So I have a data frame that is something like this
Resource 2020-06-01 2020-06-02 2020-06-03
Name1 8 7 8
Name2 7 9 9
Name3 10 10 10
Imagine that the header is literal all the days of the month. And that there are way more names than just three.
I need to reduce the columns to five. Considering the first column to be the days between 2020-06-01
till 2020-06-05
. Then from Saturday till Friday of the same week. Or the last day of the month if it is before Friday. So for June would be these weeks:
week 1: 2020-06-01 to 2020-06-05
week 2: 2020-06-06 to 2020-06-12
week 3: 2020-06-13 to 2020-06-19
week 4: 2020-06-20 to 2020-06-26
week 5: 2020-06-27 to 2020-06-30
I have no problem defining these weeks. The problem is grouping the columns based on them. I couldn't come up with anything.
Does someone have any ideas about this?
I have to use these code to generate your dataframe.
dates = pd.date_range(start='2020-06-01', end='2020-06-30')
df = pd.DataFrame({
'Name1': np.random.randint(1, 10, size=len(dates)),
'Name2': np.random.randint(1, 10, size=len(dates)),
'Name3': np.random.randint(1, 10, size=len(dates)),
})
df = df.set_index(dates).transpose().reset_index().rename(columns={'index': 'Resource'})
Then, the solution starts from here.
# Set the first column as index
df = df.set_index(df['Resource'])
# Remove the unused column
df = df.drop(columns=['Resource'])
# Transpose the dataframe
df = df.transpose()
# Output:
Resource Name1 Name2 Name3
2020-06-01 00:00:00 3 2 7
2020-06-02 00:00:00 5 6 8
2020-06-03 00:00:00 2 3 6
...
# Bring "Resource" from index to column
df = df.reset_index()
df = df.rename(columns={'index': 'Resource'})
# Add a column "week of year"
df['week_no'] = df['Resource'].dt.weekofyear
# You can simply group by the week no column
df.groupby('week_no').sum().reset_index()
# Output:
Resource week_no Name1 Name2 Name3
0 23 38 42 41
1 24 37 30 43
2 25 38 29 23
3 26 29 40 42
4 27 2 8 3
I don't know what you want to do for the next. If you want your original form, just transpose()
it back.
EDIT: OP claimed the week should start from Saturday end up with Friday
# 0: Monday
# 1: Tuesday
# 2: Wednesday
# 3: Thursday
# 4: Friday
# 5: Saturday
# 6: Sunday
df['weekday'] = df['Resource'].dt.weekday.apply(lambda day: 0 if day <= 4 else 1)
df['customised_weekno'] = df['week_no'] + df['weekday']
Output:
Resource Resource Name1 Name2 Name3 week_no weekday customised_weekno
0 2020-06-01 4 7 7 23 0 23
1 2020-06-02 8 6 7 23 0 23
2 2020-06-03 5 9 5 23 0 23
3 2020-06-04 7 6 5 23 0 23
4 2020-06-05 6 3 7 23 0 23
5 2020-06-06 3 7 6 23 1 24
6 2020-06-07 5 4 4 23 1 24
7 2020-06-08 8 1 5 24 0 24
8 2020-06-09 2 7 9 24 0 24
9 2020-06-10 4 2 7 24 0 24
10 2020-06-11 6 4 4 24 0 24
11 2020-06-12 9 5 7 24 0 24
12 2020-06-13 2 4 6 24 1 25
13 2020-06-14 6 7 5 24 1 25
14 2020-06-15 8 7 7 25 0 25
15 2020-06-16 4 3 3 25 0 25
16 2020-06-17 6 4 5 25 0 25
17 2020-06-18 6 8 2 25 0 25
18 2020-06-19 3 1 2 25 0 25
So, you can use customised_weekno
for grouping.
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