01854nam a22001937a 450000500170000000800410001702000180005804000130007608200190008910000210010824500930012925000120022226000310023430000170026550000180028252013130030054600080161365000390162120260707143322.0260707s2023    |||||||| |||| 00| 0 eng d  a9781098121228  cPK-LaUMT  a006.312bPLA-P1 aVanderPlas, Jake10aPython data science handbook :bessential tools for working with data /cJake VanderPlas  a2nd ed.  aBeijing :bO'Reilly,c2023  axxiv, 563 p.  aIndex present  aPython is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all;Python, NumPy, pandas, Matplotlib, scikit-learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you'll learn how: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms  aEng  aPython (Computer program language)