dtype=np.uint8)
+    for i from 0 <= i < n:
+        for j from 0 <= j < m:
+            val = arr[i, j]
+            if checknull_old(val):
+                result[i, j] = 1
+    return result.view(np.bool_)
+
 def list_to_object_array(list obj):
     '''
     Convert list to object ndarray. Seriously can't believe I had to write this

pandas/src/util.pxd

@@ -68,5 +68,11 @@ cdef inline bint _checknull(object val):
     except ValueError:
         return False
 
+cdef inline bint _checknull_old(object val):
+    try:
+        return bool(val is None or val != val)
+    except ValueError:
+        return False
+
 cdef inline bint _checknan(object val):
     return not cnp.PyArray_Check(val) and val != val

pandas/tests/test_common.py

@@ -6,7 +6,7 @@
 import unittest
 
 from pandas import Series, DataFrame, date_range, DatetimeIndex
-from pandas.core.common import notnull, isnull
+from pandas.core.common import notnull, isnull, use_inf_as_null
 import pandas.core.common as com
 import pandas.util.testing as tm
 
@@ -18,9 +18,17 @@ def test_notnull():
     assert notnull(1.)
     assert not notnull(None)
     assert not notnull(np.NaN)
+
+    use_inf_as_null(False)
     assert notnull(np.inf)
     assert notnull(-np.inf)
 
+    use_inf_as_null(True)
+    assert not notnull(np.inf)
+    assert not notnull(-np.inf)
+
+
+
     float_series = Series(np.random.randn(5))
     obj_series = Series(np.random.randn(5), dtype=object)
     assert(isinstance(notnull(float_series), Series))