title: Plotting time-series data with seaborn
body: <p>Say I create a fully random <code>Dataframe</code> using the following:</p>
  
  <pre><code>from pandas.util import testing
  from random import randrange
  
  def random_date(start, end):
      delta = end - start
      int_delta = (delta.days * 24 * 60 * 60) + delta.seconds
      random_second = randrange(int_delta)
      return start + timedelta(seconds=random_second)
  
  def rand_dataframe():
    df = testing.makeDataFrame()
    df['date'] = [random_date(datetime.date(2014,3,18),datetime.date(2014,4,1)) for x in xrange(df.shape[0])]
    df.sort(columns=['date'], inplace=True)      
    return df
  
  df = rand_dataframe()
  </code></pre>
  
  <p>which results in the dataframe shown at the bottom of this post. I would like to plot my columns <code>A</code>, <code>B</code>, <code>C</code> and <code>D</code> using the <a href="http://www.stanford.edu/~mwaskom/software/seaborn/examples/timeseries_from_dataframe.html" rel="noreferrer">timeseries</a> visualization features in <code>seaborn</code> so that I get something along these lines:</p>
  
  <p><img src="https://i.stack.imgur.com/pDYIh.png" alt="enter image description here"></p>
  
  <p>How can I approach this problem? From what I read on <a href="http://www.stanford.edu/~mwaskom/software/seaborn/timeseries_plots.html" rel="noreferrer">this notebook</a>, the call should be:</p>
  
  <pre><code>sns.tsplot(df, time="time", unit="unit", condition="condition", value="value")
  </code></pre>
  
  <p>but this seems to require that the dataframe is represented in a different way, with the columns somehow encoding <code>time</code>, <code>unit</code>, <code>condition</code> and <code>value</code>, which is not my case. How can I convert my dataframe (shown below) into this format?</p>
  
  <p>Here is my dataframe:</p>
  
  <pre><code>      date         A         B         C         D
  
  2014-03-18  1.223777  0.356887  1.201624  1.968612
  2014-03-18  0.160730  1.888415  0.306334  0.203939
  2014-03-18 -0.203101 -0.161298  2.426540  0.056791
  2014-03-18 -1.350102  0.990093  0.495406  0.036215
  2014-03-18 -1.862960  2.673009 -0.545336 -0.925385
  2014-03-19  0.238281  0.468102 -0.150869  0.955069
  2014-03-20  1.575317  0.811892  0.198165  1.117805
  2014-03-20  0.822698 -0.398840 -1.277511  0.811691
  2014-03-20  2.143201 -0.827853 -0.989221  1.088297
  2014-03-20  0.299331  1.144311 -0.387854  0.209612
  2014-03-20  1.284111 -0.470287 -0.172949 -0.792020
  2014-03-22  1.031994  1.059394  0.037627  0.101246
  2014-03-22  0.889149  0.724618  0.459405  1.023127
  2014-03-23 -1.136320 -0.396265 -1.833737  1.478656
  2014-03-23 -0.740400 -0.644395 -1.221330  0.321805
  2014-03-23 -0.443021 -0.172013  0.020392 -2.368532
  2014-03-23  1.063545  0.039607  1.673722  1.707222
  2014-03-24  0.865192 -0.036810 -1.162648  0.947431
  2014-03-24 -1.671451  0.979238 -0.701093 -1.204192
  2014-03-26 -1.903534 -1.550349  0.267547 -0.585541
  2014-03-27  2.515671 -0.271228 -1.993744 -0.671797
  2014-03-27  1.728133 -0.423410 -0.620908  1.430503
  2014-03-28 -1.446037 -0.229452 -0.996486  0.120554
  2014-03-28 -0.664443 -0.665207  0.512771  0.066071
  2014-03-29 -1.093379 -0.936449 -0.930999  0.389743
  2014-03-29  1.205712 -0.356070 -0.595944  0.702238
  2014-03-29 -1.069506  0.358093  1.217409 -2.286798
  2014-03-29  2.441311  1.391739 -0.838139  0.226026
  2014-03-31  1.471447 -0.987615  0.201999  1.228070
  2014-03-31 -0.050524  0.539846  0.133359 -0.833252
  </code></pre>
  
  <p>In the end, what I am looking for is an overlay of of plots (one per column), where each of them looks as follows (note that different values of CI get different values of alphas):</p>
  
  <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <img src="https://i.stack.imgur.com/5AsIv.png" alt="enter image description here"></p>