I have a Stata elapsed date variable (excerpt below) for each patient patid. But I believe I need to generate a new variable if I were to make use of only the year element within that date, that is, not just change the display format.
clear
input long patid float date
1015 18766
1018 13135
1020 13325
1025 14384
1029 14514
1050 13501
1070 14523
1071 14878
1090 14701
1092 14159
end
format %td date
How do I generate a year variable for all dates within the same year? That is all days from 1st January to 31st December of the same year?
That calls for just the function year(), which together with similar stuff is prominently documented at help datetime.
clear
input long patid float date
1015 18766
1018 13135
1020 13325
1025 14384
1029 14514
1050 13501
1070 14523
1071 14878
1090 14701
1092 14159
end
format %td date
gen year = year(date)
list
+--------------------------+
| patid date year |
|--------------------------|
1. | 1015 19may2011 2011 |
2. | 1018 18dec1995 1995 |
3. | 1020 25jun1996 1996 |
4. | 1025 20may1999 1999 |
5. | 1029 27sep1999 1999 |
|--------------------------|
6. | 1050 18dec1996 1996 |
7. | 1070 06oct1999 1999 |
8. | 1071 25sep2000 2000 |
9. | 1090 01apr2000 2000 |
10. | 1092 07oct1998 1998 |
+--------------------------+
Related
I have a table transaction_details:
transaction_id
customer_id
item_id
item_number
transaction_dttm
7765
1
23
1
2022-01-15
1254
2
12
4
2022-02-03
3332
3
56
2
2022-02-15
7658
1
43
1
2022-03-01
7231
4
56
1
2022-01-15
7231
2
23
2
2022-01-29
I need to calculate the amount spent by the client in the last month and find out the item (item_name) on which the client spent the most in the last month.
Example result:
|customer_id|amount_spent_lm|top_item_lm|
| - | ---------- | ----- |
| 1 | 700 | glasses |
| 2 | 20000 | notebook |
| 3 | 100 | cup |
When calculating, it is necessary to take into account the current price at the time of the transaction (dict_item_prices). Customers who have not made purchases in the last month are not included in the final table. he last month is defined as the last 30 days at the time of the report creation.
There is also a table dict_item_prices:
item_id
item_name
item_price
valid_from_dt
valid_to_dt
23
phone 1
1000
2022-01-01
2022-12-31
12
notebook
5000
2022-01-02
2022-12-31
56
cup
50
2022-01-02
2022-12-31
43
glasses
700
2022-01-01
2022-12-31
I have a postgresql table with this schema:
id SERIAL PRIMARY KEY,
traveltime INT,
departuredate TIMESTAMPTZ,
departurehour TIMETZ
Here is a bit of data (edited):
id | traveltime | departuredate | departurehour
----+------------+------------------------+---------------
1 | 73 | 2019-12-24 00:00:03+01 | 00:00:03+01
2 | 73 | 2019-12-24 00:12:16+01 | 00:12:16+01
53 | 115 | 2019-12-24 07:53:44+01 | 07:53:44+01
54 | 116 | 2019-12-24 07:58:45+01 | 07:58:45+01
55 | 119 | 2019-12-24 08:03:46+01 | 08:03:46+01
56 | 120 | 2019-12-24 08:08:47+01 | 08:08:47+01
57 | 121 | 2019-12-24 08:13:48+01 | 08:13:48+01
58 | 121 | 2019-12-24 08:18:48+01 | 08:18:48+01
542 | 112 | 2019-12-26 07:52:41+01 | 07:52:41+01
543 | 114 | 2019-12-26 07:57:42+01 | 07:57:42+01
544 | 116 | 2019-12-26 08:02:43+01 | 08:02:43+01
545 | 116 | 2019-12-26 08:07:44+01 | 08:07:44+01
546 | 117 | 2019-12-26 08:12:45+01 | 08:12:45+01
547 | 118 | 2019-12-26 08:17:46+01 | 08:17:46+01
548 | 118 | 2019-12-26 08:22:48+01 | 08:22:48+01
1031 | 80 | 2019-12-28 07:50:33+01 | 07:50:33+01
1032 | 81 | 2019-12-28 07:55:34+01 | 07:55:34+01
1033 | 81 | 2019-12-28 08:00:35+01 | 08:00:35+01
1034 | 82 | 2019-12-28 08:05:36+01 | 08:05:36+01
1035 | 82 | 2019-12-28 08:10:37+01 | 08:10:37+01
1036 | 83 | 2019-12-28 08:15:38+01 | 08:15:38+01
1037 | 83 | 2019-12-28 08:20:39+01 | 08:20:39+01
I'd like to get the average for all the values collected for traveltime for each 10 minutes interval for several weeks.
