I wasnt able to find anything like this yet... but here is what i need to do:
I have a query result like this:
ID Data1 Data2 Data3 Data4 ... Data7
1 12 13 15 1 ... 12
2 12 13 15 1 ... 12
3 12 13 15 1 ... 12
4 12 13 15 1 ... 12
I need to make a BarChart With 2 Values, 1 is the first row (ID=1) one is the last row (ID=4). The column headers DataX is what i need the series to be paired by.
Example:
ID Insured Uninsured Rejected
1 12 3 0
4 16 9 2
In the BarChart i need to see the number of insured or ID=1 and ID=2 next to each other, the number of Uninsured and rejected the same.
I feel like i have tried all ways possible but was not able to get anything besides a BarChart where all values of ID=1 where displayed and then all values for ID=2 where displayed next to each other.
Im sure this was a very confusing way to describe it, but i hope someone can understand what i am looking for.
NOTE: I tried to do this in Excel, and it worked within 2 minutes. I set the filter: Series on the 2 rows that i wanted, and set the Categories to the dataX Columns as described, and everything looked great. When i tried to translate this into SSRS i was able to do all the same things in the Series and Categories, but then i had to put in values and that screwed everything up.
PLEASE HELP!
I bet you need to add a grouping to your values by a spanning factor.
Related
I'm not sure how to explain this, but hopefully this makes sense. If I require 10 items of stock to be removed from this table data below, is it possible to send the number 10 into a query
and it updates the table based on the sort order, basically, it will take all it can from the first line and the rest from the second line (or third or firth if you know what I mean):
This is the start
componentid
stocklevel
so
991
5
1
1063
16
2
This is what I am trying to achieve
componentid
stocklevel
so
991
0
1
1063
11
2
Almost like, if it gets to zero, move to the next row
I am facing an issue with transforming my data using Pandas' groupby. I have a table (several million rows and 3 variables) that I am trying to group by "Date" variable.
Snippet from a raw table:
Date V1 V2
07_19_2017_17_00_06 10 5
07_19_2017_17_00_06 20 6
07_19_2017_17_00_08 15 3
...
01_07_2019_14_06_59 30 1
01_07_2019_14_06_59 40 2
The goal is to group rows with the same value of "Date" by applying a mean function over V1 and sum function over V2. So that the expected result resembles:
Date V1 V2
07_19_2017_17_00_06 15 11 # This row has changed
07_19_2017_17_00_08 15 3
...
01_07_2019_14_06_59 35 3 # and this one too!
My code:
df = df.groupby(['Date'], as_index=False).agg({'V1': 'mean', 'V2': 'sum'})
The output I am getting, however, is totally unexpected and I am can't find a reasonable explanation of why it happens. It seems like Pandas is only processing data from 01_01_2018_00_00_01 to 12_31_2018_23_58_40, instead of 07_19_2017_17_00_06 to 01_07_2019_14_06_59.
Date V1 V2
01_01_2018_00_00_01 30 3
01_01_2018_00_00_02 20 4
...
12_31_2018_23_58_35 15 3
12_31_2018_23_58_40 16 11
If you have any clue, I would really appreciate your input. Thank you!
I suspect that the issue is based around Pandas not recognizing the date format that I've used. A solution turned out to be quite simple: convert all of the dates into UNIX time format, divide by 60 and then, repeat the groupby procedure.
Hi i am new to dataframe, please help me resolve this.
My dataframe1 looks like this (It has itemID and Item name), i only have 7 items
itemID ItemName
1 abc
2 fds
3 btbtr
4 gerhet
5 dfhkwjfn
6 adaf
7 jdkj
My Dataframe2 looks like this:
which has userID, and itemID, here i have 20k users and each user has a itemid in front of it(can be multiple)
userId itemID
23213 2
31267 3
52144 1
52144 2
87467 6
how can i find item- item correlation between the items?
I want that item1 is highly correlated with item3 and item6
i tried corrwith() but all i get is NaN.
please help me find this, Thanks in advance
Here is the approach I can think of. Might be crude, but here we go.
Remove all users which have only 1 item in front of them
Now you only have users with multiple items.
Make a note of the count of co-occurrence of items. i.e. make a data frame of sort
item-item : count
1-2 : 50
3-5 : 35
and so on. Now after getting all one on one correlations normalize the count values between 0-1 and you have your correlation between all items.
Hope it helps!
i have a Datagrid that stores the number pencils produced each day of the month it looks like this:
Pencil | day 1 | day 2 | day 3 | ... |day 31
Red 0 0 13 0 0
blue 5 1 0 8 0
yellow 0 9 5 0 0
I need to save this data into SQL table but im not sure what's the most efficent way to design the table in SQL.
I was thinking about creating a table in SQL with the fields:
pencilmodel
date
quantity
and then in vb.net making a loop that saves 1 by 1 each cell of the datagrid in to the table, but i dont think this is the best way since i will have like 30 rows and a month has 31 days max so it will be 30*31= 930 times.
Im using VB.net and SQL Server
i would create the table that way (as you suggested):
ID | pencilmodel | ProducedDate | Quantity
1 blue dd-mm-yyyy 7
2 red dd-mm-yyyy 4
3 yellow dd-mm-yyyy 6
also, dont loop and insert each row to database, its not efficient, add it to a dataset first and then update it using DataAdapter.Update or bind a dataset to the datagrid view:
How to: Bind Data to the Windows Forms DataGridView Control
I dont know if this one is relevant but why dont you create a fields based on the date and time? lets say like this in your PC
12/14/2016
You can create a program that will create a field for you everyday for example when the day passes by then add a column look like this.
__________________________________
|12/14/2016|12/15/2016|12/15/2016|
so what will happen is you dont need to loop in DGV you just do your INSERT COMMAND
you just need some modifications and validations in here like
if Date_Has_Been_Changed then
Create Table Add Columns
End If
I'll clarify this: I have a data result with the twist that the two PK's (A and B) are the same, and field C doesn't.
Example:
A B C D
> 14 20 1 null
> 14 20 2 1
> 15 20 2 0
As you can see, D field has a null and a 0.
What I have to do is to change D's null value to 1 whenever A fields are the same, and there's more than 1 record with those, not touching the 0's in D.
I tried initially with NVLs and DECODEs, like this:
DECODE(migr.A,NULL,(NVL(C,1)),D) AS D
but I'm not getting all the records, only the D-1's.
I really don't want to relate to an extra table/step for validation, as my query result can be easily over 1 million records, but if that's the best, I'm ok.
Many thanks.