diffSeries / diffDataFrame compute the element-wise discrete
difference (value[i] - value[i-periods]).
shiftSeries / shiftDataFrame shift values forward or backward
by a given number of periods, filling with a configurable value.
Mirrors Series.diff(), Series.shift(),
DataFrame.diff(), and DataFrame.shift() from pandas.
Compute s[i] - s[i - periods] for each position.
The first periods entries are null.
Non-numeric values produce null.
💡 Tip: diffSeries is commonly used to compute returns, velocity, or changes over time.
Shift values forward (positive periods) or backward (negative periods).
Vacated positions are filled with fillValue (default null).
💡 Tip: combine shiftSeries with arithmetic to compute returns, lags, or leads.
axis=0 (default): diff each column independently (rows over time).
axis=1: diff across columns within each row.
Shift all columns by the same number of periods. Useful for creating lagged features in machine learning.
💡 Tip: creating multiple lagged columns is a common feature-engineering technique for time series forecasting.
// Discrete difference
diffSeries(series: Series<Scalar>, options?: DiffOptions): Series<Scalar>
diffDataFrame(df: DataFrame, options?: DataFrameDiffOptions): DataFrame
interface DiffOptions {
periods?: number; // default 1; negative = look forward
}
interface DataFrameDiffOptions extends DiffOptions {
axis?: 0 | 1 | "index" | "columns"; // default 0
}
// Value shifting
shiftSeries(series: Series<Scalar>, options?: ShiftOptions): Series<Scalar>
shiftDataFrame(df: DataFrame, options?: DataFrameShiftOptions): DataFrame
interface ShiftOptions {
periods?: number; // default 1; negative = shift backward
fillValue?: Scalar; // default null
}
interface DataFrameShiftOptions extends ShiftOptions {
axis?: 0 | 1 | "index" | "columns"; // default 0
}