# Conc
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
[](https://zenodo.org/doi/10.5281/zenodo.16358752)
## Introduction to Conc
Conc is a Python library that brings tools for corpus linguistic
analysis to [Jupyter notebooks](https://docs.jupyter.org/en/latest/).
Conc aims to allow researchers to analyse large corpora in efficient
ways using standard hardware, with the ability to produce clear,
publication-ready reports and extend analysis where required using
standard Python libraries.
<img
src="https://raw.githubusercontent.com/polsci/conc/refs/heads/master/nbs/50_conc-5.png"
data-fig-align="left" alt="Example Concordance" />
A staple of data science, [Jupyter notebooks allow researchers to
present their analysis in an interactive form that combines code,
reporting and
discussion](https://docs.jupyter.org/en/latest/#what-is-a-notebook).
They are an ideal format for collaborating with other researchers during
research or to share analysis in a way others can reproduce and interact
with.
Conc uses [spaCy](https://spacy.io/) for tokenising texts. SpaCy
functionality to annotate texts will be supported soon.
Conc uses well-supported Python libraries for processing data and
prioritises fast code libraries and data structures. The library
produces clear reports with important information to interpret result by
default. Conc makes it easy to extend analysis using other libraries or
software. [Conc’s corpus format is
well-documented](https://geoffford.nz/conc/explanations/anatomy.html)
and there are [code examples to help you work with Conc results and data
structures outside of
Conc](https://geoffford.nz/conc/tutorials/recipes.html) if you want to
extend your analysis.
Conc’s documentation site has more information on Conc, [why it was
developed and the principles guiding Conc’s
development](https://geoffford.nz/conc/explanations/why.html).
## Table of Contents
- [Acknowledgements](#acknowledgements)
- [Development Status](#development-status)
- [Installation](#installation)
- [Using Conc](#using-conc)
### Direct links to Conc documentation
- [Getting Started](https://geoffford.nz/conc/tutorials/start.html)
- [Tutorials](https://geoffford.nz/conc/tutorials) (Tutorials to get you
started with Conc)
- [Documentation](https://geoffford.nz/conc/)
([Explanations](https://geoffford.nz/conc/explanations), [Conc API
Reference](https://geoffford.nz/conc/api), information on
[Development](https://geoffford.nz/conc/development))
## Acknowledgements
Conc is developed by [Dr Geoff Ford](https://geoffford.nz/).
Work to create this Python library has been made possible by
funding/support from:
- “[Mapping LAWS](https://mappinglaws.net/): Issue Mapping and Analyzing
the Lethal Autonomous Weapons Debate” (Royal Society of New Zealand’s
Marsden Fund Grant 19-UOC-068)
- “Into the Deep: Analysing the Actors and Controversies Driving the
Adoption of the World’s First Deep Sea Mining Governance” (Royal
Society of New Zealand’s Marsden Fund Grant 22-UOC-059)
- Sabbatical, University of Canterbury, Semester 1 2025.
Thanks to Jeremy Moses and Sian Troath from the [Mapping
LAWS](https://mappinglaws.net/) project team for their support and
feedback as first users of ConText (a web-based application built on an
earlier version of Conc that I’ve [also recently
released](https://github.com/polsci/ConText)).
Dr Ford is a researcher with [Te Pokapū Aronui ā-Matihiko \| UC Arts
Digital Lab (ADL)](https://artsdigitallab.canterbury.ac.nz/). Thanks to
the ADL team and the ongoing support of the University of Canterbury’s
Faculty of Arts who make work like this possible.
Thanks to Dr Chris Thomson and Karin Stahel for their feedback on early
versions of Conc.
Above all, thanks to my family for their love, patience and kindness.
## Development Status
Conc is in active development. It is currently
[released](https://pypi.org/project/conc) for beta testing. See the
[CHANGELOG](CHANGELOG.md) for notes on releases and the
[Roadmap](https://geoffford.nz/development/roadmap.html) for planned
updates.
Although this is a Beta release, I’m currently using Conc for research
and postgraduate teaching. I’m keen to support new users. If you have
any questions, encounter hurdles using Conc or have feature requests,
[create an issue](https://github.com/polsci/conc/issues/new).
## Installation
Installing Conc is simple. Below is the essential information if you
want to use Conc. The [installation
page](https://geoffford.nz/conc/tutorials/install.html) has more
information. You can also [install the development
version](https://geoffford.nz/conc/tutorials/install.html#install-the-development-version)
of Conc, which may include new functionality and bug fixes. If you want
to download sample corpora you will need to [install optional
dependencies](https://geoffford.nz/conc/tutorials/install.html#install-optional-dependencies).
