MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04035nam a22002777i 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200301121417.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
200205s2018 nyu||||| |||| 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
978148424113 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
EG-NcFUE |
| Description conventions |
rda |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.9.D343 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Edition number |
22 |
| Classification number |
006.312 |
| Item number |
P.V.P |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
33673 |
| Personal name |
Porcu, Valentina |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Python for data mining quick syntax reference / |
| Statement of responsibility, etc |
Valentina Porcu |
| 264 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
New York : |
| Name of publisher, distributor, etc |
Apress, |
| Date of publication, distribution, etc |
[2018] |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
260 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
20 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Intro; 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<br/>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<br/>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<br/>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 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Learn how to use Python and its structures, how to install<br/> Python, and which tools are best suited for data analyst <br/> work. This book provides you with a handy reference and <br/> tutorial on topics ranging from basic Python concepts <br/> through to data mining, manipulating and importing <br/> datasets, and data analysis. Python for Data Mining Quick <br/> Syntax Reference covers each concept concisely, with many <br/> illustrative examples. You'll be introduced to several <br/> data mining packages, with examples of how to use each of <br/> them. The first part covers core Python including objects,<br/> lists, functions, modules, and error handling. The second <br/> part covers Python's most important data mining packages: <br/> NumPy and SciPy for mathematical functions and random data<br/> generation, pandas for dataframe management and data <br/> import, Matplotlib for drawing charts, and scikitlearn for<br/> machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |