I want to convert number type to string with format as:
number -> string
1 -> 001
2 -> 002
12 -> 012
340 -> 340
You can use either TO_CHAR() (preferable in this situation) function or LPAD() function to achieve the desired result:
SQL> with t1(col) as(
2 select 1 from dual union all
3 select 2 from dual union all
4 select 12 from dual union all
5 select 340 from dual
6 )
7 select to_char(col, '000') as num_1
8 , lpad(to_char(col), 3, '0') as num_2
9 from t1
10 ;
NUM_1 NUM_2
----- ------------
001 001
002 002
012 012
340 340
Related
ID
001
002
REQ
ID ITEM QUANT
001 chips 20
002 chips 100
SCHEDULE
1 001 cleaning
1 002 normal
2 001 normal
2 002 remodel
3 001 normal
3 002 remodel
4 001 remodel
4 002 cleaning
item = corn chips
id_store
1
2
3
4
001
phase
cleaning
normal
normal
remodel
quant
0
20
20
5
002
phase
normal
remodel
remodel
cleaning
quant
100
5
5
0
I want to calculate a quant given a store phase. if the store is cleaning then its 0 quant, if remodeling then 5 quant else its the quant from requirements.
normally I would do this with a switch statement in dax but the phase data is not in my table. Please assist.
it turns out a simple switch statement looking at different tables works just fine.
Num Items :=
VAR T = sum(REQ[Quant])
RETURN
SWITCH(
TRUE(),
VALUES(SCHEDULE[PHASE]) = "cleaning", 0,
VALUES(SCHEDULE[PHASE]) = "remodeling", 5,
T
)
I want to merge two columns(Sender and Receiver) and get the Transaction Type count.
Sender Receiver Type Amount Date
773787639 777611388 1 300 2/1/2019
773631898 776806843 4 450 8/20/2019
773761571 777019819 6 369 2/11/2019
774295511 777084440 34 1000 1/22/2019
774263079 776816905 45 678 6/27/2019
774386894 777202863 12 2678 2/10/2019
773671537 777545555 14 38934 9/29/2019
774288117 777035194 18 21 4/22/2019
774242382 777132939 21 1275 9/30/2019
774144715 777049859 30 6309 7/4/2019
773911674 776938987 10 3528 5/1/2019
773397863 777548054 15 35892 7/6/2019
776816905 772345091 6 1234 7/7/2019
777035194 775623065 4 453454 7/20/2019
I am try to get like this kind of table
Sender/Receiver Type_1 Type_4 Type_12...... Type_45
773787639 3 2 0 0
773631898 1 0 1 2
773397863 2 2 0 0
772345091 1 1 0 3
You are looking for a pivot query. The only twist here is that we need to first take a union of the table to combine the sender/receiver data into a single column.
SELECT
SenderReceiver,
COUNT(CASE WHEN Type = 1 THEN 1 END) AS Type_1,
COUNT(CASE WHEN Type = 2 THEN 1 END) AS Type_2,
COUNT(CASE WHEN Type = 3 THEN 1 END) AS Type_3,
...
COUNT(CASE WHEN Type = 45 THEN 1 END) AS Type_45
FROM
(
SELECT Sender AS SenderReceiver, Type FROM yourTable
UNION ALL
SELECT Receiver, Type FROM yourTable
) t
GROUP BY
SenderReceiver;
If you don't want to type out 45 separate CASE expressions, you could probably automate it to some degree using Python.
I'm using Athena and trying to find a way to create a select statement that will return a sequence in the below format:
Numer
1
2
2
3
3
3
4
4
4
4
And so on, up to 200.
Is it even possible?
Combine sequence() with UNNEST:
SELECT n FROM UNNEST(sequence(1, 5)) t(n)
CROSS JOIN UNNEST(sequence(1, n)) x(y);
presto:default> SELECT n
-> FROM UNNEST(sequence(1, 5)) t(n)
-> CROSS JOIN UNNEST(sequence(1, n)) x(y);
n
---
1
2
2
3
3
3
4
4
4
4
5
5
5
5
5
(15 rows)
(tested in Presto 326 but will work in Athena too)
Run:
SELECT numbers FROM (
SELECT * FROM (
VALUES flatten(
transform(
sequence(1, 4),
x -> repeat(x, cast(x AS INT))
)
)
) AS x (a) CROSS JOIN UNNEST(a) AS t (numbers)
);
it will return:
numbers
---------
1
2
2
3
3
3
4
4
4
4
(10 rows)
I had a DataFrame like below:
Item Date Count
a 6/1/2018 1
b 6/1/2018 2
c 6/1/2018 3
a 12/1/2018 3
b 12/1/2018 4
c 12/1/2018 1
a 1/1/2019 2
b 1/1/2019 3
c 1/1/2019 2
I would like to get the sum of Count per Item with the specified duration from 7/1/2018 to 6/1/2019. For this case, the expected output will be:
Item TotalCount
a 5
b 7
c 3
We can use query with Series.between and chain that with GroupBy.sum:
df.query('Date.between("07-01-2018", "06-01-2019")').groupby('Item')['Count'].sum()
Output
Item
a 5
b 7
c 3
Name: Count, dtype: int64
To match your exact output, use reset_index:
df.query('Date.between("07-01-2018", "06-01-2019")').groupby('Item')['Count'].sum()\
.reset_index(name='Totalcount')
Output
Item Totalcount
0 a 5
1 b 7
2 c 3
Here is one with .loc[] using lambda:
#df.Date=pd.to_datetime(df.Date)
(df.loc[lambda x: x.Date.between("07-01-2018", "06-01-2019")]
.groupby('Item',as_index=False)['Count'].sum())
Item Count
0 a 5
1 b 7
2 c 3
Here is a sample data table:
ID Number Test Type Score
001 A 81
001 A 75
001 A 93
001 B 62
001 B 87
001 B 82
002 A 91
002 A 83
002 B 94
002 B 97
What I want, in excel, is a return of the maximum score of each test type for each id number so it would look like this...
ID Number Test Type Score
001 A 93
001 B 87
002 A 91
002 B 97
Is that possible?
You can use MAXIFS(). On your second table, if you have the ID number and Test Types, in the C column you can do:
=MAXIFS(Sheet1!$C$2:$C$1000,Sheet1!$A$2:$A$1000,$A2,Sheet1!$B$2:$B$1000,$B2)
Where Sheet1 is your main table.