pandas

pandas: a Foundational Python Library for Data Analysis and Statistics. In this paper we will discuss pandas, a Python library of rich data structures and tools for working with structured data sets common to statistics, finance, social sciences, and many other fields. The library provides integrated, intuitive routines for performing common data manipulations and analysis on such data sets. It aims to be the foundational layer for the future of statistical computing in Python. It serves as a strong complement to the existing scientific Python stack while implementing and improving upon the kinds of data manipulation tools found in other statistical programming languages such as R. In addition to detailing its design and features of pandas, we will discuss future avenues of work and growth opportunities for statistics and data analysis applications in the Python language


References in zbMATH (referenced in 80 articles )

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  1. Akimitsu Ishii, Ryunosuke Kamijyo, Akinori Yamanaka, Akiyasu Yamamoto: BOXVIA: Bayesian optimization executable and visualizable application (2022) not zbMATH
  2. Alina Petukhova, Nuno Fachada: TextCL: A Python package for NLP preprocessing tasks (2022) not zbMATH
  3. Alvaro J. Garcia-Tejedor, Alberto Nogales: GEMA: An open-source Python library for self-organizing-maps (2022) arXiv
  4. Asher D Pembroke; Darren DeZeeuw; Lutz Rastaetter; Rebecca Ringuette; Oliver Gerland; Dhruv Patel; Michael Contreras: Kamodo: A functional API for space weather models and data (2022) not zbMATH
  5. Garcés, Alejandro: Mathematical programming for power systems operation. From theory to applications in Python (2022)
  6. Gellis, Jason J.; Rangel Smith, Camila; Foley, Robert A.: PyLithics: A Python package for stone tool analysis (2022) not zbMATH
  7. Jarosław Wątróbski, Aleksandra Bączkiewicz, Wojciech Sałabun: pyrepo-mcda - Reference objects based MCDA software package (2022) not zbMATH
  8. Johannes N. Hansen: nd - A Framework for the Analysis of n-dimensional Earth Observation Data (2022) not zbMATH
  9. Marek Gagolewsk: stringi: Fast and Portable Character String Processing in R (2022) not zbMATH
  10. Strickland, W. C.; Battista, N. A.; Hamlet, C. L.; Miller, L. A.: \textitPlanktos: an agent-based modeling framework for small organism movement and dispersal in a fluid environment with immersed structures (2022)
  11. Tomer Meir, Rom Gutman, Malka Gorfine: PyDTS: A Python Package for Discrete Time Survival Analysis with Competing Risks (2022) arXiv
  12. A. Buckley, J. M. Butterworth, L. Corpe, M. Habedank, D. Huang, D. Yallup, M. Altakach, G. Bassman, I. Lagwankar, J. Rocamonde, H. Saunders, B. Waugh, G. Zilgalvis: Testing new-physics models with global comparisons to collider measurements: the Contur toolkit (2021) arXiv
  13. Alessandro Sebastianelli, Maria Pia Del Rosso, Silvia Liberata Ullo: Automatic dataset builder for Machine Learning applications to satellite imagery (2021) not zbMATH
  14. Amanda D. Smith, Benjamin Stürmer, Travis Thurber, Chris R. Vernon: diyepw: A Python package for Do-It-Yourself EnergyPlus weather file generation (2021) not zbMATH
  15. Andrea Bizzego, Mengyu Lim, Gianluca Esposito: mics-library: A Python package for reproducible studies on the Multiple Indicator Cluster Survey (2021) not zbMATH
  16. Carmichael, Iain; Calhoun, Benjamin C.; Hoadley, Katherine A.; Troester, Melissa A.; Geradts, Joseph; Couture, Heather D.; Olsson, Linnea; Perou, Charles M.; Niethammer, Marc; Hannig, Jan; Marron, J. S.: Joint and individual analysis of breast cancer histologic images and genomic covariates (2021)
  17. Changjie Chen, Jasmeet Judge, David Hulse: PyLUSAT: An open-source Python toolkit for GIS-based land use suitability analysis (2021) arXiv
  18. Chathika Gunaratne, Ivan Garibay: NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces (2021) not zbMATH
  19. Derrick J.A. Chambers; M. Shawn Boltz; Calum J. Chamberlain: ObsPlus: A Pandas-centric ObsPy expansion pack (2021) not zbMATH
  20. Dmitry Soshnikov, Yana Valieva: mPyPl: Python Monadic Pipeline Library for Complex Functional Data Processing (2021) arXiv

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