API: Make datetime64 timezone naive

Fixes GH3290

With apologies to mwiebe, this rips out most of the time zone parsing from
the datetime64 type.

I think we mostly sorted out the API design in discussions last year, but
I'll be posting this to the mailing list shortly to get feedback.

Old behavior:

	# string parsing and printing defaults to your local timezone :(
    >>> np.datetime64('2000-01-01T00')
    numpy.datetime64('2000-01-01T00:00-0800','h')

New behavior:

    # datetime64 is parsed and printed as timezone naive
    >>> np.datetime64('2000-01-01T00')
    numpy.datetime64('2000-01-01T00','h')

    # you can still supply a timezone, but you get a deprecation warning
    >>> np.datetime64('2000-01-01T00Z')
    DeprecationWarning: parsing timezone aware datetimes is deprecated; this
    will raise an error in the future
    numpy.datetime64('2000-01-01T00','h')

2 files changed, 74 insertions, 25 deletions

diff --git a/doc/release/1.11.0-notes.rst b/doc/release/1.11.0-notes.rst
index b5d22d770..ac3c1578c 100644
--- a/doc/release/1.11.0-notes.rst
+++ b/doc/release/1.11.0-notes.rst
@@ -7,6 +7,8 @@ This release supports Python 2.6 - 2.7 and 3.2 - 3.5.
 Highlights
 ==========
 
+* The datetime64 type is now timezone naive. See "datetime64 changes" below
+  for more details.
 
 Dropped Support
 ===============
@@ -25,6 +27,41 @@ Future Changes
 Compatibility notes
 ===================
 
+datetime64 changes
+~~~~~~~~~~~~~~~~~~
+
+In prior versions of NumPy the experimental datetime64 type always stored
+times in UTC. By default, creating a datetime64 object from a string or
+printing it would convert from or to local time::
+
+    # old behavior
+    >>>> np.datetime64('2000-01-01T00:00:00')
+    numpy.datetime64('2000-01-01T00:00:00-0800')  # note the timezone offset -08:00
+
+A concensus of datetime64 users agreed that this behavior is undesirable
+and at odds with how datetime64 is usually used (e.g., by pandas_). For
+most use cases, a timezone naive datetime type is preferred, similar to the
+``datetime.datetime`` type in the Python standard library. Accordingly,
+datetime64 no longer assumes that input is in local time, nor does it print
+local times::
+
+    >>>> np.datetime64('2000-01-01T00:00:00')
+    numpy.datetime64('2000-01-01T00:00:00')
+
+For backwards compatibility, datetime64 still parses timezone offsets, which
+it handles by converting to UTC. However, the resulting datetime is timezone
+naive::
+
+    >>> np.datetime64('2000-01-01T00:00:00-08')
+    DeprecationWarning: parsing timezone aware datetimes is deprecated; this will raise an error in the future
+    numpy.datetime64('2000-01-01T08:00:00')
+
+As a corollary to this change, we no longer prohibit casting between datetimes
+with date units and datetimes with timeunits. With timezone naive datetimes,
+the rule for casting from dates to times is no longer ambiguous.
+
+pandas_: http://pandas.pydata.org
+
 DeprecationWarning to error
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/doc/source/reference/arrays.datetime.rst b/doc/source/reference/arrays.datetime.rst
index 0e8050b01..f5b454875 100644
--- a/doc/source/reference/arrays.datetime.rst
+++ b/doc/source/reference/arrays.datetime.rst
@@ -45,16 +45,10 @@ some additional SI-prefix seconds-based units.
     >>> np.datetime64('2005-02', 'D')
     numpy.datetime64('2005-02-01')
 
-    Using UTC "Zulu" time:
-
-    >>> np.datetime64('2005-02-25T03:30Z')
-    numpy.datetime64('2005-02-24T21:30-0600')
-
-    ISO 8601 specifies to use the local time zone
-    if none is explicitly given:
+    From a date and time:
 
     >>> np.datetime64('2005-02-25T03:30')
-    numpy.datetime64('2005-02-25T03:30-0600')
+    numpy.datetime64('2005-02-25T03:30')
 
 When creating an array of datetimes from a string, it is still possible
 to automatically select the unit from the inputs, by using the
@@ -100,23 +94,6 @@ because the moment of time is still being represented exactly.
     >>> np.datetime64('2010-03-14T15Z') == np.datetime64('2010-03-14T15:00:00.00Z')
     True
 
-An important exception to this rule is between datetimes with
-:ref:`date units <arrays.dtypes.dateunits>` and datetimes with
-:ref:`time units <arrays.dtypes.timeunits>`. This is because this kind
-of conversion generally requires a choice of timezone and
-particular time of day on the given date.
-
-.. admonition:: Example
-
-    >>> np.datetime64('2003-12-25', 's')
-    Traceback (most recent call last):
-      File "<stdin>", line 1, in <module>
-    TypeError: Cannot parse "2003-12-25" as unit 's' using casting rule 'same_kind'
-
-    >>> np.datetime64('2003-12-25') == np.datetime64('2003-12-25T00Z')
-    False
-
-
 Datetime and Timedelta Arithmetic
 =================================
 
@@ -353,6 +330,41 @@ Some examples::
     # any amount of whitespace is allowed; abbreviations are case-sensitive.
     weekmask = "MonTue Wed  Thu\tFri"
 
+Changes with NumPy 1.11
+=======================
+
+In prior versions of NumPy, the datetime64 type always stored
+times in UTC. By default, creating a datetime64 object from a string or
+printing it would convert from or to local time::
+
+    # old behavior
+    >>>> np.datetime64('2000-01-01T00:00:00')
+    numpy.datetime64('2000-01-01T00:00:00-0800')  # note the timezone offset -08:00
+
+A concensus of datetime64 users agreed that this behavior is undesirable
+and at odds with how datetime64 is usually used (e.g., by pandas_). For
+most use cases, a timezone naive datetime type is preferred, similar to the
+``datetime.datetime`` type in the Python standard library. Accordingly,
+datetime64 no longer assumes that input is in local time, nor does it print
+local times::
+
+    >>>> np.datetime64('2000-01-01T00:00:00')
+    numpy.datetime64('2000-01-01T00:00:00')
+
+For backwards compatibility, datetime64 still parses timezone offsets, which
+it handles by converting to UTC. However, the resulting datetime is timezone
+naive::
+
+    >>> np.datetime64('2000-01-01T00:00:00-08')
+    DeprecationWarning: parsing timezone aware datetimes is deprecated; this will raise an error in the future
+    numpy.datetime64('2000-01-01T08:00:00')
+
+As a corollary to this change, we no longer prohibit casting between datetimes
+with date units and datetimes with timeunits. With timezone naive datetimes,
+the rule for casting from dates to times is no longer ambiguous.
+
+pandas_: http://pandas.pydata.org
+
 Differences Between 1.6 and 1.7 Datetimes
 =========================================