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using System;
using System.Collections;
using CenterSpace.NMath.Core;
namespace CenterSpace.NMath.Examples.CSharp
{
/// <summary>
/// A .NET example in C# showing how to manipulate data using the DataFrame class.
/// </summary>
/// <remarks>
/// The statistical functions in NMath Stats support the NMath Core types
/// DoubleVector and DoubleMatrix, as well as simple arrays of doubles. In many
/// cases, these types are sufficient for storing and manipulating your
/// statistical data. However, they suffer from two limitations: they can only
/// store numeric data, and they have limited support for adding, inserting, removing,
/// and reordering data. Therefore, NMath Stats provides the DataFrame class which
/// represents a two-dimensional data object consisting of a list of columns of the
/// same length. Columns are themselves lists of different types of data: numeric,
/// string, boolean, generic, and so on.
/// </remarks>
public class DataFrameExample
{
static void Main( string[] args )
{
// Create an empty data frame.
var df = new DataFrame();
// Add some columns. These data describe the relationship between
// the size of acorns and various oak tree species. Columns in a data frame
// can be accessed by numeric index (0...n-1) or by a name supplied at
// construction time.
df.AddColumn( new DFStringColumn( "Region" ) );
df.AddColumn( new DFNumericColumn( "AcornSize" ) );
df.AddColumn( new DFNumericColumn( "TreeHeight" ) );
df.AddColumn( new DFBoolColumn( "Threatened" ) );
// Add some rows of data. Rows can be accessed by numeric index (0...n-1)
// or by a key object. The first parameter to the AddRow() method, in this
// case the name of the oak tree species, is the row key.
df.AddRow( "Quercus alba L.", "Atlantic", 1.4, 27, false );
df.AddRow( "Quercus bicolor Willd.", "Atlantic", 3.4, 21, false );
df.AddRow( "Quercus macrocarpa Michx.", "Atlantic", 9.1, 25, false );
df.AddRow( "Quercus Chapmanii Sarg.", "Atlantic", 0.9, 15, false );
df.AddRow( "Quercus Durandii Buckl.", "Atlantic", 0.8, 23, true );
df.AddRow( "Quercus laurifolia Michx.", "Atlantic", 1.1, 27, false );
df.AddRow( "Quercus marilandica Muenchh.", "Atlantic", 3.7, 9, false );
df.AddRow( "Quercus nigra L.", "Atlantic", 1.1, 24, true );
df.AddRow( "Quercus palustris Muenchh.", "Atlantic", 1.1, 23, false );
df.AddRow( "Quercus texana Buckl.", "Atlantic", 1.1, 9, false );
df.AddRow( "Quercus coccinea Muenchh.", "Atlantic", 1.2, 4, false );
df.AddRow( "Quercus Douglasii Hook. & Arn", "California", 4.1, 18, false );
df.AddRow( "Quercus dumosa Nutt.", "California", 1.6, 6, false );
df.AddRow( "Quercus Engelmannii Greene", "California", 2.0, 17, false );
df.AddRow( "Quercus Garryana Hook.", "California", 5.5, 20, true );
df.AddRow( "Quercus chrysolepis Liebm.", "California", 17.1, 15, false );
df.AddRow( "Quercus vaccinifolia Engelm.", "California", 0.4, 1, false );
df.AddRow( "Quercus tomentella Engelm", "California", 7.1, 18, true );
// Display the entire, original data frame.
Console.WriteLine();
Console.WriteLine( df );
Console.WriteLine();
// Reorder some columns. Lets move the AcornSize column to the end.
df.PermuteColumns( 0, 3, 1, 2 );
Console.WriteLine( df );
Console.WriteLine();
// If you dont know the index of a column you can query for it by name.
int acornSizeCol = df.IndexOfColumn( "AcornSize" );
int treeHeightCol = df.IndexOfColumn( "TreeHeight" );
// Sort the rows. Lets sort the rows by AcornSize in ascending order, and secondarily
// by TreeHeight in descending order.
int[] colIndices = { acornSizeCol, treeHeightCol };
SortingType[] sortingTypes = { SortingType.Ascending,
SortingType.Descending };
df.SortRows( colIndices, sortingTypes );
Console.WriteLine( df );
Console.WriteLine();
// Remove some columns and rows.
df.RemoveColumn( "Threatened" );
df.RemoveRow( "Quercus nigra L." );
df.RemoveRow( 2 );
Console.WriteLine( df );
Console.WriteLine();
// Update a value by row and column index.
int rowIndex = df.IndexOfKey( "Quercus chrysolepis Liebm." );
int colIndex = df.IndexOfColumn( "AcornSize" );
df[rowIndex, colIndex] = 17.2;
// Get a row dictionary for one species of oak tree. The keys are the column names,
// and the values are the row data.
IDictionary dict = df.GetRowDictionary( "Quercus palustris Muenchh." );
Console.WriteLine( "Quercus palustris Muenchh." );
foreach ( string key in dict.Keys )
{
Console.WriteLine( key + ": " + dict[key] );
}
Console.WriteLine();
// Get a column dictionary for the TreeHeight column. The keys are the row keys, and
// values are the column data.
dict = df.GetColumnDictionary( "TreeHeight" );
Console.WriteLine( "TreeHeight" );
foreach ( string key in dict.Keys )
{
Console.WriteLine( key + ": " + dict[key] );
}
Console.WriteLine();
// Compute some descriptive statistics
Console.WriteLine( "Acorn Size:" );
Console.WriteLine( "Mean = " + StatsFunctions.Mean( df["AcornSize"] ) );
Console.WriteLine( "Var = " + StatsFunctions.Variance( df["AcornSize"] ) );
Console.WriteLine();
// Export data to a DoubleMatrix. Non-numeric columns are ignored.
DoubleMatrix A = df.ToDoubleMatrix();
Console.WriteLine( A );
Console.WriteLine();
// Get a DoubleVector for the values in the AcornSize column.
DoubleVector v = df["AcornSize"].ToDoubleVector();
Console.WriteLine( v );
Console.WriteLine();
Console.WriteLine();
Console.WriteLine( "Press Enter Key" );
Console.Read();
} // Main
} // class
} // namespace
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