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Pandas for Everyone: Python Data Analysis, 2nd Edition

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Pandas for Everyone: Python Data Analysis, 2nd Edition

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  • Copyright 2023
  • Dimensions: 7" x 9-1/8"
  • Pages: 512
  • Edition: 2nd
  • Book
  • ISBN-10: 0-13-789115-6
  • ISBN-13: 978-0-13-789115-3

Manage and Automate Data Analysis with Pandas 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 data sets.

Pandas for Everyone, 2nd Edition, 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 data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set.

New features to the second edition include: 

  • Extended coverage of plotting and the seaborn data visualization library
  • Expanded examples and resources
  • Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries
  • Online bonus material on geopandas, Dask, and creating interactive graphics with Altair

Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets 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 data sets and handle missing data
  • Reshape, tidy, and clean data sets 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 data sets 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” one
  • Regularize to overcome overfitting and improve performance
  • Use clustering in unsupervised machine learning

Sample Content

Online Sample Chapter

Tidy Data

Sample Pages

Download the sample pages (includes Chapter 4)

Table of Contents

Foreword by Anne M. Brown     xxiii

Foreword by Jared Lander     xxv

Preface     xxvii

Changes in the Second Edition     xxxix

Part I: Introduction    1

Chapter 1. Pandas DataFrame Basics     3

       Learning Objectives      3

       1.1 Introduction      3

       1.2 Load Your First Data Set      4

       1.3 Look at Columns, Rows, and Cells      6

       1.4 Grouped and Aggregated Calculations      23

       1.5 Basic Plot      27

       Conclusion      28

Chapter 2. Pandas Data Structures Basics      31

       Learning Objectives      31

       2.1 Create Your Own Data      31

       2.2 The Series      33

       2.3 The DataFrame      42

       2.4 Making Changes to Series and DataFrames      45

       2.5 Exporting and Importing Data      52

       Conclusion      63

Chapter 3. Plotting Basics      65

       Learning Objectives      65

       3.1 Why Visualize Data?       65

       3.2 Matplotlib Basics      66

       3.3 Statistical Graphics Using matplotlib      72

       3.4 Seaborn      78

       3.5 Pandas Plotting Method      111

       Conclusion      115

Chapter 4. Tidy Data      117

       Learning Objectives      117

       Note About This Chapter       117

       4.1 Columns Contain Values, Not Variables      118

       4.2 Columns Contain Multiple Variables      122

       4.3 Variables in Both Rows and Columns      126

       Conclusion      129

Chapter 5. Apply Functions      131

       Learning Objectives      131

       Note About This Chapter      131

       5.1 Primer on Functions      131

       5.2 Apply (Basics)       133

       5.3 Vectorized Functions      138

       5.4 Lambda Functions (Anonymous Functions)       141

       Conclusion      142

Part II: Data Processing     143

Chapter 6. Data Assembly      145

       Learning Objectives      145

       6.1 Combine Data Sets      145

       6.2 Concatenation      146

       6.3 Observational Units Across Multiple Tables      154

       6.4 Merge Multiple Data Sets      160

       Conclusion      167

Chapter 7. Data Normalization      169

       Learning Objectives      169

       7.1 Multiple Observational Units in a Table (Normalization)     169

       Conclusion      173

Chapter 8. Groupby Operations: Split-Apply-Combine      175

       Learning Objectives      175

       8.1 Aggregate      176

       8.2 Transform      184

       8.3 Filter      188

       8.4 The pandas.core.groupby.DataFrameGroupBy object      190

       8.5 Working with a MultiIndex      195

       Conclusion      199

Part III: Data Types    203

Chapter 9. Missing Data      203

       Learning Objectives      203

       9.1 What Is a NaN Value?       203

       9.2 Where Do Missing Values Come From?       205

       9.3 Working with Missing Data      210

       9.4 Pandas Built-In NA Missing      216

       Conclusion      218

