I have this table :
Trans_ID Name Value Total_Item
100 I1 0.33333333 3
100 I2 0.33333333 3
400 I1 0.33333333 3
400 I2 0.33333333 3
800 I1 0.25 4
800 I2 0.25 4
900 I1 0.33333333 3
900 I2 0.33333333 3
1000 I1 0.2 5
1000 I2 0.2 5
i need to make it into :
ITEM VALUE
I1,I2 0.28999998
Value is calculated from sum of each 2 item in all transaction / total transaction
EX: item I1 & I2 at trans 100
(0.33333333 + 0.33333333) = 0.666666666
trans 400
(0.33333333 + 0.33333333) = 0.666666666
trans 800
(0.25+0.25) = 0.5
trans 900
(0.33333333 + 0.33333333) = 0.666666666
trans 1000
(0.2+0.2) = 0.4
So Value will be:
(0.666666666+0.666666666+0.5+0.666666666+0.4)/10= 0.28999998
*since total transaction in this table is example table 10. there's aprox 50k transaction in my real table
please note that Total_item value is fixed for each transaction and there's no mistake (note that trans 100 only have 2 item and i put 3 in total item)
i'm working with ms access (but general sql query is fine)
If you are correct about your process, then all the grouping you are using is not necessary - the value is the same without the grouping. That is:
((T100_I1 + T100_I2) + (T400_I1 + T400_I2)) / 4 = (T100_I1 + T100_I2 + T400_I1 + T400_I2) / 4 =
In other words, to get the value you described you want, you just need to sum all the values and divide by their count.
select sum(value)/count(*)
from table
Related
Let's assume we have this 'table' here:
Time_Order
Logic
Number
Accumulated
1
Add
20
20
2
Add
30
50
3
Add
50
100
4
Multiply
0.8
80
5
Multiply
0.5
40
6
Add
10
50
Accumulated is the results of Adding or Multiplying based on all the previous records, so in Time_Order 3 we accumulated (50 + 30 + 20) = 100, then in Time_Order 4 I want to multiply by the 0.8 so I get 100 * 0.80 = 80, then Time_Order 5 I multiply the 80 by 0.5 and get 40. I go back to Add in Time Order 6 and get 40 + 10 = 50
I have something like:
Select a.*, case when Logic = 'Add' then sum(Number) over (Order by Time_Order)
when Logic = 'Multiply' then Exp(Sum(ln(Accumulated * (1+Number)))) as Accumulated
from table a
The above won't work because I have 'Accumulated' within itself in the Multiply logic, so this is the exact problem, when faced with a conditional statement like this, how can i shift back and forth between 'add' and 'multiply' such that the accumulated number is remembered what it is from the previous row.
There is a data frame with totals and counts:
pd.DataFrame({
'categorie':['a','b','c'],
'total':[100,1000,500],
'x':[10,100,5],
'y':[100,1000,500]
})
categorie
total
x
y
a
100
10
100
b
1000
100
1000
c
500
5
500
I like to convert the counted columns into percentage based on the totals:
categorie
total
x%
y%
a
100
10
100
b
1000
10
100
c
500
1
100
Following will work for a series:
(100 * df['x'] / df['total']).round(1)
How to apply this for all columns in the data frame?
try via div(),mul() and astype() method:
df[['x%','y%']]=df[['x','y']].div(df['total'],axis=0).mul(100).astype(int)
output of df:
categorie total x y x% y%
0 a 100 10 100 10 100
1 b 1000 100 1000 10 100
2 c 500 5 500 1 100
I have a table containing a column DED with numbers that can go from 0 to infinity. I am interested in grouping them starting always in 0 (upper bound as open and lower bound as closed interval) and get the percentage totals
Suppose I have a column with
DED AMT
0.0004 4
0.0009 1
0.001 2
0.002 1
0.009 4
0.01 5
0.04 6
0.09 3
0.095 1
0.9 3
1 2
100 1
500 1
so I would want the following intervals:
DED AMT PAMT
0-0.01 12 0.3529
0.01-0.02 5 0.1470
0.04-0.05 6 0.1764
0.09-0.1 4 0.1176
0.9-1 3 0.0882
1 2 0.0588
I have tried:
SELECT CAST(DED/.02*.02 AS VARCHAR) +' - '+CAST(DED/.02*.02 +.01 AS VARCHAR) AS DED,
SUM(AMT) AS AMT,ISNULL(SUM(AMT)*1.000/NULLIF(SUM(SUM(AMT)) OVER (),0),0) AS PAMT
FROM MYTABLE
WHERE DED/.02*.02<=1
GROUP BY DED/.02*.02
Thanks for your help
SELECT
ROUND(DED, 2, 1) AS DED_lower,
ROUND(DED, 2, 1) + 0.01 AS DED_upper,
SUM(AMT) AS SUM_AMT,
SUM(AMT) * 1.0
/
SUM(AMT) OVER () AS PAMT
FROM
mytable
WHERE
DED <= 1
GROUP BY
ROUND(DED, 2, 1)
ROUND(DED, 2, 1) will round Down to two decimal places. Giving equal sized bands of 0.01 in size.
