docstr: Efficient parsing for a 1d Pandas/numpy object containing string
  representations of dates.
  
  Note: pd.to_datetime is significantly faster when no format string is
  passed, and in pandas 0.12.0 the %p strptime directive is not correctly
  handled if a format string is explicitly passed, but AM/PM is handled
  properly if format=None.
  
  Moreover, we were previously ignoring this parameter unintentionally
  because we were incorrectly passing it as a positional.  For all these
  reasons, we ignore the format_str parameter when parsing datetimes.
code: def parse_date_str_series(
      format_str, tz, date_str_series, data_frequency, trading_day
  ):
      """
      Efficient parsing for a 1d Pandas/numpy object containing string
      representations of dates.
  
      Note: pd.to_datetime is significantly faster when no format string is
      passed, and in pandas 0.12.0 the %p strptime directive is not correctly
      handled if a format string is explicitly passed, but AM/PM is handled
      properly if format=None.
  
      Moreover, we were previously ignoring this parameter unintentionally
      because we were incorrectly passing it as a positional.  For all these
      reasons, we ignore the format_str parameter when parsing datetimes.
      """
  
      # Explicitly ignoring this parameter.  See note above.
      if format_str is not None:
          logger.warn(
              "The 'format_str' parameter to fetch_csv is deprecated. "
              "Ignoring and defaulting to pandas default date parsing."
          )
          format_str = None
  
      tz_str = str(tz)
      if tz_str == pytz.utc.zone:
          parsed = pd.to_datetime(
              date_str_series.values,
              format=format_str,
              utc=True,
              errors="coerce",
          )
      else:
          parsed = (
              pd.to_datetime(
                  date_str_series.values,
                  format=format_str,
                  errors="coerce",
              )
              .tz_localize(tz_str)
              .tz_convert("UTC")
          )
  
      if data_frequency == "daily":
          parsed = roll_dts_to_midnight(parsed, trading_day)
      return parsed
  
imports: from abc import ABCMeta, abstractmethod
  from collections import namedtuple
  import hashlib
  from textwrap import dedent
  import warnings
  from logbook import Logger
  import numpy
  import pandas
  from pandas import read_csv
  import pytz
  import requests
  from six import StringIO, iteritems, with_metaclass
  from zipline.errors import MultipleSymbolsFound, SymbolNotFound, ZiplineError
  from zipline.protocol import DATASOURCE_TYPE, Event
  from zipline.assets import Equity
label_desc: Use image node_id 5 for calling the PandasCSV obj's underlying member method code with example usage: obj.parse_date_str_series(format_str, tz, date_str_series, data_frequency, trading_day) and returns: parsed