1-D labeled array — Interactive Playground. The core building block of tsb data structures.
+1-D labeled array. The core building block of tsb data structures.
2-D labeled table — interactive tutorial. Column-oriented storage, full pandas API.
+2-D labeled table. Column-oriented storage, full pandas API.
Immutable labeled axis — Interactive Playground, RangeIndex.
+Immutable labeled axis, RangeIndex.
Rich dtype system — Interactive Playground. int/float/bool/string/datetime/category.
+Rich dtype system. int/float/bool/string/datetime/category.
Split-apply-combine — interactive tutorial. groupby, agg, transform, apply, filter.
+Split-apply-combine. groupby, agg, transform, apply, filter.
Combine Series and DataFrames — interactive tutorial. axis=0/1, outer/inner join, ignoreIndex.
+Combine Series and DataFrames. axis=0/1, outer/inner join, ignoreIndex.
SQL-style DataFrame joins — interactive tutorial. inner/left/right/outer, on/left_on/right_on, suffixes.
+SQL-style DataFrame joins. inner/left/right/outer, on/left_on/right_on, suffixes.
Vectorised string operations — interactive tutorial. lower/upper/strip/pad/contains/replace/split/extract & predicates.
+Vectorised string operations. lower/upper/strip/pad/contains/replace/split/extract & predicates.
Vectorised datetime operations — interactive tutorial. Calendar components, boolean boundaries, strftime, floor/ceil/round.
+Vectorised datetime operations. Calendar components, boolean boundaries, strftime, floor/ceil/round.
Summary statistics — interactive tutorial. count/mean/std/min/percentiles/max for numeric; count/unique/top/freq for categorical. Series.quantile().
+Summary statistics. count/mean/std/min/percentiles/max for numeric; count/unique/top/freq for categorical. Series.quantile().
CSV I/O — interactive tutorial. readCsv / toCsv with dtype inference, NA handling, quoted fields, custom separators.
+CSV I/O. readCsv / toCsv with dtype inference, NA handling, quoted fields, custom separators.
JSON I/O — interactive tutorial. readJson / toJson with five orient formats: records, split, index, columns, values.
+JSON I/O. readJson / toJson with five orient formats: records, split, index, columns, values.
Pearson correlation & covariance — interactive tutorial. Series.corr(), DataFrame.corr(), DataFrame.cov(), dataFrameCorr(), dataFrameCov() with index alignment, null handling, and configurable ddof/minPeriods.
+Pearson correlation & covariance. Series.corr(), DataFrame.corr(), DataFrame.cov(), dataFrameCorr(), dataFrameCov() with index alignment, null handling, and configurable ddof/minPeriods.
Sliding-window aggregations — interactive tutorial. Series.rolling() and DataFrame.rolling() with mean, sum, std, var, min, max, count, median, apply. Supports minPeriods and centered windows.
+Sliding-window aggregations. Series.rolling() and DataFrame.rolling() with mean, sum, std, var, min, max, count, median, apply. Supports minPeriods and centered windows.
Growing-window aggregations — Series.expanding() and DataFrame.expanding() with mean, sum, std, var, min, max, count, median, apply. Window grows from start to current position.
+Growing-window aggregations. Series.expanding() and DataFrame.expanding() with mean, sum, std, var, min, max, count, median, apply. Window grows from start to current position.
Categorical operations — interactive tutorial. Series.cat with categories, codes, ordered, addCategories, removeCategories, renameCategories, setCategories, reorderCategories, valueCounts.
+Categorical operations. Series.cat with categories, codes, ordered, addCategories, removeCategories, renameCategories, setCategories, reorderCategories, valueCounts.
Exponentially Weighted Moving aggregations — interactive tutorial. Series.ewm() and DataFrame.ewm() with mean, std, var, cov, corr, apply. Decay via span, com, halflife, or alpha. Supports adjust and ignoreNa.
+Exponentially Weighted Moving aggregations. Series.ewm() and DataFrame.ewm() with mean, std, var, cov, corr, apply. Decay via span, com, halflife, or alpha. Supports adjust and ignoreNa.
