title: Compare Sales Month Over Month in Python?
body: <p>df</p>
  
  <pre><code>  Food_Id    Month_yr       Qty   Sales
  0     1       November_18     5    1920
  1     2       November_18     6    2850
  2     2       November_18     8    3852
  3     1       November_18     6    1920
  4     2       November_18     7    2650
  5     1       November_18     2    3952
  6     1       November_18     3    1320
  7     2       November_18     8    2650
  8     1       November_18     9    3152
  9     1       December_18     5    1920
  10    2       December_18     6    2150
  11    2       December_18     8    3852
  13    1       December_18     6    4920
  14    2       December_18     6    3690
  15    2       December_18     2    8952
  16    1       December_18     7    7340
  17    1       December_18     4    3650
  18    2       December_18     9    8152
  19    1       January_19      5    1920
  20    2       January_19      6    8150
  21    2       January_19      8    3852
  22    1       January_19      1    3920
  23    2       January_19      3    2690
  24    2       January_19      2    8952
  25    1       January_19      2    7340
  26    1       January_19      4    5630
  27    2       January_19      7    6152
  </code></pre>
  
  <p>i have got a big dataset around 2gb, what i need to do is to compare sales of each food_id every month.
  if the  sales difference is 1000 for a particular food id from this month to next month then mark that month.</p>
  
  <p>Output</p>
  
  <pre><code>   Food_Id    Month_yr      Sales   diff_frm_lst_month  Flag
  0    1        November_18   12264      Null             Null
  1    2        November_18   12002      Null             Null
  2    1        December_18   17830      5566            more than 1000
  3    2        December_18   26794      14792           more than 1000
  4    1        January_18    18800      970             less than 1000
  5    2        January_18    29796      3002            more than 1000
  </code></pre>
  
  <p>since the data is big, please mention how to handle heavy data.</p>