parameters: (
      arg: DatetimeScalarOrArrayConvertible,
      errors: str = "raise",
      dayfirst: bool = False,
      yearfirst: bool = False,
      utc: bool | None = None,
      format: str | None = None,
      exact: bool = True,
      unit: str | None = None,
      infer_datetime_format: bool = False,
      origin="unix",
      cache: bool = True,
  )
return_statement: return result
docstring: Convert argument to datetime.
  
      Parameters
      ----------
      arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like
          The object to convert to a datetime.
      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 : bool, 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 : bool, 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).
      utc : bool, default None
          Return UTC DatetimeIndex if True (converting any tz-aware
          datetime.datetime objects as well).
      format : str, default None
          The strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
          all the way up to nanoseconds.
          See strftime documentation for more information on choices:
          https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.
      exact : bool, True by default
          Behaves as:
          - If True, require an exact format match.
          - If False, allow the format to match anywhere in the target string.
  
      unit : str, default 'ns'
          The 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 : bool, default False
          If True and no `format` is given, attempt to infer the format of the
          datetime strings based on the first non-NaN element,
          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 '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.
      cache : bool, default True
          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. The cache is only
          used when there are at least 50 values. The presence of out-of-bounds
          values will render the cache unusable and may slow down parsing.
  
          .. versionchanged:: 0.25.0
              - changed default value from False to True.
  
      Returns
      -------
      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).
  
      See Also
      --------
      DataFrame.astype : Cast argument to a specified dtype.
      to_timedelta : Convert argument to timedelta.
      convert_dtypes : Convert dtypes.
  
      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
      <https://pandas.pydata.org/pandas-docs/stable/user_guide/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)  # doctest: +SKIP
      100 loops, best of 3: 10.4 ms per loop
  
      >>> %timeit pd.to_datetime(s, infer_datetime_format=False)  # doctest: +SKIP
      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'))
      DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
                    dtype='datetime64[ns]', freq=None)
  
      In case input is list-like and the elements of input are of mixed
      timezones, return will have object type Index if utc=False.
  
      >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'])
      Index([2018-10-26 12:00:00-05:30, 2018-10-26 12:00:00-05:00], dtype='object')
  
      >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'],
      ...                utc=True)
      DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
                    dtype='datetime64[ns, UTC]', freq=None)
docstring_summary: Convert argument to datetime.
docstring_tokens: Convert argument to datetime .
function: def to_datetime(
      arg: DatetimeScalarOrArrayConvertible,
      errors: str = "raise",
      dayfirst: bool = False,
      yearfirst: bool = False,
      utc: bool | None = None,
      format: str | None = None,
      exact: bool = True,
      unit: str | None = None,
      infer_datetime_format: bool = False,
      origin="unix",
      cache: bool = True,
  ) -> DatetimeIndex | Series | DatetimeScalar | NaTType | None:
      """
      Convert argument to datetime.
  
      Parameters
      ----------
      arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like
          The object to convert to a datetime.
      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 : bool, 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 : bool, 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).
      utc : bool, default None
          Return UTC DatetimeIndex if True (converting any tz-aware
          datetime.datetime objects as well).
      format : str, default None
          The strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
          all the way up to nanoseconds.
          See strftime documentation for more information on choices:
          https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.
      exact : bool, True by default
          Behaves as:
          - If True, require an exact format match.
          - If False, allow the format to match anywhere in the target string.
  
      unit : str, default 'ns'
          The 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 : bool, default False
          If True and no `format` is given, attempt to infer the format of the
          datetime strings based on the first non-NaN element,
          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 '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.
      cache : bool, default True
          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. The cache is only
          used when there are at least 50 values. The presence of out-of-bounds
          values will render the cache unusable and may slow down parsing.
  
          .. versionchanged:: 0.25.0
              - changed default value from False to True.
  
      Returns
      -------
      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).
  
      See Also
      --------
      DataFrame.astype : Cast argument to a specified dtype.
      to_timedelta : Convert argument to timedelta.
      convert_dtypes : Convert dtypes.
  
      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
      <https://pandas.pydata.org/pandas-docs/stable/user_guide/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)  # doctest: +SKIP
      100 loops, best of 3: 10.4 ms per loop
  
      >>> %timeit pd.to_datetime(s, infer_datetime_format=False)  # doctest: +SKIP
      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'))
      DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
                    dtype='datetime64[ns]', freq=None)
  
      In case input is list-like and the elements of input are of mixed
      timezones, return will have object type Index if utc=False.
  
