content: from functools import partial
  from datetime import datetime, time
  from collections import MutableMapping
  
  import numpy as np
  
  from pandas._libs import tslib, tslibs
  from pandas._libs.tslibs.strptime import array_strptime
  from pandas._libs.tslibs import parsing, conversion, Timestamp
  from pandas._libs.tslibs.parsing import (  # noqa
      parse_time_string,
      DateParseError,
      _format_is_iso,
      _guess_datetime_format)
  
  from pandas.core.dtypes.common import (
      ensure_object,
      is_datetime64_ns_dtype,
      is_datetime64_dtype,
      is_datetime64tz_dtype,
      is_integer_dtype,
      is_integer,
      is_float,
      is_list_like,
      is_scalar,
      is_numeric_dtype,
      is_object_dtype)
  from pandas.core.dtypes.generic import (
      ABCIndexClass, ABCSeries,
      ABCDataFrame)
  from pandas.core.dtypes.missing import notna
  from pandas.core import algorithms
  from pandas.compat import zip
  
  
  def _guess_datetime_format_for_array(arr, **kwargs):
      # Try to guess the format based on the first non-NaN element
      non_nan_elements = notna(arr).nonzero()[0]
      if len(non_nan_elements):
          return _guess_datetime_format(arr[non_nan_elements[0]], **kwargs)
  
  
  def _maybe_cache(arg, format, cache, convert_listlike):
      """
      Create a cache of unique dates from an array of dates
  
      Parameters
      ----------
      arg : integer, float, string, datetime, list, tuple, 1-d array, Series
      format : string
          Strftime format to parse time
      cache : boolean
          True attempts to create a cache of converted values
      convert_listlike : function
          Conversion function to apply on dates
  
      Returns
      -------
      cache_array : Series
          Cache of converted, unique dates. Can be empty
      """
      from pandas import Series
      cache_array = Series()
      if cache:
          # Perform a quicker unique check
          from pandas import Index
          if not Index(arg).is_unique:
              unique_dates = algorithms.unique(arg)
              cache_dates = convert_listlike(unique_dates, True, format)
              cache_array = Series(cache_dates, index=unique_dates)
      return cache_array
  
  
  def _convert_and_box_cache(arg, cache_array, box, errors, name=None):
      """
      Convert array of dates with a cache and box the result
  
      Parameters
      ----------
      arg : integer, float, string, datetime, list, tuple, 1-d array, Series
      cache_array : Series
          Cache of converted, unique dates
      box : boolean
          True boxes result as an Index-like, False returns an ndarray
      errors : string
          'ignore' plus box=True will convert result to Index
      name : string, default None
          Name for a DatetimeIndex
  
      Returns
      -------
      result : datetime of converted dates
          Returns:
  
          - Index-like if box=True
          - ndarray if box=False
      """
      from pandas import Series, DatetimeIndex, Index
      result = Series(arg).map(cache_array)
      if box:
          if errors == 'ignore':
              return Index(result, name=name)
          else:
              return DatetimeIndex(result, name=name)
      return result.values
  
  
  def _return_parsed_timezone_results(result, timezones, box, tz, name):
      """
      Return results from array_strptime if a %z or %Z directive was passed.
  
      Parameters
      ----------
      result : ndarray
          int64 date representations of the dates
      timezones : ndarray
          pytz timezone objects
      box : boolean
          True boxes result as an Index-like, False returns an ndarray
      tz : object
          None or pytz timezone object
      name : string, default None
          Name for a DatetimeIndex
  
      Returns
      -------
      tz_result : ndarray of parsed dates with timezone
          Returns:
  
          - Index-like if box=True
          - ndarray of Timestamps if box=False
  
      """
      if tz is not None:
          raise ValueError("Cannot pass a tz argument when "
                           "parsing strings with timezone "
                           "information.")
      tz_results = np.array([Timestamp(res).tz_localize(zone) for res, zone
                             in zip(result, timezones)])
      if box:
          from pandas import Index
          return Index(tz_results, name=name)
      return tz_results
  
  
  def _convert_listlike_datetimes(arg, box, format, name=None, tz=None,
                                  unit=None, errors=None,
                                  infer_datetime_format=None, dayfirst=None,
                                  yearfirst=None, exact=None):
      """
      Helper function for to_datetime. Performs the conversions of 1D listlike
      of dates
  
      Parameters
      ----------
      arg : list, tuple, ndarray, Series, Index
          date to be parced
      box : boolean
          True boxes result as an Index-like, False returns an ndarray
      name : object
          None or string for the Index name
      tz : object
          None or 'utc'
      unit : string
          None or string of the frequency of the passed data
      errors : string
          error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
      infer_datetime_format : boolean
          inferring format behavior from to_datetime
      dayfirst : boolean
          dayfirst parsing behavior from to_datetime
      yearfirst : boolean
          yearfirst parsing behavior from to_datetime
      exact : boolean
          exact format matching behavior from to_datetime
  