Expected result for the data sample: for the 10-minutes interval between 8h00 and 8h10, the rows that will be included in the avg are with id 55, 56, 544, 545, 1033 and 1034
and so on.
I can get the average for a specific interval:
select avg(traveltime) from belt where departurehour >= '10:40:00+01' and departurehour < '10:50:00+01';
To avoid creating a query for each interval, I used this query to get all the 10-minutes intervals for the complete period encoded:
select i from generate_series('2019-11-23', '2020-01-18', '10 minutes'::interval) i;
What I miss is a way to apply my AVG query to each of these generated intervals. Any direction would be helpful!
It turns out that the generate_series does not actually apply as requardless of the date range. The critical part is the 144 10Min intervals per day. Unfortunatly Postgres does not provide an interval type for minuets. (Perhaps creating one would be a useful exersize). But all is not loss you can simulate the same with BETWEEN, just need to play with the ending of the range.
The following generates this simulation using a recursive CTE. Then as previously joins to your table.
set timezone to '+1'; -- necessary to keep my local offset from effecting results.
-- create table an insert data here
-- additional data added outside of date range so should not be included)
with recursive min_intervals as
(select '00:00:00'::timetz start_10Min -- start of 1st 10Min interval
, '00:09:59.999999'::timetz end_10Min -- last microsecond in 10Min interval
, 1 interval_no
union all
select start_10Min + interval '10 min'
, end_10Min + interval '10 min'
, interval_no + 1
from Min_intervals
where interval_no < 144 -- 6 10Min intervals/hr * 24 Hr/day = No of 10Min intervals in any day
) -- select * from min_intervals;
select start_10Min, end_10Min, avg(traveltime) average_travel_time
from min_intervals
join belt
on departuredate::time between start_10Min and end_10Min
where departuredate::date between date '2019-11-23' and date '2020-01-18'
group by start_10Min, end_10Min
order by start_10Min;
-- test result for 'specified' Note added rows fall within time frame 08:00 to 08:10
-- but these should be excluded so the avg for that period should be the same for both queries.
select avg(traveltime) from belt where id in (55, 56, 544, 545, 1033, 1034);
My issue with the above is the data range is essentially hard coded (yes substitution parameter are available) and manually but that is OK for psql or an IDE but not good for a production environment. If this is to be used in that environment I'd use the following function to return a virtual table of the same results.
create or replace function travel_average_per_10Min_interval(
start_date_in date
, end_date_in date
)
returns table (Start_10Min timetz
,end_10Min timetz
,avg_travel_time numeric
)
language sql
as $$
with recursive min_intervals as
(select '00:00:00'::timetz start_10Min -- start of 1st 10Min interval
, '00:09:59.999999'::timetz end_10Min -- last microsecond in 10Min interval
, 1 interval_no
union all
select start_10Min + interval '10 min'
, end_10Min + interval '10 min'
, interval_no + 1
from Min_intervals
where interval_no < 144 -- 6 10Min intervals/hr * 24 Hr/day = No of 10Min intervals in any day
) -- select * from min_intervals;
select start_10Min, end_10Min, avg(traveltime) average_travel_time
from min_intervals
join belt
on departuredate::time between start_10Min and end_10Min
where departuredate::date between start_date_in and end_date_in
group by start_10Min, end_10Min
order by start_10Min;
$$;
-- test
select * from travel_average_per_10Min_interval(date '2019-11-23', date '2020-01-18');
This is TSQL and I'm trying to calculate repeat purchase rate for last 12 months. This is achieved by looking at sum of customers who have bought more than 1 time last 12 months and the total number of customers last 12 months.
The SQL code below will give me just that; but i would like to dynamically do this for the last 12 months. This is the part where i'm stuck and not should how to best achieve this.
Each month should include data going back 12 months. I.e. June should hold data between June 2018 and June 2018, May should hold data from May 2018 till May 2019.
[Order Date] is a normal datefield (yyyy-mm-dd hh:mm:ss)
DECLARE #startdate1 DATETIME
DECLARE #enddate1 DATETIME
SET #enddate1 = DATEADD(MONTH, DATEDIFF(MONTH, 0, GETDATE())-1, 0) -- Starting June 2018
SET #startdate1 = DATEADD(mm,DATEDIFF(mm,0,GETDATE())-13,0) -- Ending June 2019
;
with dataset as (
select [Phone No_] as who_identifier,
count(distinct([Order No_])) as mycount
from [MyCompany$Sales Invoice Header]
where [Order Date] between #startdate1 and #enddate1
group by [Phone No_]
),
frequentbuyers as (
select who_identifier, sum(mycount) as frequentbuyerscount
from dataset
where mycount > 1
group by who_identifier),
allpurchases as (
select who_identifier, sum(mycount) as allpurchasescount
from dataset
group by who_identifier
)
select sum(frequentbuyerscount) as frequentbuyercount, (select sum(allpurchasescount) from allpurchases) as allpurchasecount
from frequentbuyers
I'm hoping to achieve end result looking something like this:
...Dec, Jan, Feb, March, April, May, June each month holding both values for frequentbuyercount and allpurchasescount.
