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 0.1.63

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 0.1.63
File Size Uploaded Provenance
basedpl-0.1.63.tar.gz 2.7 MB With provenance Details

Built distributions (wheels)

Table of built distributions (wheels) for basedpl 0.1.63
File
basedpl-0.1.63-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-0.1.63-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.63-cp313-cp313-macosx_15_0_arm64.whl CPython 3.13 CPython 3.13 macOS 15.0+ ARM64 Details
basedpl-0.1.63-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-0.1.63-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.63-cp312-cp312-macosx_15_0_arm64.whl CPython 3.12 CPython 3.12 macOS 15.0+ ARM64 Details
basedpl-0.1.63-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-0.1.63-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.63-cp311-cp311-macosx_15_0_arm64.whl CPython 3.11 CPython 3.11 macOS 15.0+ ARM64 Details

Total release size: 143.2 MB

Release files / basedpl-0.1.63.tar.gz

Download URL basedpl-0.1.63.tar.gz
Size 2.7 MB
Tags Source
SHA-256 checksum
How to use checksums
e72f257687983968b5d8b720edfef731bccb1abb8dc369e5f515f33ce30c23ea
BLAKE2b-256 checksum
How to use checksums
b4b63b0221e9cf2ae92042a55d875b1460c775f4ca98f2c3fea3f755b51d7c11
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 21, 2026.

Transparency log

Release files / basedpl-0.1.63-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.63-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 17.6 MB
Tags CPython 3.13 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
2787145e181bb1719371616fc4c1784b56dadacb53e7a70a877493b0ff9de6e5
BLAKE2b-256 checksum
How to use checksums
22f8bc16bfc32599a157246fdef0c8c69fbac000a664c2a9bb582763aab8c2ca
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 21, 2026.

Transparency log

Release files / basedpl-0.1.63-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.63-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 15.1 MB
Tags CPython 3.13 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
35b238bef0d12e941719b4766f5b8526e611b78ddc41cc56a3b644648e3a4c46
BLAKE2b-256 checksum
How to use checksums
9dd1838ec13919f689f4476ac7800ca061f8b79ff2c2710e58d6b97f8a6ce795
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 21, 2026.

Transparency log

Release files / basedpl-0.1.63-cp313-cp313-macosx_15_0_arm64.whl

Download URL basedpl-0.1.63-cp313-cp313-macosx_15_0_arm64.whl
Size 14.2 MB
Tags CPython 3.13 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
31d0d1340f938af640ec6e7c804281f88476e75c4aa745639fdb4da0ec5e2dad
BLAKE2b-256 checksum
How to use checksums
c2c8ae895a7abb5cec20829238514929dd7a42132ca255974b4a1f6186dc04c7
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 21, 2026.

Transparency log

Release files / basedpl-0.1.63-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.63-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 17.6 MB
Tags CPython 3.12 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
78d7191b9257f9f755dc82be5efa821a7d2b8c224d1b074330298580bcee8d4f
BLAKE2b-256 checksum
How to use checksums
a366243813e73c698e707cb9e085ff787049ab9588e43313551614a29a9044d2
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 21, 2026.

Transparency log

Release files / basedpl-0.1.63-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.63-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 15.1 MB
Tags CPython 3.12 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
2c6e843dbef645334e5a97392344919ed3fb8debcee2ba74c0fe0d7d6e04a6b2
BLAKE2b-256 checksum
How to use checksums
271abc360b6b80a878d545f5a9645b0ae8f217949a124e01e7162b49dcfa13dd
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 21, 2026.

Transparency log

Release files / basedpl-0.1.63-cp312-cp312-macosx_15_0_arm64.whl

Download URL basedpl-0.1.63-cp312-cp312-macosx_15_0_arm64.whl
Size 14.2 MB
Tags CPython 3.12 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
9d20c5f42c94094464ca541ffb35cdfbdbbd53a24d834a7f69d70e76c6770962
BLAKE2b-256 checksum
How to use checksums
0f567a5a1a042c754b11c556fcf47479f72e6890909ac95b2b2504fab6b9f1f7
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 21, 2026.

Transparency log

Release files / basedpl-0.1.63-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.63-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 17.6 MB
Tags CPython 3.11 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
42bc3a62bba25b331cd84930b17fa385c9024adf656d65dcfbb72497208f2811
BLAKE2b-256 checksum
How to use checksums
1767b43723304d51096b933a48c7ca29a0d1e2db32a68e0fa6a1fb705bf10aca
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 21, 2026.

Transparency log

Release files / basedpl-0.1.63-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.63-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 15.1 MB
Tags CPython 3.11 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
f216750e0a1ffb2d9b00d298a7b91bffb3c9c400a69264796ae0b633630053c5
BLAKE2b-256 checksum
How to use checksums
d7160a2b0e1f7b20c9caa00fbe2b51b272756904bb3960488f8d88d39b3c9789
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 21, 2026.

Transparency log

Release files / basedpl-0.1.63-cp311-cp311-macosx_15_0_arm64.whl

Download URL basedpl-0.1.63-cp311-cp311-macosx_15_0_arm64.whl
Size 14.2 MB
Tags CPython 3.11 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
cf782240acb279f1fee6ec4920a188d95e6fe936035c318dc967fde52c641c90
BLAKE2b-256 checksum
How to use checksums
4899752cca6696b7fd14713bc195b89726ee29d90f4b0d6ccf6a93eae87cf2cf
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 21, 2026.

Transparency log

Security for basedpl 0.1.63

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-0.1.63.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 21, 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

0.1.63 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