Python for Data Analysis Data Wrangling with Pandas, NumPy, and IPython (Wes McKinney) (Z-Library)

powered by Wes McKinney Python for Data Analysis DATA WRANGLING WITH PANDAS, NUMPY, AND IPYTHON 2nd Edition

Wes McKinney Python for Data Analysis Data Wrangling with Pandas, NumPy, and IPython SECOND EDITION Boston Farnham Sebastopol TokyoBeijing

Python for Data Analysis by Wes McKinney Copyright © 2018 William McKinney. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/insti‐ tutional sales department: 800-998-9938 or corporate@oreilly.com. Editor: Marie Beaugureau Production Editor: Kristen Brown Copyeditor: Jasmine Kwityn Proofreader: Rachel Monaghan Indexer: Lucie Haskins Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Rebecca Demarest October 2012: First Edition October 2017: Second Edition Revision History for the Second Edition 2017-09-25: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781491957660 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Python for Data Analysis, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 978-1-491-95766-0 [LSI]

Table of Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 1. Preliminaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 What Is This Book About? 1 What Kinds of Data? 1 1.2 Why Python for Data Analysis? 2 Python as Glue 2 Solving the “Two-Language” Problem 3 Why Not Python? 3 1.3 Essential Python Libraries 4 NumPy 4 pandas 4 matplotlib 5 IPython and Jupyter 6 SciPy 6 scikit-learn 7 statsmodels 8 1.4 Installation and Setup 8 Windows 9 Apple (OS X, macOS) 9 GNU/Linux 9 Installing or Updating Python Packages 10 Python 2 and Python 3 11 Integrated Development Environments (IDEs) and Text Editors 11 1.5 Community and Conferences 12 1.6 Navigating This Book 12 Code Examples 13 Data for Examples 13 iii

Import Conventions 14 Jargon 14 2. Python Language Basics, IPython, and Jupyter Notebooks. . . . . . . . . . . . . . . . . . . . . . . . 15 2.1 The Python Interpreter 16 2.2 IPython Basics 17 Running the IPython Shell 17 Running the Jupyter Notebook 18 Tab Completion 21 Introspection 23 The %run Command 25 Executing Code from the Clipboard 26 Terminal Keyboard Shortcuts 27 About Magic Commands 28 Matplotlib Integration 29 2.3 Python Language Basics 30 Language Semantics 30 Scalar Types 38 Control Flow 46 3. Built-in Data Structures, Functions, and Files. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.1 Data Structures and Sequences 51 Tuple 51 List 54 Built-in Sequence Functions 59 dict 61 set 65 List, Set, and Dict Comprehensions 67 3.2 Functions 69 Namespaces, Scope, and Local Functions 70 Returning Multiple Values 71 Functions Are Objects 72 Anonymous (Lambda) Functions 73 Currying: Partial Argument Application 74 Generators 75 Errors and Exception Handling 77 3.3 Files and the Operating System 80 Bytes and Unicode with Files 83 3.4 Conclusion 84 4. NumPy Basics: Arrays and Vectorized Computation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.1 The NumPy ndarray: A Multidimensional Array Object 87 iv | Table of Contents

Creating ndarrays 88 Data Types for ndarrays 90 Arithmetic with NumPy Arrays 93 Basic Indexing and Slicing 94 Boolean Indexing 99 Fancy Indexing 102 Transposing Arrays and Swapping Axes 103 4.2 Universal Functions: Fast Element-Wise Array Functions 105 4.3 Array-Oriented Programming with Arrays 108 Expressing Conditional Logic as Array Operations 109 Mathematical and Statistical Methods 111 Methods for Boolean Arrays 113 Sorting 113 Unique and Other Set Logic 114 4.4 File Input and Output with Arrays 115 4.5 Linear Algebra 116 4.6 Pseudorandom Number Generation 118 4.7 Example: Random Walks 119 Simulating Many Random Walks at Once 121 4.8 Conclusion 122 5. Getting Started with pandas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 5.1 Introduction to pandas Data Structures 124 Series 124 DataFrame 128 Index Objects 134 5.2 Essential Functionality 136 Reindexing 136 Dropping Entries from an Axis 138 Indexing, Selection, and Filtering 140 Integer Indexes 145 Arithmetic and Data Alignment 146 Function Application and Mapping 151 Sorting and Ranking 153 Axis Indexes with Duplicate Labels 157 5.3 Summarizing and Computing Descriptive Statistics 158 Correlation and Covariance 160 Unique Values, Value Counts, and Membership 162 5.4 Conclusion 165 6. Data Loading, Storage, and File Formats. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 6.1 Reading and Writing Data in Text Format 167 Table of Contents | v

