DolphinDB Community

reciprocal(X) — element‑wise reciprocal (1/X) with DOUBLE output! 🔢 Works on scalars, vectors, and matrices—perfect for normalization, rates, and inverse transformations. #DolphinDB #MathFunctions

rdp(pointList, epsilon) — compress a POINT vector using the Ramer‑Douglas‑Peucker algorithm! 📦 Reduces number of points while preserving shape—ideal for geospatial data and curve simplification. #DolphinDB #Compression

ratios(X) — compute sequential ratios: Xᵢ / Xᵢ₋₁ (first element null)! 📈 Perfect for growth rates, returns, and period‑over‑period comparisons on vectors/matrices. #DolphinDB #TimeSeries #Ratios

polynomial(X, coeffs) — evaluate a polynomial p(x) = coeffs[0] + coeffs[1]·x + coeffs[2]·x² + … on each element of X! 📐 Fast vectorized evaluation for custom polynomial transformations and feature engineering. #DolphinDB #Polynomials

polyFit(X, Y, n, mode) — fit a degree‑n polynomial via least squares, returning coefficients (ascending powers) or a full model dict with prediction! 📈 Essential for curve fitting, trend analysis, and feature engineering. #DolphinDB #PolynomialRegression

point(X, Y) — create a 16‑byte POINT type (low 8 bytes = X, high 8 bytes = Y) for geospatial or 2D coordinates! 📍 Supports scalar/vector/matrix inputs—essential for location‑based analytics. #DolphinDB #Geospatial

plot(data, [labels], [title], [chartType], [stacking], [extras]) — create rich visualizations (LINE, BAR, PIE, SCATTER, SURFACE) from vectors, matrices, or tables! 📈 #DolphinDB #DataViz

percentile(X, percent, [interpolation]) — compute the exact percentile (0‑100) with flexible interpolation methods! 📊 Works on vectors, matrices, and tables—ignores nulls automatically. #DolphinDB #Statistics

percentChange(X, [n]) — compute element‑wise percentage change: (Xᵢ / Xᵢ₋ₙ) − 1! 📈 Ideal for growth rates, returns, and time‑series differencing—works on vectors and matrices. #DolphinDB #TimeSeries

peekAppend(engine, data) — preview calculation results without updating engine state or output! 👀 Perfect for testing, validation, and “what‑if” analysis on streaming engines. #DolphinDB #StreamProcessing

pdfChiSquare(df, X) — compute the probability density function (PDF) of a chi‑square distribution! 📊 Matches SciPy’s chi2.pdf—essential for hypothesis testing and statistical modeling. #DolphinDB #Statistics

partial(func, args…) — create a partially applied function by fixing some arguments! 🔗 Perfect for building specialized functions from generics, currying, and simplifying callbacks. #DolphinDB #FunctionalProgramming

parseInteger(X, type, [radix]) — parse strings to integers with custom radix (2‑16), ignoring trailing garbage! 🔢 Robust for hex, binary, and mixed‑format numeric parsing—empty string → null. #DolphinDB #StringParsing

parseMktData(dict) — convert a dictionary or JSON string into a strongly typed MKTDATA object (Curve, Surface, Price)! 📈 Supports FX spots, yield curves, asset price curves, and volatility surfaces—foundation for pricing models. #DolphinDB #MarketData

parseJsonTable(json, [schema], [keyCaseSensitive]) — convert JSON objects directly into DolphinDB tables, with optional schema inference! 📋 Handles single/multiple objects, custom date formats, and case‑sensitive keys—perfect for API ingestion. #DolphinDB #JSON

pair(a, b) — create a pair (interval/range) from two scalars, supporting arithmetic and comparisons! ↔️ Essential for range‑based filtering, interval joins, and vectorized boundary operations. #DolphinDB #DataTypes

parseExpr(X, [varDict], [modules], [overloadedOperators]) — convert strings to executable metacode, with optional variable binding, module loading, and operator overloading! 🔄 Powerful for dynamic SQL, expression evaluation, and metaprogramming. #DolphinDB #Metaprogramming

panel(row, col, metrics, [rowLabel], [colLabel], [parallel]) — pivot one or more vectors into matrices by row and column keys! 📊 Powerful alternative to pivotBy—supports multiple metrics and parallel execution for large datasets. #DolphinDB #Pivot

objByName(name, [sharedVar]) — fetch a variable (table/object) by name at runtime, with optional shared/local scoping! 🔍 Essential for remote execution where local parsing would fail—seamless cross‑node scripting. #DolphinDB #RemoteExecution

pack(format, args…) — pack data into a binary bytes object according to a format string (supports repeat counts & byte order)! 📦 Perfect for binary protocol serialization, file I/O, and cross‑language data exchange. #DolphinDB #BinarySerialization