I have a flight Database with the following table:
FID from fto dep arrive days flightno
---- ----- --- ---- ------ ---- --------
167 MPB KYM 1020 1040 0 EA5203
168 MPB KYM 1510 1530 0 EA5205
169 MPB KYM 1705 1725 0 EA5207
221 NEB KYM 850 1025 0 EA782
222 NEB KYM 1355 1530 0 EA784
223 NEB KYM 1810 1945 0 EA786
557 BAH NEB 1305 1500 0 EA686
558 BAH ELM 605 715 0 EA162
559 BAH ELM 1005 1115 0 EA340
560 BAH ELM 1230 1340 0 EA872
561 BAH ELM 1325 1435 0 EA442
562 BAH ELM 1400 1510 0 EA872
563 BAH ELM 1455 1605 0 EA978
564 BAH ELM 1640 1750 0 EA640
565 BAH ELM 2025 2135 0 EA940
566 BAH YDS 645 845 0 EA992
567 BAH YDS 945 1130 0 EA974
1163 PPP KYM 1040 1110 0 EA3201
1164 PPP KYM 1450 1520 0 EA3207
1190 OKR KYM 825 920 0 EA3200
1191 OKR KYM 1010 1100 0 EA3204
1192 OKR KYM 1500 1605 0 EA3214
1517 SVT KYM 810 920 0 EA3201
1518 SVT KYM 940 1050 0 EA3201
1519 SVT KYM 1215 1310 0 EA3211
1520 SVT KYM 1510 1605 0 EA3211
How do I query it to show indirect flights from BAH to KYM?
I've tried a number of ways to no avail. Any help is greatly appreciated.
select CONNECT_BY_ROOT ffrom ||SYS_CONNECT_BY_PATH(fto, '/') path_ ,level,ffrom,fto
from FLIGHTS_TABLE
where level > 1
start with ffrom='BAH' CONNECT BY PRIOR fto =ffrom ORDER SIBLINGS By ffrom ;
It retuns :
PATH_ |LEVEL| FFROM | FTO
BAH/NEB/KYM | 2 | NEB | KYM
BAH/NEB/KYM | 2 | NEB | KYM
BAH/NEB/KYM | 2 | NEB | KYM
It returns 3 rows because of there are 3 row/flight from 'NEB' to 'KYM'. I don't know which flight is indirect flight.
Related
What I have is below.
DOG
Date
Steps
Tiger
2021-11-01
164
Oakley
2021-11-01
76
Piper
2021-11-01
65
Millie
2021-11-01
188
Oscar
2021-11-02
152
Foster
2021-11-02
191
Zeus
2021-11-02
101
Benji
2021-11-02
94
Lucy
2021-11-02
186
Rufus
2021-11-02
65
Hank
2021-11-03
98
Olive
2021-11-03
122
Ellie
2021-11-03
153
Thor
2021-11-03
152
Nala
2021-11-03
181
Mia
2021-11-03
48
Bella
2021-11-03
23
Izzy
2021-11-03
135
Pepper
2021-11-03
22
Diesel
2021-11-04
111
Dixie
2021-11-04
34
Emma
2021-11-04
56
Abbie
2021-11-04
32
Guinness
2021-11-04
166
Kobe
2021-11-04
71
What I want is below. Rank by value of ['Steps'] column for each Date
DOG
Date
Steps
Rank
Tiger
2021-11-01
164
2
Oakley
2021-11-01
76
3
Piper
2021-11-01
65
4
Millie
2021-11-01
188
1
Oscar
2021-11-02
152
3
Foster
2021-11-02
191
1
Zeus
2021-11-02
101
4
Benji
2021-11-02
94
5
Lucy
2021-11-02
186
2
Rufus
2021-11-02
65
6
Hank
2021-11-03
98
6
Olive
2021-11-03
122
5
Ellie
2021-11-03
153
2
Thor
2021-11-03
152
3
Nala
2021-11-03
181
1
Mia
2021-11-03
48
7
Bella
2021-11-03
23
8
Izzy
2021-11-03
135
4
Pepper
2021-11-03
22
9
Diesel
2021-11-04
111
2
Dixie
2021-11-04
34
5
Emma
2021-11-04
56
4
Abbie
2021-11-04
32
6
Guinness
2021-11-04
166
1
Kobe
2021-11-04
71
3
I tried below, but it failed.
df['Rank'] = df.groupby('Date')['Steps'].rank(ascending=False)
First your solution for me working.
