title: Delete group if NaN is present anywhere in multiple columns
body: <p>I am trying to clean my dataframe such that if my "Base_2007" and "Base_2011" column contains NA, then I should completely drop that county. In my case since both Counties contains NA both of them will be dropped. Thus empty dataset will be returned. Is it possible to do something like this? </p>
  
  <p>Data:</p>
  
  <pre><code>  State  Year  Base_2007  Base_2011           County
  0    AL  2012        NaN       14.0  Alabama_Country
  1    AL  2013       12.0       20.0  Alabama_Country
  2    AL  2014       13.0        NaN  Alabama_Country
  3    DC  2011        NaN       20.0          Trenton
  4    DC  2012       19.0        NaN          Trenton
  5    DC  2013       20.0       21.0          Trenton
  6    DC  2014       25.0       30.0          Trenton
  </code></pre>
  
  <p>Tail section of dataframe:</p>
  
  <pre><code>{'State': {82550: 'WY', 82551: 'WY', 82552: 'WY', 82553: 'WY', 82554: 'WY', 82555: 'WY', 82556: 'WY', 82557: 'WY', 82558: 'WY', 82559: 'WY'}, 'County': {82550: 'Weston', 82551: 'Weston', 82552: 'Weston', 82553: 'Weston', 82554: 'Weston', 82555: 'Weston', 82556: 'Weston', 82557: 'Weston', 82558: 'Weston', 82559: 'Weston'}, 'FIPS code': {82550: 56045, 82551: 56045, 82552: 56045, 82553: 56045, 82554: 56045, 82555: 56045, 82556: 56045, 82557: 56045, 82558: 56045, 82559: 56045}, 'Year': {82550: 2008, 82551: 2009, 82552: 2010, 82553: 2011, 82554: 2012, 82555: 2013, 82556: 2014, 82557: 2015, 82558: 2016, 82559: 2017}, 'Annual_pct_change': {82550: 6.52, 82551: -2.93, 82552: -5.61, 82553: 1.9, 82554: 5.16, 82555: -4.03, 82556: 7.69, 82557: -2.35, 82558: 1.67, 82559: 5.56}, 'HPI': {82550: 195.73, 82551: 189.99, 82552: 179.33, 82553: 182.73, 82554: 192.15, 82555: 184.4, 82556: 198.58, 82557: 193.9, 82558: 197.14, 82559: 208.11}, 'HPI1990': {82550: nan, 82551: nan, 82552: nan, 82553: nan, 82554: nan, 82555: nan, 82556: nan, 82557: nan, 82558: nan, 82559: nan}, 'HPI2000': {82550: 190.09, 82551: 184.51, 82552: 174.16, 82553: 177.46, 82554: 186.61, 82555: 179.08, 82556: 192.86, 82557: 188.31, 82558: 191.46, 82559: 202.11}, 'CountyName': {82550: 'Weston County', 82551: 'Weston County', 82552: 'Weston County', 82553: 'Weston County', 82554: 'Weston County', 82555: 'Weston County', 82556: 'Weston County', 82557: 'Weston County', 82558: 'Weston County', 82559: 'Weston County'}}
  </code></pre>
  
  <p>Head Section of DataFrame:</p>
  
  <pre><code>{'State': {0: 'AL', 1: 'AL', 2: 'AL', 3: 'AL', 4: 'AL', 5: 'AL', 6: 'AL', 7: 'AL', 8: 'AL', 9: 'AL'}, 'County': {0: 'Autauga', 1: 'Autauga', 2: 'Autauga', 3: 'Autauga', 4: 'Autauga', 5: 'Autauga', 6: 'Autauga', 7: 'Autauga', 8: 'Autauga', 9: 'Autauga'}, 'FIPS code': {0: 1001, 1: 1001, 2: 1001, 3: 1001, 4: 1001, 5: 1001, 6: 1001, 7: 1001, 8: 1001, 9: 1001}, 'Year': {0: 1986, 1: 1987, 2: 1988, 3: 1989, 4: 1990, 5: 1991, 6: 1992, 7: 1993, 8: 1994, 9: 1995}, 'Annual_pct_change': {0: nan, 1: -2.17, 2: 3.24, 3: 4.16, 4: -0.35, 5: 2.69, 6: 2.85, 7: 3.34, 8: 4.33, 9: 3.48}, 'HPI': {0: 100.0, 1: 97.83, 2: 100.99, 3: 105.19, 4: 104.82, 5: 107.64, 6: 110.7, 7: 114.4, 8: 119.35, 9: 123.5}, 'HPI1990': {0: 95.4, 1: 93.33, 2: 96.35, 3: 100.36, 4: 100.0, 5: 102.69, 6: 105.61, 7: 109.14, 8: 113.86, 9: 117.82}, 'HPI2000': {0: 71.03, 1: 69.49, 2: 71.74, 3: 74.72, 4: 74.45, 5: 76.46, 6: 78.63, 7: 81.26, 8: 84.77, 9: 87.72}, 'CountyName': {0: 'Autauga County', 1: 'Autauga County', 2: 'Autauga County', 3: 'Autauga County', 4: 'Autauga County', 5: 'Autauga County', 6: 'Autauga County', 7: 'Autauga County', 8: 'Autauga County', 9: 'Autauga County'}}
  </code></pre>
  
  <p>Note: In above <code>dput</code> Base_2007= HPI1990, BASE_2011=HPI2000</p>