code: # -*- coding: utf-8 -*-
  """
  Created on Thu Aug 20 09:49:40 2020
  
  @author: Mehdi
  """
  
  import numpy as np
  
  a1=np.nanmean([table_11.loc['A'].accuracy,table_12.loc['A'].accuracy,table_13.loc['A'].accuracy,table_14.loc['A'].accuracy,
             table_15.loc['A'].accuracy,table_16.loc['A'].accuracy,table_17.loc['A'].accuracy,table_18.loc['A'].accuracy,
             table_19.loc['A'].accuracy,table_110.loc['A'].accuracy])
  
  
  a2=np.nanmean([table_11.loc['A'].f1_score,table_12.loc['A'].f1_score,table_13.loc['A'].f1_score,table_14.loc['A'].f1_score,
             table_15.loc['A'].f1_score,table_16.loc['A'].f1_score,table_17.loc['A'].f1_score,table_18.loc['A'].f1_score,
             table_19.loc['A'].f1_score,table_110.loc['A'].f1_score])
  
  
  a3=np.nanmean([table_11.loc['A'][2],table_12.loc['A'][2],table_13.loc['A'][2],table_14.loc['A'][2],
             table_15.loc['A'][2],table_16.loc['A'][2],table_17.loc['A'][2],table_18.loc['A'][2],
             table_19.loc['A'][2],table_110.loc['A'][2]])
  
  
  a4=np.nanmean([table_11.loc['A'][3],table_12.loc['A'][3],table_13.loc['A'][3],table_14.loc['A'][3],
             table_15.loc['A'][3],table_16.loc['A'][3],table_17.loc['A'][3],table_18.loc['A'][3],
             table_19.loc['A'][3],table_110.loc['A'][3]])
  
  a5=np.nanmean([table_11.loc['A'][4],table_12.loc['A'][4],table_13.loc['A'][4],table_14.loc['A'][4],
             table_15.loc['A'][4],table_16.loc['A'][4],table_17.loc['A'][4],table_18.loc['A'][4],
             table_19.loc['A'][4],table_110.loc['A'][4]])
  
  a6=np.nanmean([table_11.loc['A'][5],table_12.loc['A'][5],table_13.loc['A'][5],table_14.loc['A'][5],
             table_15.loc['A'][5],table_16.loc['A'][5],table_17.loc['A'][5],table_18.loc['A'][5],
             table_19.loc['A'][5],table_110.loc['A'][5]])
  
  a7=np.nanmean([table_11.loc['B'].accuracy,table_12.loc['B'].accuracy,table_13.loc['B'].accuracy,table_14.loc['B'].accuracy,
             table_15.loc['B'].accuracy,table_16.loc['B'].accuracy,table_17.loc['B'].accuracy,table_18.loc['B'].accuracy,
             table_19.loc['B'].accuracy,table_110.loc['B'].accuracy])
  
  
  a8=np.nanmean([table_11.loc['B'].f1_score,table_12.loc['B'].f1_score,table_13.loc['B'].f1_score,table_14.loc['B'].f1_score,
             table_15.loc['B'].f1_score,table_16.loc['B'].f1_score,table_17.loc['B'].f1_score,table_18.loc['B'].f1_score,
             table_19.loc['B'].f1_score,table_110.loc['B'].f1_score])
  
  
  a9=np.nanmean([table_11.loc['B'][2],table_12.loc['B'][2],table_13.loc['B'][2],table_14.loc['B'][2],
             table_15.loc['B'][2],table_16.loc['B'][2],table_17.loc['B'][2],table_18.loc['B'][2],
             table_19.loc['B'][2],table_110.loc['B'][2]])
  
  
  a10=np.nanmean([table_11.loc['B'][3],table_12.loc['B'][3],table_13.loc['B'][3],table_14.loc['B'][3],
             table_15.loc['B'][3],table_16.loc['B'][3],table_17.loc['B'][3],table_18.loc['B'][3],
             table_19.loc['B'][3],table_110.loc['B'][3]])
  
  a11=np.nanmean([table_11.loc['B'][4],table_12.loc['B'][4],table_13.loc['B'][4],table_14.loc['B'][4],
             table_15.loc['B'][4],table_16.loc['B'][4],table_17.loc['B'][4],table_18.loc['B'][4],
             table_19.loc['B'][4],table_110.loc['B'][4]])
  
  a12=np.nanmean([table_11.loc['B'][5],table_12.loc['B'][5],table_13.loc['B'][5],table_14.loc['B'][5],
             table_15.loc['B'][5],table_16.loc['B'][5],table_17.loc['B'][5],table_18.loc['B'][5],
             table_19.loc['B'][5],table_110.loc['B'][5]])
  
