Data Analysis Library Features

Numerics.NET includes classes for the following subject areas. Also see the detailed Math, Vector and Matrix, and Statistics feature lists.

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General

  • Full featured Data Frames (similar to R and pandas).
  • Vector elements can have labels. Matrices can have row and column labels.
  • A large set of aggregators and aggregator functions.
  • Large set of groupings, including moving and expanding windows, 2D pivot tables, and time-based resampling.
  • New generic Descriptives class for collecting descriptive statistics of vectors.
  • Indexes on ordered types support lookup nearest. Likewise, data frames with such indexes now support join on nearest.
  • New Recurrence type lets you specify date/time patterns for use in, for example, resampling of data frames.

Data frames

  • Column-based in-memory database tables.
  • Index rows and columns based on position or key.
  • Add, insert, remove, rename, transform columns.
  • Perform joins between data frames on indexes and/or columns.
  • Join-to-nearest on ordered row indexes.
  • Filter data frames on position, keys, boolean mask.
  • Data frames can be passed directly to statistical and machine learning models.

Indexes

  • A collection of labels for the rows and columns in a data frame or matrix or the elements of a vector..
  • Multi-level indexes are supported.
  • Indexes are automatically propagated through calculations.
  • Operations on vectors and matrices are automatically aligned on their indexes.

Aggregators

  • All the standard aggregators (Max/Min, Sum, Mean…).
  • Automatically convert to intermediate values (for example: compute the mean of a set of integers using doubles)
  • Aggregations can be applied to data frames, vectors, matrices, tensors.
  • Aggregations return a single value per vector input, or multiple values when using a group by.
  • Aggregations can be performed with or without filters for missing values or NaN’s.
  • Efficiently compute aggregates over moving windows.

Groupings

  • Group on index or column values, categorical vectors.
  • Group on date/time values using flexible recurrences.
  • Binning based on values or quantiles.
  • Moving windows: fixed size, expanding, condition-dependent.
  • Paritions, both fixed and condition-dependent.
  • Aggregate in two dimensions using pivot tables.