Apologies for typos or formating, I'm on my phone
I have a table that looks something like this
Class ID Value
A 1 300
A 2 200
A 3 500
B 1 300
B 2 300
C 1 1000
Is there a way of using SQL to calculate the percentage share each ID has to the class.
For example, the percentages for class A would be 30% to id 1, 20% to ID 2, and 50% to id 3 and so on for the other classes:
Class ID Value Percentage
A 1 300 30%
A 2 200 20%
A 3 500 50%
B 1 300 50%
B 2 300 50%
C 1 1000 100%
You can use window functions (if your database, which you did not disclose, supports them):
select
t.*,
1.0 * value / sum(value) over(partition by class) ratio
from mytable t
This gives you a ratio, that is a value between 0 and 1 - I find that this is more relevant than a percentage, but you can multiply that by 100 if you like.
I have an optimization exercise I am trying to work through and am stuck again on the syntax. Below is my attempt, and I'd really like a thorough explanation of the syntax in addition to the solution code. I think it's the specific index piece that I am having trouble with.
The problem:
I have an item that I wish to sell out of within ten weeks. I have a historical trend and wish to alter that trend by lowering price. I want maximum margin dollars. The below works, but I wish to add two constraints and can't sort out the syntax. I have spaces for these two constraints in the code, with my brief explanation of what I think they may look like. Here is a more detailed explanation of what I need each constraint to do.
inv_cap=There is only so much inventory available at each location. I wish to sell it all. For location 1 it is 800, location 2 it is 1200. The sum of the column FRC_UNITS should equal this amount, but cannot exceed it.
price_down_or_same=The price cannot bounce around, so it needs to always be less than or more than the previous week. So, price(i)<=price(i-1) where i=week.
Here is my attempt. Thank you in advance for assistance.
*read in data;
data opt_test_mkdown_raw;
input
ITM_NBR
ITM_DES_TXT $
LCT_NBR
WEEK
LY_UNITS
ELAST
COST
PRICE
TOTAL_INV;
cards;
1 stuff 1 1 300 1.2 6 10 800
1 stuff 1 2 150 1.2 6 10 800
1 stuff 1 3 100 1.2 6 10 800
1 stuff 1 4 60 1.2 6 10 800
1 stuff 1 5 40 1.2 6 10 800
1 stuff 1 6 20 1.2 6 10 800
1 stuff 1 7 10 1.2 6 10 800
1 stuff 1 8 10 1.2 6 10 800
1 stuff 1 9 5 1.2 6 10 800
1 stuff 1 10 1 1.2 6 10 800
1 stuff 2 1 400 1.1 6 9 1200
1 stuff 2 2 200 1.1 6 9 1200
1 stuff 2 3 100 1.1 6 9 1200
1 stuff 2 4 100 1.1 6 9 1200
1 stuff 2 5 100 1.1 6 9 1200
1 stuff 2 6 50 1.1 6 9 1200
1 stuff 2 7 20 1.1 6 9 1200
1 stuff 2 8 20 1.1 6 9 1200
1 stuff 2 9 5 1.1 6 9 1200
1 stuff 2 10 3 1.1 6 9 1200
;
run;
data opt_test_mkdown_raw;
set opt_test_mkdown_raw;
ITM_LCT_WK=cats(ITM_NBR, LCT_NBR, WEEK);
ITM_LCT=cats(ITM_NBR, LCT_NBR);
run;
proc optmodel;
*set variables and inputs;
set<string> ITM_LCT_WK;
number ITM_NBR{ITM_LCT_WK};
string ITM_DES_TXT{ITM_LCT_WK};
string ITM_LCT{ITM_LCT_WK};
number LCT_NBR{ITM_LCT_WK};
number WEEK{ITM_LCT_WK};
number LY_UNITS{ITM_LCT_WK};
number ELAST{ITM_LCT_WK};
number COST{ITM_LCT_WK};
number PRICE{ITM_LCT_WK};
number TOTAL_INV{ITM_LCT_WK};
*read data into procedure;
read data opt_test_mkdown_raw into
ITM_LCT_WK=[ITM_LCT_WK]
ITM_NBR
ITM_DES_TXT
ITM_LCT
LCT_NBR
WEEK
LY_UNITS
ELAST
COST
PRICE
TOTAL_INV;
var NEW_PRICE{i in ITM_LCT_WK};
impvar FRC_UNITS{i in ITM_LCT_WK}=(1-(NEW_PRICE[i]-PRICE[i])*ELAST[i]/PRICE[i])*LY_UNITS[i];
con ceiling_price {i in ITM_LCT_WK}: NEW_PRICE[i]<=PRICE[i];
/*con inv_cap {j in ITM_LCT}: sum{i in ITM_LCT_WK}=I want this to be 800 for location 1 and 1200 for location 2;*/
con supply_last {i in ITM_LCT_WK}: FRC_UNITS[i]>=LY_UNITS[i];
/*con price_down_or_same {j in ITM_LCT} : NEW_PRICE[week]<=NEW_PRICE[week-1];*/
*state function to optimize;
max margin=sum{i in ITM_LCT_WK}
(NEW_PRICE[i]-COST[i])*(1-(NEW_PRICE[i]-PRICE[i])*ELAST[i]/PRICE[i])*LY_UNITS[i];
/*expand;*/
solve;
*write output dataset;
create data results_MKD_maxmargin
from
[ITM_LCT_WK]={ITM_LCT_WK}
ITM_NBR
ITM_DES_TXT
LCT_NBR
WEEK
LY_UNITS
FRC_UNITS
ELAST
COST
PRICE
NEW_PRICE
TOTAL_INV;
*write results to window;
print
/*NEW_PRICE */
margin;
quit;
The main difficulty is that in your application, decisions are indexed by (Item,Location) pairs and Weeks, but in your code you have merged (Item,Location,Week) triplets. I rather like that use of the data step, but the result in this example is that your code is unable to refer to specific weeks and to specific pairs.
