Net4x.DapperLibrary.Pivot 1.9.9.26248

DapperLibrary.Pivot

Utilities to pivot a DataTable (rows ? columns) with a small, dependency-light API.

Purpose

• Provide an in-memory pivot operation on ADO.NET DataTable instances.
• Support two modes:
  • Count mode: when columnsWithDataToPivot is null, the pivot produces one column per distinct value encountered and a final count column.
  • Value mode: when columnsWithDataToPivot is provided, specified source columns are used to form pivoted columns and values are aggregated or joined per pivot cell.

Main types

• Pivot � factory with CreateInstance() returning an IPivot implementation.
• IPivot � interface exposing Pivot(DataTable? sourceDatatable, Dictionary<string,string[]>? columnsWithDataToPivot, params string[] columnsAsPivotPrimaryKey).
• PivotInternal � internal implementation that performs the pivot logic. It uses ArgumentGetter.Instance to obtain the default CountColumnName and logs exceptions via CoreLibrary.Logging.
• Extensions: helper methods under Extensions (DataTableHelper, PivotExtensions) assist with table operations and lookups.

API

• IPivot.Pivot(DataTable? sourceDatatable, Dictionary<string,string[]>? columnsWithDataToPivot, params string[] columnsAsPivotPrimaryKey)
  • sourceDatatable: source rows to pivot. If null, an initial pivot table structure is still returned where possible.
  • columnsWithDataToPivot: when null, the method runs in "count" mode and adds a count column; otherwise it maps source columns to lists of column names whose values become pivoted columns.
    • The dictionary keys are source column names. The dictionary values are arrays of column names whose values should be used when building the pivot cell value for that key.
    • A special dictionary key of "*" may be used to indicate wildcard columns to include additional values for composed cells.
  • columnsAsPivotPrimaryKey: one or more source column names that become the primary key columns of the resulting pivot table. Rows are grouped by these columns when building the pivot.

Behavior notes

• The pivot result uses the provided columnsAsPivotPrimaryKey as the pivot table primary key. New rows are created when a primary key combination is not present.
• Column names for pivoted values are taken from the string representation of source cell values. In count mode a numeric count column (default name from configuration, e.g. "Count") is appended.
• When columnsWithDataToPivot is provided, pivot cell values are either an aggregated count or a joined string of values (joined with " - ") depending on whether mapping lists are empty.
• The implementation logs exceptions and returns null on error.
• Default CountColumnName can be configured via ConfigurationLibrary.Arguments (argument name "CountColumnName").

Example

using System.Data;
using DapperLibrary;

var pivot = Pivot.CreateInstance();

// Source table must contain the columns used as primary key and the columns to pivot
var source = new DataTable();
source.Columns.Add("Category", typeof(string));
source.Columns.Add("Type", typeof(string));
source.Columns.Add("Value", typeof(string));

source.Rows.Add("A", "X", "10");
source.Rows.Add("A", "Y", "20");
source.Rows.Add("B", "X", "5");

// columnsWithDataToPivot maps the source column to which columns provide values for the pivot
var columnsWithDataToPivot = new Dictionary<string,string[]>
{
    { "Type", new[] { "Value" } }
};

// Use "Category" as the pivot primary key
var result = pivot.Pivot(source, columnsWithDataToPivot, "Category");

// result will have one row per Category and a column for each distinct Type value (e.g. "X", "Y").
// Each pivot cell contains the joined Value(s) for that Category/Type.

Dependencies

• The pivot implementation relies on CoreLibrary.Utility.Conversion for conversions and CoreLibrary.Logging for error reporting.
• ConfigurationLibrary.Arguments is used to read the default CountColumnName setting.

Threading and performance

• The pivot operation is in-memory and works on DataTable instances; it is not optimized for very large datasets. For large data volumes consider streaming/aggregation approaches.
• The implementation is not explicitly thread-safe for concurrent mutations of the same DataTable instance.

License / Contributing

See repository root for license and contribution guidelines.