title: nlargest(2) at row level to retrieve columns names & equal values
body: <p>I have this example df:</p>
  <pre><code>df = pd.DataFrame({
                    'Name': ['Sara', 'Adam','Ciara', 'John','Paul'],
                    'UK': [1,2,0,4,2],
                    'US': [1,3,0,1,2],
                    'IN': [1,5,0,1,0],
                    'CA': [0,0,0,7,1],
                    'JP': [0,0,0,0,1]
                     })
  
  df
  </code></pre>
  <p>Gives:</p>
  <pre><code>    Name    UK  US  IN  CA  JP
  0   Sara    1   1   1   0   0
  1   Adam    2   3   5   0   0
  2   Ciara   0   0   0   0   0
  3   John    4   1   1   7   0
  4   Paul    2   2   0   1   1
  </code></pre>
  <p>I would like to get the top 2 visited countries (Columns names) for each row, some rows have all <code>0</code> and others could have equal numbers <code>1,1,1</code></p>
  <p>I already started to get some results but the output was not as expected:</p>
  <pre><code>io = df[['UK','US','IN','CA','JP']] #getting only countries columns as a df
  </code></pre>
  <p>Then</p>
  <pre><code>pd.DataFrame({n: io.T[col].astype(int).nlargest(2).index.tolist() for n, col in enumerate(io.T)}).T.join(df)
  </code></pre>
  <p>This is what I got:</p>
  <pre><code>    0   1   Name    UK  US  IN  CA  JP
  0   UK  US  Sara    1   1   1   0   0
  1   IN  US  Adam    2   3   5   0   0
  2   UK  US  Ciara   0   0   0   0   0
  3   CA  UK  John    4   1   1   7   0
  4   UK  US  Paul    2   2   0   1   1
  </code></pre>
  <p>For the first row <code>UK,US,IN</code> columns all have equal values, so I am confused how to handle equal results and how nlargest(2) selected <code>US,UK</code> and not <code>IN</code>?</p>
  <p>Since nlargest(2) would not be applicable for ties, I would like to include all the columns names</p>
  <p>for the Third row with all <code>0,0,0,0,0</code> it still shows <code>UK,US</code> I tried to convert all 0 to NaN but got lots of errors</p>
  <p>here is an example of the expected output - the first row is an example of ties:</p>
  <pre><code>    0   1   2       Name    UK  US  IN  CA  JP
  0   UK  US  IN      Sara    1   1   1   0   0
  1   IN  US  0       Adam    2   3   5   0   0
  2   0   0   0       Ciara   0   0   0   0   0
  3   CA  UK  0       John    4   1   1   7   0
  4   UK  US  0       Paul    2   2   0   1   1
  </code></pre>