      Returns
      -------
      ndarray of parsed dates
          Returns:
  
          - Index-like if box=True
          - ndarray of Timestamps if box=False
      """
      from pandas import DatetimeIndex
      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 tz == 'utc':
              arg = arg.tz_convert(None).tz_localize(tz)
          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, name=name)
  
              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 (ValueError, TypeError, tslibs.OutOfBoundsDatetime):
                      raise ValueError("cannot convert the input to "
                                       "'%Y%m%d' date format")
  
              # fallback
              if result is None:
                  try:
                      result, timezones = array_strptime(
                          arg, format, exact=exact, errors=errors)
                      if '%Z' in format or '%z' in format:
                          return _return_parsed_timezone_results(
                              result, timezones, box, tz, name)
                  except tslibs.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, tz_parsed = tslib.array_to_datetime(
                  arg,
                  errors=errors,
                  utc=tz == 'utc',
                  dayfirst=dayfirst,
                  yearfirst=yearfirst,
                  require_iso8601=require_iso8601
              )
              if tz_parsed is not None:
                  if box:
                      # We can take a shortcut since the datetime64 numpy array
                      # is in UTC
                      return DatetimeIndex._simple_new(result, name=name,
                                                       tz=tz_parsed)
                  else:
                      # Convert the datetime64 numpy array to an numpy array
                      # of datetime objects
                      result = [Timestamp(ts, tz=tz_parsed).to_pydatetime()
                                for ts in result]
                      return np.array(result, dtype=object)
  
          if box:
              # Ensure we return an Index in all cases where box=True
              if is_datetime64_dtype(result):
                  return DatetimeIndex(result, tz=tz, name=name)
              elif is_object_dtype(result):
                  # e.g. an Index of datetime objects
                  from pandas import Index
                  return Index(result, name=name)
          return result
  
      except ValueError as e:
          try:
              values, tz = conversion.datetime_to_datetime64(arg)
              return DatetimeIndex._simple_new(values, name=name, tz=tz)
          except (ValueError, TypeError):
              raise e
  
  
  def _adjust_to_origin(arg, origin, unit):
      """
      Helper function for to_datetime.
      Adjust input argument to the specified origin
  
      Parameters
      ----------
      arg : list, tuple, ndarray, Series, Index
          date to be adjusted
      origin : 'julian' or Timestamp
          origin offset for the arg
      unit : string
          passed unit from to_datetime, must be 'D'
  
      Returns
      -------
      ndarray or scalar of adjusted date(s)
      """
      if origin == 'julian':
          original = arg
          j0 = Timestamp(0).to_julian_date()
          if unit != 'D':
              raise ValueError("unit must be 'D' for origin='julian'")
          try:
              arg = arg - j0
          except TypeError:
              raise ValueError("incompatible 'arg' type for given "
                               "'origin'='julian'")
  
          # premptively check this for a nice range
          j_max = Timestamp.max.to_julian_date() - j0
          j_min = Timestamp.min.to_julian_date() - j0
          if np.any(arg > j_max) or np.any(arg < j_min):
              raise tslibs.OutOfBoundsDatetime(
                  "{original} is Out of Bounds for "
                  "origin='julian'".format(original=original))
      else:
          # arg must be numeric
          if not ((is_scalar(arg) and (is_integer(arg) or is_float(arg))) or
                  is_numeric_dtype(np.asarray(arg))):
              raise ValueError(
                  "'{arg}' is not compatible with origin='{origin}'; "
                  "it must be numeric with a unit specified ".format(
                      arg=arg,
                      origin=origin))
  
          # we are going to offset back to unix / epoch time
          try:
              offset = Timestamp(origin)
          except tslibs.OutOfBoundsDatetime:
              raise tslibs.OutOfBoundsDatetime(
                  "origin {origin} is Out of Bounds".format(origin=origin))
          except ValueError:
              raise ValueError("origin {origin} cannot be converted "
                               "to a Timestamp".format(origin=origin))
  
          if offset.tz is not None:
              raise ValueError(
                  "origin offset {} must be tz-naive".format(offset))
          offset -= Timestamp(0)
  
          # convert the offset to the unit of the arg
          # this should be lossless in terms of precision
          offset = offset // tslibs.Timedelta(1, unit=unit)
  
          # scalars & ndarray-like can handle the addition
          if is_list_like(arg) and not isinstance(
                  arg, (ABCSeries, ABCIndexClass, np.ndarray)):
              arg = np.asarray(arg)
          arg = arg + offset
      return arg
  
  
  def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
                  utc=None, box=True, format=None, exact=True,
                  unit=None, infer_datetime_format=False, origin='unix',
                  cache=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 behavior).
  
