Successfully merging a pull request may close this issue. Is there a "better" way to rewrite a SELECT clause where multiple columns use the same CASE WHEN conditions so that the conditions are only checked once?. to nonunique unique too). Solving T(n) = 2*T(n-1)+4 witht the Master Theorem. And iscomplete. Also, I think it would be nice and efficient if this version (or all versions) of complete_cases returned a view. Let’s first create the dataframe. WRT returning a view, that makes sense, but we should have a clear policy applying to all functions (e.g. What's the etiquette for addressing a friend's partner or family in a greeting card? if I did? The optional argument should specify the column types after the first, which is fixed. An Thoughts? unique(DataTable, cols, view=false)? Arising from discussion here: https://discourse.julialang.org/t/is-there-a-dropna-for-dataframe/1777. Il controllo passa quindi all'istruzione che segue End Select.Control then passes to the statement following End Select. Let's start with what we need, trying to forget what functions currently exist and pretending we have a clean slate. Asked: April 10, 2018 - 11:27 am UTC. SELECT CASE testStatus WHEN 'A' THEN 'Authorized' WHEN 'C' THEN 'Completed' WHEN 'P' THEN 'In Progress' WHEN 'X' THEN 'Cancelled' END AS Status, CASE testStatus WHEN 'A' THEN authTime WHEN 'C' THEN cmplTime … as the recommended solution. When I do this in Excel, I use filtering and multiple columns to evaluate eligibility. So we need to identify all functions which would need to be checked and possibly changed. Return a logical vector indicating which cases are complete, i.e., have no missing values. In the discourse post (https://discourse.julialang.org/t/is-there-a-dropna-for-dataframe/1777/19) , @mwsohn suggested some code that could form a useful basis for a PR. Keeping the current methods, implementing a col-specific version of dropnull!, returning views rather than copies by default from methods that return a DataTable (the user can always copy manually), and changing the names of nonunique and completecases? is.na, na.omit, na.fail. To learn more, see our tips on writing great answers. edit 2: if we use the dim= keyword we could leave the current system (only check rows, not rows and columns) in place by setting 1 or :rows as the default. Does history use hypothesis testing using statistical methods? unique(DataTable, view=false) ^ coming from DataFrames, that's exactly what you should have expected! Using complete.cases() to remove (missing) NA and NaN values. See Also. Why was the name of Discovery's most recent episode "Unification III"? Also the term "duplicated" isn't used in the Julia API, "unique" (i.e. I think originally it was to use for sorting the datatable - e.g. Method 2: Remove or Drop rows with NA using complete.cases() function. We update unique(dt) to unique-ify rows AND columns. Once again, there is an optional ELSE clause to deal with situations where a match is not found. Each comparison is tested in turn and the associated value returned if a match is found. complete.cases; If you wouldn't mind, could you also try a few benchmarks of the before/after speeds when writing tests? The complete.cases function is often used to identify complete rows of a data frame. Thanks. Keyword arguments can be typed, the problem is that the return type of a function cannot depend on the value of an argument if we want inference to figure it out (there are also complications due to the type of keyword arguments not being taken into account by inference to compute the return type, but that's another issue). I don't think there's any precedent for this in Julia, and in DataTables generally functions do not support choosing the dimension to consider: as I said, they are not symmetric. Except for BitArray and view (which might also be suggested modifications to the existing complete_cases if BitArrays are more efficient), the only change from the current code in DataFrames is the args argument. I tried not to make too many assumptions about user patterns when thinking about isunique and iscomplete behavior. I would just like that it didn't come at the cost of being unable to support other functionality. Running complete_cases would remove rows that had NAs in columns unrelated to the function. Also, to check whether there are duplicate rows, we should rather override allunique.

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