parameters: (self, window_start, window_end, min_observations=None)
return_statement: return SArray(
              _proxy=self.__proxy__.builtin_rolling_apply(
                  agg_op, window_start, window_end, min_observations
              )
          )
docstring: Calculate a new SArray of the standard deviation of different subsets
          over this SArray.
  
          The subset that the standard deviation is calculated over is defined as
          an inclusive range relative to the position to each value in the
          SArray, using `window_start` and `window_end`. For a better
          understanding of this, see the examples below.
  
          Parameters
          ----------
          window_start : int
              The start of the subset to calculate the standard deviation
              relative to the current value.
  
          window_end : int
              The end of the subset to calculate the standard deviation relative
              to the current value. Must be greater than `window_start`.
  
          min_observations : int
              Minimum number of non-missing observations in window required to
              calculate the standard deviation (otherwise result is None). None
              signifies that the entire window must not include a missing value.
              A negative number throws an error.
  
          Returns
          -------
          out : SArray
  
          Examples
          --------
          >>> import pandas
          >>> sa = SArray([1,2,3,4,5])
          >>> series = pandas.Series([1,2,3,4,5])
  
          A rolling standard deviation with a window including the previous 2
          entries including the current:
          >>> sa.rolling_stdv(-2,0)
          dtype: float
          Rows: 5
          [None, None, 0.816496580927726, 0.816496580927726, 0.816496580927726]
  
          Pandas equivalent:
          >>> pandas.rolling_std(series, 3, ddof=0)
          0         NaN
          1         NaN
          2    0.816497
          3    0.816497
          4    0.816497
          dtype: float64
  
          Same rolling standard deviation operation, but 2 minimum observations:
          >>> sa.rolling_stdv(-2,0,min_observations=2)
          dtype: float
          Rows: 5
          [None, 0.5, 0.816496580927726, 0.816496580927726, 0.816496580927726]
  
          Pandas equivalent:
          >>> pandas.rolling_std(series, 3, ddof=0, min_periods=2)
          0         NaN
          1    0.500000
          2    0.816497
          3    0.816497
          4    0.816497
          dtype: float64
  
          A rolling standard deviation with a size of 3, centered around the
          current:
          >>> sa.rolling_stdv(-1,1)
          dtype: float
          Rows: 5
          [None, 0.816496580927726, 0.816496580927726, 0.816496580927726, None]
  
          Pandas equivalent:
          >>> pandas.rolling_std(series, 3, center=True, ddof=0)
          0         NaN
          1    0.816497
          2    0.816497
          3    0.816497
          4         NaN
          dtype: float64
  
          A rolling standard deviation with a window including the current and
          the 2 entries following:
          >>> sa.rolling_stdv(0,2)
          dtype: float
          Rows: 5
          [0.816496580927726, 0.816496580927726, 0.816496580927726, None, None]
  
          A rolling standard deviation with a window including the previous 2
          entries NOT including the current:
          >>> sa.rolling_stdv(-2,-1)
          dtype: float
          Rows: 5
          [None, None, 0.5, 0.5, 0.5]
docstring_summary: Calculate a new SArray of the standard deviation of different subsets
          over this SArray.
docstring_tokens: Calculate a new SArray of the standard deviation of different subsets over this SArray .
function: def rolling_stdv(self, window_start, window_end, min_observations=None):
          """
          Calculate a new SArray of the standard deviation of different subsets
          over this SArray.
  
          The subset that the standard deviation is calculated over is defined as
          an inclusive range relative to the position to each value in the
          SArray, using `window_start` and `window_end`. For a better
          understanding of this, see the examples below.
  
          Parameters
          ----------
          window_start : int
              The start of the subset to calculate the standard deviation
              relative to the current value.
  
          window_end : int
              The end of the subset to calculate the standard deviation relative
              to the current value. Must be greater than `window_start`.
  
          min_observations : int
              Minimum number of non-missing observations in window required to
              calculate the standard deviation (otherwise result is None). None
              signifies that the entire window must not include a missing value.
              A negative number throws an error.
  
          Returns
          -------
          out : SArray
  
          Examples
          --------
          >>> import pandas
          >>> sa = SArray([1,2,3,4,5])
          >>> series = pandas.Series([1,2,3,4,5])
  
          A rolling standard deviation with a window including the previous 2
          entries including the current:
          >>> sa.rolling_stdv(-2,0)
          dtype: float
          Rows: 5
          [None, None, 0.816496580927726, 0.816496580927726, 0.816496580927726]
  
          Pandas equivalent:
          >>> pandas.rolling_std(series, 3, ddof=0)
          0         NaN
          1         NaN
          2    0.816497
          3    0.816497
          4    0.816497
          dtype: float64
  
          Same rolling standard deviation operation, but 2 minimum observations:
          >>> sa.rolling_stdv(-2,0,min_observations=2)
          dtype: float
          Rows: 5
          [None, 0.5, 0.816496580927726, 0.816496580927726, 0.816496580927726]
  
          Pandas equivalent:
          >>> pandas.rolling_std(series, 3, ddof=0, min_periods=2)
          0         NaN
          1    0.500000
          2    0.816497
          3    0.816497
          4    0.816497
          dtype: float64
  
          A rolling standard deviation with a size of 3, centered around the
          current:
          >>> sa.rolling_stdv(-1,1)
          dtype: float
          Rows: 5
          [None, 0.816496580927726, 0.816496580927726, 0.816496580927726, None]
  
          Pandas equivalent:
          >>> pandas.rolling_std(series, 3, center=True, ddof=0)
          0         NaN
          1    0.816497
          2    0.816497
          3    0.816497
          4         NaN
          dtype: float64
  
          A rolling standard deviation with a window including the current and
          the 2 entries following:
          >>> sa.rolling_stdv(0,2)
          dtype: float
          Rows: 5
          [0.816496580927726, 0.816496580927726, 0.816496580927726, None, None]
  
          A rolling standard deviation with a window including the previous 2
          entries NOT including the current:
          >>> sa.rolling_stdv(-2,-1)
          dtype: float
          Rows: 5
          [None, None, 0.5, 0.5, 0.5]
          """
          min_observations = self.__check_min_observations(min_observations)
          agg_op = "__builtin__stdv__"
          return SArray(
              _proxy=self.__proxy__.builtin_rolling_apply(
                  agg_op, window_start, window_end, min_observations
              )
          )
function_tokens: def rolling_stdv ( self , window_start , window_end , min_observations = None ) : min_observations = self . __check_min_observations ( min_observations ) agg_op = "__builtin__stdv__" return SArray ( _proxy = self . __proxy__ . builtin_rolling_apply ( agg_op , window_start , window_end , min_observations ) )