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. 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 0.1.74

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 0.1.74
File Size Uploaded Provenance
basedpl-0.1.74.tar.gz 2.8 MB With provenance Details

Built distributions (wheels)

Table of built distributions (wheels) for basedpl 0.1.74
File
basedpl-0.1.74-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-0.1.74-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.74-cp313-cp313-macosx_15_0_arm64.whl CPython 3.13 CPython 3.13 macOS 15.0+ ARM64 Details
basedpl-0.1.74-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-0.1.74-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.74-cp312-cp312-macosx_15_0_arm64.whl CPython 3.12 CPython 3.12 macOS 15.0+ ARM64 Details
basedpl-0.1.74-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-0.1.74-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.74-cp311-cp311-macosx_15_0_arm64.whl CPython 3.11 CPython 3.11 macOS 15.0+ ARM64 Details

Total release size: 164.3 MB

Release files / basedpl-0.1.74.tar.gz

Download URL basedpl-0.1.74.tar.gz
Size 2.8 MB
Tags Source
SHA-256 checksum
How to use checksums
f52dd94369ecb045ac24aaab25baf93032b4496b64cf5b06a04f17de1f2bcc0d
BLAKE2b-256 checksum
How to use checksums
262bff4ad96781af30518301d0ab253cfd6a82b95b04a7a1ace7af3ff5105ff2
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 24, 2026.

Transparency log

Release files / basedpl-0.1.74-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.74-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 21.1 MB
Tags CPython 3.13 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
62f68dba25e8ac9b3d8fe7880269c45898a4de578dc8e59277266f2f5c10d589
BLAKE2b-256 checksum
How to use checksums
52325bd112181124e079700622d632189d99ea52b20d0cffa4a0245507ac2fe7
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 24, 2026.

Transparency log

Release files / basedpl-0.1.74-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.74-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 16.7 MB
Tags CPython 3.13 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
83092a135f819a97f91b2ae812dbf1f36b46832d2636b84f67d85778550f2c13
BLAKE2b-256 checksum
How to use checksums
c8d44402e1dda2dc637832f2435d279bf99414f16dd609bc6f439136265b3676
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 24, 2026.

Transparency log

Release files / basedpl-0.1.74-cp313-cp313-macosx_15_0_arm64.whl

Download URL basedpl-0.1.74-cp313-cp313-macosx_15_0_arm64.whl
Size 16.1 MB
Tags CPython 3.13 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
9b0e6bb230bbd01c03cee43439d0d6e9f96277ec959b499fcbbb7f5b81908980
BLAKE2b-256 checksum
How to use checksums
64094c7fccbfbb66375de582fe80413bac7f8a3d73be3e16bac0dc4990060e79
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 24, 2026.

Transparency log

Release files / basedpl-0.1.74-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.74-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 21.1 MB
Tags CPython 3.12 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
2779e4331c3d2e76b493a1c8a10e74261982721d218ae058b32c4fe429d5599c
BLAKE2b-256 checksum
How to use checksums
28e4ebbbb58a5fe8f6a9929ce8d1afa3cce2eec0324662fc7025e8da7c3c204b
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 24, 2026.

Transparency log

Release files / basedpl-0.1.74-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.74-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 16.7 MB
Tags CPython 3.12 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
c24872789e201a209eda83d3220b28fa03282eb066b8d97e82943a5c269def50
BLAKE2b-256 checksum
How to use checksums
bb5c1ad30d54dc517e4ca45dd5c1a86b069cbd157cf34a4d631e0b7799607085
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 24, 2026.

Transparency log

Release files / basedpl-0.1.74-cp312-cp312-macosx_15_0_arm64.whl

Download URL basedpl-0.1.74-cp312-cp312-macosx_15_0_arm64.whl
Size 16.1 MB
Tags CPython 3.12 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
c8aaa34a88f8f29a0ea2af49f4bf719abe161bd0028ec383615bd3388df99bc5
BLAKE2b-256 checksum
How to use checksums
c6d557137d6666ddbb1c6b0d9b05049da42b0625f422dcc852b3cc16bc1367fc
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 24, 2026.

Transparency log

Release files / basedpl-0.1.74-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.74-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 21.1 MB
Tags CPython 3.11 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
fa818faa1f1c11f53eee03330f092740304b49a3b6030d74876056f00a65e4af
BLAKE2b-256 checksum
How to use checksums
1e6a9fc9d9941eb1a62dc7d72e217a5fb50aace4d7f62a148e08b2296e7b9e4b
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 24, 2026.

Transparency log

Release files / basedpl-0.1.74-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.74-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 16.7 MB
Tags CPython 3.11 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
b5471b0c60932ea44837a5f0a0d1b998624e8cbd076ca4ba0a4861d7426ac647
BLAKE2b-256 checksum
How to use checksums
fa3424f9b1a8cef9a947a89f17504364f1c2c902697171b5939b49f9c09ba871
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 24, 2026.

Transparency log

Release files / basedpl-0.1.74-cp311-cp311-macosx_15_0_arm64.whl

Download URL basedpl-0.1.74-cp311-cp311-macosx_15_0_arm64.whl
Size 16.1 MB
Tags CPython 3.11 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
6fcceefa8c33c3bcd924b275e1412a0f7eff321cc777d0c3e66fe522e83136b6
BLAKE2b-256 checksum
How to use checksums
25124ad4cc28ac35dc4b6798af79dc7dd453440453ca13a7ac564b4eb82d30e2
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 24, 2026.

Transparency log

Security for basedpl 0.1.74

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-0.1.74.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 24, 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

0.1.74 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