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 40 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. Haan, Sebastian: GeoBO: Python package for Multi-Objective Bayesian Optimisation and Joint Inversion in Geosciences (2021) not zbMATH
  3. 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
  4. Robert Morgan; Brian Nord; Simon Birrer; Joshua Yao-Yu Lin; Jason Poh: deeplenstronomy: A dataset simulation package for strong gravitational lensing (2021) not zbMATH
  5. Arora, Rajat; Zhang, Xiaohan; Acharya, Amit: Finite element approximation of finite deformation dislocation mechanics (2020)
  6. Ashkbiz Danehkar: AtomNeb Python Package, an addendum to AtomNeb: IDL Library for Atomic Data of Ionized Nebulae (2020) not zbMATH
  7. Benjamin Edward Bolling: The DynaGUI package (2020) not zbMATH
  8. 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
  9. 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
  10. John D. Boy: textnets: A Python package for text analysis with networks (2020) not zbMATH
  11. Kshitij Aggarwal; Devansh Agarwal; Joseph W Kania; William Fiore; Reshma Anna Thomas; Scott M. Ransom; Paul B. Demorest; Robert S. Wharton; Sarah Burke-Spolaor; Duncan R. Lorimer; Maura A. Mclaughlin; Nathaniel Garver-Daniels: Your: Your Unified Reader (2020) not zbMATH
  12. 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
  13. Martin Nielsen, Guy Davies, Oliver Hall, et al.: PBjam: A Python package for automating asteroseismology of solar-like oscillators (2020) arXiv
  14. 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
  15. Morgan J. Williams, Louise Schoneveld, Yajing Mao, Jens Klump, Justin Gosses, Hayden Dalton, Adam Bath, Steve Barnes: pyrolite: Python for geochemistry (2020) not zbMATH
  16. Neal W Morton: Psifr: Analysis and visualization of free recall data (2020) not zbMATH
  17. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  18. Amir M. Mir; Jalal A. Nasiri: LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier (2019) not zbMATH
  19. 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
  20. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)

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