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 26 articles )

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  1. 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
  2. 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
  3. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  4. Amir M. Mir; Jalal A. Nasiri: LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier (2019) not zbMATH
  5. 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
  6. Johansson, Robert: Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib (2019)
  7. Rising Odegua: DataSist: A Python-based library for easy data analysis, visualization and modeling (2019) arXiv
  8. Scott Fredericks, Dean Sayre, Qiang Zhu: PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis (2019) arXiv
  9. Tai Sakuma: AlphaTwirl: A Python library for summarizing event data into multivariate categorical data (2019) arXiv
  10. 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
  11. Catherine Zucker; Hope How-Huan Chen: RadFil: a Python Package for Building and Fitting Radial Profiles for Interstellar Filaments (2018) arXiv
  12. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  13. Lionel Roubeyrie; Sébastien Celles: Windrose: A Python Matplotlib, Numpy library to manage wind and pollution data, draw windrose (2018) not zbMATH
  14. Michael J Bommarito II; Daniel Martin Katz; Eric M Detterman: OpenEDGAR: Open Source Software for SEC EDGAR Analysis (2018) arXiv
  15. Minjie Zhu, Frank McKenna, Michael H. Scott: OpenSeesPy: Python library for the OpenSees finite element framework (2018) not zbMATH
  16. Robert Gieseke; Sven N Willner; Matthias Mengel: Pymagicc: A Python wrapper for the simple climate model MAGICC (2018) not zbMATH
  17. Amen, Saeed: Using Python to analyse financial markets (2017)
  18. Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning: HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data (2017) arXiv
  19. Bryan W. Weber, Kyle E. Niemeyer: ChemKED: a human- and machine-readable data standard for chemical kinetics experiments (2017) arXiv
  20. Ehrhardt, Matthias (ed.); Günther, Michael (ed.); ter Maten, E. Jan W. (ed.): Novel methods in computational finance (2017)

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