title: Pandas: How to find a particular pattern in a dataframe column?
body: <p>I'd like to find a particular pattern in a pandas dataframe column, and return the corresponding index values in order to subset the dataframe.</p>
  
  <p>Here's a sample dataframe with a possible pattern:</p>
  
  <p><strong>Snippet to produce dataframe:</strong></p>
  
  <pre><code>import pandas as pd
  import numpy as np
  
  Observations = 10
  Columns = 2
  np.random.seed(123)
  df = pd.DataFrame(np.random.randint(90,110,size=(Observations, Columns)),
                    columns = ['ColA','ColB'])
  datelist = pd.date_range(pd.datetime(2017, 7, 7).strftime('%Y-%m-%d'),
                           periods=Observations).tolist()
  df['Dates'] = datelist
  df = df.set_index(['Dates'])
  
  pattern = [100,90,105]
  print(df)
  </code></pre>
  
  <p><strong>Dataframe:</strong>  </p>
  
  <pre><code>            ColA  ColB
  Dates                 
  2017-07-07   103    92
  2017-07-08    92    96
  2017-07-09   107   109
  2017-07-10   100    91
  2017-07-11    90   107
  2017-07-12   105    99
  2017-07-13    90   104
  2017-07-14    90   105
  2017-07-15   109   104
  2017-07-16    94    90
  </code></pre>
  
  <p>Here, the pattern of interest occurs in <code>Column A</code> on the dates <code>2017-07-10</code> to <code>2017-07-12</code>, and that's what I'd like to end up with:</p>
  
  <p><strong>Desired output:</strong></p>
  
  <pre><code>2017-07-10   100    91
  2017-07-11    90   107
  2017-07-12   105    99
  </code></pre>
  
  <p>If the same pattern occurs several times, I would like to subset the dataframe the same way, and also count how many times the pattern occurs, but I hope that's more straight forward as long as I get the first step sorted out.</p>
  
  <p>Thank you for any suggestions!</p>