Maybe need method='dense' and casting to integers.
df['Rank'] = df.groupby('Date')['Steps'].rank(ascending=False, method='dense').astype(int)
I have a data frame like this- Machine Vibration data.
datetime
tagid
value
quality
0
2021-03-01 13:43:41.440
B42
345
192
1
2021-03-01 13:43:41.440
B43
958
192
2
2021-03-01 13:43:41.440
B44
993
192
3
2021-03-01 13:43:41.440
B45
1224
192
4
2021-03-01 13:43:43.527
B188
6665
192
5
2021-03-01 13:43:43.527
B189
7162
192
6
2021-03-01 13:43:43.527
B190
7193
192
7
2021-03-01 13:43:43.747
C29
2975
192
8
2021-03-01 13:43:43.747
C30
4445
192
9
2021-03-01 13:43:43.747
C31
4015
192
I want to convert this to hourly maximum value for each tag id.
Sample Output
datetime
tagid
value
quality
01-03-2021 13:00
C91
3982
192
01-03-2021 14:00
C91
3972
192
01-03-2021 13:00
C92
9000
192
01-03-2021 14:00
C92
9972
192
01-03-2021 13:00
B42
396
192
01-03-2021 14:00
B42
370
192
01-03-2021 15:00
B42
370
192
I tried with grouper, but couldn't get output.
Use Grouper with aggregate max:
df = df.groupby([pd.Grouper(freq='H', key='datetime'), 'tagid']).max().reset_index()
I have a series;
Red 33
Blue 44
Green 22
And also this series;
0 100
1 100
2 100
3 200
4 200
5 200
I want to multiply these in a way to give the following dataframe
Red Blue Green
0 330 440 220
1 330 440 220
2 330 440 220
3 660 880 440
4 660 880 440
5 660 880 440
Can anyone see a simply / tidy way this could be done?
IIUC assuming s is the name of the first series and s1 is the name of the second series, try:
m=s.to_frame().T
pd.DataFrame(m.values*s1.values[:,None],columns=m.columns)
Red Blue Green
0 3300 4400 2200
1 3300 4400 2200
2 3300 4400 2200
3 6600 8800 4400
4 6600 8800 4400
5 6600 8800 4400
So i have a table containing below columns.
I want to compute an running average from positiondate and for example 3 days back, grouped on dealno.
I know how to do with "case by" but problem is that I have around 200 different DealNo so I do not want to write an own case by clause for every deal.
On dealNo 1 it desired output should be Average(149 243 440 + 149 224 446 + 149 243 451)
DealNo PositionDate MarketValue
1 | 2016-11-27 | 149 243 440
2 | 2016-11-27 | 21 496 418
3 | 2016-11-27 | 32 249 600
1 | 2016-11-26 | 149 243 446
2 | 2016-11-26 | 21 496 418
3 | 2016-11-26 | 32 249 600
1 | 2016-11-25 | 149 243 451
3 | 2016-11-25 | 32 249 600
2 | 2016-11-25 | 21 496 418
3 | 2016-11-24 | 32 249 600
1 | 2016-11-24 | 149 225 582
2 | 2016-11-24 | 21 498 120
1 | 2016-11-23 | 149 256 867
2 | 2016-11-23 | 21 504 181
3 | 2016-11-23 | 32 253 440
1 | 2016-11-22 | 149 256 873
2 | 2016-11-22 | 21 506 840
3 | 2016-11-22 | 32 253 440
1 | 2016-11-21 | 149 234 535
2 | 2016-11-21 | 21 509 179
3 | 2016-11-21 | 32 253 600
I tried below script but it was not very effective since my table contains around 300k rows and approx 200 different dealno.
Is there a more effective way to do this in SQL 2008?
with cte as (
SELECT ROW_NUMBER() over(order by dealno, positiondate desc) as Rownr,
dealno,
positiondate,
Currency,
MvCleanCcy
FROM T1
)
select
rownr, positiondate, DealNo, Currency,
mvcleanavg30d = (select avg(MvCleanCcy) from cte2 where Rownr between c.Rownr and c.Rownr+3)
from cte as c
You don't need window functions. You can do this using outer apply:
select t1.*, tt1.marketvalue_3day
from t1 outer apply
(select avg(tt1.marketvalue) as marketvalue_3day
from (select top 3 tt1.*
from t1 tt1
where tt1.deal1 = t1.deal1 and
tt1.positiondate <= t1.positiondate
order by tt1.positiondate desc
) tt1
) tt1;
File contains information about products per day, and I need to calculate average values for month for each product.
