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 0.1.60

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 0.1.60
File Size Uploaded Provenance
basedpl-0.1.60.tar.gz 2.6 MB With provenance Details

Built distributions (wheels)

Table of built distributions (wheels) for basedpl 0.1.60
File
basedpl-0.1.60-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-0.1.60-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.60-cp313-cp313-macosx_15_0_arm64.whl CPython 3.13 CPython 3.13 macOS 15.0+ ARM64 Details
basedpl-0.1.60-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-0.1.60-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.60-cp312-cp312-macosx_15_0_arm64.whl CPython 3.12 CPython 3.12 macOS 15.0+ ARM64 Details
basedpl-0.1.60-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-0.1.60-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ ARM64 Details
basedpl-0.1.60-cp311-cp311-macosx_15_0_arm64.whl CPython 3.11 CPython 3.11 macOS 15.0+ ARM64 Details

Total release size: 140.4 MB

Release files / basedpl-0.1.60.tar.gz

Download URL basedpl-0.1.60.tar.gz
Size 2.6 MB
Tags Source
SHA-256 checksum
How to use checksums
33609861db17c4d38f5baa95fd2eafc720084ffd8208a065c07e7adcc3a08b65
BLAKE2b-256 checksum
How to use checksums
424e4bf3129a8e77048a530139dfbe225e2b420e1b536af84a02c8f9c3df7d18
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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 20, 2026.

Transparency log

Release files / basedpl-0.1.60-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.60-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 17.2 MB
Tags CPython 3.13 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
bd5f345cba9d423d7508efb7fd06098dc2b2777b6fb9cb8fb72f6d87ad87ceef
BLAKE2b-256 checksum
How to use checksums
59deb272003eae6104ed1a0fcbdcbc86b0e1db8cb81830c0f03acf473062c0f0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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 20, 2026.

Transparency log

Release files / basedpl-0.1.60-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.60-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 14.8 MB
Tags CPython 3.13 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
efd0b6d578f18b6383d4ad8e3e715f110c30fb99ed0625ba0b3b46d143934414
BLAKE2b-256 checksum
How to use checksums
b1dd33141ebccf60dfbd4237e70c2ded1ef44e9b1550808b8cc8e0f125dd1c4a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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 20, 2026.

Transparency log

Release files / basedpl-0.1.60-cp313-cp313-macosx_15_0_arm64.whl

Download URL basedpl-0.1.60-cp313-cp313-macosx_15_0_arm64.whl
Size 13.9 MB
Tags CPython 3.13 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
c74c3653191e158401d02cc55bdc59c67610f6cc9af7f16b87c3ba38783cb6a8
BLAKE2b-256 checksum
How to use checksums
5513810d5afee5b6c34cb5bbf81bcf2f06a578404a50883ae518ecd4cb2984e0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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 20, 2026.

Transparency log

Release files / basedpl-0.1.60-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.60-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 17.2 MB
Tags CPython 3.12 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
32dac43ac1d48ed5c1da5808cabca5ae5d1f58c07a41f239b60294a3aa5f77b4
BLAKE2b-256 checksum
How to use checksums
64854ab8781ac94b5b5b8a301b56ed1a83db5e635fabba7b9812407f49e94692
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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 20, 2026.

Transparency log

Release files / basedpl-0.1.60-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.60-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 14.8 MB
Tags CPython 3.12 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
9f8117ab07976209a1af64276dec7bdec75f29889da3fb3f153a69bd7843ba82
BLAKE2b-256 checksum
How to use checksums
8d93427108e2716668ecdac4eefc625427303fdee4755614f962940ab76bdbb8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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 20, 2026.

Transparency log

Release files / basedpl-0.1.60-cp312-cp312-macosx_15_0_arm64.whl

Download URL basedpl-0.1.60-cp312-cp312-macosx_15_0_arm64.whl
Size 13.9 MB
Tags CPython 3.12 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
c3a310200d5bfce6639ee0f22a52ebf4a7801af200944f990d5abf635550a0c7
BLAKE2b-256 checksum
How to use checksums
c55db52f7d8e325f01abe5ee8647702ce63bebc446f2dacafca4aeb9251aae5d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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 20, 2026.

Transparency log

Release files / basedpl-0.1.60-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL basedpl-0.1.60-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 17.2 MB
Tags CPython 3.11 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
9f80e57386a64b55943a05e28edeaaeaab0747f1318b7ce09fee9d94e6b9f546
BLAKE2b-256 checksum
How to use checksums
8089d916bd4021a449b0ee0857d40b85d0b179a56001a0150d0c346284de4dcb
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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 20, 2026.

Transparency log

Release files / basedpl-0.1.60-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL basedpl-0.1.60-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 14.8 MB
Tags CPython 3.11 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
16ba3c2074206e8a57056d1f98fddbabc8e821c17528109d4d7beaf35339222e
BLAKE2b-256 checksum
How to use checksums
b58dff03f6ae0eb4931b72e57e855b38246cadf0c00fd04730d7d0c2a1962129
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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 20, 2026.

Transparency log

Release files / basedpl-0.1.60-cp311-cp311-macosx_15_0_arm64.whl

Download URL basedpl-0.1.60-cp311-cp311-macosx_15_0_arm64.whl
Size 14.0 MB
Tags CPython 3.11 macOS 15.0+ ARM64
SHA-256 checksum
How to use checksums
df2d181ab12772049d1cf2bdd928c4713ecdff44f7e84c1df1c2b143e298c89f
BLAKE2b-256 checksum
How to use checksums
817c57d6d9de02ab8414b7ae46093d14bce9afb27df520ca6197d65d6f984bbd
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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 20, 2026.

Transparency log

Security for basedpl 0.1.60

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-0.1.60.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 20, 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

0.1.60 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