Reading Text Files in Pieces 173 Writing Data to Text Format 175 Working with Delimited Formats 176 JSON Data 178 XML and HTML: Web Scraping 180 6.2 Binary Data Formats 183 Using HDF5 Format 184 Reading Microsoft Excel Files 186 6.3 Interacting with Web APIs 187 6.4 Interacting with Databases 188 6.5 Conclusion 190 7. Data Cleaning and Preparation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 7.1 Handling Missing Data 191 Filtering Out Missing Data 193 Filling In Missing Data 195 7.2 Data Transformation 197 Removing Duplicates 197 Transforming Data Using a Function or Mapping 198 Replacing Values 200 Renaming Axis Indexes 201 Discretization and Binning 203 Detecting and Filtering Outliers 205 Permutation and Random Sampling 206 Computing Indicator/Dummy Variables 208 7.3 String Manipulation 211 String Object Methods 211 Regular Expressions 213 Vectorized String Functions in pandas 216 7.4 Conclusion 219 8. Data Wrangling: Join, Combine, and Reshape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 8.1 Hierarchical Indexing 221 Reordering and Sorting Levels 224 Summary Statistics by Level 225 Indexing with a DataFrame’s columns 225 8.2 Combining and Merging Datasets 227 Database-Style DataFrame Joins 227 Merging on Index 232 Concatenating Along an Axis 236 Combining Data with Overlap 241 8.3 Reshaping and Pivoting 242 vi | Table of Contents

Reshaping with Hierarchical Indexing 243 Pivoting “Long” to “Wide” Format 246 Pivoting “Wide” to “Long” Format 249 8.4 Conclusion 251 9. Plotting and Visualization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 9.1 A Brief matplotlib API Primer 253 Figures and Subplots 255 Colors, Markers, and Line Styles 259 Ticks, Labels, and Legends 261 Annotations and Drawing on a Subplot 265 Saving Plots to File 267 matplotlib Configuration 268 9.2 Plotting with pandas and seaborn 268 Line Plots 269 Bar Plots 272 Histograms and Density Plots 277 Scatter or Point Plots 280 Facet Grids and Categorical Data 283 9.3 Other Python Visualization Tools 285 9.4 Conclusion 286 10. Data Aggregation and Group Operations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 10.1 GroupBy Mechanics 288 Iterating Over Groups 291 Selecting a Column or Subset of Columns 293 Grouping with Dicts and Series 294 Grouping with Functions 295 Grouping by Index Levels 295 10.2 Data Aggregation 296 Column-Wise and Multiple Function Application 298 Returning Aggregated Data Without Row Indexes 301 10.3 Apply: General split-apply-combine 302 Suppressing the Group Keys 304 Quantile and Bucket Analysis 305 Example: Filling Missing Values with Group-Specific Values 306 Example: Random Sampling and Permutation 308 Example: Group Weighted Average and Correlation 310 Example: Group-Wise Linear Regression 312 10.4 Pivot Tables and Cross-Tabulation 313 Cross-Tabulations: Crosstab 315 10.5 Conclusion 316 Table of Contents | vii

11. Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 11.1 Date and Time Data Types and Tools 318 Converting Between String and Datetime 319 11.2 Time Series Basics 322 Indexing, Selection, Subsetting 323 Time Series with Duplicate Indices 326 11.3 Date Ranges, Frequencies, and Shifting 327 Generating Date Ranges 328 Frequencies and Date Offsets 330 Shifting (Leading and Lagging) Data 332 11.4 Time Zone Handling 335 Time Zone Localization and Conversion 335 Operations with Time Zone−Aware Timestamp Objects 338 Operations Between Different Time Zones 339 11.5 Periods and Period Arithmetic 339 Period Frequency Conversion 340 Quarterly Period Frequencies 342 Converting Timestamps to Periods (and Back) 344 Creating a PeriodIndex from Arrays 345 11.6 Resampling and Frequency Conversion 348 Downsampling 349 Upsampling and Interpolation 352 Resampling with Periods 353 11.7 Moving Window Functions 354 Exponentially Weighted Functions 358 Binary Moving Window Functions 359 User-Defined Moving Window Functions 361 11.8 Conclusion 362 12. Advanced pandas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 12.1 Categorical Data 363 Background and Motivation 363 Categorical Type in pandas 365 Computations with Categoricals 367 Categorical Methods 370 12.2 Advanced GroupBy Use 373 Group Transforms and “Unwrapped” GroupBys 373 Grouped Time Resampling 377 12.3 Techniques for Method Chaining 378 The pipe Method 380 12.4 Conclusion 381 viii | Table of Contents

13. Introduction to Modeling Libraries in Python. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 13.1 Interfacing Between pandas and Model Code 383 13.2 Creating Model Descriptions with Patsy 386 Data Transformations in Patsy Formulas 389 Categorical Data and Patsy 390 13.3 Introduction to statsmodels 393 Estimating Linear Models 393 Estimating Time Series Processes 396 13.4 Introduction to scikit-learn 397 13.5 Continuing Your Education 401 14. Data Analysis Examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 14.1 1.USA.gov Data from Bitly 403 Counting Time Zones in Pure Python 404 Counting Time Zones with pandas 406 14.2 MovieLens 1M Dataset 413 Measuring Rating Disagreement 418 14.3 US Baby Names 1880–2010 419 Analyzing Naming Trends 425 14.4 USDA Food Database 434 14.5 2012 Federal Election Commission Database 440 Donation Statistics by Occupation and Employer 442 Bucketing Donation Amounts 445 Donation Statistics by State 447 14.6 Conclusion 448 A. Advanced NumPy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 A.1 ndarray Object Internals 449 NumPy dtype Hierarchy 450 A.2 Advanced Array Manipulation 451 Reshaping Arrays 452 C Versus Fortran Order 454 Concatenating and Splitting Arrays 454 Repeating Elements: tile and repeat 457 Fancy Indexing Equivalents: take and put 459 A.3 Broadcasting 460 Broadcasting Over Other Axes 462 Setting Array Values by Broadcasting 465 A.4 Advanced ufunc Usage 466 ufunc Instance Methods 466 Writing New ufuncs in Python 468 A.5 Structured and Record Arrays 469 Table of Contents | ix

Nested dtypes and Multidimensional Fields 469 Why Use Structured Arrays? 470 A.6 More About Sorting 471 Indirect Sorts: argsort and lexsort 472 Alternative Sort Algorithms 474 Partially Sorting Arrays 474 numpy.searchsorted: Finding Elements in a Sorted Array 475 A.7 Writing Fast NumPy Functions with Numba 476 Creating Custom numpy.ufunc Objects with Numba 478 A.8 Advanced Array Input and Output 478 Memory-Mapped Files 478 HDF5 and Other Array Storage Options 480 A.9 Performance Tips 480 The Importance of Contiguous Memory 480 B. More on the IPython System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 B.1 Using the Command History 483 Searching and Reusing the Command History 483 Input and Output Variables 484 B.2 Interacting with the Operating System 485 Shell Commands and Aliases 486 Directory Bookmark System 487 B.3 Software Development Tools 487 Interactive Debugger 488 Timing Code: %time and %timeit 492 Basic Profiling: %prun and %run -p 494 Profiling a Function Line by Line 496 B.4 Tips for Productive Code Development Using IPython 498 Reloading Module Dependencies 498 Code Design Tips 499 B.5 Advanced IPython Features 500 Making Your Own Classes IPython-Friendly 500 Profiles and Configuration 501 B.6 Conclusion 503 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 x | Table of Contents

