title: Normal Distribution Plot by name from pandas dataframe
body: <p>I have a dataframe like below:</p>
  
  <pre><code>dateTime        Name    DateTime        day seconds zscore
  11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
  11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
  11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
  11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
  11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
  11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
  11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
  11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
  11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
  11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
  11/1/2016 15:17 james   11/1/2016 15:17 Tue 55020   1.158266091
  11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
  11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
  11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
  11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
  11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
  11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
  11/1/2016 13:41 james   11/1/2016 13:41 Tue 49260   -0.836236954
  11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
  11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
  11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
  11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
  11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
  11/1/2016 13:42 james   11/1/2016 13:42 Tue 49320   -0.81546088
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:07  matt    11/1/2016 9:07  Tue 32820   -0.223746683
  11/1/2016 9:08  matt    11/1/2016 9:08  Tue 32880   -0.111873342
  11/1/2016 9:48  matt    11/1/2016 9:48  Tue 35280   4.363060322
  </code></pre>
  
  <p>zscore is calculated as below:</p>
  
  <pre><code>grp2 = df.groupby(['Name'])['seconds']
  df['zscore'] = grp2.transform(lambda x: (x - x.mean()) / x.std(ddof=1))
  </code></pre>
  
  <p>I would like to plot my data in a bell curve / normal distribution plot and save this as a picture/pdf file for each Name in my dataframe. </p>
  
  <p>I have tried to plot the zscores like below:</p>
  
  <pre><code>df['by_name'].plot(kind='hist', normed=True)
  range = np.arange(-7, 7, 0.001)
  plt.plot(range, norm.pdf(range,0,1))
  plt.show()
  </code></pre>
  
  <p>How would I go about plotting the by_name zscores column for each name in my data? </p>