Skip to main content

BasedPL

BasedPL, the Based-array Programming Language, is an array language derived from APL, with ideas from J and BQN. Its notation aims to be simple and consistent. Most of this documentation calls it BPL. BPL is written in Rust and comes as a native executable, a Jupyter kernel and a Python API.

LLMs: read llms.txt first. It is the primary reference index and concise overview. In a BPL session, ]help llms shows the same file.

Install and try it

pip install basedpl
bpl

Each release also has a standalone bpl that needs no Python, for macOS on Apple silicon and for Linux on x86-64 and arm64. On a Mac:

curl -Lo bpl https://github.com/AnswerDotAI/basedpl/releases/latest/download/bpl-macos-arm64
chmod +x bpl

On Linux, download bpl-linux-x86_64 or bpl-linux-arm64 instead.

At the prompt, define a mean and apply it:

avg←+/÷≢
avg 2 4 9
5

Use bpl -e 'avg←+/÷≢ ⋄ avg 2 4 9' for a shell command. The command-line guide covers source files and pipes.

Interactive use

In the REPL, type a backtick followed by a glyph name. For example, `iota becomes ⍳ when you press Tab or type a non-letter. Abbreviations and Alt-key shortcuts are available. ]help + shows help for +. •prefs changes display settings, such as boxes and how much of a large array shows. See REPL and the glyph reference, which lists each glyph’s key.

In Jupyter, select the installed BasedPL kernel. Cells share definitions and support completion, Shift-Tab help and interruption. You can also use %%bpl cells in a Python notebook. See Using BPL notebooks.

New to APL?

APL is a language built around operations on whole arrays and notation for combining functions. Here is a taste of that style in BPL.

Numbers separated by spaces form a vector. Arithmetic applies to every item:

10+1 2 3
11 12 13

Operators modify or combine functions. Reduce (/) turns addition into summation. ⍳10 generates 0…9:

+/⍳10
45

Functions can also be combined without naming their arguments. In avg←+/÷≢, sum (+/) divided by tally (≢) defines the mean:

avg 1 2 3 4
2.5

To see how these ideas express an algorithm, start from “a prime has exactly two positive divisors”. Form all remainders of 1 to 50 (|⊗⍨↦50), count the zeros down each column (+⌿0=), and find the positions (⍸) whose count is two. Positions count from 0. ≥ increments each position to recover its number:

≥⍸2=+⌿0=|⊗⍨↦50
[2 3 5 7 11 13 17 19 23 29 31 37 41 43 47]ₓ

Getting started builds a primes function from the same divisor count, step by step, and displays the divisibility matrix along the way.

What’s distinctive?

For APL users, some key differences are:

  • Based arrays, as in BQN: numbers, characters and functions are atoms; enclosure always adds a layer.
  • Vectors in brackets, and grouping by spaces. [a b c] is a vector, and a+b × c+d is (a+b)×(c+d). A run that ends in a function is a train. For example, +/÷≢ x is the mean of x, and 2× doubles its argument. 0⌷v and v₀ both select the first item of v.
  • Leading-axis broadcasting, including expansion of length-1 axes, plus string keys and names on axes.
  • Exact integers and rationals alongside approximate real and complex numbers.
  • Dfns, trains and operators, with additions such as Under, iteration histories, windows and function arrays.

Positions and axes count from 0, as in BQN and Python. Approximate comparisons use tolerance 1E¯14. See the language principles for why BPL works this way, the glyph reference for Dyalog differences and Arrays for the array model.

Numbers

Bare numbers are approximate. Use ₓ for exact integers and r for exact fractions:

1r3+1r6
1ᵣ2

Complex numbers use j between real and imaginary parts. Functions such as square root extend into the complex domain:

√¯4
0ⱼ2

See Numbers for conversion and mixed arithmetic.

Array literals and broadcasting

Write matrix rows directly in an array literal. Leading-axis agreement lets a vector supply one offset per row:

m←[1 2 3
   4 5 6]
m+10 20
11 12 13
24 25 26

See array notation and broadcasting.