Source data looks like this:
A B C D
id date rating price
1 1 2014/01/01 2 20
2 1 2014/01/02 2 20
3 1 2014/01/03 2 20
4 1 2014/01/04 1 20
5 1 2014/01/05 1 20
6 1 2014/01/06 1 20
7 1 2014/01/07 1 20
8 3 2014/01/01 5 99
9 3 2014/01/02 5 99
10 3 2014/01/03 5 99
11 3 2014/01/04 5 99
12 3 2014/01/05 5 120
13 3 2014/01/06 5 120
14 3 2014/01/07 5 120
Need to get:
A B C D
id date rating price
1 1 1.42 20
2 3 5 108
How to do that? Need some advanced formula or VB Script.
Update: I have data for long period - about 2 years. Need to calculate average values for each product for each week, and after for each month.
Source data example:
id date rating
4 2013-09-01 445
4 2013-09-02 446
4 2013-09-03 447
4 2013-09-04 448
4 2013-09-05 449
4 2013-09-06 450
4 2013-09-07 451
4 2013-09-08 452
4 2013-09-09 453
4 2013-09-10 454
4 2013-09-11 455
4 2013-09-12 456
4 2013-09-13 457
4 2013-09-14 458
4 2013-09-15 459
4 2013-09-16 460
4 2013-09-17 461
4 2013-09-18 462
4 2013-09-19 463
4 2013-09-20 464
4 2013-09-21 465
4 2013-09-22 466
4 2013-09-23 467
4 2013-09-24 468
4 2013-09-25 469
4 2013-09-26 470
4 2013-09-27 471
4 2013-09-28 472
4 2013-09-29 473
4 2013-09-30 474
4 2013-10-01 475
4 2013-10-02 476
4 2013-10-03 477
4 2013-10-04 478
4 2013-10-05 479
4 2013-10-06 480
4 2013-10-07 481
4 2013-10-08 482
4 2013-10-09 483
4 2013-10-10 484
4 2013-10-11 485
4 2013-10-12 486
4 2013-10-13 487
4 2013-10-14 488
4 2013-10-15 489
4 2013-10-16 490
4 2013-10-17 491
4 2013-10-18 492
4 2013-10-19 493
4 2013-10-20 494
4 2013-10-21 495
4 2013-10-22 496
4 2013-10-23 497
4 2013-10-24 498
4 2013-10-25 499
4 2013-10-26 500
4 2013-10-27 501
4 2013-10-28 502
4 2013-10-29 503
4 2013-10-30 504
4 2013-10-31 505
7 2013-09-01 1445
7 2013-09-02 1446
7 2013-09-03 1447
7 2013-09-04 1448
7 2013-09-05 1449
7 2013-09-06 1450
7 2013-09-07 1451
7 2013-09-08 1452
7 2013-09-09 1453
7 2013-09-10 1454
7 2013-09-11 1455
7 2013-09-12 1456
7 2013-09-13 1457
7 2013-09-14 1458
7 2013-09-15 1459
7 2013-09-16 1460
7 2013-09-17 1461
7 2013-09-18 1462
7 2013-09-19 1463
7 2013-09-20 1464
7 2013-09-21 1465
7 2013-09-22 1466
7 2013-09-23 1467
7 2013-09-24 1468
7 2013-09-25 1469
7 2013-09-26 1470
7 2013-09-27 1471
7 2013-09-28 1472
7 2013-09-29 1473
7 2013-09-30 1474
7 2013-10-01 1475
7 2013-10-02 1476
7 2013-10-03 1477
7 2013-10-04 1478
7 2013-10-05 1479
7 2013-10-06 1480
7 2013-10-07 1481
7 2013-10-08 1482
7 2013-10-09 1483
7 2013-10-10 1484
7 2013-10-11 1485
7 2013-10-12 1486
7 2013-10-13 1487
7 2013-10-14 1488
7 2013-10-15 1489
7 2013-10-16 1490
7 2013-10-17 1491
7 2013-10-18 1492
7 2013-10-19 1493
7 2013-10-20 1494
7 2013-10-21 1495
7 2013-10-22 1496
7 2013-10-23 1497
7 2013-10-24 1498
7 2013-10-25 1499
7 2013-10-26 1500
7 2013-10-27 1501
7 2013-10-28 1502
7 2013-10-29 1503
7 2013-10-30 1504
7 2013-10-31 1505
This is the job of a pivot table, and it takes about 30secs to do it
Update:
as per your update, put the date into the Report Filter and modify to suit