Wide-to-long reshape — interactive tutorial. Unpivot columns into variable/value pairs with id_vars, value_vars, var_name, value_name.
+Wide-to-long reshape. Unpivot columns into variable/value pairs with id_vars, value_vars, var_name, value_name.
Reshape with aggregation — interactive tutorial. pivot() for unique reshaping; pivotTable() for aggregation (mean/sum/count/min/max/first/last) with fill_value and dropna support.
+Reshape with aggregation. pivot() for unique reshaping; pivotTable() for aggregation (mean/sum/count/min/max/first/last) with fill_value and dropna support.
Pivot column labels to/from row index — interactive tutorial. stack() rotates columns into a compound-index Series; unstack() recovers the DataFrame. Custom sep, dropna, and fill_value support.
+Pivot column labels to/from row index. stack() rotates columns into a compound-index Series; unstack() recovers the DataFrame. Custom sep, dropna, and fill_value support.
Assign numerical ranks to values — interactive tutorial. rankSeries() and rankDataFrame() with tie methods (average/min/max/first/dense), NaN handling (keep/top/bottom), percentage ranks, and axis support.
+Assign numerical ranks to values. rankSeries() and rankDataFrame() with tie methods (average/min/max/first/dense), NaN handling (keep/top/bottom), percentage ranks, and axis support.
Return the n largest or smallest values — interactive tutorial. nlargestSeries(), nsmallestSeries(), nlargestDataFrame(), nsmallestDataFrame() with keep='first'/'last'/'all' tie-handling, NaN exclusion, and multi-column DataFrame sorting.
+Return the n largest or smallest values. nlargestSeries(), nsmallestSeries(), nlargestDataFrame(), nsmallestDataFrame() with keep='first'/'last'/'all' tie-handling, NaN exclusion, and multi-column DataFrame sorting.
Compute running totals, products, maxima, and minima — interactive tutorial. cumsum(), cumprod(), cummax(), cummin() for Series and DataFrame with skipna support and axis=0/1.
+Compute running totals, products, maxima, and minima. cumsum(), cumprod(), cummax(), cummin() for Series and DataFrame with skipna support and axis=0/1.
Element-wise transformations — interactive tutorial. clip(), seriesAbs(), seriesRound() for Series and DataFrame with min/max bounds, decimal precision, and axis support.
+Element-wise transformations. clip(), seriesAbs(), seriesRound() for Series and DataFrame with min/max bounds, decimal precision, and axis support.
Count unique values — interactive tutorial. valueCounts() for Series and dataFrameValueCounts() for DataFrame with normalize, sort, ascending, and dropna options.
+Count unique values. valueCounts() for Series and dataFrameValueCounts() for DataFrame with normalize, sort, ascending, and dropna options.
Hierarchical indexing — interactive tutorial. MultiIndex for multi-level row and column labels with fromArrays, fromTuples, fromProduct, level access, and swapLevels.
+Hierarchical indexing. MultiIndex for multi-level row and column labels with fromArrays, fromTuples, fromProduct, level access, and swapLevels.
value_counts
- Count unique values in a Series or unique row combinations in a
- DataFrame. Mirrors
- pandas.Series.value_counts()
- and
- pandas.DataFrame.value_counts().
-
Count unique values in a Series or unique row
+ combinations in a DataFrame. Mirrors
+ pandas.Series.value_counts() and
+ pandas.DataFrame.value_counts().
+ Edit any code block below and press ▶ Run
+ (or Ctrl+Enter) to execute it live in your browser.
+
import { Series } from "tsb";
-import { valueCounts } from "tsb";
+ Create a Series and count how often each unique value appears. Results are sorted
+ descending by frequency by default.
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+ const pct = valueCounts(s, { normalize: true });
-// values → [0.5, 0.333…, 0.167…] (must sum to 1)Pass normalize: true to get relative frequencies that sum to 1
+ instead of raw counts.
// Ascending order (least frequent first)
+ Control sorting: ascending: true for least-frequent first, or
+ sort: false to preserve insertion order.
