I want to filter a TableA, taking into account only those rows whose "TotalInvoice" field is within the minimum and maximum values expressed in a ViewB, based on month and year values and RepairShopId (the sample data only has one RepairShopId, but all the data has multiple IDs).
In the view I have minimum and maximum values for each business and each month and year.
TableA
RepairOrderDataId
RepairShopId
LastUpdated
TotalInvoice
1
10
2017-06-01 07:00:00.000
765
1
10
2017-06-05 12:15:00.000
765
2
10
2017-02-25 13:00:00.000
400
3
10
2017-10-19 12:15:00.000
295679
4
10
2016-11-29 11:00:00.000
133409.41
5
10
2016-10-28 12:30:00.000
127769
6
10
2016-11-25 16:15:00.000
122400
7
10
2016-10-18 11:15:00.000
1950
8
10
2016-11-07 16:45:00.000
79342.7
9
10
2016-11-25 19:15:00.000
1950
10
10
2016-12-09 14:00:00.000
111559
11
10
2016-11-28 10:30:00.000
106333
12
10
2016-12-13 18:00:00.000
23847.4
13
10
2016-11-01 17:00:00.000
22782.9
14
10
2016-10-07 15:30:00.000
NULL
15
10
2017-01-06 15:30:00.000
138958
16
10
2017-01-31 13:00:00.000
244484
17
10
2016-12-05 09:30:00.000
180236
18
10
2017-02-14 18:30:00.000
92752.6
19
10
2016-10-05 08:30:00.000
161952
20
10
2016-10-05 08:30:00.000
8713.08
ViewB
RepairShopId
Orders
Average
MinimumValue
MaximumValue
year
month
yearMonth
10
1
370343
370343
370343
2015
7
2015-7
10
1
109645
109645
109645
2015
10
2015-10
10
1
148487
148487
148487
2015
12
2015-12
10
1
133409.41
133409.41
133409.41
2016
3
2016-3
10
1
19261
19261
19261
2016
8
2016-8
10
4
10477.3575
2656.65644879821
18298.0585512018
2016
9
2016-9
10
69
15047.709565
10
90942.6052417394
2016
10
2016-10
10
98
22312.077244
10
147265.581935242
2016
11
2016-11
10
96
20068.147395
10
99974.1750708773
2016
12
2016-12
10
86
25334.053372
10
184186.985160105
2017
1
2017-1
10
69
21410.63855
10
153417.00126689
2017
2
2017-2
10
100
13009.797
10
59002.3589332934
2017
3
2017-3
10
101
11746.191287
10
71405.3391452842
2017
4
2017-4
10
123
11143.49756
10
55306.8202091131
2017
5
2017-5
10
197
15980.55406
10
204538.144334771
2017
6
2017-6
10
99
10852.496969
10
63283.9899761938
2017
7
2017-7
10
131
52601.981526
10
1314998.61355187
2017
8
2017-8
10
124
10983.221854
10
59444.0535811233
2017
9
2017-9
10
115
12467.148434
10
72996.6054527277
2017
10
2017-10
10
123
14843.379593
10
129673.931373139
2017
11
2017-11
10
111
8535.455945
10
50328.1495501884
2017
12
2017-12
I've tried:
SELECT *
FROM TableA
INNER JOIN ViewB ON TableA.RepairShopId = ViewB.RepairShopId
WHERE TotalInvoice > MinimumValue AND TotalInvoice < MaximumValue
AND TableA.RepairShopId = ViewB.RepairShopId
But I'm not sure how to compare it the yearMonth field with the datetime field "LastUpdated".
Any help is very appreciated!
here is how you can do it:
I assumed LastUpdated column is the column from tableA which indicate date of
SELECT *
FROM TableA A
INNER JOIN ViewB B
ON A.RepairShopId = B.RepairShopId
AND A.TotalInvoice > B.MinimumValue
AND A.TotalInvoice < B.MaximumValue
AND YEAR(LastUpdated) = B.year
AND MONTH(LastUpdated) = B.month
Related
I usually have to extract days, weeks and years into separate columns like this:
data['Day'] = data.SALESDATE.dt.isocalendar().day
data['Week'] = data.SALESDATE.dt.isocalendar().week
data['Year'] = data.SALESDATE.dt.isocalendar().year
But is there a way where I can assign all three in one nice line?
data[['Day', 'Week', 'Year']] = ....