  
  a13=np.nanmean([table_11.loc['C'].accuracy,table_12.loc['C'].accuracy,table_13.loc['C'].accuracy,table_14.loc['C'].accuracy,
             table_15.loc['C'].accuracy,table_16.loc['C'].accuracy,table_17.loc['C'].accuracy,table_18.loc['C'].accuracy,
             table_19.loc['C'].accuracy,table_110.loc['C'].accuracy])
  
  
  a14=np.nanmean([table_11.loc['C'].f1_score,table_12.loc['C'].f1_score,table_13.loc['C'].f1_score,table_14.loc['C'].f1_score,
             table_15.loc['C'].f1_score,table_16.loc['C'].f1_score,table_17.loc['C'].f1_score,table_18.loc['C'].f1_score,
             table_19.loc['C'].f1_score,table_110.loc['C'].f1_score])
  
  
  a15=np.nanmean([table_11.loc['C'][2],table_12.loc['C'][2],table_13.loc['C'][2],table_14.loc['C'][2],
             table_15.loc['C'][2],table_16.loc['C'][2],table_17.loc['C'][2],table_18.loc['C'][2],
             table_19.loc['C'][2],table_110.loc['C'][2]])
  
  
  a16=np.nanmean([table_11.loc['C'][3],table_12.loc['C'][3],table_13.loc['C'][3],table_14.loc['C'][3],
             table_15.loc['C'][3],table_16.loc['C'][3],table_17.loc['C'][3],table_18.loc['C'][3],
             table_19.loc['C'][3],table_110.loc['C'][3]])
  
  a17=np.nanmean([table_11.loc['C'][4],table_12.loc['C'][4],table_13.loc['C'][4],table_14.loc['C'][4],
             table_15.loc['C'][4],table_16.loc['C'][4],table_17.loc['C'][4],table_18.loc['C'][4],
             table_19.loc['C'][4],table_110.loc['C'][4]])
  
  a18=np.nanmean([table_11.loc['C'][5],table_12.loc['C'][5],table_13.loc['C'][5],table_14.loc['C'][5],
             table_15.loc['C'][5],table_16.loc['C'][5],table_17.loc['C'][5],table_18.loc['C'][5],
             table_19.loc['C'][5],table_110.loc['C'][5]])
  
  a19=np.nanmean([table_11.loc['D'].accuracy,table_12.loc['D'].accuracy,table_13.loc['D'].accuracy,table_14.loc['D'].accuracy,
             table_15.loc['D'].accuracy,table_16.loc['D'].accuracy,table_17.loc['D'].accuracy,table_18.loc['D'].accuracy,
             table_19.loc['D'].accuracy,table_110.loc['D'].accuracy])
  
  
  a20=np.nanmean([table_11.loc['D'].f1_score,table_12.loc['D'].f1_score,table_13.loc['D'].f1_score,table_14.loc['D'].f1_score,
             table_15.loc['D'].f1_score,table_16.loc['D'].f1_score,table_17.loc['D'].f1_score,table_18.loc['D'].f1_score,
             table_19.loc['D'].f1_score,table_110.loc['D'].f1_score])
  
  
  a21=np.nanmean([table_11.loc['D'][2],table_12.loc['D'][2],table_13.loc['D'][2],table_14.loc['D'][2],
             table_15.loc['D'][2],table_16.loc['D'][2],table_17.loc['D'][2],table_18.loc['D'][2],
             table_19.loc['D'][2],table_110.loc['D'][2]])
  
  
  a22=np.nanmean([table_11.loc['D'][3],table_12.loc['D'][3],table_13.loc['D'][3],table_14.loc['D'][3],
             table_15.loc['D'][3],table_16.loc['D'][3],table_17.loc['D'][3],table_18.loc['D'][3],
             table_19.loc['D'][3],table_110.loc['D'][3]])
  
  a23=np.nanmean([table_11.loc['D'][4],table_12.loc['D'][4],table_13.loc['D'][4],table_14.loc['D'][4],
             table_15.loc['D'][4],table_16.loc['D'][4],table_17.loc['D'][4],table_18.loc['D'][4],
             table_19.loc['D'][4],table_110.loc['D'][4]])
  
  a24=np.nanmean([table_11.loc['D'][5],table_12.loc['D'][5],table_13.loc['D'][5],table_14.loc['D'][5],
             table_15.loc['D'][5],table_16.loc['D'][5],table_17.loc['D'][5],table_18.loc['D'][5],
             table_19.loc['D'][5],table_110.loc['D'][5]])
  
  
  a25=np.nanmean([table_11.loc['E'].accuracy,table_12.loc['E'].accuracy,table_13.loc['E'].accuracy,table_14.loc['E'].accuracy,
             table_15.loc['E'].accuracy,table_16.loc['E'].accuracy,table_17.loc['E'].accuracy,table_18.loc['E'].accuracy,
             table_19.loc['E'].accuracy,table_110.loc['E'].accuracy])
  
