code: from datetime import datetime, timedelta, time
  import numpy as np
  from collections import MutableMapping
  
  import pandas.lib as lib
  import pandas.tslib as tslib
  
  from pandas.types.common import (_ensure_object,
                                   is_datetime64_ns_dtype,
                                   is_datetime64_dtype,
                                   is_datetime64tz_dtype,
                                   is_integer_dtype,
                                   is_list_like)
  from pandas.types.generic import (ABCIndexClass, ABCSeries,
                                    ABCDataFrame)
  from pandas.types.missing import notnull
  
  import pandas.compat as compat
  
  _DATEUTIL_LEXER_SPLIT = None
  try:
      # Since these are private methods from dateutil, it is safely imported
      # here so in case this interface changes, pandas will just fallback
      # to not using the functionality
      from dateutil.parser import _timelex
  
      if hasattr(_timelex, 'split'):
          def _lexer_split_from_str(dt_str):
              # The StringIO(str(_)) is for dateutil 2.2 compatibility
              return _timelex.split(compat.StringIO(str(dt_str)))
  
          _DATEUTIL_LEXER_SPLIT = _lexer_split_from_str
  except (ImportError, AttributeError):
      pass
  
  
  def _infer_tzinfo(start, end):
      def _infer(a, b):
          tz = a.tzinfo
          if b and b.tzinfo:
              if not (tslib.get_timezone(tz) == tslib.get_timezone(b.tzinfo)):
                  raise AssertionError('Inputs must both have the same timezone,'
                                       ' {0} != {1}'.format(tz, b.tzinfo))
          return tz
  
      tz = None
      if start is not None:
          tz = _infer(start, end)
      elif end is not None:
          tz = _infer(end, start)
      return tz
  
  
  def _guess_datetime_format(dt_str, dayfirst=False,
                             dt_str_parse=compat.parse_date,
                             dt_str_split=_DATEUTIL_LEXER_SPLIT):
      """
      Guess the datetime format of a given datetime string.
  
      Parameters
      ----------
      dt_str : string, datetime string to guess the format of
      dayfirst : boolean, default False
          If True parses dates with the day first, eg 20/01/2005
          Warning: dayfirst=True is not strict, but will prefer to parse
          with day first (this is a known bug).
      dt_str_parse : function, defaults to `compat.parse_date` (dateutil)
          This function should take in a datetime string and return
          a `datetime.datetime` guess that the datetime string represents
      dt_str_split : function, defaults to `_DATEUTIL_LEXER_SPLIT` (dateutil)
          This function should take in a datetime string and return
          a list of strings, the guess of the various specific parts
          e.g. '2011/12/30' -> ['2011', '/', '12', '/', '30']
  
      Returns
      -------
      ret : datetime format string (for `strftime` or `strptime`)
      """
      if dt_str_parse is None or dt_str_split is None:
          return None
  
      if not isinstance(dt_str, compat.string_types):
          return None
  
      day_attribute_and_format = (('day',), '%d', 2)
  
      # attr name, format, padding (if any)
      datetime_attrs_to_format = [
          (('year', 'month', 'day'), '%Y%m%d', 0),
          (('year',), '%Y', 0),
          (('month',), '%B', 0),
          (('month',), '%b', 0),
          (('month',), '%m', 2),
          day_attribute_and_format,
          (('hour',), '%H', 2),
          (('minute',), '%M', 2),
          (('second',), '%S', 2),
          (('microsecond',), '%f', 6),
          (('second', 'microsecond'), '%S.%f', 0),
      ]
  
      if dayfirst:
          datetime_attrs_to_format.remove(day_attribute_and_format)
          datetime_attrs_to_format.insert(0, day_attribute_and_format)
  
      try:
          parsed_datetime = dt_str_parse(dt_str, dayfirst=dayfirst)
      except:
          # In case the datetime can't be parsed, its format cannot be guessed
          return None
  
      if parsed_datetime is None:
          return None
  
      try:
          tokens = dt_str_split(dt_str)
      except:
          # In case the datetime string can't be split, its format cannot
          # be guessed
          return None
  
      format_guess = [None] * len(tokens)
      found_attrs = set()
  
      for attrs, attr_format, padding in datetime_attrs_to_format:
          # If a given attribute has been placed in the format string, skip
          # over other formats for that same underlying attribute (IE, month
          # can be represented in multiple different ways)
          if set(attrs) & found_attrs:
              continue
  
          if all(getattr(parsed_datetime, attr) is not None for attr in attrs):
              for i, token_format in enumerate(format_guess):
                  token_filled = tokens[i].zfill(padding)
                  if (token_format is None and
                          token_filled == parsed_datetime.strftime(attr_format)):
                      format_guess[i] = attr_format
                      tokens[i] = token_filled
                      found_attrs.update(attrs)
                      break
  
