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It can edit and format text in cells, calculate formulas, search within the spreadsheet, sort rows and columns, freeze panes, filter the columns, add comments, and create charts. It cannot add columns or rows except at the edge of the document, rearrange columns or rows, delete rows or columns, or add spreadsheet tabs.
OpenRefine is an open-source desktop application for data cleanup and transformation to other formats, an activity commonly known as data wrangling. [3] It is similar to spreadsheet applications, and can handle spreadsheet file formats such as CSV, but it behaves more like a database.
Origin Workbook with sparklines above data columns; this allows a quick glance of the data without plotting them. Origin is primarily a GUI software with a spreadsheet front end. Unlike popular spreadsheets like Excel, Origin's worksheet is column oriented. Each column has associated attributes like name, units and other user definable labels.
[4]: 114 A DataFrame is a 2-dimensional data structure of rows and columns, similar to a spreadsheet, and analogous to a Python dictionary mapping column names (keys) to Series (values), with each Series sharing an index. [4]: 115 DataFrames can be concatenated together or "merged" on columns or indices in a manner similar to joins in SQL.
Column labels are used to apply a filter to one or more columns that have to be shown in the pivot table. For instance if the "Salesperson" field is dragged to this area, then the table constructed will have values from the column "Sales Person", i.e., one will have a number of columns equal to the number of "Salesperson". There will also be ...
easily adding a new column if many elements of the new column are left blank (if the column is inserted and the existing fields are unnamed, use a named parameter for the new field to avoid adding blank parameter values to many template calls) computing fields from other fields, e.g. population density from population and area
Selecting only certain columns to load: (or selecting null columns not to load). For example, if the source data has three columns (aka "attributes"), roll_no, age, and salary, then the selection may take only roll_no and salary. Or, the selection mechanism may ignore all those records where salary is not present (salary = null).
The pandas package in Python implements this operation as "melt" function which converts a wide table to a narrow one. The process of converting a narrow table to wide table is generally referred to as "pivoting" in the context of data transformations.