Keys and named axes

Axes can have names, and positions along them can have string keys. Describe the axes once, then select by key or reduce by axis name:

axes←["city":"NY" "LA";"month":"Jan" "Feb" "Mar"]
sales←axes:[10 20 30⋄40 50 60]
"LA" "Feb"⌷sales
+/⍠"month" sales
50

["city":2]⍴["NY":60 "LA":150]

Keys and names travel with axes through operations such as transpose. Arithmetic aligns matching names and keys. See Axis keys.

Function operators

A vector of counts keeps the history: one state for each count, where count 0 is the initial value. Here, double up to four times:

2×⍣(⍳5) 1
1 2 4 8 16

Under (@) transforms the argument, applies a function, then reverses the transformation. Scale by ten, floor, and scale back to round down to tenths:

⌊@ 10× 1.25 2.78
1.2 2.7

Explore iteration and inverses, Under, windows and function selection.

Mathematical tools

Primes and factorisation are built in:

⨸360ₓ
[2 2 2 3 3 5]ₓ

Polynomials support coefficients, roots and evaluation. Polynomial functions can be differentiated: for f(x) = 1 + 2x + 3x², f′(2) = 14.

f←[1 2 3]ₓ⌻ ⋄ f∂2ₓ
14ₓ

Probability distributions provide sampling, density, CDF and quantiles. Two fair coin tosses give these probabilities for 0, 1 and 2 heads:

coin←2 0.5 •distribution "binomial"
coin.density 0 1 2
0.25 0.5 0.25

Matrix division handles linear systems and least squares.

Data and text

JSON objects become keyed arrays, with dot access to their fields:

order←•json "{""price"":10.5,""qty"":2}"
order.price×order.qty
21

CSV headers likewise name column vectors. Files, CSV and JSON covers reading, transforming and writing data. Regex supplies matching, captures and replacement through Rust’s regex engine.

Drawing

•plot draws charts from arrays. Keys label the axes and name the lines. See Plots.

["legend":"end"]•plot sales

Build SVG from element functions and keyed attributes. Notebooks display the picture directly. The same element trees serialize to XML. See XML and SVG.

[circle text]←•element "circle" "text"
c←["cx":50 "cy":40 "r":25 "fill":"orange"]circle ""
t←["x":50 "y":85 "text-anchor":"middle"]text "Hello, SVG"
["width":240 "height":240]•svg c t

Python

BPL functions are Python callables:

from basedpl import fn

mean = fn('+/÷≢')
mean([1, 2, 3])

Arrays have .py, .np and .df conversions for Python values, NumPy and pandas. Functions also have Python names and composition operators. See the Python tutorial.

For other frontends, the process interfaces provide JSON messages and interruptible workers. The BPL library contains more algorithms, codecs, interpreters and puzzles.

Metadata

Release files for basedpl 0.1.67

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for basedpl 0.1.67
File Size Uploaded Provenance
basedpl-0.1.67.tar.gz 2.8 MB With provenance Details

Built distributions (wheels)

Table of built distributions (wheels) for basedpl 0.1.67
File
basedpl-0.1.67-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ x86-64 Details
basedpl-0.1.67-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.67-cp313-cp313-macosx_15_0_arm64.whl CPython 3.13 CPython 3.13 macOS 15.0+ ARM64 Details
basedpl-0.1.67-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ x86-64 Details
basedpl-0.1.67-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.67-cp312-cp312-macosx_15_0_arm64.whl CPython 3.12 CPython 3.12 macOS 15.0+ ARM64 Details
basedpl-0.1.67-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ x86-64 Details
basedpl-0.1.67-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.67-cp311-cp311-macosx_15_0_arm64.whl CPython 3.11 CPython 3.11 macOS 15.0+ ARM64 Details

Total release size: 156.4 MB

Release files / basedpl-0.1.67.tar.gz

Download URL basedpl-0.1.67.tar.gz
Size 2.8 MB
Tags Source
SHA-256 checksum
How to use checksums
1013f834e7763a074cc347c32cfd4c0da1a8488ea2f72890c603177a6e980a07
BLAKE2b-256 checksum
How to use checksums
152b5fa8e77bb3437e887d89f34b15a038145b78a0bf0fd2944bdf6b7a16519c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