Chapter 10. Data Types      219

       Learning Objectives      219

       10.1 Data Types      219

       10.2 Converting Types      220

       10.3 Categorical Data      225

       Conclusion      227

Chapter 11. Strings and Text Data      229

       Introduction      229

       Learning Objectives      229

       11.1 Strings      229

       11.2 String Methods      233

       11.3 More String Methods      234

       11.4 String Formatting (F-Strings)       236

       11.5 Regular Expressions (RegEx)      239

       11.6 The regex Library      247

       Conclusion      247

Chapter 12. Dates and Times      249

       Learning Objectives      249

       12.1 Python's datetime Object      249

       12.2 Converting to datetime      250

       12.3 Loading Data That Include Dates      253

       12.4 Extracting Date Components      254

       12.5 Date Calculations and Timedeltas      257

       12.6 Datetime Methods      259

       12.7 Getting Stock Data      261

       12.8 Subsetting Data Based on Dates      263

       12.9 Date Ranges      266

       12.10 Shifting Values      270

       12.11 Resampling      276

       12.12 Time Zones      278

       12.13 Arrow for Better Dates and Times      280

       Conclusion      280

Part IV: Data Modeling    281

Chapter 13. Linear Regression (Continuous Outcome Variable)      283

       13.1 Simple Linear Regression      283

       13.2 Multiple Regression      287

       13.3 Models with Categorical Variables      289

       13.4 One-Hot Encoding in scikit-learn with Transformer Pipelines      294

       Conclusion      296

Chapter 14. Generalized Linear Models      297

       About This Chapter      297

       14.1 Logistic Regression (Binary Outcome Variable)       297

       14.2 Poisson Regression (Count Outcome Variable)       304

       14.3 More Generalized Linear Models      308

       Conclusion      309

Chapter 15. Survival Analysis      311

       15.1 Survival Data      311

       15.2 Kaplan Meier Curves      312

       15.3 Cox Proportional Hazard Model      314

       Conclusion      317

Chapter 16. Model Diagnostics      319

       16.1 Residuals      319

       16.2 Comparing Multiple Models      324

       16.3 k-Fold Cross-Validation      329

       Conclusion      334

Chapter 17. Regularization      335

       17.1 Why Regularize?       335

       17.2 LASSO Regression      337

       17.3 Ridge Regression      338

       17.4 Elastic Net      340

       17.5 Cross-Validation      341

       Conclusion      343

Chapter 18. Clustering      345

       18.1 k-Means      345

       18.2 Hierarchical Clustering      351

       Conclusion     356

Part V. Conclusion    357

Chapter 19. Life Outside of Pandas      359

       19.1 The (Scientific) Computing Stack      359

       19.2 Performance      360

       19.3 Dask      360

       19.4 Siuba      360

       19.5 Ibis      361

       19.6 Polars      361

       19.7 PyJanitor      361

       19.8 Pandera      361

       19.9 Machine Learning      361

       19.10 Publishing      362

       19.11 Dashboards      362

       Conclusion      362

Chapter 20. It's Dangerous To Go Alone!      363

       20.1 Local Meetups      363

       20.2 Conferences      363

       20.3 The Carpentries      364

       20.4 Podcasts      364

       20.5 Other Resources      365

       Conclusion      365

Appendices      367

A.      Concept Maps      369
B.      Installation and Setup     373
C.      Command Line     377
D.      Project Templates     379
E.      Using Python       381
F.       Working Directories       383
G.      Environments       385
H.      Install Packages       389
I.       Importing Libraries       391
J.       Code Style       393
K.      Containers: Lists, Tuples, and Dictionaries       395
L.      Slice Values       399
M.     Loops       401
N.     Comprehensions       403
O.     Functions       405
P.      Ranges and Generators       409
Q.     Multiple Assignment       413
R.     Numpy ndarray       415
S.     Classes       417
T.      SettingWithCopyWarning       419
U.     Method Chaining       423
V.      Timing Code       427
W.     String Formatting       429
X.      Conditionals (if-elif-else)        433
Y.      New York ACS Logistic Regression Example       435
Z.      Replicating Results in R       443

Index      451


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