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The two most commonly used classes are "wikitable" and "wikitable sortable"; the latter allows the reader to sort the table by clicking on the header cell of any column. |+ caption Required for accessibility purposes on data tables, and placed only between the table start and the first table row. ! header cell Optional.
For complex tables, when a header spans two columns or rows, use ! scope="colgroup" colspan="2" | or ! scope="rowgroup" rowspan="2" | respectively to clearly identify the header as a column header of two columns or a row header of two rows.
In a database, a table is a collection of related data organized in table format; consisting of columns and rows.. In relational databases, and flat file databases, a table is a set of data elements (values) using a model of vertical columns (identifiable by name) and horizontal rows, the cell being the unit where a row and column intersect. [1]
Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
A pivot table usually consists of row, column and data (or fact) fields. In this case, the column is ship date, the row is region and the data we would like to see is (sum of) units. These fields allow several kinds of aggregations, including: sum, average, standard deviation, count, etc.
CSV is a delimited data format that has fields/columns separated by the comma character and records/rows terminated by newlines. A CSV file does not require a specific character encoding , byte order , or line terminator format (some software do not support all line-end variations).
A circle record might contain a numeric radius and a center that is a point record containing x and y coordinates. Notable applications include the programming language record type and for row-based storage, data organized as a sequence of records, such as a database table, spreadsheet or comma-separated values (CSV) file.
Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").