Meansalong

Meansalong is a data-analysis operation that computes the arithmetic mean across elements along one or more axes of a multi-dimensional data structure, such as an N-dimensional array. It is commonly used to summarize or normalize data by removing variation along a chosen dimension, producing a reduced representation.

Typically, meansalong takes two primary inputs: the data array and the axis parameter (or axes) that specify the dimension(s) along which to compute the mean. The result is an array with those axes reduced, unless a keepdims option is used to retain singleton dimensions. The operation can be applied to simple matrices or higher-dimensional tensors, and its exact behavior depends on the language or library in use.

Variants include nan-safe or missing-value handling, such as ignoring NaNs when calculating the mean, and weighted means, where each element contributes proportional to a weight. When applied to multiple axes, meansalong can produce higher- or lower-dimensional outputs depending on the axes specified and the keepdims setting.

Implementation details vary by programming language. In many environments, the operation is implemented as a vectorized kernel that leverages optimized linear algebra or tensor libraries. Efficient implementations require attention to memory layout and broadcasting rules to maximize performance.

Applications include data aggregation in statistics, feature engineering in machine learning, and image or signal processing, where means across time, space, or channels are common. The concept of meansalong is closely related to general reduction operations and to the notion of taking means along a specified axis in multi-dimensional data structures.

See also: Mean, Reduction operation, Axis in multi-dimensional arrays, Weighted average.