title: Cycling through a dictionary of dataframes for values above a threshold
body: <p>I am trying to set up a way to cycle though a 100 element dictionary, where each element is a 42,000 row dataframe and check whether the value is above a threshold</p>
  
  <p>I am having problems working out how to store the row data so it is not overwritten.<br>
  I've made a simple example of what I would like to do: </p>
  
  <p>I have a three element dictionary (my_dic) where each element is a dataframe 
  I want to cycle through each row of every dataframe and check if any of the columns are above a threshold number.
  I have been trying to use .any() and .where but I dont know how to capture the data for each df separately.</p>
  
  <p>I would like to end up with three separate new df that have boolean values in each column that is above the threshold.</p>
  
  <p>Any help would be great!</p>
  
  <pre><code>d1 = np.random.rand(3,3)
  d2 = np.random.rand(3,3)
  d3 = np.random.rand(3,3)
  
  df1 =pd.DataFrame(d1, index=['a', 'b', 'c'])
  df2 =pd.DataFrame(d2, index=['a', 'b', 'c'])
  df3 =pd.DataFrame(d3, index=['a', 'b', 'c'])
  
  my_dic = {}
  my_dic['a'] = df1   
  my_dic['b'] = df2  
  my_dic['c'] = df3   
  
  threshold = 0.5
  </code></pre>
  
  <p>I want to cycle through every row of each of the keys in my_dic and change the value to booleans if it is greater than a threshold number</p>
  
  <pre><code>for k in my_dic:
      print k
      data = my_dic[k]
      for row in range(len(data)):
          print row
          np.where(data.iloc[row,:] &gt; threshold)
  </code></pre>
  
  <p>This is the point I am struggling with, Im not sure how to keep this data, without it being overwritten        </p>