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+ tsb — idxmin / idxmax
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Loading TypeScript compiler…
+
idxmin / idxmax
+
+ Return the index label of the minimum or maximum value in a
+ Series or each column of a DataFrame.
+ Mirrors pandas.Series.idxmin(), idxmax(),
+ pandas.DataFrame.idxmin(), and DataFrame.idxmax().
+
+
+
+
+
1 · Series.idxmin — label of the minimum value
+
Returns the index label at the position of the minimum value.
+ NaN / null values are skipped by default.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
2 · Series.idxmax — label of the maximum value
+
Returns the index label at the position of the maximum value.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
3 · NaN handling — skipna option
+
By default NaN / null values are skipped. Set skipna: false
+ to propagate NaN (returns null if any value is NaN).
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
4 · DataFrame.idxmin — row label of column minima
+
Returns a Series indexed by column names. Each value is the row label
+ where that column achieves its minimum.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
5 · DataFrame.idxmax — row label of column maxima
+
Returns a Series indexed by column names, where each entry is the row
+ label of that column's maximum value.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
6 · Edge cases — empty, all-NaN, all-equal
+
Behavior for empty series, series where every value is NaN, and series
+ where all values are equal.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
API Reference
+
// Series
+idxminSeries(series, { skipna?: boolean }): Label // default skipna=true
+idxmaxSeries(series, { skipna?: boolean }): Label
+
+// DataFrame (axis=0 — min/max per column)
+idxminDataFrame(df, { skipna?: boolean }): Series // indexed by column names
+idxmaxDataFrame(df, { skipna?: boolean }): Series
+
+ 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.
+
+
+
+
+
1 · isna / notna on scalars
+
+ Returns true / false for individual values.
+ null, undefined, and NaN are all
+ considered "missing".
+
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
2 · isna on a Series
+
+ When passed a Series, isna returns a boolean Series of the
+ same length — true where values are missing.
+
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
3 · isna on a DataFrame
+
+ Returns a DataFrame of booleans with the same shape — one column per
+ original column, true where missing.
+
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
4 · Forward-fill (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.
+
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
5 · Backward-fill (bfillSeries)
+
+ Propagates the next valid value backward to fill gaps. Trailing
+ nulls that have no following value remain null.
+
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
6 · DataFrame forward-fill & backward-fill
+
+ dataFrameFfill and dataFrameBfill apply fill
+ column-wise by default (axis=0). Pass axis: 1 to fill
+ row-wise across columns.
+
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
API Reference
+
// 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
+
+
+
Initializing playground…
+
📊 pct_change — Interactive Playground
+ Compute the fractional change between each element and a prior element.
+ Mirrors pandas.Series.pct_change() /
+ pandas.DataFrame.pct_change().
+ Edit any code block below and press ▶ Run
+ (or Ctrl+Enter) to execute it live in your browser.
+
+
+
+
+
1 · Basic pct_change on a Series
+
pctChangeSeries(series) returns the fractional (not percentage) change
+ from each previous element. The first element is always null.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
2 · Multi-period change
+
The periods option controls the lag. Use periods: 2 to
+ compare each value to the one two steps earlier — useful for month-over-month
+ comparisons in quarterly data.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
3 · Handling missing values
+
By default, pctChangeSeries forward-fills (fillMethod: "pad")
+ NaN/null values before computing the ratio — so gaps don't break the chain.
+ Set fillMethod: null to propagate NaN instead.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
4 · Limit consecutive fills
+
The limit option caps how many consecutive NaN values get forward-filled.
+ Useful when you want to tolerate short gaps but not bridge large ones.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
5 · DataFrame column-wise pct_change
+
pctChangeDataFrame(df) applies pctChangeSeries to every
+ column independently. Ideal for comparing multiple assets or metrics simultaneously.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
6 · Negative periods (look-forward change)
+
A negative periods value computes the forward change: how much will
+ this element change by the time we reach |periods| steps ahead.
+ Useful for computing returns on a "hold for N periods" strategy.
+
+
+
+
+
Click ▶ Run to execute
+
Ctrl+Enter to run · Tab to indent
+
+
+
API Reference
+
All functions return a new Series/DataFrame of the same shape — inputs are never mutated.
+
// Series
+pctChangeSeries(series, {
+ periods?: number, // default 1 (positive = look back, negative = look forward)
+ fillMethod?: "pad" | "bfill" | null, // default "pad"
+ limit?: number | null, // max consecutive fills; default unlimited
+}): Series
+
+// DataFrame
+pctChangeDataFrame(df, {
+ periods?: number,
+ fillMethod?: "pad" | "bfill" | null,
+ limit?: number | null,
+ axis?: 0 | 1 | "index" | "columns", // default 0 (column-wise)
+}): DataFrame
+