|
- How to Sum Based on Column and Row Criteria in Excel (7 Ways)
7 Easy Useful methods to sum based on column and row criteria in MS Excel Download our Excel workbook, practice modify data!
- pandas get_level_values for multiple columns - Stack Overflow
There's a workaround in which one could use get_level_values over each desired column and zip them together: For example: a_list = df index get_level_values('a') values c_list = df index get_level_values('c') values print([i for i in zip(a_list,c_list)]) [(1, 10), (1, 11), (1, 12), (2, 13), (2, 14), (3, 15)]
- Taguchi 2-level Designs - Real Statistics Using Excel
Taguchi’s L8 design supports a fractional factorial experiment with only 8 test runs Similarly, a full factorial experiment with 15 factors requires 2 15 = 32,768 test runs Taguchi’s L16 design supports such an experiment with only 16 test runs
- Catalogue of Taguchi designs - Minitab
For example, an L8 design has 8 runs (2^3) or (2 3 ) means 3 factors at 2 levels If your notation is L(runs) (number ^ exponent number ^ exponent) then you have a mixed-level design
- How to Return All Rows That Match Criteria in Excel
We want to return the rows based on the value of the Achieved Target column Steps: Create a Criteria table like the following one Enter the lower and upper end of the criteria values of the Target Achieved column Here, Column indicates the column number where your criteria is located
- How to simply add a column level to a pandas dataframe
You could just assign the columns like: >>> df columns = [df columns, ['C', 'C']] >>> df A B C C a 0 0 b 1 1 c 2 2 d 3 3 e 4 4 >>> Or for unknown length of columns: >>> df columns = [df columns get_level_values(0), np repeat('C', df shape[1])] >>> df A B C C a 0 0 b 1 1 c 2 2 d 3 3 e 4 4 >>>
- Calculated Column Based on Another Column in Power BI - SPGuides
Learn how to create a calculated column based on another column in Power BI Follow this step-by-step guide with DAX formulas and examples of adding calculated column
- r - list all factor levels of a data. frame - Stack Overflow
We loop through the 'data' with sapply and get the levels of each column (assuming that all the columns are factor class) Or if we need to pipe (%>%) it, this can be done as sapply(levels) Or another option is summarise_each from dplyr where we specify the levels within the funs data %>% summarise_each(funs(list(levels( ))))
|
|
|