You can use: df = pd.DataFrame([A]) print (df) 0 1 2 3 4 0 1 d p bab If all values are strings: df = pd.DataFrame(np.array(A).reshape(-1,len(A))) print (df) 0 1 2 3 4 0 1 d p bab Thank you AKS: df = pd.DataFrame(A).T print (df) 0 1 2 3 4 0 1 d p … Read more
Maybe I read the problem wrong, but I believe you already have inserted the course 11 records and simply need to update those that meet the criteria you listed with course 6’s data. If this is the case, you’ll want to use an UPDATE…FROM statement: UPDATE MyTable SET complete = 1, complete_date = newdata.complete_date, post_score … Read more
You can do it calling setRowSelectionInterval : table.setRowSelectionInterval(0, 0); to select the first row.
The method selectionModel() return a QItemSelectionModel. You can use QItemSelectionModel class to check/change/other selection(s) Example: QItemSelectionModel *select = table->selectionModel(); select->hasSelection() //check if has selection select->selectedRows() // return selected row(s) select->selectedColumns() // return selected column(s) …
Columns: awk ‘{print NF}’ file | sort -nu | tail -n 1 Use head -n 1 for lowest column count, tail -n 1 for highest column count. Rows: cat file | wc -l or wc -l < file for the UUOC crowd.
Oh yes, yes it is! CSS: table { border-collapse: collapse; } td { position: relative; padding: 5px 10px; } tr.strikeout td:before { content: ” “; position: absolute; top: 50%; left: 0; border-bottom: 1px solid #111; width: 100%; } HTML: <table> <tr> <td>Stuff</td> <td>Stuff</td> <td>Stuff</td> </tr> <tr class=”strikeout”> <td>Stuff</td> <td>Stuff</td> <td>Stuff</td> </tr> <tr> <td>Stuff</td> <td>Stuff</td> <td>Stuff</td> … Read more
You need to add the table-hover class to the <table/> element. Like this: <table class=”table table-striped table-hover”> <tbody> <tr> <td>Col 1</td> <td>Col 2 </td> </tr> </tbody> </table> Source: Documentation
You can call apply pass axis=1 to apply row-wise, then convert the dtype to str and join: In [153]: df[‘ColumnA’] = df[df.columns[1:]].apply( lambda x: ‘,’.join(x.dropna().astype(str)), axis=1 ) df Out[153]: Column1 Column2 Column3 Column4 Column5 ColumnA 0 a 1 2 3 4 1,2,3,4 1 a 3 4 5 NaN 3,4,5 2 b 6 7 8 NaN … Read more