Preface New for the Second Edition The first edition of this book was published in 2012, during a time when open source data analysis libraries for Python (such as pandas) were very new and developing rap‐ idly. In this updated and expanded second edition, I have overhauled the chapters to account both for incompatible changes and deprecations as well as new features that have occurred in the last five years. I’ve also added fresh content to introduce tools that either did not exist in 2012 or had not matured enough to make the first cut. Finally, I have tried to avoid writing about new or cutting-edge open source projects that may not have had a chance to mature. I would like readers of this edition to find that the content is still almost as relevant in 2020 or 2021 as it is in 2017. The major updates in this second edition include: • All code, including the Python tutorial, updated for Python 3.6 (the first edition used Python 2.7) • Updated Python installation instructions for the Anaconda Python Distribution and other needed Python packages • Updates for the latest versions of the pandas library in 2017 • A new chapter on some more advanced pandas tools, and some other usage tips • A brief introduction to using statsmodels and scikit-learn I also reorganized a significant portion of the content from the first edition to make the book more accessible to newcomers. xi

Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program ele‐ ments such as variable or function names, databases, data types, environment variables, statements, and keywords. Constant width bold Shows commands or other text that should be typed literally by the user. Constant width italic Shows text that should be replaced with user-supplied values or by values deter‐ mined by context. This element signifies a tip or suggestion. This element signifies a general note. This element indicates a warning or caution. Using Code Examples You can find data files and related material for each chapter is available in this book’s GitHub repository at http://github.com/wesm/pydata-book. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this xii | Preface

book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a signifi‐ cant amount of example code from this book into your product’s documentation does require permission. We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Python for Data Analysis by Wes McKinney (O’Reilly). Copyright 2017 Wes McKinney, 978-1-491-95766-0.” If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at permissions@oreilly.com. O’Reilly Safari Safari (formerly Safari Books Online) is a membership-based training and reference platform for enterprise, government, educators, and individuals. Members have access to thousands of books, training videos, Learning Paths, interac‐ tive tutorials, and curated playlists from over 250 publishers, including O’Reilly Media, Harvard Business Review, Prentice Hall Professional, Addison-Wesley Profes‐ sional, Microsoft Press, Sams, Que, Peachpit Press, Adobe, Focal Press, Cisco Press, John Wiley & Sons, Syngress, Morgan Kaufmann, IBM Redbooks, Packt, Adobe Press, FT Press, Apress, Manning, New Riders, McGraw-Hill, Jones & Bartlett, and Course Technology, among others. For more information, please visit http://oreilly.com/safari. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North Sebastopol, CA 95472 800-998-9938 (in the United States or Canada) 707-829-0515 (international or local) 707-829-0104 (fax) We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at http://bit.ly/python_data_analysis_2e. Preface | xiii

