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 67 articles )

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  1. 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
  2. Alessandro Sebastianelli, Maria Pia Del Rosso, Silvia Liberata Ullo: Automatic dataset builder for Machine Learning applications to satellite imagery (2021) not zbMATH
  3. 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
  4. Andrea Bizzego, Mengyu Lim, Gianluca Esposito: mics-library: A Python package for reproducible studies on the Multiple Indicator Cluster Survey (2021) not zbMATH
  5. Changjie Chen, Jasmeet Judge, David Hulse: PyLUSAT: An open-source Python toolkit for GIS-based land use suitability analysis (2021) arXiv
  6. Chathika Gunaratne, Ivan Garibay: NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces (2021) not zbMATH
  7. Derrick J.A. Chambers; M. Shawn Boltz; Calum J. Chamberlain: ObsPlus: A Pandas-centric ObsPy expansion pack (2021) not zbMATH
  8. Dmitry Soshnikov, Yana Valieva: mPyPl: Python Monadic Pipeline Library for Complex Functional Data Processing (2021) arXiv
  9. Garyfallidis et al.: FURY: advanced scientific visualization (2021) not zbMATH
  10. Haan, Sebastian: GeoBO: Python package for Multi-Objective Bayesian Optimisation and Joint Inversion in Geosciences (2021) not zbMATH
  11. Hyemin Han: BayesFactorFMRI: Implementing Bayesian Second-Level fMRI Analysis with Multiple Comparison Correction and Bayesian Meta-Analysis of fMRI Images with Multiprocessing (2021) not zbMATH
  12. James D. Gaboardi, Sergio Rey, Stefanie Lumnitz: spaghetti: spatial network analysis in PySAL (2021) not zbMATH
  13. Jonathan Bac, Evgeny M. Mirkes, Alexander N. Gorban, Ivan Tyukin, Andrei Zinovyev: Scikit-dimension: a Python package for intrinsic dimension estimation (2021) arXiv
  14. Justin Shenk, Wolf Byttner, Saranraj Nambusubramaniyan, Alexander Zoeller: Traja: A Python toolbox for animal trajectory analysis (2021) not zbMATH
  15. Luis Cabañero-Gomez, Ramon Hervas, Ivan Gonzalez, Luis Rodriguez-Benitez: eeglib: A Python module for EEG feature extraction (2021) not zbMATH
  16. Lukas Stappen, Lea Schumann, Benjamin Sertolli, Alice Baird, Benjamin Weigel, Erik Cambria, Björn W. Schuller: MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox (2021) arXiv
  17. Matteo Zampieri, Andrea Toreti, Andrej Ceglar, Pierluca De Palma, Thomas Chatzopoulos, Melania Michetti: Analysing the resilience of agricultural production systems with ResiPy, the Python production resilience estimation package (2021) not zbMATH
  18. Matthew T. Herritt; Jacob C. Long; Mike D. Roybal; David C. Moller Jr.; Todd C. Mockler; Duke Pauli; Alison L.Thompson: FLIP: FLuorescence Imaging Pipeline for field-based chlorophyll fluorescence images (2021) not zbMATH
  19. Md Motiur Rahman, Tahmina Tasnim Nahar, Dookie Kim: FeView: Finite element model (FEM) visualization and post-processing tool for OpenSees (2021) not zbMATH
  20. Mehdi Bahrami, N.C. Shrikanth, Shade Ruangwan, Lei Liu, Yuji Mizobuchi, Masahiro Fukuyori, Wei-Peng Chen, Kazuki Munakata, Tim Menzies: PyTorrent: A Python Library Corpus for Large-scale Language Models (2021) arXiv

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