PeopleofSuperintelligence

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Pierre Gérard-Marchant

Also known as P Gérard-Marchant, Pierre GM, pierregm

Pierre Gérard-Marchant is a Python developer whose work on missing data and time series belongs to the NumPy ecosystem. In 2006, while at the University of Georgia, he rewrote numpy.ma, the NumPy package for missing or invalid values. With Matt Knox, he announced the scikits.timeseries package in 2009, and in 2011 he said that further work on it was in limbo. He was one of the 26 authors of "Array programming with NumPy", published in Nature in 2020, a paper that notes how data science, machine learning and artificial intelligence have boosted the scientific use of Python. By 2020, he was affiliated with the University of Georgia and with France-IX Services in Paris.

Timeline

  1. 2006

    Rewriting masked arrays in NumPy

    In 2006, while at the University of Georgia, he completely rewrites numpy.ma, the NumPy package for missing or invalid values, so that the MaskedArray class becomes a subclass of ndarray and structured arrays gain better support. The package began as a numarray project by Paul F. Dubois at Lawrence Livermore National Laboratory.

  2. 2009

    The scikits.timeseries package

    In April 2009, he and Matt Knox announce scikits.timeseries 0.91.0 as the first official public release of their module for manipulating, reporting and plotting time series of various frequencies. Version 0.91.3 follows in November 2009.

  3. 2011

    Pausing the time series package

    In July 2011, he tells the SciPy-User mailing list that he and Matt Knox are busy with other projects, so further development of scikits.timeseries is in limbo. Wes McKinney, who works on time series in pandas, answers by floating a merger of the two projects.

  4. 2020

    Array programming with NumPy

    He is one of 26 authors of "Array programming with NumPy", posted to arXiv in June 2020 and published in Nature in September 2020. The paper presents NumPy as the primary array programming library for Python and as an interoperability layer between array libraries, and it notes that data science, machine learning and artificial intelligence have boosted the scientific use of Python.

  5. 2020

    Georgia and France-IX Services

    In 2020, his affiliations are the Department of Biological and Agricultural Engineering at the University of Georgia in Athens and France-IX Services in Paris.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University of Georgia. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  2. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  3. The United States employs an estimated 48% of people shaping AIWe estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  4. 43% of living people shaping AI work outside their country of birthWe estimate that 43% of living people shaping AI work outside their country of birth (520 of 1,198 people with known birth and employment countries).

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. numpy/numpy/ma/core.py at main · numpy/numpy · GitHub github.com. Checked 22 October 2026.
  2. scikits.timeseries 0.91.0 released [LWN.net] lwn.net. Checked 22 October 2026.
  3. scikits.timeseries · PyPI pypi.org. Checked 22 October 2026.
  4. [SciPy-User] Status of TimeSeries SciKit python.org. Checked 22 October 2026.
  5. Array programming with NumPy | Nature nature.com. Checked 22 October 2026.
  6. [2006.10256] Array Programming with NumPy arxiv.org. Checked 22 October 2026.