title: How to read binary files in Python using NumPy?
body: <p>I know how to read binary files in Python using NumPy's <code>np.fromfile()</code> function. The issue I'm faced with is that when I do so, the array has exceedingly large numbers of the order of 10^100 or so, with random <code>nan</code> and <code>inf</code> values. </p>
  
  <p>I need to apply machine learning algorithms to this dataset and I cannot work with this data. I cannot normalise the dataset because of the <code>nan</code> values.</p>
  
  <p>I've tried <code>np.nan_to_num()</code> but that doesn't seem to work. After doing so, my min and max values range from 3e-38 and 3e+38 respectively, so I could not normalize it.</p>
  
  <p>Is there any way to scale this data down? If not, how should I deal with this?</p>
  
  <p>Thank you.</p>
  
  <p><strong>EDIT:</strong></p>
  
  <p>Some context. I'm working on a malware classification problem. My dataset consists of live malware binaries. They are files of the type .exe, .apk etc. My idea is store these binaries as a numpy array, convert to a grayscale image and then perform pattern analysis on it. </p>