000			02856nam a22002537a 4500
999			_c889 _d889
005			20210301133004.0
008			210301b ||||| |||| 00| 0 eng d
020			_a9781484242452
082			_a005.133 _bJOH
100			_aJohansson, Robert. _91740
245			_aNumerical Python: scientific computing and data science applications with Numpy, SciPy and Matplotlib
250			_a2nd
260			_bApress Media _aNew York _c2019
300			_axxiii, 700 p.
365			_aEURO _b37.99
504			_aTable of Contents Introduction to computing with Python Vectors, matrices, and multidimensional arrays Symbolic computing Plotting and visualization Equation solving Optimization Interpolation Integration Ordinary differential equations Sparse matrices and graphs Partial differential equations Data processing and analysis Statistics Statistical modeling Machine learning Bayesian statistics Signal processing Data input and output Code optimization Appendix: Installation.
520			_aLeverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. What You'll Learn Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing.
650			_aPython (Computer program language) _92745
650			_aComputer programming _92746
650			_aBig data _9212
650			_aArtificial intelligence _91478
650			_aComputer software _92747
942			_2ddc _cBK