--- title: "Who cites Data structures for statistical computing in Python" description: "111 papers of the Elamur Research library cite «Data structures for statistical computing in Python», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/mckinney-2010-data-structures-statistical-computing-python/cited-by/" --- [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/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Data structures for statistical computing in…](https://md.research.elamur.ai/free/en/papers/mckinney-2010-data-structures-statistical-computing-python/) / cited by # Who cites «Data structures for statistical computing in Python» 111 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «Data structures for statistical computing in Python»](https://md.research.elamur.ai/free/en/papers/mckinney-2010-data-structures-statistical-computing-python/) 1. [**PyTorch: An Imperative Style, High-Performance Deep Learning Library arXiv:1912.01703**Adam Paszke, Sam Gross, Francisco Massa, et al.*2019 · 18,645 citations*](https://md.research.elamur.ai/free/en/papers/paszke-2019-pytorch-imperative-style-high-performance/) 2. [**Progress measures for grokking via mechanistic interpretability arXiv:2301.05217**Neel Nanda, Lawrence Chan, Tom Lieberum, et al.*2023 · 228 citations*](https://md.research.elamur.ai/free/en/papers/nanda-2023-progress-measures-grokking-mechanistic-interpretability/) 3. [**DiscoveryBench: Towards Data-Driven Discovery with Large Language Models arXiv:2407.01725**Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, et al.*2024 · 55 citations*](https://md.research.elamur.ai/free/en/papers/majumder-2024-discoverybench-data-driven-discovery/) 4. **[STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval](https://md.research.elamur.ai/free/en/papers/conforti-2020-stander-expert-annotated-dataset-news/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, et al.*2020 · 22 citations* 5. [**Traditional Readability Formulas Compared for English arXiv:2301.02975**Bruce W. Lee, Jason Hyung-Jong Lee*2023 · 9 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-traditional-readability-formulas-compared-english/) 6. **[GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities](https://md.research.elamur.ai/free/en/papers/misra-2026-gitchameleon-2-0-evaluating-ai/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Diganta Misra, Nizar Islah, Victor May, et al.*2026 · 2 citations* 7. [**The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP arXiv:2606.03250 · BMC Med Inform Decis Mak 26, 296 (2026)**Henry He, Johann Frei, Raphael Schmitt*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/he-2026-word-way-strategies-domain-specific/) 8. **[Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection](https://md.research.elamur.ai/free/en/papers/san-2018-random-decision-syntax-trees-semeval/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Aidan San*2018 · 2 citations* 9. **[Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists](https://md.research.elamur.ai/free/en/papers/pietruszka-2026-can-models-help-us-create/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Michał Pietruszka, Łukasz Borchmann, Aleksander Jędrosz, et al.*2026 · 1 citation* 10. [**AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation arXiv:2506.14634 · Survey Research Methods (2025)**Leah von der Heyde, Anna-Carolina Haensch, Bernd Weiß, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/heyde-2025-ain-t-nothing-but-survey/) 11. **[OLEA: Tool and Infrastructure for Offensive Language Error Analysis in English](https://md.research.elamur.ai/free/en/papers/grace-2023-olea-tool-infrastructure-offensive-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Marie Grace, Jay Seabrum, Dananjay Srinivas, et al.*2023 · 1 citation* 12. **[I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning](https://md.research.elamur.ai/free/en/papers/diaz-2022-i2c-semeval-2022-task-6/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Pablo Gonzalez Diaz, Pablo Cordon, Jacinto Mata, et al.*2022 · 1 citation* 13. **[Entity-Level Sentiment Analysis with Sentence Relevance Detection](https://md.research.elamur.ai/free/en/papers/ronningstad-2026-entity-level-sentiment-analysis-sentence/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Egil Rønningstad, Roman Klinger, Lilja Øvrelid, et al.*2026* 14. **[Studying Expert-ese: Profiling and Classification of Domain-Specific Language Variation in Architecture with Traditional Machine Learning and LLMs](https://md.research.elamur.ai/free/en/papers/schacht-2026-studying-expert-ese-profiling-classification/) [Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL)](https://md.research.elamur.ai/free/en/venues/latechclfl/)**Carmen Schacht, Renate Delucchi Danhier*2026* 97 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/mckinney-2010-data-structures-statistical-computing-python/) — Data structures for statistical computing in Python