Here is the info i have on my tables, what i need is to create a report based on certain dates and make a sum of every stock movement of the same id
Table one Table Two
Items Stocks
---------- ---------------------------
ID - NAME items_id - altas - bajas - created_at
1 White 4 5 0 8/10/2016
2 Black 2 1 5 8/10/2016
3 Red 3 3 2 8/11/2016
4 Blue 4 1 4 8/11/2016
2 10 2 8/12/2016
So based on a customer choice of dates (on this case lets say it selects all the data available on the table), i need to group them by items_id and then SUM all altas, and all bajas for that items_id, having the following at the end:
items_id altas bajas
1 0 0
2 11 7
3 3 2
4 6 4
Any help solving this?
Hope this will help:
Stock.select("sum(altas) as altas, sum(bajas) as bajas").group("item_id")
Related
I would appreciate some help for below issue. I have below table
id
items
1
Product
2
Tea
3
Coffee
4
Sugar
5
Product
6
Rice
7
Wheat
8
Product
9
Beans
10
Oil
I want output like below. Basically I want to increase the rank when item is 'Product'. May I know how can I do that? For data privacy and compliance purposes I have modified the data and column names
id
items
ranks
1
Product
1
2
Tea
1
3
Coffee
1
4
Sugar
1
5
Product
2
6
Rice
2
7
Wheat
2
8
Product
3
9
Beans
3
10
Oil
3
I have tried Lag and lead functions but unable to get expected output
Here is solution using a derived value of 1 or 0 to denote data boundaries SUM'ed up with the ROWS UNBOUNDED PRECEDING option, which is key here.
SELECT
id,
items,
SUM(CASE WHEN items='Product' THEN 1 ELSE 0 END) OVER (ORDER BY id ROWS UNBOUNDED PRECEDING) as ranks
FROM
I'm new to CTEs, and I am slowly starting to understand the basics.
I have a table that essentially goes in this pattern:
CUSTOMER X
CUSTOMER Y
1
1
1
2
2
3
3
4
3
5
4
5
5
6
I was wondering if a CTE would help return the numbers 1 through 6 (CUSTOMER Y) if Number 1 in CUSTOMER X had a specific column flagged for relevancy.
Customer 1 would be considered the main customer, while 2 - 6 could be stores related to said customer.
My end goal would be propogating down this relevancy flag for customers 2 - 6 if customer 1 has it and I'm currently trying to figure out how to get that full list.
I'd want the CTE to return a distinct list of customers.
CUSTOMER
1
2
3
4
5
6
I have a history of customer IDs and purchase IDs where no customer has ever bought the same product. However, for each purchase ID (which is unique), how can I find out the number of times the customer has made a previous purchase
I have tried using groupby() and sort_values()
df = pd.DataFrame({'id_cust': [1,2,1,3,2,4,1],
'id_purchase': ['20A','143C','99B','78R','309D','90J','78J']})
df.sort_values(by='id_cust')
df.groupby('id_cust')['id_purchase'].cumcount()
This is what I expect:
id_cust id_purchase value
1 20A 1
2 143C 1
1 99B 2
3 78R 1
2 3097D 2
4 900J 1
1 78J 3
You can just use the cumcount() on the id_cust column since the id_purchase is unique:
df['value']=df.groupby('id_cust')['id_cust'].cumcount()+1
print(df)
id_cust id_purchase value
0 1 20A 1
1 2 143C 1
2 1 99B 2
3 3 78R 1
4 2 309D 2
5 4 90J 1
6 1 78J 3
I have a track and Field Database with these tables (simplified):
Performance Table
Row Athlete Event Mark Meet
1 1 3 0:55 A
2 2 2 2:25 A
3 3 3 0:54 A
4 4 4 4:10 A
5 2 2 2:11 A
6 3 2 2:12 B
7 1 1 10 C
Athlete Table
Row Name Age Sex
1 Joe 13 M
2 Amy 15 F
3 John 16 M
4 Tim 17 M
So I understand how to implement this for an event with only 1 athlete (e.g. 100 m dash), but how would I include a relay event with 4 athletes. So, for example a 4x400 relay would need 4 athletes. In other words, some events have only 1 athlete and some have more than one. I am not sure if I should use:
Linking Table
Add 4 Columns
Do a table like below.
