Hi All,
There are pivot table where I calculate the % of the parent row total can someone help me with the same feature in alteryx.
Regards
Rohini
Solved! Go to Solution.
Hi @rohini, does the attached solution seem like an approach that would be applicable to your dataset?
Please let us know if this isn't what you are looking for, and perhaps share a sample of your dataset to build something more specific to how your data is structured.
You can use a combination of a Tranpose Tool and a Cross Tab tool.
When you select the field values in the Cross Tab tool, make sure not to select Sum, but rather "Percent Column."
I use this feature in a lot of my daily use.
Please Direct Message me if you need more help or reach out!
Good luck solving,
Jacob
Thank you This worked but now I have got summarization result of two tables, I want to have the % of casualty like 11/61 based on Business and month. is there possibility for this.
year | month | Business | split |
2016 | 1 | Casualty | 11 |
2016 | 1 | Motor | 50 |
2016 | 10 | Casualty | 138 |
2016 | 10 | Motor | 167 |
2016 | 11 | Casualty | 110 |
2016 | 11 | Motor | 156 |
2016 | 12 | Casualty | 140 |
2016 | 12 | Motor | 177 |
2016 | 2 | Casualty | 25 |
2016 | 2 | Motor | 123 |
2016 | 3 | Casualty | 34 |
2016 | 3 | Motor | 102 |
2016 | 4 | Casualty | 52 |
2016 | 4 | Motor | 155 |
2016 | 5 | Casualty | 67 |
2016 | 5 | Motor | 159 |
2016 | 6 | Casualty | 69 |
2016 | 6 | Motor | 157 |
2016 | 7 | Casualty | 61 |
2016 | 7 | Motor | 169 |
year | month | Total |
2016 | 1 | 61 |
2016 | 10 | 305 |
2016 | 11 | 266 |
2016 | 12 | 317 |
2016 | 2 | 148 |
2016 | 3 | 136 |
2016 | 4 | 207 |
2016 | 5 | 226 |
2016 | 6 | 226 |
2016 | 7 | 230 |
2016 | 8 | 299 |
2016 | 9 | 299 |
Hi @rohini, I would still use a similar approach in summarizing data and joining it back to the original dataset for us to use the Formula tool, just that I used Join Tool in this case. We used the Append tool in the previous approach given there was only one Total value (think of append as a cross join between datasets), and it applied the Total value to all rows of the original dataset. While we use the Join tool in the updated approach given we have multiple Totals.