000			01916nam a22002177a 4500
005			20260707143322.0
008			260707s2023 |||||||| |||| 00| 0 eng d
020			_a9781098121228
040			_cPK-LaUMT
082			_a006.312 _bPLA-P
100	1		_aVanderPlas, Jake _98371
245	1	0	_aPython data science handbook : _bessential tools for working with data / _cJake VanderPlas
250			_a2nd ed.
260			_aBeijing : _bO'Reilly, _c2023
300			_axxiv, 563 p.
500			_aIndex present
520			_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
546			_aEng
650			_aPython (Computer program language) _93584
942			_cBK
999			_c141317 _d141317