To comment or ask technical questions about this book, send email to bookques‐ tions@oreilly.com. For more information about our books, courses, conferences, and news, see our web‐ site at http://www.oreilly.com. Find us on Facebook: http://facebook.com/oreilly Follow us on Twitter: http://twitter.com/oreillymedia Watch us on YouTube: http://www.youtube.com/oreillymedia Acknowledgments This work is the product of many years of fruitful discussions, collaborations, and assistance with and from many people around the world. I’d like to thank a few of them. In Memoriam: John D. Hunter (1968–2012) Our dear friend and colleague John D. Hunter passed away after a battle with colon cancer on August 28, 2012. This was only a short time after I’d completed the final manuscript for this book’s first edition. John’s impact and legacy in the Python scientific and data communities would be hard to overstate. In addition to developing matplotlib in the early 2000s (a time when Python was not nearly so popular), he helped shape the culture of a critical gen‐ eration of open source developers who’ve become pillars of the Python ecosystem that we now often take for granted. I was lucky enough to connect with John early in my open source career in January 2010, just after releasing pandas 0.1. His inspiration and mentorship helped me push forward, even in the darkest of times, with my vision for pandas and Python as a first-class data analysis language. John was very close with Fernando Pérez and Brian Granger, pioneers of IPython, Jupyter, and many other initiatives in the Python community. We had hoped to work on a book together, the four of us, but I ended up being the one with the most free time. I am sure he would be proud of what we’ve accomplished, as individuals and as a community, over the last five years. Acknowledgments for the Second Edition (2017) It has been five years almost to the day since I completed the manuscript for this book’s first edition in July 2012. A lot has changed. The Python community has grown immensely, and the ecosystem of open source software around it has flourished. xiv | Preface

This new edition of the book would not exist if not for the tireless efforts of the pan‐ das core developers, who have grown the project and its user community into one of the cornerstones of the Python data science ecosystem. These include, but are not limited to, Tom Augspurger, Joris van den Bossche, Chris Bartak, Phillip Cloud, gfyoung, Andy Hayden, Masaaki Horikoshi, Stephan Hoyer, Adam Klein, Wouter Overmeire, Jeff Reback, Chang She, Skipper Seabold, Jeff Tratner, and y-p. On the actual writing of this second edition, I would like to thank the O’Reilly staff who helped me patiently with the writing process. This includes Marie Beaugureau, Ben Lorica, and Colleen Toporek. I again had outstanding technical reviewers with Tom Augpurger, Paul Barry, Hugh Brown, Jonathan Coe, and Andreas Müller contri‐ buting. Thank you. This book’s first edition has been translated into many foreign languages, including Chinese, French, German, Japanese, Korean, and Russian. Translating all this content and making it available to a broader audience is a huge and often thankless effort. Thank you for helping more people in the world learn how to program and use data analysis tools. I am also lucky to have had support for my continued open source development efforts from Cloudera and Two Sigma Investments over the last few years. With open source software projects more thinly resourced than ever relative to the size of user bases, it is becoming increasingly important for businesses to provide support for development of key open source projects. It’s the right thing to do. Acknowledgments for the First Edition (2012) It would have been difficult for me to write this book without the support of a large number of people. On the O’Reilly staff, I’m very grateful for my editors, Meghan Blanchette and Julie Steele, who guided me through the process. Mike Loukides also worked with me in the proposal stages and helped make the book a reality. I received a wealth of technical review from a large cast of characters. In particular, Martin Blais and Hugh Brown were incredibly helpful in improving the book’s exam‐ ples, clarity, and organization from cover to cover. James Long, Drew Conway, Fer‐ nando Pérez, Brian Granger, Thomas Kluyver, Adam Klein, Josh Klein, Chang She, and Stéfan van der Walt each reviewed one or more chapters, providing pointed feed‐ back from many different perspectives. I got many great ideas for examples and datasets from friends and colleagues in the data community, among them: Mike Dewar, Jeff Hammerbacher, James Johndrow, Kristian Lum, Adam Klein, Hilary Mason, Chang She, and Ashley Williams. Preface | xv