      # Only consider it a valid guess if we have a year, month and day
      if len(set(['year', 'month', 'day']) & found_attrs) != 3:
          return None
  
      output_format = []
      for i, guess in enumerate(format_guess):
          if guess is not None:
              # Either fill in the format placeholder (like %Y)
              output_format.append(guess)
          else:
              # Or just the token separate (IE, the dashes in "01-01-2013")
              try:
                  # If the token is numeric, then we likely didn't parse it
                  # properly, so our guess is wrong
                  float(tokens[i])
                  return None
              except ValueError:
                  pass
  
              output_format.append(tokens[i])
  
      guessed_format = ''.join(output_format)
  
      # rebuild string, capturing any inferred padding
      dt_str = ''.join(tokens)
      if parsed_datetime.strftime(guessed_format) == dt_str:
          return guessed_format
  
  
  def _guess_datetime_format_for_array(arr, **kwargs):
      # Try to guess the format based on the first non-NaN element
      non_nan_elements = notnull(arr).nonzero()[0]
      if len(non_nan_elements):
          return _guess_datetime_format(arr[non_nan_elements[0]], **kwargs)
  
  
  def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
                  utc=None, box=True, format=None, exact=True,
                  unit=None, infer_datetime_format=False):
      """
      Convert argument to datetime.
  
      Parameters
      ----------
      arg : integer, float, string, datetime, list, tuple, 1-d array, Series
  
          .. versionadded: 0.18.1
  
             or DataFrame/dict-like
  
      errors : {'ignore', 'raise', 'coerce'}, default 'raise'
  
          - If 'raise', then invalid parsing will raise an exception
          - If 'coerce', then invalid parsing will be set as NaT
          - If 'ignore', then invalid parsing will return the input
      dayfirst : boolean, default False
          Specify a date parse order if `arg` is str or its list-likes.
          If True, parses dates with the day first, eg 10/11/12 is parsed as
          2012-11-10.
          Warning: dayfirst=True is not strict, but will prefer to parse
          with day first (this is a known bug, based on dateutil behavior).
      yearfirst : boolean, default False
          Specify a date parse order if `arg` is str or its list-likes.
  
          - If True parses dates with the year first, eg 10/11/12 is parsed as
            2010-11-12.
          - If both dayfirst and yearfirst are True, yearfirst is preceded (same
            as dateutil).
  
          Warning: yearfirst=True is not strict, but will prefer to parse
          with year first (this is a known bug, based on dateutil beahavior).
  
          .. versionadded: 0.16.1
  
      utc : boolean, default None
          Return UTC DatetimeIndex if True (converting any tz-aware
          datetime.datetime objects as well).
      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", 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, allow the format to match anywhere in the target string.
  
      unit : string, default 'ns'
          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.
      infer_datetime_format : boolean, default False
          If True and no `format` is given, attempt to infer the format of the
          datetime strings, and if it can be inferred, switch to a faster
          method of parsing them. In some cases this can increase the parsing
          speed by ~5-10x.
  
      Returns
      -------
      ret : datetime if parsing succeeded.
          Return type depends on input:
  
          - list-like: DatetimeIndex
          - Series: Series of datetime64 dtype
          - scalar: Timestamp
  
          In case when it is not possible to return designated types (e.g. when
          any element of input is before Timestamp.min or after Timestamp.max)
          return will have datetime.datetime type (or correspoding array/Series).
  
