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
basedpl-install
bpl

The setup command installs the Jupyter kernel and BPL fonts for your user. On macOS it also installs a native keyboard layout.

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 (|⊗⍨↦50ₓ), count the zeros down each column (+⌿0ₓ=), and find the positions (⍸) whose count is two. Positions count from 0. ≥ increments each position to recover its number:

≥⍸2ₓ=+⌿0ₓ=|⊗⍨↦50ₓ
[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. Default Mathish mode uses relative tolerance 1E¯14 for 64-bit comparisons and defines 0÷0=0 for Mathish types. See the language principles for the language design, 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 0.1.76

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 0.1.76
File Size Uploaded Provenance Actions
basedpl-0.1.76.tar.gz 3.4 MB With provenance Details for basedpl-0.1.76.tar.gz

Built distributions (wheels)

Table of built distributions (wheels) for basedpl 0.1.76
File Actions
basedpl-0.1.76-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ x86-64 Details for basedpl-0.1.76-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
basedpl-0.1.76-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ ARM64 Details for basedpl-0.1.76-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
basedpl-0.1.76-cp313-cp313-macosx_15_0_arm64.whl CPython 3.13 CPython 3.13 macOS 15.0+ ARM64 Details for basedpl-0.1.76-cp313-cp313-macosx_15_0_arm64.whl
basedpl-0.1.76-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ x86-64 Details for basedpl-0.1.76-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
basedpl-0.1.76-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ ARM64 Details for basedpl-0.1.76-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
basedpl-0.1.76-cp312-cp312-macosx_15_0_arm64.whl CPython 3.12 CPython 3.12 macOS 15.0+ ARM64 Details for basedpl-0.1.76-cp312-cp312-macosx_15_0_arm64.whl
basedpl-0.1.76-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ x86-64 Details for basedpl-0.1.76-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
basedpl-0.1.76-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ ARM64 Details for basedpl-0.1.76-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
basedpl-0.1.76-cp311-cp311-macosx_15_0_arm64.whl CPython 3.11 CPython 3.11 macOS 15.0+ ARM64 Details for basedpl-0.1.76-cp311-cp311-macosx_15_0_arm64.whl

Total release size: 129.9 MB

Release files / basedpl-0.1.76.tar.gz

Download URL basedpl-0.1.76.tar.gz
Size 3.4 MB
Tags Source
SHA-256 checksum
How to use checksums
bd100ffa25a46867c54afe86e9eddd56e40a5fdc6ee19d481d53d11743673a25
BLAKE2b-256 checksum
How to use checksums
6d064443f1a73f7ea78e78f3b71fffd53239b0468d64aa8002549347652f1387
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 28, 2026.

Transparency log

Release files / basedpl-0.1.76-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.76-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 12.7 MB
Tags CPython 3.13 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
4ebb352cdbef76c9bbd0422d344421bb1ac2d96534eface47340a5c66dd78b93
BLAKE2b-256 checksum
How to use checksums
6eed71dc3fc9d68f9b0093729dd78d7e798e545a275b1e666c2b489b5d008ec9
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 28, 2026.

Transparency log

Release files / basedpl-0.1.76-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.76-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 9.9 MB
Tags CPython 3.13 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
47ba1c5b93bb03c1cb6bcfa628b650fb8be2585435c9305619390a787e94d76d
BLAKE2b-256 checksum
How to use checksums
b671dd8282397169822b6e97c4d11a386a4c66ae8ab555acbe4d9d7edb3026eb
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 28, 2026.

Transparency log

Release files / basedpl-0.1.76-cp313-cp313-macosx_15_0_arm64.whl

Download URL basedpl-0.1.76-cp313-cp313-macosx_15_0_arm64.whl
Size 19.5 MB
Tags CPython 3.13 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
15c8bd7d9497ba53df82f63036ec36f2b2723a6c47936ce473bd4ff942ecc1cb
BLAKE2b-256 checksum
How to use checksums
46a49fa1ccf19e8bc620561ba8b6e23a5d37ced831edafacda27975b0c448e6d
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 28, 2026.

Transparency log

Release files / basedpl-0.1.76-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.76-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 12.7 MB
Tags CPython 3.12 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
3725271eee8d50a62b25a79ce88d0816a758e1d48e3d1447a69d88fa229af855
BLAKE2b-256 checksum
How to use checksums
416dd0303dfdd49c80a080c46231acf16bbe27c3dc197a275b8d1682b5b90396
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 28, 2026.

Transparency log

Release files / basedpl-0.1.76-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.76-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 9.9 MB
Tags CPython 3.12 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
9f487861aaabaa3e17f56bcb9217dccb5e76f4c70360e3b9edf422a760521266
BLAKE2b-256 checksum
How to use checksums
a529ff3ae9001d1a67ba4d94bce58a40c1ce6d08896bf3e8b0fc7125c8aecefb
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 28, 2026.

Transparency log

Release files / basedpl-0.1.76-cp312-cp312-macosx_15_0_arm64.whl

Download URL basedpl-0.1.76-cp312-cp312-macosx_15_0_arm64.whl
Size 19.5 MB
Tags CPython 3.12 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
4e64ca7303ac961bac1985d41784ec3ccb97682cf401f41aa2f13b9d05beaaf7
BLAKE2b-256 checksum
How to use checksums
1fddb1841f4a42372006474480c1b4ed7ef183d73ba4dac41854c98fca37e1c7
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 28, 2026.

Transparency log

Release files / basedpl-0.1.76-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.76-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 12.7 MB
Tags CPython 3.11 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
318825edc63e7ce1100e9dbb1d0c0340b31b642da56ba6c1211b9bb9632bf92e
BLAKE2b-256 checksum
How to use checksums
2dc32ee3f7dd59d8f7c9cb7dbf7a33d0cc578cd6032f62407d8a0da55a0b8283
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 28, 2026.

Transparency log

Release files / basedpl-0.1.76-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.76-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 9.9 MB
Tags CPython 3.11 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
705fc8502d59a7c61de36f60a10651a814a422c60f0e568b1af372fc54516262
BLAKE2b-256 checksum
How to use checksums
57c9c266c335334250952402151934a2b1be4aa861a59b83be450c8386ec8352
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 28, 2026.

Transparency log

Release files / basedpl-0.1.76-cp311-cp311-macosx_15_0_arm64.whl

Download URL basedpl-0.1.76-cp311-cp311-macosx_15_0_arm64.whl
Size 19.5 MB
Tags CPython 3.11 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
27096920dfbaa0bef15bdb4999ba9e8de5ea5dafd392070908f5946d3a4b677e
BLAKE2b-256 checksum
How to use checksums
cd72c74b0bc471449ea5d46397f11c91d2b3a1d7d4ca8860687c4da24faa18f5
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 28, 2026.

Transparency log

Security for basedpl 0.1.76

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-0.1.76.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 28, 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.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

0.1.76 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