columna API reference - v0.5.0
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    Migrating to columna

    Side-by-side vocabulary for the three APIs people usually come from. Every columna snippet is the fluent API on DataFrame / LazyFrame; lazy() … collect() is where the optimizer and the engines run, the sync DataFrame helpers are the same kernels without a plan. Full API: docs/api.

    import { DataFrame, col } from 'columna'

    const out = await DataFrame.fromRows(rows)
      .lazy()
      .filter((c) => c.age.gt(18))
      .groupBy('city')
      .agg((c) => ({ avgSalary: c.salary.mean(), n: c.salary.count() }))
      .sort(col('avgSalary').desc())
      .collect()
    out.toArray()
    Arquero columna Notes
    aq.from(rows) / aq.table({ a: [...] }) DataFrame.fromRows(rows) / DataFrame.fromColumns({ a: [...] }) fromColumns takes TypedArrays zero-copy ({ copy: true } to own them).
    aq.fromCSV(text) / aq.loadCSV(url) DataFrame.fromCSV(text) / await DataFrame.readCsv({ url }) Sources are explicit: { text }, { path }, { url }; policy via setIoPolicy.
    .filter(d => d.age > 18) .filter((c) => c.age.gt(18)) or .filter(col('age').gt(18)) Expressions, not closures: they plan, push down and run on typed kernels. mapElements(fn) is the escape hatch.
    .derive({ y: d => d.a * 2 }) .withColumn('y', col('a').mul(2)) / .withColumns((c) => [c.a.mul(2).alias('y')])
    .select('a', 'b') / .select(aq.not('c')) .select('a', 'b') / .drop('c') / .exclude('c')
    .rename({ a: 'b' }) .rename({ a: 'b' }) Type of the frame follows the rename.
    .groupby('g').rollup({ n: aq.op.count(), m: aq.op.mean('x') }) .groupBy('g').agg((c) => ({ n: c.x.count(), m: c.x.mean() })) Also agg({ x: ['sum', 'mean'] }) → x_sum, x_mean; shortcuts .groupBy('g').mean().
    .orderby('a', aq.desc('b')) .sort('a', col('b').desc()) nullsLast per key.
    .join(other, ['k1', 'k2']) / join_left .join(other, { on: 'k' }) / .join(other, { on, how: 'left' }) how: inner, left, right, outer, semi, anti, cross; suffix, validate.
    .dedupe('a') .unique(['a'])
    .pivot('key', 'value') / .fold(['a', 'b']) .pivot(...) / .melt(...)
    .objects() / .array('a') .toArray() / .getColumn('a').toArray()
    .toArrow() .toArrowIpc() Real Arrow IPC bytes (Arquero's toArrow gives an apache-arrow Table: tableFromIPC(df.toArrowIpc())).
    aq.fromArrow(table) DataFrame.fromArrowIpc(tableToIPC(table))
    window: aq.op.lag('x'), aq.op.cume_dist() col('x').shift(1), .over(...), rolling / expanding

    What changes conceptually: transformations are lazy (lazy() … collect() is async) and the runtime picks kernels (native / workers / GPU) and reports what ran (collectWithReport). Arquero's per-row closures become expressions; anything an expression cannot say goes through mapElements.

    Polars columna Notes
    pl.DataFrame({...}) / pl.read_csv DataFrame.fromColumns({...}) / DataFrame.readCsv dtypes: f64, f32, i32, u32, bool, utf8, category, datetime (ms). No i64 / decimal / list / struct.
    df.lazy() … .collect() same .explain() prints the optimized plan; collect({ signal, timeoutMs, memory }).
    pl.col('a').gt(1) col('a').gt(1) Same expression style; when(...).then(...).otherwise(...), .isIn, .isBetween, .cast, .fillNull, .str.*, .dt.*.
    .group_by('g').agg(pl.col('x').sum().alias('s')) .groupBy('g').agg({ s: col('x').sum() })
    .sort('a', descending=True) .sort(col('a').desc())
    .join(other, on='k', how='left') .join(other, { on: 'k', how: 'left' }) join_asof → joinAsof.
    .with_columns(...) .withColumns(...)
    .unique(subset=['a']) .unique(['a'])
    .select(pl.col('^x.*$')) .selectDtypes(...) / .select(...names) No regex selectors.
    .over('g') .over('g')
    df.write_ipc(path) / pl.read_ipc_stream(bytes) df.writeArrowIpc(path, { format: 'file' }) / DataFrame.fromArrowIpc(bytes) Polars' Utf8View / Categorical / Enum / Datetime(us) are read; columna writes Utf8 + Dictionary + Timestamp(ms).
    df.write_parquet / pl.read_parquet df.writeParquet / DataFrame.readParquet via hyparquet.
    pl.SQLContext — No SQL; see DuckDB-Wasm exchange through Arrow IPC in the README.
    streaming engine / out-of-core Node: collect({ memory: { maxBytes } }) spills sort / unique / join intermediates Not a streaming query engine; see positioning.md.