The fix that changes your code the least is to add these relationships by using defined sets and inputs that OPTMODEL can compute for you. Then you will know which triplets refer to each combination of (Item,Location) pair and week:
/* This code creates a set version of the Item x Location pairs
that you already have as strings */
set ITM_LCTS = setof{ilw in ITM_LCT_WK} itm_lct[ilw];
/* For each Item x Location pair, define a set of which
Item x Location x Week entries refer to that Item x Location */
set ILWperIL{il in ITM_LCTS} = {ilw in ITM_LCT_WK: itm_lct[ilw] = il};
With this relationship you can add the other two constraints.
I left your code as is, but applied to the new code a convention I find useful, especially when there are similar names like itm_lct and ITM_LCTS:
sets as all caps;
input parameters start with lowercase;
output (vars, impvars, and constraints) start with Uppercase */
Here is the new OPTMODEL code:
proc optmodel;
*set variables and inputs;
set<string> ITM_LCT_WK;
number ITM_NBR{ITM_LCT_WK};
string ITM_DES_TXT{ITM_LCT_WK};
string ITM_LCT{ITM_LCT_WK};
number LCT_NBR{ITM_LCT_WK};
number WEEK{ITM_LCT_WK};
number LY_UNITS{ITM_LCT_WK};
number ELAST{ITM_LCT_WK};
number COST{ITM_LCT_WK};
number PRICE{ITM_LCT_WK};
number TOTAL_INV{ITM_LCT_WK};
*read data into procedure;
read data opt_test_mkdown_raw into
ITM_LCT_WK=[ITM_LCT_WK]
ITM_NBR
ITM_DES_TXT
ITM_LCT
LCT_NBR
WEEK
LY_UNITS
ELAST
COST
PRICE
TOTAL_INV;
var NEW_PRICE{i in ITM_LCT_WK} <= price[i];
impvar FRC_UNITS{i in ITM_LCT_WK} =
(1-(NEW_PRICE[i]-PRICE[i])*ELAST[i]/PRICE[i]) * LY_UNITS[i];
* Moved to bound
con ceiling_price {i in ITM_LCT_WK}: NEW_PRICE[i] <= PRICE[i];
con supply_last{i in ITM_LCT_WK}: FRC_UNITS[i] >= LY_UNITS[i];
/* This code creates a set version of the Item x Location pairs
that you already have as strings */
set ITM_LCTS = setof{ilw in ITM_LCT_WK} itm_lct[ilw];
/* For each Item x Location pair, define a set of which
Item x Location x Week entries refer to that Item x Location */
set ILWperIL{il in ITM_LCTS} = {ilw in ITM_LCT_WK: itm_lct[ilw] = il};
/* I assume that for each item and location
the inventory is the same for all weeks for convenience,
i.e., that is not a coincidence */
num inventory{il in ITM_LCTS} = max{ilw in ILWperIL[il]} total_inv[ilw];
con inv_cap {il in ITM_LCTS}:
sum{ilw in ILWperIL[il]} Frc_Units[ilw] = inventory[il];
num lastWeek = max{ilw in ITM_LCT_WK} week[ilw];
/* Concatenating indexes is not the prettiest, but gets the job done here*/
con Price_down_or_same {il in ITM_LCTS, w in 2 .. lastWeek}:
New_Price[il || w] <= New_Price[il || w - 1];*/
*state function to optimize;
max margin=sum{i in ITM_LCT_WK}
(NEW_PRICE[i]-COST[i])*(1-(NEW_PRICE[i]-PRICE[i])*ELAST[i]/PRICE[i])*LY_UNITS[i];
expand;
solve;
*write output dataset;
create data results_MKD_maxmargin
from
[ITM_LCT_WK]={ITM_LCT_WK}
ITM_NBR
ITM_DES_TXT
LCT_NBR
WEEK
LY_UNITS
FRC_UNITS
ELAST
COST
PRICE
NEW_PRICE
TOTAL_INV;
*write results to window;
print
NEW_PRICE FRC_UNITS
margin
;
quit;