  
  a26=np.nanmean([table_11.loc['E'].f1_score,table_12.loc['E'].f1_score,table_13.loc['E'].f1_score,table_14.loc['E'].f1_score,
             table_15.loc['E'].f1_score,table_16.loc['E'].f1_score,table_17.loc['E'].f1_score,table_18.loc['E'].f1_score,
             table_19.loc['E'].f1_score,table_110.loc['E'].f1_score])
  
  
  a27=np.nanmean([table_11.loc['E'][2],table_12.loc['E'][2],table_13.loc['E'][2],table_14.loc['E'][2],
             table_15.loc['E'][2],table_16.loc['E'][2],table_17.loc['E'][2],table_18.loc['E'][2],
             table_19.loc['E'][2],table_110.loc['E'][2]])
  
  
  a28=np.nanmean([table_11.loc['E'][3],table_12.loc['E'][3],table_13.loc['E'][3],table_14.loc['E'][3],
             table_15.loc['E'][3],table_16.loc['E'][3],table_17.loc['E'][3],table_18.loc['E'][3],
             table_19.loc['E'][3],table_110.loc['E'][3]])
  
  a29=np.nanmean([table_11.loc['E'][4],table_12.loc['E'][4],table_13.loc['E'][4],table_14.loc['E'][4],
             table_15.loc['E'][4],table_16.loc['E'][4],table_17.loc['E'][4],table_18.loc['E'][4],
             table_19.loc['E'][4],table_110.loc['E'][4]])
  
  a30=np.nanmean([table_11.loc['E'][5],table_12.loc['E'][5],table_13.loc['E'][5],table_14.loc['E'][5],
             table_15.loc['E'][5],table_16.loc['E'][5],table_17.loc['E'][5],table_18.loc['E'][5],
             table_19.loc['E'][5],table_110.loc['E'][5]])
  
  
  a31=np.nanmean([table_11.loc['F'].accuracy,table_12.loc['F'].accuracy,table_13.loc['F'].accuracy,table_14.loc['F'].accuracy,
             table_15.loc['F'].accuracy,table_16.loc['F'].accuracy,table_17.loc['F'].accuracy,table_18.loc['F'].accuracy,
             table_19.loc['F'].accuracy,table_110.loc['F'].accuracy])
  
  
  a32=np.nanmean([table_11.loc['F'].f1_score,table_12.loc['F'].f1_score,table_13.loc['F'].f1_score,table_14.loc['F'].f1_score,
             table_15.loc['F'].f1_score,table_16.loc['F'].f1_score,table_17.loc['F'].f1_score,table_18.loc['F'].f1_score,
             table_19.loc['F'].f1_score,table_110.loc['F'].f1_score])
  
  
  a33=np.nanmean([table_11.loc['F'][2],table_12.loc['F'][2],table_13.loc['F'][2],table_14.loc['F'][2],
             table_15.loc['F'][2],table_16.loc['F'][2],table_17.loc['F'][2],table_18.loc['F'][2],
             table_19.loc['F'][2],table_110.loc['F'][2]])
  
  
  a34=np.nanmean([table_11.loc['F'][3],table_12.loc['F'][3],table_13.loc['F'][3],table_14.loc['F'][3],
             table_15.loc['F'][3],table_16.loc['F'][3],table_17.loc['F'][3],table_18.loc['F'][3],
             table_19.loc['F'][3],table_110.loc['F'][3]])
  
  a35=np.nanmean([table_11.loc['F'][4],table_12.loc['F'][4],table_13.loc['F'][4],table_14.loc['F'][4],
             table_15.loc['F'][4],table_16.loc['F'][4],table_17.loc['F'][4],table_18.loc['F'][4],
             table_19.loc['F'][4],table_110.loc['F'][4]])
  
  a36=np.nanmean([table_11.loc['F'][5],table_12.loc['F'][5],table_13.loc['F'][5],table_14.loc['F'][5],
             table_15.loc['F'][5],table_16.loc['F'][5],table_17.loc['F'][5],table_18.loc['F'][5],
             table_19.loc['F'][5],table_110.loc['F'][5]])
  
  a37=np.nanmean([table_11.loc['G'].accuracy,table_12.loc['G'].accuracy,table_13.loc['G'].accuracy,table_14.loc['G'].accuracy,
             table_15.loc['G'].accuracy,table_16.loc['G'].accuracy,table_17.loc['G'].accuracy,table_18.loc['G'].accuracy,
             table_19.loc['G'].accuracy,table_110.loc['G'].accuracy])
  
  
  a38=np.nanmean([table_11.loc['G'].f1_score,table_12.loc['G'].f1_score,table_13.loc['G'].f1_score,table_14.loc['G'].f1_score,
             table_15.loc['G'].f1_score,table_16.loc['G'].f1_score,table_17.loc['G'].f1_score,table_18.loc['G'].f1_score,
             table_19.loc['G'].f1_score,table_110.loc['G'].f1_score])
  
  
  a39=np.nanmean([table_11.loc['G'][2],table_12.loc['G'][2],table_13.loc['G'][2],table_14.loc['G'][2],
             table_15.loc['G'][2],table_16.loc['G'][2],table_17.loc['G'][2],table_18.loc['G'][2],
             table_19.loc['G'][2],table_110.loc['G'][2]])
  
  
  a40=np.nanmean([table_11.loc['G'][3],table_12.loc['G'][3],table_13.loc['G'][3],table_14.loc['G'][3],
             table_15.loc['G'][3],table_16.loc['G'][3],table_17.loc['G'][3],table_18.loc['G'][3],
             table_19.loc['G'][3],table_110.loc['G'][3]])
  
  a41=np.nanmean([table_11.loc['G'][4],table_12.loc['G'][4],table_13.loc['G'][4],table_14.loc['G'][4],
             table_15.loc['G'][4],table_16.loc['G'][4],table_17.loc['G'][4],table_18.loc['G'][4],
             table_19.loc['G'][4],table_110.loc['G'][4]])
  
  a42=np.nanmean([table_11.loc['G'][5],table_12.loc['G'][5],table_13.loc['G'][5],table_14.loc['G'][5],
             table_15.loc['G'][5],table_16.loc['G'][5],table_17.loc['G'][5],table_18.loc['G'][5],
             table_19.loc['G'][5],table_110.loc['G'][5]])
  
  
  a43=np.nanmean([table_11.loc['H'].accuracy,table_12.loc['H'].accuracy,table_13.loc['H'].accuracy,table_14.loc['H'].accuracy,
             table_15.loc['H'].accuracy,table_16.loc['H'].accuracy,table_17.loc['H'].accuracy,table_18.loc['H'].accuracy,
             table_19.loc['H'].accuracy,table_110.loc['H'].accuracy])
  
  
  a44=np.nanmean([table_11.loc['H'].f1_score,table_12.loc['H'].f1_score,table_13.loc['H'].f1_score,table_14.loc['H'].f1_score,
             table_15.loc['H'].f1_score,table_16.loc['H'].f1_score,table_17.loc['H'].f1_score,table_18.loc['H'].f1_score,
             table_19.loc['H'].f1_score,table_110.loc['H'].f1_score])
  
  
  a45=np.nanmean([table_11.loc['H'][2],table_12.loc['H'][2],table_13.loc['H'][2],table_14.loc['H'][2],
             table_15.loc['H'][2],table_16.loc['H'][2],table_17.loc['H'][2],table_18.loc['H'][2],
             table_19.loc['H'][2],table_110.loc['H'][2]])
  
  
  a46=np.nanmean([table_11.loc['H'][3],table_12.loc['H'][3],table_13.loc['H'][3],table_14.loc['H'][3],
             table_15.loc['H'][3],table_16.loc['H'][3],table_17.loc['H'][3],table_18.loc['H'][3],
             table_19.loc['H'][3],table_110.loc['H'][3]])
  
  a47=np.nanmean([table_11.loc['H'][4],table_12.loc['H'][4],table_13.loc['H'][4],table_14.loc['H'][4],
             table_15.loc['H'][4],table_16.loc['H'][4],table_17.loc['H'][4],table_18.loc['H'][4],
             table_19.loc['H'][4],table_110.loc['H'][4]])
  
  a48=np.nanmean([table_11.loc['H'][5],table_12.loc['H'][5],table_13.loc['H'][5],table_14.loc['H'][5],
             table_15.loc['H'][5],table_16.loc['H'][5],table_17.loc['H'][5],table_18.loc['H'][5],
             table_19.loc['H'][5],table_110.loc['H'][5]])
  
  
  a49=np.nanmean([table_11.loc['I'].accuracy,table_12.loc['I'].accuracy,table_13.loc['I'].accuracy,table_14.loc['I'].accuracy,
             table_15.loc['I'].accuracy,table_16.loc['I'].accuracy,table_17.loc['I'].accuracy,table_18.loc['I'].accuracy,
             table_19.loc['I'].accuracy,table_110.loc['I'].accuracy])
  
  
  a50=np.nanmean([table_11.loc['I'].f1_score,table_12.loc['I'].f1_score,table_13.loc['I'].f1_score,table_14.loc['I'].f1_score,
             table_15.loc['I'].f1_score,table_16.loc['I'].f1_score,table_17.loc['I'].f1_score,table_18.loc['I'].f1_score,
             table_19.loc['I'].f1_score,table_110.loc['I'].f1_score])
  
  
  a51=np.nanmean([table_11.loc['I'][2],table_12.loc['I'][2],table_13.loc['I'][2],table_14.loc['I'][2],
             table_15.loc['I'][2],table_16.loc['I'][2],table_17.loc['I'][2],table_18.loc['I'][2],
             table_19.loc['I'][2],table_110.loc['I'][2]])
  
