| 000 | a | ||
|---|---|---|---|
| 999 |
_c9678 _d9678 |
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| 005 | 20191102104801.0 | ||
| 008 | 191102b xxu||||| |||| 00| 0 eng d | ||
| 020 | _a9789352136414 | ||
| 040 | _cAIKTC-KRRC | ||
| 041 | _aENG | ||
| 082 |
_2DDC23 _a005.133 _bMCK |
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| 100 |
_aMcKinney, Wes _910246 |
||
| 245 |
_aPython for data analysis _b: Data, wrangling with pandas, numpy, and ipython |
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| 250 | _a2nd. | ||
| 260 |
_bO'reilly _aSebastopol _c2018 |
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| 300 |
_axvi, 522p. _bPaperback _c23.2*17.8 cm |
||
| 520 |
_aGet complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. YouÂll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. ItÂs ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the IPython shell and Jupyter notebook for exploratory computingLearn basic and advanced features in NumPy (Numerical Python)Get started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples _b Table of Contents 1.Preliminaries 2.Python Language Basics, IPython, and Jupyter Notebooks 3.Built-in Data Structures, Functions, and Files 4. NumPy Basics: Arrays and Vectorized Computation 5.Getting Started with pandas 6.Data Loading, Storage, and File Formats 7. Data Cleaning and Preparation 8. Data Wrangling: Join, Combine, and Reshape 9. Plotting and Visualization 10.Data Aggregation and Group Operations 11.Time Series 12. Advanced pandas 13. Introduction to Modeling Libraries in Python 14. Data Analysis Examples |
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| 650 | 0 |
_94619 _aEXTC Engineering |
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| 942 |
_2ddc _cBK |
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