      >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'])
      Index([2018-10-26 12:00:00-05:30, 2018-10-26 12:00:00-05:00], dtype='object')
  
      >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'],
      ...                utc=True)
      DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
                    dtype='datetime64[ns, UTC]', freq=None)
      """
      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,
      )
  
      result: Timestamp | NaTType | Series | Index
  
      if isinstance(arg, Timestamp):
          result = arg
          if tz is not None:
              if arg.tz is not None:
                  # error: Too many arguments for "tz_convert" of "NaTType"
                  result = result.tz_convert(tz)  # type: ignore[call-arg]
              else:
                  # error: Too many arguments for "tz_localize" of "NaTType"
                  result = result.tz_localize(tz)  # type: ignore[call-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:
              values = convert_listlike(arg._values, format)
              result = arg._constructor(values, index=arg.index, name=arg.name)
      elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):
          result = _assemble_from_unit_mappings(arg, errors, tz)
      elif isinstance(arg, Index):
          cache_array = _maybe_cache(arg, format, cache, convert_listlike)
          if not cache_array.empty:
              result = _convert_and_box_cache(arg, cache_array, name=arg.name)
          else:
              result = convert_listlike(arg, format, name=arg.name)
      elif is_list_like(arg):
          try:
              cache_array = _maybe_cache(arg, format, cache, convert_listlike)
          except OutOfBoundsDatetime:
              # caching attempts to create a DatetimeIndex, which may raise
              # an OOB. If that's the desired behavior, then just reraise...
              if errors == "raise":
                  raise
              # ... otherwise, continue without the cache.
              from pandas import Series
  
              cache_array = Series([], dtype=object)  # just an empty array
          if not cache_array.empty:
              result = _convert_and_box_cache(arg, cache_array)
          else:
              result = convert_listlike(arg, format)
      else:
          result = convert_listlike(np.array([arg]), format)[0]
  
      #  error: Incompatible return value type (got "Union[Timestamp, NaTType,
      # Series, Index]", expected "Union[DatetimeIndex, Series, float, str,
      # NaTType, None]")
      return result
function_tokens: def to_datetime ( arg : DatetimeScalarOrArrayConvertible , errors : str = "raise" , dayfirst : bool = False , yearfirst : bool = False , utc : bool | None = None , format : str | None = None , exact : bool = True , unit : str | None = None , infer_datetime_format : bool = False , origin = "unix" , cache : bool = True , ) -> DatetimeIndex | Series | DatetimeScalar | NaTType | None : 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 , ) result : Timestamp | NaTType | Series | Index if isinstance ( arg , Timestamp ) : result = arg if tz is not None : if arg . tz is not None : # error: Too many arguments for "tz_convert" of "NaTType" result = result . tz_convert ( tz ) # type: ignore[call-arg] else : # error: Too many arguments for "tz_localize" of "NaTType" result = result . tz_localize ( tz ) # type: ignore[call-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 : values = convert_listlike ( arg . _values , format ) result = arg . _constructor ( values , index = arg . index , name = arg . name ) elif isinstance ( arg , ( ABCDataFrame , abc . MutableMapping ) ) : result = _assemble_from_unit_mappings ( arg , errors , tz ) elif isinstance ( arg , Index ) : cache_array = _maybe_cache ( arg , format , cache , convert_listlike ) if not cache_array . empty : result = _convert_and_box_cache ( arg , cache_array , name = arg . name ) else : result = convert_listlike ( arg , format , name = arg . name ) elif is_list_like ( arg ) : try : cache_array = _maybe_cache ( arg , format , cache , convert_listlike ) except OutOfBoundsDatetime : # caching attempts to create a DatetimeIndex, which may raise # an OOB. If that's the desired behavior, then just reraise... if errors == "raise" : raise # ... otherwise, continue without the cache. from pandas import Series cache_array = Series ( [ ] , dtype = object ) # just an empty array if not cache_array . empty : result = _convert_and_box_cache ( arg , cache_array ) else : result = convert_listlike ( arg , format ) else : result = convert_listlike ( np . array ( [ arg ] ) , format ) [ 0 ] #  error: Incompatible return value type (got "Union[Timestamp, NaTType, # Series, Index]", expected "Union[DatetimeIndex, Series, float, str, # NaTType, None]") return result