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+ const sWithNull = new Series({ data: ["a", null, "b", null, "a"] });
+ By default nulls are excluded (dropna: true). Set
+ dropna: false to include them in the count.
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+ import { DataFrame } from "tsb";
-import { dataFrameValueCounts } from "tsb";
+ Count unique row combinations across all columns. Each unique
+ (city, temp) pair becomes an index label.
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+ // Restrict to a subset of columns
-const vc2 = dataFrameValueCounts(df, { subset: ["city"] });
-// Only counts by city: NYC=3, LA=1Restrict counting to a subset of columns with the subset option.
The result Series is indexed by the unique values (or composite
+ "v1|v2|…" strings for DataFrames). Use .index.values and
+ .values to inspect the labels and counts respectively.
// Series
valueCounts(series, {
- normalize?: boolean, // default false — return proportions instead of counts
+ normalize?: boolean, // default false — return proportions
sort?: boolean, // default true — sort by frequency
- ascending?: boolean, // default false — highest count first when sort=true
+ ascending?: boolean, // default false — highest count first
dropna?: boolean, // default true — exclude missing values
}): Series<number>
@@ -132,13 +379,14 @@ 7 · API reference
ascending?: boolean,
dropna?: boolean,
}): Series<number>"v1|v2|…" strings for DataFrames). Use .index.values and
- .values to inspect the labels and counts respectively.
- blocks to
interactive with editable
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+
-
- 2 — Nulls are encoded as -1
- Missing values (null) are not counted as categories and get a code of -1.
- import { Series } from "tsb";
+
+
+ 2 · Nulls are encoded as -1
+ Missing values (null) are not counted as categories and get a
+ code of -1.
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+
-
- 3 — Add and remove categories
- Use addCategories() to register new labels (without needing them in the data yet)
- and removeCategories() to drop labels (values become null).
- import { Series } from "tsb";
+
+
+ 3 · Add and remove categories
+ Use addCategories() to register new labels (without needing them in the
+ data yet) and removeCategories() to drop labels (values become null).
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+ 4 · Remove unused categories
+ removeUnusedCategories() drops any categories not present in the data.
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+
-
- 5 — Rename categories
- Pass an object mapping old names → new names, or an array of replacement names.
- import { Series } from "tsb";
+
+
+ 5 · Rename categories
+ Pass an object mapping old names → new names, or an array of replacement names.
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+ 6 · Set and reorder categories
+ setCategories() replaces the entire category list (values not in the new
+ list become null). reorderCategories() changes the order without adding
+ or removing.
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+
-
- 7 — Value counts per category
- valueCounts() returns a Series with count of each category (zero for unused ones).
- import { Series } from "tsb";
+
+
+ 7 · Value counts per category
+ valueCounts() returns a Series with the count of each category
+ (zero for unused ones).
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+ 8 · Ordered categories
+ asOrdered() marks a categorical as ordered (enabling comparisons).
+ The order is determined by the categories array index.
+
+
+ TypeScript
+
+
+
+
+
+
+ Click ▶ Run to execute
+ Ctrl+Enter to run · Tab to indent
+
+
+
+
+
+ API Reference
+ The .cat accessor is available on any Series containing string or
+ categorical data. It provides pandas-compatible category management methods.
+ series.cat.categories // Index of sorted unique labels
+series.cat.codes // Series<number> of integer codes (-1 for null)
+series.cat.nCategories // number of distinct categories
+series.cat.ordered // boolean — is the categorical ordered?
+
+series.cat.addCategories(newCats: string[]): Series
+series.cat.removeCategories(cats: string[]): Series
+series.cat.removeUnusedCategories(): Series
+series.cat.renameCategories(mapping: Record | string[]): Series
+series.cat.setCategories(cats: string[]): Series
+series.cat.reorderCategories(cats: string[], ordered?: boolean): Series
+series.cat.asOrdered(): Series
+series.cat.asUnordered(): Series
+series.cat.valueCounts(): Series<number>
+
+
+
+