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 30 articles )

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  1. Arora, Rajat; Zhang, Xiaohan; Acharya, Amit: Finite element approximation of finite deformation dislocation mechanics (2020)
  2. Iago Pereira Lemos; Antônio Marcos Gonçalves Lima; Marcus Antônio Viana Duarte: thresholdmodeling: A Python package for modeling excesses over a threshold using the Peak-Over-Threshold Method and the Generalized Pareto Distribution (2020) not zbMATH
  3. Iva Laginja; Hannah R. Wakeford: ExoTiC-ISM: A Python package for marginalised exoplanet transit parameters across a grid of systematic instrument models (2020) not zbMATH
  4. Lukas Adamowicz; Shyamal Patel: Sit2StandPy: An Open-Source Python Package for Detecting and Quantifying Sit-to-Stand Transitions Using an Accelerometer on the Lower Back (2020) not zbMATH
  5. Matthew J. Gidden; Daniel Huppmann: pyam: a Python Package for the Analysis and Visualization of Models of the Interaction of Climate, Human, and Environmental Systems (2020) not zbMATH
  6. Morgan J. Williams, Louise Schoneveld, Yajing Mao, Jens Klump, Justin Gosses, Hayden Dalton, Adam Bath, Steve Barnes: pyrolite: Python for geochemistry (2020) not zbMATH
  7. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  8. Amir M. Mir; Jalal A. Nasiri: LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier (2019) not zbMATH
  9. Hsieh-Fu Tsai, Joanna Gajda, Tyler F.W. Sloan, Andrei Rares, Jason Ting-Chun Chou, Amy Q. Shen: Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning (2019) not zbMATH
  10. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)
  11. Rising Odegua: DataSist: A Python-based library for easy data analysis, visualization and modeling (2019) arXiv
  12. Scott Fredericks, Dean Sayre, Qiang Zhu: PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis (2019) arXiv
  13. Tai Sakuma: AlphaTwirl: A Python library for summarizing event data into multivariate categorical data (2019) arXiv
  14. Benyuan Liu; Bin Yang; Canhua Xu; Junying Xia; Meng Dai; Zhenyu Ji; Fusheng You; Xiuzhen Dong; Xuetao Shi; Feng Fu: pyEIT: A python based framework for Electrical Impedance Tomography (2018) not zbMATH
  15. Catherine Zucker; Hope How-Huan Chen: RadFil: a Python Package for Building and Fitting Radial Profiles for Interstellar Filaments (2018) arXiv
  16. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  17. Lionel Roubeyrie; Sébastien Celles: Windrose: A Python Matplotlib, Numpy library to manage wind and pollution data, draw windrose (2018) not zbMATH
  18. Michael J Bommarito II; Daniel Martin Katz; Eric M Detterman: OpenEDGAR: Open Source Software for SEC EDGAR Analysis (2018) arXiv
  19. Minjie Zhu, Frank McKenna, Michael H. Scott: OpenSeesPy: Python library for the OpenSees finite element framework (2018) not zbMATH
  20. Robert Gieseke; Sven N Willner; Matthias Mengel: Pymagicc: A Python wrapper for the simple climate model MAGICC (2018) not zbMATH

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