Other
Option 3 Table
Performance Table (Event 5 is a relay)
Row Athlete Event Mark Meet
1 1 3 0:55 A
2 2 2 2:25 A
3 3 3 0:54 A
4 4 4 4:10 A
5 2 2 2:11 A
6 3 2 2:12 B
7 1 5 9:34 C
8 2 5 9:34 C
9 3 5 9:34 C
10 4 5 9:34 C
Are you going to have events in the system before they are finished? For example, today's meet will include a 4x400 and here are the runners...
If that's the case then you'll need the linking table that you referred to because you want to be able to have that data stand on its own. It would just have the event_id and athlete_id in it so that you could have that set up. That would also be the PK (Primary Key) for the table and you would then use those two columns as the FK (Foreign Key) to the Performance table that you have at the end. If the data will never exist without times then you could just skip the link table and have the Performance table, although having the link table still wouldn't hurt in that case.
I am working with MS Access 2007. I have 2 tables: Types of Soda, and Likeability.
Types of Soda are: Coke, Pepsi, Dr. Pepper, and Mello Yellow
Likeability is a lookup with these options: Liked, Disliked, No preference
I know how to count the number of Cokes or Mello Yellows in the table using DCount("[Types]", "[Types of Soda]", "[Types]" = 'Coke')
I also know how to count the number of Liked, Disliked, No preference.
("[Perception]", "[Likeability]", "[Perception]" = 'Liked')
But, what if I need to count the number of "Likes" by Type.
i.e. the table should look like this:
Coke | Pepsi | Dr. Pepper | Mello Yellow
Likes 9 2 12 19
Dislikes 2 45 1 0
No Preference 0 12 14 15
I know in Access I can create a cross tab queries, but my tables are joined by an ID. So my [Likeability] table has an ID column, which is the same as the ID column in my [Types] table. That's the relationship, and that's what connects my tables.
My problem is that I don't know how to apply the condition for counting the likes, dislikes, etc, for ONLY the Types that I specify. It seems like I first have to check the [Likeability] table for "Likes", and cross reference the ID with the ID in the [Types] table.
I am very confused, and you may be too, now. But all I want to do is count the # of Likes and Dislikes for each type of soda.
Please help.
Its not really clear (to me anyway) what your tables look like so lets assume the following
tables
Soda
------
Soda_ID (Long Integer (Increment))
Soda_Name (Text(50)
Perception
------
Perception_ID (Long Integer (Increment))
Perception_Name (Text(50)
Likeability
-----------
Likeability_ID (Long Integer (Increment))
Soda_ID (Long Integer)
Perception_ID (Long Integer)
User_ID (Long Integer)
Data
Soda_Id Soda_Name
------- ---------
1 Coke
2 Pepsi
3 Dr. Pepper
4 Mello Yellow
Perception_ID Perception_Name
------------- ---------
1 Likes
2 Dislikes
3 No Preference
Likeability_ID Soda_ID Perception_ID User_ID
-------------- ------- ------------- -------
1 1 1 1
2 2 1 1
3 3 1 1
4 4 1 1
5 1 2 2
6 2 2 2
7 3 2 2
8 4 2 2
9 1 3 3
10 2 3 3
11 3 3 3
12 4 3 3
13 1 1 5
14 2 2 6
15 2 2 7
16 3 3 8
17 3 3 9
18 3 3 10
Transform query You could write a query like this
TRANSFORM
Count(l.Likeability_ID) AS CountOfLikeability_ID
SELECT
p.Perception_Name
FROM
Soda s
INNER JOIN (Perception p
INNER JOIN Likeability l
ON p.Perception_ID = l.Perception_ID)
ON s.Soda_Id = l.Soda_ID
WHERE
p.Perception_Name<>"No Preference"
GROUP BY
p.Perception_Name
PIVOT
s.Soda_Name;
query output
Perception_Name Coke Dr_ Pepper Mello Yellow Pepsi
--------------- ---- ---------- ------------ -----
Dislikes 1 1 1 3
Likes 2 1 1 1