Release files / basedpl-0.1.67-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.67-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 19.7 MB
Tags CPython 3.13 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
a736cdc727d2cb60576cb788a1420294a97072008a37aefb1941c9aab370a58f
BLAKE2b-256 checksum
How to use checksums
3e9bdc934f820bfd826d0fec226df15bbd11a6a9cdf3869f9dd247c493d19d5d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

Release files / basedpl-0.1.67-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.67-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 16.1 MB
Tags CPython 3.13 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
c64cc2cb7251d4bc410116a5c87a77d12e3de0c4ddadb212fc0ea7f6549f25b5
BLAKE2b-256 checksum
How to use checksums
83c4f1f58442a5ed3e5b1945745b60c11e11026e2d6404efd5d08dc93376c15c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

Release files / basedpl-0.1.67-cp313-cp313-macosx_15_0_arm64.whl

Download URL basedpl-0.1.67-cp313-cp313-macosx_15_0_arm64.whl
Size 15.4 MB
Tags CPython 3.13 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
1adabf6d8d2b0bffc04d1a88f8a395ae84d27b002dc5ec2e4e9143fcb0f7a63e
BLAKE2b-256 checksum
How to use checksums
1512bd51d8fb4e7d1d10af1c0d2c322f5464a1e2d4c3c29f335a4c04358643a5
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

Release files / basedpl-0.1.67-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.67-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 19.7 MB
Tags CPython 3.12 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
22d10926a002ef4dc2907b9db7e90753bbf6f81aa3ed69cad622d9e463b0bb81
BLAKE2b-256 checksum
How to use checksums
2923dfc3775773ff62b0d93a1789082e9f82db994950e432643a8d1a7b00e11a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

Release files / basedpl-0.1.67-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.67-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 16.1 MB
Tags CPython 3.12 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
405c799f2f99c83b6adfb83daf7c109d96c44472f2703f133ddb78cc9c4e4474
BLAKE2b-256 checksum
How to use checksums
5f7f1e145b0805468dd8137b2144b2a178170b3019ca207bddf6fa07eeb726f4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

Release files / basedpl-0.1.67-cp312-cp312-macosx_15_0_arm64.whl

Download URL basedpl-0.1.67-cp312-cp312-macosx_15_0_arm64.whl
Size 15.4 MB
Tags CPython 3.12 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
0e773ab6db8a3e3b5b97403b6954b2dcbe876be1ca63c534d8de2c6c97d9b7b5
BLAKE2b-256 checksum
How to use checksums
06673e53ce3f298bc95fc9cf370bdb64bd4ce51987f526191fe06f9b34ce9067
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

Release files / basedpl-0.1.67-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.67-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 19.7 MB
Tags CPython 3.11 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
16a3d8c921abbbedb2d25cc0fe8470e08aded306e381e7ead5f142b2810b3822
BLAKE2b-256 checksum
How to use checksums
08acf37c2ac6a97fd3b212a4ff87985f4a04cf7bac386db64e0b06d99ffe00f4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

Release files / basedpl-0.1.67-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.67-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 16.1 MB
Tags CPython 3.11 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
abf36c673f77b28ec678f703d20804949e78a7875ab1c3cdbaf988a81b62bb5c
BLAKE2b-256 checksum
How to use checksums
e258c03ea00d7301ea505d4934efe50939f33a7edaee1797499951dfe0e2182d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

Release files / basedpl-0.1.67-cp311-cp311-macosx_15_0_arm64.whl

Download URL basedpl-0.1.67-cp311-cp311-macosx_15_0_arm64.whl
Size 15.4 MB
Tags CPython 3.11 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
10a42cf45f6c8b5307358e96a2458795a4b823c7dcdb6250ed308bb31b1a238b
BLAKE2b-256 checksum
How to use checksums
f1d38eb286319f39521eab4a0d2a063535c29bae6e0d7f75987c0314a5829164
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

Security for basedpl 0.1.67

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

All 10 files in this release have been uploaded with provenance metadata.

Select from 10 files

Attestation for basedpl-0.1.67.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 22, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Release history Release notifications | RSS feed

This release

0.1.67 This release

10 release files Full provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page