class_docstr: Does simple seasonality _calculations on data.
  
      
class_docstr_tok: ["Does","simple","seasonality","_calculations","on","data","."]
docstr: Adjusted time series which exhibit strong seasonality. If time series do not exhibit any seasonality will return
  NaN values.
  
  Parameters
  ----------
  data_frame : DataFrame
      Data to be seasonally adjusted
  window : int
      Number of points to use in our rolling window (eg. for monthly data 60 = 5 year * 12 months)
  likely_period
      What is the likely period of the data (eg. for monthly data, likely to be 12, for daily, likely to be 252)
  
  Returns
  -------
  DataFrame
      Time series seasonally adjusted
docstr_tok: ["Adjusted","time","series","which","exhibit","strong","seasonality",".","If","time","series","do","not","exhibit","any","seasonality","will","return","NaN","values",".","Parameters","--","--","--","--","--","data_frame",":","DataFrame","Data","to","be","seasonally","adjusted","window",":","int","Number","of","points","to","use","in","our","rolling","window","(","eg",".","for","monthly","data","60","=","5","year","*","12","months",")","likely_period","What","is","the","likely","period","of","the","data","(","eg",".","for","monthly","data",",","likely","to","be","12",",","for","daily",",","likely","to","be","252",")","Returns","--","--","--","-","DataFrame","Time","series","seasonally","adjusted"]
code: def adjust_rolling_seasonality(
      self, data_frame, window=None, likely_period=None
  ):
      """Adjusted time series which exhibit strong seasonality. If time series do not exhibit any seasonality will return
      NaN values.
  
      Parameters
      ----------
      data_frame : DataFrame
          Data to be seasonally adjusted
      window : int
          Number of points to use in our rolling window (eg. for monthly data 60 = 5 year * 12 months)
      likely_period
          What is the likely period of the data (eg. for monthly data, likely to be 12, for daily, likely to be 252)
  
      Returns
      -------
      DataFrame
          Time series seasonally adjusted
      """
      data_frame = data_frame.copy()
  
      for c in data_frame.columns:
          data_frame[c] = (
              data_frame[c]
              .rolling(center=False, window=window, min_periods=window)
              .apply(
                  lambda x: self._remove_seasonality(
                      x, likely_period=likely_period
                  )
              )
          )
  
      return data_frame
  
file_path: finmarketpy/finmarketpy/economics/seasonality.py
imports: import numpy
  import pandas
  from findatapy.timeseries import Calculations, Filter
  from findatapy.util.commonman import CommonMan
  from findatapy.util.configmanager import ConfigManager
  from findatapy.util.loggermanager import LoggerManager
label_desc: Use image node_id 7 for calling the Seasonality obj's underlying member method code with example usage: obj.adjust_rolling_seasonality(data_frame, window, likely_period) and returns: data_frame