BUG: fix GH8989 to parse nanoseconds with %f format

doc/source/whatsnew/v0.15.2.txt

@@ -78,14 +78,15 @@ Enhancements
 - Added ``gbq.generate_bq_schema`` function to the gbq module (:issue:`8325`).
 - ``Series`` now works with map objects the same way as generators (:issue:`8909`).
 - Added context manager to ``HDFStore`` for automatic closing (:issue:`8791`).
+- ``to_datetime`` gains an ``exact`` keyword to allow for a format to not require an exact match for a provided format string (if its ``False). ``exact`` defaults to ``True`` (meaning that exact matching is still the default)  (:issue:`8904`)
 
 .. _whatsnew_0152.performance:
 
 Performance
 ~~~~~~~~~~~
 - Reduce memory usage when skiprows is an integer in read_csv (:issue:`8681`)
 
-- Performance boost for ``to_datetime`` conversions with a passed ``format=`` kw, and the new ``exact=False`` for kw with non-exact matching (:issue:`8904`)
+- Performance boost for ``to_datetime`` conversions with a passed ``format=``, and the ``exact=False`` (:issue:`8904`)
 
 .. _whatsnew_0152.experimental:
 
@@ -143,6 +144,7 @@ Bug Fixes
 
 - Report a ``TypeError`` when invalid/no paramaters are passed in a groupby (:issue:`8015`)
 - Regression in DatetimeIndex iteration with a Fixed/Local offset timezone (:issue:`8890`)
+- Bug in ``to_datetime`` when parsing a nanoseconds using the ``%f`` format (:issue:`8989`)
 - Bug in packaging pandas with ``py2app/cx_Freeze`` (:issue:`8602`, :issue:`8831`)
 - Bug in ``groupby`` signatures that didn't include \*args or \*\*kwargs (:issue:`8733`).
 - ``io.data.Options`` now raises ``RemoteDataError`` when no expiry dates are available from Yahoo and when it receives no data from Yahoo (:issue:`8761`), (:issue:`8783`).

pandas/tseries/tests/test_timeseries.py

@@ -4125,14 +4125,30 @@ def test_to_datetime_format_time(self):
 
     def test_to_datetime_with_non_exact(self):
 
+        # 8904
+        # exact kw
         if sys.version_info < (2, 7):
             raise nose.SkipTest('on python version < 2.7')
 
-        s = Series(['19MAY11','foobar19MAY11','19MAY11:00:00:00','19MAY11 00:00:00Z']*10000)
+        s = Series(['19MAY11','foobar19MAY11','19MAY11:00:00:00','19MAY11 00:00:00Z'])
         result = to_datetime(s,format='%d%b%y',exact=False)
         expected = to_datetime(s.str.extract('(\d+\w+\d+)'),format='%d%b%y')
         assert_series_equal(result, expected)
 
+    def test_parse_nanoseconds_with_formula(self):
+
+        # GH8989
+        # trunctaing the nanoseconds when a format was provided
+        for v in ["2012-01-01 09:00:00.000000001",
+                  "2012-01-01 09:00:00.000001",
+                  "2012-01-01 09:00:00.001",
+                  "2012-01-01 09:00:00.001000",
+                  "2012-01-01 09:00:00.001000000",
+                  ]:
+            expected = pd.to_datetime(v)
+            result =  pd.to_datetime(v, format="%Y-%m-%d %H:%M:%S.%f")
+            self.assertEqual(result,expected)
+
     def test_to_datetime_format_weeks(self):
         data = [
                 ['2009324', '%Y%W%w', Timestamp('2009-08-13')],

pandas/tseries/tools.py

@@ -194,10 +194,11 @@ def to_datetime(arg, errors='ignore', dayfirst=False, utc=None, box=True,
     box : boolean, default True
         If True returns a DatetimeIndex, if False returns ndarray of values
     format : string, default None
-        strftime to parse time, eg "%d/%m/%Y"
+        strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
+        all the way up to nanoseconds
     exact : boolean, True by default
-        if True, require an exact format match
-        if False, search for a matching format non-exclusive to the endpoints
+        If True, require an exact format match.
+        If False, allow the format to match anywhere in the target string.
     coerce : force errors to NaT (False by default)
     unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch
         (e.g. a unix timestamp), which is an integer/float number

pandas/tslib.pyx

@@ -2137,8 +2137,9 @@ def array_strptime(ndarray[object] values, object fmt, bint exact=True, bint coe
         Py_ssize_t i, n = len(values)
         pandas_datetimestruct dts
         ndarray[int64_t] iresult
-        int year, month, day, minute, hour, second, fraction, weekday, julian, tz
+        int year, month, day, minute, hour, second, weekday, julian, tz
         int week_of_year, week_of_year_start
+        int64_t us, ns
         object val, group_key, ampm, found
         dict found_key
 
@@ -2237,7 +2238,7 @@ def array_strptime(ndarray[object] values, object fmt, bint exact=True, bint coe
 
         year = 1900
         month = day = 1
-        hour = minute = second = fraction = 0
+        hour = minute = second = ns = us = 0
         tz = -1
         # Default to -1 to signify that values not known; not critical to have,
         # though
@@ -2302,9 +2303,11 @@ def array_strptime(ndarray[object] values, object fmt, bint exact=True, bint coe
                 second = int(found_dict['S'])
             elif parse_code == 10:
                 s = found_dict['f']
-                # Pad to always return microseconds.
-                s += "0" * (6 - len(s))
-                fraction = int(s)
+                # Pad to always return nanoseconds
+                s += "0" * (9 - len(s))
+                us = long(s)
+                ns = us % 1000
+                us = us / 1000
             elif parse_code == 11:
                 weekday = locale_time.f_weekday.index(found_dict['A'].lower())
             elif parse_code == 12:
@@ -2369,7 +2372,8 @@ def array_strptime(ndarray[object] values, object fmt, bint exact=True, bint coe
         dts.hour = hour
         dts.min = minute
         dts.sec = second
-        dts.us = fraction
+        dts.us = us
+        dts.ps = ns * 1000
 
         iresult[i] = pandas_datetimestruct_to_datetime(PANDAS_FR_ns, &dts)
         try:
@@ -4311,7 +4315,7 @@ class TimeRE(dict):
         base.__init__({
             # The " \d" part of the regex is to make %c from ANSI C work
             'd': r"(?P<d>3[0-1]|[1-2]\d|0[1-9]|[1-9]| [1-9])",
-            'f': r"(?P<f>[0-9]{1,6})",
+            'f': r"(?P<f>[0-9]{1,9})",
             'H': r"(?P<H>2[0-3]|[0-1]\d|\d)",
             'I': r"(?P<I>1[0-2]|0[1-9]|[1-9])",
             'j': r"(?P<j>36[0-6]|3[0-5]\d|[1-2]\d\d|0[1-9]\d|00[1-9]|[1-9]\d|0[1-9]|[1-9])",

vb_suite/timeseries.py

@@ -159,7 +159,7 @@ def date_range(start=None, end=None, periods=None, freq=None):
 setup = common_setup + """
 s = Series(['19MAY11','19MAY11:00:00:00']*100000)
 """
-timeseries_with_format = Benchmark("to_datetime(s,format='%d%b%y',exact=False)", \
+timeseries_with_format_no_exact = Benchmark("to_datetime(s,format='%d%b%y',exact=False)", \
      setup, start_date=datetime(2014, 11, 26))
 timeseries_with_format_replace = Benchmark("to_datetime(s.str.replace(':\S+$',''),format='%d%b%y')", \
      setup, start_date=datetime(2014, 11, 26))