    What you gain: the same code in the browser, and columna/advanced (hypothesis tests, ANOVA, regression diagnostics, DOE, SPC, capability, reliability, time series). What you give up: Polars' native multi-threaded core, i64 / nested types and the streaming engine.

    The cheat-sheet; the long form with worked examples is pandas-to-columna.md.

    pandas columna Notes
    pd.DataFrame(rows) / pd.read_csv DataFrame.fromRows(rows) / DataFrame.readCsv No index: rows are positional; set_index has no equivalent — keep the key as a column.
    df[df.age > 18] df.filter((c) => c.age.gt(18))
    df['y'] = df.a * 2 df.withColumn('y', col('a').mul(2)) Frames are immutable; every step returns a new frame / plan.
    df.groupby('g').agg(n=('x', 'count'), m=('x', 'mean')) df.groupBy('g').agg({ x: ['count', 'mean'] })
    df.sort_values(['a', 'b'], ascending=[True, False]) df.sort('a', col('b').desc())
    df.merge(other, on='k', how='left') df.join(other, { on: 'k', how: 'left' })
    df.drop_duplicates(['a']) df.unique(['a'])
    df.pivot_table / df.melt df.pivot / df.melt
    df.describe() df.describe() Quantiles: quantileMethod: 'minitab' for Minitab's type-6 positions.
    df['m'] = df.x.rolling(3).mean() df.rolling('m', 'x', 3, 'mean') expanding(name, column, agg) likewise.
    df.fillna(0) / ffill() df.fillNull(0) / df.ffill()
    df.astype({ a: 'int32' }) df.withColumn('a', col('a').cast('i32')) cast refuses overflow instead of wrapping.
    df.to_feather / pd.read_feather df.writeArrowIpc(path, { format: 'file' }) / DataFrame.readArrowIpc({ path })
    scipy.stats.ttest_ind(a, b) ttest2(a, b) from columna/advanced Same conventions where scipy and Minitab agree; documented where they differ (advanced-roadmap.md).
    statsmodels.OLS ols(y, X) / df.regress('y', ['x1', 'x2']) Coefficients, SE, t, p, CI, R², adjusted R², F, residual diagnostics, VIF.
    • No SQL. DuckDB-Wasm next to columna, exchanging Arrow IPC, is the intended pairing.
    • No i64 / decimal / nested types. Int64 columns arrive as exact f64 when every value is within ±(2⁵³ − 1), otherwise the read fails unless { int64: 'string' } (exact decimal strings) or { int64: 'number' } is set; lists and structs are refused.
    • No mutable frames, no index. Positional rows; keys are columns.
    • Cancellation is per operator. collect({ signal }) lands between operators, not inside a kernel.
    • Browser has no disk spill. Memory budgets spill only on Node.

    0.3.0 is a minor under the 0.x rules in compatibility.md: new Tier 1 surfaces plus behaviour fixes that change results for some existing inputs. Install with npm install columna@0.3.0.

    • Apache Arrow IPC: prefer toArrowIpc() / fromArrowIpc() / readArrowIpc / writeArrowIpc for exchange with Polars, DuckDB, pyarrow and apache-arrow. toArrow() / fromArrow() still work but are deprecated (they speak columna's JSON ArrowLike, not Arrow).
    • Cancellation / deadlines: collect({ signal, timeoutMs }) — rejects with ExecutionAbortedError.
    • Sessions: createSession({ io, runtime, persist }) for per-tenant runtime, persist cache and IO policy floor; new Runtime({ memory }) no longer sets the process-wide memory policy (use setMemoryPolicy or collect({ memory })).
    • Non-blocking spill and SQL nRows pushdown under memory / read options (see operations.md).
    • groupBy().agg({ x: 'count' }) counts non-null values of x on every kernel (was row-count on some fast paths).
    • sort(...).head(k) matches sort(...).collect().slice(0, k) for ties and NaN keys.
    • Join projection of collision names (score_right) and select(col('x').alias('y')) after a join keep the expected schema.
    • unique keeps multi-key null rows and does not merge null with NaN.
    • CommonJS + collect({ memory }) spill works (empty import.meta in the CJS bundle no longer breaks createRequire).

    Full list: CHANGELOG.md [0.3.0].

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    Migrating to columna