title: Convert a list of tuples into a dataframe in pandas
body: <p>I have a list of tuples(y) that I wish to convert to a DataFrame x. There are five tuples in y. Each tuple in y has 33 elements. Element 1 in all 5 tuples is text and is the same. Element two in all five tuples is text and is the same. Element three in each tuple is text and is the same. </p>
  
  <p>I'd like to first three elements in y to be the column names in the DataFrame. I want to convert the list of tuples into a 10 x 3 DataFrame. The tricky part is row 1 in the dataframe would be elements 4,5,6 in y[1], row 2 in the dataframe would be elements 7,8,9 in y[1], row 3 would be 10,11,12...etc.</p>
  
  <p>y looks like this (not showing the entire list) :</p>
  
  
  
  <pre class="lang-none prettyprint-override"><code>List of tuples y                
  y[0]    y[1]    y[2]    y[3]    y[4]
  
  Formula Formula Formula Formula Formula
  Phase   Phase   Phase   Phase   Phase
  Value   Value   Value   Value   Value
  "a" "a" "a" "a" "a"
  "nxxx"  "nxxx"  "nxxx"  "nxxx"  "nxxx"
  3.2 3.7 22.4    18.2    9.7
  "h45"   "h45"   "h45"   "h45"   "h45"
  "cacpp" "cacpp" "cacpp" "cacpp" "cacpp"
  45.2    61.76   101.2   171.89  203.7
  "trx"   "trx"   "trx"   "trx"   "trx"
  "v2o5p" "v2o5p" "v2o5p" "v2o5p" "v2o5p"
  0.24    0.81    0.97    1.2 1.98
  "blnt"  "blnt"  "blnt"  "blnt"  "blnt"
  "g2o3"  "g2o3"  "g2o3"  "g2o3"  "g2o3"
  807.2   905.8   10089   10345   10979
  </code></pre>
  
  <p>I want to convert y into DataFrame x as follows:</p>
  
  <pre class="lang-none prettyprint-override"><code>DataFrame x     
  column 1 column 2 column 3
  
  Formula Phase   Value
  "a" "nxxx"  3.2
  "h45"   "cacpp" 45.2
  "trx"   "v2o5p" 0.24
  "blnt"  "g2o3"  807.2
  "a" "nxxx"  3.7
  "h45"   "cacpp" 61.76
  "trx"   "v2o5p" 0.81
  "blnt"  "g2o3"  905.8
  "a" "nxxx"  22.4
  "h45"   "cacpp" 101.2
  "trx"   "v2o5p" 0.97
  "blnt"  "g2o3"  10089
  etc etc etc
  </code></pre>
  
  <p>I know there must be an easy way to iterate through the list of tuples. But new to Pandas and relatively new to Python so I'm struggling with a clean way to do this.</p>