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240503 ||| eng |
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|a 1394213263
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|a 9781394213269
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|a 9781394213276
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|a 1394213255
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|a 1394213271
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|a 9781394213252
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| 050 |
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4 |
|a QA76.9.D343
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| 100 |
1 |
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|a Cremonini, Marco
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| 245 |
0 |
0 |
|a Data science fundamentals with R, Python, and open data
|c Marco Cremonini
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| 260 |
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|a Hoboken, New Jersey
|b Wiley
|c 2024
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| 300 |
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|a 1 online resource
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| 505 |
0 |
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|a Preface xiii -- About the Companion Website xvii -- Introduction xix -- 1 Open-Source Tools for Data Science 1 -- 1.1 R Language and RStudio 1 -- 1.2 Python Language and Tools 5 -- 1.3 Advanced Plain Text Editor 8 -- 1.4 CSV Format for Datasets 8 -- 2 Simple Exploratory Data Analysis 13 -- 2.1 Missing Values Analysis 13 -- 2.2 R: Descriptive Statistics and Utility Functions 15 -- 2.3 Python: Descriptive Statistics and Utility Functions 17 -- 3 Data Organization and First Data Frame Operations 23 -- 3.1 R: Read CSV Datasets and Column Selection 24 -- 3.2 R: Rename and Relocate Columns 36 -- 3.3 R: Slicing, Column Creation, and Deletion 38 -- 3.4 R: Separate and Unite Columns 45 -- 3.5 R: Sorting Data Frames 49 -- 3.6 R: Pipe 55 -- 3.7 Python: Column Selection 59 -- 3.8 Python: Rename and Relocate Columns 67 -- 3.9 Python: NumPy Slicing, Selection with Index, Column Creation and Deletion 69 -- 3.10 Python: Separate and Unite Columns 81 -- 3.11 Python: Sorting Data Frame 85 --
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| 505 |
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|a 9.3 Python: User-defined and Lambda Functions 330 -- 10 Join Data Frames 347 -- 10.1 Basic Concepts 348 -- 10.2 Python: Join Operations 369 -- 11 List/Dictionary Data Format 393 -- 11.1 R: List Data Format 395 -- 11.2 R: JSON Data Format and Use Cases 410 -- 11.3 Python: Dictionary Data Format 422 -- Questions 443 -- Index 447
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| 505 |
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|a 4 Subsetting with Logical Conditions 99 -- 4.1 Logical Operators 99 -- 4.2 R: Row Selection 101 -- 5 Operations on Dates, Strings, and Missing Values 127 -- 5.1 R: Operations on Dates and Strings 129 -- 5.2 R: Handling Missing Values and Data Type Transformations 141 -- 5.3 R: Example with Dates, Strings, and Missing Values 154 -- 5.4 Pyhton: Operations on Dates and Strings 165 -- 5.5 Python: Handling Missing Values and Data Type Transformations 173 -- 5.6 Python: Examples with Dates, Strings, and Missing Values 182 -- 6 Pivoting and Wide-long Transformations 195 -- 6.1 R: Pivoting 197 -- 6.2 Python: Pivoting 202 -- 7 Groups and Operations on Groups 221 -- 7.1 R: Groups 222 -- 7.2 Python: Groups 244 -- 8 Conditions and Iterations 271 -- 8.1 R: Conditions and Iterations 272 -- 8.2 Python: Conditions and Iterations 284 -- 9 Functions and Multicolumn Operations 307 -- 9.1 R: User-defined Functions 308 -- 9.2 R: Multicolumn Operations 316 --
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| 653 |
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|a R (Langage de programmation)
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Data mining / http://id.loc.gov/authorities/subjects/sh97002073
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| 653 |
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|a R (Computer program language) / http://id.loc.gov/authorities/subjects/sh2002004407
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
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|a Exploration de données (Informatique)
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Includes index
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| 024 |
8 |
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|a 10.1002/9781394213276
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| 776 |
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|z 1394213271
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| 776 |
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|z 1394213247
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| 776 |
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|z 9781394213245
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| 776 |
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|z 9781394213276
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781394213245/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3/12
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| 520 |
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|a "Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data."--
|