``
For one line solution use DataFrame.join with rename columns if necessary:
rng = pd.date_range('2017-04-03', periods=10)
data = pd.DataFrame({'SALESDATE': rng, 'a': range(10)})
data = data.join(data.SALESDATE.dt.isocalendar().rename(columns=lambda x: x.title()))
print (data)
SALESDATE a Year Week Day
0 2017-04-03 0 2017 14 1
1 2017-04-04 1 2017 14 2
2 2017-04-05 2 2017 14 3
3 2017-04-06 3 2017 14 4
4 2017-04-07 4 2017 14 5
5 2017-04-08 5 2017 14 6
6 2017-04-09 6 2017 14 7
7 2017-04-10 7 2017 15 1
8 2017-04-11 8 2017 15 2
9 2017-04-12 9 2017 15 3
Or change order of list and assign:
data[['Year', 'Week', 'Day']] = data.SALESDATE.dt.isocalendar()
print (data)
SALESDATE a Year Week Day
0 2017-04-03 0 2017 14 1
1 2017-04-04 1 2017 14 2
2 2017-04-05 2 2017 14 3
3 2017-04-06 3 2017 14 4
4 2017-04-07 4 2017 14 5
5 2017-04-08 5 2017 14 6
6 2017-04-09 6 2017 14 7
7 2017-04-10 7 2017 15 1
8 2017-04-11 8 2017 15 2
9 2017-04-12 9 2017 15 3
If need changed order of values in list:
data[['Day', 'Week', 'Year']] = data.SALESDATE.dt.isocalendar()[['day','week','year']]
print (data)
SALESDATE a Day Week Year
0 2017-04-03 0 1 14 2017
1 2017-04-04 1 2 14 2017
2 2017-04-05 2 3 14 2017
3 2017-04-06 3 4 14 2017
4 2017-04-07 4 5 14 2017
5 2017-04-08 5 6 14 2017
6 2017-04-09 6 7 14 2017
7 2017-04-10 7 1 15 2017
8 2017-04-11 8 2 15 2017
9 2017-04-12 9 3 15 2017
I have a data frame as shown below. which is a sales data of two health care product starting from December 2016 to November 2018.
product profit sale_date discount
A 50 2016-12-01 5
A 50 2017-01-03 4
B 200 2016-12-24 10
A 50 2017-01-18 3
B 200 2017-01-28 15
A 50 2017-01-18 6
B 200 2017-01-28 20
A 50 2017-04-18 6
B 200 2017-12-08 25
A 50 2017-11-18 6
B 200 2017-08-21 20
B 200 2017-12-28 30
A 50 2018-03-18 10
B 300 2018-06-08 45
B 300 2018-09-20 50
A 50 2018-11-18 8
B 300 2018-11-28 35
From the above I would like to prepare below dataframe and plot that into line plot.
Expected Output
bought_year total_profit
2016 250
2017 1250
2018 1000
X axis = bought_year
Y axis = profit
use groupby with dt.year and .agg to name your column.
df1 = df.groupby(df['sale_date'].dt.year).agg(total_profit=('profit','sum'))\
.reset_index().rename(columns={'sale_date': 'bought_year'})
print(df1)
bought_year total_profit
0 2016 250
1 2017 1250
2 2018 1000
df1.set_index('bought_year').plot(kind='bar')
I have a running balance sheet showing customer balances after inflows and (outflows) by date. It looks something like this:
ID DATE AMOUNT RUNNING AMOUNT
-- ---------------- ------- --------------
10 27/06/2019 14:30 100 100
10 29/06/2019 15:26 -100 0
10 03/07/2019 01:56 83 83
10 04/07/2019 17:53 15 98
10 05/07/2019 15:09 -98 0
10 05/07/2019 15:53 98.98 98.98
10 05/07/2019 19:54 -98.98 0
10 07/07/2019 01:36 90.97 90.97
10 07/07/2019 13:02 -90.97 0
10 07/07/2019 16:32 39.88 39.88
10 08/07/2019 13:41 50 89.88
20 08/01/2019 09:03 890.97 890.97
20 09/01/2019 14:47 -91.09 799.88
20 09/01/2019 14:53 100 899.88
20 09/01/2019 14:59 -399 500.88
20 09/01/2019 18:24 311 811.88
20 09/01/2019 23:25 50 861.88
20 10/01/2019 16:18 -861.88 0
20 12/01/2019 16:46 894.49 894.49
20 25/01/2019 05:40 -871.05 23.44
I have attempted using lag() but I seem not to understand how to use it yet.
