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 
CodePudding user response：
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