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
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References in zbMATH (referenced in 74 articles )
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Sorted by year (- Akimitsu Ishii, Ryunosuke Kamijyo, Akinori Yamanaka, Akiyasu Yamamoto: BOXVIA: Bayesian optimization executable and visualizable application (2022) not zbMATH
- Alvaro J. Garcia-Tejedor, Alberto Nogales: GEMA: An open-source Python library for self-organizing-maps (2022) arXiv
- Gellis, Jason J.; Rangel Smith, Camila; Foley, Robert A.: PyLithics: A Python package for stone tool analysis (2022) not zbMATH
- Johannes N. Hansen: nd - A Framework for the Analysis of n-dimensional Earth Observation Data (2022) not zbMATH
- Tomer Meir, Rom Gutman, Malka Gorfine: PyDTS: A Python Package for Discrete Time Survival Analysis with Competing Risks (2022) arXiv
- 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
- Alessandro Sebastianelli, Maria Pia Del Rosso, Silvia Liberata Ullo: Automatic dataset builder for Machine Learning applications to satellite imagery (2021) not zbMATH
- 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
- Andrea Bizzego, Mengyu Lim, Gianluca Esposito: mics-library: A Python package for reproducible studies on the Multiple Indicator Cluster Survey (2021) not zbMATH
- 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)
- Changjie Chen, Jasmeet Judge, David Hulse: PyLUSAT: An open-source Python toolkit for GIS-based land use suitability analysis (2021) arXiv
- Chathika Gunaratne, Ivan Garibay: NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces (2021) not zbMATH
- Derrick J.A. Chambers; M. Shawn Boltz; Calum J. Chamberlain: ObsPlus: A Pandas-centric ObsPy expansion pack (2021) not zbMATH
- Dmitry Soshnikov, Yana Valieva: mPyPl: Python Monadic Pipeline Library for Complex Functional Data Processing (2021) arXiv
- Garyfallidis et al.: FURY: advanced scientific visualization (2021) not zbMATH
- Haan, Sebastian: GeoBO: Python package for Multi-Objective Bayesian Optimisation and Joint Inversion in Geosciences (2021) not zbMATH
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- James D. Gaboardi, Sergio Rey, Stefanie Lumnitz: spaghetti: spatial network analysis in PySAL (2021) not zbMATH
- Jonathan Bac, Evgeny M. Mirkes, Alexander N. Gorban, Ivan Tyukin, Andrei Zinovyev: Scikit-dimension: a Python package for intrinsic dimension estimation (2021) arXiv
- Justin Shenk, Wolf Byttner, Saranraj Nambusubramaniyan, Alexander Zoeller: Traja: A Python toolbox for animal trajectory analysis (2021) not zbMATH