title: Cant use numpy.ndarray or pandas groupby to create individual plots
body: <p>My purpose is to plot measurement data (var1, var2,Timestamp) from each locations (ID) in a large dataset. My data:</p>
  
  <pre><code>ID  var1    var2    TimeStamp
  AA  2       5    8/8/2010 0:00
  AA  2.5     5    1/1/2010 0:00
  B7  1.1     7    1/9/2010 0:00
  B7  2       6    1/8/2010 0:00
  B7  3       7    1/1/2010 0:00
  B7  3       6    1/9/2010 0:00
  C0  4       7    1/8/1900 0:00
  ...
  </code></pre>
  
  <p>However, as shown, the number of data (or rows) for each location ID varies (usually varies between 3-30)
  To plot variables for each respective location, i'm unsure on how to proceed: 
  from what I've experienced so far, something similar to the following: </p>
  
  <pre><code>np.array(data).reshape(500,20,15)
  </code></pre>
  
  <p>that creates a numpy ndarray by np.reshape of the original data would not let me partition the array into individual locations because of irregular shape of data. subsequently, using Pands groupby:</p>
  
  <pre><code>data_df.groupby('ID').plot('var1','var2')
  </code></pre>
  
  <p>Instantly plots graphs for var1, var2 for each location ID but does not give opportunity to iterate over each graph to create settings like labels / axis / limits etc. (I dont know yet how to iterate over pandas groupby objects to create and manage individual plots)</p>
  
  <p><strong>tldr:</strong></p>
  
  <p>Any nice way to create <code>np.ndarray</code> from irregular shaped data (as shown above) based on a criteria (for each ID)?</p>
  
  <p>Any way to iterate and control over pandas <code>dataframe.groupby</code> object to plot (not just talking about summarized mean/ min /max etc) variables ?</p>