class_docstr: Date/Time methods for pandas-on-Spark Series
class_docstr_tok: ["Date\/Time","methods","for","pandas-on-Spark","Series"]
docstr: Convert to a string Series using specified date_format.
  
  Return an series of formatted strings specified by date_format, which
  supports the same string format as the python standard library. Details
  of the string format can be found in the python string format
  doc.
  
  Parameters
  ----------
  date_format : str
      Date format string (example: "%%Y-%%m-%%d").
  
  Returns
  -------
  Series
      Series of formatted strings.
  
  See Also
  --------
  to_datetime : Convert the given argument to datetime.
  normalize : Return series with times to midnight.
  round : Round the series to the specified freq.
  floor : Floor the series to the specified freq.
  
  Examples
  --------
  >>> series = ps.Series(pd.date_range(pd.Timestamp("2018-03-10 09:00"),
  ...                                  periods=3, freq='s'))
  >>> series
  0   2018-03-10 09:00:00
  1   2018-03-10 09:00:01
  2   2018-03-10 09:00:02
  dtype: datetime64[ns]
  
  >>> series.dt.strftime('%B %d, %Y, %r')
  0    March 10, 2018, 09:00:00 AM
  1    March 10, 2018, 09:00:01 AM
  2    March 10, 2018, 09:00:02 AM
  dtype: object
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code: def strftime(self, date_format: str) -> "ps.Series":
      """
      Convert to a string Series using specified date_format.
  
      Return an series of formatted strings specified by date_format, which
      supports the same string format as the python standard library. Details
      of the string format can be found in the python string format
      doc.
  
      Parameters
      ----------
      date_format : str
          Date format string (example: "%%Y-%%m-%%d").
  
      Returns
      -------
      Series
          Series of formatted strings.
  
      See Also
      --------
      to_datetime : Convert the given argument to datetime.
      normalize : Return series with times to midnight.
      round : Round the series to the specified freq.
      floor : Floor the series to the specified freq.
  
      Examples
      --------
      >>> series = ps.Series(pd.date_range(pd.Timestamp("2018-03-10 09:00"),
      ...                                  periods=3, freq='s'))
      >>> series
      0   2018-03-10 09:00:00
      1   2018-03-10 09:00:01
      2   2018-03-10 09:00:02
      dtype: datetime64[ns]
  
      >>> series.dt.strftime('%B %d, %Y, %r')
      0    March 10, 2018, 09:00:00 AM
      1    March 10, 2018, 09:00:01 AM
      2    March 10, 2018, 09:00:02 AM
      dtype: object
      """
  
      def pandas_strftime(s) -> ps.Series[str]:  # type: ignore[no-untyped-def]
          return s.dt.strftime(date_format)
  
      return self._data.pandas_on_spark.transform_batch(pandas_strftime)
  
imports: from typing import Any, Optional, Union, no_type_check
  import numpy
  import pandas
  from pandas.tseries.offsets import DateOffset
  import pyspark.pandas
  import pyspark.sql.functions
  from pyspark.sql.types import DateType, TimestampType, TimestampNTZType, IntegerType
  from pyspark.pandas import DataFrame
  from pyspark.pandas.config import option_context
label_desc: Use image node_id 30 for calling the DatetimeMethods obj's underlying member method code with example usage: obj.strftime(date_format) and returns: self, s