I have a customer table with their closest delivery hub on a grid based system and need to calculate what is the most populated area using a query.
This is the current query I have that lists all of the Co-ordinates per Customer.
SELECT Customers.HubID, TO_CHAR(Hubs.HubCoordX, 'FM999999999999') as "X Co-ordinate", TO_CHAR(Hubs.HubCoordX, 'FM999999999999') AS "Y Co-ordinate" FROM Customers INNER JOIN Hubs ON Customers.HubID = Hubs.DestinationID ORDER BY Hubs.HubCoordX, Hubs.HubCoordY
This query creates the following result.
HubID
X Co-ord
Y Co-ord
9
-3
1
11
-2
18
2
0
0
3
0
0
3
0
0
1
0
0
1
0
0
3
0
0
4
3
1
5
3
1
7
7
3
But I need a result like this
X Co-ordinate
Y Co-ordinate
Population
-3
1
1
-2
18
1
0
0
6
3
1
2
7
3
1
Thanks in advance
I have attempted use Count Unique however it resulted in only counting individual Co-ordinates once.
SELECT TO_CHAR(Hubs.HubCoordX, 'FM999999999999') as "X Co-ordinate",
TO_CHAR(Hubs.HubCoordX, 'FM999999999999') AS "Y Co-ordinate", Count(HubID) as "Population"
FROM Customers
INNER JOIN Hubs ON Customers.HubID = Hubs.DestinationID
Group BY Hubs.HubCoordX, Hubs.HubCoordY
Related
If I have a dataframe of the form:
tag element_id
1 12
1 13
1 15
2 12
2 13
2 19
3 12
3 15
3 22
how can I compute the overlaps of the tags in terms of the element_id ? The result I guess should be an overlap matrix of the form:
1 2 3
1 X 2 2
2 2 X 1
3 2 1 X
where I put X on the diagonal since the overlap of a tag with itself is not relevant and where the numbers in the matrix represent the total element_ids that the two tags share.
My attempts:
You can try and use a for loop like :
for item in df.itertuples():
element_lst += [item.element_id]
element_tag = item.tag
# then intersect the element_list row by row.
# This is extremely costly for large datasets
The second thing I was thinking about was to use df.groupby('tag') and try to somehow intersect on element_id, but it is not clear to me how I can do that with grouped data.
merge + crosstab
# Find element overlap, remove same tag matches
res = df.merge(df, on='element_id').query('tag_x != tag_y')
pd.crosstab(res.tag_x, res.tag_y)
Output:
tag_y 1 2 3
tag_x
1 0 2 2
2 2 0 1
3 2 1 0
I have a list of index numbers that represent index locations for a DF. list_index = [2,7,12]
I want to sum from a single column in the DF by rolling through each number in list_index and totaling the counts between the index points (and restart count at 0 at each index point). Here is a mini example.
The desired output is in OUTPUT column, which increments every time there is another 1 from COL 1 and RESTARTS the count at 0 on the location after the number in the list_index.
I was able to get it to work with a loop but there are millions of rows in the DF and it takes a while for the loop to run. It seems like I need a lambda function with a sum but I need to input start and end point in index.
Something like lambda x:x.rolling(start_index, end_index).sum()? Can anyone help me out on this.
You can try of cummulative sum and retrieving only 1 values related information , rolling sum with diffferent intervals is not possible
a = df['col'].eq(1).cumsum()
df['output'] = a - a.mask(df['col'].eq(1)).ffill().fillna(0).astype(int)
Out:
col output
0 0 0
1 1 1
2 1 2
3 0 0
4 1 1
5 1 2
6 1 3
7 0 0
8 0 0
9 0 0
10 0 0
11 1 1
12 1 2
13 0 0
14 0 0
15 1 1
For each row, I need to calculate the integer part from dividing by 4. For each subsequent row, we add the remainder of the division by 4 previous and current lines and look at the whole part and the remainders from dividing by 4. Consider the example below:
id val
1 22
2 1
3 1
4 2
5 1
6 6
7 1
After dividing by 4, we look at the whole part and the remainders. For each id we add up the accumulated points until they are divided by 4:
id val wh1 rem1 wh2 rem2 RESULT(wh1+wh2)
1 22 5 2 0 2 5
2 1 0 1 (3/4=0) 3%4=3 0
3 1 0 1 (4/4=1) 4%4=0 1
4 2 0 2 (2/4=0) 2%4=2 0
5 1 0 1 (3/4=0) 3%4=3 0
6 7 1 2 (5/4=1) 5%4=1 2
7 1 0 1 (2/4=0) 2%4=1 0
How can I get the next RESULT column with sql?
