code: def operations(df, features):
      df_new = df[features]
      df_new = df_new - df_new.min()
  
      sqr_name = [str(fa) + "_POWER_2" for fa in df_new.columns]
      log_p_name = [str(fa) + "_LOG_p_one_abs" for fa in df_new.columns]
      rec_p_name = [str(fa) + "_RECIP_p_one" for fa in df_new.columns]
      sqrt_name = [str(fa) + "_SQRT_p_one" for fa in df_new.columns]
  
      df_sqr = pd.DataFrame(
          np.power(df_new.values, 2), columns=sqr_name, index=df.index
      )
      df_log = pd.DataFrame(
          np.log(df_new.add(1).abs().values),
          columns=log_p_name,
          index=df.index,
      )
      df_rec = pd.DataFrame(
          np.reciprocal(df_new.add(1).values),
          columns=rec_p_name,
          index=df.index,
      )
      df_sqrt = pd.DataFrame(
          np.sqrt(df_new.abs().add(1).values),
          columns=sqrt_name,
          index=df.index,
      )
  
      dfs = [df, df_sqr, df_log, df_rec, df_sqrt]
  
      df = pd.concat(dfs, axis=1)
  
      return df
  
imports: import pandas
  import numpy
  import statsmodels.api
  from scipy import signal, integrate
  from pykalman import UnscentedKalmanFilter
  from tsaug import *
  from fbprophet import Prophet
  import pylab
  from seasonal.periodogram import periodogram
label_desc: Use image node_id 7 for calling a global function with example usage: operations(df, features) and returns: df