I am of course indebted to the many leaders in the open source scientific Python community who’ve built the foundation for my development work and gave encour‐ agement while I was writing this book: the IPython core team (Fernando Pérez, Brian Granger, Min Ragan-Kelly, Thomas Kluyver, and others), John Hunter, Skipper Sea‐ bold, Travis Oliphant, Peter Wang, Eric Jones, Robert Kern, Josef Perktold, Francesc Alted, Chris Fonnesbeck, and too many others to mention. Several other people pro‐ vided a great deal of support, ideas, and encouragement along the way: Drew Con‐ way, Sean Taylor, Giuseppe Paleologo, Jared Lander, David Epstein, John Krowas, Joshua Bloom, Den Pilsworth, John Myles-White, and many others I’ve forgotten. I’d also like to thank a number of people from my formative years. First, my former AQR colleagues who’ve cheered me on in my pandas work over the years: Alex Reyf‐ man, Michael Wong, Tim Sargen, Oktay Kurbanov, Matthew Tschantz, Roni Israelov, Michael Katz, Chris Uga, Prasad Ramanan, Ted Square, and Hoon Kim. Lastly, my academic advisors Haynes Miller (MIT) and Mike West (Duke). I received significant help from Phillip Cloud and Joris Van den Bossche in 2014 to update the book’s code examples and fix some other inaccuracies due to changes in pandas. On the personal side, Casey provided invaluable day-to-day support during the writ‐ ing process, tolerating my highs and lows as I hacked together the final draft on top of an already overcommitted schedule. Lastly, my parents, Bill and Kim, taught me to always follow my dreams and to never settle for less. xvi | Preface

CHAPTER 1 Preliminaries 1.1 What Is This Book About? This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. My goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. While “data analysis” is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis. What Kinds of Data? When I say “data,” what am I referring to exactly? The primary focus is on structured data, a deliberately vague term that encompasses many different common forms of data, such as: • Tabular or spreadsheet-like data in which each column may be a different type (string, numeric, date, or otherwise). This includes most kinds of data commonly stored in relational databases or tab- or comma-delimited text files. • Multidimensional arrays (matrices). • Multiple tables of data interrelated by key columns (what would be primary or foreign keys for a SQL user). • Evenly or unevenly spaced time series. This is by no means a complete list. Even though it may not always be obvious, a large percentage of datasets can be transformed into a structured form that is more suitable for analysis and modeling. If not, it may be possible to extract features from a dataset 1

into a structured form. As an example, a collection of news articles could be pro‐ cessed into a word frequency table, which could then be used to perform sentiment analysis. Most users of spreadsheet programs like Microsoft Excel, perhaps the most widely used data analysis tool in the world, will not be strangers to these kinds of data. 1.2 Why Python for Data Analysis? For many people, the Python programming language has strong appeal. Since its first appearance in 1991, Python has become one of the most popular interpreted pro‐ gramming languages, along with Perl, Ruby, and others. Python and Ruby have become especially popular since 2005 or so for building websites using their numer‐ ous web frameworks, like Rails (Ruby) and Django (Python). Such languages are often called scripting languages, as they can be used to quickly write small programs, or scripts to automate other tasks. I don’t like the term “scripting language,” as it car‐ ries a connotation that they cannot be used for building serious software. Among interpreted languages, for various historical and cultural reasons, Python has devel‐ oped a large and active scientific computing and data analysis community. In the last 10 years, Python has gone from a bleeding-edge or “at your own risk” scientific com‐ puting language to one of the most important languages for data science, machine learning, and general software development in academia and industry. For data analysis and interactive computing and data visualization, Python will inevi‐ tably draw comparisons with other open source and commercial programming lan‐ guages and tools in wide use, such as R, MATLAB, SAS, Stata, and others. In recent years, Python’s improved support for libraries (such as pandas and scikit-learn) has made it a popular choice for data analysis tasks. Combined with Python’s overall strength for general-purpose software engineering, it is an excellent option as a pri‐ mary language for building data applications. Python as Glue Part of Python’s success in scientific computing is the ease of integrating C, C++, and FORTRAN code. Most modern computing environments share a similar set of legacy FORTRAN and C libraries for doing linear algebra, optimization, integration, fast Fourier transforms, and other such algorithms. The same story has held true for many companies and national labs that have used Python to glue together decades’ worth of legacy software. Many programs consist of small portions of code where most of the time is spent, with large amounts of “glue code” that doesn’t run often. In many cases, the execution time of the glue code is insignificant; effort is most fruitfully invested in optimizing 2 | Chapter 1: Preliminaries