title: Why does "numpy.mean" return 'inf'?
body: <p>I need to calculate the mean in columns of an array with more than 1000 rows.</p>
  
  <p><code>np.mean(some_array)</code> gives me 
  <code>inf</code> as output</p>
  
  <p>but i am pretty sure the values are ok. I am loading a csv from <a href="http://www.bliasoft.com/Documents/DataConcrete.txt" rel="noreferrer">here</a> into my <code>Data</code> variable and column 'cement' is "healthy" from my point of view.</p>
  
  <pre><code>In[254]:np.mean(Data[:230]['Cement'])
  Out[254]:275.75
  </code></pre>
  
  <p>but if I increase the number of rows
  the problem starts:</p>
  
  <pre><code>In [259]:np.mean(Data[:237]['Cement'])
  Out[259]:inf
  </code></pre>
  
  <p>but when i look at the Data</p>
  
  <pre><code>In [261]:Data[230:237]['Cement']
  Out[261]:
   array([[ 425. ],
          [ 333.  ],
          [ 250.25],
          [ 491.  ],
          [ 160.  ],
          [ 229.75],
          [ 338.  ]], dtype=float16)
  </code></pre>
  
  <p>i do not find a reason for this behaviour
  P.S This happens in Python 3.x using wakari (cloud based Ipython)</p>
  
  <p>Numpy Version '1.8.1'</p>
  
  <p>I am loading the Data with:</p>
  
  <pre><code>No_Col=9
  conv = lambda valstr: float(valstr.replace(',','.'))
  
  c={}
  for i in range(0,No_Col,1):
      c[i] = conv
  
  Data=np.genfromtxt(get_data,dtype=float16 , delimiter='\t', skip_header=0, names=True,   converters=c)
  </code></pre>