A few months ago, I submitted a proposal for including a Join-Object cmdlet to the standard PowerShell equipment #14994. Besides complexer joins based on a related property, the idea is to also be able to do a side-by-side join (by omiting the -On parameter). Taken this Paste command in Linux as an example: $State=”Arunachal Pradesh”, … Read more
You can do it with a crosstab like this – SELECT `year`, `month`, SUM(IF(`transporttype` = ‘inbound’, 1, 0)) AS `inbound`, SUM(IF(`transporttype` = ‘LocalPMB’, 1, 0)) AS `LocalPMB`, SUM(IF(`transporttype` = ‘Long Distance’, 1, 0)) AS `Long Distance`, SUM(IF(`transporttype` = ‘shuttle’, 1, 0)) AS `shuttle`, SUM(IF(`transporttype` = ‘export’, 1, 0)) AS `export`, SUM(IF(`transporttype` = ‘Extrusions-LongDistance’, 1, 0)) … Read more
How about this: scala> def transpose[A](xs: List[List[A]]): List[List[A]] = xs.filter(_.nonEmpty) match { | case Nil => Nil | case ys: List[List[A]] => ys.map{ _.head }::transpose(ys.map{ _.tail }) | } warning: there were unchecked warnings; re-run with -unchecked for details transpose: [A](xs: List[List[A]])List[List[A]] scala> val ls = List(List(1, 2, 3), List(4, 5), List(6, 7, 8)) ls: … Read more
Since you are using SQL Server there are several different ways that you can convert the rows into columns. You can use an aggregate function with a CASE expression: select empid, max(case when empindex = 1 then empstate end) empState1, max(case when empindex = 1 then empStDate end) empStDate1, max(case when empindex = 1 then … Read more
NumPy’s transpose() effectively reverses the shape of an array. If the array is one-dimensional, this means it has no effect. In NumPy, the arrays array([1, 2, 3]) and array([1, 2, 3]) are actually the same – they only differ in whitespace. What you probably want are the corresponding two-dimensional arrays, for which transpose() would work … Read more
Definition General array slicing can be implemented (whether or not built into the language) by referencing every array through a dope vector or descriptor — a record that contains the address of the first array element, and then the range of each index and the corresponding coefficient in the indexing formula. This technique also allows … Read more
If no data are missing, you can use numpy.reshape: print (np.reshape(df.values,(2,5))) [[‘Andrew’ ‘School of Music’ ‘Music: Sound of the wind’ ‘Dr. Seuss’ ‘Dr.Sass’] [‘Michelle’ ‘School of Theatrics’ ‘Music: Voice’ ‘Dr. A’ ‘Dr. B’]] print (pd.DataFrame(np.reshape(df.values,(2,5)), columns=[‘Name’,’School’,’Music’,’Mentor1′,’Mentor2′])) Name School Music Mentor1 Mentor2 0 Andrew School of Music Music: Sound of the wind Dr. Seuss Dr.Sass 1 … Read more
=ARRAYFORMULA(TRIM(TRANSPOSE(SPLIT(QUERY( REPT(A1:A3&”♠”, 5), ,999^99), “♠”)))) =SORT(TRIM(TRANSPOSE(SPLIT(QUERY(ARRAYFORMULA( REPT(A1:A3&”♠”, 5)), ,999^99), “♠”))), 1, 0)
As I mentioned in the comments, it’s not practical to represent matrices using vector-of-vector for a few reasons: It is fiddly to set up; It is difficult to change; Cache locality is bad. Here is a very simple class I have created that will hold a 2D matrix in a single vector. This is pretty … Read more