Analysis of the use of the Python programming language for statistical calculations.
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Abstract
This article presents a review of the use of Python for data analysis. It discusses the main specialized libraries and the advantages of using Python for data analysis. Also, the main phases of data analysis are explained in general and in an example with the programming language. It is concluded that Python presents multiple advantages in its use and that in spite of being an open source software, it allows professional and sophisticated work in data management and statistical calculations.
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References
Bødker, M. S., Wilkinson, C. J., Mauro, J. C., & Smedskjaer, M. M. (2022). StatMechGlass: Python based software for composition-structure prediction in oxide glasses using statistical mechanics. SoftwareX, 17, 100-913. https://doi.org/10.1016/j.softx.2021.100913.
Boelens, A. M. P., & Tchelepi, H. A. (2021). QuantImPy: Minkowski functionals and functions with Python. SoftwareX, 16, 100-109. https://doi.org/10.1016/j.softx.2021.100823.
de Rosa, G. H., & Papa, J. P. (2021). OPFython: A Python implementation for Optimum-Path Forest[Formula presented]. Software Impacts,9 (July), 100-113. https://doi.org/10.1016/j.simpa.2021.100113
Giroux, B. (2021). ttcrpy: A Python package for traveltime computation and raytracing. SoftwareX, 16, 100-134. https://doi.org/10.1016/j.softx.2021.100834.
Gunaratne, C., & Garibay, I. (2021). NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces. SoftwareX,16 , 100801. https://doi.org/10.1016/j.softx.2021.100801. https://doi.org/10.1016/j.softx.2021.100801
Hudson Pérez, M. C., Förster Marín, C. E., Rojas-Barahona, C. A., Valenzuela Hasenohr, M. F., Valdés, P. R., & Ferré, A. R. (2013). Comparison of the effectiveness of two strategies teaching methodology in the development of the reading comprehension in the first school year. Perfiles Educativos,35 (140), 100-118. https://doi.org/10.1016/s0185-2698(13)71824-5.
Iweka, S. C., Owuama, K. C., Chukwuneke, J. L., & Falowo, O. A. (2021). Optimization of biogas yield from anaerobic co-digestion of corn-chaff and cow dung digestate: RSM and python approach. Heliyon,7 (11), 255-260. https://doi.org/10.1016/j.heliyon.2021.e08255.
Matsuda, F., Maeda, K., Taniguchi, T., Kondo, Y., Yatabe, F., Okahashi, N., & Shimizu, H. (2021). mfapy: An open-source Python package for 13C-based metabolic flux analysis. Metabolic Engineering Communications, 13(July), 177-188. https://doi.org/10.1016/j.mec.2021.e00177.
Meng, S., Fu, Z., Qin, J., Ma, X., Li, Y., Hao, L., Liu, Y., Sun, K., & Chen, D. (2021). magcoilcalc: A Python package for modeling and optimization of axisymmetric magnet coils generating uniform magnetic field for noble gas spin-polarizers. SoftwareX, 16, 100-105. https://doi.org/10.1016/j.softx.2021.100805.
Pérez-García, F., Sparks, R., & Ourselin, S. (2021). TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Computer Methods and Programs in Biomedicine,208 , 200-230. https://doi.org/10.1016/j.cmpb.2021.106236. https://doi.org/10.1016/j.cmpb.2021.106236.
Schwarz, S., Uerlich, S. A., & Monti, A. (2021). pycity_scheduling-A Python framework for the development and assessment of optimisation-based power scheduling algorithms for multi-energy systems in city districts. SoftwareX, 16, 100-104. https://doi.org/10.1016/j.softx.2021.100839.
Silvestri, L. G., Stanek, L. J., Dharuman, G., Choi, Y., & Murillo, M. S. (2022). Sarkas: A fast pure-python molecular dynamics suite for plasma physics. Computer Physics Communications, 272, 1100-1140. https://doi.org/10.1016/j.cpc.2021.108245.
Takefuji, Y. (2021). SCORECOVID: A Python Package Index for scoring the individual policies against COVID-19. Healthcare Analytics,1 (August), 100-105. https://doi.org/10.1016/j.health.2021.100005
Terven, J. R., & Córdova-Esparza, D. M. (2021). KinZ an Azure Kinect toolkit for Python and Matlab. Science of Computer Programming,211 , 102-109. https://doi.org/10.1016/j.scico.2021.102702. https://doi.org/10.1016/j.scico.2021.102702
Watcharasupat, K. N., Lee, J., & Lerch, A. (2022). Latte: Cross-framework Python package for evaluation of latent-based generative models. Software Impacts, 100-222. https://doi.org/10.1016/j.simpa.2022.100222.
Zolotov, O., Romanovskaya, Y., & Knyazeva, M. (2021). pyFIRI - A free and open source Python software package of the non-auroral Earth's lower ionosphere. SoftwareX, 16, 100-105. https://doi.org/10.1016/j.softx.2021.100885. https://doi.org/10.1016/j.softx.2021.100885
Zurheide, F. T., Hermann, E., & Lampesberger, H. (2021). PyBNBowTie: Python library for Bow-Tie Analysis based on Bayesian Networks. Procedia Computer Science,180 (2019), 344-351. https://doi.org/10.1016/j.procs.2021.01.172.