--- title: "Data structures for statistical computing in Python (2010)" description: "McKinney presents pandas, a library first built at AQR Capital Management for quantitative finance. 1 reference, cited by 111 papers here." authors: ["Wes McKinney"] year: 2010 venue: "Proc. 9th Python in Science Conf. (SciPy 2010), 56–61" source: "https://proceedings.scipy.org/articles/Majora-92bf1922-00a" licence: "CC BY (SciPy Proceedings)" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/mckinney-2010-data-structures-statistical-computing-python/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mckinney-2010-data-structures-statistical-computing-python/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2010](https://md.research.elamur.ai/free/en/papers/2010/) / Data structures for statistical computing in Python # Data structures for statistical computing in Python **Authors:** Wes McKinney · **Year:** 2010 · **Venue:** [Proc. 9th Python in Science Conf. (SciPy 2010), 56–61](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-python-in-science-conferences/) ([Proceedings of the Python in Science Conferences 2010](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-python-in-science-conferences/2010/)) · **Licence:** CC BY (SciPy Proceedings) **Links:** [Publisher's page](https://doi.org/10.25080/majora-92bf1922-00a) · [PDF](https://pub.curvenote.com/01908378-3686-7168-a380-d82bbf21c799/public/mckinney-57fc0d4e8a08cd7f26a4b8bf468a71f4.pdf) · [OpenAlex](https://openalex.org/W2342249984) **Citations:** 11,032 citations (OpenAlex, 2026-10-14) · [111](https://md.research.elamur.ai/free/en/papers/mckinney-2010-data-structures-statistical-computing-python/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary The paper addresses why Python has been slow to win over statisticians compared with R. A key reason is the lack of convenient in-memory structures for labeled, mixed-type tabular data. NumPy structured arrays can hold such data, but they fit poorly with the rest of NumPy and are awkward next to R's data.frame. McKinney presents pandas, a library first built at AQR Capital Management for quantitative finance. It provides labeled 1-, 2- and 3-dimensional containers, including Series and DataFrame. The design choices are illustrated with stock price and volume examples for GOOG and AAPL and with a table of country and industry categories. Central ideas include: - axes tied to Index objects that act as ordered sets backed by hash maps; - automatic label-based alignment when combining objects; - NaN-based handling of missing values; - joins on the index or on key columns; - reshaping with pivot; - calendar-aware date ranges and frequency conversion. The main contribution is a practical, R-like data model that fits the scientific Python stack. It is offered as a foundation for statistical modeling tools in Python. ## Key points - The name pandas comes from "panel data", the econometric term for 3-dimensional data sets. Higher dimensions were deliberately left out as rare in practice. - Index keeps labels both as an ndarray and as a dict, which makes lookups and membership checks constant-time. Arithmetic between objects aligns on labels and returns their union, filling gaps with NaN. Core routines were sped up with Cython. - NaN was chosen over NumPy MaskedArrays for performance. The trade-off is that it does not work in integer arrays. Helpers such as isnull/notnull, valid and fillna handle missing values, and reductions like sum and count skip them. - DateRange (an Index subclass) and DateOffset subclasses support custom frequencies, asfreq and shift. For example, business month-ends from 2000 to 2010 give 120 dates. Relying on Python's built-in datetime may limit performance on high-frequency data. - Related packages include la/larry, tabular and pydataframe. Closer integration with scikits.statsmodels is planned. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎯 Decision Sciences](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · 249 works · [🎯 Information Systems and Management](https://md.research.elamur.ai/free/en/shelves/decision-sciences/information-systems-and-management/) · 52 works ## Cite Cite as: Wes McKinney. 2010. Data structures for statistical computing in Python. Proc. 9th Python in Science Conf. (SciPy 2010), 56–61. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{mckinney2010data, title = {{Data structures for statistical computing in Python}}, author = {Wes McKinney}, booktitle = {Proc. 9th Python in Science Conf. (SciPy 2010), 56–61}, year = {2010}, doi = {10.25080/majora-92bf1922-00a}, url = {https://doi.org/10.25080/majora-92bf1922-00a} } ``` - [next → Statsmodels: econometric and statistical modeling with Python](https://md.research.elamur.ai/free/en/papers/seabold-2010-statsmodels-econometric-statistical-modeling-python/) - [↑ back to papers of 2010](https://md.research.elamur.ai/free/en/papers/2010/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work. 1. [pandas] W. McKinney, AQR Capital Management, pandas: a python data analysis library, [http://pandas.sourceforge.net](http://pandas.sourceforge.net) [Larry] K. Goodman. la / larry: ndarray with labeled axes, http://larry. sourceforge.net/ [SciTS] M. Knox, P. Gerard-Marchant, scikits.timeseries: python time series analysis, http://p… [↗](http://pandas.sourceforge.net) *Updated 2 November 2026*