Here is the code. I made a little modification for the frequentbuyerscount and allpurchasescount. If you use a sumif like expression you don't need a second cte.
if object_id('tempdb.dbo.#tmpMonths') is not null drop table #tmpMonths
create table #tmpMonths ( MonthID datetime, StartDate datetime, EndDate datetime)
declare #MonthCount int = 12
declare #Month datetime = DATEADD(MONTH, DATEDIFF(MONTH, 0, GETDATE()), 0)
while #MonthCount > 0 begin
insert into #tmpMonths( MonthID, StartDate, EndDate )
select #Month, dateadd(month, -12, #Month), #Month
set #Month = dateadd(month, -1, #Month)
set #MonthCount = #MonthCount - 1
end
;with dataset as (
select m.MonthID as MonthID, [Phone No_] as who_identifier,
count(distinct([Order No_])) as mycount
from [MyCompany$Sales Invoice Header]
inner join #tmpMonths m on [Order Date] between m.StartDate and m.EndDate
group by m.MonthID, [Phone No_]
),
buyers as (
select MonthID, who_identifier
, sum(iif(mycount > 1, mycount, 0)) as frequentbuyerscount --sum only if count > 1
, sum(mycount) as allpurchasescount
from dataset
group by MonthID, who_identifier
)
select
b.MonthID
, max(tm.StartDate) StartDate, max(tm.EndDate) EndDate
, sum(b.frequentbuyerscount) as frequentbuyercount
, sum(b.allpurchasescount) as allpurchasecount
from buyers b inner join #tmpMonths tm on tm.MonthID = b.MonthID
group by b.MonthID
Be aware, that the code was tested only syntax-wise.
After the test data, this is the result:
MonthID | StartDate | EndDate | frequentbuyercount | allpurchasecount
-----------------------------------------------------------------------------
2018-08-01 | 2017-08-01 | 2018-08-01 | 340 | 3702
2018-09-01 | 2017-09-01 | 2018-09-01 | 340 | 3702
2018-10-01 | 2017-10-01 | 2018-10-01 | 340 | 3702
2018-11-01 | 2017-11-01 | 2018-11-01 | 340 | 3702
2018-12-01 | 2017-12-01 | 2018-12-01 | 340 | 3703
2019-01-01 | 2018-01-01 | 2019-01-01 | 340 | 3703
2019-02-01 | 2018-02-01 | 2019-02-01 | 2 | 8
2019-03-01 | 2018-03-01 | 2019-03-01 | 2 | 3
2019-04-01 | 2018-04-01 | 2019-04-01 | 2 | 3
2019-05-01 | 2018-05-01 | 2019-05-01 | 2 | 3
2019-06-01 | 2018-06-01 | 2019-06-01 | 2 | 3
2019-07-01 | 2018-07-01 | 2019-07-01 | 2 | 3
I have delivery slots that has a from column (datetime).
Delivery slots are stored as 1 hour to 1 hour and 30 minute intervals, daily.
i.e. 3.00am-4.30am, 6.00am-7.30am, 9.00am-10.30am and so forth
id | from
------+---------------------
1 | 2016-01-01 03:00:00
2 | 2016-01-01 04:30:00
3 | 2016-01-01 06:00:00
4 | 2016-01-01 07:30:00
5 | 2016-01-01 09:00:00
6 | 2016-01-01 10:30:00
7 | 2016-01-01 12:00:00
8 | 2016-01-02 03:00:00
9 | 2016-01-02 04:30:00
10 | 2016-01-02 06:00:00
11 | 2016-01-02 07:30:00
12 | 2016-01-02 09:00:00
13 | 2016-01-02 10:30:00
14 | 2016-01-02 12:00:00
I’m trying to get all delivery_slots between the hours of 3.00am - 4.30 am. Ive got the following so far:
SELECT * FROM delivery_slots WHERE EXTRACT(HOUR FROM delivery_slots.from) >= 3 AND EXTRACT(MINUTE FROM delivery_slots.from) >= 0 AND EXTRACT(HOUR FROM delivery_slots.from) <= 4 AND EXTRACT(MINUTE FROM delivery_slots.from) <= 30;
Which kinda works. Kinda, because it is only returning delivery slots that have minutes of 00.
