The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python   Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.   Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems.   Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.   Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem.  Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning

Table of Contents:
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