MatPlotLibNet.DataFrame 1.10.0

MatPlotLibNet.DataFrame

Microsoft.Data.Analysis.DataFrame extension methods for MatPlotLibNet — plot directly from typed DataFrames with optional hue grouping and full fluent API support.

Install

dotnet add package MatPlotLibNet.DataFrame

Quick start

using MatPlotLibNet;                    // extension methods live in the top-level namespace
using Microsoft.Data.Analysis;

// Load from CSV or build in memory
var df = DataFrame.LoadCsv("prices.csv");

// Line chart — two numeric columns
df.Line("date", "close")
  .WithTitle("Closing Price")
  .Save("price.svg");

// Scatter with hue grouping — one series per ticker
df.Scatter("date", "close", hue: "ticker")
  .WithTitle("Portfolio")
  .Save("portfolio.svg");

// Histogram with hue grouping
df.Hist("returns", bins: 40, hue: "sector")
  .WithTitle("Return Distribution by Sector")
  .Save("returns.svg");

API

Charting — DataFrameFigureExtensions

// All three methods return FigureBuilder — chainable before .Build() / .ToSvg() / .Save()

FigureBuilder df.Line(string x, string y, string? hue = null, Color[]? palette = null)
FigureBuilder df.Scatter(string x, string y, string? hue = null, Color[]? palette = null)
FigureBuilder df.Hist(string column, int bins = 30, string? hue = null, Color[]? palette = null)

Financial Indicators — DataFrameIndicatorExtensions

All indicator methods resolve the named column(s) to double[] and delegate to the core indicator types. Output arrays are trimmed (not NaN-padded) — length equals n - warmUp where warmUp depends on the indicator period.

// Price indicators (single close/price column)
double[]      df.Sma(string priceCol, int period)
double[]      df.Ema(string priceCol, int period)
double[]      df.Rsi(string priceCol, int period = 14)
BandsResult   df.BollingerBands(string priceCol, int period = 20, double stdDev = 2.0)
double[]      df.Obv(string closeCol, string volumeCol)
MacdResult    df.Macd(string priceCol, int fast = 12, int slow = 26, int signal = 9)
double[]      df.DrawDown(string priceCol)

// Candle indicators (high / low / close columns)
double[]      df.Adx(string highCol, string lowCol, string closeCol, int period = 14)
AdxResult     df.AdxFull(string highCol, string lowCol, string closeCol, int period = 14)
double[]      df.Atr(string highCol, string lowCol, string closeCol, int period = 14)
double[]      df.Cci(string highCol, string lowCol, string closeCol, int period = 20)
double[]      df.WilliamsR(string highCol, string lowCol, string closeCol, int period = 14)
StochasticResult df.Stochastic(string highCol, string lowCol, string closeCol, int period = 14)
double[]      df.ParabolicSar(string highCol, string lowCol, double step = 0.02, double max = 0.2)
BandsResult   df.KeltnerChannels(string highCol, string lowCol, string closeCol, int period = 20, double atrMultiplier = 1.5)
double[]      df.Vwap(string highCol, string lowCol, string closeCol, string volumeCol)

Result types:

// Example: candlestick + SMA + Bollinger Bands overlay
double[]    sma20 = df.Sma("close", 20);
BandsResult bb    = df.BollingerBands("close", period: 20, stdDev: 2.0);

string svg = Plt.Create()
    .AddSubPlot(1, 1, 1, ax =>
    {
        ax.UseBarSlotX()
          .Candlestick(open, high, low, close)
          .Signal(sma20, label: "SMA 20")
          .FillBetween(xVals, bb.Upper, bb.Lower, s => s.Alpha = 0.2);
    })
    .WithTitle("Price + Bollinger Bands")
    .ToSvg();

Polynomial Regression — DataFrameNumericsExtensions

// Fit a polynomial of the given degree to two numeric columns
double[] coeffs = df.PolyFit(string xCol, string yCol, int degree)

// Evaluate the fitted polynomial at every value in the X column
double[] fitY   = df.PolyEval(string xCol, double[] coefficients)

// Compute a confidence band for the fitted polynomial
ConfidenceBand band = df.ConfidenceBand(string xCol, string yCol,
                          double[] coefficients, double[] evalX, double level = 0.95)
// ConfidenceBand has Upper[] and Lower[]

// Example: scatter + linear fit + 95 % confidence band
double[] xVals  = DataFrameColumnReader.ToDoubleArray(df["x"]);
double[] yVals  = DataFrameColumnReader.ToDoubleArray(df["y"]);
double[] coeffs = df.PolyFit("x", "y", degree: 1);
double[] fitY   = df.PolyEval("x", coeffs);
ConfidenceBand band = df.ConfidenceBand("x", "y", coeffs, evalX: xVals);

string svg = Plt.Create()
    .AddSubPlot(1, 1, 1, ax =>
    {
        ax.Scatter(xVals, yVals, s => s.Label = "Data")
          .Plot(xVals, fitY, s => s.Label = "Linear fit")
          .FillBetween(xVals, band.Upper, band.Lower,
              s => { s.Alpha = 0.2; s.Label = "95 % CI"; });
    })
    .WithTitle("Regression with Confidence Band")
    .ToSvg();

Column name resolution throws ArgumentException with the unknown column name when a column is not found.

Column type support

How it works

The extensions materialise named DataFrame columns to double[] or string[] via DataFrameColumnReader, then delegate all grouping, palette cycling, and series-creation logic to the existing EnumerableFigureExtensions.Line / Scatter / Hist methods in the core package. No grouping code is duplicated — the DataFrame package is ~100 lines of pure plumbing.

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