Thats because of the last where condition (EXTRACT(MINUTE FROM delivery_slots.from) <= 30)
To give you an idea, of what I am trying to expect:
id | from
-------+---------------------
1 | 2016-01-01 03:00:00
2 | 2016-01-01 04:30:00
8 | 2016-01-02 03:00:00
9 | 2016-01-02 04:30:00
15 | 2016-01-03 03:00:00
16 | 2016-01-03 04:30:00
etc...
Is there a better way to go about this?
Try this: (not tested)
SELECT * FROM delivery_slots WHERE delivery_slots.from::time >= '03:00:00' AND delivery_slots.from::time <= '04:30:00'
Hope this helps.
Cheers.
The easiest way to do this, in my mind, is to cast the from column as a type time and do a where >= and <=, like so
select * from testing where (date::time >= '3:00'::time and date::time <= '4:30'::time);
I am doing data analysis with trading data. I would like to use Pandas in order to examine the times when the traders are active.
In particular, I try to extract the difference in minutes between the dates of every first trade of every trader for each day and cumulate it to a monthly basis
The data looks like this:
Timestamp (Datetime) | Buyer | Volume
--------------------------------------
2012-01-01 09:00:00 | John | 10
2012-01-01 10:00:00 | Mark | 10
2012-01-01 16:00:00 | Mark | 10
2012-01-01 11:00:00 | Kevin | 10
2012-02-01 10:00:00 | Mark | 10
2012-02-01 09:00:00 | John | 10
2012-02-01 17:00:00 | Mark | 10
Right now I use resampling to retrieve the first trade on a daily basis. However, I want to group also by the buyer to calculate the differences in their trading dates. Like this
Timestamp (Datetime) | Buyer | Volume
--------------------------------------
2012-01-01 09:00:00 | John | 10
2012-01-01 10:00:00 | Mark | 10
2012-01-01 11:00:00 | Kevin | 10
2012-01-02 10:00:00 | Mark | 10
2012-01-02 09:00:00 | John | 10
Overall I am looking to calculate the differences in minutes between the first trades on a daily basis for each trader.
Update
For example in the case of John on the 2012-01-01: Dist = 60 (Diff John-Mark) + 120 (Diff John-Kevin) = 180
I would highly appreciate if anyone has an idea how to do this.
Thank you
Your original frame (the resampled one)
In [71]: df_orig
Out[71]:
buyer date volume
0 John 2012-01-01 09:00:00 10
1 Mark 2012-01-01 10:00:00 10
2 Kevin 2012-01-01 11:00:00 10
3 Mark 2012-01-02 10:00:00 10
4 John 2012-01-02 09:00:00 10
Set the index to the date column, keeping the date column in place
In [75]: df = df_orig.set_index('date',drop=False)
Create this aggregation function
def f(frame):
frame.sort('date',inplace=True)
frame['start'] = frame.date.iloc[0]
return frame
Groupby the single date
In [74]: x = df.groupby(pd.TimeGrouper('1d')).apply(f)
Create the differential in minutes
In [86]: x['diff'] = (x.date-x.start).apply(lambda x: float(x.item().total_seconds())/60)
In [87]: x
Out[87]:
buyer date volume start diff
date
2012-01-01 2012-01-01 09:00:00 John 2012-01-01 09:00:00 10 2012-01-01 09:00:00 0
2012-01-01 10:00:00 Mark 2012-01-01 10:00:00 10 2012-01-01 09:00:00 60
2012-01-01 11:00:00 Kevin 2012-01-01 11:00:00 10 2012-01-01 09:00:00 120
2012-01-02 2012-01-02 09:00:00 John 2012-01-02 09:00:00 10 2012-01-02 09:00:00 0
2012-01-02 10:00:00 Mark 2012-01-02 10:00:00 10 2012-01-02 09:00:00 60
Here's the explanation. We use the TimeGrouper to have the grouping by date, where a frame is passed to the function f. This function, then uses the first date of the day (the sort is necessary here). You subtract this from the date on the entry to get a timedelta64, which is then massaged to minutes (this is a bit hacky right now because of some numpy issues, should be more natural in 0.12)
Thanks for you update, I originally thought you wanted the diff per buyer, not from the first buyer, but that's just a minor tweak.
Update:
To track the buyer name as well (which corresponds to the start date), just include
it in the function f
def f(frame):
frame.sort('date',inplace=True)
frame['start'] = frame.date.iloc[0]
frame['start_buyer'] = frame.buyer.iloc[0]
return frame
Then can groupby on this at the end:
In [14]: x.groupby(['start_buyer']).sum()
Out[14]:
diff
start_buyer
John 240