title: Reading daily time-series using pandas and re-sampling to monthly
body: <p>I am very new to Python. I usually use <code>scikits.timeseries</code> to process time-series data. Now I would like to use <code>Panda</code> such as <code>read_csv</code> to do the same as the code shown below. I used the read_csv manual to read the file, but I don't know how to convert the daily time-series to monthly time-series. </p>
  
  <p>The input is one column daily data starting from 2002-01-01 to 2011-12-31, so the length is 3652. The output will be one column monthly data starting from 2002-01 to 2011-12, so the length is 120.</p>
  
  <pre><code>import numpy as np
  import pandas as pd
  import scikits.timeseries as ts
  stgSim = ts.time_series(np.loadtxt('examp.txt', delimiter = ',' , skiprows  = 1    ,
                                                         usecols   = [37] ),
                                              start_date ='2002-01-01',
                                              freq='d'                     )
  
  v4 = ts.time_series(np.random.rand(3652),start_date='2002-01-01',freq='d')                              
  startD = stgSim.date_to_index(v4.start_date) 
  stgSim = stgSim[startD:]
  stgSimAnMonth = stgSim.convert(freq='m',func=np.ma.mean)
  </code></pre>