class_docstr: Arranging underlying volatility market in easier to read format. Also provides methods for calculating various
  volatility metrics, such as realized_vol volatility and volatility risk premium. Has extensive support for estimating
  implied_vol volatility addons.
class_docstr_tok: ["Arranging","underlying","volatility","market","in","easier","to","read","format",".","Also","provides","methods","for","calculating","various","volatility","metrics",",","such","as","realized_vol","volatility","and","volatility","risk","premium",".","Has","extensive","support","for","estimating","implied_vol","volatility","addons","."]
code: def adjust_implied_ON_fri_vol(self, data_frame):
      cols_ON = [x for x in data_frame.columns if "VON" in x]
  
      for c in cols_ON:
          data_frame[c][data_frame.index.dayofweek == 4] = data_frame[
              c
          ][data_frame.index.dayofweek == 4] * math.sqrt(3)
  
      # data_frame[data_frame.index.dayofweek == 4] = data_frame[data_frame.index.dayofweek == 4] * math.sqrt(3)
  
      return data_frame
  
file_path: finmarketpy/finmarketpy/curve/volatility/volstats.py
imports: import math
  import pandas
  from pandas.tseries.offsets import CustomBusinessDay
  from findatapy.timeseries import Calculations, Filter, Timezone
  from findatapy.timeseries import Calendar
label_desc: Use image node_id 5 for calling the VolStats obj's underlying member method code with example usage: obj.adjust_implied_ON_fri_vol(data_frame) and returns: data_frame