000			04035nam a22002777i 4500
999			_c11752 _d11752
005			20200301121417.0
008			200205s2018 nyu||||| |||| 001 0 eng d
020			_a978148424113
040			_aEG-NcFUE _erda
050			_aQA76.9.D343
082	0	4	_222 _a006.312 _bP.V.P
100	1		_933673 _aPorcu, Valentina
245			_aPython for data mining quick syntax reference / _cValentina Porcu
264			_aNew York : _bApress, _c[2018]
300			_a260 pages : _billustrations (some color) ; _c20 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
500			_aIncludes index
505	0		_aIntro; Table of Contents; About the Author; About the Technical Reviewer; Introduction; Chapter 1: Getting Started; Installing Python; Editor and IDEs; Differences between Python2 and Python3; Work Directory; Using a Terminal; Summary; Chapter 2: Introductory Notes; Objects in Python; Reserved Terms for the System; Entering Comments in the Code; Types of Data; File Format; Operators; Mathematical Operators; Comparison and Membership Operators; Bitwise Operators; Assignment Operators; Operator Order; Indentation; Quotation Marks; Summary; Chapter 3: Basic Objects and Structures; Numbers Container ObjectsTuples; Lists; Dictionaries; Sets; Strings; Files; Immutability; Converting Formats; Summary; Chapter 4: Functions; Some words about functions in Python; Some Predefined Built-in Functions; Obtain Function Information; Create Your Own Functions; Save and run Your Own Modules and Files; Summary; Chapter 5: Conditional Instructions and Writing Functions; Conditional Instructions; if; if + else; elif; Loops; for; while; continue and break; Extend Functions with Conditional Instructions; map() and filter() Functions; The lambda Function; Scope; Summary Chapter 6: Other Basic ConceptsObject-oriented Programming; More on Objects; Classes; Inheritance; Modules; Methods; List Comprehension; Regular Expressions; User Input; Errors and Exceptions; Summary; Chapter 7: Importing Files; .csv Format; From the Web; In JSON; Other Formats; Summary; Chapter 8: pandas; Libraries for Data Mining; pandas; pandas: Series; pandas: Data Frames; pandas: Importing and Exporting Data; pandas: Data Manipulation; pandas: Missing Values; pandas: Merging Two Datasets; pandas: Basic Statistics; Summary; Chapter 9: SciPy and NumPy; SciPy; NumPy NumPy: Generating Random Numbers and SeedsSummary; Chapter 10: Matplotlib; Basic Plots; Pie Charts; Other Plots and Charts; Saving Plots and Charts; Selecting Plot and Chart Styles; More on Histograms; Summary; Chapter 11: Scikit-learn; What Is Machine Learning?; Import Datasets Included in Scikit-learn; Creation of Training and Testing Datasets; Preprocessing; Regression; K-Nearest Neighbors; Cross-validation; Support Vector Machine; Decision Trees; KMeans; Managing Dates; Data Sources; Index
520			_aLearn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis. Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning
650			_aPython (Computer program language)
650			_aData mining
942			_2ddc _cBK