title: Finding equal frequency from discrete data
body: <p>i have to find equal width from time series data.</p>
  
  <p>So far,i could do it by manually selecting every single column,and then applying the condition. But i need a faster way to do it.</p>
  
  <p>The time-series data:</p>
  
  <pre><code>Time    ulaR    trxA
  
  0       0.6457325   0.4040438
  50      0.4594477   0.4172161
  100     0.4244469   0.3878299
  150     0.391452    0.49735
  200     0.3570379   0.4930038
  250     0.3730624   0.4221448
  300     0.3676819   0.3796647
  350     0.3688949   0.4228213
  400     0.4018654   0.439482
  450     0.3934677   0.4039933
  500     0.3571651   0.3264575
  550     0.5451287   0.3471816
  600     0.6520524   0.3710635
  650     0.6776012   0.4173777
  700     0.684412    0.3812378
  750     0.7298819   0.3735065
  800     0.739083    0.3195176
  850     0.6394782   0.213515
  900     0.6483277   0.3721211
  950     0.7003584   0.3528451
  1000    0.6926971   0.3867717
  </code></pre>
  
  <p>My Code:</p>
  
  <pre><code>import numpy as np
  import pandas as pd
  import csv
  import array as ar
  
  infile="Ecoli-1_dream4_timeseries.tsv"
  outfile="tempecoli.csv"
  df=pd.read_csv(infile,delimiter="\t",dtype=float)
  
  a1=np.array(df['ulaR'])
  s=df.sort_values(['ulaR'])
  s1=np.array(s['ulaR'])
  gr=list()
  
  for i in range(len(s1)):
    for j in range(len(a1)):
      if s1[i]==a1[j]:
          if j&lt;=7:
              gr.append(0)
          elif j&gt;7 and j&lt;=14:
              gr.append(1)
          else:
              gr.append(2)
  
  
  ##########
  
  a1=np.array(df['trxA'])
  s=df.sort_values(['trxA'])
  s1=np.array(s['trxA'])
  gr1=list()
  
  for i in range(len(s1)):
    for j in range(len(a1)):
       if s1[i]==a1[j]:
           if j&lt;=7:
              gr1.append(0)
           elif j&gt;7 and j&lt;=14:
              gr1.append(1)
           else:
              gr1.append(2)
  
  #############
  
  
  group1=pd.Series(gr,name="ulaR")
  group2=pd.Series(gr1,name="trxA")
  df2=pd.concat([group1,group2],axis=1)
  df2.to_csv("ecoli1.csv")
  print("Completed")
  </code></pre>
  
  <p>If you run this code,you will get the result. I do not want any new result,all i want it a more time efficient code to get the desired result. Because,writing the names of each code and then applying the conditions takes a lot of time. 
  A little bit of help will be appreciated. Thanks in advance.</p>