SELECT ID, MEDIAN(DIFF) MEDIAN_AGE
FROM
(
SELECT *, DATEDIFF(day, Lag(DATE, 1) OVER(ORDER BY ID), DATE
)AS DIFF
FROM TABLE 1
WHERE RUNNING AMOUNT = 0
)
GROUP BY ID;
The expected result would be:
ID MEDIAN_AGE
-- ----------
10 1
20 2
Please help in writing out the query that gives the expected result.
As already pointed out, you are using syntax that isn't valid for Oracle, including functions that don't exist and column names that aren't allowed.
You seem to want to calculate the number of days between a zero running-amount and the following non-zero running-amount; lead() is probably easier than lag() here, and you can use a case expression to only calculate it when needed:
select id, date_, amount, running_amount,
case when running_amount = 0 then
lead(date_) over (partition by id order by date_) - date_
end as diff
from your_table;
ID DATE_ AMOUNT RUNNING_AMOUNT DIFF
---------- -------------------- ---------- -------------- ----------
10 2019-06-27 14:30:00 100 100
10 2019-06-29 15:26:00 -100 0 3.4375
10 2019-07-03 01:56:00 83 83
10 2019-07-04 17:53:00 15 98
10 2019-07-05 15:09:00 -98 0 .0305555556
10 2019-07-05 15:53:00 98.98 98.98
10 2019-07-05 19:54:00 -98.98 0 1.2375
10 2019-07-07 01:36:00 90.97 90.97
10 2019-07-07 13:02:00 -90.97 0 .145833333
10 2019-07-07 16:32:00 39.88 39.88
10 2019-07-08 13:41:00 50 89.88
20 2019-01-08 09:03:00 890.97 890.97
20 2019-01-09 14:47:00 -91.09 799.88
20 2019-01-09 14:53:00 100 899.88
20 2019-01-09 14:59:00 -399 500.88
20 2019-01-09 18:24:00 311 811.88
20 2019-01-09 23:25:00 50 861.88
20 2019-01-10 16:18:00 -861.88 0 2.01944444
20 2019-01-12 16:46:00 894.49 894.49
20 2019-01-25 05:40:00 -871.05 23.44
Then use the median() function, rounding if desired to get your expected result:
select id, median(diff) as median_age, round(median(diff)) as median_age_rounded
from (
select id, date_, amount, running_amount,
case when running_amount = 0 then
lead(date_) over (partition by id order by date_) - date_
end as diff
from your_table
)
group by id;
ID MEDIAN_AGE MEDIAN_AGE_ROUNDED
---------- ---------- ------------------
10 .691666667 1
20 2.01944444 2
db<>fiddle
I need to create a new grouping every time I have a period of more than 60 days since my previous record.
Basically, I need too take the data I have here:
RowNo StartDate StopDate DaysBetween
1 3/21/2017 3/21/2017 14
2 4/4/2017 4/4/2017 14
3 4/18/2017 4/18/2017 14
4 6/23/2017 6/23/2017 66
5 7/5/2017 7/5/2017 12
6 7/19/2017 7/19/2017 14
7 9/27/2017 9/27/2017 70
8 10/24/2017 10/24/2017 27
9 10/31/2017 10/31/2017 7
10 11/14/2017 11/14/2017 14
And turn it into this:
RowNo StartDate StopDate DaysBetween Series
1 3/21/2017 3/21/2017 14 1
2 4/4/2017 4/4/2017 14 1
3 4/18/2017 4/18/2017 14 1
4 6/23/2017 6/23/2017 66 2
5 7/5/2017 7/5/2017 12 2
6 7/19/2017 7/19/2017 14 2
7 9/27/2017 9/27/2017 70 3
8 10/24/2017 10/24/2017 27 3
9 10/31/2017 10/31/2017 7 3
10 11/14/2017 11/14/2017 14 3
Once I have that I'll group by Series and get the min(StartDate) and max(StopDate) for individual durations.
