title: Group items in a data frame using a conditions
body: <pre><code>| ID | CUSTOMER_ID | LAST_TRAN_DATE | is_active | NO_OF_ACC |  |
  |----|-------------|----------------|-----------|-----------|--|
  |    |             |                |           |           |  |
  |  1 |           1 | 3-Apr-15       |         0 |         5 |  |
  |  2 |           2 | 26-Mar-04      |         0 |         4 |  |
  |  3 |           2 | 25-Jul-14      |         0 |         4 |  |
  |  4 |           2 | 3-Jan-13       |         0 |         4 |  |
  |  5 |           2 | 28-Jun-13      |         0 |         4 |  |
  |  6 |           3 | 19-Nov-08      |         0 |         3 |  |
  |  7 |           3 | 21-May-09      |         0 |         3 |  |
  |  8 |           3 | 24-Feb-12      |         0 |         3 |  |
  |  9 |           1 | 1-Jun-16       |         0 |         5 |  |
  | 10 |           1 | 8-Apr-19       |         1 |         5 |  |
  | 11 |           1 | 25-Nov-17      |         0 |         5 |  |
  | 12 |           1 | 22-Feb-19      |         1 |         5 |  |
  
  </code></pre>
  <p>My data is like above and I want to calculate no of active accounts for each customer id, create a new column and display them in front of each row.</p>
  <p>I used</p>
  <pre><code>df.groupby(['CUSTOMER_ID', 'is_active']).size()
  
  </code></pre>
  <p>which gave me the following result.</p>
  <pre><code>| CUSTOMER_ID  | is_active |      |
  |--------------|-----------|------|
  | 1            |         0 |    3 |
  |              |         1 |    2 |
  | 2            |         0 |    4 |
  | 3            |         0 |    3 |
  | dtype: int64 |           |      |
  
  </code></pre>
  <p>But I have no idea how to map them in front of each row by creating a new column.</p>
  <p>Please help me</p>