class_docstr: Not sure if I can avoid the state machine here
class_docstr_tok: ["Not","sure","if","I","can","avoid","the","state","machine","here"]
docstr: Find the appropriate frequency string to describe the inferred
  frequency of self.values
  
  Returns
  -------
  freqstr : str or None
docstr_tok: ["Find","the","appropriate","frequency","string","to","describe","the","inferred","frequency","of","self.values","Returns","--","--","--","-","freqstr",":","str","or","None"]
code: def get_freq(self):
      """
      Find the appropriate frequency string to describe the inferred
      frequency of self.values
  
      Returns
      -------
      freqstr : str or None
      """
      if not self.is_monotonic or not self.index._is_unique:
          return None
  
      delta = self.deltas[0]
      if _is_multiple(delta, _ONE_DAY):
          return self._infer_daily_rule()
  
      # Business hourly, maybe. 17: one day / 65: one weekend
      if self.hour_deltas in ([1, 17], [1, 65], [1, 17, 65]):
          return "BH"
      # Possibly intraday frequency.  Here we use the
      # original .asi8 values as the modified values
      # will not work around DST transitions.  See #8772
      elif not self.is_unique_asi8:
          return None
  
      delta = self.deltas_asi8[0]
      if _is_multiple(delta, _ONE_HOUR):
          # Hours
          return _maybe_add_count("H", delta / _ONE_HOUR)
      elif _is_multiple(delta, _ONE_MINUTE):
          # Minutes
          return _maybe_add_count("T", delta / _ONE_MINUTE)
      elif _is_multiple(delta, _ONE_SECOND):
          # Seconds
          return _maybe_add_count("S", delta / _ONE_SECOND)
      elif _is_multiple(delta, _ONE_MILLI):
          # Milliseconds
          return _maybe_add_count("L", delta / _ONE_MILLI)
      elif _is_multiple(delta, _ONE_MICRO):
          # Microseconds
          return _maybe_add_count("U", delta / _ONE_MICRO)
      else:
          # Nanoseconds
          return _maybe_add_count("N", delta)
  
imports: from datetime import timedelta
  import re
  import numpy
  from pytz import AmbiguousTimeError
  from pandas._libs.algos import unique_deltas
  from pandas._libs.tslibs import Timedelta, Timestamp
  from pandas._libs.tslibs.ccalendar import MONTH_ALIASES, int_to_weekday
  from pandas._libs.tslibs.conversion import tz_convert
  from pandas._libs.tslibs.fields import build_field_sarray
  import pandas._libs.tslibs.frequencies
  from pandas._libs.tslibs.offsets import _offset_to_period_map
  import pandas._libs.tslibs.resolution
  from pandas._libs.tslibs.resolution import Resolution
  from pandas._libs.tslibs.timezones import UTC
  import pandas.compat
  from pandas.compat import zip
  from pandas.util._decorators import cache_readonly
  from pandas.core.dtypes.common import is_datetime64_dtype, is_period_arraylike, is_timedelta64_dtype
  from pandas.core.dtypes.generic import ABCSeries
  from pandas.core.algorithms import unique
  from pandas.tseries.offsets import DateOffset, Day, Hour, Micro, Milli, Minute, Nano, Second, prefix_mapping
label_desc: Use image node_id 6 for calling the _FrequencyInferer obj's underlying member method code with example usage: obj.get_freq() and returns: None, self, str, _maybe_add_count, None, _maybe_add_count, _maybe_add_count, _maybe_add_count, _maybe_add_count, _maybe_add_count