class_docstr: Computes the expanding standard deviation for events over a given window.
  
  Description:
      Given a list of datetimes, returns the expanding standard deviation
      starting at the row `gap` rows away from the current row. An expanding
      primitive calculates the value of a primitive for a given time
      with all the data available up to the corresponding point in time.
  
      Input datetimes should be monotonic.
  
  Args:
      gap (int, optional): Specifies a gap backwards from each instance before the
          usable data begins. Corresponds to number of rows. Defaults to 1.
      min_periods (int, optional): Minimum number of observations required for performing calculations
          over the window. Defaults to 1.
  
  
  Examples:
      >>> import pandas as pd
      >>> expanding_std = ExpandingSTD()
      >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
      >>> ans = expanding_std(times, [5, 4, 3, 2, 1]).tolist()
      >>> [round(x, 2) for x in ans]
      [nan, nan, 0.71, 1.0, 1.29]
  
      We can also control the gap before the expanding calculation.
  
      >>> import pandas as pd
      >>> expanding_std = ExpandingSTD(gap=0)
      >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
      >>> ans = expanding_std(times, [5, 4, 3, 2, 1]).tolist()
      >>> [round(x, 2) for x in ans]
      [nan, 0.71, 1.0, 1.29, 1.58]
  
      We can also control the minimum number of periods required for the rolling calculation.
  
      >>> import pandas as pd
      >>> expanding_std = ExpandingSTD(min_periods=3)
      >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
      >>> ans = expanding_std(times, [5, 4, 3, 2, 1]).tolist()
      >>> [round(x, 2) for x in ans]
      [nan, nan, nan, 1.0, 1.29]
code: def __init__(self, gap=1, min_periods=1):
      self.gap = gap
      self.min_periods = min_periods
  
file_path: featuretools/featuretools/primitives/standard/transform/time_series/expanding/expanding_std.py
imports: import pandas
  from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import Datetime, Double
  from featuretools.primitives.base.transform_primitive_base import TransformPrimitive
  from featuretools.primitives.standard.transform.time_series.utils import _apply_gap_for_expanding_primitives
label_desc: Use image node_id 1 to create a new ExpandingSTD object from inherited base classes: TransformPrimitive with example: obj = ExpandingSTD(gap, min_periods)