  
  a52=np.nanmean([table_11.loc['I'][3],table_12.loc['I'][3],table_13.loc['I'][3],table_14.loc['I'][3],
             table_15.loc['I'][3],table_16.loc['I'][3],table_17.loc['I'][3],table_18.loc['I'][3],
             table_19.loc['I'][3],table_110.loc['I'][3]])
  
  a53=np.nanmean([table_11.loc['I'][4],table_12.loc['I'][4],table_13.loc['I'][4],table_14.loc['I'][4],
             table_15.loc['I'][4],table_16.loc['I'][4],table_17.loc['I'][4],table_18.loc['I'][4],
             table_19.loc['I'][4],table_110.loc['I'][4]])
  
  a54=np.nanmean([table_11.loc['I'][5],table_12.loc['I'][5],table_13.loc['I'][5],table_14.loc['I'][5],
             table_15.loc['I'][5],table_16.loc['I'][5],table_17.loc['I'][5],table_18.loc['I'][5],
             table_19.loc['I'][5],table_110.loc['I'][5]])
  
  
  a55=np.nanmean([table_11.loc['J'].accuracy,table_12.loc['J'].accuracy,table_13.loc['J'].accuracy,table_14.loc['J'].accuracy,
             table_15.loc['J'].accuracy,table_16.loc['J'].accuracy,table_17.loc['J'].accuracy,table_18.loc['J'].accuracy,
             table_19.loc['J'].accuracy,table_110.loc['J'].accuracy])
  
  
  a56=np.nanmean([table_11.loc['J'].f1_score,table_12.loc['J'].f1_score,table_13.loc['J'].f1_score,table_14.loc['J'].f1_score,
             table_15.loc['J'].f1_score,table_16.loc['J'].f1_score,table_17.loc['J'].f1_score,table_18.loc['J'].f1_score,
             table_19.loc['J'].f1_score,table_110.loc['J'].f1_score])
  
  
  a57=np.nanmean([table_11.loc['J'][2],table_12.loc['J'][2],table_13.loc['J'][2],table_14.loc['J'][2],
             table_15.loc['J'][2],table_16.loc['J'][2],table_17.loc['J'][2],table_18.loc['J'][2],
             table_19.loc['J'][2],table_110.loc['J'][2]])
  
  
  a58=np.nanmean([table_11.loc['J'][3],table_12.loc['J'][3],table_13.loc['J'][3],table_14.loc['J'][3],
             table_15.loc['J'][3],table_16.loc['J'][3],table_17.loc['J'][3],table_18.loc['J'][3],
             table_19.loc['J'][3],table_110.loc['J'][3]])
  
  a59=np.nanmean([table_11.loc['J'][4],table_12.loc['J'][4],table_13.loc['J'][4],table_14.loc['J'][4],
             table_15.loc['J'][4],table_16.loc['J'][4],table_17.loc['J'][4],table_18.loc['J'][4],
             table_19.loc['J'][4],table_110.loc['J'][4]])
  
  a60=np.nanmean([table_11.loc['J'][5],table_12.loc['J'][5],table_13.loc['J'][5],table_14.loc['J'][5],
             table_15.loc['J'][5],table_16.loc['J'][5],table_17.loc['J'][5],table_18.loc['J'][5],
             table_19.loc['J'][5],table_110.loc['J'][5]])
  
  a61=np.nanmean([table_11.loc['K'].accuracy,table_12.loc['K'].accuracy,table_13.loc['K'].accuracy,table_14.loc['K'].accuracy,
             table_15.loc['K'].accuracy,table_16.loc['K'].accuracy,table_17.loc['K'].accuracy,table_18.loc['K'].accuracy,
             table_19.loc['K'].accuracy,table_110.loc['K'].accuracy])
  
  
  a62=np.nanmean([table_11.loc['K'].f1_score,table_12.loc['K'].f1_score,table_13.loc['K'].f1_score,table_14.loc['K'].f1_score,
             table_15.loc['K'].f1_score,table_16.loc['K'].f1_score,table_17.loc['K'].f1_score,table_18.loc['K'].f1_score,
             table_19.loc['K'].f1_score,table_110.loc['K'].f1_score])
  
  
  a63=np.nanmean([table_11.loc['K'][2],table_12.loc['K'][2],table_13.loc['K'][2],table_14.loc['K'][2],
             table_15.loc['K'][2],table_16.loc['K'][2],table_17.loc['K'][2],table_18.loc['K'][2],
             table_19.loc['K'][2],table_110.loc['K'][2]])
  
  
  a64=np.nanmean([table_11.loc['K'][3],table_12.loc['K'][3],table_13.loc['K'][3],table_14.loc['K'][3],
             table_15.loc['K'][3],table_16.loc['K'][3],table_17.loc['K'][3],table_18.loc['K'][3],
             table_19.loc['K'][3],table_110.loc['K'][3]])
  