          .. 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 or Index-like object
          - 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, which is an
          integer or float number. This will be based off the origin.
          Example, with unit='ms' and origin='unix' (the default), this
          would calculate the number of milliseconds to the unix epoch start.
      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.
      origin : scalar, default is 'unix'
          Define the reference date. The numeric values would be parsed as number
          of units (defined by `unit`) since this reference date.
  
          - If 'unix' (or POSIX) time; origin is set to 1970-01-01.
          - If 'julian', unit must be 'D', and origin is set to beginning of
            Julian Calendar. Julian day number 0 is assigned to the day starting
            at noon on January 1, 4713 BC.
          - If Timestamp convertible, origin is set to Timestamp identified by
            origin.
  
          .. versionadded:: 0.20.0
      cache : boolean, default False
          If True, use a cache of unique, converted dates to apply the datetime
          conversion. May produce significant speed-up when parsing duplicate
          date strings, especially ones with timezone offsets.
  
          .. versionadded:: 0.23.0
  
      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 corresponding
          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
  
      Using a unix epoch time
  
      >>> pd.to_datetime(1490195805, unit='s')
      Timestamp('2017-03-22 15:16:45')
      >>> pd.to_datetime(1490195805433502912, unit='ns')
      Timestamp('2017-03-22 15:16:45.433502912')
  
      .. warning:: For float arg, precision rounding might happen. To prevent
          unexpected behavior use a fixed-width exact type.
  
      Using a non-unix epoch origin
  
      >>> pd.to_datetime([1, 2, 3], unit='D',
                         origin=pd.Timestamp('1960-01-01'))
      0    1960-01-02
      1    1960-01-03
      2    1960-01-04
  
      See also
      --------
      pandas.DataFrame.astype : Cast argument to a specified dtype.
      pandas.to_timedelta : Convert argument to timedelta.
      """
      if arg is None:
          return None
  
      if origin != 'unix':
          arg = _adjust_to_origin(arg, origin, unit)
  
      tz = 'utc' if utc else None
      convert_listlike = partial(_convert_listlike_datetimes, tz=tz, unit=unit,
                                 dayfirst=dayfirst, yearfirst=yearfirst,
                                 errors=errors, exact=exact,
                                 infer_datetime_format=infer_datetime_format)
  
      if isinstance(arg, Timestamp):
          result = arg
      elif isinstance(arg, ABCSeries):
          cache_array = _maybe_cache(arg, format, cache, convert_listlike)
          if not cache_array.empty:
              result = arg.map(cache_array)
          else:
              from pandas import Series
              values = convert_listlike(arg._values, True, format)
              result = Series(values, index=arg.index, name=arg.name)
      elif isinstance(arg, (ABCDataFrame, MutableMapping)):
          result = _assemble_from_unit_mappings(arg, errors=errors)
      elif isinstance(arg, ABCIndexClass):
          cache_array = _maybe_cache(arg, format, cache, convert_listlike)
          if not cache_array.empty:
              result = _convert_and_box_cache(arg, cache_array, box, errors,
                                              name=arg.name)
          else:
              convert_listlike = partial(convert_listlike, name=arg.name)
              result = convert_listlike(arg, box, format)
      elif is_list_like(arg):
          cache_array = _maybe_cache(arg, format, cache, convert_listlike)
          if not cache_array.empty:
              result = _convert_and_box_cache(arg, cache_array, box, errors)
          else:
              result = convert_listlike(arg, box, format)
      else:
          result = convert_listlike(np.array([arg]), box, format)[0]
  
      return result
  
  
  # 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 specified 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: [{required}] "
                           "is missing".format(required=','.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: "
                           "[{excess}]".format(excess=','.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: {error}".format(error=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 [{value}]: "
                                   "{error}".format(value=value, error=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 = parsing.try_parse_year_month_day(carg / 10000,
                                                    carg / 100 % 100,
                                                    carg % 100)
          return tslib.array_to_datetime(parsed, errors=errors)[0]
  
      def calc_with_mask(carg, mask):
          result = np.empty(carg.shape, dtype='M8[ns]')
          iresult = result.view('i8')
          iresult[~mask] = tslibs.iNaT
  
          masked_result = calc(carg[mask].astype(np.float64).astype(np.int64))
          result[mask] = masked_result.astype('M8[ns]')
          return result
  
      # try intlike / strings that are ints
      try:
          return calc(arg.astype(np.int64))
      except ValueError:
          pass
  
      # a float with actual np.nan
      try:
          carg = arg.astype(np.float64)
          return calc_with_mask(carg, notna(carg))
      except ValueError:
          pass
  
      # string with NaN-like
      try:
          mask = ~algorithms.isin(arg, list(tslib.nat_strings))
          return calc_with_mask(arg, mask)
      except ValueError:
          pass
  
      return None
  
  
  # 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 = notna(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':
                          msg = ("Cannot convert {element} to a time with given "
                                 "format {format}").format(element=element,
                                                           format=format)
                          raise ValueError(msg)
                      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]
  