I am trying to make a pivot table that has a list of inventory on one axis and customers on another axis. With this table I could replace all non-'1' values in the table with 1s and then use the column count to determine the total number of items purchased by each customer. However, Excel claims that there's far too much information and that it wouldn't be able to complete the task. Is there another way to do this that doesn't involve pivot tables?
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I have a data I want to sort into pivot-based graphs and I need help with it.
Below is an example of the data im sorting
Basically, I want the pivot to have a week number and category filter. Then for it to sort the data like a frequency based line-chart based on how many people have unit output in certain ranges. End product should look like this.
I am having trboule because every category has very different min and max points so it can't be done manually.
I tried to use a traditional pivot table but I cant get the frequency part down.
Thank you
I have a table with more than 1 million rows of data so I can't put the information into Excel. The first column is the identifier and the second column is the percent increase on that identifier. The table records all the increases over a year so the ID entries in the first column can have more than one increase during the year.
I want to calculate the total increase. If the data was small enough to fit in excel I would just pivot the table and make the rows the ID in the Rows bucket and the Product of the Increase in the Values bucket. This give me one row for each unique ID and the total percentage increase for the year.
The matrix visualization in Power BI doesn't help since it doesn't have a similar Product summarization. That and there are more than 1 million unique IDs in the dataset so I can't export it due to the 150K row limit. I need to create a new table in BI that does the same thing because I want to bring in related data from another table that includes categories and then average the category.
Is there a way to do this? Please let me know any questions you have and if you need any additional information or clarification. Thanks.
I am having issues translating the following formula to a pivot table; either through a regular pivot table, or through DAX and powerpivot.
=SUMPRODUCT((C$2:C$11)*(D$2:D$11)*(A$2:A$11=A2)*(B$2:B$11=B2))/SUMIFS(D$2:D$11,A$2:A$11,A2,B$2:B$11,B2)
The background is, I have a number of products that appear on an e-commerce site, and I need to find out their price per day. However, these prices change daily, based on things like promo codes, visitor location etc. Therefore, I need their weighted price based on the number of visitors that saw a particular price.
Can anyone help with this translation, or alternatively, offer a better way to approach this problem?
PS- I need it in a pivot table due to the volume of data. At 250,000 rows, standard Excel cannot handle this formula.
The following is in Excel 2010 sans Powerpivot. However, the general approach should work:
Explanation:
I added a column that multiplies the Prices and Visits. The pivot table uses Dates, then Product SKU as the row labels. Then I added a calculated field that divides the Price*Visits by the Visits.
I'm trying to build a Pivot table out of 4 tables that i have loaded in PowerPivot.
the information in the rows of the Pivot is like this:
Region
account
Products
One of the tables has the forecasted values for all 4 quarters for each Region but when i add it as Sum Values it repeats the entire number for each product but i just want the total. Is there a way to fix it without having to change the color of the font to white to hide it?
Without more info can't be sure, but this looks like you have not set up the model to support the Quarterly Forecast Measures in PowerPivot. To get this to work, there needs to be one-to-many relationships between identifying fields in each table.
PowerDax answered based on the assumption that you intended for the Quarterly Forecast to roll up to the region because you phrased you're question to indicate the forecast data is at the regional granularity.
If the forecast data is in one fact table, and the EXT_SELL_PRICE is in another, you will need to set up dimension tables each containing unique lists of the members of each field you are using to slice and dice the report (looks like End User Region, Child Customer, forecast product name). Relate all you're fact tables (tables that contain you're actual data) to these and put the dimension table fields in the Rows area of the pivot table. Then the measures will have some way of understanding how they relate to the rows they are being applied to.
Hope that helps
it would be best for these to be actual calculated fields rather than implicit calculated fields (i.e. dropping the column onto the pivot table). This way you can utilize the isfiltered function to drive what you are looking for.
i.e. if your data is at this granularity:
Region Customer Product Ext_Sell_Price Q1 Q2 Q3
Brazil Acuntia 39XX 17246 743799 672599 748339
Brazil Acuntia 51XX 130918 743799 672599 748339
or you have the sales table and a region forecast table with a relationship to the sales table, you could check to ensure customer/product were not filtered in order to show the forecast:
Q1 Total:=IF( ISFILTERED( Table1[Customer]) || ISFILTERED( Table1[Product]), BLANK(), MIN( Table1[Q1]) )
I have created some pivot tables which shows sales of PC from different brands for multiple years. for example 2013 PC sales of dell,HP,Apple etc...
Now I want to compare these sales but I just want to compare top 10 results...I am trying to create a table which will show only the top ten values from the pivot table. I dont want to filter the pivot table to top 10 values.
Thanks and Regards
If you have to create your output as a table without filtering the pivot table, you can use cube functions. Create a cubeset that is all PC brands and order them by PC Sales. Create cubrankedmember functions for positions 1 through 10. Then write a cubevalue formula next to each cuberankedmember to retrieve the associated value. If you want to post a little more about your data model, I could write more specific cube function formulas.