  a65=np.nanmean([table_11.loc['K'][4],table_12.loc['K'][4],table_13.loc['K'][4],table_14.loc['K'][4],
             table_15.loc['K'][4],table_16.loc['K'][4],table_17.loc['K'][4],table_18.loc['K'][4],
             table_19.loc['K'][4],table_110.loc['K'][4]])
  
  a66=np.nanmean([table_11.loc['K'][5],table_12.loc['K'][5],table_13.loc['K'][5],table_14.loc['K'][5],
             table_15.loc['K'][5],table_16.loc['K'][5],table_17.loc['K'][5],table_18.loc['K'][5],
             table_19.loc['K'][5],table_110.loc['K'][5]])
  
  a67=np.nanmean([table_11.loc['L'].accuracy,table_12.loc['L'].accuracy,table_13.loc['L'].accuracy,table_14.loc['L'].accuracy,
             table_15.loc['L'].accuracy,table_16.loc['L'].accuracy,table_17.loc['L'].accuracy,table_18.loc['L'].accuracy,
             table_19.loc['L'].accuracy,table_110.loc['L'].accuracy])
  
  
  a68=np.nanmean([table_11.loc['L'].f1_score,table_12.loc['L'].f1_score,table_13.loc['L'].f1_score,table_14.loc['L'].f1_score,
             table_15.loc['L'].f1_score,table_16.loc['L'].f1_score,table_17.loc['L'].f1_score,table_18.loc['L'].f1_score,
             table_19.loc['L'].f1_score,table_110.loc['L'].f1_score])
  
  
  a69=np.nanmean([table_11.loc['L'][2],table_12.loc['L'][2],table_13.loc['L'][2],table_14.loc['L'][2],
             table_15.loc['L'][2],table_16.loc['L'][2],table_17.loc['L'][2],table_18.loc['L'][2],
             table_19.loc['L'][2],table_110.loc['L'][2]])
  
  
  a70=np.nanmean([table_11.loc['L'][3],table_12.loc['L'][3],table_13.loc['L'][3],table_14.loc['L'][3],
             table_15.loc['L'][3],table_16.loc['L'][3],table_17.loc['L'][3],table_18.loc['L'][3],
             table_19.loc['L'][3],table_110.loc['L'][3]])
  
  a71=np.nanmean([table_11.loc['L'][4],table_12.loc['L'][4],table_13.loc['L'][4],table_14.loc['L'][4],
             table_15.loc['L'][4],table_16.loc['L'][4],table_17.loc['L'][4],table_18.loc['L'][4],
             table_19.loc['L'][4],table_110.loc['L'][4]])
  
  a72=np.nanmean([table_11.loc['L'][5],table_12.loc['L'][5],table_13.loc['L'][5],table_14.loc['L'][5],
             table_15.loc['L'][5],table_16.loc['L'][5],table_17.loc['L'][5],table_18.loc['L'][5],
             table_19.loc['L'][5],table_110.loc['L'][5]])
  
  
  a73=np.nanmean([table_11.loc['M'].accuracy,table_12.loc['M'].accuracy,table_13.loc['M'].accuracy,table_14.loc['M'].accuracy,
             table_15.loc['M'].accuracy,table_16.loc['M'].accuracy,table_17.loc['M'].accuracy,table_18.loc['M'].accuracy,
             table_19.loc['M'].accuracy,table_110.loc['M'].accuracy])
  
  
  a74=np.nanmean([table_11.loc['M'].f1_score,table_12.loc['M'].f1_score,table_13.loc['M'].f1_score,table_14.loc['M'].f1_score,
             table_15.loc['M'].f1_score,table_16.loc['M'].f1_score,table_17.loc['M'].f1_score,table_18.loc['M'].f1_score,
             table_19.loc['M'].f1_score,table_110.loc['M'].f1_score])
  
  
  a75=np.nanmean([table_11.loc['M'][2],table_12.loc['M'][2],table_13.loc['M'][2],table_14.loc['M'][2],
             table_15.loc['M'][2],table_16.loc['M'][2],table_17.loc['M'][2],table_18.loc['M'][2],
             table_19.loc['M'][2],table_110.loc['M'][2]])
  
  
  a76=np.nanmean([table_11.loc['M'][3],table_12.loc['M'][3],table_13.loc['M'][3],table_14.loc['M'][3],
             table_15.loc['M'][3],table_16.loc['M'][3],table_17.loc['M'][3],table_18.loc['M'][3],
             table_19.loc['M'][3],table_110.loc['M'][3]])
  
  a77=np.nanmean([table_11.loc['M'][4],table_12.loc['M'][4],table_13.loc['M'][4],table_14.loc['M'][4],
             table_15.loc['M'][4],table_16.loc['M'][4],table_17.loc['M'][4],table_18.loc['M'][4],
             table_19.loc['M'][4],table_110.loc['M'][4]])
  
