000 | 01556nam a22002297a 4500 | ||
---|---|---|---|
005 | 20240627143658.0 | ||
008 | 240627b |||||||| |||| 00| 0 eng d | ||
020 | _a978-9355421906 (PB) | ||
041 | _aeng | ||
082 |
_a005.133 _bMCK |
||
100 | _aMcKinney,Wes | ||
245 |
_aPython for Data Analysis _bData Wrangling with Pandas, NumPy, and Jupyter _cWes McKinney |
||
250 | _a3 | ||
260 |
_a Beijing _bO'Reilly _c2022 |
||
300 | _a xvi, 561 p | ||
505 | _aPreliminaries Python language basics IPython, and Jupyter notebooks Built-in data structures, functions, and files NumPy basics: arrays and vectorized computation Getting started with pandas Data loading, storage, and file formats Data cleaing and preparation Data wrangling: join, combine, reshape Plotting and visualization Data aggregation and group operations Time series Introduction to modeling libraries in Python Data analysis examples Advanced NumPy More on the IPython system | ||
520 | _aGet the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third 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, and Jupyter in the process | ||
650 | _a Data mining Exploration de données (Informatique) | ||
650 | _aProgramming languages (Electronic computers) | ||
650 | _aPython (Computer program language) | ||
942 |
_2ddc _cBK |
||
999 |
_c2283 _d2283 |