Be the following pandas DataFrame in Python:
| Hours | 2022-06-06 | 2022-06-07 | 2022-06-08 | 2022-06-09 | 2022-06-10 | 2022-06-13 | 2022-06-14 | 2022-06-15 | 2022-06-16 | 2022-06-17 |
|---|---|---|---|---|---|---|---|---|---|---|
| 00:00 | 3 | 0 | 0 | 3 | 23 | 2 | 3 | 3 | 7 | 3 |
| 05:00 | 5 | 4 | 0 | 3 | 32 | 9 | 3 | 3 | 5 | 3 |
| 10:00 | 0 | 3 | 3 | 34 | 45 | 6 | 3 | 0 | 3 | 3 |
| 15:00 | 10 | 31 | 10 | 3 | 53 | 3 | 3 | 3 | 5 | 3 |
| 20:00 | 20 | 33 | 33 | 3 | 86 | 3 | 3 | 21 | 3 | 3 |
| 23:00 | 31 | 34 | 45 | 63 | 43 | 12 | 1 | 0 | 2 | 5 |
I want to get a new DataFrame containing the arithmetic mean of each row, for each column containing a date. Example:
| Hours | avg_count |
|---|---|
| 00:00 | 4.7 |
| 05:00 | 6.7 |
| 10:00 | 10 |
| 15:00 | 12.4 |
| 20:00 | 20.8 |
| 23:00 | 23.6 |
Finally I want to round to an integer value:
| Hours | avg_count |
|---|---|
| 00:00 | 5 |
| 05:00 | 7 |
| 10:00 | 10 |
| 15:00 | 12 |
| 20:00 | 21 |
| 23:00 | 24 |
CodePudding user response：
Use:
df1 = df.set_index('Hour').mean(axis=1).round(0).astype(int).reset_index(name='avg_count')