Let it be the following Python Panda Dataframe (the original could include dates for several months):
| Hours | 2022-06-06 | 2022-06-07 | 2022-06-08 | 2022-06-09 | 2022-06-10 | 2022-06-11 | 2022-06-12 | 2022-06-13 | 2022-06-14 | 2022-06-15 | 2022-06-16 | 2022-06-17 | 2022-06-18 | 2022-06-19 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00:00 | 3 | 0 | 0 | 3 | 23 | 43 | 1 | 2 | 3 | 3 | 7 | 3 | 1 | 0 |
| 05:00 | 5 | 4 | 0 | 3 | 32 | 31 | 3 | 9 | 3 | 3 | 5 | 3 | 0 | 3 |
| 10:00 | 0 | 3 | 3 | 34 | 45 | 63 | 43 | 6 | 3 | 0 | 3 | 3 | 0 | 1 |
| 15:00 | 10 | 31 | 10 | 3 | 53 | 0 | 3 | 3 | 3 | 3 | 5 | 3 | 12 | 3 |
| 20:00 | 20 | 33 | 33 | 3 | 86 | 3 | 0 | 3 | 3 | 21 | 3 | 3 | 0 | 3 |
| 23:00 | 31 | 34 | 45 | 63 | 43 | 64 | 23 | 12 | 1 | 0 | 2 | 5 | 2 | 3 |
I want to create 2 dataframes by following the steps below:
- Remove the columns corresponding to Saturdays and Sundays.
| 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 |
- Separate the resulting Dataframe into 2, one with only Fridays and the other with the rest of the days (Monday, Tuesday, Wednesday and Thursday).
| Hours | 2022-06-10 | 2022-06-17 |
|---|---|---|
| 00:00 | 23 | 3 |
| 05:00 | 32 | 3 |
| 10:00 | 45 | 3 |
| 15:00 | 53 | 3 |
| 20:00 | 86 | 3 |
| 23:00 | 43 | 5 |
| Hours | 2022-06-06 | 2022-06-07 | 2022-06-08 | 2022-06-09 | 2022-06-13 | 2022-06-14 | 2022-06-15 | 2022-06-16 |
|---|---|---|---|---|---|---|---|---|
| 00:00 | 3 | 0 | 0 | 3 | 2 | 3 | 3 | 7 |
| 05:00 | 5 | 4 | 0 | 3 | 9 | 3 | 3 | 5 |
| 10:00 | 0 | 3 | 3 | 34 | 6 | 3 | 0 | 3 |
| 15:00 | 10 | 31 | 10 | 3 | 3 | 3 | 3 | 5 |
| 20:00 | 20 | 33 | 33 | 3 | 3 | 3 | 21 | 3 |
| 23:00 | 31 | 34 | 45 | 63 | 12 | 1 | 0 | 2 |
CodePudding user response：
You can use pandas.to_datetime and weekday to create a boolean indexer, then use boolean indexing:
# set index aside
df = df.set_index('Hours')
# get weekday (4 is Fri, 5 and 6 are Sat and Sun)
# compare to 5 (<5) to get True on weekdays
weekdays = pd.to_datetime(df.columns).weekday < 5
# or
# monthu = pd.to_datetime(df.columns).weekday < 4
# compare to 4 to get Fridays
fri = pd.to_datetime(df.columns).weekday == 4
# slice
mon_thu_df = df.loc[:, weekdays & ~fri].reset_index()
# or
# mon_thu_df = df.loc[:, monthu].reset_index()
fri_df = df.loc[:, fri].reset_index()
Mon-Thu:
Hours 2022-06-06 2022-06-07 2022-06-08 2022-06-09 2022-06-13 2022-06-14 2022-06-15 2022-06-16
0 00:00 3 0 0 3 2 3 3 7
1 05:00 5 4 0 3 9 3 3 5
2 10:00 0 3 3 34 6 3 0 3
3 15:00 10 31 10 3 3 3 3 5
4 20:00 20 33 33 3 3 3 21 3
5 23:00 31 34 45 63 12 1 0 2
Fri:
Hours 2022-06-10 2022-06-17
0 00:00 23 3
1 05:00 32 3
2 10:00 45 3
3 15:00 53 3
4 20:00 86 3
5 23:00 43 5
CodePudding user response：
For the first Step you can make something like this :
def allsundays(year):
d = date(year, 1, 1) # January 1st
d = timedelta(days = 6 - d.weekday()) # First Sunday
while d.year == year:
yield d
d = timedelta(days = 7)
for d in allsundays(2022):
print(d)
This little code can help you to get all Sunday
After getting the sunday list you can drop the columns :
df.drop(columns=sunday_list)