If you have an older computer with a pre-2013 CPU, you will probably
need to install a version of Polars compiled for older machines, see the
[install page for
details](https://geoffford.nz/conc/tutorials/install.html#pre-2013-cpu-install-polars-with-support-for-older-machines).
### 1. Install via pip
Conc is tested with Python 3.10+. You can install Conc from
[pypi](https://pypi.org/project/conc) using this command:
``` sh
pip install conc
```
Add the `-U` flag to upgrade if you are already running Conc.
### 2. Install a spaCy model for tokenization
Conc uses a SpaCy language model for tokenization. After installing
Conc, install a model. If you are working with English-language texts,
install SpaCy’s small English model (which is Conc’s default) like this:
``` sh
python -m spacy download en_core_web_sm
```
If you are working with a different language or want to use a different
‘en’ model, check the [SpaCy models
documentation](https://spacy.io/models/) for the relevant model name.
## Using Conc
### Getting started
A good place to start is the [Get started with
Conc](https://geoffford.nz/conc/tutorials/start.html) tutorial, which
demonstrates how to build a corpus and output Conc reports. There are
also [simple code
recipes](https://geoffford.nz/conc/tutorials/recipes.html) for common
Conc tasks.
### Conc Documentation
There is a dedicated [Conc documentation
site](https://geoffford.nz/conc/). This includes tutorials, examples
demonstrating how to create reports for analysis, explanation of Conc
functionality and its Corpus format, and a reference to Conc’s classes
and methods. Here are links to the documentation site sections:
- [Tutorials](https://geoffford.nz/conc/tutorials) to get you started
with Conc
- The [Explanations](https://geoffford.nz/conc/explanations) section
includes information on how Conc works, how to work with the Conc
corpus format and Conc results with other Python libraries
- The [Conc API Reference](https://geoffford.nz/conc/api) provides
detailed documentation of Conc classes and functions
- The [Development](https://geoffford.nz/conc/development) section gives
information on Conc development, including a Roadmap and Developer’s
Guide
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"description": "# Conc\n\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n\n[](https://zenodo.org/doi/10.5281/zenodo.16358752)\n\n## Introduction to Conc\n\nConc is a Python library that brings tools for corpus linguistic\nanalysis to [Jupyter notebooks](https://docs.jupyter.org/en/latest/).\nConc aims to allow researchers to analyse large corpora in efficient\nways using standard hardware, with the ability to produce clear,\npublication-ready reports and extend analysis where required using\nstandard Python libraries.\n\n<img\nsrc=\"https://raw.githubusercontent.com/polsci/conc/refs/heads/master/nbs/50_conc-5.png\"\ndata-fig-align=\"left\" alt=\"Example Concordance\" />\n\nA staple of data science, [Jupyter notebooks allow researchers to\npresent their analysis in an interactive form that combines code,\nreporting and\ndiscussion](https://docs.jupyter.org/en/latest/#what-is-a-notebook).\nThey are an ideal format for collaborating with other researchers during\nresearch or to share analysis in a way others can reproduce and interact\nwith.\n\nConc uses [spaCy](https://spacy.io/) for tokenising texts. SpaCy\nfunctionality to annotate texts will be supported soon.\n\nConc uses well-supported Python libraries for processing data and\nprioritises fast code libraries and data structures. The library\nproduces clear reports with important information to interpret result by\ndefault. Conc makes it easy to extend analysis using other libraries or\nsoftware. [Conc\u2019s corpus format is\nwell-documented](https://geoffford.nz/conc/explanations/anatomy.html)\nand there are [code examples to help you work with Conc results and data\nstructures outside of\nConc](https://geoffford.nz/conc/tutorials/recipes.html) if you want to\nextend your analysis.\n\nConc\u2019s documentation site has more information on Conc, [why it was\ndeveloped and the principles guiding Conc\u2019s\ndevelopment](https://geoffford.nz/conc/explanations/why.html).