title: How to arrange numbers creating new columns in pandas
body: <h1>Example dataset in pandas</h1>
  <p>Below is a dataset with 3 columns.</p>
  <pre><code>c1 c2 c3
  1  2   0
  3  4   0
  5  6   1
  7  8   1
  </code></pre>
  <p>Now I want to arrange above dataset in such a way that the numbers <strong>[3,4] and [7,8]</strong> should fall under the columns <strong>c3 and c4</strong>.</p>
  <pre><code>c1 c2 c3 c4 c5
  1  2  3  4  0
  5  6  7  8  1
  </code></pre>
  <p><strong>Code for the dataset,</strong></p>
  <pre><code>data = {'c1':[1,2,4,6], 'c2':[2,3,5,7], 'c3':[0,0,1,1]}
  data = pd.DataFrame(data)
  </code></pre>
  <h3>Excercise</h3>
  <p>Assume there are 1000 such rows with classes [0 or 1] for each row.<br>
  How do you arrange numbers in every 2 rows in columns c3 and c4?</p>
  <pre><code>df = {'c1':[1,2,4,6,15,143,114,104,80,\
              89,100,104,70,99,70,46], 'c2':[2,3,5,7,85,80,\
              89,100,104,70,123,43,32,90,123,87], 'c3':[0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1]}
  
       c1   c2  c3
  0     1    2   0
  1     2    3   0
  2     4    5   0
  3     6    7   0
  4    15   85   0
  5   143  109   0
  6   114   80   0
  7   104   89   0
  9    80  104   1
  10   89   70   1
  11  100  123   1
  12  104   43   1
  13   70   32   1
  14   99   90   1
  15   70  101   1
  16   46  123   1
  </code></pre>
  <h3>Excercise 2</h3>
  <p>Assign classes accordingly.</p>
  <pre><code>df = pd.DataFrame(df)
  a = df[['c1','c2']].to_numpy().reshape(8, -1)
  
  Expected output :-
          c1  c2  c3  c4  c5
           1   2   2   3  0
           4   5   6   7  0
          15  85 143  109 0
         114  80 104   89 0
         109 100  80  104 1
          89  70 100  123 1
         104  43 70   32  1
          99  90 70   101 1
  </code></pre>
  <h2>Excercise 3</h2>
  <p>Increasing columns<br></p>
  <pre><code>Expected output :-
          c1  c2  c3  c4  c5  c6  c7   c8  c9
           1   2   2   3  4   5   6    7    0
           
          15  85 143  109 114  80 104  89   0
         
         109 100  80  104 89  70  100  123  1
          
         104  43 70   32  99  90   70  101  1 
                     
  </code></pre>