  a78=np.nanmean([table_11.loc['M'][5],table_12.loc['M'][5],table_13.loc['M'][5],table_14.loc['M'][5],
             table_15.loc['M'][5],table_16.loc['M'][5],table_17.loc['M'][5],table_18.loc['M'][5],
             table_19.loc['M'][5],table_110.loc['M'][5]])
  
  
  a79=np.nanmean([table_11.loc['.'].accuracy,table_12.loc['.'].accuracy,table_13.loc['.'].accuracy,table_14.loc['.'].accuracy,
             table_15.loc['.'].accuracy,table_16.loc['.'].accuracy,table_17.loc['.'].accuracy,table_18.loc['.'].accuracy,
             table_19.loc['.'].accuracy,table_110.loc['.'].accuracy])
  
  
  a80=np.nanmean([table_11.loc['.'].f1_score,table_12.loc['.'].f1_score,table_13.loc['.'].f1_score,table_14.loc['.'].f1_score,
             table_15.loc['.'].f1_score,table_16.loc['.'].f1_score,table_17.loc['.'].f1_score,table_18.loc['.'].f1_score,
             table_19.loc['.'].f1_score,table_110.loc['.'].f1_score])
  
  
  a81=np.nanmean([table_11.loc['.'][2],table_12.loc['.'][2],table_13.loc['.'][2],table_14.loc['.'][2],
             table_15.loc['.'][2],table_16.loc['.'][2],table_17.loc['.'][2],table_18.loc['.'][2],
             table_19.loc['.'][2],table_110.loc['.'][2]])
  
  
  a82=np.nanmean([table_11.loc['.'][3],table_12.loc['.'][3],table_13.loc['.'][3],table_14.loc['.'][3],
             table_15.loc['.'][3],table_16.loc['.'][3],table_17.loc['.'][3],table_18.loc['.'][3],
             table_19.loc['.'][3],table_110.loc['.'][3]])
  
  a83=np.nanmean([table_11.loc['.'][4],table_12.loc['.'][4],table_13.loc['.'][4],table_14.loc['.'][4],
             table_15.loc['.'][4],table_16.loc['.'][4],table_17.loc['.'][4],table_18.loc['.'][4],
             table_19.loc['.'][4],table_110.loc['.'][4]])
  
  a84=np.nanmean([table_11.loc['.'][5],table_12.loc['.'][5],table_13.loc['.'][5],table_14.loc['.'][5],
             table_15.loc['.'][5],table_16.loc['.'][5],table_17.loc['.'][5],table_18.loc['.'][5],
             table_19.loc['.'][5],table_110.loc['.'][5]])
  
  
  A=[[a1,a2,a3,round(a4),a5,round(a6)],[a7,a8,a9,round(a10),a11,round(a12)],[a13,a14,a15,round(a16),a17,round(a18)],
      [a19,a20,a21,round(a22),a23,round(a24)]
  ,[a25,a26,a27,round(a28),a29,round(a30)],[a31,a32,a33,round(a34),a35,round(a36)],
  [a37,a38,a39,round(a40),a41,round(a42)],[a43,a44,a45,round(a46),a47,round(a48)],
  [a49,a50,a51,round(a52),a53,round(a54)],[a55,a56,a57,round(a58),a59,round(a60)],
  [a61,a62,a63,round(a64),a65,round(a66)],[a67,a68,a69,round(a70),a71,round(a72)],
  [a73,a74,a75,round(a76),a77,round(a78)],[a79,a80,a81,round(a82),a83,round(a84)]]
  
  vv1=np.mean([v1[0],v2[0],v3[0],v4[0],v5[0],v6[0],v7[0],v8[0],v9[0],v10[0]])
  vv2=np.mean([v1[1],v2[1],v3[1],v4[1],v5[1],v6[1],v7[1],v8[1],v9[1],v10[1]])
  vv3=np.mean([v1[2],v2[2],v3[2],v4[2],v5[2],v6[2],v7[2],v8[2],v9[2],v10[2]])
  vv4=np.mean([v1[3],v2[3],v3[3],v4[3],v5[3],v6[3],v7[3],v8[3],v9[3],v10[3]])
  vv5=np.mean([v1[4],v2[4],v3[4],v4[4],v5[4],v6[4],v7[4],v8[4],v9[4],v10[4]])
  vv6=np.mean([v1[5],v2[5],v3[5],v4[5],v5[5],v6[5],v7[5],v8[5],v9[5],v10[5]])
  table_111= pd.DataFrame(A,columns=['accuracy', 'f1_score', 'accuracy for unknown words',
  'number of unknown words','accuracy for known words','number of known words']
  ,index=['A','B','C','D','E','F','G','H','I','J','K','L','M','.'])
  