      Examples
      --------
  
      Assembling a datetime from multiple columns of a DataFrame. The keys can be
      common abbreviations like ['year', 'month', 'day', 'minute', 'second',
      'ms', 'us', 'ns']) or plurals of the same
  
      >>> df = pd.DataFrame({'year': [2015, 2016],
                             'month': [2, 3],
                             'day': [4, 5]})
      >>> pd.to_datetime(df)
      0   2015-02-04
      1   2016-03-05
      dtype: datetime64[ns]
  
      If a date does not meet the `timestamp limitations
      <http://pandas.pydata.org/pandas-docs/stable/timeseries.html
      #timeseries-timestamp-limits>`_, passing errors='ignore'
      will return the original input instead of raising any exception.
  
      Passing errors='coerce' will force an out-of-bounds date to NaT,
      in addition to forcing non-dates (or non-parseable dates) to NaT.
  
      >>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore')
      datetime.datetime(1300, 1, 1, 0, 0)
      >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
      NaT
  
      Passing infer_datetime_format=True can often-times speedup a parsing
      if its not an ISO8601 format exactly, but in a regular format.
  
      >>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000']*1000)
  
      >>> s.head()
      0    3/11/2000
      1    3/12/2000
      2    3/13/2000
      3    3/11/2000
      4    3/12/2000
      dtype: object
  
      >>> %timeit pd.to_datetime(s,infer_datetime_format=True)
      100 loops, best of 3: 10.4 ms per loop
  
      >>> %timeit pd.to_datetime(s,infer_datetime_format=False)
      1 loop, best of 3: 471 ms per loop
  
      """
  
      from pandas.tseries.index import DatetimeIndex
  
      tz = 'utc' if utc else None
  
      def _convert_listlike(arg, box, format, name=None, tz=tz):
  
          if isinstance(arg, (list, tuple)):
              arg = np.array(arg, dtype='O')
  
          # these are shortcutable
          if is_datetime64tz_dtype(arg):
              if not isinstance(arg, DatetimeIndex):
                  return DatetimeIndex(arg, tz=tz, name=name)
              if utc:
                  arg = arg.tz_convert(None).tz_localize('UTC')
              return arg
  
          elif is_datetime64_ns_dtype(arg):
              if box and not isinstance(arg, DatetimeIndex):
                  try:
                      return DatetimeIndex(arg, tz=tz, name=name)
                  except ValueError:
                      pass
  
              return arg
  
          elif unit is not None:
              if format is not None:
                  raise ValueError("cannot specify both format and unit")
              arg = getattr(arg, 'values', arg)
              result = tslib.array_with_unit_to_datetime(arg, unit,
                                                         errors=errors)
              if box:
                  if errors == 'ignore':
                      from pandas import Index
                      return Index(result)
  
                  return DatetimeIndex(result, tz=tz, name=name)
              return result
          elif getattr(arg, 'ndim', 1) > 1:
              raise TypeError('arg must be a string, datetime, list, tuple, '
                              '1-d array, or Series')
  
          arg = _ensure_object(arg)
          require_iso8601 = False
  
          if infer_datetime_format and format is None:
              format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
  
          if format is not None:
              # There is a special fast-path for iso8601 formatted
              # datetime strings, so in those cases don't use the inferred
              # format because this path makes process slower in this
              # special case
              format_is_iso8601 = _format_is_iso(format)
              if format_is_iso8601:
                  require_iso8601 = not infer_datetime_format
                  format = None
  
          try:
              result = None
  
              if format is not None:
                  # shortcut formatting here
                  if format == '%Y%m%d':
                      try:
                          result = _attempt_YYYYMMDD(arg, errors=errors)
                      except:
                          raise ValueError("cannot convert the input to "
                                           "'%Y%m%d' date format")
  
                  # fallback
                  if result is None:
                      try:
                          result = tslib.array_strptime(arg, format, exact=exact,
                                                        errors=errors)
                      except tslib.OutOfBoundsDatetime:
                          if errors == 'raise':
                              raise
                          result = arg
                      except ValueError:
                          # if format was inferred, try falling back
                          # to array_to_datetime - terminate here
                          # for specified formats
                          if not infer_datetime_format:
                              if errors == 'raise':
                                  raise
                              result = arg
  
              if result is None and (format is None or infer_datetime_format):
                  result = tslib.array_to_datetime(
                      arg,
                      errors=errors,
                      utc=utc,
                      dayfirst=dayfirst,
                      yearfirst=yearfirst,
                      require_iso8601=require_iso8601
                  )
  
