code: def standard_scaler(df, drop):
      if drop:
          keep = df[drop]
          df = df.drop(drop, axis=1)
      mean = np.mean(df, axis=0)
      scale = np.std(df, axis=0)
      df = (df - mean) / scale
      if drop:
          df = pd.concat((keep, df), 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 3 for calling a global function with example usage: standard_scaler(df, drop) and returns: df