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Daniel Chen, Daniel Y. Chen

Pandas for Everyone

Name: Pandas for Everyone
Author: Daniel Chen

Python Data Analysis

Paperback Engels 2018 9780134546933

Verwachte levertijd ongeveer 9 werkdagen

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This tutorial teaches students everything they need to get started with Python programming for the fast-growing field of data analysis. Daniel Chen tightly links each new concept with easy-to-apply, relevant examples from modern data analysis.

Unlike other beginner's books, this guide helps today's newcomers learn both Python and its popular Pandas data science toolset in the context of tasks they'll really want to perform. Following the proven Software Carpentry approach to teaching programming, Chen introduces each concept with a simple motivating example, slowly offering deeper insights and expanding your ability to handle concrete tasks.

Specificaties

ISBN13:9780134546933

Taal:Engels

Bindwijze:Paperback

Uitgever:Pearson Education

Serie:Addison-Wesley Data & Analytics Series

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Inhoudsopgave

Foreword xix Preface xxi Acknowledgments xxvii About the Author xxxi   Part I: Introduction 1   Chapter 1: Pandas DataFrame Basics 3 1.1 Introduction 3 1.2 Loading Your First Data Set 4 1.3 Looking at Columns, Rows, and Cells 7 1.4 Grouped and Aggregated Calculations 18 1.5 Basic Plot 23 1.6 Conclusion 24   Chapter 2: Pandas Data Structures 25 2.1 Introduction 25 2.2 Creating Your Own Data 26 2.3 The Series 28 2.4 The DataFrame 36 2.5 Making Changes to Series and DataFrames 38 2.6 Exporting and Importing Data 43 2.7 Conclusion 47   Chapter 3: Introduction to Plotting 49 3.1 Introduction 49 3.2 Matplotlib 51 3.3 Statistical Graphics Using matplotlib 56 3.4 Seaborn 61 3.5 Pandas Objects 83 3.6 Seaborn Themes and Styles 86 3.7 Conclusion 90   Part II: Data Manipulation 91   Chapter 4: Data Assembly 93 4.1 Introduction 93 4.2 Tidy Data 93 4.3 Concatenation 94 4.4 Merging Multiple Data Sets 102 4.5 Conclusion 107   Chapter 5: Missing Data 109 5.1 Introduction 109 5.2 What Is a NaN Value? 109 5.3 Where Do Missing Values Come From? 111 5.4 Working with Missing Data 116 5.5 Conclusion 121   Chapter 6: Tidy Data 123 6.1 Introduction 123 6.2 Columns Contain Values, Not Variables 124 6.3 Columns Contain Multiple Variables 128 6.4 Variables in Both Rows and Columns 133 6.5 Multiple Observational Units in a Table (Normalization) 134 6.6 Observational Units Across Multiple Tables 137 6.7 Conclusion 141   Part III: Data Munging 143   Chapter 7: Data Types 145 7.1 Introduction 145 7.2 Data Types 145 7.3 Converting Types 146 7.4 Categorical Data 152 7.5 Conclusion 153   Chapter 8: Strings and Text Data 155 8.1 Introduction 155 8.2 Strings 155 8.3 String Methods 158 8.4 More String Methods 160 8.5 String Formatting 161 8.6 Regular Expressions (RegEx) 164 8.7 The regex Library 170 8.8 Conclusion 170   Chapter 9: Apply 171 9.1 Introduction 171 9.2 Functions 171 9.3 Apply (Basics) 172 9.4 Apply (More Advanced) 177 9.5 Vectorized Functions 182 9.6 Lambda Functions 185 9.7 Conclusion 187   Chapter 10: Groupby Operations: Split–Apply–Combine 189 10.1 Introduction 189 10.2 Aggregate 190 10.3 Transform 197 10.4 Filter 201 10.5 The pandas.core.groupby.DataFrameGroupBy Object 202 10.6 Working with a MultiIndex 207 10.7 Conclusion 211   Chapter 11: The datetime Data Type 213 11.1 Introduction 213 11.2 Python’s datetime Object 213 11.3 Converting to datetime 214 11.4 Loading Data That Include Dates 217 11.5 Extracting Date Components 217 11.6 Date Calculations and Timedeltas 220 11.7 Datetime Methods 221 11.8 Getting Stock Data 224 11.9 Subsetting Data Based on Dates 225 11.10 Date Ranges 227 11.11 Shifting Values 230 11.12 Resampling 237 11.13 Time Zones 238 11.14 Conclusion 240   Part IV: Data Modeling 241   Chapter 12: Linear Models 243 12.1 Introduction 243 12.2 Simple Linear Regression 243 12.3 Multiple Regression 247 12.4 Keeping Index Labels From sklearn 251 12.5 Conclusion 252   Chapter 13: Generalized Linear Models 253 13.1 Introduction 253 13.2 Logistic Regression 253 13.3 Poisson Regression 257 13.4 More Generalized Linear Models 260 13.5 Survival Analysis 260 13.6 Conclusion 264   Chapter 14: Model Diagnostics 265 14.1 Introduction 265 14.2 Residuals 265 14.3 Comparing Multiple Models 270 14.4 k-Fold Cross-Validation 275 14.5 Conclusion 278   Chapter 15: Regularization 279 15.1 Introduction 279 15.2 Why Regularize? 279 15.3 LASSO Regression 281 15.4 Ridge Regression 283 15.5 Elastic Net 285 15.6 Cross-Validation 287 15.7 Conclusion 289   Chapter 16: Clustering 291 16.1 Introduction 291 16.2 k-Means 291 16.3 Hierarchical Clustering 297 16.4 Conclusion 301   Part V: Conclusion 303   Chapter 17: Life Outside of Pandas 305 17.1 The (Scientific) Computing Stack 305 17.2 Performance 306 17.3 Going Bigger and Faster 307   Chapter 18: Toward a Self-Directed Learner 309 18.1 It’s Dangerous to Go Alone! 309 18.2 Local Meetups 309 18.3 Conferences 309 18.4 The Internet 310 18.5 Podcasts 310 18.6 Conclusion 311   Part VI: Appendixes 313   Appendix A: Installation 315 A.1 Installing Anaconda 315 A.2 Uninstall Anaconda 316   Appendix B: Command Line 317 B.1 Installation 317 B.2 Basics 318   Appendix C: Project Templates 319   Appendix D: Using Python 321 D.1 Command Line and Text Editor 321 D.2 Python and IPython 322 D.3 Jupyter 322 D.4 Integrated Development Environments (IDEs) 322   Appendix E: Working Directories 325   Appendix F: Environments 327   Appendix G: Install Packages 329 G.1 Updating Packages 330   Appendix H: Importing Libraries 331   Appendix I: Lists 333   Appendix J: Tuples 335   Appendix K: Dictionaries 337   Appendix L: Slicing Values 339   Appendix M: Loops 341   Appendix N: Comprehensions 343   Appendix O: Functions 345 O.1 Default Parameters 347 O.2 Arbitrary Parameters 347   Appendix P: Ranges and Generators 349   Appendix Q: Multiple Assignment 351   Appendix R: numpy ndarray 353   Appendix S: Classes 355   Appendix T: Odo: The Shapeshifter 357   Index 359

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