Separate parsing functions out from tslib

pandas/_libs/period.pyx

@@ -40,8 +40,9 @@ from tslib cimport (
     _get_dst_info,
     _nat_scalar_rules)
 
+from tslibs.parsing import parse_time_string, NAT_SENTINEL
+
 from pandas.tseries import offsets
-from pandas.core.tools.datetimes import parse_time_string
 from pandas.tseries import frequencies
 
 cdef int64_t NPY_NAT = util.get_nat()
@@ -1178,6 +1179,8 @@ class Period(_Period):
                 value = str(value)
             value = value.upper()
             dt, _, reso = parse_time_string(value, freq)
+            if dt is NAT_SENTINEL:
+                ordinal = iNaT
 
             if freq is None:
                 try:

pandas/_libs/src/inference.pyx

@@ -8,6 +8,13 @@ iNaT = util.get_nat()
 
 cdef bint PY2 = sys.version_info[0] == 2
 
+from pandas._libs.tslibs.parsing import (
+    try_parse_dates,
+    try_parse_date_and_time,
+    try_parse_year_month_day,
+    try_parse_datetime_components)
+
+
 from util cimport (UINT8_MAX, UINT16_MAX, UINT32_MAX, UINT64_MAX,
                    INT8_MIN, INT8_MAX, INT16_MIN, INT16_MAX,
                    INT32_MAX, INT32_MIN, INT64_MAX, INT64_MIN)
@@ -1383,165 +1390,6 @@ def convert_sql_column(x):
     return maybe_convert_objects(x, try_float=1)
 
 
-def try_parse_dates(ndarray[object] values, parser=None,
-                    dayfirst=False, default=None):
-    cdef:
-        Py_ssize_t i, n
-        ndarray[object] result
-
-    n = len(values)
-    result = np.empty(n, dtype='O')
-
-    if parser is None:
-        if default is None: # GH2618
-            date=datetime.now()
-            default=datetime(date.year, date.month, 1)
-
-        try:
-            from dateutil.parser import parse
-            parse_date = lambda x: parse(x, dayfirst=dayfirst, default=default)
-        except ImportError: # pragma: no cover
-            def parse_date(s):
-                try:
-                    return datetime.strptime(s, '%m/%d/%Y')
-                except Exception:
-                    return s
-        # EAFP here
-        try:
-            for i from 0 <= i < n:
-                if values[i] == '':
-                    result[i] = np.nan
-                else:
-                    result[i] = parse_date(values[i])
-        except Exception:
-            # failed
-            return values
-    else:
-        parse_date = parser
-
-        try:
-            for i from 0 <= i < n:
-                if values[i] == '':
-                    result[i] = np.nan
-                else:
-                    result[i] = parse_date(values[i])
-        except Exception:
-            # raise if passed parser and it failed
-            raise
-
-    return result
-
-
-def try_parse_date_and_time(ndarray[object] dates, ndarray[object] times,
-                            date_parser=None, time_parser=None,
-                            dayfirst=False, default=None):
-    cdef:
-        Py_ssize_t i, n
-        ndarray[object] result
-
-    from datetime import date, time, datetime, timedelta
-
-    n = len(dates)
-    if len(times) != n:
-        raise ValueError('Length of dates and times must be equal')
-    result = np.empty(n, dtype='O')
-
-    if date_parser is None:
-        if default is None: # GH2618
-            date=datetime.now()
-            default=datetime(date.year, date.month, 1)
-
-        try:
-            from dateutil.parser import parse
-            parse_date = lambda x: parse(x, dayfirst=dayfirst, default=default)
-        except ImportError: # pragma: no cover
-            def parse_date(s):
-                try:
-                    return date.strptime(s, '%m/%d/%Y')
-                except Exception:
-                    return s
-    else:
-        parse_date = date_parser
-
-    if time_parser is None:
-        try:
-            from dateutil.parser import parse
-            parse_time = lambda x: parse(x)
-        except ImportError: # pragma: no cover
-            def parse_time(s):
-                try:
-                    return time.strptime(s, '%H:%M:%S')
-                except Exception:
-                    return s
-
-    else:
-        parse_time = time_parser
-
-    for i from 0 <= i < n:
-        d = parse_date(str(dates[i]))
-        t = parse_time(str(times[i]))
-        result[i] = datetime(d.year, d.month, d.day,
-                             t.hour, t.minute, t.second)
-
-    return result
-
-
-def try_parse_year_month_day(ndarray[object] years, ndarray[object] months,
-                             ndarray[object] days):
-    cdef:
-        Py_ssize_t i, n
-        ndarray[object] result
-
-    from datetime import datetime
-
-    n = len(years)
-    if len(months) != n or len(days) != n:
-        raise ValueError('Length of years/months/days must all be equal')
-    result = np.empty(n, dtype='O')
-
-    for i from 0 <= i < n:
-        result[i] = datetime(int(years[i]), int(months[i]), int(days[i]))
-
-    return result
-
-
-def try_parse_datetime_components(ndarray[object] years,
-                                  ndarray[object] months,
-                                  ndarray[object] days,
-                                  ndarray[object] hours,
-                                  ndarray[object] minutes,
-                                  ndarray[object] seconds):
-
-    cdef:
-        Py_ssize_t i, n
-        ndarray[object] result
-        int secs
-        double float_secs
-        double micros
-
-    from datetime import datetime
-
-    n = len(years)
-    if (len(months) != n or len(days) != n or len(hours) != n or
-        len(minutes) != n or len(seconds) != n):
-        raise ValueError('Length of all datetime components must be equal')
-    result = np.empty(n, dtype='O')
-
-    for i from 0 <= i < n:
-        float_secs = float(seconds[i])
-        secs = int(float_secs)
-
-        micros = float_secs - secs
-        if micros > 0:
-            micros = micros * 1000000
-
-        result[i] = datetime(int(years[i]), int(months[i]), int(days[i]),
-                             int(hours[i]), int(minutes[i]), secs,
-                             int(micros))
-
-    return result
-
-
 def sanitize_objects(ndarray[object] values, set na_values,
                      convert_empty=True):
     cdef: