new_contents: from __future__ import absolute_import
  
  import pandas as pd
  import numpy as np
  
  
  import pyaf.ForecastEngine as autof
  
  def convery_date(x):
      try:
          y = datetime.datetime.strptime(x, "%Y-%m")
          return y
      except:
          pass
      return np.nan
  
  
  csvfile_link = "https://raw.githubusercontent.com/antoinecarme/pyaf/master/data/ozone-la_missing_dates.csv"
  csvfile_link = "data/ozone-la_missing_dates.csv"
  df = pd.read_csv(csvfile_link);
  import datetime
  df['Month'] = df['Month'].apply(convery_date)
  
  lEngine = autof.cForecastEngine()
  lEngine
  
  H = 12;
  lTimeVar = 'Month'
  lSignalVar = 'Ozone'
  # lEngine.mOptions.enable_slow_mode();
  # lEngine.mOptions.mDebugPerformance = True;
  lEngine.train(df , lTimeVar , lSignalVar, H);
  lEngine.getModelInfo();
  print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
  
  lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
  
  lEngine.standardPlots("outputs/my_ozone_missing_signal");
  
  dfapp_in = df.copy();
  dfapp_in.tail()
  
  #H = 12
  dfapp_out = lEngine.forecast(dfapp_in, H);
  #dfapp_out.to_csv("outputs/ozone_apply_out.csv")
  dfapp_out.tail(2 * H)
  print("Forecast Columns " , dfapp_out.columns);
  Forecast_DF = dfapp_out[[lTimeVar , lSignalVar, lSignalVar + '_Forecast']]
  print(Forecast_DF.info())
  print("Forecasts\n" , Forecast_DF.tail(H));
  
  print("\n\n<ModelInfo>")
  print(lEngine.to_json());
  print("</ModelInfo>\n\n")
  print("\n\n<Forecast>")
  print(Forecast_DF.tail(2*H).to_json(date_format='iso'))
  print("</Forecast>\n\n")
  
  
subject: Handle Missing Data in the Time Column #80 Added a failing script