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Each bar can also have a comment, such as "comment7=xx" to show "(xx)" after the number in bar 7. For a 2-column bar chart, the 2nd column items have prefix "col2_" such as scale maximum, col2_data_max=110, and col2_data3=67 with col2_comment3=zz. See below: "Example with two data columns". Each bar chart can be formatted typically within 1/5 ...
A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a column chart and has been identified as the prototype of charts. [1]
Module:Chart creates bar and pie charts on Wikipedia without need for external tools; Many spreadsheet, drawing, and desktop publishing programs allow you to create graphs and export them as images. gnuplot can produce a wide variety of charts and graphs; see samples with source code at Commons. In Python using matplotlib
The whole image is scaled by a command, e.g. "ImageSize=width:180 height:90", and using the keyword "bar" triggers the bar chart features. However, the {{ Bar chart }} can easily format a horizontal bar chart (scrolling down a page), with one or two or four columns of bars in a chart.
Matplotlib can create plots in a variety of output formats, such as PNG and SVG. Matplotlib mainly does 2-D plots (such as line, contour, bar, scatter, etc.), but 3-D functionality is also available. A simple SVG line plot with Matplotlib. Here is a minimal line plot (output image is shown on the right):
A chart (sometimes known as a graph) is a graphical representation for data visualization, in which "the data is represented by symbols, such as bars in a bar chart, lines in a line chart, or slices in a pie chart". [1] A chart can represent tabular numeric data, functions or some kinds of quality structure and provides different info.