title: Mean and Standard deviation across multiple arrays using numpy
body: <p>I’m importing 2-D matrix data for a multi year climate time series testing on a 5 year annual dataset. I’ve created a for loop to import the 2D matrix data by year into a series of 5 separate arrays of size (1500, 3600). I append the matrix time series data into a single combined (5, 1500, 3600) array with each year being one dimension in the array. I then run the np.mean and np.std to create (1500, 3600) matrices calculating the 5 year mean and stddev of the data at each matrix point. Code is below. The numbers look to be coming out correctly when I test this but I would like to know ..</p>
  
  <p>Is there a faster way to do this? I will eventually need to run this type of analysis for daily data over an 18 time span which would be building and operating on a (6570, 1500, 3600) array. Any suggestions? I’m fairly new to Python and still finding my way. </p>
  
  <pre><code>StartYear=2009
  EndYear=2014
  for x in range(StartYear, EndYear):
      name = "/dir/climate_variable" + str(x) + ".gz"
      Q_WBM = rg.grid(name)
      Q_WBM.Load()
      q_wbm = Q_WBM.Data  # .flatten()
      q_wbm[np.isnan(q_wbm)] = 0
      if x == StartYear:
          QTS_array = q_wbm
      else:
          QTS_array = np.append(QTS_array, q_wbm, axis=0)
  
  DischargeMEAN = np.mean(QTS_array, axis=0)
  DischargeSTD = np.std(QTS_array, axis=0)
  </code></pre>