DataFrameMath extension
- on
Methods
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anova(
String groupCol, String valueCol) → ANOVAResult -
Available on DataFrame, provided by the DataFrameMath extension
One-way ANOVA comparing means across multiple groups. -
autocorrelation(
int columnIndex, int maxLag) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Computes autocorrelation up to a specified lag. -
bootstrap(
{required String valueCol, required double statistic(List< double> ), int nBoot = 1000}) → BootstrapResult -
Available on DataFrame, provided by the DataFrameMath extension
Bootstrapped sampling distribution of a user-defined statistic. -
chiSquare(
String colX, String colY) → ChiSquareResult -
Available on DataFrame, provided by the DataFrameMath extension
Chi-Square test (Pearson) for independence between two categorical columns. -
convolve(
String xCol, String yCol) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Computes the discrete linear convolution of two columns. -
countNulls(
dynamic colName) → int -
Available on DataFrame, provided by the DataFrameMath extension
Counts the number ofnullorNaNvalues in the specified column. -
countZeros(
dynamic colName, {List< Object> zeroValues = const <Object>[0]}) → int -
Available on DataFrame, provided by the DataFrameMath extension
Counts the number of zero values in a specified column. -
covarianceMatrix(
List< String> cols) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Covariance matrix for a list of columns (population). -
crossCorrelate(
String xCol, String yCol) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Computes the discrete cross-correlation of two columns. -
ewmCorr(
int columnIndexX, int columnIndexY, double alpha) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
EWM correlation between two columns. -
ewmCov(
int columnIndexX, int columnIndexY, double alpha, {bool useLaggedMean = false}) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Exponential Weighted Moving Covariance (EWMCov) between two columns. -
ewmMean(
int columnIndex, double alpha) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Computes the exponentially weighted moving average (EWM) for a column. Thealphaparameter is the smoothing factor and must be in the range(0, 1]. -
ewmVar(
int columnIndex, double alpha) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Computes the exponentially weighted moving variance for a column (population). Thealphaparameter is the smoothing factor and must be in the range(0, 1]. -
expandingMax(
int columnIndex) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Computes the expanding maximum for a column. -
expandingMean(
int columnIndex) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Computes the expanding mean for a column. -
expandingMin(
int columnIndex) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Computes the expanding minimum for a column. -
expandingVar(
int columnIndex, {int ddof = 1}) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Computes the expanding variance for a column. Theddofparameter determines the type of variance:0for population,1for sample. -
exponentialSmoothing(
String valueCol, int period, {String method = 'hw', double alpha = 0.2, double beta = 0.1, double gamma = 0.1, String seasonalType = 'additive'}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Applies single, double, or triple exponential smoothing to a series. -
fft(
String timeCol, String valueCol) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Computes the discrete Fourier transform (DFT) of a numeric column using FFT logic. -
filterNulls(
dynamic columnIndex, {bool skipNull = true}) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Filters out null and NaN values from a specified column and returns a list of doubles. -
groupBy(
String byColName, {String? valueColName, double transform(List< double> )?, bool filter(List<double> )?, double aggregate(List<double> )?}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
GroupBy with transform, filter, or aggregate. -
interpolate(
String timeCol, String colName, {InpMethod method = InpMethod.polynomial, int degree = 3, int? precision}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Interpolates missing values in a column using time-aware or index-based methods. -
knnImputer(
{required List< String> featureCols, required List<String> targetCols, int k = 5}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
K-Nearest Neighbors (KNN) imputation for missing values. -
localOutlierFactor(
{List< String> ? cols, int k = 20}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Computes Local Outlier Factor (LOF) scores for multivariate outlier detection. -
m(
dynamic colName, double operation(num), {bool asList = false, bool inplace = false}) → dynamic -
Available on DataFrame, provided by the DataFrameMath extension
Applies a mathematical function to all elements in a column. -
max(
int columnIndex) → double -
Available on DataFrame, provided by the DataFrameMath extension
Returns the maximum value in a specified column. -
mean(
int columnIndex) → double -
Available on DataFrame, provided by the DataFrameMath extension
Calculates the mean (average) of the values in a specified column. -
melt(
List< String> idVars, List<String> valueVars, {String varName = 'variable', String valueName = 'value'}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Converts wide-form data to long-form. -
min(
int columnIndex) → double -
Available on DataFrame, provided by the DataFrameMath extension
Returns the minimum value in a specified column. -
outlierIQR(
String valueCol, {double k = 1.5}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Flags outliers using the interquartile range (IQR) method. -
outlierZScore(
String valueCol, {double threshold = 3.0}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Flags outliers using the Z-score method. -
partialAutocorrelation(
int columnIndex, int maxLag) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Computes partial autocorrelation using the Durbin–Levinson algorithm. -
pca(
{List< String> ? cols, bool center = true, bool scale = false}) → PCAModel -
Available on DataFrame, provided by the DataFrameMath extension
Principal Component Analysis (PCA) on a group of columns. -
pivotTable(
{required String indexCol, required String columnCol, required String valueCol, double agg(List< double> )?}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Dynamic pivot table for reshaping data. -
resample(
String timeCol, String valueCol, Duration freq, {double agg(List< double> )?, String interpolation = 'linear'}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Resamples a time series by upsampling or downsampling with optional interpolation. -
rollingApply(
int columnIndex, int window, double func(List< double> )) → List<double> -
Available on DataFrame, provided by the DataFrameMath extension
Apply a custom function over each fixed-size window. -
rollingMad(
String valueCol, int window) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Computes the rolling median absolute deviation (MAD) for a numeric column. -
rollingMean(
int columnIndex, int window) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Rolling mean (average) over a fixed-size window. -
rollingStd(
int columnIndex, int window) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Rolling standard deviation (population std) over a fixed-size window. -
rollingSum(
int columnIndex, int window) → List< double> -
Available on DataFrame, provided by the DataFrameMath extension
Computes the rolling sum over a fixed-size window on a numeric column. -
seasonalDecompose(
int columnIndex, int period) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Classical seasonal-trend decomposition using an additive model. -
sumCol(
int columnIndex) → double -
Available on DataFrame, provided by the DataFrameMath extension
Sums the values in a specified column. -
svd(
{List< String> ? cols, int sweeps = 100}) → SVDResult -
Available on DataFrame, provided by the DataFrameMath extension
Singular Value Decomposition (SVD) on a group of columns. -
tTest(
String groupCol, String valueCol) → TTestResult -
Available on DataFrame, provided by the DataFrameMath extension
Two-sample t-test comparing means between two groups. -
unstack(
{required String indexCol, required String varName, required String valueName}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Converts long-form data back to wide-form (unstack). -
weightedHistogram(
String valueCol, String weightCol, {int? bins}) → DataFrame -
Available on DataFrame, provided by the DataFrameMath extension
Computes a weighted histogram of a column using another column as weights (Freedman–Diaconis).