              if is_datetime64_dtype(result) and box:
                  result = DatetimeIndex(result, tz=tz, name=name)
              return result
  
          except ValueError as e:
              try:
                  values, tz = tslib.datetime_to_datetime64(arg)
                  return DatetimeIndex._simple_new(values, name=name, tz=tz)
              except (ValueError, TypeError):
                  raise e
  
      if arg is None:
          return arg
      elif isinstance(arg, tslib.Timestamp):
          return arg
      elif isinstance(arg, ABCSeries):
          from pandas import Series
          values = _convert_listlike(arg._values, False, format)
          return Series(values, index=arg.index, name=arg.name)
      elif isinstance(arg, (ABCDataFrame, MutableMapping)):
          return _assemble_from_unit_mappings(arg, errors=errors)
      elif isinstance(arg, ABCIndexClass):
          return _convert_listlike(arg, box, format, name=arg.name)
      elif is_list_like(arg):
          return _convert_listlike(arg, box, format)
  
      return _convert_listlike(np.array([arg]), box, format)[0]
  
  
  # mappings for assembling units
  _unit_map = {'year': 'year',
               'years': 'year',
               'month': 'month',
               'months': 'month',
               'day': 'day',
               'days': 'day',
               'hour': 'h',
               'hours': 'h',
               'minute': 'm',
               'minutes': 'm',
               'second': 's',
               'seconds': 's',
               'ms': 'ms',
               'millisecond': 'ms',
               'milliseconds': 'ms',
               'us': 'us',
               'microsecond': 'us',
               'microseconds': 'us',
               'ns': 'ns',
               'nanosecond': 'ns',
               'nanoseconds': 'ns'
               }
  
  
  def _assemble_from_unit_mappings(arg, errors):
      """
      assemble the unit specifed fields from the arg (DataFrame)
      Return a Series for actual parsing
  
      Parameters
      ----------
      arg : DataFrame
      errors : {'ignore', 'raise', 'coerce'}, default 'raise'
  
          - If 'raise', then invalid parsing will raise an exception
          - If 'coerce', then invalid parsing will be set as NaT
          - If 'ignore', then invalid parsing will return the input
  
      Returns
      -------
      Series
      """
      from pandas import to_timedelta, to_numeric, DataFrame
      arg = DataFrame(arg)
      if not arg.columns.is_unique:
          raise ValueError("cannot assemble with duplicate keys")
  
      # replace passed unit with _unit_map
      def f(value):
          if value in _unit_map:
              return _unit_map[value]
  
          # m is case significant
          if value.lower() in _unit_map:
              return _unit_map[value.lower()]
  
          return value
  
      unit = {k: f(k) for k in arg.keys()}
      unit_rev = {v: k for k, v in unit.items()}
  
      # we require at least Ymd
      required = ['year', 'month', 'day']
      req = sorted(list(set(required) - set(unit_rev.keys())))
      if len(req):
          raise ValueError("to assemble mappings requires at "
                           "least that [year, month, day] be specified: "
                           "[{0}] is missing".format(','.join(req)))
  
      # keys we don't recognize
      excess = sorted(list(set(unit_rev.keys()) - set(_unit_map.values())))
      if len(excess):
          raise ValueError("extra keys have been passed "
                           "to the datetime assemblage: "
                           "[{0}]".format(','.join(excess)))
  
      def coerce(values):
          # we allow coercion to if errors allows
          values = to_numeric(values, errors=errors)
  
          # prevent overflow in case of int8 or int16
          if is_integer_dtype(values):
              values = values.astype('int64', copy=False)
          return values
  
      values = (coerce(arg[unit_rev['year']]) * 10000 +
                coerce(arg[unit_rev['month']]) * 100 +
                coerce(arg[unit_rev['day']]))
      try:
          values = to_datetime(values, format='%Y%m%d', errors=errors)
      except (TypeError, ValueError) as e:
          raise ValueError("cannot assemble the "
                           "datetimes: {0}".format(e))
  
      for u in ['h', 'm', 's', 'ms', 'us', 'ns']:
          value = unit_rev.get(u)
          if value is not None and value in arg:
              try:
                  values += to_timedelta(coerce(arg[value]),
                                         unit=u,
                                         errors=errors)
              except (TypeError, ValueError) as e:
                  raise ValueError("cannot assemble the datetimes "
                                   "[{0}]: {1}".format(value, e))
  
      return values
  
  
  def _attempt_YYYYMMDD(arg, errors):
      """ try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,
          arg is a passed in as an object dtype, but could really be ints/strings
          with nan-like/or floats (e.g. with nan)
  
