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This page has summary information about developing the fpdf2 library.

Repository structure

  • .github/ - GitHub Actions configuration
  • docs/ - documentation folder
  • fpdf/ - library sources
  • scripts/ - utilities to validate PDF files & publish the package on Pypi
  • test/ - non-regression tests
  • tutorial/ - tutorials (see also Tutorial)
  • - Github and PyPI ReadMe
  • - details of each release content
  • LICENSE - code license information
  • CODEOWNERS - define individuals or teams responsible for code in this repository
  • - the people who helped build this library ❤️
  • setup.cfg,, - packaging configuration to publish a package on Pypi
  • mkdocs.yml - configuration for MkDocs
  • tox.ini - configuration for Tox
  • .banditrc.yml - configuration for bandit
  • .pylintrc - configuration for Pylint

Installing fpdf2 from a local git repository

pip install --editable $path/to/fpdf/repo

This will link the installed Python package to the repository location, basically meaning any changes to the code package will get reflected directly in your environment.

Code auto-formatting

We use black as a code prettifier. This "uncomprimising Python code formatter" must be installed in your development environment in order to auto-format source code before any commit:

pip install black
black .  # inside fpdf2 root directory


We use pylint as a static code analyzer to detect potential issues in the code.

In case of special "false positive" cases, checks can be disabled locally with #pylint disable=XXX code comments, or globally through the .pylintrc file.

Pre-commit hook

This project uses git pre-commit hooks:

Those hooks are configured in .pre-commit-config.yaml.

They are intended to abort your commit if pylint found issues or black detected non-properly formatted code. In the later case though, it will auto-format your code and you will just have to run git commit -a again.

To install pre-commit hooks on your computer, run:

pip install pre-commit
pre-commit install


Running tests

To run tests, cd into fpdf2 repository, install the dependencies using pip install -r test/requirements.txt, and run pytest.

You may also need to install SWIG and Ghostscript, because they are dependencies for camelot, a library for table extraction in PDF that we test in test/table/ Those tests will always be executed by the GitHub Actions pipeline, so you can also not bother installing those tools and skip those tests by running pytest -k "not camelot".

You can run a single test by executing: pytest -k function_name.

Alternatively, you can use Tox. It is self-documented in the tox.ini file in the repository. To run tests for all versions of Python, simply run tox. If you do not want to run tests for all versions of python, run tox -e py39 (or your version of Python).

Why is a test failing?

If there are some failing tests after you made a code change, it is usually because there are difference between an expected PDF generated and the actual one produced.

Calling pytest -vv will display the difference of PDF source code between the expected & actual files, but that may be difficult to understand,

You can also have a look at the PDF files involved by navigating to the temporary test directory that is printed out during the test failure:

=================================== FAILURES ===================================
____________________________ test_html_simple_table ____________________________

tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/test_html_simple_table0')

This directory contains the actual & expected files, that you can vsualize to spot differences:

$ ls /tmp/pytest-of-runner/pytest-0/test_html_simple_table0

assert_pdf_equal & writing new tests

When a unit test generates a PDF, it is recommended to use the assert_pdf_equal utility function in order to validate the output. It relies on the very handy qpdf CLI program to generate a PDF that is easy to compare: annotated, strictly formatted, with uncompressed internal streams. You will need to have its binary in your $PATH, otherwise assert_pdf_equal will fall back to hash-based comparison.

All generated PDF files (including those processed by qpdf) will be stored in /tmp/pytest-of-USERNAME/pytest-current/NAME_OF_TEST/. By default, three last test runs will be saved and then automatically deleted, so you can check the output in case of a failed test.

Generating PDF files for testing

In order to generate a "reference" PDF file, simply call assert_pdf_equal once with generate=True.

import fpdf

svg = fpdf.svg.SVGObject.from_file("path/to/file.svg")
pdf = fpdf.FPDF(unit="pt", format=(svg.width, svg.height))


Testing performances

Code speed & profiling

First, try to write a really MINIMAL Python script that focus strictly on the performance point you are investigating. Try to choose the input dataset so that the script execution time is between 1 and 15 seconds.

Then, you can use cProfile to profile your code and produce a .pstats file:

python -m cProfile -o profile.pstats

Finally, you can quickly convert this .pstats file into a SVG flamegraph using flameprof:

pip install flameprof
flameprof profile.pstats > script-flamegraph.svg
You will get something like this:

Source GitHub thread where this was produced: issue #907

Tracking memory usage

A good way to track memory usage is to insert calls to fpdf.util.print_mem_usage() in the code you are investigating. This function will display the current process resident set size (RSS) which is currently, to the maintainer knowledge, one of the best way to get an accurate measure of Python scripts memory usage.

There is an example of using this function to track fpdf2 memory usage in this issue comment: issue #641. This thread also includes some tests of other libs & tools to track memory usage.

Non-regression performance tests

We try to have a small number of unit tests that ensure that the library performances do not degrade over time, when refactoring are made and new features added.

We have 2 test decorators to help with this:

As of fpdf2 v2.7.6, we only keep 3 non-regression performance tests:

GitHub pipeline

A GitHub Actions pipeline is executed on every commit on the master branch, and for every Pull Request.

It performs all validation steps detailed above: code checking with black, static code analysis with pylint, unit tests... Pull Requests submitted must pass all those checks in order to be approved. Ask maintainers through comments if some errors in the pipeline seem obscure to you.

Release checklist

  1. complete and add the version & date of the new release
  2. bump FPDF_VERSION in fpdf/ Also (optionnal, once every year), update contributors/contributors-map-small.png based on
  3. update the announce block in docs/overrides/main.html to mention the new release
  4. git commit & git push (if editing in a fork: submit and merge a PR)
  5. check that the GitHub Actions succeed, and that a new release appears on Pypi
  6. perform a GitHub release, taking the description from the It will create a new git tag.
  7. Announce the release on r/pythonnews, and add an announcement to the documentation website: docs/overrides/main.html


The standalone documentation is in the docs subfolder, written in Markdown. Building instructions are contained in the configuration file mkdocs.yml and also in .github/workflows/continuous-integration-workflow.yml.

Additional documentation is generated from inline comments, and is available in the project home page.

After being committed to the master branch, code documentation is automatically uploaded to GitHub Pages.

There is a useful one-page example Python module with docstrings illustrating how to document code: pdoc3 example_pkg.

To preview the Markdown documentation, launch a local rendering server with:

mkdocs serve

To preview the API documentation, launch a local rendering server with:

pdoc --html -o public/ fpdf --http :

PDF spec & new features

The PDF 1.7 spec is available on Adobe website: PDF32000_2008.pdf.

The PDF 2.0 spec is available on the Adobe website or on the PDF Association website

It may be intimidating at first, but while technical, it is usually quite clear and understandable.

It is also a great place to look for new features for fpdf2: there are still many PDF features that this library does not support.

Useful tools to manipulate PDFs


qpdf is a very powerful tool to analyze PDF documents.

One of it most useful features is the QDF mode that can convert any PDF file to a human-readable, decompressed & annotated new PDF document:

qpdf --qdf doc.pdf doc-qdf.pdf

This is extremely useful to peek into the PDF document structure. is a small Python script that can rebuild a PDF xref table.

This is very useful, as a PDF with an invalid xref cannot be opened. An xref table is basically an index of the document internal sections. When manually modifying a PDF file (for example one produced by qpdf --qdf), if the characters count in any of its sections changes, the xref table must be rebuilt.

With doc.pdf --inplace, you can change some values inside any PDF file, and then quickly make it valid again to be viewed in a PDF viewer.