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The process of converting a narrow table to wide table is generally referred to as "pivoting" in the context of data transformations. The "pandas" python package provides a "pivot" method which provides for a narrow to wide transformation.
If just 2 columns are being swapped within 1 table, then cut/paste editing (of those column entries) is typically faster than column-prefixing, sorting and de-prefixing. Another alternative is to copy the entire table from the displayed page, paste the text into a spreadsheet, move the columns as you will.
Note that you may also specify the § height of individual rows, and if they add up to more than the table height you specified or if word wrapping increases row height, the table height you specified will be ignored and the table height increased as needed to accommodate all the rows (except on mobile where the bottom of the table will be cut ...
See the Width section of Help:Table.To summarize, max-width is the preferred way to limit widths on tables. It works on divs too. Note though that in both tables and divs there needs to be spaces in long lines of text or wikitext.
This is a test of the default settings for a very wide table with much text to show the extent of the margins when enough data is present to push the table to its maximum width allowed by the table properties. A table with long text, or many wide columns, will expand to fit 98% of the width of the window.
The dataset structure for observations is a flat file representing a table with one or more rows and columns. Normally, one dataset is submitted for each domain. Each row of the dataset represents a single observation and each column represents one of the variables. Each dataset or table is accompanied by metadata definitions that provide ...
A frequency distribution table is an arrangement of the values that one or more variables take in a sample. Each entry in the table contains the frequency or count of the occurrences of values within a particular group or interval, and in this way, the table summarizes the distribution of values in the sample.
Graphical examination of count data may be aided by the use of data transformations chosen to have the property of stabilising the sample variance. In particular, the square root transformation might be used when data can be approximated by a Poisson distribution (although other transformation have modestly improved properties), while an inverse sine transformation is available when a binomial ...