  #table_10= pd.DataFrame(A,
  #columns=['accuracy', 'f1_score', 'accuracy for unknown words',
  #         'number of unknown words','accuracy for known words','number of known words']
  #,index=[list(tag2idx.keys())[0], list(tag2idx.keys())[1], list(tag2idx.keys())[2] , list(tag2idx.keys())[3] 
  #, list(tag2idx.keys())[4] , list(tag2idx.keys())[5],list(tag2idx.keys())[6],list(tag2idx.keys())[7]
  #,list(tag2idx.keys())[8],list(tag2idx.keys())[9],list(tag2idx.keys())[10],list(tag2idx.keys())[11],
  #list(tag2idx.keys())[12],list(tag2idx.keys())[13]])
  
  str_pythontex=[float("{0:.2f}".format(list(table_111.loc["A"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["A"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["A"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["A"])[4]*100)),
  round(list(table_111.loc["A"])[3]),round(list(table_111.loc["A"])[5]),
  float("{0:.2f}".format(list(table_111.loc["B"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["B"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["B"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["B"])[4]*100)),
  round(list(table_111.loc["B"])[3]),round(list(table_111.loc["B"])[5]),
  float("{0:.2f}".format(list(table_111.loc["C"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["C"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["C"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["C"])[4]*100)),
  round(list(table_111.loc["C"])[3]),round(list(table_111.loc["C"])[5]),
  float("{0:.2f}".format(list(table_111.loc["D"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["D"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["D"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["D"])[4]*100)),
  round(list(table_111.loc["D"])[3]),round(list(table_111.loc["D"])[5]),
  float("{0:.2f}".format(list(table_111.loc["E"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["E"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["E"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["E"])[4]*100)),
  round(list(table_111.loc["E"])[3]),round(list(table_111.loc["E"])[5]),
  float("{0:.2f}".format(list(table_111.loc["F"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["F"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["F"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["F"])[4]*100)),
  round(list(table_111.loc["F"])[3]),round(list(table_111.loc["F"])[5]),
  float("{0:.2f}".format(list(table_111.loc["G"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["G"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["G"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["G"])[4]*100)),
  round(list(table_111.loc["G"])[3]),round(list(table_111.loc["G"])[5]),
  float("{0:.2f}".format(list(table_111.loc["H"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["H"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["H"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["H"])[4]*100)),
  round(list(table_111.loc["H"])[3]),round(list(table_111.loc["H"])[5]),
  float("{0:.2f}".format(list(table_111.loc["I"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["I"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["I"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["I"])[4]*100)),
  round(list(table_111.loc["I"])[3]),round(list(table_111.loc["I"])[5]),
  float("{0:.2f}".format(list(table_111.loc["J"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["J"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["J"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["J"])[4]*100)),
  round(list(table_111.loc["J"])[3]),round(list(table_111.loc["J"])[5]),
  float("{0:.2f}".format(list(table_111.loc["K"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["K"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["K"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["K"])[4]*100)),
  round(list(table_111.loc["K"])[3]),round(list(table_111.loc["K"])[5]),
  float("{0:.2f}".format(list(table_111.loc["L"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["L"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["L"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["L"])[4]*100)),
  round(list(table_111.loc["L"])[3]),round(list(table_111.loc["L"])[5]),
  float("{0:.2f}".format(list(table_111.loc["M"])[0]*100)),float("{0:.2f}".format(list(table_111.loc["M"])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["M"])[2]*100)),float("{0:.2f}".format(list(table_111.loc["M"])[4]*100)),
  round(list(table_111.loc["M"])[3]),round(list(table_111.loc["M"])[5]),
  float("{0:.2f}".format(list(table_111.loc["."])[0]*100)),float("{0:.2f}".format(list(table_111.loc["."])[1]*100)),
  float("{0:.2f}".format(list(table_111.loc["."])[2]*100)),float("{0:.2f}".format(list(table_111.loc["."])[4]*100)),
  round(list(table_111.loc["."])[3]),round(list(table_111.loc["."])[5]),float("{0:.2f}".format(vv1))
  ,float("{0:.2f}".format(vv2))
  ,float("{0:.2f}".format(vv3))
  ,float("{0:.2f}".format(vv4)),round(vv5)
  ,float("{0:.2f}".format(vv6))
  ]
  
  
  L=[]
  for x in str_pythontex:
      if math.isnan(x):
          L.append('NULL')
      else:
          L.append(str(x))
  
  L1=[]
  i=0
  for x in L:
      i=i+1
      if i!=5 and i!=6 and x!="NULL":
          L1.append(x+" \%")
      elif x=="NULL":
          L1.append(x)
      elif i==5:
          L1.append(x)
      else:
          L1.append(x)
          i=0
  
  L1[-1]=L1[-1]+" \%"
  
  
apis: numpy.nanmean numpy.mean