I have a table like below containing values for multiple IDs:

ID	value	date
1	20	2022-01-01 12:20
2	25	2022-01-04 18:20
1	10	2022-01-04 11:20
1	150	2022-01-06 16:20
2	200	2022-01-08 13:20
3	40	2022-01-04 21:20
1	75	2022-01-09 08:20

I would like to calculate week wise sum of values for all IDs:

• The start date is given (for example, 01-01-2022).

• Weeks are calculated based on range:

  • every Saturday 00:00 to next Friday 23:59 (i.e. Week 1 is from 01-01-2022 00:00 to 07-01-2022 23:59)

ID	Week 1 sum	Week 2 sum	Week 3 sum	...
1	180	75	--	--
2	25	200	--	--
3	40	--	--	--

CodePudding user response：

There's a pandas function (pd.Grouper) that allows you to specify a groupby instruction.¹ In this case, that specification is to "resample" date by a weekly frequency that starts on Fridays.² Since you also need to group by ID as well, add it to the grouper.

# convert to datetime
df['date'] = pd.to_datetime(df['date'])
# pivot the dataframe
df1 = (
    df.groupby(['ID', pd.Grouper(key='date', freq='W-FRI')])['value'].sum()
    .unstack(fill_value=0)
)
# rename columns
df1.columns = [f"Week {c} sum" for c in range(1, df1.shape[1] 1)]
df1 = df1.reset_index()

---

¹ What you actually need is a pivot_table result but groupby unstack is equivalent to pivot_table and groupby unstack is more convenient here.

² Because Jan 1, 2022 is a Saturday, you need to specify the anchor on Friday.

CodePudding user response：

You can compute a week column. In case you've data for same year, you can extract just week number, which is less likely in real-time scenarios. In case you've data from multiple years, it might be wise to derive a combination of Year & week number.

df['Year-Week'] = df['Date'].dt.strftime('%Y-%U')

In your case the dates 2022-01-01 & 2022-01-04 18:2 should be convert to 2022-01 as per the scenario you considered.

To calculate your pivot table, you can use the pandas pivot_table. Example code:

pd.pivot_table(df, values='value', index=['ID'], columns=['year_weeknumber'], aggfunc=np.sum)

CodePudding user response：

Let's define a formatting helper.

def fmt(row):
    return f"{row.year}-{row.week:02d}"  # We ignore row.day

Now it's easy.

>>> df = pd.DataFrame([dict(id=1, value=20, date="2022-01-01 12:20"),
                       dict(id=2, value=25, date="2022-01-04 18:20")])
>>> df['date'] = pd.to_datetime(df.date)
>>> df['iso'] = df.date.dt.isocalendar().apply(fmt, axis='columns')
>>> df
   id  value                date      iso
0   1     20 2022-01-01 12:20:00  2021-52
1   2     25 2022-01-04 18:20:00  2022-01

Just groupby the ISO week.