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| 001 | 1005140249 | ||
| 005 | 20190411094216.0 | ||
| 007 | cr cnu|||unuuu | ||
| 008 | 171003s2018 cau o 001 0 eng d | ||
| 020 | _a9789352136414 | ||
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| 037 |
_a7B9FDAC1-3C9F-458A-9A81-766B38EAFEC3 _bOverDrive, Inc. _nhttp://www.overdrive.com |
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| 049 | _aBNYC | ||
| 050 | 4 | _aQA76.73.P98 | |
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_a005.262P _223 _bMCK |
| 100 | 1 |
_aMcKinney, Wes, _eauthor. |
|
| 245 | 1 | 0 |
_aPython for data analysis : _bdata wrangling with Pandas, NumPy, and IPython / _cWes McKinney. |
| 250 | _a2nd ed. | ||
| 260 |
_aMumbai : _bShroff Publishers & Distributors, _c2018. |
||
| 300 | _axvi, 522 p. | ||
| 500 | _aIncludes index. | ||
| 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. You'll 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. It's 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. | ||
| 650 | 0 | _aPython (Computer program language) | |
| 650 | 0 | _aProgramming languages (Electronic computers) | |
| 650 | 0 | _aData mining. | |
| 942 |
_2ddc _cBK |
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