title: Replace a row in a pandas DataFrame with a dict item based on a unique column value
body: <p>Assume that you have a pandas dataframe as follows.</p>
  <pre><code>df = pd.DataFrame([[100,1000111,1.25,10,1,-1],
      [200,1000115,2.2,20,2,-2],
      [300,1000311,3.2,30,3,-3],
      [400,1000351,8.7,90,7,-1]],
      columns=['a','unique','c','d','e','f'])
  </code></pre>
  <p>Which looks like this:</p>
  <pre><code>     a   unique     c   d  e  f
   0  100  1000111  1.25  10  1 -1
   1  200  1000115  2.20  20  2 -2
   2  300  1000311  3.20  30  3 -3
   3  400  1000351  8.70  90  7 -1
  </code></pre>
  <p>Now you have a dictionary like this:</p>
  <pre><code>my_dtnry = {'a':50, 'unique':1000115, 'c':700, 'd':1, 'e':-10, 'f':3}
  </code></pre>
  <p>We focus on the <strong>unique</strong> column and seek the unique value from the dictionary. In this example the unique value is 1000115 and exists in the second row (ie. row 1).</p>
  <p>So the question is, how can one easily [1] <strong>find the correct row</strong> and then [2] <strong>replace all the values</strong> of row 1 with the dict values ?</p>
  <p>So the desired output would be:</p>
  <pre><code>     a   unique     c    d   e  f
    0  100  1000111  1.25  10  1 -1
    1  50   1000115  700   1  -10 3   # &lt;- this row replaced with the dict
    2  300  1000311  3.20  30  3 -3
    3  400  1000351  8.70  90  7 -1
  </code></pre>
  <p>In particular, is there a specific (known standard pythonic) method that would do this efficiently as opposed to iterating the rows and columns?</p>