Bins30

Bins30 is a binning configuration used in data analysis to partition numerical data into 30 equal-width intervals, or bins. It is not a formal standard; rather, it is an informal label that appears in plotting templates, tutorials, and codebases to indicate a fixed-bin histogram with thirty bins.

Definition and calculation: Given a dataset with minimum a and maximum b, the 30-bin scheme creates edges e0 = a, e1, …, e30 = b, with bin width w = (b − a) / 30 and ei = a + i·w. Each value x is assigned to the bin i for which ei ≤ x < ei+1, with the final bin including b. This yields a histogram that reflects the distribution over 30 segments of the data range.

Usage: Commonly used for exploratory data analysis and feature engineering—partitioning a continuous variable into categories that reflect value ranges; used in visualization to compare distributions across datasets or groups. In software, similar behavior is achieved by specifying the number of breaks or bins as 30 (for example, breaks=30 in R’s hist function or bins=30 in Python plotting and data libraries).

Advantages and limitations: Provides a consistent bin count across plots, aiding comparability and interpretability of distributions. Limitations include sensitivity to the data range and outliers; fixed bin width may create empty or sparsely populated bins, and slight changes near edges can shift counts between bins.

Variations: Other bin counts (e.g., 20, 40) or adaptive binning rules such as Freedman–Diaconis or Sturges. When distributions are highly skewed, alternative binning may be preferable.

See also: histogram, data binning, bin width, breaks parameter, Freedman–Diaconis rule, Sturges’ rule.