\n\n## Table of Contents\n\n- [Acknowledgements](#acknowledgements) \n- [Development Status](#development-status) \n- [Installation](#installation) \n- [Using Conc](#using-conc)\n\n### Direct links to Conc documentation\n\n- [Getting Started](https://geoffford.nz/conc/tutorials/start.html) \n- [Tutorials](https://geoffford.nz/conc/tutorials) (Tutorials to get you\n started with Conc) \n- [Documentation](https://geoffford.nz/conc/)\n ([Explanations](https://geoffford.nz/conc/explanations), [Conc API\n Reference](https://geoffford.nz/conc/api), information on\n [Development](https://geoffford.nz/conc/development))\n\n## Acknowledgements\n\nConc is developed by [Dr Geoff Ford](https://geoffford.nz/).\n\nWork to create this Python library has been made possible by\nfunding/support from:\n\n- \u201c[Mapping LAWS](https://mappinglaws.net/): Issue Mapping and Analyzing\n the Lethal Autonomous Weapons Debate\u201d (Royal Society of New Zealand\u2019s\n Marsden Fund Grant 19-UOC-068) \n- \u201cInto the Deep: Analysing the Actors and Controversies Driving the\n Adoption of the World\u2019s First Deep Sea Mining Governance\u201d (Royal\n Society of New Zealand\u2019s Marsden Fund Grant 22-UOC-059)\n- Sabbatical, University of Canterbury, Semester 1 2025.\n\nThanks to Jeremy Moses and Sian Troath from the [Mapping\nLAWS](https://mappinglaws.net/) project team for their support and\nfeedback as first users of ConText (a web-based application built on an\nearlier version of Conc that I\u2019ve [also recently\nreleased](https://github.com/polsci/ConText)).\n\nDr Ford is a researcher with [Te Pokap\u016b Aronui \u0101-Matihiko \\| UC Arts\nDigital Lab (ADL)](https://artsdigitallab.canterbury.ac.nz/). Thanks to\nthe ADL team and the ongoing support of the University of Canterbury\u2019s\nFaculty of Arts who make work like this possible.\n\nThanks to Dr Chris Thomson and Karin Stahel for their feedback on early\nversions of Conc.\n\nAbove all, thanks to my family for their love, patience and kindness.\n\n## Development Status\n\nConc is in active development. It is currently\n[released](https://pypi.org/project/conc) for beta testing. See the\n[CHANGELOG](CHANGELOG.md) for notes on releases and the\n[Roadmap](https://geoffford.nz/development/roadmap.html) for planned\nupdates.\n\nAlthough this is a Beta release, I\u2019m currently using Conc for research\nand postgraduate teaching. I\u2019m keen to support new users. If you have\nany questions, encounter hurdles using Conc or have feature requests,\n[create an issue](https://github.com/polsci/conc/issues/new).\n\n## Installation\n\nInstalling Conc is simple. Below is the essential information if you\nwant to use Conc. The [installation\npage](https://geoffford.nz/conc/tutorials/install.html) has more\ninformation. You can also [install the development\nversion](https://geoffford.nz/conc/tutorials/install.html#install-the-development-version)\nof Conc, which may include new functionality and bug fixes. If you want\nto download sample corpora you will need to [install optional\ndependencies](https://geoffford.nz/conc/tutorials/install.html#install-optional-dependencies).\nIf you have an older computer with a pre-2013 CPU, you will probably\nneed to install a version of Polars compiled for older machines, see the\n[install page for\ndetails](https://geoffford.nz/conc/tutorials/install.html#pre-2013-cpu-install-polars-with-support-for-older-machines).\n\n### 1. Install via pip\n\nConc is tested with Python 3.10+. You can install Conc from\n[pypi](https://pypi.org/project/conc) using this command:\n\n``` sh\npip install conc\n```\n\nAdd the `-U` flag to upgrade if you are already running Conc.\n\n### 2. Install a spaCy model for tokenization\n\nConc uses a SpaCy language model for tokenization. After installing\nConc, install a model. If you are working with English-language texts,\ninstall SpaCy\u2019s small English model (which is Conc\u2019s default) like this:\n\n``` sh\npython -m spacy download en_core_web_sm\n```\n\nIf you are working with a different language or want to use a different\n\u2018en\u2019 model, check the [SpaCy models\ndocumentation](https://spacy.io/models/) for the relevant model name.\n\n## Using Conc\n\n### Getting started\n\nA good place to start is the [Get started with\nConc](https://geoffford.nz/conc/tutorials/start.html) tutorial, which\ndemonstrates how to build a corpus and output Conc reports. There are\nalso [simple code\nrecipes](https://geoffford.nz/conc/tutorials/recipes.html) for common\nConc tasks.\n\n### Conc Documentation\n\nThere is a dedicated [Conc documentation\nsite](https://geoffford.nz/conc/). 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