isna / notna — detect missing values in scalars,
Series, and DataFrames.
ffill / bfill — propagate the last (or next) valid
value to fill gaps.
Mirrors pd.isna(), Series.ffill(), and
DataFrame.bfill() from pandas.
isna / notna on scalars
Returns true / false for individual values.
null, undefined, and NaN are all
considered "missing".
isna on a Series
When passed a Series, isna returns a boolean Series of the
same length — true where values are missing.
isna on a DataFrame
Returns a DataFrame of booleans with the same shape — one column per
original column, true where missing.
ffillSeries)
Propagates the last valid value forward to fill gaps. Leading
nulls that have no preceding value remain null.
Use the optional limit to cap consecutive fills.
bfillSeries)
Propagates the next valid value backward to fill gaps. Trailing
nulls that have no following value remain null.
dataFrameFfill and dataFrameBfill apply fill
column-wise by default (axis=0). Pass axis: 1 to fill
row-wise across columns.
// Module-level missing-value detection
isna(value: Scalar): boolean
isna(value: Series): Series<boolean>
isna(value: DataFrame): DataFrame
notna(value: Scalar): boolean
notna(value: Series): Series<boolean>
notna(value: DataFrame): DataFrame
// Aliases
isnull(...) // same as isna
notnull(...) // same as notna
// Series forward / backward fill
ffillSeries(series, options?: { limit?: number | null }): Series
bfillSeries(series, options?: { limit?: number | null }): Series
// DataFrame forward / backward fill
dataFrameFfill(df, options?: {
limit?: number | null, // max consecutive fills (default: no limit)
axis?: 0 | 1 | "index" | "columns", // default 0 (column-wise)
}): DataFrame
dataFrameBfill(df, options?: {
limit?: number | null,
axis?: 0 | 1 | "index" | "columns",
}): DataFrame