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|---|---|---|---|
| 001 | 23792222 | ||
| 003 | OSt | ||
| 005 | 20250113105358.0 | ||
| 008 | 240720t20222022maua 001 0 eng d | ||
| 010 | _a 2023549174 | ||
| 015 |
_aGBC2D5219 _2bnb |
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| 016 | 7 |
_a020700153 _2Uk |
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| 020 |
_a9781098104030 _q(paperback) |
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| 020 |
_a109810403X _q(paperback) |
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| 035 | _a(OCoLC)on1338676391 | ||
| 040 |
_aUKMGB _beng _erda _cUKMGB _dOCLCF _dIMD _dJOZ _dSISPL _dQGE _dUKMGB _dLML _dVNVGU _dOCL _dTRC _dOCLCO _dDLC |
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| 042 | _alccopycat | ||
| 050 | 0 | 0 |
_aQA76.73.P98 _bM42 2022 |
| 082 | 0 | 4 |
_a006.312 _223 |
| 100 | 1 |
_aMcKinney, Wes, _eauthor. _91059 |
|
| 245 | 1 | 0 |
_aPython for data analysis : _bdata wrangling with pandas, NumPy, and Jupyter / _cWes McKinney. |
| 250 | _aThird edition. | ||
| 264 | 1 |
_aBeijing : _bO'Reilly, _c[2022] |
|
| 264 | 4 | _c©2022 | |
| 300 |
_axvi, 561 pages : _billustrations ; _c24 cm |
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| 336 |
_atext _btxt _2rdacontent |
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| 336 |
_astill image _bsti _2rdacontent |
||
| 337 |
_aunmediated _bn _2rdamedia |
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| 338 |
_avolume _bnc _2rdacarrier |
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| 500 | _aPrevious edition: 2017. | ||
| 500 | _aIncludes index. | ||
| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _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 |
_a"Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, 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. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing." -- _cProvided by publisher |
||
| 650 | 0 |
_aPython (Computer program language) _91060 |
|
| 650 | 0 |
_aProgramming languages (Electronic computers) _91061 |
|
| 650 | 0 |
_aData mining. _91062 |
|
| 650 | 6 |
_aExploration de données (Informatique) _91063 |
|
| 650 | 6 |
_aPython (Langage de programmation) _91064 |
|
| 650 | 7 |
_aProgramming languages (Electronic computers) _2fast _91065 |
|
| 650 | 7 |
_aData mining _2fast _91066 |
|
| 650 | 7 |
_aPython (Computer program language) _2fast _91067 |
|
| 906 |
_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg |
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| 942 |
_2lcc _cBK _e3rd. |
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| 999 |
_c8465 _d8465 |
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