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I have a value in a table that was changed unexpectedly. The column in question is CreatedDate : this is set when my item is created, but it's being changed by a stored procedure. Could I write some type of SELECT statement to get all the procedure names that reference this column from my table? huMpty duMpty One option is to create a script file. Right click on the database -> Tasks -> Generate Scripts Then you can select all the stored procedures and generate the script with all the sps. So you can find the reference from there. Or -- Search in All Objects SELECT OBJECT_NAME(OBJECT_ID),

I get how to use pd.MultiIndex.from_tuples() in order to change something like Value (A,a) 1 (B,a) 2 (B,b) 3 into Value Caps Lower A a 1 B a 2 B b 3 But how do I change column tuples in the form (A, a) (A, b) (B,a) (B,b) index 1 1 2 2 3 2 2 3 3 2 3 3 4 4 1 into the form Caps A B Lower a b a b index 1 1 2 2 3 2 2 3 3 2 3 3 4 4 1 Many thanks. Edit: The reason I have a tuple column header is that when I joined a DataFrame with a single level column onto a DataFrame with a Multi-Level column it turned the Multi-Column into a tuple of strings format and left the single level as single string. Edit

This question already has an answer here: Group by multiple columns in dplyr, using string vector input 9 answers How can I pass column names to dplyr if I do not know the column name, but want to specify it through a variable? e.g. this works: require(dplyr) df <- as.data.frame(matrix(seq(1:9),ncol=3,nrow=3)) df$group <- c("A","B","A") gdf <- df %.% group_by(group) %.% summarise(m1 =mean(V1),m2 =mean(V2),m3 =mean(V3)) But this does not require(dplyr) someColumn = "group" df <- as.data.frame(matrix(seq(1:9),ncol=3,nrow=3)) df$group <- c("A","B","A") gdf <- df %.% group_by(someColumn) %.%

I recently discovered pandas "assign" method which I find very elegant. My issue is that the name of the new column is assigned as keyword, so it cannot have spaces or dashes in it. df = DataFrame({'A': range(1, 11), 'B': np.random.randn(10)}) df.assign(ln_A = lambda x: np.log(x.A)) A B ln_A 0 1 0.426905 0.000000 1 2 -0.780949 0.693147 2 3 -0.418711 1.098612 3 4 -0.269708 1.386294 4 5 -0.274002 1.609438 5 6 -0.500792 1.791759 6 7 1.649697 1.945910 7 8 -1.495604 2.079442 8 9 0.549296 2.197225 9 10 -0.758542 2.302585 but what if I want to name the new column "ln(A)" for example? E.g. df.assign

Do the following function pairs generate exactly the same results? Pair 1) names() & colnames() Pair 2) rownames() & row.names() Dirk Eddelbuettel As Oscar Wilde said Consistency is the last refuge of the unimaginative. R is more of an evolved rather than designed language, so these things happen. names() and colnames() work on a data.frame but names() does not work on a matrix: R> DF <- data.frame(foo=1:3, bar=LETTERS[1:3]) R> names(DF) [1] "foo" "bar" R> colnames(DF) [1] "foo" "bar" R> M <- matrix(1:9, ncol=3, dimnames=list(1:3, c("alpha","beta","gamma"))) R> names(M) NULL R> colnames(M) [1]

Is there any way to edit column names in a DataGridView? I don't think there is a way to do it without writing custom code. I'd implement a ColumnHeaderDoubleClick event handler, and create a TextBox control right on top of the column header. Ryan Spears You can also change the column name by using: myDataGrid.Columns[0].HeaderText = "My Header" but the myDataGrid will need to have been bound to a DataSource . Bjørn Otto Vasbotten You can edit the header directly: dataGridView1.Columns[0].HeaderCell.Value = "Created"; dataGridView1.Columns[1].HeaderCell.Value = "Name"; And so on for as many