title: Convert a Pandas DataFrame to bin frequencies
body: <p>using pandas, i know how to bin a single column but i'm struggling to figure how to do multiple columns  and then find a count (frequency) of the bins, as my dataframe has 20 columns. I know I could do the method I use for a single column 20 times, but I'm interested in learning a new better method.  here is head of the dataframe with 4 columns showing:</p>
  
  <pre><code>      Percentile1 Percentile2 Percentile3   Percentile4
  395     0.166667    0.266667    0.266667    0.133333
  424     0.266667    0.266667    0.133333    0.032258
  511     0.032258    0.129032    0.129032    0.387097
  540     0.129032    0.129032    0.387097    0.612903
  570     0.129032    0.387097    0.612903    0.741935
  </code></pre>
  
  <p>i created the following bin array</p>
  
  <pre><code>output = ['0-10','10-20','20-30','30-40','40-50','50-60','60-70','70-80','80-90','90-100']
  </code></pre>
  
  <p>here is my desired output:</p>
  
  <pre><code>      Percentile1 Percentile2 Percentile3   Percentile4
  395     10-20        20-30      20-30           10-20
  424     20-30        20-30      10-20           0-10
  511     0-10         10-20      10-20           30-40
  540     10-20        10-20      30-40           60-70
  570     10-20        30-40      60-70           70-80
  </code></pre>
  
  <p>after this i would ideally do a frequency/value count to get something like this:</p>
  
  <pre><code>      Percentile1 Percentile2 Percentile3   Percentile4
  0-10    frequency #'s        
  10-20   
  20-30   
  30-40   
  40-50   
  etc...
  </code></pre>
  
  <p>any help would be greatly appreciated</p>