      Parameters
      ----------
      arg : passed value
      errors : 'raise','ignore','coerce'
      """
  
      def calc(carg):
          # calculate the actual result
          carg = carg.astype(object)
          parsed = lib.try_parse_year_month_day(carg / 10000,
                                                carg / 100 % 100,
                                                carg % 100)
          return tslib.array_to_datetime(parsed, errors=errors)
  
      def calc_with_mask(carg, mask):
          result = np.empty(carg.shape, dtype='M8[ns]')
          iresult = result.view('i8')
          iresult[~mask] = tslib.iNaT
          result[mask] = calc(carg[mask].astype(np.float64).astype(np.int64)). \
              astype('M8[ns]')
          return result
  
      # try intlike / strings that are ints
      try:
          return calc(arg.astype(np.int64))
      except:
          pass
  
      # a float with actual np.nan
      try:
          carg = arg.astype(np.float64)
          return calc_with_mask(carg, notnull(carg))
      except:
          pass
  
      # string with NaN-like
      try:
          mask = ~lib.ismember(arg, tslib._nat_strings)
          return calc_with_mask(arg, mask)
      except:
          pass
  
      return None
  
  
  def _format_is_iso(f):
      """
      Does format match the iso8601 set that can be handled by the C parser?
      Generally of form YYYY-MM-DDTHH:MM:SS - date separator can be different
      but must be consistent.  Leading 0s in dates and times are optional.
      """
      iso_template = '%Y{date_sep}%m{date_sep}%d{time_sep}%H:%M:%S.%f'.format
      excluded_formats = ['%Y%m%d', '%Y%m', '%Y']
  
      for date_sep in [' ', '/', '\\', '-', '.', '']:
          for time_sep in [' ', 'T']:
              if (iso_template(date_sep=date_sep,
                               time_sep=time_sep
                               ).startswith(f) and f not in excluded_formats):
                  return True
      return False
  
  
  def parse_time_string(arg, freq=None, dayfirst=None, yearfirst=None):
      """
      Try hard to parse datetime string, leveraging dateutil plus some extra
      goodies like quarter recognition.
  
      Parameters
      ----------
      arg : compat.string_types
      freq : str or DateOffset, default None
          Helps with interpreting time string if supplied
      dayfirst : bool, default None
          If None uses default from print_config
      yearfirst : bool, default None
          If None uses default from print_config
  
      Returns
      -------
      datetime, datetime/dateutil.parser._result, str
      """
      from pandas.core.config import get_option
      if not isinstance(arg, compat.string_types):
          return arg
  
      from pandas.tseries.offsets import DateOffset
      if isinstance(freq, DateOffset):
          freq = freq.rule_code
  
      if dayfirst is None:
          dayfirst = get_option("display.date_dayfirst")
      if yearfirst is None:
          yearfirst = get_option("display.date_yearfirst")
  
      return tslib.parse_datetime_string_with_reso(arg, freq=freq,
                                                   dayfirst=dayfirst,
                                                   yearfirst=yearfirst)
  
  
  DateParseError = tslib.DateParseError
  normalize_date = tslib.normalize_date
  
  # Fixed time formats for time parsing
  _time_formats = ["%H:%M", "%H%M", "%I:%M%p", "%I%M%p",
                   "%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p"]
  
  
  def _guess_time_format_for_array(arr):
      # Try to guess the format based on the first non-NaN element
      non_nan_elements = notnull(arr).nonzero()[0]
      if len(non_nan_elements):
          element = arr[non_nan_elements[0]]
          for time_format in _time_formats:
              try:
                  datetime.strptime(element, time_format)
                  return time_format
              except ValueError:
                  pass
  
      return None
  
  
  def to_time(arg, format=None, infer_time_format=False, errors='raise'):
      """
      Parse time strings to time objects using fixed strptime formats ("%H:%M",
      "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
      "%I%M%S%p")
  
      Use infer_time_format if all the strings are in the same format to speed
      up conversion.
  