I could do this using a cursor but I'm sure someone much smarter than me has figured out a more elegant solution. Thanks in advance!
You can use the window function sum() over with a conditional FLAG
Example
Select *
,Series= 1+sum(case when [DaysBetween]>60 then 1 else 0 end) over (Order by RowNo)
From YourTable
Returns
RowNo StartDate StopDate DaysBetween Series
1 2017-03-21 2017-03-21 14 1
2 2017-04-04 2017-04-04 14 1
3 2017-04-18 2017-04-18 14 1
4 2017-06-23 2017-06-23 66 2
5 2017-07-05 2017-07-05 12 2
6 2017-07-19 2017-07-19 14 2
7 2017-09-27 2017-09-27 70 3
8 2017-10-24 2017-10-24 27 3
9 2017-10-31 2017-10-31 7 3
10 2017-11-14 2017-11-14 14 3
EDIT - 2008 Version
Select A.*
,B.*
From YourTable A
Cross Apply (
Select Series=1+sum( case when [DaysBetween]>60 then 1 else 0 end)
From YourTable
Where RowNo <= A.RowNo
) B
I work with Cloudera VM 5.2.0 pandas 0.18.0.
I have the following data
adclicksDF = pd.read_csv('/home/cloudera/Eglence/ad-clicks.csv',
parse_dates=['timestamp'],
skipinitialspace=True).assign(adCount=1)
adclicksDF.head(n=5)
Out[65]:
timestamp txId userSessionId teamId userId adId adCategory \
0 2016-05-26 15:13:22 5974 5809 27 611 2 electronics
1 2016-05-26 15:17:24 5976 5705 18 1874 21 movies
2 2016-05-26 15:22:52 5978 5791 53 2139 25 computers
3 2016-05-26 15:22:57 5973 5756 63 212 10 fashion
4 2016-05-26 15:22:58 5980 5920 9 1027 20 clothing
adCount
0 1
1 1
2 1
3 1
4 1
I want to do a group by for the field timestamp
adCategoryclicks = adclicksDF[['timestamp','adId','adCategory','userId','adCount']]
agrupadoDF = adCategoryclicks.groupby(pd.Grouper(key='timestamp', freq='1H'))['adCount'].agg(['count','sum'])
agrupadoDF.head(n=5)
Out[68]:
count sum
timestamp
2016-05-26 15:00:00 14 14
2016-05-26 16:00:00 24 24
2016-05-26 17:00:00 13 13
2016-05-26 18:00:00 16 16
2016-05-26 19:00:00 16 16
I want to add to agrupado more columns adCategory, idUser .
How can I do this?
There is multiple values in userId and adCategory for each group, so aggreagate by join:
In this sample last two datetime are changed for better output
print (adclicksDF)
timestamp txId userSessionId teamId userId adId adCategory \
0 2016-05-26 15:13:22 5974 5809 27 611 2 electronics
1 2016-05-26 15:17:24 5976 5705 18 1874 21 movies
2 2016-05-26 15:22:52 5978 5791 53 2139 25 computers
3 2016-05-26 16:22:57 5973 5756 63 212 10 fashion
4 2016-05-26 16:22:58 5980 5920 9 1027 20 clothing
adCount
0 1
1 1
2 1
3 1
4 1
#cast int to string
adclicksDF['userId'] = adclicksDF['userId'].astype(str)
adCategoryclicks = adclicksDF[['timestamp','adId','adCategory','userId','adCount']]
agrupadoDF = adCategoryclicks.groupby(pd.Grouper(key='timestamp', freq='1H'))
.agg({'adCount': ['count','sum'],
'userId': ', '.join,
'adCategory': ', '.join})
agrupadoDF.columns = ['adCategory','count','sum','userId']
print (agrupadoDF)
adCategory count sum \
timestamp
2016-05-26 15:00:00 electronics, movies, computers 3 3
2016-05-26 16:00:00 fashion, clothing 2 2
userId
timestamp
2016-05-26 15:00:00 611, 1874, 2139
2016-05-26 16:00:00 212, 1027