Forum Discussion
Grouped norm dist
Hi all,
I'm trying to make a grouped norm.dist calculation but can't yet figure it out. Below an example of my dataset (item, price):
| chair | 30 |
| couch | 400 |
| table | 200 |
| table | 150 |
| chair | 11 |
| desk | 100 |
| chair | 60 |
| couch | 180 |
| chair | 100 |
If I would do a normal norm.dist calculation it wouldn't do any good since there are different products. That's why I want to group the calculation based on the product column. Each item should be calculated on it's own median and mean prices, and not on all the prices. It's btw big data with around 1K different items.
Any idea? Thanks in advance!
Aha, Anonymous try to add three columns like:
mean2 = VAR _table = FILTER( TableName, TableName[item] = EARLIER(TableName[item]) ) RETURN AVERAGEX( _table, TableName[Price] ) med2 = VAR _table = FILTER( TableName, TableName[item] = EARLIER(TableName[item]) ) RETURN CONVERT( MEDIANX( _table, TableName[Price] ), INTEGER ) std.dev2 = VAR _table = FILTER( TableName, TableName[item] = EARLIER(TableName[item]) ) RETURN STDEVX.P( _table, TableName[Price] )it worked like:
6 Replies
hi Anonymous
try to plot a table visual with the product column and measures like:
mean = MEAN(TableName[Price])median = MEDIAN(TableName[Price])Or?hi Anonymous
try to create a calculated table like:Table =ADDCOLUMNS(VALUES(TableName[Product]),"mean",CALCULATE(MEAN(TableName[Price])),"median",CALCULATE(MEDIAN(TableName[Price])))