      Parameters
      ----------
      arg : string in time format, datetime.time, list, tuple, 1-d array,  Series
      format : str, default None
          Format used to convert arg into a time object.  If None, fixed formats
          are used.
      infer_time_format: bool, default False
          Infer the time format based on the first non-NaN element.  If all
          strings are in the same format, this will speed up conversion.
      errors : {'ignore', 'raise', 'coerce'}, default 'raise'
          - If 'raise', then invalid parsing will raise an exception
          - If 'coerce', then invalid parsing will be set as None
          - If 'ignore', then invalid parsing will return the input
  
      Returns
      -------
      datetime.time
      """
      from pandas.core.series import Series
  
      def _convert_listlike(arg, format):
  
          if isinstance(arg, (list, tuple)):
              arg = np.array(arg, dtype='O')
  
          elif getattr(arg, 'ndim', 1) > 1:
              raise TypeError('arg must be a string, datetime, list, tuple, '
                              '1-d array, or Series')
  
          arg = _ensure_object(arg)
  
          if infer_time_format and format is None:
              format = _guess_time_format_for_array(arg)
  
          times = []
          if format is not None:
              for element in arg:
                  try:
                      times.append(datetime.strptime(element, format).time())
                  except (ValueError, TypeError):
                      if errors == 'raise':
                          raise ValueError("Cannot convert %s to a time with "
                                           "given format %s" % (element, format))
                      elif errors == 'ignore':
                          return arg
                      else:
                          times.append(None)
          else:
              formats = _time_formats[:]
              format_found = False
              for element in arg:
                  time_object = None
                  for time_format in formats:
                      try:
                          time_object = datetime.strptime(element,
                                                          time_format).time()
                          if not format_found:
                              # Put the found format in front
                              fmt = formats.pop(formats.index(time_format))
                              formats.insert(0, fmt)
                              format_found = True
                          break
                      except (ValueError, TypeError):
                          continue
  
                  if time_object is not None:
                      times.append(time_object)
                  elif errors == 'raise':
                      raise ValueError("Cannot convert arg {arg} to "
                                       "a time".format(arg=arg))
                  elif errors == 'ignore':
                      return arg
                  else:
                      times.append(None)
  
          return times
  
      if arg is None:
          return arg
      elif isinstance(arg, time):
          return arg
      elif isinstance(arg, Series):
          values = _convert_listlike(arg._values, format)
          return Series(values, index=arg.index, name=arg.name)
      elif isinstance(arg, ABCIndexClass):
          return _convert_listlike(arg, format)
      elif is_list_like(arg):
          return _convert_listlike(arg, format)
  
      return _convert_listlike(np.array([arg]), format)[0]
  
  
  def format(dt):
      """Returns date in YYYYMMDD format."""
      return dt.strftime('%Y%m%d')
  
  
  OLE_TIME_ZERO = datetime(1899, 12, 30, 0, 0, 0)
  
  
  def ole2datetime(oledt):
      """function for converting excel date to normal date format"""
      val = float(oledt)
  
      # Excel has a bug where it thinks the date 2/29/1900 exists
      # we just reject any date before 3/1/1900.
      if val < 61:
          raise ValueError("Value is outside of acceptable range: %s " % val)
  
      return OLE_TIME_ZERO + timedelta(days=val)
  
apis: pandas.Series pandas.tseries.index.DatetimeIndex._simple_new pandas.tslib.array_with_unit_to_datetime pandas.types.common.is_datetime64_ns_dtype pandas.tslib.get_timezone pandas.types.missing.notnull pandas.lib.ismember pandas.core.config.get_option pandas.types.common.is_datetime64tz_dtype pandas.to_numeric pandas.types.common.is_list_like pandas.tseries.index.DatetimeIndex pandas.tslib.array_to_datetime pandas.types.common.is_datetime64_dtype pandas.types.common.is_integer_dtype pandas.lib.try_parse_year_month_day pandas.Index numpy.array pandas.tslib.datetime_to_datetime64 pandas.tslib.parse_datetime_string_with_reso numpy.empty pandas.DataFrame pandas.types.common._ensure_object pandas.tslib.array_strptime