title: Remove Holidays and Weekends in a very long time-serie, how to model time-series in Python?
body: <p>Is there some function in Python to handle this. GoogleDocs has a Weekday -operation so perhaps there is something like that in Python. I am pretty sure someone must have solved this, similar problems occur in sparse data such as in finance and research. I am basically just trying to organize a huge amount of different sized vectors indexed by days, time-series, I am not sure how I should hadle the days -- mark the first day with 1 and the last day with N or with unix -time or how should that be done? I am not sure whether the time-series should be saved into matrix so I could model them more easily to calculate correlation matrices and such things, any ready thing to do such things?</p>
  
  <p>Let's try to solve this problem without the "practical" extra clutter:</p>
  
  <pre><code>import itertools
  seq = range(100000)
  criteria  = cycle([True]*10 + [False]*801)
  list(compress(seq, criteria))
  </code></pre>
  
  <p>now  have to change them into days and then change the $\mathbb R$ into $( \mathbb R, \mathbb R)$, tuple. So $V : \mathbb R \mapsto \mathbb R^{2}$ missing, investigating.</p>
  
  <p><strong>[Update]</strong> </p>
  
  <p>Let's play! Below code solves the subproblem -- creates some test data to test things -- now we need to create arbitrary days and valuations there to try to test it on arbitrary timeseries. If we can create some function $V$, we are very close to solve this problem...it must consider though the holidays and weekends so maybe not easy (not sure).</p>
  
  <pre><code>import itertools as i
  import time
  import math
  import numpy
  
  
  
  def createRandomData():
      samples=[]
  
      for x in range(5):
          seq = range(5)
          criteria  = i.cycle([True]*x+ [False]*3)
  
          samples += [list(i.compress( seq, criteria ))] 
  
      return samples
  
  def createNNtriangularMatrix(data):
      N = len(data)
      return [aa+[0]*(N-len(aa)) for aa in data]
  
  
  A= createNNtriangularMatrix(createRandomData())
  print numpy.array(A)
  print numpy.corrcoef(A)
  </code></pre>