Data of project:
http://sqlfiddle.com/#!18/9e18f/2
The whole part from the division into 4 is easy, the problem is to calculate the accumulated remains for each id, and to calculate which of them will also be divided into 4
I am creating an excel sheet that has three columns. Detail, month and month count
1 -- I would like for the formula to look at the detail column and if there is text add the previous cell number plus 1 to new month count, if not insert 0
2-- I would like the formula to add the previous cell before the cell with 0 and for the cell with 0 not to impact the other cells or reset the cells back to 1 witch is the problem am having
3-- I also need the formula to reset for every month from what ever number it was back to 0 or 1 depending if the new month first cell has text or not. for this I need the formula to look at the month column
This is what I have so far:
=IF(ISTEXT(G95), I94+ 1, 0)
The formula for the count column should be as follows.
=IF(A2<>"",COUNTIF($B$1:B2,B2)-COUNTIFS($A$1:A2,"",$B$1:B2,B2),0)
Breakdown of how this works:
A2<>"" Will check if the detail column is populated
COUNTIF($B$1:B2,B2) will figure out how many entries are above this row that reference the same month.
COUNTIFS($A$1:A2,"",$B$1:B2,B2) Will find how many cells are blank provided that it also matches the month. This subtracted from the previous section gives you how many are not blank.
The IF will return 0 if the detail is empty.
Which returned the following data
Orderly Random
Det Mon Count Det Mon Count
X 1 1 2 0
X 1 2 X 1 1
X 1 3 X 1 2
1 0 2 0
X 1 4 X 2 1
X 2 1 X 1 3
X 2 2 X 1 4
2 0 1 0
2 0 1 0
2 0 2 0
3 0 3 0
X 3 1 X 3 1
3 0 1 0
X 3 2 3 0
X 3 3 X 1 5
3 0 X 2 2
X 3 4 X 3 2
3 0 3 0
X 3 5 3 0
X 3 6 2 0
It sounds like you want to keep a running total for the month count in the column and put a 0 if there is not text. If that is the case, you can put this formula in I95.
=IF(ISTEXT(G95),MAX($I$2:I94)+1, 0)
I have 1 Virtual cube consists of 2 cubes.
Example of fact table of 1st cube.
id object_id time_id date_id state
1 10 2 1 0
2 11 5 1 0
3 10 7 1 1
4 10 3 1 0
5 11 4 1 0
6 11 7 1 1
7 10 8 1 0
8 11 5 1 0
9 10 7 1 1
10 10 9 1 2
Where State: 0 - Ok, 1 - Down, 2 - Unknown
For this cube I have one measure StateCount it should count States for each object_id.
Here for example we have such result:
for 10 : 3 times Ok , 2 times Down, 1 time Unknown
for 11 : 3 times Ok , 1 time Down
Second cube looks like this:
id object_id time_id date_id status
1 10 2 1 0
2 11 5 1 0
3 10 7 1 1
4 10 3 1 1
5 11 4 1 1
Where Status: 0 - out, 1 - in. I keep this in StatusDim.
In this table I keep records that should not be count. If object have status 1 that means that I have exclude it from count.
If we intersect these tables and use StateCount we will receive this result:
for 10 : 2 times Ok , 1 times Down, 1 time Unknown
for 11 : 2 times Ok , 1 time Down
As far as i know, i must use calculated member with IIF condition. Currently I'm trying something like this.
WITH MEMBER [Measures].[StateTimeCountDown] AS(
iif(
[StatusDimDown.DowntimeHierarchy].[DowntimeStatus].CurrentMember.MemberValue
<> "in"
, [Measures].[StateTimeCount]
, null )
)
The multidimensional way to do this would be to make attributes from your state and status columns (hopefully with user understandable members, i. e. using "Ok" and not "0"). Then, you can just use a normal count measure on the fact tables, and slice by these attributes. No need for complex calculation definitions.