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  2. pandas (software) - Wikipedia

    en.wikipedia.org/wiki/Pandas_(software)

    [4]: 114 A DataFrame is a 2-dimensional data structure of rows and columns, similar to a spreadsheet, and analogous to a Python dictionary mapping column names (keys) to Series (values), with each Series sharing an index. [4]: 115 DataFrames can be concatenated together or "merged" on columns or indices in a manner similar to joins in SQL.

  3. Row- and column-major order - Wikipedia

    en.wikipedia.org/wiki/Row-_and_column-major_order

    Note how the use of A[i][j] with multi-step indexing as in C, as opposed to a neutral notation like A(i,j) as in Fortran, almost inevitably implies row-major order for syntactic reasons, so to speak, because it can be rewritten as (A[i])[j], and the A[i] row part can even be assigned to an intermediate variable that is then indexed in a separate expression.

  4. Reorder point - Wikipedia

    en.wikipedia.org/wiki/Reorder_point

    The reorder point (ROP), also reorder level (ROL) or "optimal re-order level", [1] is the level of inventory which triggers an action to replenish that particular inventory. It is a minimum amount of an item which a firm holds in stock, such that, when stock falls to this amount, the item must be reordered.

  5. Economic order quantity - Wikipedia

    en.wikipedia.org/wiki/Economic_order_quantity

    If there are backorders, the reorder point is: =; with m being the largest integer and μ the lead time demand. Additionally, the economic order interval [ 8 ] can be determined from the EOQ and the economic production quantity model (which determines the optimal production quantity) can be determined in a similar fashion.

  6. Data augmentation - Wikipedia

    en.wikipedia.org/wiki/Data_augmentation

    Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.

  7. Floating point operations per second - Wikipedia

    en.wikipedia.org/wiki/Floating_point_operations...

    The exponentiation inherent in floating-point computation assures a much larger dynamic range – the largest and smallest numbers that can be represented – which is especially important when processing data sets where some of the data may have extremely large range of numerical values or where the range may be unpredictable.