I have a pandas dataframe.
| id | Score | Action_flag |
|---|---|---|
| S11 | 0.585366 | Not Traded |
| P555 | 0.457778 | Not Traded |
| B28 | 0.636154 | Not Traded |
| A859 | 0.000000 | Traded |
| P556 | 0.761905 | Not Traded |
| Y461 | 0.333333 | Not Traded |
| S121 | 0.444444 | Not Traded |
| K481 | 0.000000 | Traded |
| S122 | 1.000000 | Not Traded |
| R556 | 0.000000 | Traded |
| R627 | 0.602778 | Traded |
In excel using pivot table and grouping I was able to do this.
How to achieve this using pandas
And pivot table 
>Solution :
IIUC, you could create bins using pd.cut, then use crosstab to get the counts each each action flag. Finally, assign the row-wise totals to a new column "Grand_total":
out = pd.crosstab(pd.cut(df['Score'], np.linspace(0,1,21), include_lowest=True), df['Action_flag']).assign(Grand_total=lambda x: x.sum(axis=1))
Output:
Action_flag Not Traded Traded Grand_total
Score
(-0.001, 0.05] 0 3 3
(0.3, 0.35] 1 0 1
(0.4, 0.45] 1 0 1
(0.45, 0.5] 1 0 1
(0.55, 0.6] 1 0 1
(0.6, 0.65] 1 1 2
(0.75, 0.8] 1 0 1
(0.95, 1.0] 1 0 1